EURO 2025 Leeds
Abstract Submission

2403. Stochastic Optimization for Efficient Incentive Policies of Renewable Energy Communities

Invited abstract in session WA-9: Optimization methods and models for finance, stream OR in Finance and Insurance .

Wednesday, 8:30-10:00
Room: Clarendon SR 2.01

Authors (first author is the speaker)

1. Alessandra Ruffini
Department of Economics and Management, University of Brescia
2. Paolo Falbo
Department of Economics and Management, University of Brescia
3. Carlos Ruiz
Universidad Carlos III de Madrid

Abstract

In our study, we extend the formulation of a bi-level problem to explore the dynamics between a policy maker (upper level) and Renewable Energy Community (REC, lower level). We introduce stochastic optimization, departing from deterministic approaches, and, following the approach of Falbo et al. (2023), we augment the objective functions to incorporate both expected value and Conditional Value at Risk (CVaR).
At the upper level, the central authority determines a sharing rule for self-consumption incentive to maximize the REC self-consumption expectation, fostering fair distribution among participants.
At the lower level, RECs are modelled with heterogeneous agents, with households installing photovoltaic plants (PV) and biogas producers installing biodigestors and turbines. This choice reflects the prevalence of PV in urban settings and of biogas in agricultural contexts. Households and biogas producers seek to maximize profits within capacity and budget constraints, leading to pure Nash equilibrium.
This bi-level problem is implemented in Python using Pyomo. By implementing stochastic optimization, CVaR, and by leveraging scenario simulations through Markov Chain Bootstrapping, we uncover intricate dependencies among variables, shedding new light on REC member interactions. Therefore, our study offers a nuanced understanding of the interplay between policymakers and RECs, providing valuable insights for sustainable energy policy design and implementation.

Keywords

Status: accepted

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