Optimal design of urban energy systems with demand side management and distributed generation

To tackle prominent societal challenges such as increasing energy demands and climate change due to greenhouse gas (GHGs) emissions demand side management (DSM) and distributed generation (DG) have been proposed as effective solutions particularly for urban areas with high energy densities and diverse types of energy demands. However, urban energy systems are complex, involving supply-demand interconnections, interaction of whole system level with various stakeholders (e.g. end-users, centralised suppliers) and regulation effects of policy instruments. The potential roles of integrated DSM and DG for the climate change mitigation under the urban energy system context haven been yet well understood. Our research aims to advance the understanding of such promising urban energy solutions and generate new insights via modelling framework development. In this study, an optimisation model is developed to simulate the combined effects of DSM and DG strategies in the optimal design of urban energy systems, and investigate the trade-offs between environmental and economic targets. The results of our case study on a hypothetical urban area suggest that the effects of just DSM on climate change mitigation are relatively low whereas urban system would benefit significantly from the introduction of more carbon efficient and economically competitive DG technologies. The set of Pareto optimal solutions derived from the model provide insights into the trade-offs between conflicting GHG and economic objectives: the environmentally optimal solutions with up to 39-43% of the GHG reduction are derived at the expenses of a cost increase by 73-87%; relatively cost efficient systems with marginal increase in economic profiles (4-5%) but significant GHG reductions (32-33%) are achievable. This study demonstrates the insights such model could provide for the decision-making and paradigm shifts towards sustainable urban energy systems and smart operational strategies.