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Multi-objective capacity optimization allocation of wind-PV-diesel-battery stand-alone microgrid based on SPEA2
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Xin LI, Junwei LI, Wei CHEN, Mou HOU, Zefeng JIA, Kun QIU
Thermal Power Generation | 2024, 53(8) : 9 - 19
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Thermal Power Generation | 2024, 53(8): 9-19
Economic study on grid-forming energy storage technology
Multi-objective capacity optimization allocation of wind-PV-diesel-battery stand-alone microgrid based on SPEA2
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Xin LI, Junwei LI, Wei CHEN, Mou HOU, Zefeng JIA, Kun QIU
Affiliations
  • School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
Published: 2024-08-25 doi: 10.19666/j.rlfd.202405104
Outline
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The stability and cost-effectiveness of power supply has been a pressing issue in areas such as isolated islands where power resources are relatively scarce and natural resources is abundant. Conventional stand-alone microgrids mostly rely on the non-dominated sorting genetic algorithm (NSGA-II) for capacity allocation, which has slightly insufficient local search capability when dealing with multi-objective optimization problems with real loads. In order to overcome this limitation, the improved strength Pareto evolutionary algorithm (SPEA2) is used to optimize the capacity allocation of wind-PV-diesel-battery stand-alone microgrid, which takes the economic cost, loss-of-load probability, and carbon emission as the optimization objectives, to achieve a more comprehensive and efficient capacity allocation. By importing the weather and load data of an isolated island and generating the real Pareto frontier of the independent microgrid with wind, PV, diesel and storage, the analysis results of SPEA2 are compared with that of multi-objective search based on indicator selection (IBEA) and NSGA-II algorithms. Compared with the NSGA-II algorithm, the anti generational distance evaluation IGD index of the SPEA2 increases by 46.83%, the spatial evaluation method Spacing index rises by 60.28%, and the real Pareto coverage CPF index grows by 35.14%, indicating the SPEA2 shows a more excellent performance. Finally, the parameters of each part are reasonably configured according to the results of capacity optimization. It shows that the joint output meets the load demand, which provides a new way of thinking for the energy management of isolated islands and other areas with scarce power resources, and also provides a valuable reference for the optimal design of multi-energy microgrids.

microgrids  /  SPEA2  /  multi-objective optimization  /  wind-PV-diesel-battery system
Xin LI, Junwei LI, Wei CHEN, Mou HOU, Zefeng JIA, Kun QIU. Multi-objective capacity optimization allocation of wind-PV-diesel-battery stand-alone microgrid based on SPEA2[J]. Thermal Power Generation, 2024 , 53 (8) : 9 -19 . DOI: 10.19666/j.rlfd.202405104
  • National Natural Science Foundation of China(62202138)
Year 2024 volume 53 Issue 8
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doi: 10.19666/j.rlfd.202405104
  • Receive Date:2024-05-31
  • Online Date:2026-01-07
  • Published:2024-08-25
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  • Received:2024-05-31
Funding
National Natural Science Foundation of China(62202138)
Affiliations
    School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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