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Decision Making, Optimisation and Computational Intelligence

Classic and emerging optimisation methods with applications in energy systems, methodologies for multi-criteria decision support, including risk models and methodologies based on metaheuristics and evolutionary computation for optimisation and decision making. Computational intelligence based models (e.g. fuzzy systems, neural networks) for applications in energy systems.

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Forecasting models for short-term and very short-term load and renewable energy production, forecasting error analysis with probabilistic descriptions of uncertainties, time series studies and knowledge discovery in databases, application and selection of regression technologies: classic models, neural networks, regression trees, evaluation and estimation on confidence levels.

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Static and Dynamic analysis of Energy Grids

Classic and fuzzy models for electricity grid in steady load analysis, analysis of dynamic behaviour in isolated and interconnected networks, dynamic models for energy conversion systems, dynamic simulation models for microgeneration systems and microgrids and the design of models for the integration of electric vehicles into the electricity grid.

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Using models to analyse reliability in energy systems, reliability in static, spinning and operational capacity power systems looking at renewable and variable energy production, reliability of composite distribution systems (Generation + Transmission), microgeneration and microgrids, models to represent maintenance and the transport network.

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