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() In this article, PROSA—a new multi-criteria decision-making method—is proposed. While PROSA has In this article, PROSA—a new multi-criteria decision-making method—is proposed.

While PROSA has advantages derived from the PROMETHEE method, it is characterized by a lower degree of criteria compensation, thus better adhering to the strong sustainability paradigm. As wind is the most used Renewable Energy Source (RES) in the European Union and Poland, the PROSA method is used to evaluate and select offshore wind farm locations in Poland, based on a sustainability assessment. In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed.

Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and the possibility to meet the target power demand at a given hour according to each customer’s availability. The micro direct methanol fuel cell (MicroDMFC) is an emergent technology due to its special interest for portable applications. This work presents the results of a set of experiments conducted at room temperature using an active metallic MicroDMFC with an active area of 2.25 cm 2. The MicroDMFC uses available commercial materials with low platinum content in order to reduce the overall fuel cell cost.

The main goal of this work is to provide useful information to easily design an active MicroDMFC with a good performance recurring to cheaper commercial Membrane Electrode Assemblies MEAs. A performance/cost analysis for each MEA tested is provided. The maximum power output obtained was 18.1 mW/cm 2 for a hot-pressed MEA with materials purchased from Quintech with very low catalyst loading (3 mg/cm 2 Pt–Ru at anode side and 0.5 mg/cm 2 PtB at the cathode side) costing around 15 euros. Similar power values are reported in literature for the same type of micro fuel cells working at higher operating temperatures and substantially higher cathode catalyst loadings. Experimental studies using metallic active micro direct methanol fuel cells operating at room temperature are very scarce.

The results presented in this work are, therefore, very useful for the scientific community. Hybrid vehicles usually have several braking systems, and braking mode switches are significant events during braking. It is difficult to coordinate torque fluctuations caused by mode switches because the dynamic characteristics of braking systems are different. In this study, a new type of plug-in hybrid vehicle is taken as the research object, and braking mode switches are divided into two types. The control strategy of type one is achieved by controlling the change rates of clutch hold-down and motor braking forces.

The control strategy of type two is achieved by simultaneously changing the target braking torque during different mode switch stages and controlling the motor to participate in active coordination control. Finally, the torque coordination control strategy is modeled in MATLAB/Simulink, and the results show that the proposed control strategy has a good effect in reducing the braking torque fluctuation and vehicle shocks during braking mode switches. Smart Grids are electricity networks that use digital technology to co-ordinate the needs and capabilities of all generators, grid operators, end users and electricity market stakeholders in such a way that it can optimize asset utilization and operation while maintaining system reliability, resilience and stability.

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However, Smart Grids are increasingly proposing a much more distributed architecture with the integration of multiple Distributed Energy Resources (DERs) that demand different control and protection schemes. In that sense, the implementation of standards such as IEC 61850 and the integration with Ethernet-based communication networks provide novel tools to manage DER efficiently. This paper analyses the potential usage and benefits of ANSI 67/67N protection in combination with Generic Object Oriented Substation Event (GOOSE) communication service, from the standard 61850 of the International Electro-technical Commission (IEC), for providing adaptive network protection, specifying the configuration and implementation and exposing the obtained results. Even though wind energy is one of the most mature renewable technologies, it is in continuous development not only because of the trend towards larger wind turbines but also because of the development of new technological solutions. The gearbox is one of the components of the drive train in which the industry is concentrating more effort on research and development. Larger rotor blades lead to more demanding requirements for this component as a consequence of a higher mechanical torque and multiplication ratio (due to lower rotational speed of blades while the rotational speed on the generator side remains at similar values).

In addition, operating conditions become increasingly demanding in terms of reliability, performance, and compactness. This paper analyses the different gearbox arrangements that are implemented by manufacturers of onshore wind turbines, as well as their market penetration (including different aspects that affect the design of the gearbox, such as drive train configuration and turbine size). The analysis carried out shows a clear convergence towards gearboxes with three stages. However, there is a noticeable diversity in the types of gears used, depending to a large extent on the preferences of each manufacturer but also on the nominal power of the wind turbine and drive train configuration.

A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE) is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM) algorithm.

Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

This work assesses the performance of five transposition models that estimate the global and diffuse solar irradiance on tilted planes based on the global horizontal irradiance. The modelled tilted irradiance values are compared to measured one-minute values from pyranometers and silicon sensors tilted at different angles at Hannover (Germany) and NREL (Golden, CO, USA).

It can be recognized that the deviations of the model of Liu and Jordan, Klucher and Perez from the measurements increases as the tilt angle increases and as the sensors are oriented away from the south direction, where they receive lower direct radiation than south-oriented surfaces. Accordingly, the vertical E, W and N planes show the highest deviation. Best results are found by the models from Hay and Davies and Reindl, when horizontal pyranometer measurements and a constant albedo value of 0.2 are used. The relative root mean squared difference (rRMSD) of the anisotropic models does not exceed 11% for south orientation and low inclination angles ( β = 10–60°), but reaches up to 28.9% at vertical planes.

For sunny locations such as Golden, the Perez model provides the best estimates of global tilted irradiance for south-facing surfaces. The relative mean absolute difference (rMAD) of the Perez model at NREL ranges from 4.2% for 40° tilt to 8.7% for 90° tilt angle, when horizontal pyranometer measurements and a measured albedo value are used; the use of measured albedo values instead of a constant value of 0.2 leads to a reduction of the deviation to 3.9% and 6.0%, respectively. The use of higher albedo values leads to a significant increase of rMAD. We also investigated the uncertainty resulting from using horizontal pyranometer measurements, in combination with constant albedo values, to estimate the incident irradiance on tilted photovoltaic (PV) modules.

We found that these uncertainties are small or negligible. Mandarin (Citrus reticulata) is one of the most popular fruits in tropical and sub-tropical countries around the world. It contains about 22–34 seeds per fruit.

This study investigated the potential of non-edible mandarin seed oil as an alternative fuel in Australia. The seeds were prepared after drying in the oven for 20 h to attain an optimum moisture content of around 13.22%. The crude oil was extracted from the crushed seed using 98% n-hexane solution. The biodiesel conversion reaction (transesterification) was designed according to the acid value (mg KOH/g) of the crude oil. The study also critically examined the effect of various reaction parameters (such as effect of methanol: oil molar ratio,% of catalyst concentration, etc.) on the biodiesel conversion yield.

After successful conversion of the bio-oil into biodiesel, the physio-chemical fuel properties of the virgin biodiesel were measured according to relevant ASTM standards and compared with ultra-low sulphur diesel (ULSD) and standard biodiesel ASTM D6751. The fatty acid methyl esters (FAMEs) were analysed by gas chromatography (GC) using the EN 14103 standard.

The behaviour of the biodiesel (variation of density and kinematic viscosity) at various temperatures (10–40 °C) was obtained and compared with that of diesel fuel. Finally, mass and energy balances were conducted for both the oil extraction and biodiesel conversion processes to analyse the total process losses of the system. The study found 49.23 wt% oil yield from mandarin seed and 96.82% conversion efficiency for converting oil to biodiesel using the designated transesterification reaction. The GC test identified eleven FAMEs. The biodiesel mainly contains palmitic acid (C16:0) 26.80 vol%, stearic acid (C18:0) 4.93 vol%, oleic acid (C18:1) 21.43 vol% (including cis. And trans.), linoleic acid (C18:2) 4.07 vol%, and less than one percent each of other fatty acids. It is an important source of energy because it has a higher heating value of 41.446 MJ/kg which is close to ULSD (45.665 MJ/kg).

In mass and energy balances, 49.23% mass was recovered as crude bio-oil and 84.48% energy was recovered as biodiesel from the total biomass. This work presents a methodology to adjust the electronic control system of a reciprocating internal combustion engine test bench and the effect of the control parameters on emissions produced by the engine under two extreme situations: unadjusted and adjusted, both under transient operation. The aim is to provide a tuning guide to those in charge of this equipment not needed to be experts in control engineering.

The proposed methodology covers from experimental plant modelling to control parameters determination and experimental validation. The methodology proposed includes the following steps: (i) Understanding of test bench and mathematical modeling; (ii) Model parameters identification; (iii) Control law proposal and tuning from simulation and (iv) Experimental validation. The work has been completed by presenting a comparative experimental study about the effect of the test bench control parameters on engine performance profiles (engine speed, engine torque and relative fuel air ratio) and on regulated gaseous emissions (nitrogen oxides and hydrocarbons concentrations) and the profile of number of particles emitted. The whole process, including experimental validation, has been carried out in a test bench composed of a turbocharged, with common rail injection system, light duty diesel engine coupled to a Schenck E-90 eddy current dynamometric brake and its related Schenk X-act control electronics. The work demonstrates the great effect of the test bench control tuning under transient operation on performance and emissions produced by the engine independently of the engine accelerator position demanded before and after the test bench tuning.

Geothermal energy is a renewable form of energy, however due to misuse, processing and management issues, it is necessary to use the resource more efficiently. To increase energy efficiency, energy systems engineers carry out careful energy control studies and offer alternative solutions. With this aim, this study was conducted to improve the performance of a real operating air-cooled organic Rankine cycle binary geothermal power plant (GPP) and its components in the aspects of thermodynamic modeling, exergy analysis and optimization processes. In-depth information is obtained about the exergy (maximum work a system can make), exergy losses and destruction at the power plant and its components. Thus the performance of the power plant may be predicted with reasonable accuracy and better understanding is gained for the physical process to be used in improving the performance of the power plant.

The results of the exergy analysis show that total exergy production rate and exergy efficiency of the GPP are 21 MW and 14.52%, respectively, after removing parasitic loads. The highest amount of exergy destruction occurs, respectively, in condenser 2, vaporizer HH2, condenser 1, pumps 1 and 2 as components requiring priority performance improvement. To maximize the system exergy efficiency, the artificial bee colony (ABC) is applied to the model that simulates the actual GPP. Under all the optimization conditions, the maximum exergy efficiency for the GPP and its components is obtained. Two of these conditions such as Case 4 related to the turbine and Case 12 related to the condenser have the best performance. As a result, the ABC optimization method provides better quality information than exergy analysis.

Based on the guidance of this study, the performance of power plants based on geothermal energy and other energy resources may be improved. This research conducted a study specially to systematically analyze combined recovery of exhaust gas and engine coolant and related influence mechanism, including a detailed theoretical study and an assistant experimental study. In this research, CO 2-based transcritical Rankine cycle (CTRC) was used for fully combining the wastes heats. The main objective of theoretical research was to search an ‘ideal point’ of the recovery system and related influence mechanism, which was defined as operating condition of complete recovery of two waste heats.

The theoretical methodology of this study could also provide a design reference for effective combined recovery of two or multiple waste heats in other fields. Based on a kW-class preheated CTRC prototype that was designed by the ‘ideal point’ method, an experimental study was conducted to verify combined utilization degree of two engine waste heats by the CTRC system. The operating results showed that the prototype can gain 44.4–49.8 kW and 22.7–26.7 kW heat absorption from exhaust gas and engine coolant, respectively. To direct practical operation, an experimental optimization work on the operating process was conducted for complete recovery of engine coolant exactly, which avoided deficient or excessive recovery. This paper presents the application of support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) models that are amalgamated with synchronized phasor measurements for on-line voltage stability assessment. As the performance of SVR model extremely depends on the good selection of its parameters, the recently developed ant lion optimizer (ALO) is adapted to seek for the SVR’s optimal parameters.

In particular, the input vector of ALO-SVR and ANFIS soft computing models is provided in the form of voltage magnitudes provided by the phasor measurement units (PMUs). In order to investigate the effectiveness of ALO-SVR and ANFIS models towards performing the on-line voltage stability assessment, in-depth analyses on the results have been carried out on the IEEE 30-bus and IEEE 118-bus test systems considering different topologies and operating conditions. Two statistical performance criteria of root mean square error (RMSE) and correlation coefficient (R) were considered as metrics to further assess both of the modeling performances in contrast with the power flow equations. The results have demonstrated that the ALO-SVR model is able to predict the voltage stability margin with greater accuracy compared to the ANFIS model. Burning fuels in an O 2/H 2O atmosphere is regarded as the next generation of oxy-fuel combustion for CO 2 capture and storage (CCS). By combining oxy-fuel combustion and biomass utilization technology, CO 2 emissions could be further reduced.

Therefore, this work focuses on investigating the combustion characteristics of single particles from bituminous coal (BC) and pine sawdust (PS) in O 2/N 2, O 2/CO 2 and O 2/H 2O atmospheres at different O 2 mole fractions (21%, 30%, and 40%). The experiments were carried out in a drop tube furnace (DTF), and a high-speed camera was used to record the combustion processes of fuel particles. The combustion temperatures were measured by a two-color method.

The results reveal that the particles from BC and PS all ignite homogeneously. Replacing N 2 by CO 2 results in a longer ignition delay time and lower combustion temperatures. After substituting H 2O for N 2, the ignition delay time is shortened, which is mainly caused by the steam gasification reaction (C + H 2O → CO + H 2) and steam shift reaction (CO + H 2O → CO 2 + H 2). In addition, the combustion temperatures are first decreased at low O 2 mole fractions, and then increased at high O 2 mole fractions because the oxidation effect of H 2O performs a more important role than its volumetric heat capacity and thermal radiation capacity.

At the same condition, particles from PS ignite earlier because of their higher reactivity, but the combustion temperatures are lower than those of BC, which is owing to their lower calorific values. In the present study, eight kinds plant growth regulators—salicylic acid (SA), 1-naphthaleneacetic acid (NAA), gibberellic acid (GA 3), 6-benzylaminopurine (6-BA), 2, 4-epi-brassinolide (EBR), abscisic acid (ABA), ethephon (ETH), and spermidine (SPD)—were used to investigate the impact on microalgal biomass, lipid, total soluble protein, carotenoids, and polyunsaturated fatty acids (PUFAS) production of Chlorella pyrenoidosa ZF strain. Wave Energy Converters (WECs) need to be deployed in large numbers in an array layout in order to have a significant power production. Each WEC has an impact on the incoming wave field, by diffracting, reflecting and radiating waves.

Simulating the wave transformations within and around a WEC array is complex; it is difficult, or in some cases impossible, to simulate both these near-field and far-field wake effects using a single numerical model, in a time- and cost-efficient way in terms of computational time and effort. Within this research, a generic coupling methodology is developed to model both near-field and far-field wake effects caused by floating (e.g., WECs, platforms) or fixed offshore structures. The methodology is based on the coupling of a wave-structure interaction solver (Nemoh) and a wave propagation model. In this paper, this methodology is applied to two wave propagation models (OceanWave3D and MILDwave), which are compared to each other in a wide spectrum of tests. Additionally, the Nemoh-OceanWave3D model is validated by comparing it to experimental wave basin data.

The methodology proves to be a reliable instrument to model wake effects of WEC arrays; results demonstrate a high degree of agreement between the numerical simulations with relative errors lower than 5% and to a lesser extent for the experimental data, where errors range from 4% to 17%. In this paper, physical experiments and numerical simulations were applied to systematically investigate the non-Newtonian flow characteristics of heavy oil in porous media. Rheological experiments were carried out to determine the rheology of heavy oil. Threshold pressure gradient (TPG) measurement experiments performed by a new micro-flow method and flow experiments were conducted to study the effect of viscosity, permeability and mobility on the flow characteristics of heavy oil.

An in-house developed novel simulator considering the non-Newtonian flow was designed based on the experimental investigations. The results from the physical experiments indicated that heavy oil was a Bingham fluid with non-Newtonian flow characteristics, and its viscosity-temperature relationship conformed to the Arrhenius equation. Its viscosity decreased with an increase in temperature and a decrease in asphaltene content. The TPG measurement experiments was impacted by the flow rate, and its critical flow rate was 0.003 mL/min.

The TPG decreased as the viscosity decreased or the permeability increased and had a power-law relationship with mobility. In addition, the critical viscosity had a range of 42–54 mPa∙s, above which the TPG existed for a given permeability.

The validation of the designed simulator was positive and acceptable when compared to the simulation results run in ECLIPSE V2013.1 and Computer Modelling Group (CMG) V2012 software as well as when compared to the results obtained during physical experiments. The difference between 0.0005 and 0.0750 MPa/m in the TPG showed a decrease of 11.55% in the oil recovery based on the simulation results, which demonstrated the largely adverse impact the TPG had on heavy oil production. A critical speed controller for avoiding a certain rotational speed is presented. The controller is useful for variable speed wind turbines with a natural frequency in the operating range. The controller has been simulated, implemented and tested on an open site 12 kW vertical axis wind turbine prototype.

The controller is based on an adaptation of the optimum torque control. Two lookup tables and a simple state machine provide the control logic of the controller.

The controller requires low computational resources, and no wind speed measurement is needed. The results suggest that the controller is a feasible method for critical speed control. The skipping behavior can be adjusted using only two parameters. While tested on a vertical axis wind turbine, it may be used on any variable speed turbine with the control of generator power. A general pattern, which can include different types of permanent magnet (PM) arrangement in PM synchronous motors (PMSMs) is presented.

By varying the geometric parameters of the general pattern, the template can automatically produce different types of PM arrangement in the rotor. By choosing the best arrangement of PMs using optimization method, one can obtain a better performance and lower manufacturing cost. Six of the most widely used conventional types of rotor structures can be obtained through the parameter variation of the general pattern. These types include five embedded PM types and a traditional surface-mounted PM type. The proposed approach combines optimization method embedded with finite element method (FEM) for solving the multi-objective optimization for the PM structures. To save computing load, this paper employs a strategy of sub-group optimization, which is on account of the impact levels of the design parameters on the objective functions, and a parallel computation, which is a valid method to shorten the computing time. As an application example, a PMSM is optimally designed.

Its simulation results and prototype experiments are provided to showcase the effectiveness of the proposed method. Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses.

The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and “reshape” the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system. The ascending trend of photo-voltaic (PV) utilization on a domestic scale in Finland, calls for a technical aspects review of low voltage (LV) networks. This work investigates the technical factors that limit the PV hosting capacity, in realistic case networks, designed relative to different geographical areas of Finland. A Monte Carlo method based analysis was performed, in order to quantify the hosting capacity of the formulated networks, with balanced and unbalanced feeds, in PV systems and their limiting constraints were evaluated. Finally, the effectiveness of on-load tap changer (OLTC) in increasing the PV penetration, when employed in the LV system, was investigated.

Cushioning is an important aspect in hydraulic cylinder performance. The piston has to be decelerated before it strikes the end cap in order to avoid stresses in the cylinder components and reduce vibration that can be transmitted to the machine. One of the least-studied methods is internal cushioning by grooves in the piston.

In this method, the flow is throttled with adequately designed grooves when the piston reaches the outlet port position. The purpose of the present work is to present a method to estimate the pressure-drop coefficients for a certain design of piston grooves in order to provide a model to develop a dynamic system simulation of the cushion system. The method is based on a computational fluid dynamic simulation of flow through piston grooves to the outlet port for each piston’s static position. The results are compared with experimental measurements, and a correction, based on Reynolds number, is proposed. Good agreement, below 16%, was obtained for all the positions but particularly for the last grooves, for which the numerical result’s deviation to the experimental measurements was less than 10%.

In general, the numerical simulation tended to underestimate the pressure drop for the first grooves and overestimate the calculation for the last grooves. Abstract: In this paper, the endothermic methanol decomposition reaction is used to obtain syngas by transforming middle and low temperature solar energy into chemical energy. A two-dimensional multiphysics coupling model of a middle and low temperature of 150~300 °C solar receiver/reactor was developed, which couples momentum equation in porous catalyst bed, the governing mass conservation with chemical reaction, and energy conservation incorporating conduction/convection/radiation heat transfer. The complex thermochemical conversion process of the middle and low temperature solar receiver/reactor (MLTSRR) system was analyzed.

The numerical finite element method (FEM) model was validated by comparing it with the experimental data and a good agreement was obtained, revealing that the numerical FEM model is reliable. The characteristics of chemical reaction, coupled heat transfer, the components of reaction products, and the temperature fields in the receiver/reactor were also revealed and discussed.

The effects of the annulus vacuum space and the glass tube on the performance of the solar receiver/reactor were further studied. It was revealed that when the direct normal irradiation increases from 200 W/m 2 to 800 W/m 2, the theoretical efficiency of solar energy transformed into chemical energy can reach 0.14–0.75. When the methanol feeding rate is 13 kg/h, the solar flux increases from 500 W/m 2 to 1000 W/m 2, methanol conversion can fall by 6.8–8.9% with air in the annulus, and methanol conversion can decrease by 21.8–28.9% when the glass is removed from the receiver/reactor. Passive and active solar heating systems have drawn much attention and are widely used in residence buildings in the Qinghai-Tibetan plateau due to its high radiation intensity. In fact, there is still lack of quantitative evaluation of the passive and active heating effect, especially for residential building in the Qinghai-Tibetan plateau areas. In this study, three kinds of heating strategies, including reference condition, passive solar heating condition and active solar heating condition, were tested in one demonstration residential building.

The hourly air temperatures of each room under different conditions were obtained and analyzed. The results show the indoor air temperature in the living room and bedrooms (core zones) was much higher than that of other rooms under both passive and active solar heating conditions.

In addition, the heating effect with different strategies for core zones of the building was evaluated by the ratio of indoor and outdoor degree hour, which indicates that solar heating could effectively reduce the traditional energy consumption and improve the indoor thermal environment. The passive solar heating could undertake 49.8% degree hours for heating under an evaluation criterion of 14 °C and the active solar heating could undertake 75% degree hours for heating under evaluation criterion of 18 °C, which indicated that solar heating could effectively reduce the traditional energy consumption and improve the indoor thermal environment in this area. These findings could provide reference for the design and application of solar heating in similar climate areas. The transition to a more sustainable personal transportation sector requires the widespread adoption of electric vehicles. However, a dominant design has not yet emerged and a standards battle is being fought between battery and hydrogen fuel cell powered electric vehicles. The aim of this paper is to analyze which factors are most likely to influence the outcome of this battle, thereby reducing the uncertainty in the industry regarding investment decisions in either of these technologies. We examine the relevant factors for standard dominance and apply a multi-criteria decision-making method, best worst method, to determine the relative importance of these factors.

The results indicate that the key factors include technological superiority, compatibility, and brand reputation and credibility. Our findings show that battery powered electric vehicles have a greater chance of winning the standards battle. This study contributes to theory by providing further empirical evidence that the outcome of standards battles can be explained and predicted by applying factors for standard success. We conclude that technology dominance in the automotive industry is mostly driven by technological characteristics and characteristics of the format supporter. This paper focuses on Hybrid Energy Storage Systems (HESS), consisting of a combination of batteries and Electric Double Layer Capacitors (EDLC), for electric urban busses. The aim of the paper is to develop a methodology to determine the hybridization percentage that allows the electric bus to work with the highest efficiency while reducing battery aging, depending on the chosen topology, control strategy, and driving cycle. Three power electronic topologies are qualitatively analyzed based on different criteria, with the topology selected as the favorite being analyzed in detail.

The whole system under study is comprised of the following elements: a battery pack (LiFePO4 batteries), an EDLC pack, up to two DC-DC converters (depending on the topology), and an equivalent load, which behaves as an electric bus drive (including motion resistances and inertia). Mathematical models for the battery, EDLCs, DC-DC converter, and the vehicle itself are developed for this analysis. The methodology presented in this work, as the main scientific contribution, considers performance variation (energy efficiency and battery aging) and hybridization percentage (ratio between batteries and EDLCs, defined in terms of mass), using a power load profile based on standard driving cycles. The results state that there is a hybridization percentage that increases energy efficiency and reduces battery aging, maximizing the economic benefits of the vehicle, for every combination of topology, type of storage device, control strategy, and driving cycle. The realization of a comfortable thermal environment with low energy consumption and improved ventilation in a car has become the aim of manufacturers in recent decades. Novel ventilation concepts with more flexible cabin usage and layouts are appealing owing to their potential for improving passenger comfort and driving power. In this study, three variant ventilation concepts are investigated and their performance is compared with respect to energy efficiency and human comfort of the driver and passenger in front and a child in the rear compartment.

FLUENT 16.0, a commercial three-dimensional (3D) software, are used for the simulation. A surface-to-surface radiation model is applied under transient conditions for a car parked in summer conditions with its engine in the running condition. The results for the standard Fanger’s model and modified Fanger’s model are analyzed, discussed, and compared for the driver, passenger, and child. The modified Fanger’s model determines the thermal sensation on the basis of mean arterial pressure. While the technical layout of smart energy systems is well advanced, the implementation of these systems is slowed down by the current decision-making practice regarding such energy infrastructures. We call for a reorganisation of the decision-making process on local energy planning and address the question ‘how can decision-making on the design and implementation of Smart Energy Systems be accelerated?’ Inspired by engineering design thinking and based on two workshop sessions, we identify five design phases and an implementation phase, and distinguish between a design component and a participation component. This allows for the effective participation of external stakeholders at four specific moments in the decision-making process.

This way, efficiency and effectiveness in smart energy system planning can be increased, without compromising on participation. When applied to the Dutch context of energy planning, the developed decision-making model is useful for project participants as well as policy-makers in a wide variety of settings. This paper develops fifth-generation-sized silicon thin-film tandem photovoltaic (PV) modules with animated images. Front PV cell stripes are created using a laser scribing technique, and specially edited and shifted images are printed onto the back glass.

After encapsulating the front PV module with the back glass, the animated image effect can then be clearly seen from various positions. The PV module that can display three images has a stabilized power output of 87 W. The remarkable features of this module such as its animated image display, semitransparency, and acceptable power loss give it great potential for use in building-integrated photovoltaics. This paper could help improve the aesthetic appearance of PV modules, which may increase users’ or architects’ willingness to install PV modules on buildings. Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA).

The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters.

Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others.

In this paper, the effect of renewable energy resources (RERs), demand response (DR) programs and electric vehicles (EVs) is evaluated on the optimal operation of a smart distribution company (SDISCO) in the form of a new bi-level model. According to the existence of private electric vehicle parking lots (PLs) in the network, the aim of both levels is to maximize the profits of SDISCO and the PL owners. Furthermore, due to the uncertainty of RERs and EVs, the conditional value-at-risk (CVaR) method is applied in order to limit the risk of expected profit. The model is transformed into a linear single-level model by the Karush–Kuhn–Tucker (KKT) conditions and tested on the IEEE 33-bus distribution system over a 24-h period. The results show that by using a proper charging/discharging schedule, as well as a time of use program, SDISCO gains more profit. Furthermore, by increasing the risk aversion parameter, this profit is reduced.

Core loss is one of the significant factors affecting the high power density of permanent magnet machines; thus, it is necessary to consider core loss in machine design. This paper presents a novel method for calculating the core loss of permanent magnet synchronous machines under space vector pulse width modulation (SVPWM) excitation, taking magnetic saturation and cross coupling into account. In order to accurately obtain the direct and quadrature (d-q) axis, current in the given load condition, the permanent magnet motor model under SVPWM excitation has been modified, so as to consider the influence of magnetic saturation and cross coupling effects on the d-q axis flux-linkage. Based on the magnetic field distribution caused by permanent magnet and armature reactions, the stator core loss can be calculated with the core loss analytical model, corresponding to the rotational magnetic field.

In this study, the method has been applied to analyze core loss in an interior permanent magnet synchronous machine, and has been validated by the experimental results. The influence of pole/slot number combinations on core loss in the same on-load condition is also investigated. This study provides a potential method to guide motor design optimization.

The main feature of a run-off river hydroelectric system is a small size intake pond that overspills when river flow is more than turbines’ intake. As river flow fluctuates, a large proportion of the potential energy is wasted due to the spillages which can occur when turbines are operated manually. Manual operation is often adopted due to unreliability of water level-based controllers at many remote and unmanned run-off river hydropower plants. In order to overcome these issues, this paper proposes a novel method by developing a controller that derives turbine output set points from computed mass flow rate of rivers that feed the hydroelectric system. The computed flow is derived by summation of pond volume difference with numerical integration of both turbine discharge flows and spillages. This approach of estimating river flow allows the use of existing sensors rather than requiring the installation of new ones.

All computations, including the numerical integration, have been realized as ladder logics on a programmable logic controller. The implemented controller manages the dynamic changes in the flow rate of the river better than the old point-level based controller, with the aid of a newly installed water level sensor. The computed mass flow rate of the river also allows the controller to straightforwardly determine the number of turbines to be in service with considerations of turbine efficiencies and auxiliary power conservation. System frequency may change from defined values while transmitting power from one area to another in an interconnected power system due to various reasons such as load changes and faults. This frequency change causes a frequency error in the system.

However, the system frequency should always be maintained close to the nominal value even in the presence of model uncertainties and physical constraints. This paper proposes an Active Disturbance Rejection Controller (ADRC)-based load frequency control (LFC) of an interconnected power system. The controller incorporates effects of generator inertia and generator electrical proximity to the point of disturbances. The proposed controller reduces the magnitude error of the area control error (ACE) of an interconnected power system compared to the standard controller.

The simulation results verify the effectiveness of proposed ADRC in the application of LFC of an interconnected power system. This study examines the effect of the complementarity between the variable generation resources (VGRs) and the load on the flexibility of the power system. The complementarity may change the ramping capability requirement, and thereby, the flexibility. This effect is quantified using a flexibility index called the ramping capability shortage expectation (RSE). The flexibility is evaluated for different VGR mix scenarios under the same VGR penetration level, and an optimal VGR mix (i.e., one that maximizes flexibility) is obtained.

The effect of the complementarity of the wind and PV outputs on the flexibility is investigated for the peak-load day of 2016 for the Korean power system. The result shows that the RSE value for the optimal VGR mix scenario is 6.95% larger than that for the original mix scenario. Solar power generation is intermittent in nature. It is nearly impossible for a photovoltaic (PV) system to supply power continuously and consistently to a varying load. Operating a controllable source like a fuel cell in parallel with PV can be a solution to supply power to variable loads. In order to coordinate the power supply from fuel cells and PVs, a power management system needs to be designed for the microgrid system. This paper presents a power management system for a grid-connected PV and solid oxide fuel cell (SOFC), considering variation in the load and solar radiation.

The objective of the proposed system is to minimize the power drawn from the grid and operate the SOFC within a specific power range. Since the PV is operated at the maximum power point, the power management involves the control of SOFC active power where a proportional and integral (PI) controller is used. The control parameters of the PI controller K p (proportional constant) and T i (integral time constant) are determined by the genetic algorithm (GA) and simplex method. In addition, a fuzzy logic controller is also developed to generate appropriate control parameters for the PI controller. The performance of the controllers is evaluated by minimizing the integral of time multiplied by absolute error (ITAE) criterion. Simulation results showed that the fuzzy-based PI controller outperforms the PI controller tuned by the GA and simplex method in managing the power from the hybrid source effectively under variations of load and solar radiation.

Wind velocity distribution and the vortex around the wind turbine present a significant challenge in the development of straight-bladed vertical axis wind turbines (VAWTs). This paper is intended to investigate influence of tip vortex on wind turbine wake by Computational Fluid Dynamics (CFD) simulations. In this study, the number of blades is two and the airfoil is a NACA0021 with chord length of c = 0.265 m. To capture the tip vortex characteristics, the velocity fields are investigated by the Q-criterion iso-surface ( Q = 100) with shear-stress transport (SST) k-ω turbulence model at different tip speed ratios (TSRs). Then, mean velocity, velocity deficit and torque coefficient acting on the blade in the different spanwise positions are compared. The wind velocities obtained by CFD simulations are also compared with the experimental data from wind tunnel experiments. As a result, we can state that the wind velocity curves calculated by CFD simulations are consistent with Laser Doppler Velocity (LDV) measurements.

The distribution of the vortex structure along the spanwise direction is more complex at a lower TSR and the tip vortex has a longer dissipation distance at a high TSR. In addition, the mean wind velocity shows a large value near the blade tip and a small value near the blade due to the vortex effect. This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers.

First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions.

Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently.

MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored. This study investigated the heat problems that occur during the operation of power batteries, especially thermal runaway, which usually take place in high temperature environments. The study was conducted on a ternary polymer lithium-ion battery.

In addition, a lumped parameter thermal model was established to analyze the thermal behavior of the electric bus battery system under the operation conditions of the driving cycles of the Harbin city electric buses. Moreover, the quantitative relationship between the optimum heat transfer coefficient of the battery and the ambient temperature was investigated. The relationship between the temperature rise ( T r), the number of cycles ( c), and the heat transfer coefficient ( h) under three Harbin bus cycles have been investigated at 30 °C, because it can provide a basis for the design of the battery thermal management system. The results indicated that the heat transfer coefficient that meets the requirements of the battery thermal management system is the cubic power function of the ambient temperature. Therefore, if the ambient temperature is 30 °C, the heat transfer coefficient should be at least 12 W/m 2K in the regular bus lines, 22 W/m 2K in the bus rapid transit lines, and 32 W/m 2K in the suburban lines. As a promising technology to improve shale gas (SG) recovery and CO 2 storage capacity, the multi-well pads (MWPs) scheme has gained more and more attention.

The semi-analytical pressure-buildup method has been used to estimate CO 2 storage capacity. It focuses on single multi-fractured horizontal wells (SMFHWs) and does not consider multi-well pressure interference (MWPI) induced by the MWPs scheme. This severely limits the application of this method as incidences of multi-well pressure interference have been widely reported. This paper proposed a new methodology to optimize the injection strategy of the MWPs scheme and maximize CO 2 storage capacity.

The new method implements numerical discretization, the superposition theory, Gauss elimination, and the Stehfest numerical algorithm to obtain pressure-buildup solutions for the MWPs scheme. The solution by the new method was validated with numerical simulation and pressure-buildup curves were generated to identify MWPI. Using the new method, we observed that the fracture number and fracture half-length have a positive influence on CO 2 storage capacity. Both can be approximately related to the CO 2 storage capacity by a linear correlation. For a given injection pressure, there is an optimal fracture number; the bigger the limited injection pressure, the smaller the optimal fracture number. Stress sensitivity has positive influences on CO 2 storage capacity, thus extending the injection period would improve CO 2 storage capacity.

This work gains some insights into the CO 2 storage capacity of the MWPs scheme in depleted SG reservoirs, and provides considerable guidance on injection strategies to maximize CO 2 storage capacity in depleted SG reservoirs. When orchards reach the end of the productive cycle, the stumps removal becomes a mandatory operation to allow new soil preparation and to establish new cultivations. The exploitation of the removed stump biomass seems a valuable option, especially in the growing energy market of the biofuels; however, the scarce quality of the material obtained after the extraction compromises its marketability, making this product a costly waste to be disposed.

In this regard, the identification of affordable strategies for the extraction and the cleaning of the material will be crucial in order to provide to plantation owners the chance to sell the biomass and offset the extraction costs. Mechanical extraction and cleaning technologies have been already tested on forest stumps, but these systems work on the singular piece and would be inefficient in the conditions of an intensive orchard, where stumps are small and numerous. The objective of this study was to test the possibility to exploit a natural stumps cleaning system through open-air storage. The tested stumps were obtained from two different vineyards, extracted with an innovative stump puller specifically designed for continuous stump removal in intensively-planted orchards. The effects of weathering were evaluated to determine the fuel quality immediately after the extraction and after a storage period of six months with respect to moisture content, ash content, and heating value. Results indicated interesting storage performance, showing also different dynamics depending on the stumps utilized. Wind turbine driven doubly-fed induction generators (DFIGs) are widely used in the wind power industry.

With the increasing penetration of wind farms, analysis of their effect on power systems has become a critical requirement. This paper presents the modeling of wind turbine driven DFIGs using the conventional vector controls in a detailed model of a DFIG that represents power electronics (PE) converters with device level models and proposes an average model eliminating the PE converters.

The PSCAD/EMTDC™ (4.6) electromagnetic transient simulation software is used to develop the detailed and the proposing average model of a DFIG. The comparison of the two models reveals that the designed average DFIG model is adequate for simulating and analyzing most of the transient conditions. This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency.

In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions.

The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers. This paper investigates the controllability of a closed-loop tracking synchronization network based on multiple linear-switched reluctance machines (LSRMs). The LSRM network is constructed from a global closed-loop manner, and the closed loop only replies to the input and output information from the leader node. Then, each local LSRM node is modeled as a general second-order system, and the model parameters are derived by the online system identification method based on the least square method.

Next, to guarantee the LSRM network’s controllability condition, a theorem is deduced that clarifies the relationship among the LSRM network’s controllability, the graph controllability of the network and the controllability of the node dynamics. A state feedback control strategy with the state observer located on the leader is then proposed to improve the tracking performance of the LSRM network. Last, both the simulation and experiment results prove the effectiveness of the network controller design scheme and the results also verify that the leader-based global feedback strategy not only improves the tracking performance but also enhances the synchronization accuracy of the LSRM network experimentally. Wave tank tests often involve simulating extreme wave conditions as they enable the maximum expected loads to be inferred: a vital parameter for structural design. The definition, and simulation of, extreme conditions are often fairly simplistic, which can result in conditions and associated loads that are not representative of those that would be observed at the deployment location. Here we present a method of defining, simulating at scale, and validating realistic site-specific extreme wave conditions for survival testing of wave energy converters. Bivariate inverse-first order reliability method (I-FORM) environmental contours define extreme pairs of significant wave height and energy period ( H m 0– T E), while observed extreme conditions are used to define realistic frequency and directional distributions.

These sea states are scaled, simulated and validated at the FloWave Ocean Energy Research Facility to demonstrate that the site-specific extreme wave conditions can be re-created with accuracy. The presented approach enables greater realism to be incorporated into tank testing with survival sea states. The techniques outlined and explored here can provide further and more realistic insight into the response of offshore structures and devices, and can help make important design decisions prior to full-scale deployment. Abstract: Information technology (IT) has brought significant changes in people’s lives.

As an important part of the IT industry, data centres (DCs) have been rapidly growing in both the number and size over the past 40 years. Around 30% to 40% of electricity consumption in DCs is used for space cooling, thus leading to very inefficient DC operation. To identify ways to reduce the energy consumption for space cooling and increase the energy efficiency of DCs’ operation, a dedicated investigation into the energy usage in DCs has been undertaken and a novel high performance dew point cooling system was introduced into a DC operational scheme. Based on the cooling load in DCs, a case study was carried out to evaluate the energy consumptions and energy usage effectiveness when using the novel dew point cooling system in different scales of DCs in various climates.

It was found that by using the novel dew point cooling system, for 10 typical climates a DC can have a much lower power usage effectiveness (PUE) of 1.10 to 1.22 compared to that of 1.7 to 3.7 by using existing traditional cooling systems, leading to significantly increased energy efficiency of the DC operation. In addition, the energy performance by managing the cooling air supply at the different levels in DCs, i.e., room, row and rack level, was simulated by using a dynamic computer model. It was found that cooling air supply at rack level can provide a higher energy efficiency in DCs. Based on the above work, the energy saving potential in DCs was conducted by comparing DCs using an the novel dew point cooling system and the optimum management scheme for the cooling air supply to that using traditional air cooling systems and the same supply air management. Annual electricity consumptions for the two cases were given. It was found that by using the novel dew point cooling system and optimum management system for the cooling air supply, an 87.7~91.6% electricity consumption saving for space cooling in DCs could be achieved in 10 typical cities at 10 selected climatic conditions. This paper presents the optimal placement of multiple Dispersed Generators using multi-objective optimization.

The optimization is carried out with objectives namely active power loss, reactive power loss, voltage deviation and overall economy. The multi-objective optimization and accounting conflicting objectives are realized through Particle Swarm Optimization with fuzzy decision approach to find the optimal sizes and sites of Dispersed Generators for voltage dependent residential, commercial and industrial loads. The clusters of buses are formulated from base case load flow to limit the search space for finding the placement of the Dispersed Generators. The effectiveness of the proposed approach is tested on a 69-bus radial distribution. It is found that the optimal placement of the Dispersed Generators improves the overall performance of the system and the optimal allocation is affected by the type of load. Supercritical water gasification (SCWG) is an emerging technology for the valorization of (wet) biomass into a valuable fuel gas composed of hydrogen and/or methane.

The harsh temperature and pressure conditions involved in SCWG ( T >375 °C, p >22 MPa) are definitely a challenge for the manufacturing of the reactors. Metal surfaces are indeed subject to corrosion under hydrothermal conditions, and expensive special alloys are needed to overcome such drawbacks. A ceramic reactor could be a potential solution to this issue.

Finding a suitable material is, however, complex because the catalytic effect of the material can influence the gas yield and composition. In this work, a research reactor featuring an internal alumina inlay was utilized to conduct long-time (16 h) batch tests with real biomasses and model compounds. The same experiments were also conducted in batch reactors made of stainless steel and Inconel 625. The results show that the three devices have similar performance patterns in terms of gas production, although in the ceramic reactor higher yields of C 2+ hydrocarbons were obtained. The SEM observation of the reacted alumina surface revealed a good resistance of such material to supercritical conditions, even though some intergranular corrosion was observed.

The properties of brushless DC motor (BLDCM) are similar to the fractional, slot-concentrated winding of permanent-magnet synchronous machines, and they fit well for electric vehicle application. However, BLDCM still suffers from the high commutation torque ripple in the case of the traditional square-wave current control (SWC) method, where the current vector rotates asynchronously with back-EMF. A current optimizing control (COC) method for BLDCM is proposed in the paper to minimize the commutation torque ripple. The trajectories of the three phase currents are planned by the given torque and the optimized result of the copper loss and motor torque equations.

The properties of COC are analyzed and compared with that of SWC in the stationary reference frame. The results show that the way of making the current vector rotate synchronously with back-EMF (back-Electromotive Force) can minimize the modulus and velocity of the current vector in the commutation region, and reduce the torque ripple. Experimental tests obtained from an 82 W BLDCM are done to confirm the theoretical findings. In order to reduce the online calculations for power system simulations of transient stability, and dramatically improve numerical integration efficiency, a transient stability numerical integration algorithm for variable step sizes based on virtual input is proposed. The method for fully constructing the nonhomogeneous virtual input for a certain integration scheme is given, and the calculation method for the local truncation error of the power angle for the corresponding integration scheme is derived. A step size control strategy based on the predictor corrector variable step size method is proposed, which performs an adaptive control of the step size in the numerical integration process.

The proposed algorithm was applied to both the IEEE39 system and a regional power system (5075 nodes, 496 generators) in China, and demonstrated a high level of accuracy and efficiency in practical simulations compared to the conventional numerical integration algorithm. It is a difficult and important project to coordinate active front steering (AFS) and direct yaw moment control (DYC), which has great potential to improve vehicle dynamic stability.

Moreover, the balance between driver’s operation and advanced technologies’ intervention is a critical problem. This paper proposes a human-machine-cooperative-driving controller (HMCDC) with a hierarchical structure for vehicle dynamic stability and it consists of a supervisor, an upper coordination layer, and two lower layers (AFS and DYC). The range of AFS additional angle is constrained, with consideration of the influence of AFS on drivers’ feeling. First, in the supervisor, a nonlinear vehicle model was utilized to predict vehicle states, and the reference yaw rate, and side slip angle values were calculated. Then, the upper coordination layer decides the control object and control mode.

At last, DYC and AFS calculate brake pressures and the range of active steering angle, respectively. The proposed HMCDC is evaluated by co-simulation of CarSim and MATLAB. Results show that the proposed controller could improve vehicle dynamic stability effectively for the premise of ensuring the driver’s intention. Operation of power system within specified limits of voltage and frequency are the major concerns in power system stability studies. As power system is always prone to disturbances, which consequently affect the voltage instability and optimal power flow, and therefore risks the power systems stability and security. In this paper, a novel technique based on the “Artificial Algae Algorithm” (AAA) is introduced, to identify the optimal location and the parameters setting of Unified Power Flow Controller (UPFC) under N-1 contingency criterion. In the first part, we have carried out a contingency operation and ranking process for the most parlous lines outage contingencies while taking the transmission lines overloading (NOLL) and voltage violation of buses (NVVB) as a performance parameter (PP = NOLL + NVVB).

As UPFC possesses too much prohibitive cost and larger size, its optimal location and size must be identified before the actual deployment. In the second part, we have applied a novel AAA technique to identify the optimal location and parameters setting of UPFC under the discovered contingencies. The simulations have been executed on IEEE 14 bus and 30 bus networks. The results reveals that the location of UPFC is significantly optimized using AAA technique, which has improved the stability and security of the power system by curtailing the overloaded transmission lines and limiting the voltage violations of buses. Complex power electronic conversion devices, most of which have high transmission performance, are important power conversion units in modern aircraft power systems.

However, these devices can also affect the stability of the aircraft power system more and more prominent due to their dynamic and nonlinear characteristics. To analyze the stability of aircraft power systems in a simple, accurate and comprehensive way, this paper develops a unified large signal model of aircraft power systems. In this paper, first the Lyapunov linearization method and the mixed potential theory are employed to analyze small signal and large signal stability, respectively, and then a unified stability criterion is proposed to estimate small and large signal stability problems. Simulation results show that the unified large signal model of aircraft power systems presented in this paper can be used to analyze the stability problem of aircraft power systems in an accurate and comprehensive way.

Furthermore, with simplicity, universality and structural uniformity, the unified large signal model lays a good foundation for the optimal design of aircraft power systems. Multi-state weighted k-out-of- n systems are widely applied in various scenarios, such as multiple line (power/oil transmission line) transmission systems where the capability of fault tolerance is desirable. However, the complex operating environment and the dynamic features of load demands influence the evaluation of system reliability.

In this paper, a stochastic multiple-valued (SMV) approach is proposed to efficiently predict the reliability of two models of systems with non-repairable components and dynamically repairable components. The weights/performances and reliabilities of multi-state components (MSCs) are represented by stochastic sequences consisting of a fixed number of multi-state values with the positions being randomly permutated. Using stochastic sequences with L multiple values, linear computational complexities with parameters n and L are required by the SMV approach to compute the reliability of different multi-state k-out-of- n systems at a reasonable accuracy, compared to the complexities of universal generating functions (UGF) and fuzzy universal generating functions (FUGF) that increase exponentially with the value of n. The analysis of two benchmarks shows that the proposed SMV approach is more efficient than the analysis using UGF or FUGF. A non-stoichiometric, amorphous a-Mn(BH 4) (2x) hydride, accompanied by a NaCl-type salt, was mechanochemically synthesized from the additive-free mixture of (2NaBH 4 + MnCl 2), as well as from the mixtures containing the additives of ultrafine filamentary carbonyl nickel (Ni), graphene, and LiNH 2. It is shown that both graphene and LiNH 2 suppressed the release of B 2H 6 during thermal gas desorption, with the LiNH 2 additive being the most effective suppressor of B 2H 6.

During solvent filtration and extraction of additive-free, as well as additive-bearing, (Ni and graphene) samples from diethyl ether (Et 2O), the amorphous a-Mn(BH 4) (2x) hydride transformed into a crystalline c-Mn(BH 4) 2 hydride, exhibiting a microstructure containing nanosized crystallites (grains). In contrast, the LiNH 2 additive most likely suppressed the formation of a crystalline c-Mn(BH 4) 2 hydride during solvent filtration/extraction. In a differential scanning calorimeter (DSC), the thermal decomposition peaks of both amorphous a-Mn(BH 4) (2x) and crystalline c-Mn(BH 4) 2 were endothermic for the additive-free samples, as well as the samples with added graphene and Ni. The samples with LiNH 2 exhibited an exothermic DSC decomposition peak. Shock absorbers allow the damping of suspension vibrations, by dissipating kinetic energy. This energy theoretically can be harvested; however, practical solutions are not easily obtainable. This paper is dedicated to analyzing and evaluating the vibration energy in a vehicle’s suspension that is generated by road excitations.

Also, it estimates the possible amount of harvested energy required to diminish accelerations of the vehicle body, the driver, or the passenger center of mass. The control of damper is realized by optimizing the best damping coefficient for different road roughness. Analytical results, obtained from the proposed dynamic model of the car, were compared with experimental data, showing a good coherence between them.

These results allow us to evaluate the amount of energy circulating within shock absorbers and give information about the amount of the possible harvested energy. There is a very good relationship between energy needed for control and gained energy. For studying the law of crack propagation around a gas drilling borehole, an experimental study about coal with a cavity under uniaxial compression was carried out, with the digital speckle correlation method capturing the images of coal failure. A sequence of coal failure images and the full-field strain of failure were obtained. The strain softening characteristic was shown by the curve. A method of curve dividing—named fitting-damaging—was proposed, combining the least square fitting residual norm and damage fraction. By this method, the five stages and four key points of a stress-strain curve were defined.

Then, the full-field stress was inverted by means of the theory of elasticity and the adjacent element weight sharing model. The results show that σ ci was 30.28–41.71 percent of σ f and σ cd was 83.08–87.34 percent of σ f, calculated by the fitting-damaging method, agreeing with former research.

The results of stress inversion showed that under a low stress level (0.15 σ f. One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited.

The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations. Shale gas, with its lower carbon content and pollution potential, is the most promising natural gas resource in China.

When modeling the shale gas supply in a specific gas field, it is of paramount importance to determine the gas supply under economic considerations. Two common calculation methods are used in China for this purpose: Method 1 (M1) is the breakeven analysis, where the gas supply is based on the relationship between costs and revenues, while Method 2 (M2) is the Geologic Resource Supply-Demand Model, where the supply relies on demand and price scenarios. No comparisons has been made between these two methods. In this study, the Fuling shale gas field in the Sichuan Basin was chosen as a study case to forecast the shale gas supply using these two different methods. A sensitivity analysis was performed to discuss the influencing factors of each method and error measures were used to compare the different shale gas supply values calculated by each method. The results shows that M1 is more sensitive to initial production, while M2 is more sensitive to gas price.

In addition, M2 is more feasible for its simplicity and accuracy at high price scenarios and M1 is considered to be reliable for low price scenarios with profit. This study can provide a quick and comprehensive assessment method for the shale gas supply in China. Solar-driven ammonia-water absorption refrigeration system (AARS) has been considered as an alternative for the conventional refrigeration and air-conditioning systems. However, its high initial cost seems to be the main problem that postpones its wide spread use. In the present study, a single-stage NH 3/H 2O ARS is analyzed in depth on the basis of energetic and exergetic coefficients of performance (COP and ECOP, respectively) to decrease its cut in/off temperature. This study was carried out to lower the required heat source temperature, so that a less-expensive solar collector could be used.

Effects of all parameters that could influence the system’s performance and cut in/off temperature were investigated in detail. Presence of water in the refrigerant and evaporator temperature glide was considered. Results revealed that appropriate selection of system’s working condition can effectively reduce the driving temperature. Besides, the cut in/off temperature can be significantly decreased by inserting an effective solution heat exchanger (SHX). Required driving temperature can be lowered by up to 10 °C using SHX with 0.80 effectiveness. The results also showed that effects of water content in the refrigerant could not be neglected in studying NH 3/H 2O ARS because it affects both COP and ECOP. Additionally, a large temperature glide in the evaporator can substantially decrease the ECOP.

Climate change has gained widespread attention, and the rapid growth of the economy in China has generated a considerable amount of carbon emissions. Zhejiang Province was selected as a study area. First, the energy-related carbon emissions from 2000 to 2014 were accounted for, and then the Logarithmic Mean Divisia Index (LMDI) decomposition model was applied to analyse the driving factors underlying the carbon emissions. Finally, three scenarios (inertia, comparative decoupling and absolute decoupling) for 2020 and 2030 were simulated based on the low-carbon city and Human Impact Population Affluence Technology (IPAT) models. Given the abundant straw resources in Northeast China and the huge external costs associated with fossil fuels, straw-based biomass power plants have emerged as a popular alternative to coal-fired power plants. The sustainability of these green alternatives depends on straw supply from farmers, yet little is known about their perceptions regarding such supply because of a lack of cooperation in the supply chain.

To better understand farmers’ opinions on supplying straw, this study examined their trust in middlemen, perceptions regarding risk in straw supply, the possibility of reducing transaction costs, and their willingness to supply straw. Data were collected from 275 farmers in the national bioenergy industry area in Wangkui County, Northeast China. We investigated the theoretical and empirical connections between trust and risk perception, trust and the possibility of reducing transaction costs, and trust and willingness to supply straw.

The results indicated that education, income, and trust factors explained farmers’ risk perceptions, the possibility that they will reduce transaction costs, and their willingness to supply straw. On the basis of the analysis, a model of the influence of trust on straw supply was established.

The overall findings indicated that biomass power plants and middlemen must build trusting relationships with farmers to ensure sustainable biomass supply. The long term ambition of energy security and solidarity, coupled with the environmental concerns of problematic waste accumulation, is addressed via the proposed waste-to-fuel technology.

Plastic waste is converted into automotive diesel fuel via a two-step thermochemical process based on pyrolysis and hydrotreatment. Plastic waste was pyrolyzed in a South East Asia plant rendering pyrolysis oil, which mostly consisted of middle-distillate (naphtha and diesel) hydrocarbons. The diesel fraction (170–370 °C) was fractionated, and its further upgrade was assessed in a hydroprocessing pilot plant at the Centre for Research and Technology Hellas (CERTH) in Greece. The final fuel was evaluated with respect to the diesel fuel quality specifications EN 590, which characterized it as a promising alternative diesel pool component with excellent ignition quality characteristics and low back end volatility. State-of-charge (SOC) estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. Canoscan Lide 20 Software Windows 7 64 Bit. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation.

This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter.

After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC) of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters.

This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea. While solar energy is the most efficient energy source for heating, many problems can occur when the capacity selection of the system is wrong: a definite possibility in a place where the seasonal climate change is large, such as Korea. For example, if a system is designed for use in the winter, the system will be overloaded if it does not discard the energy it collects during the summer months. Conversely, if the capacity of the system is in accordance with the summer season demand, it will be necessary to input supplementary energy in the winter season. Solar energy also depends on the altitude and azimuth of the sun, and the amount of energy collected on the slope depends on the latitude of the area in which it is installed.

Therefore, this study is divided into investigating the collection energy, heat radiation energy and auxiliary energy input according to the installation angle of the solar collector and the capacity of the heat storage tank according to latitude of the installation area. To this end, we formulate appropriate energy equations. Simulation coding was performed to track the temperature changes in each part.

Additionally, we considered the amount of solar energy that can be effectively used, not simply the amount of solar energy collected, by substituting the actual hot water usage schedule. In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispatching electric power systems. Although this type of source cannot be programmed, their production can be predicted using soft computing techniques that consider weather forecasts, reducing the imbalance costs paid to the transmission system operator (TSO). The problem is mainly that the forecasting procedures used by the TSO, distribution system operator (DSO) or large producers and they are too expensive, as they use complex algorithms and detailed meteorological data that have to be bought, this can represent an excessive charge for small-scale producers, such as prosumers. In this paper, a cheap photovoltaic (PV) production forecasting method, in terms of reduced computational effort, free-available meteorological data and implementation is discussed, and the economic results regarding the imbalance costs due to the utilization of this method are analyzed. The economic analysis is carried out considering several factors, such as the month, the day type, and the accuracy of the forecasting method.

The user can utilize the implemented method to know and reduce the imbalance costs, by adopting particular load management strategies. Wind is the most used renewable energy source (RES) in the European Union and Poland. Due to the legal changes in the scope of RES in Poland, there are plans to develop offshore wind farms at the expense of onshore ones. On the other hand, the success of an offshore wind farm is primarily determined by its location.

Therefore, the aim of this study is to select offshore wind farm locations in Poland, based on sustainability assessment, which is an inherent aspect of RES decision-making issues. To accomplish the objectives of this research, PROSA (PROMETHEE for Sustainability Assessment) method, a new multi-criteria method is proposed. Like PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation), PROSA is transparent for decision makers and is easy to use; moreover, it provides the analytical tools available in PROMETHEE, i.e., the sensitivity and GAIA (Geometrical Analysis for Interactive Assistance) analyses. However, PROSA is characterized by a lower degree of criteria compensation than PROMETHEE. Thus, it adheres in a higher degree to the strong sustainability paradigm.

The study also compared the solutions of the decision problem obtained with the use of PROSA and PROMETHEE methods. The compared methods demonstrated a high concurrence of the recommended decision-making variant of location selection, from methodological and practical points of view.

At the same time, the conducted research allowed to confirm that the PROSA method recommends more sustainable decision-making variants, and that the ranking it builds is less sensitive to changes in criteria weights. Therefore, it is more stable than the PROMETHEE-based ranking.

() Alternative fuels are an important aspect of transportation and energy production. The study of Alternative fuels are an important aspect of transportation and energy production.

The study of bioalcohols combustion and pollutant formation in spark ignition power units is essential, especially for direct injection engines. Within this context, and with particle emissions becoming an ever-pressing matter, optical techniques provide substantial insight into local phenomena and constitute a solid background for developing optimized control strategies. Waste management and energy systems are often interlinked, either directly by waste-to-energy technologies, or indirectly as processes for recovery of resources—such as materials, oils, manure, or sludge—use energy in their processes or substitute conventional production of the commodities for which the recycling processes provide raw materials. A special issue in Energies on the topic of “Energy and Waste Management” attained a lot of attention from the scientific community. In particular, papers contributing to improved understanding of the combined management of waste and energy were invited.

In all, 9 papers were published out of 24 unique submissions. The papers cover technical topics such as leaching of heavy metals, pyrolysis, and production of synthetic natural gas in addition to different systems assessments of horse manure, incineration, and complex future scenarios at a national level. All papers except one focused on energy recovery from waste. That particular paper focused on waste management of infrastructure in an energy system (wind turbines). Published papers illustrate research in the field of energy and waste management on both a current detailed process level as well as on a future system level. Knowledge gained on both types is necessary to be able to make progress towards a circular economy.

In this research, the development of a diesel engine thermal overload monitoring system is presented with applications and test results. The designed diesel engine thermal overload monitoring system consists of two set of sensors, i.e., a lambda sensor to measure the oxygen concentration and a fast response thermocouple to measure the temperature of the gas leaving the cylinder. A medium speed Ruston diesel engine is instrumented to measure the required engine process parameters, measurements are taken at constant load and variable fuel delivery i.e., normal and excessive injection. It is indicated that with excessive injection, the test engine is of high risk to be operated at thermal overload condition. Further tests were carried out on a Sulzer 7RTA84T engine to explore the influence of engine operating at thermal overload condition on exhaust gas temperature and oxygen concentration in the blow down gas.

It is established that a lower oxygen concentration in the blow down gas corresponds to a higher exhaust gas temperature. The piston crown wear rate will then be much higher due to the high rate of heat transfer from a voluminous flame. Various crops can be considered as potential bioenergy and biofuel production feedstocks.

The selection of the crops to be cultivated for that purpose is based on several factors. For an objective comparison between different crops, a common framework is required to assess their economic or energetic performance. In this paper, a computational tool for the energy cost evaluation of multiple-crop production systems is presented. All the in-field and transport operations are considered, providing a detailed analysis of the energy requirements of the components that contribute to the overall energy consumption. A demonstration scenario is also described.

The scenario is based on three selected energy crops, namely Miscanthus, Arundo donax and Switchgrass. The tool can be used as a decision support system for the evaluation of different agronomical practices (such as fertilization and agrochemicals application), machinery systems, and management practices that can be applied in each one of the individual crops within the production system.

Within the context of ever wider expansion of direct injection in spark ignition engines, this investigation was aimed at improved understanding of the correlation between fuel injection strategy and emission of nanoparticles. Measurements performed on a wall guided engine allowed identifying the mechanisms involved in the formation of carbonaceous structures during combustion and their evolution in the exhaust line. In-cylinder pressure was recorded in combination with cycle-resolved flame imaging, gaseous emissions and particle size distribution. This complete characterization was performed at three injection phasing settings, with butanol and commercial gasoline.

Optical accessibility from below the combustion chamber allowed visualization of diffusive flames induced by fuel deposits; these localized phenomena were correlated to observed changes in engine performance and pollutant species. With gasoline fueling, minor modifications were observed with respect to combustion parameters, when varying the start of injection. The alcohol, on the other hand, featured marked sensitivity to the fuel delivery strategy.

Even though the start of injection was varied in a relatively narrow crank angle range during the intake stroke, significant differences were recorded, especially in the values of particle emissions. This was correlated to the fuel jet-wall interactions; the analysis of diffusive flames, their location and size confirmed the importance of liquid film formation in direct injection engines, especially at medium and high load. In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance.

The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective. From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox. The paper presents the concept of a hybrid power system with additional energy storage to support electric vehicles (EVs) charging stations. The aim is to verify the possibilities of mutual cooperation of individual elements of the system from the point of view of energy balances and to show possibilities of utilization of accumulation for these purposes using mathematical modeling. The description of the technical solution of the concept is described by a mathematical model in the Matlab Simulink programming environment.

Individual elements of the assembled model are described in detail, together with the algorithm of the control logic of charging the supporting storage system. The resulting model was validated via an actual small-scale hybrid system (HS). Within the outputs of the mathematical model, two simulation scenarios are presented, with the aid of which the benefits of the concept presented were verified. Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available.

The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system.

In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging.

Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation. In this work, a practical methodology is proposed to analyze, before undertaking a large investment, an outdoor lighting installation renewal with light-emitting diode (LED) luminaires. The main problems found in many of the luminaires tested are associated with inrush peak currents in cold start (which may cause ignition problems with random shutdowns), the harmonic distortions caused by their AC/DC associated electronic nature driver, and their working and efficiency dependency on the ambient temperature.

All these issues have been tested in the context of a large metal halide (MH) to LED luminaires lighting point renewal where six commercial LED projectors have been analyzed with the above considerations. This research has isolated a single-phase circuit powered with constant stabilized 230 V AC voltage source in a real public lighting installation. All of them have been sequentially installed and their main electrical and power-quality parameters measured and recorded. The results indicate that each luminaire option will influence the expected long-term reliability (>50.000 h or more as expressed by the U.S. Department of Energy) of the lighting installation (in the case poor power quality is generated on the grid). The economic analysis made to estimate the profitability of the investment may be severely affected by the difference between the declared and the real consumption values in which they perform in our specific installation. In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields.

First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques. Current commercial battery management systems (BMSs) do not provide adequate information in real time to mitigate issues of battery cells such as thermal runway.

This paper explores and evaluates the integration of fiber optic Bragg grating (FBG) sensors inside lithium-ion battery (LiB) coin cells. Strain and internal and external temperatures were recorded using FBG sensors, and the battery cells were evaluated at a cycling C/20 rate.

The preliminary results present scanning electron microscope (SEM) images of electrode degradation upon sensor integration and the systematic process of sensor integration to eliminate degradation in electrodes during cell charge/discharge cycles. Recommendation for successful FBG sensor integration is given, and the strain and temperature data is presented.

The FBG sensor was placed on the inside of the coin cell between the electrodes and the separator layers towards the most electrochemically active area. On the outside, the temperature of the coin cell casing as well as the ambient temperature was recorded. Results show stable strain behavior within the cell and about 10 °C difference between the inside of the coin cell and the ambient environment over time during charging/discharging cycles. This study is intended to contribute to the safe integration of FBG sensors inside hermetically sealed batteries and to detection of real-time temperature and strain gradient inside a cell, ultimately improving reliability of current BMSs. This paper evaluates the performance of a fuel cell/battery vehicle with an on-board autothermal reformer, fed by different liquid and gaseous hydrocarbon fuels. A sensitivity analysis is performed to investigate the system behavior under the variation of the steam to carbon and oxygen to carbon ratios. This is done in order to identify the most suitable operating conditions for a direct on-board production of hydrogen to be used in a high temperature polymer electrolyte membrane fuel cell.

The same system should be able to process different fuels, to allow the end-user to freely decide which one to use to refuel the vehicle. Hence, the obtained operating conditions result in a trade-off between system flexibility as the feeding fuel changes, CO poisoning effect on the fuel cell and overall efficiency. The system is thus coupled to a high temperature fuel cell, modeled by means of a self-made tool, able to reproduce the polarization curve as the input syngas composition varies, and the overall system is afterwards tested on a plug-in fuel cell/battery vehicle simulator, in order to provide a thorough feasibility analysis, focusing on the entire system efficiency. Results show that a proper energy management strategy can mitigate the effect of the fuel variation on the reformer efficiency, allowing for good overall powertrain performance.

In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems. Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems into future sustainable energy systems. The models are diverse and often end up with different results and recommendations. This paper analyses this diversity of models and their implicit or explicit theoretical backgrounds. In particular, two archetypes are defined and compared. On the one hand, the prescriptive investment optimisation or optimal solutions approach.

On the other hand the analytical simulation or alternatives assessment approach. Awareness of the dissimilar theoretical assumption behind the models clarifies differences between the models, explains dissimilarities in results, and provides a theoretical and methodological foundation for understanding and interpreting results from the two archetypes. In this study, the environmental impacts of monolithic silicon heterojunction organometallic perovskite tandem cells (SHJ-PSC) and single junction organometallic perovskite solar cells (PSC) are compared with the impacts of crystalline silicon based solar cells using a prospective life cycle assessment with a time horizon of 2025.

This approach provides a result range depending on key parameters like efficiency, wafer thickness, kerf loss, lifetime, and degradation, which are appropriate for the comparison of these different solar cell types with different maturity levels. The life cycle environmental impacts of SHJ-PSC and PSC solar cells are similar or lower compared to conventional crystalline silicon solar cells, given comparable lifetimes, with the exception of mineral and fossil resource depletion. A PSC single-junction cell with 20% efficiency has to exceed a lifetime of 24 years with less than 3% degradation per year in order to be competitive with the crystalline silicon single-junction cells. If the installed PV capacity has to be maximised with only limited surface area available, the SHJ-PSC tandem is preferable to the PSC single-junction because their environmental impacts are similar, but the surface area requirement of SHJ-PSC tandems is only 70% or lower compared to PSC single-junction cells. The SHJ-PSC and PSC cells have to be embedded in proper encapsulation to maximise the stability of the PSC layer as well as handled and disposed of correctly to minimise the potential toxicity impacts of the heavy metals used in the PSC layer.

The injection of CO 2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance.

This study proposed the comprehensive evaluations of a water-alternating-CO 2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO 2 production, and net CO 2 storage were accurately reflected by integrating the generated network models. More importantly, since the networks could simulate a series of injection processes, the sequent variations of those technical issues were well presented, indicating the distinct application of ANN in this study compared to previous works. The economic terms were also briefly introduced for a given fiscal condition which included sufficient concerns for a general CO 2 flooding project, in a range of possible oil prices. Using the ANN models, the net present value (NPV) optimization results for several specific cases apparently expressed the profitability of the present enhanced oil recovery (EOR) project according to the unstable oil prices, and most importantly provided the most relevant injection schedules corresponding with each different scenario.

Obviously, the methodology of applying traditional ANN as shown in this study can be adaptively adjusted for any other EOR project, and in particular, since the models have demonstrated their flexible capacity for economic analyses, the method can be promisingly developed to engage with other economic tools on comprehensively assessing the project. Multi-terminal high voltage direct current transmission based on voltage source converter (VSC-HVDC) grids can connect non-synchronous alternating current (AC) grids to a hybrid alternating current and direct current (AC/DC) power system, which is one of the key technologies in the construction of smart grids. However, it is still a problem to control the converter to achieve the function of each AC system sharing the reserve capacity of the entire network. This paper proposes an improved control strategy based on the slope control of the DC voltage and AC frequency (V–f slope control), in which the virtual inertia is introduced. This method can ensure that each AC sub-system shares the primary frequency control function. Additionally, with the new control method, it is easy to apply the secondary frequency control method of traditional AC systems to AC/DC hybrid systems to achieve the steady control of the DC voltage and AC frequency of the whole system. Most importantly, the new control method is better than the traditional control method in terms of dynamic performance.

In this paper, a new control method is proposed, and the simulation model has been established in Matlab/Simulink to verify the effectiveness of the proposed control method. Many previous contributions to methods of forecasting the performance of polymer flooding using artificial neural networks (ANNs) have been made by numerous researchers previously. In most of those forecasting cases, only a single polymer slug was employed to meet the objective of the study. The intent of this manuscript is to propose an efficient recovery factor prediction tool at different injection stages of two polymer slugs during polymer flooding using an ANN.

In this regard, a back-propagation algorithm was coupled with six input parameters to predict three output parameters via a hidden layer composed of 10 neurons. Evaluation of the ANN model performance was made with multiple linear regression. With an acceptable correlation coefficient, the proposed ANN tool was able to predict the recovery factor with errors of. Wind towers or wind catchers, as passive cooling systems, can provide natural ventilation in buildings located in hot, arid regions. These natural cooling systems can provide thermal comfort for the building inhabitants throughout the warm months. In this paper, a modular design of a wind tower is introduced.

The design, called a modular wind tower with wetted surfaces, was investigated experimentally and analytically. To determine the performance of the wind tower, air temperature, relative humidity (RH) and air velocity were measured at different points. Measurements were carried out when the wind speed was zero. The experimental results were compared with the analytical ones. The results illustrated that the modular wind tower can decrease the air temperature significantly and increase the relative humidity of airflow into the building. The average differences for air temperature and air relative humidity between ambient air and air exiting from the wind tower were approximately 10 °C and 40%, respectively. The main advantage of the proposed wind tower is that it is a modular design that can reduce the cost of wind tower construction.

When medium- or high-voltage power conversion is preferred for renewable energy sources, multilevel power converters have received much of the interest in this area as methods for enhancing the conversion efficiency and cost effectiveness. In such cases, multilevel, multi-input multi-output (MIMO) configurations of DC-DC converters come to the scenario for integrating several sources together, especially considering the stringent regulatory needs and the requirement of multistage power conversion systems.

Considering the above facts, a three-level dual input dual output (DIDO) buck-boost converter, as the simplest form of MIMO converter, is proposed in this paper for DC-link voltage regulation. The capability of this converter for cross regulating the DC-link voltage is analyzed in detail to support a three-level neutral point clamped inverter-based grid connection in the future. The cross-regulation capability is examined under a new type of pulse delay control (PDC) strategy and later compared with a three-level boost converter (TLBC). Compared to conventional boost converters, the high-voltage three-level buck boost converter (TLBBC) with PDC exhibits a wide controllability range and cross regulation capability.

These enhanced features are extremely important for better regulating variable output renewable energy sources such as solar, wind, wave, marine current, etc. The simulation and experimental results are provided to validate the claim. Modern economies run on the backbone of electricity as one of major factors behind industrial development. India is endowed with plenty of natural resources and the majority of electricity within the country is generated from thermal and hydro-electric plants. A few nuclear plants assist in meeting the national requirements for electricity but still many rural areas remain uncovered. As India is primarily a rural agrarian economy, providing electricity to the remote, undeveloped regions of the country remains a top priority of the government.

A vital, untapped source is livestock generated biomass which to some extent has been utilized to generate electricity in small scale biogas based plants under the government's thrust on rural development. This study is a preliminary attempt to correlate developments in this arena in the Asian region, as well as the developed world, to explore the possibilities of harnessing this resource in a better manner. The current potential of 2600 million tons of livestock dung generated per year, capable of yielding 263,702 million m 3 of biogas is exploited. Our estimates suggest that if this resource is utilized judiciously, it possesses the potential of generating 477 TWh (Terawatt hour) of electrical energy per annum. The objective of this work was to optimize and to evaluate a solar-driven trigeneration system which operates with nanofluid-based parabolic trough collectors. The trigeneration system includes an organic Rankine cycle (ORC) and an absorption heat pump operating with LiBr-H 2O which is powered by the rejected heat of the ORC.

Toluene, n-octane, Octamethyltrisiloxane (MDM) and cyclohexane are the examined working fluids in the ORC. The use of CuO and Al 2O 3 nanoparticles in the Syltherm 800 (base fluid) is investigated in the solar field loop. The analysis is performed with Engineering Equation Solver (EES) under steady state conditions in order to give the emphasis in the exergetic optimization of the system. Except for the different working fluid investigation, the system is optimized by examining three basic operating parameters in all the cases. The pressure in the turbine inlet, the temperature in the ORC condenser and the nanofluid concentration are the optimization variables. According to the final results, the combination of toluene in the ORC with the CuO nanofluid is the optimum choice. The global maximum exergetic efficiency is 24.66% with pressure ratio is equal to 0.7605, heat rejection temperature 113.7 °C and CuO concentration 4.35%.

Solar desiccant cooling is widely considered as an attractive replacement for conventional vapor compression air conditioning systems because of its environmental friendliness and energy efficiency advantages. The system performance of solar desiccant cooling strongly depends on the input parameters associated with the system components, such as the solar collector, storage tank and backup heater, etc. In order to understand the implications of different design parameters on the system performance, this study has conducted a parametric analysis on the solar collector area, storage tank volume, and backup heater capacity of a solid solar desiccant cooling system for an office building in Brisbane, Australia climate. In addition, a parametric analysis on the outdoor air humidity ratio control set-point which triggers the operation of the desiccant wheel has also been investigated. The simulation results have shown that either increasing the storage tank volume or increasing solar collector area would result in both increased solar fraction ( SF) and system coefficient of performance ( COP), while at the same time reduce the backup heater energy consumption.

However, the storage tank volume is more sensitive to the system performance than the collector area. From the economic aspect, a storage capacity of 30 m 3/576 m 2 has the lowest life cycle cost ( LCC) of $405,954 for the solar subsystem. In addition, 100 kW backup heater capacity is preferable for the satisfaction of the design regeneration heating coil hot water inlet temperature set-point with relatively low backup heater energy consumption.

Moreover, an outdoor air humidity ratio control set-point of 0.008 kgWater/kgDryAir is more reasonable, as it could both guarantee the indoor design conditions and achieve low backup heater energy consumption. In this study, the physical properties of briquettes produced from two different biomass feedstocks (sawdust and date palm trunk) and different plastic wastes, without using any external binding agent, were investigated.

The biomass feedstocks were blended with different ratios of two waste from electrical and electronic equipment (WEEE) plastics (halogen-free wire and printed circuit boards (PCBs)) and automotive shredder residues (ASR). The briquettes production is studied at different waste proportions (10–30%), pressures (22–67 MPa) and temperatures (room–130 °C).

Physical properties as density and durability rating were measured, usually increasing with temperature. Palm trunk gave better results than sawdust in most cases, due to its moisture content and the extremely fine particles that are easily obtained. This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm.

Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production. As an alternative to the greedy control strategy, we study a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions for maximizing the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ a data-driven optimization method that seeks to find the optimum control actions using only the power measurement data collected from the wind turbines in a wind farm. In particular, we employ the Bayesian Ascent (BA) algorithm, a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept.

Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production; and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data. The focus on alternative energy sources has increased significantly throughout the last few decades, leading to a considerable development in the wave energy sector. In spite of this, the sector cannot yet be considered commercialized, and many challenges still exist, in which mooring of floating wave energy converters is included. Different methods for assessment and design of mooring systems have been described by now, covering simple quasi-static analysis and more advanced and sophisticated dynamic analysis. Design standards for mooring systems already exist, and new ones are being developed specifically forwave energy converter moorings, which results in other requirements to the chosen tools, since these often have been aimed at other offshore sectors. The present analysis assesses a number of relevant commercial software packages for full dynamic mooring analysis in order to highlight the advantages and drawbacks.

The focus of the assessment is to ensure that the software packages are capable of fulfilling the requirements of modeling, as defined in design standards and thereby ensuring that the analysis can be used to get a certified mooring system. Based on the initial assessment, the two software packages DeepC and OrcaFlex are found to best suit the requirements. They are therefore used in a case study in order to evaluate motion and mooring load response, and the results are compared in order to provide guidelines for which software package to choose. In the present study, the OrcaFlex code was found to satisfy all requirements. Understanding mechanical behavior and permeability of coal at ambient and high temperature is key in optimizing high-temperature in-situ processes such as underground coal gasification. The main objectives of this study were to characterize thermal deformation, stress-strain behavior, and gas permeability of coal samples acquired from the Genesee coal mine in Central Alberta, Canada under various temperatures and confining stresses. These measurements were conducted in a high-pressure high-temperature triaxial apparatus.

Initial thermal expansion of the coal was followed by contraction in both axial and lateral directions at about 140 °C. This temperature corresponds to occurrence of pyrolysis in the coal. All specimens showed brittle behavior during shear while forming complex shear planes. The specimens exhibited compressional volumetric strain responses at all temperatures. Deformation localization initiated at various stage during shearing.

Specimens sheared at 200 °C showed higher peak stresses and larger axial strains compared to those tested at room temperature (24 °C). Fluctuations of permeability were observed with confining stress and temperature. Permeability dropped at 80 °C due to thermal expansion of coal and closure of initial fractures; however, it increased at 140 and 200 °C due to a combined response of thermal expansion and pyrolysis. Small axial strain during shear was observed to reduce permeability. The utilization of the captured CO 2 as a carbon source for the production of energy storage media offers a technological solution for overcoming crucial issues in current energy systems.

Solar energy production generally does not match with energy demand because of its intermittent and non-programmable nature, entailing the adoption of storage technologies. Hydrogen constitutes a chemical storage for renewable electricity if it is produced by water electrolysis and is also the key reactant for CO 2 methanation (Sabatier reaction). The utilization of CO 2 as a feedstock for producing methane contributes to alleviate global climate changes and sequestration related problems.

The produced methane is a carbon neutral gas that fits into existing infrastructure and allows issues related to the aforementioned intermittency and non-programmability of solar energy to be overcome. In this paper, an experimental apparatus, composed of an electrolyzer and a tubular fixed bed reactor, is built and used to produce methane via Sabatier reaction. The objective of the experimental campaign is the evaluation of the process performance and a comparison with other CO 2 valorization paths such as methanol production. The investigated pressure range was 2–20 bar, obtaining a methane volume fraction in outlet gaseous mixture of 64.75% at 8 bar and 97.24% at 20 bar, with conversion efficiencies of, respectively, 84.64% and 99.06%.

The methanol and methane processes were compared on the basis of an energy parameter defined as the spent energy/stored energy. It is higher for the methanol process (0.45), with respect to the methane production process (0.41–0.43), which has a higher energy storage capability. The latest technological developments are challenging for finding new solutions to mitigate the massive integration of renewable-based electricity generation in the electrical networks and to support new and dynamic energy and ancillary services markets.

Smart meters have become ubiquitous equipment in the low voltage grid, enabled by the decision made in many countries to support massive deployments. The smart meter is the only equipment mandatory to be mounted when supplying a grid connected user, as it primarily has the function to measure delivered and/or produced energy on its common coupling point with the network, as technical and legal support for billing. Active distribution networks need new functionalities, to cope with the bidirectional energy flow behaviour of the grid, and many smart grid requirements need to be implemented in the near future. However there is no real coupling between smart metering systems and smart grids, as there is not yet a synergy using the opportunity of the high deployment level in smart metering. The paper presents a new approach for managing the smart metering and smart grid orchestration by presenting a new general design based on an unbundled smart meter (USM) concept, labelled as next generation open real-time smart meters (NORM), for integrating the smart meter, phasor measurement unit (PMU) and cyber-security through an enhanced smart metering gateway (SMG). NORM is intended to be deployed everywhere at the prosumer’s interface to the grid, as it is usually now done with the standard meter. Furthermore, rich data acquired from NORM is used to demonstrate the potential of assessing grid data inconsistencies at a higher level, as function to be deployed in distribution security monitoring centers, to address the higher level cyber-security threats, such as false data injections and to allow secure grid operations and complex market activities at the same time.

The measures are considering only non-sensitive data from a privacy perspective, and is therefore able to be applied everywhere in the grid, down to the end-customer level, where a citizen’s personal data protection is an important aspect. Smart grid (SG) will be one of the major application domains that will present severe pressures on future communication networks due to the expected huge number of devices that will be connected to it and that will impose stringent quality transmission requirements. To address this challenge, there is a need for a joint management of both monitoring and communication systems, so as to achieve a flexible and adaptive management of the SG services. This is the issue addressed in this paper, which provides the following major contributions. We define a new strategy to optimize the accuracy of the state estimation (SE) of the electric grid based on available network bandwidth resources and the sensing intelligent electronic devices (IEDs) installed in the field.

In particular, we focus on phasor measurement units (PMUs) as measurement devices. We propose the use of the software defined networks (SDN) technologies to manage the available network bandwidth, which is then assigned by the controller to the forwarding devices to allow for the flowing of the data streams generated by the PMUs, by considering an optimization routine to maximize the accuracy of the resulting SE. Additionally, the use of SDN allows for adding and removing PMUs from the monitoring architecture without any manual intervention. We also provide the details of our implementation of the SDN solution, which is used to make simulations with an IEEE 14-bus test network in order to show performance in terms of bandwidth management and estimation accuracy. A post-fracturing evaluation is essential to optimize a fracturing design for a multi-stage fractured well located in unconventional reservoirs. To accomplish this task, a production logging tool (PLT) can be utilized to provide the oil production rate of each fracturing stage. In this research, a practical method is proposed to integrate PLT and surface production data into a reservoir model.

It applies the ensemble smoother for history-matching to integrate various kinds of dynamic data. To investigate the validity of the proposed method, three cases are designed according to the frequency of PLT surveys. Each fracture half-length calibrated by PLT data is similar to the true value, and the dynamic behavior also has the same trend as true production behavior. Integration with PLT data can reduce error ratios for fracture half-length down to 48%. In addition, it presents the applicability of reserve prediction and uncertainty assessment.

It has been proven that the more frequently PLTs are surveyed, the more accurate the results. By sensitivity analysis of PLT frequency—a cost-effective strategy—a combination of only one PLT survey and continuous surface production data is employed to demonstrate this proposed concept. Industrial hydrogen production via alkaline water electrolysis (AEL) is a mature hydrogen production method. One argument in favor of AEL when supplied with renewable energy is its environmental superiority against conventional fossil-based hydrogen production.

However, today electricity from the national grid is widely utilized for industrial applications of AEL. Also, the ban on asbestos membranes led to a change in performance patterns, making a detailed assessment necessary.

This study presents a comparative Life Cycle Assessment (LCA) using the GaBi software (version 6.115, thinkstep, Leinfelden-Echterdingen, Germany), revealing inventory data and environmental impacts for industrial hydrogen production by latest AELs (6 MW, Zirfon membranes) in three different countries (Austria, Germany and Spain) with corresponding grid mixes. The results confirm the dependence of most environmental effects from the operation phase and specifically the site-dependent electricity mix. Construction of system components and the replacement of cell stacks make a minor contribution. At present, considering the three countries, AEL can be operated in the most environmentally friendly fashion in Austria. Concerning the construction of AEL plants the materials nickel and polytetrafluoroethylene in particular, used for cell manufacturing, revealed significant contributions to the environmental burden. Many future electricity scenarios, including those from the International Energy Agency, use natural gas to bridge the transition to renewables, in particular as a means of balancing intermittent generation from new renewables.

Given that such strategies may be inconsistent with strategies to limit climate change to below 2 °C, we address the question of whether such use of gas is necessary or cost effective. We conduct a techno-economic case study of Switzerland, using a cost optimization model.

We explore a range of electricity costs, comparing scenarios in which gas is used as a source of base-load power, a source of balancing capacity, and not used at all. Costs at the high end of the range show that a complete decarbonization increases system-wide costs by 3% compared to a gas bridging scenario, and 13–46% compared to a carbon-intensive scenario, depending on the relative shares of solar and wind. Costs at the low end of the range show that system-wide costs are equal or lower for both completely decarbonized and gas bridging scenarios.

In conclusion, gas delivers little to no cost savings as a bridging fuel in a system that switches to wind and solar. The latest forecasts on the upcoming effects of climate change are leading to a change in the worldwide power production model, with governments promoting clean and renewable energies, as is the case of tidal energy. Nevertheless, it is still necessary to improve the efficiency and lower the costs of the involved processes in order to achieve a Levelized Cost of Energy (LCoE) that allows these devices to be commercially competitive. In this context, this paper presents a novel complementary control strategy aimed to maximize the output power of a Tidal Stream Turbine (TST) composed of a hydrodynamic turbine, a Doubly-Fed Induction Generator (DFIG) and a back-to-back power converter. In particular, a global control scheme that supervises the switching between the two operation modes is developed and implemented. When the tidal speed is low enough, the plant operates in variable speed mode, where the system is regulated so that the turbo-generator module works in maximum power extraction mode for each given tidal velocity. For this purpose, the proposed back-to-back converter makes use of the field-oriented control in both the rotor side and grid side converters, so that a maximum power point tracking-based rotational speed control is applied in the Rotor Side Converter (RSC) to obtain the maximum power output.

Analogously, when the system operates in power limitation mode, a pitch angle control is used to limit the power captured in the case of high tidal speeds. Both control schemes are then coordinated within a novel complementary control strategy. The results show an excellent performance of the system, affording maximum power extraction regardless of the tidal stream input. This work was devoted to study experimentally and numerically the oxygen carrier (NiO/NiAl 2O 4) performances for Chemical-Looping Combustion applications.

Various kinetic models including Shrinking Core, Nucleation Growth and Modified Volumetric models were investigated in a one-dimensional approach to simulate the reactive mass transfer in a fixed bed reactor. The preliminary numerical results indicated that these models are unable to fit well the fuel breakthrough curves. Therefore, the oxygen carrier was characterized after several operations using Scanning Electronic Microscopy (SEM) coupled with equipped with an energy dispersive X-ray spectrometer (EDX). These analyses showed a layer rich in nickel on particle surface. Below this layer, to a depth of about 10 µm, the material was low in nickel, being the consequence of nickel migration. From these observations, two reactive sites were proposed relative to the layer rich in nickel (particle surface) and the bulk material, respectively.

Then, a numerical model, taking into account of both reactive sites, was able to fit well fuel breakthrough curves for all the studied operating conditions. The extracted kinetic parameters showed that the fuel oxidation was fully controlled by the reaction and the effect of temperature was not significant in the tested operating conditions range. The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun’s radiation.

The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. Voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions.

This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools.

Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances. The current generation of nuclear reactors are evolutionary in design, mostly based on the technology originally designed to power submarines, and dominated by light water reactors.

The aims of the Generation IV consortium are driven by sustainability, safety and reliability, economics, and proliferation resistance. The aims are extended here to encompass the ultimate and universal vision for strategic development of energy production, the “perpetuum mobile”—at least as close as possible. We propose to rethink nuclear reactor design with the mission to develop an innovative system which uses no fresh resources and produces no fresh waste during operation as well as generates power safe and reliably in economic way. The results of the innovative simulations presented here demonstrate that, from a theoretical perspective, it is feasible to fulfil the mission through the direct reuse of spent nuclear fuel from currently operating reactors as the fuel for a proposed new reactor. The produced waste is less burdensome than current spent nuclear fuel which is used as feed to the system. However, safety, reliability and operational economics will need to be demonstrated to create the basis for the long term success of nuclear reactors as a major carbon free, sustainable, and applied highly reliable energy source.

There is a need for energy storage to improve the efficiency and effectiveness of energy distribution with the increasing penetration of renewable energy sources. Among the various energy storage technologies being developed, ‘power-to-gas’ is one such concept which has gained interest due to its ability to provide long term energy storage and recover the energy stored through different energy recovery pathways. Incorporation of such systems within the energy infrastructure requires analysis of the key factors influencing the operation of electrolyzers and hydrogen storage.

This study focusses on assessing the benefits power-to-gas energy storage while accounting for uncertainty in the following three key parameters that could influence the operation of the energy system: (1) hourly electricity price; (2) the number of fuel cell vehicles serviced; and (3) the amount of hydrogen refueled. An hourly time index is adopted to analyze how the energy hub should operate under uncertainty. The results show that there is a potential economic benefit for the power-to-gas system if it is modeled using the two-stage stochastic programming approach in comparison to a deterministic optimization study. The power-to-gas system also offers environmental benefits both from the perspective of the producer and end user of hydrogen. Screw-type expanders offer excellent prospects for energy conversion in lower and medium power ranges, for instance as expansion engines in Rankine cycles with regard to either waste or geothermal heat recovery. With the aim of identifying the potential in organic Rankine cycle (ORC) power systems, an oil-flooded twin-screw expander without timing gears was designed and experimentally investigated in an ORC with R245fa as working fluid.

Here, the scope for the experimental determination of the expander characteristic map was limited by the test rig specifications. Based on the experimental results, a multi-chamber model of the test twin-screw expander was calibrated and theoretical approaches according to mechanical and hydraulic loss calculation were applied. Consequently, the expander’s complete characteristic map could be calculated. Furthermore, relevant mechanisms influencing the operational behaviour of oil-flooded twin-screw expanders were identified and analysed in-depth.

This paper presents a study to assess how wind turbines could increase their energy yield when their grid connection point is not strong enough for the rated power. It is state of the art that in such situations grid operators impose feed-in management on the affected wind turbines, i.e., the maximum power is limited. For this study a 5 MW wind turbine is introduced in a small grid that has only limited power transfer capabilities to the upstream power system. Simulations of one particular day are conducted with the electric load, the temperature, and the wind speed as measured on that day. This simulation is conducted twice: once with the 5 MW wind turbine controlled with conventional feed-in management, and a second time when its power is controlled flexibly, i.e., with continuous feed-in management. The results of these two simulations are compared in terms of grid performance, and in terms of mechanical stress on the 5 MW wind turbine.

Finally, the conclusion can be drawn that continuous feed-in management is clearly superior to conventional feed-in management. It exhibits much better performance in the grid in terms of energy yield and also in terms of constancy of voltage and temperature of grid equipment. Although it causes somewhat more frequent stress for the wind turbine, the maximum stress level is not increased. A DC-DC converter that can be applied for battery chargers with the power-capacity of over 7-kW for electric vehicles (EVs) is presented in this paper. Due to a new architecture, the proposed converter achieves a reduction of conduction losses at the primary side by as much as 50% and has many benefits such as much smaller circulating current, less duty-cycle loss, and lower secondary-voltage stress. In addition, its power handing capacity can be upsized easily with the use of two full-bridge inverters and two transformers. Besides, all the switches in the converter achieve zero-voltage switching (ZVS) during whole battery charging process, and the size of output filter can be significantly reduced.

The circuit configuration, operation, and relevant analysis are presented, followed by the experiment on a prototype realized with a 7-kW charger. The experimental results validate the theoretical analysis and show the effectiveness of the proposed converter as battery charger.

Turbo machinery is an essential part in the power generation cycle. However, it is the main source of noise that annoys workers and users, and contributes to environmental problems. Thus, it is important to reduce this noise when operating the power generation cycle. This noise is created by a flow instability on the trailing edge of the rotor blade—an airfoil that becomes a section of the rotor blade of the rotating machine—manufactured as a blunt trailing edge (T.E.), with a round or flatback shape, rather than the ideal sharp T.E. Shape, for the purposes of production and durability. This increases the tonal noise and flow-induced vibrations at a low frequency, owing to vortex shedding behind T.E.

When compared with a sharp T.E. In order to overcome this problem, the present study investigates the oblique T.E.

Shape using numerical simulations. In order to do so, flow was simulated using large eddy simulation (LES) and the noise was analyzed by acoustic analogy coupled with the LES result. Once the simulation results were verified using the flatback airfoil measurements of the Sandia National Laboratories, numerical prediction was performed to analyze the flow and the noise characteristics for the airfoils, which were modified to have oblique trailing edge angles of 60°, 45°, and 30°. From the simulation results of the oblique T.E. Airfoil, it could be seen that the vortex shedding frequency moves in accordance with the oblique angle and that the vortex shedding noise characteristics change according to the angle, when compared to the flatback T.E. Therefore, it is considered that modifying the flatback T.E.

Airfoil with an appropriate oblique angle can reduce noise and change the tonal frequency to a bandwidth that is suitable for mechanical systems. Multi-terminal Direct Current Transmission (MTDC) is an emerging and promising technology for the transmission of electricity and the main initiator of the development of MTDC grids is offshore wind generation.

However, prior to their construction, a thorough investigation of different aspects of their implementation and operation is required. In this research, an MTDC grid with voltage margin control consisting of voltage source converters (VSCs) and a high frequency cable model was implemented in Matlab/SIMULINK (R2015b, The MathWorks, Inc., Natick, MA, USA). Small-signal stability analysis was carried out to investigate the sensitivity of the grid’s interaction modes to the operating point, the structure of the grid, and the selection of the voltage controlling converter. Based on the findings of these analyses, a strategy for droop control method is proposed and demonstrated.

Accurate and reliable forecasting on energy-related carbon dioxide (CO 2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO 2 emissions with its driving forces, the accurate forecasting for CO 2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO 2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO 2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method.

Taking China’s CO 2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO 2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO 2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO 2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application. The influence of recycling on double-pass solar air collectors with welding of the V-corrugated absorber has been studied experimentally and theoretically. Welding the V-corrugated absorber and the recycle-effect concept to the solar air collector was proposed to strengthen the convective heat-transfer coefficient due to turbulence promotion.

Both the recycle effect and the V-corrugated absorber can effectively enhance the heat transfer efficiency compared to various designs such as single-pass, flat-plate double-pass, and double-pass wire mesh packed devices. Recycling operations and welding the V-corrugated absorber could enhance the collector efficiency by increasing the recycle ratio, incident solar radiations, and air mass flow rates. The most efficient and economical operating conditions were found at R ≈ 0.5, with relatively small hydraulic dissipated energy compensation. It was found that the turbulence intensity increase from welding the V-corrugated absorber into the solar air collector channel could compensate for the power consumption increase, when considering economic feasibility.

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data.

Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model.

The proposed model is also effective and practical in forecasting existing grid-connected PV power generation. The use of non-edible, second-generation feedstocks for the production of biodiesel has been an active area of research, due to its potential in replacing fossil diesel as well as its environmentally friendly qualities.

Despite this, more needs to be done to remove the technical barriers associated with biodiesel production and usage, to increase its quality as well as to widen the choice of available feedstocks; so as to avoid over-dependence on limited sources. This paper assesses the feasibility of using a local plant, Reutealis trisperma, whose seeds contain a high percentage of oil of up to 51%, as one of the possible feedstocks. The techno-economic and sensitivity analysis of biodiesel production from Reutealis trisperma oil as well as implementation aspects and environmental effects of the biodiesel plant are discussed. Analysis indicates that the 50 kt Reutealis trisperma biodiesel production plant has a life cycle cost of approximately $710 million, yielding a payback period of 4.34 years.

The unit cost of the biodiesel is calculated to be $0.69/L with the feedstock cost accounting for the bulk of the cost. The most important finding from this study is that the biodiesel from Reutealis trisperma oil can compete with fossil diesel, provided that appropriate policies of tax exemptions and subsidies can be put in place.

To conclude, further studies on biodiesel production and its limitations are necessary before the use of biodiesel from Reutealis trisperma oil may be used as a fuel source to replace fossil diesel. Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. Armin Van Buuren A State Of Trance 437 Download Games. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed.

The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC). The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD.

For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively.

The influence voltage level ( vl), voltage type ( vt), polarity/phases ( pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline ( d), wind velocity ( wv), and rainfall ( rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively. Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market.

However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability. The major challenges for the integration of solar collecting devices into a building envelope are related to the poor aesthetic view of the appearance of buildings in addition to the low efficiency in collection, transportation, and utilization of the solar thermal and electrical energy. To tackle these challenges, a novel design for the integration of solar collecting elements into the building envelope was proposed and discussed. This involves the dedicated modular and multiple-layer combination of the building shielding, insulation, and solar collecting elements.

On the basis of the proposed modular structure, the energy performance of the solar envelope was investigated by using the Energy-Plus software. It was found that the solar thermal efficiency of the modular envelope is in the range of 41.78–59.47%, while its electrical efficiency is around 3.51% higher than the envelopes having photovoltaic (PV) alone. The modular solar envelope can increase thermal efficiency by around 8.49% and the electrical efficiency by around 0.31%, compared to the traditional solar photovoltaic/thermal (PV/T) envelopes. Thus, we have created a new envelope solution with enhanced solar efficiency and an improved aesthetic view of the entire building. This paper presents a single-phase bidirectional current-source AC/DC converter for vehicle to grid (V2G) applications. The presented converter consists of a line frequency commutated unfolding bridge and an interleaved buck-boost stage. The low semiconductor losses of the line frequency commutated unfolding bridge contribute to the comparatively good efficiency of this converter.

The buck and boost operating modes of the interleaved buck-boost stage provide operation over a wide battery voltage range. The interleaved structure of the interleaved buck-boost stage results in lower battery current ripple.

In addition, sinusoidal input current, bidirectional power flow and reactive power compensation capability are also guaranteed. This paper presents the topology and operating principles of the presented converter. The feasibility of the converter is validated using MATLAB simulations, as well as experimental results. Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause.

Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds.

The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives. The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character.

The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations. Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids.

This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids.

The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation. Accurate modeling and forecasting monthly electricity consumption are the keys to optimizing energy management and planning.

This paper examines the seasonal characteristics of electricity consumption in Hong Kong—a subtropical city with 7 million people. Using the data from January 1970 to December 2014, two novel nonlinear seasonal models for electricity consumption in the residential and commercial sectors were obtained.

The models show that the city’s monthly residential and commercial electricity consumption patterns have different seasonal variations. Specifically, monthly residential electricity consumption (mainly for appliances and cooling in summer) has a quadratic relationship with monthly mean air temperature, while monthly commercial electricity consumption has a linear relationship with monthly mean air temperature. The nonlinear seasonal models were used to predict residential and commercial electricity consumption for the period January 2015–December 2016. The correlations between the predicted and actual values were 0.976 for residential electricity consumption and 0.962 for commercial electricity consumption, respectively. The root mean square percentage errors for the predicted monthly residential and commercial electricity consumption were 7.0% and 6.5%, respectively. The new nonlinear seasonal models can be applied to other subtropical urban areas, and recommendations on the reduction of commercial electricity consumption are given.

Fe 3O 4 nanoparticles were prepared by a simple solid-state method under ambient conditions. The obtained nanoparticles, with small size and large surface area, were used as a catalyst for direct coal liquefaction (DCL).

The results display that high conversion and oil yield were achieved with the nanocatalysts in direct liquefaction of two kinds of coals, i.e., Heishan coal and Dahuangshan coal. The effects of the temperature, initial H 2 pressure, and holding time on conversion and product distribution have been investigated in the catalytic hydrogenation of Dahuangshan coal. The optimal reaction condition for DCL in which conversion and oil yield are 96.6 and 60.4 wt% was determined with Fe 3O 4 nanocatalysts. This facile solid-state route is beneficial for scale-up synthesis of iron-based catalysts with good performance for DCL. In this paper, an effective low-speed oscillating wave power generator and its energy storage system have been proposed.

A vertical flux-switching permanent magnet (PM) machine is designed as the generator while supercapacitors and batteries are used to store the energy. First, the overall power generation system is established and principles of the machine are introduced. Second, three modes are proposed for the energy storage system and sliding mode control (SMC) is employed to regulate the voltage of the direct current (DC) bus, observe the mechanical input, and feedback the status of the storage system. Finally, experiments with load and sinusoidal mechanical inputs are carried out to validate the effectiveness and stability of power generation for wave energy. The results show that the proposed power generation system can be employed in low-speed environment around 1 m/s to absorb random wave power, achieving over 60% power efficiency. The power generation approach can be used to capture wave energy in the future. The dual power flow wind energy conversion system (DPF-WECS) is a novel system which is based on the electrical variable transmission (EVT) machine.

The proposed sensorless control for the DPF-WECS is based on the model reference adaptive system (MRAS) observer by combining the sliding mode (SM) theory. The SM-MRAS observer is on account of the calculations without the requirement of the proportional-integral (PI) loop which exists in the classical MRAS observer.

Firstly, the sensorless algorithm is applied in the maximum power point tracking (MPPT) control considering the torque loss for the outer rotor of the EVT. Secondly, the sensorless control is adopted for the inner rotor control of the EVT machine.

The proposed sensorless control method based on the SM-MRAS for the DPF-WECS is verified by the simulation and experimental results. Herein, the nitridophosphate Na 3V(PO 3) 3N is synthesized by solid state method. X-ray diffraction (XRD) and Rietveld refinement confirm the cubic symmetry with P2 13 space group.

The material exhibits very good thermal stability and high operating voltage of 4.0 V vs. Na/Na + due to V 3+/V 4+ redox couple. In situ X-ray diffraction studies confirm the two-phase (de-)sodiation process to occur with very low volume changes. The refinement of the sodium occupancies reveal the low accessibility of sodium cations in the Na2 and Na3 sites as the main origin for the lower experimental capacity (0.38 eq. Na +, 28 mAh g −1) versus the theoretical one (1.0 eq.

Na +, 74 mAh g −1). These observations provide valuable information for the further optimization of this materials class in order to access their theoretical electrochemical performance as a potentially interesting zero-strain and safe high-voltage cathode material for sodium-ion batteries. The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change.

Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours.

The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system. 6.5 kV level IGBT (Insulated Gate Bipolar Transistor) modules are widely applied in megawatt locomotive (MCUs) traction converters, to achieve an upper 3.5 kV DC link, which is beneficial for decreasing power losses and increasing the power density.

Reverse Conducting IGBT (RC-IGBT) constructs the conventional IGBT function and freewheel diode function in a single chip, which has a greater flow ability in the same package volume. In the same cooling conditions, RC-IGBT allows for a higher operating temperature. In this paper, a mathematic model is developed, referring to the datasheets and measurement data, to study the 6.5 kV/1000 A RC-IGBT switching features. The relationship among the gate desaturated pulse, conducting losses, and recovery losses is discussed.

Simulations and tests were carried out to consider the influence of total losses on the different amplitudes and durations of the desaturated pulse. The RC-IGBT traction converter system with gate pulse desaturated control is built, and the simulation and measurements show that the total losses of RC-IGBT with desaturated control decreased comparing to the RC-IGBT without desaturated control or conventional IGBT. Finally, a proportional small power platform is developed, and the test results prove the correction of the theory analysis.

The strong coupling between electric power and heat supply highly restricts the electric power generation range of combined heat and power (CHP) units during heating seasons. This makes the system operational flexibility very low, which leads to heavy wind power curtailment, especially in the region with a high percentage of CHP units and abundant wind power energy such as northeastern China. The heat storage capacity of pipelines and buildings of the district heating system (DHS), which already exist in the urban infrastructures, can be exploited to realize the power and heat decoupling without any additional investment. We formulate a combined heat and power dispatch model considering both the pipelines’ dynamic thermal performance (PDTP) and the buildings’ thermal inertia (BTI), abbreviated as the CPB-CHPD model, emphasizing the coordinating operation between the electric power and district heating systems to break the strong coupling without impacting end users’ heat supply quality. Simulation results demonstrate that the proposed CPB-CHPD model has much better synergic benefits than the model considering only PDTP or BTI on wind power integration and total operation cost savings.

High-voltage direct current (HVDC) grids are emerging, and their reliability has been an increasing concern for the utilities. HVDC grids are different from typical two-terminal HVDC transmission systems due to the loops in their topology, which makes it difficult to evaluate the reliability by conventional analytical methods. This paper proposes an innovative hybrid method to evaluate the reliability of meshed HVDC grids. First, steady-state models and reliability models are established for the components in HVDC grids, especially for converters and power flow controllers. In the models, virtual buses are introduced to represent the external AC connections to the HVDC grid. Then a hybrid reliability evaluation method is proposed based on an analytical approach and Monte Carlo simulation. One innovation of the paper is the application of an analytical analysis method to accelerate state evaluation in Monte Carlo simulation by skipping unnecessary optimization.

The proposed models and methods are verified on two HVDC grids. Test results show that HVDC grids under most failure states (approximately 70%) tend to shed no load except on buses connected to faulted converters, and the application of the analytical method could promote evaluation efficiency significantly. This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs). The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC) and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation. It is easy to implement in real-time and the engineering significance is explained in details.

In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA). The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin’s minimum principle (PMP)-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions.

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios.

The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers. The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning.

First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the tra.