Research on the Operation of Complementary Microgrid System for Wind-Solar-Hydro-Storage Based on DEPSO

With the increasing demand for green energy transition, multi-energy complementary microgrid systems that integrate wind, solar, hydro, and storage have become a popular research focus due to their efficient, clean, and stable energy supply characteristics. This system combines wind power, photovolt...

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Vydané v:2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2) s. 974 - 978
Hlavní autori: Liu, Dongge, Yi, Likui, Meng, Fanhua, Feng, Rui
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 29.11.2024
Predmet:
Costs
differential evolution particle swarm optimization algorithm
economic optimization
Fluctuations
Maintenance
Microgrids
Optimization
output fluctuation
Particle swarm optimization
Power system stability
Stability analysis
System integration
Wind power generation
wind-solar-hydro-storage complementary system
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Shrnutí:With the increasing demand for green energy transition, multi-energy complementary microgrid systems that integrate wind, solar, hydro, and storage have become a popular research focus due to their efficient, clean, and stable energy supply characteristics. This system combines wind power, photovoltaics, hydropower, and energy storage, effectively mitigating the output fluctuations of renewable energy sources, achieving self-sufficiency, and enabling flexible energy regulation. In response, this paper establishes a model aimed at minimizing system fluctuations and optimizing economic performance, taking into account various system constraints. The model is solved using the Differential Evolution Particle Swarm Optimization (DEPSO) algorithm. Through case analysis, both the Particle Swarm Optimization (PSO) algorithm and the DEPSO algorithm are applied to the multi-energy complementary microgrid system, allowing a comparative analysis of equipment output and total operating costs. The experimental results confirm the effectiveness of the proposed algorithm.
DOI:10.1109/EI264398.2024.10990606