Advanced Analytics to Empower the Small Flexible Consumers of Electricity

PI: Juan Miguel Morales González

Acronym: FlexAnalytics

Abstract: Activating the demand response, although a major challenge, may also bring tremendous benefits to society, with potential cost savings in the billions of euros. This project will exploit methods of inverse problems, multi-level programming and machine learning to develop a pioneering system that enables the active participation of a group of price-responsive consumers of electricity in the wholesale electricity markets. Through this, they will be able to make the most out of their flexible consumption. FlexAnalytics proposes a generalized scheme for so-called inverse optimization that materializes into a novel data-driven approach to the market bidding problem that, unlike existing approaches, combines the tasks of forecasting, model formulation and estimation, and decision-making in an original unified theoretical framework. The project will also address big-data challenges, as the proposed system will leverage weather, market, and demand information to capture the many factors that may affect the price-response of a pool of flexible consumers. On a fundamental level, FlexAnalytics will produce a novel mathematical framework for data-driven decision making. On a practical level, FlexAnalytics will show that this framework can facilitate the best use of a large amount and a wide variety of data to efficiently operate the sustainable energy systems of the future.