The Impact of Renewable Energy Communities in the Italian Day-Ahead Electricity Market: A Scenario Analysis

TL;DR

This paper develops a two-stage framework to quantify how Renewable Energy Communities (RECs) influence Italy’s day-ahead electricity market. First, it constructs a detailed bottom-up engineering model of REC prosumers/producers across seven zones, using PVGIS yields, category-specific load profiles, and self-consumption targets to generate hourly injection and self-consumption time series; second, it applies a synthetic counterfactual approach to the DA market by shifting demand and supply curves based on REC activity to identify MOE-driven effects under multiple deployment scenarios, including a 5 GW target by 2027. Results show REC impacts are modest but regionally and temporally heterogeneous, with positive effects during daylight hours and in spring, and potential price reductions under certain deployments and self-consumption mixes; in winter, higher self-consumption can damp wholesale volumes. The study provides policy insights, suggesting tailoring incentives to PV coverage and supporting storage integration to unlock additional welfare gains, while highlighting data gaps and the need for broader replication to other markets. Overall, RECs can contribute to system efficiency and grid resilience when coupled with storage and well-designed governance, though their macro impact remains limited under current Italian conditions.

Abstract

This paper evaluates the economic impact of Renewable Energy Communities (RECs) on the Italian wholesale power market. Combining a bottom-up engineering approach with a short-run economic impact assessment, the study begins by mapping existing and emerging RECs in Italy. We identify key characteristics of RECs, such as average installed capacity, institutional profiles of members, types of renewable systems used, and transmission across Italy's electricity market zones. This mapping yields representative REC configurations, which are employed within a bottom-up engineering model to generate energy injection and self-consumption profiles for different REC prosumer and producer categories (residential, public, small and medium enterprise, non-profit organization, and standalone installation), considering the different levels of solar irradiance in Italy based on latitude. These zonal results, aggregated on an hourly basis, inform the implementation of the synthetic counterfactual approach, which develops alternative scenarios (e.g., 5 GW target for REC-driven capacity set by Italian policy for 2027) to assess the impact of REC-driven injection and self-consumption on the Italian day-ahead power market. The findings suggest that REC deployment can increase equilibrium quantities during daylight in most of the time, while decreasing equilibrium quantities mostly during the cold months, as electrified heating drives greater self-consumption and offsets lower grid injections. Both positive and negative effects on equilibrium quantities suggest that REC deployment also has a potential to reduce wholesale electricity prices. Moreover, by reducing grid exchanges through higher self-consumption, REC proliferation can alleviate pressure on the distribution system.

Figures (27)

• Figure 1: A net cash flow for REC members in Italy (excl. explicit capital subsidy).
• Figure 2: Graphical representation of the prosumer energy modeling framework used to simulate hourly energy flows for different prosumer/producer categories. The flowchart illustrates the input data (PV production and load profiles), the modeling process for each prosumer/producer category (residential, school, commercial, office, standalone), and the generation of annual hourly datasets for energy production, self-consumption, and grid injection across multiple market zones and self-consumption scenarios. These outputs are then used as inputs for the scenario-based projection and economic simulations in the day-ahead electricity market.
• Figure 3: Hourly energy profiles for Milan (NORD) over a full year for four prosumer user categories. Each subplot shows PV generation (yellow line), electricity consumption (blue line), and energy injected into the grid (green line).
• Figure 4: Hourly energy profiles for Milan (NORD) over an average day in January for four prosumer user categories. Each subplot shows PV generation (yellow bars), electricity consumption (blue bars), and energy injected into the grid (green bars).
• Figure 5: Hourly energy profiles for Milan (NORD) over an average day in April for four prosumer user categories. Each subplot shows PV generation (yellow bars), electricity consumption (blue bars), and energy injected into the grid (green bars).