Optimal joint bid-offer strategy of the GenCo in electricity-carbon coupling markets with the uncertainty from renewable power

Ren, Yanzhe, Zhou, Yue, Li, Gengfeng, Li, Jialu, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Bie, Zhaohong 2026. Optimal joint bid-offer strategy of the GenCo in electricity-carbon coupling markets with the uncertainty from renewable power. International Journal of Electrical Power & Energy Systems 180 , 112030. 10.1016/j.ijepes.2026.112030

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Abstract

With the implementation of the carbon-emission cap-and-trade mechanism, generation companies (GenCos) inherently couple the electricity market and the carbon market. In this context, this paper first proposes a clearing and settlement mechanism for electricity-carbon coupling markets. Next, to provide effective decision-making support for the strategic GenCo in this market mechanism, a bi-level stochastic optimization (SO) model is established to determine its optimal joint bid-offer strategy in electricity-carbon coupling markets with the uncertainty from renewable power. Compared to traditional bid-offer models in the electricity market, this proposed bid-offer model of the strategic GenCo comprehensively characterizes the two-stage carbon market structure and explicitly captures two complex coupling relationships, thereby achieving a transformation from mechanism to model. Then, to tackle the uncertainty within it, an all-scenario-feasible (ASF) method is extended to the bid-offer problem, thus simplifying it into an ASF bi-level SO model. Finally, based on the test system, the comparison of numerical results from different cases and the uncertainty analysis of wind power not only investigate the effectiveness of the proposed model and solution methodology, but also reveal the ability of the strategic GenCo to exercise market power and increase its profit.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0142-0615
Date of First Compliant Deposit:	3 August 2026
Date of Acceptance:	22 June 2026
Last Modified:	03 Aug 2026 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/188682

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