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
陈荃 1郇嘉嘉 1郑淇源 2张丹宏 1余达 1朱建全2
作者信息
- 1. 广东电网有限责任公司,广州 510699
- 2. 华南理工大学电力学院,广州 510640
- 折叠
摘要
针对碳交易和绿证交易机制下发电商策略报价与市场出清问题,提出了一种基于值函数近似的双层优化算法.首先,以发电商利润最大为目标,综合考虑报价约束、潮流约束与碳配额约束,建立了发电商策略报价和市场出清的双层优化模型.其次,提出基于响应面的值函数近似技术,可以在不引入整数变量的情况下将双层模型转换为单层模型,实现快速求解.再次,从紧支光滑权函数的角度对所提基于响应面的值函数近似技术进行改进,通过赋予采样点不同的权重因子,解决非光滑值函数难以准确近似的问题,提高计算精度.最后,通过算例分析验证了所提算法的有效性.仿真结果表明,相较于传统的双层优化算法,所提方法的求解效率提升了3倍以上,且不损失计算精度,为电-碳-证耦合下的发电商报价与市场出清提供一种有效工具.
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
出版年
2024
国家科技期刊平台
NSTL
万方数据