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

Vishal Kachhad, Amit Joshi, Luigi Glielmo

TL;DR

The paper tackles optimal sizing of PV and BESS for individual REC participants to maximize Net Present Value over long planning horizons under dynamic pricing and seasonal variability. It combines a MINLP formulation with periodic daily signals extracted via FISTA to reduce runtime, and introduces a fairness mechanism using the individual marginal contribution to fairly allocate incentives. The approach is validated on a five-household case study in Roseto Valfortore, showing recovered NPVs within payback periods and reduced bills due to energy sharing and optimized storage. The work provides a practical roadmap for equitable, economically viable Renewable Energy Communities and highlights extensions to broader settings and resources.

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.

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

TL;DR

The paper tackles optimal sizing of PV and BESS for individual REC participants to maximize Net Present Value over long planning horizons under dynamic pricing and seasonal variability. It combines a MINLP formulation with periodic daily signals extracted via FISTA to reduce runtime, and introduces a fairness mechanism using the individual marginal contribution to fairly allocate incentives. The approach is validated on a five-household case study in Roseto Valfortore, showing recovered NPVs within payback periods and reduced bills due to energy sharing and optimized storage. The work provides a practical roadmap for equitable, economically viable Renewable Energy Communities and highlights extensions to broader settings and resources.

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.

Paper Structure

This paper contains 13 sections, 39 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Mathematical Modeling
  3. PV
  4. BESS
  5. Power Flows
  6. Electricity Rate
  7. NPV
  8. Optimal Sizing Problem Formulation
  9. Exploiting Periodicity
  10. Optimal Sizing using Periodic Signals
  11. Individual Marginal Contribution
  12. Simulation Results
  13. Conclusion

Figures (4)

  • Figure 1: Schematic representation of the REC. In this model, the TF signs a contract with all the participants. This contract covers the real energy management, as well as the operation and maintenance costs for the solar PV and BESS installed on each participant's rooftop.
  • Figure 2: Italian grid electricity rate structure.
  • Figure 3: NPV, electricity bills, incentives of individual participants on shared energy and total amount of energy shared within the REC.
  • Figure 4: Four periodic days profile for demand, generation, self-consumption, charging/discharging power, and selling power for individual participants.

Theorems & Definitions (1)

  • Remark 1