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Summary: This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic‐based modelling and modern optimization. Firstly, a robust Adaptive Network‐based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective and simultaneous two-problem allocation of a hybrid distributed generation (HDG) system comprises of solar panels, wind turbines, and battery storage is proposed in a 33-bus unbalanced distribution network which can decrease total losses and improve power quality (PQ). The PQ indices are defined as voltage swell, total harmonic distortion, voltage sag, and voltage unbalance. In this study, the two problems of hybrid system design and its allocation in the distribution network are solved simultaneously. In the allocation problem, the HDG is placed ideally in the network to reduce energy losses and enhance PQ indices. The HDG is measured to minimize the cost of energy generation, including the initial investment, maintenance, and operation costs. The decision variable including the size of HDG components and its location is optimally determined via escaping bird search (EBS) algorithm which is inspired by the maneuvers of the swift bird to avoid predation. The results cleared that the proposed methodology using the wind and solar resources integrated with battery storage reduced the losses, voltage swell, total harmonic distortion, voltage sag, and voltage unbalance by 34.31%, 49.60%, 0.25%, 40.19%, and 2.18%, respectively, than the base network via the EBS and the results demonstrated the better network performance using all renewable resources against wind or solar application only. The outcomes demonstrated the superiority of the EBS in achieving the highest improvement of the different objectives compared with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO). Moreover, the superior capability of the EBS-based methodology is proved in comparison with previous studies. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show. [ABSTRACT FROM AUTHOR]

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low‐carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power‐to‐gas associated gas storage (FP2G‐AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model's capability to promote new energy integration, reduce operational costs, and decrease CO2 emissions across various scenarios. This research significantly contributes to achieving low‐carbon operations on OOGPs and promotes the sustainable development of marine resources. [ABSTRACT FROM AUTHOR]

Summary: Battery storage performance optimization is crucial in ensuring the reliable operation of renewable energy integrated power systems and emergency backup applications. Considering the long life cycle and several other merits such as deep discharge capacity, scalability of power, and energy capacity, Vanadium Redox Flow Battery (VRFB) has become one of the most potential storage solutions for stationary storage applications compared to the other conventional batteries. In this paper, a kW scale VRFB storage system power loss optimization based on the particle swarm optimization (PSO) technique has been demonstrated for the first time. It is a multi‐variable optimization considering both the VRFB pump power loss and stack power loss simultaneously. The electrolyte flow rate has been considered as the control variable in optimizing the overall VRFB system power loss. Charge‐discharge operations of VRFB with four sets of electrolyte flow rates at three different levels of stack terminal current have been taken for validation of the proposed work. It has been observed that for a 1 kW 6kWh VRFB storage system, the overall system power loss becomes the minimum at an optimal flow rate of 6 L/min for a range of (3‐18) L/min of electrolyte flow rate at 40, 45, and 50 A charge‐discharge operations. The proposed work formulation demonstrated in this paper is a generalized one and can be very useful for maximizing the overall system efficiency of VRFB storage and ensuring the reliability of VRFB integrated power system applications. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards. [ABSTRACT FROM AUTHOR]

The power-voltage (P-V) characteristic curve of the centralized thermoelectric generation (TEG) system under nonuniform temperature distribution (NTD) exhibits multiple extreme point characteristics, and the traditional maximum power point tracking (MPPT) algorithm is prone to fall into the local maximum power point (LMPP) and takes a long time to track. This paper designs a BP-IPSO algorithm based on back propagation neural network (BPNN) and improved particle swarm optimization (IPSO) for MPPT. The algorithm firstly utilizes the good nonlinear function fitting ability of BPNN to obtain the fitting curve of the relationship between system control input and power output to establish the TEG array power prediction model. Then, the dynamic learning factor and weight coefficient are introduced into the traditional particle swarm optimization (PSO) algorithm to search the output power prediction model and realize MPPT control. MATLAB/Simulink experiment results show that BP-IPSO algorithm can effectively avoid falling into LMPP, quickly and accurately track the global maximum power point (GMPP), and effectively suppress the oscillation of voltage and power during the tracking process. Especially in the start-up test experiment, compared with perturb and observe (P&O), PSO, and grey wolf optimizer (GWO), the energy generated by BP-IPSO increased by 12.84%, 3.18%, and 4.75%, respectively, which improved the system power generation efficiency. [ABSTRACT FROM AUTHOR]

Summary: This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS. [ABSTRACT FROM AUTHOR]

Summary: "Electric vehicles (EVs) are one of the most promising technologies to green the transportation systems. However, high penetration of EVs brings heavy electricity demand to the power grid." Due to the intermittent character of renewable energy sources (RESs), it turns out to be very challenging to manage EV charging with other renewable generation and grid load. This paper aims to introduce a dispatch strategy assisted with the optimization concept for enhancing the economy of the microgrid system. The major objective is to minimize the cost of system operation and environmental control when meeting system load requirements. The output constraints related to the distributed power supply, such as power limits, are subjected to optimization. To solve this optimization issue, a new "Fitness Sorted Moth Search algorithm (FS‐MSA)" is introduced. Finally, the proposed work is compared and validated with other existing works with respect to various measures. The enhanced outcomes prove the efficacy of the implemented FS‐MSA model. This work concentrates on EV adoption integrated with RES for sustainable mobility. On observing the result, it can be noticed that the adopted scheme was 81.97%, 82.03%, 82%, and 81.97%, better than existing genetic algorithm, particle swarm optimization (PSO), moth search algorithm, and lagrange multiplier optimization (LMO) models for mean case scenario. [ABSTRACT FROM AUTHOR]

Summary: Accurate and reliable model parameters are not only a prerequisite for model‐based estimation but also a significant part of battery operating characteristics. However, the measurement signal inevitably contains noise, which brings great challenges to model identification. This paper focuses on the noise immunity performance of model identification based on two‐swarm cooperative particle swarm optimization. An adaptive dynamic sliding window based on the current rate criterion and the identification results feedback is designed to avoid data redundancy and improve the robustness of model identification. The model parameters are obtained using two‐swarm cooperative particle swarm optimization based on the adaptive dynamic sliding window. The proposed method effectively improves the accuracy and speed of parameter identification through optimization of data fragments and particle update rules. Compared with two existing parameter identification methods, simulation studies illustrate that the average mean square deviation of the proposed method is reduced by at least 35 dB. The proposed method is superior to existing parameter identification methods in noise immunity performance, parameter identification reliability, and state‐of‐charge estimation accuracy. By employing the proposed method, the maximum errors of state‐of‐charge estimation are limited within 1% under experimental verification. The experiment results verify that the proposed method has the potential to extract reliable model features online. [ABSTRACT FROM AUTHOR]

Summary: As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation. [ABSTRACT FROM AUTHOR]

Optimal operation of micro grids (MGs) with high penetration of renewable energy sources (RESs) has become inflexible as a consequence of the uncertainty and intermittency nature of RESs. Energy management along with battery energy storage system (BESS) is thus essential to minimize these uncertainties of MGs and to ensure continuity of energy supply and maintain the reliability of the system. In this paper, a dual-stage MG optimal energy management approach considering the BESS model and other dispatchable resources is introduced to minimize the operating cost. The proposed approach comprises of the schedule and dispatch stages. The mixed-integer linear programming is applied to solve the dual-stage energy management problem and the performance of the suggested technique is compared with the particle swarm optimization technique. The obtained results prove that the incorporation of the suggested BESS plant can significantly reduce the operating costs up to 7% and 6% when committed to supplying power in the scheduling and dispatch stages. The results also show that power loss has a considerable impact on MG energy management. The optimal simulation results confirm the effectiveness of the proposed microgrid energy resources optimization strategy. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, concurrent participation of distributed energy resources in providing electrical energy and ancillary services brings many benefits. Also, energy storage systems (ESSs) along with renewable generations have a growing penetration rate in modern power systems aimed at declining environmental issues. Due to the uncertainties of intermittent nonpredictable power resources in microgrids (MGs), high integration of these products leads to an increase in ancillary services requirements and it necessitates the coordinated management of these technologies with the ESSs. In this paper, for the first time, a robust model based on particle swarm optimization (PSO) metaheuristic is developed to cope with the uncertainty of renewables in concurrent active/reactive and reserve management problem in the MG with ESS. The robust framework provides a medium priority in comparison with deterministic and stochastic techniques. The objective function of the robust concurrent active/reactive and reserve scheduling problem in MGs is expressed as maximizing social welfare (SW). The proposed model is carried out using a max‐min optimization scheme. The robust design will be attained in such a way that the maximizer at the outer level seeks an optimal solution against the worst‐case objective function achieved through the minimizer at the inner level taking into account the uncertainty neighborhood. The performance of the presented approach has been evaluated on a typical MG. Simulation results verify that the suggested robust‐PSO technique will aid MG operators to decrease daily operational costs and to yield a higher SW. [ABSTRACT FROM AUTHOR]

Summary: Considering prediction accuracy and adaptability to unpredictable operating conditions simultaneously, this paper presents an application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles. Under static and dynamic operating conditions, three commonly used online model parameters identification algorithms, including extended Kalman filter (EKF), particle swarm optimization, and recursive least square, are compared first, whose comparison results show that EKF's comprehensive performance is optimal. Taking identified open‐circuit voltage as observation information, two first‐order EKFs are established to online estimate state‐of‐charge (SOC) and state‐of‐energy (SOE). To maintain high accuracy and reliability under unpredicted operating conditions, fixed accumulation charge and fixed accumulation energy are innovatively seen as triggers, successfully realizing periodically capacity (state‐of‐health) and maximum available energy prediction with estimated SOC and SOE. Finally, with identified model parameters and estimated battery states, peak discharge/charge power can be further calculated in real time. Notably, parameters tuning for multistate estimation is also discussed in this work. Furthermore, the feasibility and prediction accuracy of the proposed multistate estimation framework is verified with sophisticated driving simulation under different temperatures. The validation results indicate that the presented framework can provide precise and reliable multistate estimation with relatively low computation cost. Highlights: An application‐oriented multistate estimation framework is proposedDifferent online model parameters identification methods are comparedParameters tuning for multistate estimation is discussedFixed accumulation charge is innovatively taken as capacity updating triggerThe feasibility of the proposed framework is verified under various temperatures [ABSTRACT FROM AUTHOR]

Summary: The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta‐heuristic algorithm named parasitism‐predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work; the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical 5KW dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri‐competitive mechanism (MCSO), opposition‐based learning competitive PSO (OBLCPSO), ranking‐based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e−6 in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e−6. Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e−2 at 973 K; meanwhile, the most peer (CLPSO) attains 5.44e−1. [ABSTRACT FROM AUTHOR]

Summary: Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously. [ABSTRACT FROM AUTHOR]

Summary: The main objective of this paper is to determine the optimal sizing of a biomass and fuel cell micro‐grid. The biomass generator will be utilized as the main source of power generation for the study area, while the fuel cell generator will be used as a backup generator to be used if the biomass generator fails to meet the energy requirements of the study area. In this research, excess energy will be used to produce hydrogen which is to be used by the fuel cells to generate energy instead of using batteries. To achieve the goal of this paper, a multi‐objective particle swarm optimization (MOPSO) technique has been proposed to solve the sizing problem for the introduced micro‐grid via an economical perspective which is the cost of energy (COE). The MOPSO algorithm tries to mitigate the COE to the lower values by keeping the loss of power supply probability (LPSP) as minimal as possible. Likewise, statistical analysis has been concluded to study the accuracy of the outcomes of the introduced technique. Three indicators have been offered, which are the process capability indices, the normal probability, and the control chart. The final contribution of the consequences of the two presented objective functions clears that the algorithm process is under statistical control, stable, and very precise. The optimum system from the economic perspective consists of two biomass gensets, 31 fuel cells, 65 electrolyzers, and 186 H2 tanks with an NPC of $ 2 314 842, COE of 0.335 $/kWh at an LPSP of 1.929%. [ABSTRACT FROM AUTHOR]

SUMMARY This paper describes the problem of short-term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Since the economy of the energy storage system (ESS) participating in power grid ancillary services is greatly affected by electricity price factors, a flexible control method of the ESS participating in grid ancillary services based on electricity price forecasting is proposed in this paper, and the economic evaluation of the ESS participating in ancillary services is realized by the net present value (NPV) method. To improve the accuracy, the Markov chain is adopted to correct the predicted electricity price value obtained by the combination of the particle swarm optimization (PSO) and back propagation (BP) neural network according to the systematic errors in the modelling process. Based on the compatibility of each ancillary service, taking the maximum revenue of the system as the objective function, a flexible control model of the ESS participating in ancillary services based on electricity price forecasting is established, and the YALIMP+CPLEX software toolbox in MATLAB is adopted to solve the problem of electricity price forecasting, and the capacity division of the ESS under the ancillary service application scenarios is realized. The NPV method is used to evaluate the economics of the ESS participating in ancillary services under this strategy. The simulation analysis of the actual operation data from a power grid in China validates the effectiveness of the proposed method. The simulation results show the proposed method can provide theoretical support for the ESS participating in power grid ancillary service markets. [ABSTRACT FROM AUTHOR]

Summary: In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three‐stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost‐effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Summary: In order to improve the comprehensive energy utilization rate of combined cooling, heating, and power (CCHP) system, a hybrid energy storage system (HESS) is proposed in this paper consisting of electric and thermal energy storage systems. And the overall optimization design and operation of CCHP system with HESS are the main problems to be solved in application. Therefore, the topology and the energy flow model of CCHP system with HESS are established and analyzed according to the energy conversion characteristics of the component equipment. Moreover, combined with five evaluative restrictions for HESS system, a rule‐based energy management strategy is designed to realize the decoupling regulation of electric energy and thermal energy in CCHP system. On this basis, a multi‐objective optimization model is studied by taking the indicators of annual cost ratio, the primary energy consumption ratio, and loss energy ratio, and then the capacity parameters are optimized by particle swarm optimization algorithm (PSOA). Finally, a case is carried out to compare the energy allocation situations and capacity optimization results between CCHP system with HESS and CCHP system with single thermal energy storage system (ST). Results show that the capacity of ICE is reduced by 34%, and the annual cost and the primary energy consumption are saved about 7.69% and 18.47%, respectively, demonstrating that HESS has better optimization effect and applicable for small‐scale CCHP system. [ABSTRACT FROM AUTHOR]

Dynamics modeling is of fundamental importance to Li-ion battery design and manufacturing. Accurate dynamics models established can be used to optimize the operation strategy, manage the life cycle, and thus ensure the economic and safe operation of the batteries. The Li-ion dynamics model examined in this paper has integrated both empirical and electrochemical aspects, and it has been specifically validated for electric vehicle applications using experimental data. Previously, the model parameter estimation was done by manually picking three key points from the discharge curve obtained from the datasheet. The approach is quite subjective and error prone. The resulted model may deviate greatly from the experimental curve. To address this issue, this paper proposes to use particle swarm optimization to more objectively estimate the model parameters. Results from case studies show that the proposed approach provides more accurate estimation of the true parameters, and thus the new approach can more precisely capture the battery dynamics. In addition, this approach is generic because it is independent of specific battery chemistries and applicable to many different types of Li-ion batteries. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi-objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO-patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The increasing use of inverter‐based distributed generation requires a comprehensive study of its effects on fault analysis and the effectiveness of protection systems in distribution networks. This study examines the impact of different inverter control modes on multiple types of protective relay schemes. These include different overcurrent relay (OCR) schemes, both standard and nonstandard tripping characteristics, optimal coordination approaches, and different grid operation scenarios. The investigation is conducted through the utilization of grid‐connected and islanding operation modes, with fault resistance values of 0 and 5 Ω. The study is carried out on a 14‐bus CIGRE network which includes two photovoltaic (PV) farms with a capacity of 10 MVA each. The research provides several significant contributions, including the analysis of fault current contributions, examination of issues in OCR protection, investigation of the influence of fault impedance levels on OCR performance, evaluation of ideal coordination methods, and carrying out a comparative analysis utilizing optimization technique by using the water cycle optimization algorithm (WCA) and the particle swarm optimization algorithm (PSO). In addition, to guarantee the robustness of the suggested protection strategy, this work adopts a hardware‐in‐the‐loop approach. The OMICRON‐256 system is utilized to carry out real‐time testing on a SIPROTEC 7SJ62 multifunction protection relay, which validates the effectiveness of the proposed method in protecting microgrids under different PV control strategies. The total operating time for the nonstandard tripping scheme was 12.077–12.3003 s for PSO and WCA, respectively, while for the standard tripping scheme, it was 12.1226 and 12.1564 s. Moreover, these findings offer practical insights that can assist operators in effectively designing the power networks with grid‐connected PV systems by showing OCR miscoordination and no tripping events in power systems with PVs under inverters controllers. [ABSTRACT FROM AUTHOR]

Summary: Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise. [ABSTRACT FROM AUTHOR]

Summary: The optimization of the total annual cost in heat exchanger networks has been one of the overarching goals when synthesizing these networks. Several methodologies and techniques have been developed to achieve optimal costs in mixed material heat exchanger networks. This paper demonstrates the application of two decomposition methodologies (total decomposition and partial decomposition) for typical cost rules. The objective function was defined as the optimization and minimization of the total annual cost in mixed materials heat exchanger network. Three optimization algorithms, hybrid genetic‐particle swarm optimization (GA‐PSO), shuffled frog leaping algorithm (SFLA) techniques, and ant colony optimization (ACO), were used to further optimize the total cost in mixed materials heat exchanger network. The results indicate that the total annual cost in partial decomposition method was smaller than that in full integration method and total decomposition method. The reduction of the total annual cost was about 27% for GA‐PSO algorithm, 24% for SFLA and 10% for ACO relative to the results reported in this work. In partial decomposition method, at least one mixed material of heat exchanger was used to reduce the hot and cold utility for decreasing the total annual cost. Partial decomposition method resulted in the highest reduction of the total annual cost compared with other methods. Percentage of difference of the total annual cost were 0.36%, 1.92%, and 5.05% for full integration, total decomposition, and partial decomposition methods, respectively, in comparison with the previous studies. Results have been compared with the results of other studies to demonstrate the accuracy of the applied algorithms. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (Npv) and ESU batteries (Nbat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed Npv and Nbat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station. Methodology for modeling of PV‐ESU grid‐based EV charging system is described.Main features of existing popular system sizing techniques are compared.An energy management algorithm (EMA) is developed to control the charging system.Optimum sizes of PV modules and ESU batteries are determined by means of PSO.Proposed system is benchmarked against the standard grid charging system. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a novel dynamic semiempirical model for the proton exchange membrane fuel cell (PEMFC). The proposed model not only considers the stack output voltage but also provides valid waveforms of component voltages, such as the no‐load, activation, ohmic, and concentration voltages of the PEMFC stack system. Experiments under no‐load, ramping load, and dynamic load conditions are performed to obtain various voltage components. According to experimental results, model parameters are optimised using the lightning search algorithm by providing valid theoretical ranges of parameters to the lightning search algorithm code. In addition, the correlation between the vapour and water pressures of the PEMFC is obtained to model the component voltages. Finally, all component voltages and the stack output voltage are validated by using the experimental/theoretical waveforms mentioned in previous research. The proposed model is also compared with a recently developed semiempirical model of PEMFC through particle swarm optimisation. The proposed dynamic model may be used in future in‐depth studies on PEMFC behaviour and in dynamic applications for health monitoring and fault diagnosis. [ABSTRACT FROM AUTHOR]

Summary: Biofuel supply chain design plays a critical role in facilitating the large‐scale substitution of biofuel for traditional fossil fuels with a cost‐effective and environmentally friendly manner towards sustainability. This paper proposes a multiobjective optimization model for a 4‐layer biofuel supply chain network using mixed integer nonlinear programming while considering the benefits from economic, environmental, and societal aspects. The model can be used either to optimize an existing biofuel supply chain network or to guide the construction of a new biofuel supply chain network. The profit, the greenhouse gas emissions in transportation, and the market share of biofuel were set as targets for optimization. The selection of the participators at each layer, and the amount of the material flow between each pair of selected supplier and customer located at two adjacent layers were modeled as decision variables. The conventional weighted aggregation method was used to unify 3 objectives after normalization. Particle swarm optimization was used to solve this high‐dimension multiobjective problem to obtain a near optimal solution. A numerical case study based on the state of Missouri in the United States was implemented to verify the effectiveness of the proposed model. The results of the case study illustrate that the benefits in terms of transportation emission, profit, and market share can be achieved simultaneously. Using the equal weights configuration in conventional weighted aggregation as an example, a 21% reduction of the transportation emission, a 33% increase of the profit, and a 2% augmentation of the market share were achieved compared to the benchmark scenario. [ABSTRACT FROM AUTHOR]

Summary: In recent scenario, there is abundant availability of renewable energy resources to satisfy the significant increase in residential, industrial, and commercial demand. This paper presents a novel framework to determine the preeminent size of renewable distributed generators (RDGs) by optimizing the system components such as area required for solar‐photovoltaic modules, swept area occupied by wind turbine blades, and area used by fuel cell. A microgrid with hybrid RDG (h‐RDG) is integrated in distribution system to minimize the distribution loss, substation energy requirement, and improve the voltage level of the load. The power loss minimization is formulated as a nonlinear problem and optimized by the proposed Hybrid Nelder Mead‐Particle Swarm Optimization algorithm. The microgrid location is identified by voltage stability index to improve the stability of system. Further, the system is analyzed for energy flow in different seasonal loading conditions with mixture of residential, industrial, and commercial load. The effective performance of the proposed technique is applied to standard 12‐bus, 69‐bus, and a practical Tamil Nadu (TN) 84‐bus radial distribution system (RDS) for different hybrid combinations of h‐RDG in microgrid. The result proves that the proposed method provides a simple and efficient tool for optimal and flexible use of h‐RDG in microgrid under different climatic changes by simultaneously reducing distribution energy loss and improving voltage profile. [ABSTRACT FROM AUTHOR]

Summary: The optimal design of a hybrid system with different configurations including renewable generation is presented in this paper. A novel multi‐objective function consisting of 6 different objectives of hybrid system is reported using GA, PSO, and TLBO to decide the optimal configurations of parameters. The technical (loss of power supply probability, renewable factor), economical (cost of energy, penalty and fuel consumption), and social (job creation, human development index, and particular matter) features are investigated as objectives simultaneously for optimal design of hybrid system. The different objective indices namely cost of energy, loss of power supply probability, particular matter, human development index, job creation, and renewable factor indices are considered. The newly invented particular matter factor for design consideration of hybrid system directly shows the human health impacts, while pollutant emission is measured in the hybrid system design. The optimum values of objective indices are decided on the basis of the minimum value of multi‐objective function. The distinct cases from I to VI of hybrid system are examined for optimal configuration including different combinations of PV, wind, biomass, diesel generator, and battery bank. The resulting analysis of each case reveals that the performance of TLBO is better than PSO, and PSO is better than GA in all respect through new multi‐objective function and found case I is more efficient solution. [ABSTRACT FROM AUTHOR]

Recently, bio-inspired metaheuristic algorithms have been widely used as powerful optimization tools to estimate crucial parameters of photovoltaic (PV) models. However, the computational cost involved in terms of the time increases as data size or the complexity of the applied PV electrical model increases. Hence, to overcome these limitations, this paper presents the parallel particle swarm optimization (PPSO) algorithm implemented in Open Computing Language ( Open CL) to solve the parameter estimation problem for a wide range of PV models. Experimental and simulation results demonstrate that the PPSO algorithm not only has the capability of obtaining all the parameters with extremely high accuracy but also dramatically improves the computational speed. This is possible and is shown in this work via the inherent capabilities of the parallel processing framework. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well-known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: For a remote area or an isolated island, where the grid has not extended, a standalone hybrid energy system can provide cheap and adequate power for local users. However, with the development of society, the load demand will increase and the original system cannot completely meet the load demand. This situation occurs in Xiaojin, Sichuan, China. The existing photovoltaic‐pumped hydro storage (PV‐PHS) hybrid system in this area as the original system cannot completely meet the load requirements at present. The term "repowering" aims to maximize the reliability of power supply and the utilization of the PV‐PHS hybrid energy system that differs from traditional planning optimization to build all components. The repowering strategy is to integrate wind turbines (WTs) and battery into the original system. For the repowering system, a power management strategy is proposed to determine the operating modes of the PHS and battery. Three objectives, which are minimizing percentage of the demand not supplied, levelized cost of energy, and curtailment rate of renewable energy, are considered in the optimization model. Simulation is conducted by single‐objective, biobjective, and triobjective particle swarm optimization (PSO) techniques. For the single‐objective optimization, the comparison of PSO and genetic algorithm (GA) is made. For the double‐objective optimization, multiobjective PSO (MOPSO) is compared with weighted sum approach (WSA), and fuzzy satisfying method is utilized to find the win‐win solution. The results reveal that the repowering strategy can help to achieve maximum reliability of power supply after load demand increases significantly, and the battery plays an important role in such a hybrid system. [ABSTRACT FROM AUTHOR]

The renewable energy cluster can reduce the total power deviation of renewable energy stations and also bring cooperative benefits to renewable energy stations. Shared energy storage can assist in tracking the power generation plan of renewable energy and has advantages in the scale of investment, utilization rate, and other aspects. Therefore, this article proposes a study on the grid-connected optimal operation mode between renewable energy cluster and shared energy storage on the power supply side. Firstly, for the complementary characteristics blurring each member's contribution to the cluster power deviation, an improved Shapley value method is used to build a rational mechanism for apportioning the cluster power deviation penalty. Secondly, based on the Nash negotiation model and allocation mechanism, the revenue model of the renewable energy cluster is constructed, and an improved particle swarm optimization algorithm is proposed to solve the cluster operation strategy. Then, based on the charge and discharge demand of renewable energy cluster, with the goal of maximizing the benefits of shared energy storage, capacity pricing is optimized, as well as capacity allocation and participation in the frequency control auxiliary service market. Finally, the solving process of grid-connected optimal operation mode is proposed, and the rationality of the grid-connected optimal operation strategy between renewable energy cluster and shared energy storage is verified by example analysis. The results indicate that renewable energy cluster and shared energy storage can effectively increase both benefits, and a win-win situation for all parties can be realized. On the one hand, the cooperation mode and allocation mechanism can effectively guarantee the benefit of each renewable energy station. On the other hand, shared energy storage is beneficial for assisting in tracking the power generation plan of renewable energy and reducing the renewable energy power curtailment situation. There also has residual capacity in the shared energy storage system; it can participate in the frequency control ancillary service market, which can improve the capacity utilization rate and benefit of shared energy storage. [ABSTRACT FROM AUTHOR]

SUMMARY For a solid oxide fuel cell (SOFC) and micro gas turbine (MGT) hybrid system, optimal control of load changes requires optimal dynamic scheduling of set points for the system's controllers. Thus, this paper proposes an improved iterative particle swarm optimization (PSO) algorithm to optimize the operating parameters under various loads. This method combines the iteration method and the PSO algorithm together, which can execute the discrete PSO iteratively until the control profile would converge to an optimal one. In MATLAB environment, the simulation results show that the SOFC/MGT hybrid model with the optimized parameters can effectively track the output power with high efficiency. Hence, the improved iterative PSO algorithm can be helpful for system analysis, optimization design, and real-time control of the SOFC/MGT hybrid system. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, an electrochemical-based proton exchange membrane fuel cell (PEMFC) model suitable for engineering applications is presented. In order to improve the accuracy of this model so that it can reflect the actual PEMFC performance better, its parameters are optimized by means of a modified particle swarm optimization (MPSO). The MPSO is a modified method for the PSO's inertia weight. The proposed inertia weight is calculated according to the distance of the particle's current position from the best solution of the entire swarm. The obtained results of the PEMFC model with optimized parameters agree with experimental data well. Therefore, the MPSO is a helpful and reliable technique for optimizing the model parameters and can be used to solve other complex parameter optimization problems of fuel cell models. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The comprehension of severe criticality accident is a key issue in Gen‐IV neutronics and safety. Within the future zero‐power experimental physics reactor (ZEPHYR), to be built in Cadarache in the next decade, innovative approaches to reproduce high temperature partially degraded Gen‐IV cores into a critical facility is being investigated. This work presents the first attempt to represent a fuel assembly of sodium‐cooled fast reactor severe criticality accident based on surrogate models. One identified way to construct such representative configuration is to use MASURCA plates stockpile (MOX, UOx, Na, U, and Pu metal) in a fast/thermal coupled core to model a stratified molten assembly. The present study is the first step in a more global approach to full core analysis. The approach is based on a nature‐inspired metaheuristic algorithm, the particle swarm optimization algorithm, to find relevant ZEPHYR configuration at 20°C that exhibits characteristics of (2000‐3000°C) molten MOX assembly in a stratified metal arrangement in a reference sodium‐cooled fast reactor core. Thus, the underlying research question of this study is whether it is possible to represent temperature‐related reactivity effects occurring at fuel meltdown temperatures in a power reactor as density‐related reactivity effects at the operation temperature of a zero‐power reactor, and if so, how should it be done? The calculations are based on a Serpent‐2 Monte Carlo sensitivity and representativity analysis using the Cadarache's cross sections covariance data (COMAC). The single fuel assembly studies show that it is possible to represent the multiplication factor with a representativity factor greater than 0.98. As for reactivity variations, it is possible to achieve a satisfactory representativity factor of above 0.85 in all the presented cases. The representativity process demonstrates that temperature effects could be translated into density effects with good confidence. A complementary analysis on modified nuclear data covariance matrix demonstrates the importance of selecting consistent and robust uncertainties in the particle swarm optimization algorithm. This work provides insights on the behavior of the representativity scheme in different core states and shades some light on the problem in hand. [ABSTRACT FROM AUTHOR]

Management of plug-in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2-area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta-heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost. [ABSTRACT FROM AUTHOR]

The electric field distribution of insulator surface is nonuniform, and the maximum electric field is visible around two terminals of the insulator. Using a microvaristor layer is one of the methods of field control that can reduce the electric field stresses to prevent an extension of discharges on the insulator surface and a complete flashover caused by the subsequent development of arcing. This study targets the effect of zinc oxide (ZnO) microvaristors on the electric field distribution along the contaminated and clean composite insulators that have been investigated. In addition, the impact of the insertion of microvaristor layers on the critical flashover voltage (CFO) of the insulators through a mathematical formula has been presented for the first time. The estimation of electric field distribution is conducted through finite element method (FEM) on a 400 kV insulator using COMSOL Multiphysics, in which the optimal dimensions of the microvaristor layer were obtained using the accelerated particle swarm optimization (APSO) algorithm. Then, for the first time, the analysis of the influence of the ZnO insertion on the transient performance of the insulator, i.e., the outage rate of the network, is performed in EMTP software for the insulator with the optimized insertion of the microvaristor layer. Modelling techniques were used to simulate the components of a transmission network according to the valid models. Finally, by setting different values for CFO, Monte Carlo simulation, and linking EMTP and MATLAB software, the lightning flashover rate (LFOR) and the failure risk (F.R.) of the different insulator models are calculated. It is shown that the proposed method reduces the maximum electric field of the inside and outside of the insulator, which in turn leads to a reduction in the outage rate of the power network and the insulation risk of the insulator, and an increase in CFO of the insulator. [ABSTRACT FROM AUTHOR]

Summary: The existing impedance estimation methods for proton exchange membrane fuel cell (PEMFC) based on single equivalent circuit model (ECM) cannot accurately measure electrochemical impedance spectroscopy (EIS) at all times in the future or in the whole frequency range, which affects the accuracy of state‐of‐health (SOH) estimation with EIS as aging index. Therefore, a novel health assessment approach based on multi‐model probability fusion is proposed for improving the accuracy and reliability of SOH estimation. Five typical ECMs are selected to describe PEMFC, and chaotic particle swarm optimization algorithm with dynamic inertia weight (DIW‐CPSO) is used for identifying the model parameters. The parameters related to aging are extracted for predicting the SOH, and Bayesian theorem‐based multi‐model probability approach is adopted to calculate the optimal weights of synthesizing the SOHs estimated by the five ECMs. This approach is applied to real EIS data sets of Stack FC at current 20A, 45A and 70A. The results show that the EIS estimation results of the proposed approach have better performance in the sense of root mean square error and Euclidean error. The accuracies of SOH evaluation are 46.38%, 28.69% and 10.94% higher than the single ECM respectively, and have higher accuracy compared with previous studies. [ABSTRACT FROM AUTHOR]

Summary: Proton Exchange Membrane Fuel Cells (PEMFC) have been one of the most promising energy alternatives to the non‐renewable resources of energy in the last few years owing to their numerous advantages such as reliability and pollution‐free, sustainability, steady power generation, and non‐self‐discharging. Due to this, fuel cells have a wide range of applications in different areas. Therefore, it is very crucial to do an accurate and precise estimation of the parameters of PEMFC as modeling their characteristics has huge significance in the study, simulation, and development of highly efficient fuel cells. In this study, a new hybrid algorithm based on two widely used meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO) and Dingo Optimizer (DOX) is developed in order to estimate the parameters of PEMFC. First of all, the proposed algorithm is benchmarked on 10 functions in order to justify the algorithm. Further, the results of PEMFC parameter estimation obtained by the new proposed Hybrid Particle Swarm Optimization Dingo Optimizer (HPSODOX) algorithm are compared with other meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO), Dingo Optimizer (DOX), Grey Wolf Optimization (GWO) algorithm, and hybrid algorithms that is, Grey Wolf Optimization ‐Cuckoo Search (GWOCS), and PSOGWO. Then, the evaluation metric used in this manuscript is the Sum of Square Error (SSE). The new proposed hybrid algorithm performs better than other meta‐heuristic algorithms as it has the minimum value of SSE. For the datasheet of Ballard Mark V, the complete statistical error analysis and non‐parametric tests are carried out in order to evaluate the performance and superiority of the new proposed hybrid algorithm. [ABSTRACT FROM AUTHOR]

Summary: In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fast‐tracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex‐MSX‐60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck‐boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast‐tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10°C,15°C,20°C,25°C,30°C,35°C. [ABSTRACT FROM AUTHOR]

Summary: This article deals with the problem of energy losses in distribution networks (DNs) under electric vehicle (EV) penetration. The problem of charging overlaps causing severe power losses and voltage drops is faced under appropriate EV charging. An optimized EV charging schedule is proposed under EV time‐of‐arrival consideration. The solution algorithm is based on a particle swarm optimization (PSO) variant, and both grid‐to‐vehicle (G2V) and vehicle‐to‐grid (V2G) schemes are included regarding the power interactions of the EVs with the grid. The goal is to optimally allocate the charging and/or discharging time periods of the EVs in order to minimize energy losses while delivering fully charges EVs to the owners. New departures after the first EVs' arrival are also handled. The results indicate that with the proposed scheduling, daily energy loss reduction could be reached up to 25% under an improved voltage profile that indicates more uniform loading for the DN. [ABSTRACT FROM AUTHOR]

Summary: Liquid cooling systems are effective for keeping the battery modules in the safe temperature range. This study focuses on decreasing the power consumption of the pump without compromising the cooling performance. Artificial neural network (ANN) models are created to predict the effects of the height and width of the cooling channel and the mass flow rate on the maximum temperature, convective heat transfer coefficient, and pressure drop. The ANN models are used as surrogate models for the design and optimization of the liquid cooling battery system. Particle swarm optimization (PSO) and genetic algorithm (GA), which are commonly utilized optimization methods in many areas, and chaos game optimization (CGO) and coot optimization algorithm (COOT) methods, which are recently presented methods, are adopted to minimize the power consumption of the pump. The results are compared in terms of computational performance and best, worst, average, and SD values. Despite all of the optimization methods used giving similar results, the CGO method comes forward due to fast converging, SD, and finding the minimum power consumption of the pump among other optimization methods. A 22.4% decrease in the power consumption of the pump is achieved with the use of the ANN‐based CGO method while conserving the cooling performance. When comparing the ANN predicted and CFD results, the relative errors are less than 1%. [ABSTRACT FROM AUTHOR]

Summary: Natural gas purification, especially with high sulfur content, is an energy‐intensive chemical production process, for which there are considerable differences in the energy consumption patterns of different purification plants. Therefore, these purification plants are required to establish a universal evaluation standard for energy consumption performance. This article proposed a novel approach to evaluate the energy efficiency of the natural gas purification process from the systems engineering perspective. An evaluation system is established for the hierarchical indicators of energy consumption using this technique providing the detailed definition of evaluation indicators for process, unit, and device. At the same time, a technical route is proposed for intelligent algorithm optimization and artificial neural network modeling based on historical operation data of the plant to discover the energy consumption benchmarks under various raw gas flow rates. Using this proposed method, the energy consumption efficiency can be evaluated while analyzing the energy‐savings potential of these natural gas purification plants with various process types or raw gas characteristics. Furthermore, the model based on historical operating data can objectively and truly reflect the plant's energy consumption features; therefore, the plant's energy consumption can be decreased to benchmark by adjusting the corresponding operation parameters. Ultimately, the computational process of the energy consumption benchmark is described thoroughly for a high‐sulfur natural gas purification plant. [ABSTRACT FROM AUTHOR]