期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 12, 期 10, 页码 2369-2377出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2017.0401
关键词
particle swarm optimisation; game theory; energy management systems; battery storage plants; distributed power generation; power generation economics; energy consumption; power markets; distributed algorithms; cooperative optimisation; noncooperative optimisation; battery energy storage system; energy management; multimicrogrid; distributed generation cost; load management; peak shaving; energy consumption scheduling; BESS capacity optimisation; bidirectional energy trading; BESS cost; individual-oriented optimisation; coalition-based optimisation; noncooperative game; optimal energy consumption strategy; distributed algorithms; particle swarm optimisation; interior point method
资金
- National Science Foundation of China [51367018]
- Fundamental Research Funds for the Central Universities [2242015R30024]
- Science and Technology Program of State Grid Corporation of China
Multi-microgrid is an integrated system of microgrids, distributed generations, and battery energy storage system (BESS). As the significant equipment in microgrid, BESS can perform multitasking, such as load management and peak shaving. This study mainly focuses on the energy consumption scheduling of multi-microgrid considering the optimisation of BESS capacity. Energy management with BESS optimisation is studied by considering the cost of distributed generations, cost of BESS, and bidirectional energy trading. The optimisation problem is tackled from two different aspects: an individual-oriented optimisation and a coalition-based optimisation. In the first approach, each microgrid is optimised individually with a non-cooperative game; while in the second approach, the joint optimisation of all microgrids is formulated through cooperation among multi-microgrids. In order to achieve the optimal energy consumption strategy and BESS capacity, distributed algorithms for two formulations are presented, which combine particle swarm optimisation and interior point method. Simulation results show that both approaches can contribute to peak shaving and reducing the daily cost of multi-microgrid.
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