基于遗传蚁群的光储电站运行效益提升策略研究

以并网光储电站为研究对象，以充分发挥储能系统灵活调节作用并提高系统运行经济性为出发点，在考虑度电成本的基础上，提出一种实时调整储能系统运行状态的控制策略，建立以净收益最大、向大电网取电量最少为目标的优化模型，采用基于精英策略的带有惩罚函数的遗传-蚁群算法对优化模型进行求解，从投资人角度对并网光储系统进行投资收益分析。最后通过对江苏省某地区实际数据仿真分析，给出该地区“光伏+储能”优化控制策略及其经济效益分析结果，验证了该模型及算法的可行性。

Research on Operational Benefit Improvement Strategy of Optical Storage Power Station Based on Genetic Ant Colony Algorithm

This article takes the grid-connected optical storage regional power grid as the research object, and starts the research with the full use of the flexible regulation role of the energy storage system and the improvement of the system operation economy. On the basis of considering the cost of electricity, a control strategy for real-time adjustment of the BESS operating state is proposed, and an optimization model is established with the goal of maximizing net income, minimizing total cost, and extracting electricity from the large grid. The genetic-ant colony algorithm of the penalty function solves the optimization model, and analyzes the investment income of the grid-connected optical storage system from the perspective of investors. Finally, through the simulation analysis of actual data in a certain area of Jiangsu, the optimization control strategy of "photovoltaic and energy storage" in this area and the results of economic benefit analysis are given to verify the feasibility of this model and algorithm.