A Framework for Energy Management Modelling in Hubs for Circularity

The concept Hubs for Circularity (H4C) represents integrated systems that combine efficient use of clean energy and circular economy to enhance resource efficiency within a region. H4C benefit from the geographical proximity of different industries within industrial zones and the surrounding urban and rural areas, allowing them to share resources, technology, and infrastructure. They reduce the use of virgin resources through Industrial Symbiosis (IS), where one company uses waste of another company as resource. Energy management is crucial in H4C, as these systems often integrate renewable energy sources, involve energy-intensive industries, include numerous energy consumers, and may rely on energy-based industrial symbiosis exchanges. This study presents a modelling framework for energy management in H4C, developed through a systematic literature review of related systems including energy-based IS. The framework provides a guideline for researchers and practitioners on which modelling aspects to consider when optimising the energy flows within a hub. We argue that effective energy management in H4C requires combining conventional modelling aspects - such as objective functions, uncertainty, operational flexibility, and market participation - with IS-specific factors like the type of symbiosis, the degree of information sharing, and collaboration structures. In H4C with extensive IS, decentralised resource and energy exchanges often lead to similarly decentralised information flows and decision-making. Yet, our review shows a persistent reliance on centralised model structures, suggesting a path dependency rooted in traditional energy optimisation approaches. This highlights the need for models that better align with the distributed and collaborative nature of H4C systems.

💡 Research Summary

This paper introduces a modelling framework for energy management in “Hubs for Circularity” (H4C), integrated systems that combine clean energy use with circular‑economy principles across co‑located industrial, urban, and rural zones. By conducting a systematic literature review (SLR) following PRISMA 2020 guidelines, the authors screened 603 records from Scopus and Web of Science, narrowing them to 52 peer‑reviewed studies that implement multi‑carrier energy optimisation models. The review reveals that existing research on related concepts—energy communities, energy hubs, positive energy districts, and eco‑industrial parks—focuses largely on conventional optimisation aspects: objective functions (cost, emissions, efficiency), uncertainty handling (renewable output, demand, price), operational flexibility (demand response, storage, load shifting), and market participation (electricity, gas, carbon trading). However, studies that explicitly incorporate industrial symbiosis (IS) are scarce, and those that do tend to treat waste flows only superficially.

The authors categorize IS‑related decisions using the 9R circularity hierarchy, distinguishing between waste‑reuse (energy cascade), waste‑to‑bio‑energy, and waste‑to‑fuel‑replacement, each offering a different level of circularity. They argue that the type of IS, the degree of information sharing among partners, and the collaboration structure profoundly influence model formulation. Despite H4C’s inherently decentralised information flows, the literature overwhelmingly adopts centralised optimisation architectures (linear, mixed‑integer, or non‑linear programming). This path dependency limits the ability to capture the distributed decision‑making that characterises real‑world H4C ecosystems.

To address this gap, the paper proposes a comprehensive modelling framework that integrates conventional optimisation components with IS‑specific factors. The framework outlines ten key elements: (1) hierarchical objective functions and constraints, (2) modular treatment of uncertainty and flexibility, (3) IS‑type‑specific sub‑models (cascade, bio‑energy, fuel replacement), (4) selection of centralised versus decentralised solution approaches based on information‑sharing levels, (5) incorporation of multiple market mechanisms, (6) explicit representation of storage technologies, (7) demand‑response strategies, (8) spatial considerations (distance, transport costs), (9) performance indicators linking energy KPIs with circularity metrics, and (10) governance and policy interfaces.

The authors conclude that future research should move beyond centralised formulations toward distributed optimisation techniques (e.g., cooperative game theory, distributed Lagrangian methods, blockchain‑enabled smart contracts) that reflect the collaborative nature of H4C. They also call for the development of integrated platforms capable of simultaneously handling multi‑carrier energy flows, waste streams, and real‑time data, as well as for policy designs that incentivise information sharing and joint market participation. This framework aims to guide both scholars and practitioners in designing energy‑efficient, circular, and resilient hub systems.