Integrating load profiling and multi-objective optimisation: An open-source tool for the design of renewable energy communities

¹ Alma Mater Studiorum - University of Bologna, Department of Industrial Engineering, Viale Risorgimento 2, Bologna, 40136, Italy
² University of Modena and Reggio Emilia, Department of Sciences and Method for Engineering, Piazzale Europa, 1, 42100, Reggio Emilia, Italy
³ En&Tech Interdepartmental Centre, University of Modena and Reggio Emilia, Piazzale Europa 1, Reggio Emilia, 42124, Italy

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The increasing interest in Renewable Energy Communities (RECs) has prompted stakeholders to gain greater confidence in simulating electric energy flows among different consumers and prosumers within the community. Within the framework of the SACER project, an open-source numerical tool was developed to design the optimal configuration of a REC. First, the trends of electric energy consumption for a series of typical days can be determined for a generic REC member at three levels of accuracy. When hourly consumption data are available, typical profiles can be defined. In this work, two algorithms were used and compared for this purpose: the Aggregate approXimation method and the Self-Organising Map (SOM) model. If the aforementioned data are unavailable, representative patterns are reconstructed from monthly data, which are typically available from energy bills for existing buildings. If no data are available, the profiles can be estimated as a function of the building intended use and floor area. Then, the optimal configuration of the REC can be assessed using a multi-objective mixed-integer linear programming model (Analytic Hierarchy Process). Given the relative importance of each user-defined objective function, the revenues for REC components can be maximized by optimizing investment costs in new renewable energy generators and batteries while minimizing energy costs. To test the proposed methodology, the optimal configuration of a new REC comprising 23 residential and 2 tertiary buildings was determined.