摘要
针对直流输电对跨省潮流模型的影响,提出了一种考虑特高压直流输电影响的省间电力现货市场(以下简称“省间市场”)协调交易方法。将省间市场展开过程拆分为省内代理报价曲线模型和省间市场协调交易模型,采用交-直流解耦的方法进行省间及省内的潮流计算。省内代理报价曲线模型以省内机组发电量/负荷需求为决策变量,通过区域等效模型得到省内机组/负荷参与省间市场的代理报价曲线。省间市场协调交易模型以省间市场交易值为决策变量实现省间市场出清,通过ADMM(交替方向乘子法)实现协调交易的分布式转化,避免交易过程中泄露省内机组信息和网络拓扑信息。算例表明,该方法实现了省间市场综合收益最大化,保障了信息隐私安全。
The impact of high-voltage direct current(HVDC)transmission systems on inter-provincial power flow models is addressed by proposing a coordinated trading method for the inter-provincial electricity spot market(here⁃inafter referred to as the inter-provincial market).The process of inter-provincial market expansion is divided into a provincial agent bidding curve model and a coordinated inter-provincial market trading model.The method employs an AC-DC decoupling approach for power flow calculations within and between provinces.The provincial agent bid⁃ding curve model,with the provincial generation and load demand as decision variables,derives the agent bidding curves for provincial units/loads participating in the inter-provincial market through a regional equivalent model.The coordinated inter-provincial market trading model,using the inter-provincial market trading values as decision vari⁃ables,achieves market clearing through ADMM(alternating direction method of multipliers),ensuring a distrib⁃uted transformation of coordinated trading to prevent leakage of information regarding provincial units and network topology during the trading process.Case studies demonstrate that this method maximizes the overall revenue of the inter-provincial market while safeguarding information privacy and security.
作者
张潮
顾卫祥
雍康倩
朱超
ZHANG Chao;GU Weixiang;YONG Kangqian;ZHU Chao(National Grid Jiangsu Electric Power Co.,Ltd.EHV Branch,Nanjing 211102,China;National Grid Jiangsu Electric Power Co.,Ltd.Nantong Power supply Branch,Nantong,Jiangsu 226000,China)
出处
《浙江电力》
2024年第5期83-90,共8页
Zhejiang Electric Power
基金
国网江苏省电力有限公司科技项目(J2022066)。
关键词
特高压直流输电
省间市场
交-直流解耦
交替方向乘子法
分布式优化
UHVDC transmission systems
inter-provincial market
AC/DC decoupling
ADMM algorithm
distrib⁃uted optimization
