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王凌云,徐嘉阳,尚勇.基于Multi-agent改进粒子群优化算法的分时电价机制下多微网系统联合优化调度[J].电源学报,2019,17(4):130-139
基于Multi-agent改进粒子群优化算法的分时电价机制下多微网系统联合优化调度
Coordinated Optimal Scheduling of Multi-microgrid System under Mechanism of Time-of-use Electricity Price Based on Multi-agent Improved Particle Swarm Optimization Algorithm
投稿时间:2017-06-19  修订日期:2019-01-15
DOI:10.13234/j.issn.2095-2805.2019.4.130
中文关键词:  多微网  储能装置  分时电价  优化调度  多智能体粒子群优化
英文关键词:multi-microgrid  energy storage device  time-of-use (TOU) electricity price  optimal scheduling  multi-agent particle swarm optimization (PSO)
基金项目:国家自然科学基金资助项目(51407104);三峡大学研究生科研创新基金资助项目(SDYC2016043)
作者单位E-mail
王凌云 三峡大学电气与新能源学院, 宜昌 443002
三峡大学新能源微电网湖北省协同创新中心, 宜昌 443002 
 
徐嘉阳 三峡大学电气与新能源学院, 宜昌 443002
三峡大学新能源微电网湖北省协同创新中心, 宜昌 443002 
x6822313@163.com 
尚勇 国网湖北省电力公司襄阳供电公司检修分公司, 襄阳 441000  
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中文摘要:
      随着分布式能源与微网技术的发展,大量分布式电源或微网并入主电网,对电网运行带来一定影响。针对多个微网接入主电网的情况,在计及分时电价机制的基础上,从微网整合角度考虑各微网间的互补性。结合微网中储能装置对电网削峰填谷的作用,提出一种基于分时电价机制下多微网系统联合优化调度方法。考虑不同时刻电价差异,分别制定峰、平、谷不同时刻调度策略,建立以整个多微网系统的运行成本和环境治理成本最小为目标的数学优化模型,并采用基于multi-agent改进粒子群优化算法对模型求解。通过算例分析,比较了各微网单独运行和多微网联合调度两种策略下的多微网系统成本,验证了所提方法的有效性。
英文摘要:
      Along with the development of distributed energy and microgrid technology, a large number of distributed generations or microgrids have been integrated into the main power grid, which has a certain impact on the operation of the power grid. In this paper, for the case of access of multiple microgrids to the power grid, the complementarity among each microgrid is considered from the aspect of microgrid integration based on the mechanism of time-of-use(TOU) electricity price. By combining the function of energy storage devices in microgrid, i.e., peak shaving and valley filling, a coordinated optimization scheduling method for the multi-microgrid system based on the TOU mechanism is proposed. Considering the difference in prices at different time points, a scheduling strategy is formulated for the peak, flat and valley time, respectively, and a mathematical optimization model is established to minimize the operating cost of the whole multi-microgrid system and environmental governance cost. In addition, an improved particle swarm optimization algorithm based on multi-agent is adopted to solve this model. Finally, through the analysis of a numerical example, the operating costs of the multi-microgrid system under two strategies(i.e., independent operation of each micr-ogrid and coordinated scheduling of multi-microgrid) are compared, which verifies the effectiveness of the proposed method.
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