计及深度调峰的风-光-水-火-抽蓄多能源联网系统优化调度研究

Research on Optimal Dispatching of Wind-Photovoltaic-Hydropower-Thermal Power-Pumped Storage Combined Power Generation System with Deep Peak Regulation

多种能源联网运行逐步成为未来电力系统发展的方向,多能源联网系统的优化调度是应对规模化新能源并网消纳的重要手段。文中建立了计及火电机组深度调峰成本的风-光-水-火-抽蓄联网系统成本模型和考虑调峰主动性的风-光-水-火-抽蓄联网系统日前优化调度模型。针对多能源耦合系统结构复杂的情况,提出了分层优化调度的模型求解策略。上层模型以净负荷波动最小为优化目标,得到等效负荷曲线;下层模型根据上层模型的调度结果,以调峰运行的经济性最优和新能源弃电率最低为目标函数,计及调峰主动性,求解考虑火电机组深度调峰煤耗特性的含新能源系统的经济调度问题。仿真算例表明所建模型实现了多能源联网系统的互补运行,提升了火电机组的深度调峰能力,降低新能源弃电率和系统运行成本。

The interconnection of multiple energy sources has gradually become the direction of future power system development,and the optimization and scheduling of multi energy interconnection systems is an important means to cope with the large-scale consumption of new energy connected to the grid.We have established a cost model for the wind solar water fire pumping and storage interconnected system that takes into account the cost of deep peak shaving for thermal power units,and a day ahead optimization scheduling model for the wind solar water fire pumping and storage interconnected system that takes into account the initiative of peak shaving.A hierarchical optimization scheduling model solving method is proposed for the complex structure of multi energy coupled systems.The upper level model aims to minimize the fluctuation of net load and obtain the equivalent load curve;The lower level model,based on the scheduling results of the upper level model,takes the economic optimization of peak shaving operation and the minimum amount of new energy waste as the objective function,taking into account the initiative of peak shaving,to solve the economic scheduling problem of a new energy system that considers the deep peak shaving coal consumption characteristics of thermal power units.The simulation examples show that the constructed model achieves complementary operation of multi energy interconnected systems,improves the deep peak shaving ability of thermal power units,reduces the abandonment rate of new energy and system operating costs.