Research Smart Energy SystemsOptimization and Management of Microgrids

Optimization and Management of Microgrids

Optimization and Management of Microgrids

ARES develops optimization frameworks and digital tools for the scheduling and design of renewable-based microgrids, combining technical modeling and data-driven approaches to ensure reliability, sustainability, and cost-effectiveness in hybrid energy systems.

Scheduling

Research activities focus on the development of robust scheduling strategies for hybrid microgrids integrating renewable sources, batteries, and hydrogen technologies. The group investigates optimization formulations that account for the uncertainty in renewable forecasts and component degradation, enabling reliable day-ahead operation and energy management in isolated and grid-connected systems. These models ensure optimal coordination among renewable generators, batteries, and power-to-gas-to-power storage, improving the resilience and self-sufficiency of microgrids such as hybrid power stations.

Design

In the design phase, the ARES group applies advanced optimization techniques, including Mixed-Integer Linear Programming and stochastic algorithms, to identify the most efficient configuration of renewable generators and storage systems. The frameworks developed integrate detailed component models, long-term degradation effects, and future cost projections to support techno-economic analyses and investment decisions. Recent studies demonstrated the benefits of hybrid storage systems combining batteries and hydrogen, highlighting their potential to reduce costs and enable fully renewable, self-sufficient energy systems for small islands and remote communities.

 

Advanced tools for more accurate techno-economic assessments

ARES develops and applies advanced simulation frameworks to improve the accuracy of techno-economic assessments of hybrid energy systems. By combining detailed short-term simulations with long-term performance projections, these tools explicitly account for the degradation of components such as batteries, electrolyzers, and fuel cells. This approach allows for realistic lifetime modeling and cost evaluation, providing a more reliable estimation of system performance over time. The integration of degradation effects within the optimization and design process leads to more robust solutions, ensuring that technical, operational, and economic indicators remain consistent throughout the system’s lifespan.