南方电网技术2024，Vol.18Issue(1)：121-133,13.DOI:10.13648/j.cnki.issn1674-0629.2024.01.013
电-碳-绿证市场耦合下发电商报价与出清双层优化
Bi-Level Optimization Strategy for Biddings and Clearing of Power Suppliers Under the Coupling of Electricity,Carbon,and Green Certificate Market
摘要
Abstract
This paper focuses on the bi-level optimization problem of biddings and clearing of power suppliers under the coupling of electricity,carbon,and green certificate market.Firstly,a bi-level optimization decision model of multi-entities under the coupling of electricity market,carbon market and green certificate market is formulated,in which the constraints of DC power flow,the piecewise linear bidding method and the inverse-demand linear relationship between volume and price of green certificates are considered.The model can help power suppliers optimize their bidding strategies and improve their ability to benefit from competition.Secondly,a bi-level algorithm based on the optimal value function approximation is proposed to solve the problem.The algorithm constructs the optimal value function of the lower model based on the polynomial basis and least square method,and then converts the bi-level optimization model into a single-level optimization model.Different from the traditional Karush-Kuhn-Tucker method,this method does not introduce new integer variables when simplifying the model,and can achieve fast solution of the model.Finally,simulation analysis verifies the rationality of the proposed model and the effectiveness of the proposed algorithm.关键词
值函数近似算法/双层模型/碳交易/绿证交易/分段线性报价Key words
value function approximation algorithm/bi-level model/carbon trading/green certificate trading/piecewise linear bidding分类
动力与电气工程引用本文复制引用
陈荃,张丹宏,郑淇源,郇嘉嘉,赵敏彤,朱建全..电-碳-绿证市场耦合下发电商报价与出清双层优化[J].南方电网技术,2024,18(1):121-133,13.基金项目
国家自然科学基金资助项目(51977081) （51977081）
广东电网有限责任公司科技项目(0300002022030201GH00033). Supported by the National Natural Science Foundation of China(51977081) （0300002022030201GH00033）
the Science and Technology Project of Guangdong Power Grid Co.,Ltd.,(0300002022030201GH00033). （0300002022030201GH00033）
