This project focuses on the mathematical modeling and optimization of a Renewable Energy Community (CER). Developed as part of the Optimization Methods course at the University of Campania "Luigi Vanvitelli," the study provides a robust framework for determining the ideal technical and economic balance between local green energy production and member consumption.
A CER is a legal entity comprised of citizens, local authorities, and businesses that share energy produced from renewable sources. The primary objective of this project is to optimize the sizing of photovoltaic (PV) plants and battery storage systems to maximize collective efficiency and economic benefits.
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Mathematical Optimization: The project uses a Branch and Cut algorithm to solve complex Mixed-Integer Linear Programming (MILP) problems. This approach guarantees global optimality, ensuring the most reliable operational and investment strategies.
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Comprehensive Sizing Model: The model accounts for various building typologies (Houses, Schools, Gyms, Restaurants, etc.) and their specific load profiles across different seasons.
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Physical & Economic Constraints: The optimization respects strict real-world limits, including available rooftop areas, regulatory power limits (1000 kW), battery State of Charge (SOC) dynamics, and specific budget requirements.
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Incentive Analysis: The study evaluates different financial scenarios, including the impact of government incentives, non-repayable grants (such as PNRR funds), and shared energy premium tariffs.
The optimization demonstrates that active participation in a CER significantly outperforms standalone installations.
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Return on Investment (ROI): In a subsidized scenario for small municipalities, the project achieves a payback period of just 2.4 years, with projected earnings of €1.6M over 20 years.
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Synergy vs. Standalone: While a "DIY" individual installation may take 12.5 years to break even, the CER's "Virtual Battery" effect and shared incentives drastically accelerate the return on investment.
The project further explores Real-Time Optimal Management, using cloud computing and Weather APIs to forecast production and manage energy arbitrage, peak shaving, and collective self-consumption minute-by-minute.
For a detailed explanation of the mathematical model, please refer to the technical report:
Academic Year: 2025/2026