Abstract
This paper investigates the optimal operation of an energy hub engaged in both day-ahead and real-time trading. A two-stage optimization framework Information Gap Decision Theory (IGDT) for day-ahead bidding and stochastic programming with Monte Carlo scenarios for real-time recourse is applied. Risk-neutral, risk-averse, and risk-taking strategies are considered to capture different risk preferences. The hub integrates combined heat and power, renewable energy, plug-in electric vehicles, and vehicle-to-grid and grid-to-vehicle technologies. Price and load forecasts are generated using a hybrid recurrent convolutional network (HRCN). Results highlight the trade-off between risk management and economic performance: costs are 16.5 % higher in the risk-averse mode than in the risk-neutral mode, and 55.6 % higher than in the risk-taking mode. Natural gas accounts for the most in the risk-taking case, at ∼33 % of the total cost. Under the tested conditions, the proposed IGDT–stochastic–HRCN framework improves expected costs relative to baselines, though outcomes may vary under different market rules, fuel prices, or volatility regimes.
Recommended Citation
A. Heidari et al., "Applying Two-stage Risk-Based Market Structures for Energy Hub-Based Plug-in Electric Vehicles using Information Decision Gap Theory and a Hybrid Recurrent Convolutional Network," Sustainable Energy Grids and Networks, vol. 45, article no. 102085, Elsevier, Mar 2026.
The definitive version is available at https://doi.org/10.1016/j.segan.2025.102085
Department(s)
Electrical and Computer Engineering
Publication Status
Full Text Access
Keywords and Phrases
And plug-in electric vehicles; Energy hub; Hybrid recurrent convolutional network; Information gap decision theory; Intelligent structures
International Standard Serial Number (ISSN)
2352-4677
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Elsevier, All rights reserved.
Publication Date
01 Mar 2026
