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| Low-Carbon Economic Dispatch in Integrated Energy Systems: A Set-Based Interval Optimization with Decision Support Under Uncertainties |
| Jiehui Zheng, Member, IEEE,Lexian Zhai,Mingming Tao,Wenhu Tang, Senior Member, IEEE,Zhigang Li, Senior Member, IEEE |
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| Abstract: |
| The incorporation of high percentages of renewable resources into integrated energy systems (IES) is accelerating, and it becomes challenging to identify low-carbon economic dispatch options with significant uncertainties. This paper proposes an enhanced structure that combines hydrogen storage, power-to-gas, carbon capture and storage, and hydrogen fuel cells to extend CO2 reduction pathways. The structure is embedded within an IES that considers multi-energy network constraints. First, the low-carbon economic dispatch model is formulated as a multi-objective interval optimization problem minimizing the total fuel cost and carbon emissions of the IES comprising electricity, heat, gas, and hydrogen subsystems. Then, the multi-objective optimization problem is solved by set-based group search interval optimizer (Set-GSIO) to construct an interval-based Pareto frontier while preserving the uncertainty information for decision-making. In addition, a decision support method based on Shannon entropy and the technique of ordering preferences for similarity of ideal solutions (TOPSIS) evaluates the interval solutions in terms of convergence, stability, and security. Finally, case studies are conducted on a modified IEEE30-bus system integrated with a 15-node gas network and a 32-node heat network to verify the effectiveness of the proposed architecture and approach. Furthermore, the proposed approach is demonstrated on a larger-scale test case, and simulation results verify its scalability. |
| Key words: Integrated energy systems, low-carbon, uncertainty, interval optimization, decision-making. |
| DOI：10.23919/PCMP.2025.000183 |
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| Fund:This work is supported by the National Natural Science Foundation of China (No. 52477097), and the GuangDong Basic and Applied Basic Research Foundation (No. 2024A1515240034). |
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