电-碳-绿证市场耦合下发电商报价与出清双层优化OA北大核心CSTPCD
Bi-Level Optimization Strategy for Biddings and Clearing of Power Suppliers Under the Coupling of Electricity,Carbon,and Green Certificate Market
针对电力市场、碳市场和绿证市场耦合下发电商的报价与出清双层优化问题开展了研究工作.首先,考虑直流潮流约束、分段线性报价方法及逆需求线性的绿证量价关系,建立了电力市场、碳市场和绿证市场耦合的双层多主体优化决策模型.该模型可以帮助发电商优化报价策略,提高自身竞争获益的能力.其次,提出了一种基于最优值函数近似的双层模型求解算法.该算法基于多项式基和最小二乘法构造下层模型最优值函数,进而将双层优化模型转换为单层优化模型.与传统Karush-Kuhn-Tucker(KKT)方法不同,该方法在简化模型时不会引入新的整数变量,能够实现模型的快速求解.最后,通过仿真分析验证了所提模型的合理性和算法的有效性.
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.
陈荃;张丹宏;郑淇源;郇嘉嘉;赵敏彤;朱建全
广东电网有限责任公司,广州 510060华南理工大学电力学院,广州 510640
动力与电气工程
值函数近似算法双层模型碳交易绿证交易分段线性报价
value function approximation algorithmbi-level modelcarbon tradinggreen certificate tradingpiecewise linear bidding
《南方电网技术》 2024 (001)
121-133 / 13
国家自然科学基金资助项目(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);the Science and Technology Project of Guangdong Power Grid Co.,Ltd.,(0300002022030201GH00033).
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