面向电力市场多时间尺度场景应用的风储系统调度方法
Dispatching Method of Combined Wind-Storage System for Multi-Time Scale Scenarios Application in Electricity Markets
殷高文 1沈非凡 1黄晟 1魏娟 1屈尹鹏 1王鹏达1
作者信息
- 1. 湖南大学 电气与信息工程学院,长沙 410082
- 折叠
摘要
针对风储系统参与电力市场不同调用时间尺度场景的耦合问题,提出面向电力市场多时间尺度场景应用的风储系统优化调度方法,指导风储系统进行短时风功率波动平抑,并参与电能量市场及备用辅助服务市场,实现不同调用时间尺度场景间协同优化,最大化风储系统经济效益.首先,考虑不同场景盈利机制,以风储系统多场景经济收益最大为目标建立目标函数.然后,建立风储系统参与各应用场景条件约束以及多调用时间尺度耦合约束.最后,算例仿真验证了所提方法可在保证风功率波动不越限的同时,提升风储系统在日前电能量市场及备用辅助服务市场中的综合运行效益.
Abstract
Aimed at the coupling problem of the combined wind-storage system participating in different call time scale scenarios in electricity markets,an optimal dispatching method of the combined wind-storage system oriented to the application of multi-time scale scenarios in electricity markets is proposed to guide the combined wind-storage system to suppress short-term wind power fluctuation,and participate in the electric energy market and the reserve ancillary service market,so as to realize the collaborative optimization among different call time scale scenarios application and maximize the economic benefits of the combined wind-storage system.First,considering the profit mechanism of different scenarios,the objective function is established with the objective of maximizing the economic benefits of multiple scenarios of the combined wind-storage system.Then,the constraints of the combined wind-storage system participating in various application scenarios and multi call time scale coupling constraints are established.Finally,the numerical simulation verifies that the proposed method can improve the comprehensive operation profit of the combined wind-storage system in the day-ahead electric energy market and the reserve ancillary service market while ensuring that the wind power fluctuation does not exceed the limit.
关键词
电力市场/风储系统/多场景应用/多调用时间尺度协同优化Key words
electricity markets/combined wind-storage system/multi-scenario application/multi call time scale collaborative optimization引用本文复制引用
基金项目
湖南省科技创新计划(2021RC2046)
国家自然科学基金(52207096)
出版年
2024
NETL
NSTL
万方数据