Set of instances used in article "Optimal management of collective Photovoltaic Systems in Spanish Multi-dwelling buildings"

The dataset presented is used in the article "Optimal management of collective Photovoltaic Systems in Spanish Multi-dwelling buildings" by Antonio Alonso-Ayuso, Silvia Benito-Orea, F. Javier Martín-Campo, Elisenda Molina and Paula Terán-Viadero, submitted for publication (2026).

This paper proposes a deterministic mathematical optimization model for the allocation of photovoltaic energy in multi-dwelling residential buildings, developed in accordance with the regulatory framework governing the Spanish energy market. The model considers the electricity consumption profiles of individual users, as well as the demand associated with communal facilities such as entrance halls, elevators, garages, and swimming pools, among others. 

The dataset contains the input data and optimization results required to reproduce the computational experiments presented in the paper.

Six residential communities are considered: I20a, I20b, I40a, I40b, I60a, and I60b. For each community, three CSV files are provided:

• <community>_demand.csv: Hourly electricity demand for the entire study year. The file contains the electricity consumption of all community supply points and individual supply points in the corresponding residential community.
• <community>_weight.csv: Participation coefficients of the individual supply points (households and commercial units) in each community supply point. Rows correspond to individual supply points, while columns correspond to community supply points. The coefficients of each community supply point sum to 1.
• <community>_tariff.csv: Hourly electricity purchase prices and surplus energy compensation prices for the entire study year.

The file ProduccionFotovoltaica.csv contains the hourly photovoltaic generation profiles for the five cities considered in the study: Barcelona, Bilbao, Huelva, Madrid, and Ourense.

The file Results.zip contains the optimization results, Pareto front points, and payoff matrices. The archive is organized into two main folders:

• PV50: Results assuming that the photovoltaic installation is sized to cover 50% of the annual electricity demand of the residential community.
• PV100: Results assuming that the photovoltaic installation is sized to cover 100% of the annual electricity demand.

Each of these folders contains five subfolders corresponding to the analyzed cities (Barcelona, Bilbao, Huelva, Madrid, and Ourense). Within each city folder, there are six subfolders corresponding to the six residential communities (I20a, I20b, I40a, I40b, I60a, and I60b).

Each community folder contains six Excel workbooks, corresponding to the six energy-sharing policies considered in the study (hourly, daily, weekly, monthly, bimonthly, and four-monthly), together with two additional folders: ParetoValues and PayoffMatrices.

Each Excel workbook contains four worksheets:

• Input_data: Summary of the case study, including the residential community, total number of supply points, number of community and individual supply points, time horizon, city, minimum percentage of photovoltaic generation allocated to community supply points, and other model parameters.
• Energy_summary: Summary of the energy-sharing results, including photovoltaic generation, self-consumption, electricity demand, community demand, electricity imported from the grid, exported surplus energy, monetary savings, and surplus compensation.
• Distribution: Hourly values for every supply point throughout the entire study horizon. The reported variables include photovoltaic generation, electricity demand, grid imports, photovoltaic energy allocation, surplus energy, and the energy-sharing coefficient (beta). Although all data are reported at hourly resolution, the sharing policy determines the time intervals over which the beta coefficient remains constant. For example, under the daily policy, the beta values are identical for all 24 hours of the same day.
• Model_performance: Optimization model statistics, including the objective function value, solution time, solver status, and the number of variables and constraints.

The ParetoValues folder contains one Excel workbook with two worksheets:

• Self-Consuming: Pareto front points obtained using the self-consumption objective.
• Savings: Pareto front points obtained using the monetary savings objective.

The PayoffMatrices folder contains six Excel workbooks, one for each energy-sharing policy, reporting the payoff matrices for the self-consumption and monetary savings objectives.