中国电力2025，Vol.58Issue(3)：55-64,10.DOI:10.11930/j.issn.1004-9649.202403093
多能互补发电系统电/热储能容量双层优化配置方法
Bi-level Capacity Optimization for Battery/Thermal Energy Storage System in Multi-energy Complementary Power Generation System
摘要
Abstract
Multi-energy complementary power generation system can fully utilize the complementary advantages of wind-PV-thermal-battery sources and improve energy efficiency.It is of great significance for the construction of low-carbon new power system.Capacity coordinated optimization of battery/thermal energy storage in multi-energy complementary power generation system can reduce the investment costs of power system,and improve the utilization rate of renewable energy and continuous power supply.In this paper,multi-energy complementary power generation system with wind-PV-thermal-battery sources is studied,and a bi-level capacity coordinated optimization model of this system is established.The upper-level model optimizes the capacity of battery/thermal energy storage with the maximum annual net income of this system.The lower-level model optimizes the power shortage in this system,and optimize the generation operation status of this system by considering the constraints such as continuous power supply,wind-PV-thermal-battery sources operation,utilization rate of renewable energy,etc.To solve this bi-level model,a linearization method for nonlinear constraints model is proposed,and a heuristic algorithm based on the combination of value function and branch-bound is designed to obtain the efficient solution.Based on a typical example of multi-energy complementary power generation system,the experimental results verify the effectiveness of the proposed bi-level model and algorithm.关键词
多能互补发电系统/电/热储能/容量配置/双层模型/启发式算法Key words
multi-energy complementary power generation system/battery/thermal energy storage/capacity optimization/bi-level/heuristic algorithm引用本文复制引用
李湃,卢慧,李驰,杜洪博..多能互补发电系统电/热储能容量双层优化配置方法[J].中国电力,2025,58(3):55-64,10.基金项目
中国电力科学研究院有限公司研究开发项目(融合深度学习与最优化决策算法的灵活资源优化规划方法研究,NY83-21-006). This work is supported by Research and Development Project of China Electric Power Research Institute(Research on Flexible Resource Optimization Planning Method Integrating Deep Learning and Optimization Decision Algorithm,No.NY83-21-006). （融合深度学习与最优化决策算法的灵活资源优化规划方法研究,NY83-21-006）
