The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies
•Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity optimization.•Comprehensive performance metrics of multi-objective algorithms are proposed.•Sensibility analysis of different load profile and resources level a...
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| Vydané v: | Energy conversion and management Ročník 229; s. 113779 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford
Elsevier Ltd
01.02.2021
Elsevier Science Ltd |
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| ISSN: | 0196-8904, 1879-2227 |
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| Abstract | •Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity optimization.•Comprehensive performance metrics of multi-objective algorithms are proposed.•Sensibility analysis of different load profile and resources level are conducted.•Thermal energy storage is the most cost-effective energy storage alternative.
To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology. |
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| AbstractList | To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology. •Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity optimization.•Comprehensive performance metrics of multi-objective algorithms are proposed.•Sensibility analysis of different load profile and resources level are conducted.•Thermal energy storage is the most cost-effective energy storage alternative. To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology. |
| ArticleNumber | 113779 |
| Author | He, Yi Wu, Feng Zhou, Jianxu Guo, Su Pei, Huanjin Huang, Jing |
| Author_xml | – sequence: 1 givenname: Yi surname: He fullname: He, Yi email: 1334159303@qq.com – sequence: 2 givenname: Su surname: Guo fullname: Guo, Su email: guosu81@126.com – sequence: 3 givenname: Jianxu surname: Zhou fullname: Zhou, Jianxu – sequence: 4 givenname: Feng surname: Wu fullname: Wu, Feng – sequence: 5 givenname: Jing surname: Huang fullname: Huang, Jing – sequence: 6 givenname: Huanjin surname: Pei fullname: Pei, Huanjin |
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| Snippet | •Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity... To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery,... |
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| SubjectTerms | accuracy administrative management Algorithms batteries Capacity optimization Cost analysis cost effectiveness degradation Economics energy conversion Energy management Energy storage Evolutionary algorithms Genetic algorithms Hybrid systems hybrids hydrogen Hydrogen storage Hydrogen-based energy Model accuracy Multi-objective evolutionary algorithm Multiple objective analysis Operation characteristics Optimization Particle swarm optimization Performance assessment Photovoltaics probability Quantitative techno-economic comparison Reliability sorting Sorting algorithms storage supply Thermal energy Wind |
| Title | The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies |
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