• The collaborative integration of carbon trading and green certificate trading effectively reduces carbon emissions and enhances economic performance. • A bi-Level game model is leveraged to achieve collaborative optimization between operators and users. • This framework provides a practical pathway to accelerate low-carbon transition and green development in energy-intensive industrial clusters. Traditional energy-intensive industrial clusters consume multiple energy types, suffering from poor energy synergy, high carbon emissions, and excessive consumption. To advance their energy transition, a system-integrated energy management framework that incorporates synergistic carbon-green-certificate collaborative trading and integrates electrochemical, thermal, and natural gas storage for system regulation is proposed. First, a mathematical model for multi-energy coupling in a cluster is developed. Then a bi-level optimization architecture for energy scheduling is adopted: the upper level establishes a market-driven pricing mechanism integrating carbon and green certificate trading; the lower level adjusts demand-side responses and power dispatch via real-time price signals, aiming to maximize operator revenues while minimizing user costs and carbon emissions. Results show collaborative trading boosts operator revenues by 81.57% and cuts user costs by 6.44%. Green certificate trading alone reduces operator profits by 9.56% but lowers user costs by 8.12%; independent carbon trading raises operator revenues by 48.22% but only cuts user costs by 0.94%. Environmentally, coordinated trading slashes carbon emissions by 65.94%, outperforming single markets’ 48.89–63.36%. This study demonstrates that the innovative carbon-green-certificate collaborative trading with multiple energy storage technologies successfully achieves operator-user collaboration through market-driven incentives, effectively resolving the threefold challenges of economic sustainability, green energy integration, and decarbonization imperatives in energy-intensive industrial clusters.
Xiaohua et al. (Sun,) studied this question.
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