Research on Optimization and Scheduling of Multi-Integrated Energy System Based on Step-by-Step Carbon Trading and Hybrid Games

• Published in: International transactions on electrical energy systems Vol. 2023; pp. 1 - 19
• Main Authors: Zeng, Xianqiang, Feng, Yujie, Zhou, Yun, Li, Hengjie
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2023; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Alternative energy sources; Carbon; Clean technology; Consumers; Cooperation; Emissions trading; Energy; Energy industry; Energy management; Energy storage; Fossil fuels; Game theory; Games; Greenhouse gases; Integrated energy systems; Load; Operating costs; Optimization; Optimization models; Pareto optimum; Scheduling; Supply & demand; Wind power; China
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1155/2023/2063273

In light of the current integrated energy system (IES) transactions, the interaction between the supply and demand sides is not fully considered. With the overall development of the carbon trading market, there is a need to deeply explore the interests of multiple entities in energy trading under the step-by-step carbon trading mechanism. Therefore, research on the optimization and scheduling of multicomprehensive energy systems based on step-by-step carbon trading and hybrid games is proposed. Firstly, the interests and demands of various integrated energy systems and users in multi-integrated energy system energy trading should be comprehensively considered. Based on the master-slave game and Nash bargaining theory, an optimization model of multiple integrated energy systems is built under the step-by-step carbon trading mechanism and load aggregation, representing the interests of the entire user side. Secondly, the proposed game optimization model has been established to prove it can maximize social benefits. IES and user game optimization models have been established, with multi-IES being leaders with the goal of maximizing their own benefits, guiding the optimization between IES and load by formulating energy prices. Users are followers who aim to maximize comprehensive benefits and respond to IES’s decisions through cooperation. Finally, using the improved gray wolf algorithm to solve the built model, it has been proven through comparison of calculation examples that the proposed method can reduce carbon emissions, effectively coordinate the optimization scheduling of multi-IES, and achieve the fair distribution of multi-IES cooperation benefits. This improves the effectiveness of individual and social benefits.