A review of integrated energy system modeling and operation

The integration of multiple energy sectors through integrated energy systems (IES) can enhance energy efficiency, stimulate economic performance, and accelerate the adoption of renewable energy, thereby reducing carbon emissions and fostering sustainable energy transitions. This paper presents a systematic review of IES modeling and operation, focusing on modeling frameworks, analytical techniques, and emerging research frontiers. We summarize various IES models, including bus injection and branch flow models for power flow, as well as steady-state and dynamic models for gas, heat, hydrogen, and ammonia flow. Due to technological and institutional barriers, energy sectors are often managed by separate entities, necessitating distributed energy management to address challenges such as the curse of dimensionality and privacy concerns. As a result, distributed optimization algorithms are commonly applied in the IES operation. The IES is decomposed into smaller subproblems, each managed by the relevant entity, with coordination achieved through the exchange of limited information. As the proportion of renewable energy grows, so do the associated uncertainties, which in turn complicate the IES operation. Our analysis provides a comparative assessment of uncertainty management strategies, with a detailed evaluation of stochastic programming, robust optimization, and distributionally robust optimization. Finally, we identify critical research directions, especially regarding the strategic interactions among stakeholders, the application of operations research, and the potential role of artificial intelligence in the IES.