计及火电机组深度调峰成本的大规模风电并网鲁棒优化调度

规模风电并网背景下,电力系统加大火电机组的调峰深度,充分挖掘现有下调备用空间,将是应对风电出力不确定性的有效方式之一。文中考虑火电机组工作在深度调峰(DPR)方式下的附加煤耗损失和机组寿命损耗,提出了计及火电机组DPR成本的规模风电并网鲁棒优化调度模型。考虑风电出力不确定性,建立了包括基于风电出力预测场景的调度主问题和基于极端场景的调控子问题的两阶段鲁棒优化模型,并引入不确定度参数控制调度计划的保守性,最终优化出经济性最优的鲁棒日前调度方案。基于算例分析证明所述模型的合理性与有效性。

Robust Optimal Dispatch of Large-scale Wind Power Integration Considering Deep Peak Regulation Cost of Thermal Power Units

Under the background of large-scale wind power integration, the power system raises the deep peak regulation (DPR) of the thermal power unit to fully exploit the existing downward spinning reserve capacity, which will be one of the effective ways to deal with the uncertainty of wind power. Considering the additional coal loss and unit life loss of the thermal power units which work in the DPR state, a robust optimal dispatching model taking the DPR cost of the thermal power unit into account is established in this paper. Considering the uncertainty of wind power output, a two-stage robust optimal model consisting of the main scheduling problem in forecasting scenes and the control sub-problem in extreme scenes is established, and the uncertain parameter is introduced to control the conservativeness of scheduling scheme. Finally, the economical optimal robust day-ahead scheduling plan is obtained. The rationality and effectiveness of the model are proved by case analysis.