考虑多风能预测场景的虚拟电厂日内滚动柔性优化调度方法

随着分布式可再生能源接入电网的比例日益提高,其出力间歇性和随机性给电力系统的安全稳定和经济运行带来了一系列的负面影响,虚拟电厂为分布式可再生能源可靠并网提供了新的途径。针对虚拟电厂中风电机组出力的不确定性及预测误差,考虑了由多个风能预测服务商基于不同预测方法提供多个风电出力预测场景,以期望运行成本最小为目标,制定虚拟电厂每个场景对应的最优调度计划。为应对风力状况的随机波动,虚拟电厂当前时段考虑下一时段所有风电出力场景下的计划调整成本。并以期望运行总成本最小为目标,构建基于多风能预测结果的虚拟电厂柔性优化调度模型,并设计了日内虚拟电厂出力计划的滚动调度策略。通过数值仿真,对比分析了不同预测场景下虚拟电厂的运行成本,验证了所提模型的可行性和有效性。

Day-ahead flexible rolling optimization dispatch of virtual power plant based on multi-wind forecasting results

With the increasing proportion of distributed renewable energy access to the power grid, the intermittent and stochastic output of power system has brought a series of negative effects on the safety, stability and economic operation, and the Virtual Power Plant (VPP) provides a new way for the reliable interconnection of distributed renewable energy sources. Aiming at the uncertainty and prediction error of the output of the stroke generator in the VPP, this paper considers that several wind power forecasting service providers provide a number of air power output prediction scenarios based on different forecasting methods, and the virtual plant sets the optimal scheduling plan for each scene with the goal of minimizing the expected operating cost. In response to random fluctuations in wind conditions, the current time slot of a VPP considers the planned adjustment costs under all wind power output scenarios in the next period. Aiming at the minimum total cost of expected operation, the flexible optimal scheduling model of VPP based on multi-wind energy prediction results is constructed, and the rolling scheduling strategy of the output plan of intraday VPP is designed. Through numerical simulation, the running cost of VPP under different prediction scenarios is compared and analyzed, and the feasibility and effectiveness of the proposed model are verified. This work is supported by National Natural Science Foundation of China (No. 71420107027), Science and Technology Projects of Hunan Province (No. 2017CT5015 and No. 2017WK2053), Postgraduate Research and Innovation Project of Hunan Province (No. CX2017B465), and Strategic Emerging Industries Project in Hunan Province (No. 2018GK4002).