Multi-objective optimization of an energy community: an integrated and dynamic approach for full decarbonisation in the European Alps

Abstract

At the local level, energy communities are at the forefront of the European Green Deal strategy offering new opportunities for citizens to get actively involved in energy markets. The scope of this study is to propose a multi-objective optimization framework to minimize both carbon dioxide emissions and total annual costs in an energy community, considering, within different constraints, a wide availability of decision variables including local renewable energy sources, sector coupling, storage and hydrogen. The methodology involves the coupling of the software EnergyPLAN with a multi-objective evolutionary algorithm, considering 2030 and 2050 as target years and modelling a set of eight types of scenarios, each consisting of 100 optimal systems out of 10,000. The case study is an energy community in the European Alps. The results show, on the one hand, the key role of sector coupling technologies such as cogeneration, heat pumps and electric vehicles in exploiting local renewable energy sources and, on the other hand, the higher costs in introducing both electricity storage to achieve a complete decarbonisation and hydrogen as an alternative strategy in the electricity, thermal and transport sectors.

Keywords

• Energy community
• Energy System Integration
• Renewable energy
• Low-carbon economy
• Multi-Objective Evolutionary Algorithm