Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability

Ghorbani, Narges; Kasaeian, Alibakhsh; Toopshekan, Ashkan; Bahrami, Leyli; Maghami, Amin

doi:10.1016/j.energy.2017.12.057

Cited by 373 publications

(112 citation statements)

References 43 publications

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“…C Inv ESS , C O&M ESS , and C Rep ESS are the investment cost, the O&M cost, and the replacement cost of the BESS, respectively; their sum is taken to 350 €/kWh in this study. Furthermore, the lifetime of PV and battery systems is defined as 20 years and 10 years, respectively [28]. Thus, the first objective function can be determined, as follows:…”

Section: Tco Minimizationmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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Section: Tco Minimizationmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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“…Other studies investigated technical characteristics of various battery sizes [14,15], focusing on different spatial scales, from individual buildings up to municipalities. Considerable research efforts concerning the ideal configuration of such hybrid systems using wind and solar power exist for particular locations (see e.g., [16][17][18]). These studies focus on the techno-economic feasibility of a hybridized energy system and consider different optimization algorithms [16], demand response strategies [17], system net present costs or costs of energy generation [18] among others.…”

Section: Introductionmentioning

confidence: 99%

“…Considerable research efforts concerning the ideal configuration of such hybrid systems using wind and solar power exist for particular locations (see e.g., [16][17][18]). These studies focus on the techno-economic feasibility of a hybridized energy system and consider different optimization algorithms [16], demand response strategies [17], system net present costs or costs of energy generation [18] among others. The studies observed, however, vary in the spatial and temporal resolution as well as their methodological approach.…”

Section: Introductionmentioning

confidence: 99%

“…-While there are several studies on PV or PV-battery systems [4,10,11,[13][14][15]24,30], the investigation of self-sufficient hybrid systems (solar and wind energy combined with electrical storage) for residential use are scarce. -Many of the studies are mostly based on environmental data (wind speeds, solar irradiation) of single locations [16][17][18] not accounting for local long-and short-term availability and intermittency of natural resources, which are crucial to sustain self-sufficiency at all times. Furthermore, these studies usually neglect the potential of harnessing complementarity of wind and solar resources to decrease total system costs while increasing efficiency, stability and reliability of electricity generation [16,25].…”

Section: Introductionmentioning

confidence: 99%

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Potential Analysis of Hybrid Renewable Energy Systems for Self-Sufficient Residential Use in Germany and the Czech Republic

et al. 2019

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Independence from the power grid can be pursued by achieving total self-sufficient electricity supply. Such an energy supply model might be particularly interesting for settlements located in rural areas where enough space is available for energy generation installations. This article evaluates how and at what cost electricity demand of residential users across Germany and the Czech Republic could be covered by hybrid renewable energy generation systems consisting of photovoltaics, micro-generation wind turbines and batteries. High-resolution reanalysis data are used to calculate necessary system sizes over a large area by simultaneously accounting for the temporal variability of renewable energy. For every potential location in the research area, the hybrid system requirements for clusters of 50 self-sufficient single-family houses are calculated. The results indicate no general trend regarding the size of the respective technologies, although larger areas where PV-wind power complementarity enables lowering the total system costs and required storage capacities were determined. Assuming that the cluster of households could be constituted and depending on the location, the total installation and operation costs for the proposed systems for a lifetime of 20 years range between EUR 1.8 Million and EUR 5 Million without considering costs of financing. Regions with the lowest costs were identified mainly in the south of Germany.

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“…e second is to combine the PSO algorithm with other heuristic algorithms to supplement each other. Ghorbani et al [21] built GA-PSO algorithm by incorporating PSO and Genetic Algorithm (GA) to optimize the sizing of an off-grid house with photovoltaic panels, wind turbines, and battery. Accordingly, the favorable results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Holonic Particle Swarm Optimization

Li¹,

Jin²,

Wang³

et al. 2019

Mathematical Problems in Engineering

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For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. e HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. erefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. e performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. erefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. e HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. is paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

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Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability

Cited by 373 publications

References 43 publications

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Potential Analysis of Hybrid Renewable Energy Systems for Self-Sufficient Residential Use in Germany and the Czech Republic

Improved Adaptive Holonic Particle Swarm Optimization

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