Techno-Economic Modelling and Component Sizing in Renewable Energy Communities: A Participant Perspective

TL;DR

This paper develops an optimization framework using MINLP and representative signals to determine optimal PV and BESS sizes for participants in renewable energy communities, considering economic, technical, and fairness aspects.

Contribution

It introduces a novel approach combining FISTA-based representative signals with MINLP for component sizing and incorporates fairness via marginal contribution for equitable incentive distribution.

Findings

Optimized PV and BESS sizes improve Net Present Value for participants.

Representative signals reduce simulation runtime significantly.

Fair incentive distribution enhances participant engagement.

Abstract

This article proposes an optimization problem formulation to find the optimal sizes of Photovoltaics (PV) and Battery Energy Storage Systems (BESS) for individual participants within the context of the Renewable Energy Community (REC). An optimization problem considered the dynamic nature of electricity pricing, solar irradiation levels, financial aspects such as capital investment, and operational and maintenance expenditures of PV and BESS. The analysis also considered replacement costs and the efficiency of charging and discharging the BESS unit. We employed Mixed-Integer Non-Linear Programming (MINLP) to determine the optimal system size that maximizes the Net Present Value (NPV) of individual participants. Furthermore, in this study, we used daily representative signals for each season of the year to reduce simulation runtime. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was used to extract these signals. Then, these representative signals obtained were used in the optimization problem formulation to reduce simulation time and extend our analysis to a wider planning horizon. In addition, the study introduced fairness by applying the individual marginal contribution method to distribute incentives equitably among REC participants, ensuring that each member benefited from their contribution. A simulation study was conducted using a real demand dataset of five houses located in Roseto Valfortore, a small town and commune in the Foggia Province of the Apulia region in southern Italy, to demonstrate the practical relevance and usefulness of the ideas discussed. Ultimately, the goal of the article was to empower the REC with the knowledge necessary to make informed decisions and shape the future of sustainable energy.