A Framework for Energy Management Modelling in Hubs for Circularity

TL;DR

This paper introduces a comprehensive modelling framework for energy management in Hubs for Circularity, emphasizing the integration of renewable energy, industrial symbiosis, and decentralised decision-making to optimize resource efficiency.

Contribution

It systematically reviews existing models and proposes a guideline that incorporates both conventional and IS-specific factors for energy flow optimisation in H4C.

Findings

Decentralised exchanges often lead to decentralised information flows.

Traditional models tend to rely on centralised structures, indicating a path dependency.

Effective models should better align with the distributed nature of H4C systems.

Abstract

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.