Mixed-integer linear programming based optimization strategies for renewable energy communities - Cosic, Armin; Stadler, Michael; Mansoor, Muhammad; Zellinger, Michael (Energy (Oxford), 2021)
Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality because of several limitation factors. Challenges are already encountered during the planning phase since a large number of decision variables have to be considered depending on the number and type of community participants and distributed technologies. This paper overcomes these challenges by establishing a mixed-integer linear programming based optimal planning approach for renewable energy communities. A real case study is analyzed by creating an energy community testbed with a leading energy service provider in Austria. The case study considers nine energy community members of a municipality in Austria, distributed photovoltaic systems, energy storage systems, different electricity tariff scenarios and market signals including feed-in tariffs. The key results indicate that renewable energy communities can significantly reduce the total energy costs by 15% and total carbon dioxide emissions by 34% through an optimal selection and operation of the energy technologies. In all the optimization scenarios considered, each community participant can benefit both economically and ecologically. •Novel optimization strategies for renewable energy communities in microgrids.•Advanced mixed-integer linear programming model with 8760-hour time horizon.•A real Austrian case study with nine community participants and six scenarios.•Renewable energy transfer with different tariff scenarios and market signals.•Reduction of community costs by 15% and community carbon emissions by 34%.