1.郑州轻工业大学电气信息工程学院,河南省郑州市 450002
2.国家能源投资集团有限责任公司,北京市 100011
赵琛(1988—),女,通信作者,博士,讲师,硕士生导师,主要研究方向:电力市场博弈分析、风险管理等。E-mail:zhaochenmail@163.com
彭思远(2001—),男,硕士研究生,主要研究方向:虚拟电厂优化调度。E-mail:psy2440394904@163.com
和萍(1980—),女,博士,教授,博士生导师,主要研究方向:电力系统稳定性分析等。E-mail:hplkz@126.com
收稿:2025-02-21,
修回:2025-06-26,
网络首发:2025-09-15,
纸质出版:2026-01-10
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赵琛,彭思远,和萍等.电-碳-绿证交易耦合下多虚拟电厂动态定价模型与博弈分析[J].电力系统自动化,2026,50(1):188-198.
ZHAO Chen,PENG Siyuan,HE Ping,et al.Dynamic Pricing Model and Game Analysis of Multiple Virtual Power Plants Under Electricity-Carbon-Green Certificate Trading Coupling[J].Automation of Electric Power Systems,2026,50(1):188-198.
赵琛,彭思远,和萍等.电-碳-绿证交易耦合下多虚拟电厂动态定价模型与博弈分析[J].电力系统自动化,2026,50(1):188-198. DOI: 10.7500/AEPS20250221005.
ZHAO Chen,PENG Siyuan,HE Ping,et al.Dynamic Pricing Model and Game Analysis of Multiple Virtual Power Plants Under Electricity-Carbon-Green Certificate Trading Coupling[J].Automation of Electric Power Systems,2026,50(1):188-198. DOI: 10.7500/AEPS20250221005.
随着新型电力系统市场逻辑向多层联动和多市场耦合的复杂开放性市场演变,研究虚拟电厂(VPP)在电力、碳和绿证交易耦合的多市场环境下的定价问题及博弈策略具有重要意义。为此,文中提出一种电-碳-绿证市场耦合交易框架,涉及配电系统运营商(DSO)和多个VPP的联合交易,并建立了以DSO为领导者、多个VPP为跟随者的主从博弈模型,探讨了耦合市场中DSO的动态定价机制及VPP的竞价策略。为进一步求解该模型,提出一种基于神经网络增强的区域蜣螂优化算法。该算法通过模型预测,减少上下层信息交互,降低下层模型调用次数,显著提高了计算速度和精度。仿真结果验证了所提理论模型的合理性和有效性,表明该框架与模型增强了VPP在多市场中的自主调节能力,降低了区域内总交易成本,并实现了系统的碳减排。
As the market logic of new power systems evolves into a complex
open market with multi-layer interactions and multi-market coupling
it is of great significance to study the pricing issues and game strategies of virtual power plants (VPPs) in the multi-market environment with coupling of electricity
carbon
and green certificate trading. Therefore
this paper proposes a coupled trading framework for the electricity-carbon-green certificate markets
involving the joint trading between distribution system operators (DSO) and multiple VPPs. A Stackelberg game model is established with the DSO as the leader and multiple VPPs as followers
exploring the dynamic pricing mechanism of the DSO and the bidding strategies of the VPPs in the coupled market. To further solve this model
an artificial neural network-enhanced regional dung beetle optimization (ANN-RDBO) algorithm is proposed. This algorithm
through model prediction
reduces the information interaction between upper and lower layers and lowers the number of lower-layer model calls
significantly improving the computation speed and accuracy. Simulation results verify the rationality and effectiveness of the proposed theoretical model
indicating that the framework and model enhance the autonomous regulation capability of VPPs in the multi-market environment
reduce the total trading costs within the region
and achieve carbon emission reduction in the system.
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