考虑可再生能源出力不确定的孤岛型微电网优化调度

Optimal Scheduling of Islanded microgrid Considering Uncertain Output of Renewable Energy

风电与光伏并网显著增加了电力系统运行调度的不确定性,在某一时隙其所能发出的最大功率往往在某一确定值附近按一定的概率分布随机波动,而经过预测所得到的功率与实际功率之间存在预测误差。当风机与光伏实际所能发出的最大功率小于调度方案中安排的功率时,会影响所制定的调度策略,甚至可能会造成运行成本的大幅上升。为了研究可再生能源出力不确定性对微电网调度的影响,该文将微源按照调频特性分成基础负荷功率电源和调频电源两类,提出基于电源分类的两步式优化调度方法,采用蒙特卡洛法,对考虑可再生能源不确定性的成本函数值的期望和方差进行估计,并通过采样得到一定数量的样本,采用粒子群优化算法(particle swarm optimization,PSO)找到使得样本成本均值最小的调频电源装机计划以及基础负荷功率电源调度计划。最后利用BP神经网络构建调频电源的调频策略。在大量抽样样本背景下,通过算例分析验证所提的两步式调度方法的合理性和有效性。

Wind power and photovoltaic grid-connected significantly increase the uncertainty of power system operation scheduling. The maximum power that it can emit often fluctuates randomly according to a certain probability distribution around a certain value in a certain time slot, and there is a prediction error between the predicted power and the actual power. When the maximum power that the wind power and photovoltaic can actually emit is less than the power scheduled in the scheduling plan, it will affect the scheduling strategy formulated, and may even cause a substantial increase in operating costs. In order to studying the influence of the uncertainty of renewable energy output on microgrid dispatching, this paper divided microsources into two types of basic power supply and requency modulation(FM) power supply according to the frequency modulation characteristics, and proposed a two-step optimal scheduling method based on power classification. Using Monte Carlo lestimated the expected and variance of the cost function value considering the uncertainty of renewable energy and obtained a certain number of samples through sampling. Using the particle swarm optimization(PSO) algorithm found the FM power installation plan and the basic load power supply scheduling plan that minimize the average sample cost. In the context of a large number of sampling samples, the rationality and effectiveness of the proposed two-step scheduling method were verified through example.