Stochastic optimization of integrated electricity-heat-gas energy system considering uncertainty of indirect carbon emission intensity

Abstract

The inherent intermittency and fluctuation of renewable energy generation introduce uncertainty in electricity carbon emission intensity, posing challenges to the low-carbon and economic scheduling of integrated energy systems. To this end, this paper proposes an optimization model for integrated electricity-heat-gas energy system operation considering uncertain indirect carbon emission intensity. First, considering the tiered carbon trading mechanism, an optimization framework for a park-level model is developed. Then, polyhedral uncertainty sets are introduced to capture the uncertainties in renewable energy generation and carbon emission intensity, offering a flexible and intuitive approach to handling diverse uncertainty types. These uncertainty sets are integrated into the optimization problem to enhance robustness. Finally, the uncertainty set-based stochastic optimization method is introduced to improve the model's robustness, addressing both cost minimization and carbon reduction under uncertain conditions. Case studies using data from a Mediterranean-region hospital demonstrate the model's effectiveness in addressing uncertainties at park-level systems and providing robust solutions that balance cost efficiency and environmental impact.