电能量市场下负荷聚合商参与负荷削减投标策略  

Strategy of load aggregator participating in load curtailment bidding under power market

作　　者：杨苹[1] 曾凯林 余雁琳 林文智 YANG Ping;ZENG Kailin;YU Yanlin;LIN Wenzhi(School of Electric Power,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]华南理工大学电力学院,广东广州510640

出　　处：《电力工程技术》2022年第1期2-10,共9页Electric Power Engineering Technology

基　　金：广东省自然科学基金资助项目(2019A1515010689)。

摘　　要：针对负荷聚合商(LA)在电能量市场下参与负荷削减投标(LCB)时面临负荷与价格的不确定性问题,文中提出一种可使LA代理成本最小的LCB参与策略。首先,将LA的购电申报、LCB申报与可调节负荷的控制问题统一描述为混合整数线性规划问题。其次,利用双层规划模型处理负荷功率的不确定性,利用历史场景法结合场景缩减处理市场价格的不确定性。最后,基于宾夕法尼亚州-新泽西州-马里兰州(PJM)电力市场数据,针对所提策略及其在测试场景集中的表现进行分析。测试结果表明,所提策略及场景缩减方法能够降低聚合商4%以上的代理成本与10%以上的风险损失,可提高最多17.8%的履约率,为LA在不确定问题下的投标提供技术支撑。Load aggregators(LA)face the uncertainty of load and price when they participate in load curtailment bidding(LCB)in power market.A day ahead optimization model with the objective of minimizing the agency cost of LA is proposed.In this paper,the power purchase,LCB and the control of adjustable load are described as a mixed integer linear programming model.A bi-level programming model is used to deal with the uncertain load,so as to the historical scenario method to uncertain price combined with scenario reduction.Based on the data of Pennsylvania-New Jersey-Maryland(PJM)power market,the proposed strategy is analysed.The results show that the proposed strategy and scenario reduction method can reduce the aggregators′agency cost by more than 4%and risks by more than 10%,improve the performance rate by up to 17.8%,and provide bidding technical support for the LA under uncertain problems.

关 键 词：电能量市场 负荷聚合商(LA) 负荷削减投标(LCB) 需求响应 场景缩减 神经网络 

分 类 号：TM73[电气工程—电力系统及自动化]