基于多代理的实时电价机制下微网需求侧协同调控优化
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(1.三峡大学电气与新能源学院, 湖北 宜昌 443002;2.国网湖北省电力公司,湖北 武汉 430077)

作者简介:

王凌云(1981—),男,通信作者,博士,副教授,研究方向为电力系统运行优化与控制;E-mail:wly@ctgu.edu.cn
胡兴媛(1992—),女,硕士研究生,研究方向为新能源微电网运行与控制。E-mail:437135005@qq.com

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国家自然科学基金项目资助(51407104,61603212)


Collaborative regulation control optimization on demand side of microgrid based on multi-agent under real time price mechanism
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(1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;2. State Grid Hubei Electric Power Company, Wuhan 430077, China)

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    摘要:

    为使微网中各元件稳定运行且经济效益提高,采用多代理技术对需求侧负荷、蓄电池以及发电侧微源进行协调控制,使微网中各代理间信息及时反馈,减少系统故障。其次在微网并网运行的实时电价机制下,制定蓄电池充放电控制策略,分析蓄电池输出功率特性,以协调一天内不同时刻的各微源出力。同时采用多Agent混沌粒子群优化算法对考虑用户满意度的微网经济运行模型进行求解。并通过算例对比分析以验证所提出的基于多代理协调控制的微网能有效提高运行效益。

    Abstract:

    In order to ensure stable operation of each component in the microgrid and improve its economic efficiency, firstly, the multi-agent technology is used to coordinate the demand side load, the battery and the generator side micro source, so that the information between agents in the microgrid can be timely output to reduce the system failure. Secondly, a battery charging and discharging control strategy is designed based on the real-time electricity price microgrid mechanism connected to the grid. The output power characteristics of the battery are analyzed to coordinate the output of each micro source at different times of the day. Meanwhile, the Multi-Agent Chaotic Particle Swarm Optimization (MACPSO) algorithm is adopted to solve this proposed model. Through the simulation contrastive analysis, it is verified that the microgrid based on multi-agent coordinated control can effectively improve the economic operation efficiency. This work is supported by National Natural Science Foundation of China (No. 51407104 and No. 61603212).

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王凌云,胡兴媛,李.基于多代理的实时电价机制下微网需求侧协同调控优化[J].电力系统保护与控制,2019,47(5):69-76.[WANG Lingyun, HU Xingyuan, LI Sheng. Collaborative regulation control optimization on demand side of microgrid based on multi-agent under real time price mechanism[J]. Power System Protection and Control,2019,V47(5):69-76]

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  • 收稿日期:2018-03-20
  • 最后修改日期:2018-07-03
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  • 在线发布日期: 2019-03-06
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