摘要
兼顾分布式电源(DG)出力和负荷动态变化提出一种基于需求侧响应的配电网动态无功优化策略。该策略采用灰色关联度映射方法划分时段,在同一时段内协同优化不同类型变量,然后固定多个时段内并联电容器组(SCB)和有载调压变压器(OLTC)的状态,进行二次静态无功优化校正。针对模型特征,对不同类型变量采用混合协同进化算法进行求解,并提出基于Tent混沌映射和Levy飞行策略的改进麻雀算法提高协同求解效率。仿真结果表明:计及新能源出力变化和需求侧响应的所提策略能在降低求解规模的同时获取较高满意度的无功优化结果,且该混合协同进化算法在求解混合整数的非凸、非线性优化问题上具有一定优势。
In response to the call of"carbon peak,carbon neutral",a large number of distributed energy(DG)are connected to the distribution network.This paper proposes a dynamic reactive power optimization strategy based on demand side response considering DG output and load dynamic change.In this strategy,the gray relational mapping method is used to divide the time periods,and different types of variables are optimized in the same time period.Then,the shunt capacitor bank(SCB)and on-line tap changer(OLTC)is fixed in multiple time periods,and the secondary static reactive power optimization correction is carried out.According to the characteristics of the model,different types of variables are solved by hybrid co-evolution algorithm,and an improved sparrow algorithm based on tent chaotic map and Levy flight strategy is proposed to improve the efficiency of solution.The simulation results show that the proposed strategy considering new energy output and demand response can optimize the load characteristics and obtain satisfactory reactive power optimization results while reducing the solution scale,moreover the hybrid co-evolution algorithm has certain advantages in solving mixed integer non convex nonlinear optimization problems.
作者
陈倩
王维庆
王海云
武家辉
Chen Qian;Wang Weiqing;Wang Haiyun;Wu Jiahui(Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid-connected Control,Xinjiang University,Urumqi 830047,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第1期525-535,共11页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(52067020)
自治区重点实验室开放课题(2018D04005)
自治区教育厅重点项目(XJEDU2019I009)。
关键词
分布式电源
无功功率
配电网
灰色关联度
协同进化算法
Tent混沌
distributed generations
reactive power
distribution network
gray relational degree
co-evolution algorithm
tent chaos
