家庭智能用电的随机&可调节鲁棒优化混合调度策略研究  

Research of stochastic and adjustable robust optimization hybrid co-scheduling strategy for smart residential electricity consumption

作　　者：端凌立 杨镜非[1] 王硕 胡继匀 傅长熠 Duan Lingli;Yang Jingfei;Wang Shuo;Hu Jiyun;Fu Changyi(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《电测与仪表》2020年第20期1-9,共9页Electrical Measurement & Instrumentation

基　　金：国家重点研发计划资助项目(2018YFB0905200)。

摘　　要：针对包含光伏及储能系统的家庭用户,考虑光伏发电的预测距离运行点越远精度越低的特点,以及家庭用户用电行为具有不确定性的特点,建立了分布式电源、家庭负荷及用户用电行为不确定性的模型。结合随机优化与可调节鲁棒优化的优点,提出了一种家庭智能用电的随机与可调节鲁棒优化混合调度策略,以系统运行成本最小为目标,保证用户用电自由与舒适度,同时提高光伏的本地消纳水平。基于工程博弈论思想和改进粒子群算法,将优化模型转换为混合整数线性规划问题进行求解。通过算例仿真,验证了该调度策略的有效性。For residential loads with photovoltaic(PV)system and energy storage system,in view of the characteristics that the forecast accuracy of PV systems decreases gradually with the growing time-scale,as well as the consumption behavior of residential consumers has the characteristics of uncertainty,the models for distributed energy sources,residential loads and the uncertainty of residential consumers behavior are built in this paper.On this basis,a stochastic and adjustable robust optimization hybrid co-scheduling strategy for smart residential electricity consumption is proposed,taking advantages of both optimization methods.It aims at minimizing the operating cost of the system and increasing the PV local consumption,meeting the requirements of comfort level and consumption freedom.With the engineering game theory and the improved particle swarm optimization(PSO),the optimization model is transformed to mixed integer linear programming(MILP).Through the simulation results of the test system,the effectiveness of the proposed strategy is proved.

关 键 词：家庭智能用电 不确定性 随机优化 鲁棒优化 

分 类 号：TM925[电气工程—电力电子与电力传动]