引用本文: | 陈 寒,唐 忠,鲁家阳,等.基于CVaR量化不确定性的微电网优化调度研究[J].电力系统保护与控制,2021,49(5):105-115.[点击复制] |
CHEN Han,TANG Zhong,LU Jiayang,et al.Research on optimal dispatch of a microgrid based on CVaR quantitative uncertainty[J].Power System Protection and Control,2021,49(5):105-115[点击复制] |
|
摘要: |
分布式可再生能源(Distributed Energy Resources, DER)以微电网的形式大规模并网,其稳定运行面临着挑战。针对微电网中可再生能源出力的不确定性及调控过程中柔性负荷调整量过大可能会使用户满意度下降的问题,建立了考虑DER出力不确定性和计及用户满意度的日前优化调度模型。首先,采用基于条件风险价值(Conditional Value at Risk, CVaR)理论对微电网不确定性风险进行量化处理,提高系统运行稳定性,并将其转化为风险成本。其次,将优化前后的新旧负荷曲线差异程度作为评判用户满意度大小的指标,在满足微电网经济运行的同时,提高用户侧的用电体验感。以微电网综合运行成本最小和用户满意度最大为目标函数,建立优化调度模型。最后,采用改进的NSGA-II算法求解该模型,并通过仿真分析了不同置信水平及三种方案下的优化结果,从而验证了所提模型的有效性。 |
关键词: 分布式可再生能源 微电网 条件风险价值 不确定性 用电满意度 |
DOI:DOI: 10.19783/j.cnki.pspc.200603 |
投稿时间:2020-05-29修订日期:2020-11-16 |
基金项目:国家自然科学基金项目资助(61672337) |
|
Research on optimal dispatch of a microgrid based on CVaR quantitative uncertainty |
CHEN Han,TANG Zhong,LU Jiayang,MEI Guangyin,LI Zhengnan,SHI Chenhao |
(College of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China) |
Abstract: |
Distributed Renewable Energy (DRE) is connected to the power grid on a large scale in the form of a microgrid, and the stable operation of the power grid faces challenges. There is uncertainty on the renewable energy output in the microgrid and excessively large flexible load adjustments in the regulation process may reduce user satisfaction. Thus a day-ahead optimal scheduling model considering the uncertainty of DRE output and user satisfaction is established. First, the theory of Conditional Value at Risk (CVaR) is used to quantify the uncertainty risk of the microgrid and improve the stability of the system, and the uncertainty is converted into risk costs. Secondly, the degree of difference between the new and old load curves before and after optimization is used as an index to judge user satisfaction, while the economic operation of the microgrid is satisfied and the user’s sense of power consumption is also improved. The objective function of the optimal scheduling model is to minimize the overall operating cost and maximize user satisfaction. Finally, the NSGA-II algorithm generated by improved crossover operator coefficients is used, and the optimization results under different confidence levels and three schemes are analyzed through simulation, thereby verifying the effectiveness of the proposed model.
This work is supported by the National Natural Science Foundation of China (No. 61672337). |
Key words: distributed renewable energy microgrid conditional value at risk uncertainty electricity satisfaction |