基于模型预测控制的光储场站与SVG协同优化策略

Collaborative optimization strategy of optical storage station and SVG based on model predictive control

随着新能源高渗透接入电网,电网的电压控制问题变得越来越复杂。针对多无功源之间协同优化存在多时间尺度特征的问题,采用了模型预测控制方法,从日前计划与日内短期调度出发,建立了不同时间尺度下的多无功源协同优化模型,并制定了日前—日内多无功源协同优化调度策略。仿真验证表明,所提策略能够显著缩减离散无功设备的动作次数,并高效协调光储场站与SVG设备,在保证电网经济运行的同时,避免电网节点电压越限的问题。

With the high penetration of new energy into the power grid,the voltage control problem of the power grid becomes more and more complicated.Aiming at the problem of multi-time scale characteristics in cooperative optimization among multiple reactive sources,this paper adopted model predictive control method,established multi-reactive source cooperative optimization models under different time scales from day-ahead planning and day-day short-term scheduling,and formulated day-ahead to day-day multi-reactive source cooperative optimal scheduling strategies.Simulation results show that the proposed strategy can significantly reduce the number of discrete reactive power devices,and efficiently coordinate optical storage stations and SVG devices,while ensuring the economic operation of the power grid,avoid the problem of voltage exceeding the limit at grid nodes.