Machine learning applied to microgrids, electric vehicles and energy management
Milestone 5.1. Prediction algorithms for renewable resource in microgrids
Tasks
- Analysis of the state-of-the-art in prediction techniques for renewables energy generation (photovoltaic and wind) and their application to microgrids
- Development and implementation of algorithms and tools for predicting short and very short term photovoltaic generation using machine learning techniques
- Development and implementation of algorithms and tools for short and very short term wind generation prediction using machine learning techniques
Participants: Research Groups GHEODE and IMDEA-USE
Milestone 5.2. Optimisation algorithms for microgrid design and planning
Tasks
- Analysis of the state-of-the-art in optimisation using machine learning techniques for scheduling and planning of Energy Storage Systems in microgrids
- Design and development of machine learning algorithms for optimal positioning of renewable generation in microgrids
- Design and planning of microgrids as stand-alone systems
Participants: Research Groups GHEODE and GCP
Milestone 5.3. Machine learning algorithms for problems related to the incorporation of electric vehicles into microgrids
Tasks
- State-of-the-art analysis of the impact of the inclusion of electric vehicles in microgrids
- Design and development of sizing and scheduling algorithms for charging and discharging electric vehicle batteries and their impact on the microgrid
- Impact of renewable resource forecasting on electric vehicle charging and off-loading scheduling
Participants: Research Groups GHEODE, IMDEA-USE and GCP