基于MAS微电网的需求响应互动定价优化模型
发布时间：2018-08-23 11:48
【摘要】：新能源发电不同于配电网中常规机组发电,其可调节性差,调度弃电损失大。文章考虑到微电网中柔性负荷的可调度性,使用需求响应作为经济杠杆,以尽可能利用新能源供电,减少弃电量和提高用户侧与供电侧经济性为目标,提出含弹性因子的分时定价策略对负荷功率进行日前调度。为实现微电网的发电侧与用户侧的互动及其复杂调度,采用JADE软件平台,构建基于Multi-Agent System(MAS)的微电网需求响应互动定价优化模型,通过联合多个代理体使用改进的粒子群算法寻优验证了所提方法的有效性。
[Abstract]:New energy generation is different from conventional generating units in distribution network. In this paper, considering the schedulability of flexible load in microgrid, the demand response is used as the economic lever, and the goal is to make use of the new energy as much as possible, to reduce the power abandonment and to improve the economy of the user side and the power supply side. A time-sharing pricing strategy with elastic factors is proposed to schedule load power before day. In order to realize the interaction between generation side and user side of microgrid and its complex dispatching, the interactive pricing optimization model of demand response of microgrid based on Multi-Agent System (MAS) is constructed based on JADE software platform. The effectiveness of the proposed method is verified by combining multiple agents with improved particle swarm optimization (PSO).
【作者单位】： 三峡大学电气与新能源学院;新能源微电网湖北省协同创新中心(三峡大学);
【基金】：国家自然科学基金项目(51407104)
【分类号】：F426.61;TM73
[Abstract]:New energy generation is different from conventional generating units in distribution network. In this paper, considering the schedulability of flexible load in microgrid, the demand response is used as the economic lever, and the goal is to make use of the new energy as much as possible, to reduce the power abandonment and to improve the economy of the user side and the power supply side. A time-sharing pricing strategy with elastic factors is proposed to schedule load power before day. In order to realize the interaction between generation side and user side of microgrid and its complex dispatching, the interactive pricing optimization model of demand response of microgrid based on Multi-Agent System (MAS) is constructed based on JADE software platform. The effectiveness of the proposed method is verified by combining multiple agents with improved particle swarm optimization (PSO).
【作者单位】： 三峡大学电气与新能源学院;新能源微电网湖北省协同创新中心(三峡大学);
【基金】：国家自然科学基金项目(51407104)
【分类号】：F426.61;TM73
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