IGDT-based two-layer optimization of trading strategies in multi-energy markets

Yanni Huang, Tianran Li, Yunting Yao, Gang Ma, Xingshuo Li, Xiuyong Yu, Wenjun Xu and Jinran Wang

Energy, 2025, vol. 333, issue C

Abstract: Integrated Energy Systems (IES) encounter uncertainty when engaging in multi-energy coupled market trading. To secure market returns by optimizing the system's procurement costs and capacity allocation in different markets, that is, the trading strategy to deal with uncertainty fluctuations warrants further investigation. Therefore, this paper first puts forward the two-layer optimization trading model of the integrated energy system, employing Monte Carlo method to generate wind turbine and PV scenes, and subsequently solving it using Karush-Kuhn-Tucker (KKT) condition and big M method. Secondly, introducing the theory of Information Gap Decision Theory (IGDT) on the user side load uncertainty is described. This paper offers guidance to decision makers on the two distinct strategies opportunity pursuit and risk avoidance. Finally, the feasibility and effectiveness of the two-layer model are substantiated through the example analysis of both deterministic and uncertain scenarios. The results show that the net benefit of two-layer optimization is 2.92 % and 6.51 % higher than that of traditional stochastic optimization and robust optimization. In addition, the two strategies of IGDT model ensure that their net income is not less than 3367.84 yuan and 3898.75 yuan, which helps decision makers to choose corresponding strategies according to different business objectives.

Keywords: Monte Carlo simulation; Two-layer optimization; Energy market decision-making; Information gap decision theory (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225030166

DOI: 10.1016/j.energy.2025.137374

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