Agent-Based Modelling of Urban District Energy System Decolonisation – A Systematic Literature Review

 

Abstract and Figures

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decolonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

The built environment is known as a major contributor to both sustainability problems and solutions. Life Cycle Sustainability Assessment (LCSA) which is a promising approach to evaluating the environmental, economic, and social dimensions of building performance, is progressively drawing the building researcher’s attention. This chapter aims to review the roots and evolution of building sustainability assessment and discusses the associated challenges of LCSA in building and energy retrofit design. Through a critical review, different assumptions and limitations will be reviewed, and the main challenges of integrating LCSA into building energy retrofit design will be classified and discussed. In the end, the new research lines such as developing integrated LCSA models, application of optimization methods, and Building Information Modeling (BIM) in LCSA will be discussed.