考虑火电深度调峰的风光火储系统日前优化调度

深入挖掘火电机组深度调峰能力、实现风光火储多能互补运行是应对规模化新能源并网消纳的重要手段。提出火电机组深度调峰和爬坡成本、污染物惩罚成本、储能系统运行成本及新能源弃电惩罚成本的计算方法，建立了考虑火电深度调峰的风光火储系统日前优化调度模型。分别以风光出力最大、净负荷波动最小和系统运行成本最低为优化目标，并设定火电机组的不同调峰深度，对含高比例新能源的风光火储系统在典型日的优化调度策略进行仿真计算。结果表明：所建立的模型能够满足不同优化目标下的风光火储优化调度策略计算；通过提升火电机组深度调峰能力，可有效降低新能源弃电率。

Day-Ahead Optimal Dispatching of Wind-Solar-Thermal Power Storage System Considering Deep Peak Shaving of Thermal Power

Realizing the complementary operation of wind, solar, and thermal power storage by deeply mining the deep peak-shaving capacity of thermal power units is an important means to deal with the large-scale grid-connected consumption of new energy. This paper proposes the calculation methods of the deep peak shaving and climbing cost of thermal power units, pollutant penalty cost, operation cost of the energy storage system, and penalty cost of new energy power abandonment. In addition, it builds the day-ahead optimal dispatching model of the wind-solar-thermal power storage system considering deep peak shaving of thermal power. Taking the maximum wind and solar output, the minimum net load fluctuation, and the lowest system operation cost as the optimization objectives separately, this study simulates and calculates the optimal dispatching strategies of the wind-solar-thermal power storage system with a high proportion of new energy on a typical day under different peak-shaving depths of thermal power units. The results show that the built model can realize the calculation of the optimal dispatching strategies of wind, solar, and thermal power storage under different optimization objectives. A larger deep peak-shaving capacity of thermal power units can effectively reduce the power abandonment rate of new energy.