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Towards Network-Aware Operation of Integrated Energy Systems: A Comprehensive Review

Alessandra Parisio

Abstract

Integrated Energy Systems (IES) are systems of interconnected electricity, gas, heating, and cooling networks, where the carriers interact and depend on one another. Beyond these core vectors, IES may also incorporate additional infrastructures, such as hydrogen, transportation and water networks, whenever sector coupling or cross-vector exchanges are relevant. Although modern cities already function as multi-energy systems, these networks are still planned and operated in isolation, which leads to inefficiencies and unused flexibility. As distributed energy resources (DERs) grow, local coupling among electricity, heating, and gas networks becomes stronger, so coordinated operation across carriers and infrastructures is essential. IES can improve efficiency, flexibility, and renewable integration, yet operation is challenging because of complex interdependencies, non-convex behaviors, and multi-scale dynamics of the energy networks. A key point that the literature often overlooks is the explicit role of network constraints and topology, which shape feasible operating regions, affect scalability, and determine how uncertainty and formal guarantees can be addressed. This review provides a first comprehensive analysis of network-aware modeling, optimization, and control methods for IES. We identify methodological limitations related to tractability, feasibility guarantees, and scalability. Building on these insights, we outline research directions that include distributed optimization with theoretical guarantees and control approaches informed by operational data. The review offers a foundation for scalable, network-aware operational frameworks for future low-carbon energy systems.

Towards Network-Aware Operation of Integrated Energy Systems: A Comprehensive Review

Abstract

Integrated Energy Systems (IES) are systems of interconnected electricity, gas, heating, and cooling networks, where the carriers interact and depend on one another. Beyond these core vectors, IES may also incorporate additional infrastructures, such as hydrogen, transportation and water networks, whenever sector coupling or cross-vector exchanges are relevant. Although modern cities already function as multi-energy systems, these networks are still planned and operated in isolation, which leads to inefficiencies and unused flexibility. As distributed energy resources (DERs) grow, local coupling among electricity, heating, and gas networks becomes stronger, so coordinated operation across carriers and infrastructures is essential. IES can improve efficiency, flexibility, and renewable integration, yet operation is challenging because of complex interdependencies, non-convex behaviors, and multi-scale dynamics of the energy networks. A key point that the literature often overlooks is the explicit role of network constraints and topology, which shape feasible operating regions, affect scalability, and determine how uncertainty and formal guarantees can be addressed. This review provides a first comprehensive analysis of network-aware modeling, optimization, and control methods for IES. We identify methodological limitations related to tractability, feasibility guarantees, and scalability. Building on these insights, we outline research directions that include distributed optimization with theoretical guarantees and control approaches informed by operational data. The review offers a foundation for scalable, network-aware operational frameworks for future low-carbon energy systems.
Paper Structure (62 sections, 19 equations, 6 figures, 3 tables)

This paper contains 62 sections, 19 equations, 6 figures, 3 tables.

Figures (6)

  • Figure 1: Publication trend related to integrated/multi‑energy systems (2005–2025). Source: Scopus, retrieved [21/02/2026]. Query: TITLE-ABS-KEY( "integrated energy system*" OR "multi* energy system*" OR "multi* carrier energy system*" OR "sector coupling" OR "energy hub*" OR "integrated electricity heat gas" OR "electric* heat gas network*" OR "multienergy system*" OR "multi-energy network*" ) AND PUBYEAR > 2004 AND PUBYEAR < 2026 AND ( LIMIT-TO (SUBJAREA, "ENGI") OR LIMIT-TO (SUBJAREA, "ENER") ).
  • Figure 2: Integrated Energy Systems conceptual architecture.
  • Figure 3: An example of an energy hub.
  • Figure 4: Centralized, distributed and decentralized architectures. LC = Local Controller; DER = Distributed Energy Resource; CC = Central Coordinator. Centralized offers system‑wide visibility but can be a scalability and resilience bottleneck; distributed reduces computation/privacy burdens via coordination but incurs communication/consensus overhead; decentralized enables autonomy with no global guarantees on coupled network constraints.
  • Figure 5: Model Predictive Control architecture.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2