Optimal Scheduling of Source-Load Dual Layer Based on Node Carbon Potential and Green Certificate-Carbon Joint Trading Mechanism 

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Yu Zhang
Zhijia Zhu
Xiran Zhou
Haoqian Cui
Huihui Yang
Zhuo Liu
Huiyan Li
Yanjun Jing
Haixin Wang
Junyou Yang

Abstract

 To address the issues of insufficient load-side responsiveness and the lack of effective synergy between the renewable energy certificate market and carbon trading scheme on the power production side, this paper presents a bilevel optimal dispatching framework that integrates both supply and demand sides, centered on node carbon potential guidance and a mechanism linking green certificate and carbon trading. From the demand-side, nodal carbon potential values are computed using carbon emission flow theory and used as a direct steering signal to drive two types of demand response, thereby unlocking the full carbon abatement potential of the demand sector. On the power production side, a coordinated framework is set up to integrate green certificate transactions with carbon allowance trading. Green certificates, with their carbon reduction functions, can be used to offset a portion of the carbon allowance purchase volume, carbon trading costs are effectively reduced, enabling the synergistic operation of the two markets. Based on the above analysis, this paper constructs a bi-level optimization model. the upper layer problem targets the minimization of overall power production costs under the constraints of the coupled green certificate and carbon emission trading rules, and the lower level aims to minimize user electricity expenses under the guidance of nodal carbon potential signals. Finally, simulation and validation are conducted using the IEEE 14-node system. The results show that the presented scheduling scheme can fully tap into the carbon reduction potential on the load side, enhance the interactivity of market mechanisms on the generation side.

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Optimal Scheduling of Source-Load Dual Layer Based on Node Carbon Potential and Green Certificate-Carbon Joint Trading Mechanism . (2026). International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 1(1). https://ingeniot.uclm.es/editorial/index.php/ingenio26/article/view/65

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