摘要
为解决用电尖峰负荷不断提升、能源电力保供困难等问题,我国正在积极推动峰谷分时电价引导用户削峰填谷。合理设置用户侧峰谷电价有利于保障独立型源网荷储一体化项目的基本收益。采用调整峰谷电价的方法对该类项目进行成本疏导,既可以完善用户侧峰谷分时电价,又可以促进该类项目的发展。基于峰谷电价信号,建立了该系统进行柔性负荷激励响应和绿证交易的典型日经济效益模型;在兼顾其他用户及电网效益的基础上,以保障系统经济效益为目标,对用户侧峰谷电价提出了优化方法。算例分析表明,重新测算的峰谷电价使夏季典型日成本削减136.87万元,并在冬季典型日盈利9.17万元;此外,峰谷电价使一般工商业的整体最大净负荷减少近1%,净负荷峰谷差减少近2%。
In order to solve the problems of rising peak loads and difficulties in keeping energy and power supply,China is actively promoting peak and valley time-sharing tariffs to guide users for peak shaving and valley filling.Reasonable setting of peak and valley electricity prices is beneficial for ensuring the benefits of independent source-grid-load-storage integration projects.Adopting the method of adjusting peak and valley electricity prices to guide the cost of such projects can not only improve the user side peak and valley time of use electricity prices,but also promote the development of such projects.The typical daily economic benefit model of flexible load incentive response and green certificate trading based on peak and valley prices signals for such system was established.On the basis of taking into account the benefits of other users and the grid,the optimization method of user-side peak and valley tariffs was proposed with the objective of guaranteeing the economic benefits of such system.Example analyses show that the re-measured peak and valley prices result in a typical day cost reduction of CNY 1368700 in summer and a profit of CNY 91700 on a typical day in winter.Moreover,the peak and valley tariffs reduce the overall maximum net load of the general industrial and commercial sector by nearly 1%,and reduce the peak-valley difference of the net load by nearly 2%.
作者
恩格贝
张岩
田冰颖
EN Gebei;ZHANG Yan;TIAN Bingying(School of Economic and Management,North China Electric Power University,Beijing 102206,China)
出处
《山东电力技术》
2023年第11期42-50,共9页
Shandong Electric Power
关键词
独立型源网荷储一体化系统
柔性负荷激励响应
绿证交易
峰谷电价优化模型
independent source-gird-load-storage system
flexible load excitation response
green card trading
peak-valley electricity price optimization model