摘要
鉴于可再生能源出力及负荷需求的不确定性会对配电网储能配置效果造成不利影响,该文构建了基于分类概率机会约束信息间隙决策理论(information gap decision theory,IGDT)的配电网储能鲁棒优化配置模型;该模型不仅利用IGDT提高了配置方案的鲁棒性,还通过引入分类概率机会约束机制消除了常规IGDT需预先设定偏差因子的主观性,同时充分体现了风电、光伏和负荷不确定性分布的概率差异性。根据不确定理论将机会约束转化为等价确定性约束,并采用非劣排序复合微分进化,实现模型求解。算例应用表明,所提出的储能优化配置方法在经济性、电压改善效果、鲁棒性、灵活性和高效性等方面具有优越性。
In view of the fact that the uncertainty of renewable energy output and load demand will badly affect the allocation of the energy storage system in distribution network,this paper constructs a robust optimal allocation of energy storage in distribution network based on classified probability chance constraint information gap decision theory(IGDT).The model not only improves the robustness of the allocation scheme by using IGDT,but also eliminates the subjectivity that conventional IGDT needs to pre-set the deviation factor by introducing classified probability chance constraint mechanism,and fully reflects the probability differences of the uncertainty distributions of wind power,photovoltaic and load.Then the chance constraint was transformed into the equivalent deterministic constraint according to the uncertainty theory,and the model was solved by non-dominated sorting compound differential evolution.The application of the example shows that the proposed optimal allocation method of energy storage has advantages in terms of economy,voltage improvement,robustness,flexibility and efficiency.
作者
彭春华
陈露
张金克
孙惠娟
PENG Chunhua;CHEN Lu;ZHANG Jinke;SUN Huijuan(School of Electric and Automation Engineering,East China Jiaotong University,Nanchang 330013,Jiangxi Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2020年第9期2809-2818,共10页
Proceedings of the CSEE
基金
国家自然科学基金项目(51867008,51567007)
江西省自然科学基金项目(20192ACBL20007)
江西省研究生创新资金项目(YC2018-S242)。
关键词
储能配置
配电网
信息间隙决策理论
鲁棒优化
分类概率机会约束
energy storage allocation
distribution network
information gap decision theory
robust optimization
classified probability chance constraint
作者简介
彭春华(1973),男,博士,教授,博士生导师,研究方向为电力系统优化调度、智能电网规划与优化运行,chinapch@163.com;陈露(1995),女,硕士研究生,研究方向为电力系统优化运行,565490467@qq.com。
