An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems
Min Xie, Lei Qing, Jia-Nan Ye, Yan-Xuan Lu
TL;DR
This paper introduces a new optimization model for urban energy systems that improves efficiency and resilience by considering energy quality and uncertainties.
Contribution
The novel contribution is integrating exergy efficiency into a stochastic optimization framework for electricity-hydrogen systems under uncertainty.
Findings
The proposed model improves exergy efficiency by up to 2.18%.
The method maintains cost robustness across different confidence levels.
Case studies demonstrate the model's effectiveness in real-time energy scheduling.
Abstract
Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for…
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Taxonomy
TopicsIntegrated Energy Systems Optimization · Thermodynamic and Exergetic Analyses of Power and Cooling Systems · Hybrid Renewable Energy Systems