售用双方协同优化的家庭柔性负荷管理策略

提出一种家庭柔性负荷管理和售电公司售电决策协同优化模型。在居民用户侧,利用洗衣机、热水器、洗碗机和电动汽车充电过程的可时移性和周期性,以及电动汽车的储能特性,建立了以上述设备为对象的柔性负荷运行模型,以家庭用电费用最优为目标,制定了动态电价激励下的家庭负荷优化策略,用响应程度衡量参与优化调度的用户比例,并将总用电需求信息传递给售电公司;在售电公司侧,以售电公司经济收益最大为目标,建立了售电决策模型,制定日前购电方案和动态电价,并将价格信息传递给用户,因此构成了售用相互协同的双层迭代模型。算例基于遗传算法和yalmip优化工具箱进行仿真,采用情景分析法处理不同家庭用电需求预测的不确定性,调整响应程度分析日前购电问题。千个家庭的仿真结果验证了本优化策略能够协调解决用售双方利益,提高双方经济性,同时还有降低短时负荷峰值的能力。

Management Strategy for Domestic Flexible Load to Achieve Retailer-user Coordinated Optimization

A coordinated optimization model of domestic flexible load management and electricity retailers’ decision making is proposed. At residential user side, on the basis of time-delay and periodicity of washing machines, water heaters,dishwashers and electric vehicle charging process, and energy storage characteristics of electric vehicle, a flexible load operation model is built. With the goal of optimal electricity-purchase cost, a optimization strategy of domestic load inspired by dynamic electricity price is established. The degree of demand response measures the proportion of the users involving in the optimal scheduling. Then the total demand information is sent to electricity retailers. At electricity retailer side, in order to maximize economic benefits, a retailers’ decision-making model is established to formulate a day-ahead purchase plan and dynamic electricity price. Then, price information is transferred to the users. Thus, a bi-level optimization model of retailers and users is constituted. An example is simulated based on genetic algorithm and Yalmip optimization toolbox. Scenario analysis method is used to deal with the uncertainty of electricity demand prediction of different households, and the response degree is adjusted to analyze the electricity purchase before the day.Simulation results of thousands of households verify that the optimal strategy has ability to coordinate interests and improve economic efficiency on both sides, and reduce short-term load peak.