Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning  

作　　者：Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang 

机构地区：[1]Department of Electrical and Computer Engineering,State University of New York at Binghamton,Binghamton,USA [2]School of Artificial Intelligence,Anhui University,Hefei 230601,China [3]School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China [4]Brookhaven National Lab,Upton,USA [5]School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2025年第1期365-378,共14页现代电力系统与清洁能源学报(英文)

基　　金：supported by the U.S.Department of Energy's Office of Energy Efficiency and Renewable Energy(EERE)under the Solar Energy Technologies Office Award(No.DE-EE0009341)。

摘　　要：The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market.This paper proposes two coherent models to address these challenges.Firstly,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation penalties.Secondly,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast errors.The role of the DEQ model is to guide the knowledgedriven bidding model.Notably,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational optimum.These two models have independent optimizations based on misaligned objectives.To address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding strategy.The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).

关 键 词：Hybrid power plant(HPP) photovoltaic(PV) battery energy storage system(BESS) optimization uncertainty bidding strategy error quantification knowledge-data-complementary learning 

分 类 号：TM73[电气工程—电力系统及自动化]