基于概率潮流的主动配电网日前-实时两级优化调度

为有效应对主动配电网(ADN)中间歇性电源、虚拟微网与柔性负荷等不确定变量给电网安全经济运行带来的挑战,基于概率潮流技术建立了ADN日前—实时两级优化调度模型。日前调度以ADN中各单元日前功率预测结果为依据,以运行成本最小为目标,确定次日各时段内各调度单元运行计划;在此基础上,针对实际运行中各单元的波动性和超短期预测结果进行实时调度,以不确定单元作为随机变量进行概率潮流计算,对日前调度计划进行调整,使得ADN中各节点电压和支路功率在3倍标准差内波动仍满足约束条件,提高ADN的安全裕度。最后,结合引力搜索算法和粒子群算法对调度模型进行求解,并通过算例验证模型的有效性。

Day-ahead and Real-time Optimal Scheduling for Active Distribution Network Based on Probabilistic Power Flow

In order to effectively handle the challenges of safe operation brought by the intermittent power supply, virtual micro-network, flexible load and other uncertain variables in the active distribution network(ADN), a two-level optimal scheduling model based on probabilistic power flow is proposed, which is constituted by day-head and real-time optimal scheduling model. The day-ahead scheduling determines the operation plan of each unit to minimum the operation cost of ADN based on the power forecasting results. Real-time scheduling is performed for the volatility of each unit in the actual operation and the results of the short-term forecasting. The plan made by day-ahead scheduling is adjusted in accordance to the probabilistic power flow, which is calculated regarding the uncertain units as random variables. Node voltage and branch power in ADN still satisfy the constraint condition when fluctuating in triple standard deviation. Therefore, the safety margin of ADN is improved. The gravitational search algorithm and particle swarm optimization algorithm are applied to solve the scheduling model. In the end, the effectiveness of the scheduling model is verified by case study.