高电压技术2024，Vol.50Issue(11)：4875-4885,中插6,12.DOI:10.13336/j.1003-6520.hve.20231325
基于值函数近似的发电商报价与市场出清双层优化算法
Value Function Approximation-based Bi-level Optimization Algorithm for Strategic Biddings of Power Suppliers and Clearing of Markets
摘要
Abstract
Aiming at the strategic bidding of power suppliers and clearing of markets with carbon and green certificate trading,a bi-level algorithm based on value function approximation is proposed.Firstly,a bi-level model is established,aiming to maximize the profit of power suppliers while considering pricing constraints,power flow constraints,and car-bon quota constraints.Secondly,a response surface method-based value function approximation technique is proposed,which can transform the bi-level model into a single-layer model without introducing integer variables,enabling rapid computation.Furthermore,the response surface method-based value function approximation technique is improved from the perspective of compactly supported smooth weight functions,which assigns distinct weight factors to different sample points.This addresses the challenge of accurately fitting non-smooth value functions and enhances computational preci-sion.Finally,simulation analysis is used to verify the effectiveness of the proposed algorithm.The simulation results show that the efficiency of the proposed method is more than 3 times that of the traditional bi-level algorithm without loss of calculation accuracy,which provides an effective tool for clearing of markets and the strategic bidding of power sup-pliers under coupled markets.关键词
双层模型/值函数近似/响应面方法/绿证交易/电力市场Key words
bi-level model/value function approximation/response surface method/green certificate trading/electricity market引用本文复制引用
陈荃,郇嘉嘉,郑淇源,张丹宏,余达,朱建全..基于值函数近似的发电商报价与市场出清双层优化算法[J].高电压技术,2024,50(11):4875-4885,中插6,12.基金项目
国家自然科学基金(51977081) （51977081）
广东电网有限责任公司科技项目(0300002022030201GH00033).Project supported by National Natural Science Foundation of China(51977081),Science and Technology Project of Guangdong Power Grid Corporation(0300002022030201GH00033). （0300002022030201GH00033）
