Multi-Uncertainty Optimal Scheduling of Integrated Electricity and Heat Energy Systems Based on Fuzzy-IGDT

The presence of multiple uncertainties in integrated electricity–heat energy systems (E-HIES) poses significant challenges to system dispatch. To achieve an effective balance between economy and robustness, this paper proposes an optimal scheduling method based on fuzzy chance-constrained Information Gap Decision Theory (Fuzzy-IGDT), accounting for uncertainties in wind power output, photovoltaic output, electrical load, and thermal load. The method employs trapezoidal fuzzy numbers to model the four types of uncertain variables and constructs a fuzzy robust model (F-RM) for conservative decision-makers and a fuzzy opportunity model (F-OM) for aggressive decision-makers. An Adaptive Step Ratio (ASR) optimization method is then developed to solve the proposed models. Case studies demonstrate the effectiveness of the proposed methodology. Results show that: compared with conventional IGDT, pure fuzzy and stochastic programming, Fuzzy-IGDT simultaneously optimizes economy, stability and reliability: daily operating cost is reduced by 12.7%, the standard deviation of cost volatility shrinks by 34.5%, and the loss-of-load probability is only 0.3%. Relative to the traditional Weighted Offset Coefficient (WOC) method, ASR directly coordinates the deviation ratios of multiple variables through its step-ratio mechanism, cutting system risk cost by 21.3%, raising solution efficiency by 42%, and improving convergence stability by a factor of 3.8. This research provides new theoretical support and practical tools for optimal scheduling of E-HIES under multiple uncertainties.