引用本文:李文云,蒋亚坤,雷炳银,等.基于Multi-Agent系统的含分布式电源电网能源优化管理[J].电力系统保护与控制,2015,43(12):21-27.
LI Wenyun,JIANG Yakun,LEI Bingyin,et al.MAS based energy management strategies of microgrid[J].Power System Protection and Control,2015,43(12):21-27
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基于Multi-Agent系统的含分布式电源电网能源优化管理
李文云1, 蒋亚坤2, 雷炳银3, 于峰3
1.云南电网公司科技部，云南 昆明650011;2.云南电力调度控制中心，云南 昆明 650041;3.易能(中国)电力科技有限公司，北京 100093
摘要:
在市场运行环境下，为了寻求微电网成本利益和环境效益的最大化，提出基于Multi-Agent系统能源优化管理策略。该研究首先构建Multi-Agent系统。此系统包括市场管理Agent (MOA)，微电网管理Agent (MMA)，公共电网Agent (UGA)和分布式发电Agent (DGA)。根据各微电网管理Agent上报的发电计划，在市场管理Agent中设计电价竞标策略用来确定各微电网的最佳交易电价和中标电量，以确保微电网经济利益最大化。在此基础上，在各微电网管理Agent中设计能量管理策略，并采用改进粒子群算法来确定微电网内部各分布式发电Agents的最佳功率分派，从而最小化微电网的运行成本。最后仿真验证了该方案的有效性。
关键词:  MG  Multi-Agent系统  能源管理策略  电价竞标策略  粒子群算法
DOI：10.7667/j.issn.1674-3415.2015.12.004
分类号:
基金项目:
MAS based energy management strategies of microgrid
LI Wenyun1, JIANG Yakun2, LEI Bingyin3, YU Feng3
1.The Ministry of Science and Technology of Yunnan Power Grid Co., Kunming 650011, China;2.Yunnan Electric Power Dispatching Control Center, Kunming 650041, China;3.YINENG (CHINA) Power Technology Co., Ltd., Beijing 100093, China
Abstract:
A Multi-Agent System (MAS) based energy optimization management strategy for microgrid in market operation environment is proposed in order to maximize the cost and environment benefit. Firstly, the MAS is constructed, which consists of one market operator Agent (MOA), several MG Management Agents (MMA), one Utility Grid Agent (UGA), and many DG Agents (DGA). Then the power price bidding strategy is designed in the MOA according to the generation power plan provided by all the MMAs. By the power price bidding strategy, the best trade price and bid-winning power are determined in order to maximize economic benefit. On the basis of this, the energy management strategy is designed in MMAs, by which the optimal power dispatches for all DGAs are obtained by using an improved particle swarm optimization method for minimizing the operating cost. Finally, the validity of proposed method is demonstrated by means of simulation results.
Key words:  MG  MAS  energy management strategy  price bidding strategy  particle swarm algorithm
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