A Review and Prospect of Optimal Scheduling for Integrated Energy Systems

Ning Guo; Yingying Zhang

doi:10.6919/ICJE.202607_12(7).0012

Authors

Ning Guo School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China
Yingying Zhang School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China

DOI:

https://doi.org/10.6919/ICJE.202607_12(7).0012

Keywords:

Integrated Energy System; Optimal Scheduling; Energy Hub; Demand Response; Game Theory; Uncertainty Handling.

Abstract

Optimal scheduling of integrated energy systems (IESs) is a key technical approach for coordinating energy supply and demand, improving multi-energy utilisation efficiency and supporting low-carbon energy transition. To address the fragmented coverage of existing studies, this paper reviews the research progress of IES optimal scheduling from five aspects: theoretical foundations and modelling frameworks, optimisation objectives and constraint systems, core optimisation algorithms, demand response and multi-agent interaction mechanisms, and uncertainty handling methods. The review shows that IES modelling has gradually developed from physical architecture description to energy-hub, microgrid and virtual-power-plant-based aggregation frameworks. Optimisation objectives have evolved from single economic cost minimisation to coordinated economic, environmental and technical performance. Algorithmic research has moved from classical mathematical programming to intelligent optimisation, game-theoretic modelling and data-driven hybrid methods. Integrated demand response and multi-agent coordination have become important sources of operational flexibility, while stochastic optimisation, robust optimisation and distributionally robust optimisation provide complementary tools for handling renewable generation, load, network and market uncertainties. However, current research still faces challenges in dynamic modelling, real-time scheduling, multi-agent benefit coordination, uncertainty modelling efficiency, policy-market coupling and life-cycle optimisation. Future research should further integrate digital twins, blockchain, machine learning and scheduling theory to improve the engineering applicability of IES optimal scheduling.

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