Portfolio optimization of energy communities to meet reductions in costs and emissions

Cities are expected to grow further, and energy communities are one promising approach to promote distributed energy resources and implement energy efficiency measures. To understand the motivation of those communities, this work improves two existing open source models with a Pareto Optimization and two objectives: costs and carbon emissions. Clustering algorithms support the improvement of the models' scalability and performance. The methods developed in this work gives stakeholders the tool to calculate the capabilities and restrictions of the local energy system. The models are applied to a case study using data from an Austrian city, Linz. Four scenarios help to understand aspects of the energy community, such as the lock-in effect of existing infrastructure and future developments. The results show that it is possible to reduce both objectives, but the solutions for minimum costs and minimum carbon emissions are contrary to each other. This work quantifies the highest effect of emission reduction by the electrification of the system. It may be concluded, that a steady transformation of the local energy systems is necessary to reach economically sustainable goals. (C) 2019 Elsevier Ltd. All rights reserved.