含超前控制的风储系统滚动最优控制策略

新一轮电力体制改革要求进一步提高风电跟踪计划能力,电池储能为此提供了途径但成本较高。为提高其经济性,提出一种风电场电池储能最优控制策略,其以最优化模型为核心,并包含超前滚动优化算法。最优化模型以储能下令次数最少为优化目标,包含统计学控制效果约束及荷电状态、死区控制等储能运行约束。超前滚动优化算法利用超短期风电功率预测,在统计学控制效果的每一个10min考核周期内进行实时的滚动超前控制,并不断修正预测误差的影响,保证各项约束得到满足。仿真结果表明,所提最优控制策略显著提高了风电跟踪计划精度,并在此前提下大幅减少了储能下令次数,且对超短期风电功率预测误差具备一定的抗干扰能力。

Rolling Optimal Control Strategy with Lead Control for Wind Power and Energy Storage Systems

The new round of power system reformation requires further improvement on the capability of wind power tracking plan. The battery energy storage system(BESS)provides a way for this but the cost is high. To enhance the economy, an optimal control strategy for wind farms equipped with BESS is proposed, which contains an optimal control model as the core, and a rolling optimization algorithm with lead control. The proposed optimal control model takes the minimization of numbers of BESS orders as its objective, and contains statistical control effect constraints and BESS operation constraints including state of charge and dead band. The rolling optimization algorithm utilizes the ultra-short-term wind power prediction(WPP)to perform a real-time lead control within each 10 min evaluation period of statistical control effect, and continuously correct the impact of prediction error, ensuring all constraints to be satisfied. Finally, the simulation results show that the proposed control strategy does improve the accuracy of wind power tracking plan and reduce numbers of BESS orders under this premise. Besides, it has anti-interference feature against ultra-short-term WPP error.