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New algorithm optimizes energy consumption in buildings and urban grids

Researchers from the University of Málaga develop a predictive system to improve the management of renewable energy, batteries, and electric vehicles.

Abstract visualization of energy flow in a smart urban grid.
AI

Abstract visualization of energy flow in a smart urban grid.

A team from the University of Málaga has designed a predictive algorithm that optimizes electricity consumption, storage, and trading in buildings, neighborhoods, and urban grids, integrating renewables, batteries, and electric vehicles.

A research team from the University of Málaga has developed an energy management system that schedules one day in advance when it is most convenient to consume, store, buy, or sell electricity. This comprehensive tool coordinates the use of renewable energy, batteries, and electric vehicles to reduce costs and anticipate demand.
Simulation results indicate that the integration of these resources significantly improves energy management compared to conventional systems. The algorithm can decide, for instance, to charge batteries with solar surpluses, use that energy during peak demand hours, or even return stored electricity from an electric vehicle if it proves advantageous.
The project's main innovation lies in its multi-level approach: from smart buildings with home automation systems, through energy communities grouping several buildings, to the urban distribution grid. This complete vision contrasts with previous studies that focused on a single level.
Simulations showed considerable savings: photovoltaic energy reduced operational costs by around 29% compared to systems without renewables or batteries. When including air conditioning and hot water, savings reached 45.65%, and the use of electric vehicles as energy support provided an additional reduction of 8.59%.
The model also incorporates risk management, planning energy not only under ideal forecasts but also considering potential deviations in demand, electricity prices, or renewable generation. This allows for more stable planning amidst the uncertainty of the electrical system.
The research, published in Sustainable Energy, Grids and Networks, uses real and historical data to create representative variability scenarios, optimizing daily energy scheduling and adjusting plans to minimize costs and risks.
The next steps for the TEP-144 Electric Power Systems group at the University of Málaga will include the integration of emerging technologies such as hydrogen systems and new artificial intelligence techniques. The research has received funding from the Consejería de Universidad, Industria, Energía e Innovación of the Junta de Andalucía, the Ministry of Science, Innovation and Universities, and European projects like Horizon Europe.
Based on information from the official source: Fundación Descubre (divulgación científica, Junta de Andalucía) (03/10/2026)