水电站跨省跨区月度合同电量曲线分解模型

Model for Curve Decomposition of Monthly Contract Electricity of Hydropower Plant

水电站跨省跨区送电涉及多省份、多品种、多时间尺度等多维度电量曲线分解,且需要考虑复杂的电网调峰等需求,是电站每月发电运行中亟待解决的重要难题。依托实际工程的生产需求,构建了适合跨省、跨区送电的水电站月度合同电量曲线分解模型,考虑保量保价、保量竞价、市场化三类品种电价差异,以及电网调峰、市场和电站运行复杂约束,提出了多省多品种发电收益总和最大的二次规划目标,并采用混合整数线性规划方法实现了模型高效求解。以溪洛渡右岸水电站为依托工程,通过枯期、汛期两个不同的应用场景进行了模型验证,结果表明在满足电网要求的调峰边界条件下,通过优化两省多尺度多品种电量和出力分配,市场化电量优先分配至负荷高峰和平段,可以有效提高电站发电收益。

The trans-provincial and trans-regional power transmission of large hydropower plant involves the decomposition of power curve among multiple provinces,multiple varieties and multiple time scales,and needs to consider the needs of complex peak shaving in power grids.This is an important problem to be solved in the monthly power generation and operation of the power plant.Based on the production demand of real engineering,this paper constructs a monthly contract power curve decomposition model,which is suitable for trans-provincial and trans-regional power transmission of giant hydropower plants.Considering the differences of electricity prices of three types of hydropower plants,including guaranteed quantity and guaranteed price,guaranteed quantity bidding and marketization,as well as the complex constraints of power grid peak shaving,market and power station operation,this paper puts forward the secondary planning goal of maximizing the total revenue of multi-provincial and multi-variety power generation.The mixed integer linear programming method is used to solve the model efficiently.The model is verified by two different application scenarios of right bank of Xiluodu Hydropower Plant in dry season and flood season.The results show that under the boundary conditions of peak shaving meeting the requirements of power grid,by optimizing the multi-scale and multi-variety power and output distribution of the two provinces,the market-oriented power is preferentially distributed to the peak and flat sections of load,the power generation income of right bank of Xiluodu Hydropower Plant can be effectively improved.