Juan M. Morales is currently the head of the research group OASYS ― Optimization and Analytics for Sustainable Energy Systems (oasys.uma.es) at the University of Málaga in Spain, where he also holds a tenured associate professorship position in the Department of Applied Mathematics. Juan M. Morales received his M.Sc. degree in Industrial Engineering from the University of Málaga and his Ph.D. in Electrical Engineering from the University of Castilla – La Mancha, Spain, in 2006 and 2010, respectively. In 2011, he was awarded a Hans Christian Ørsted research fellowship by the Technical University of Denmark, where he was also an associate professor in Stochastic Optimization in Energy Systems within the Department of Applied Mathematics and Computer Science, until 2016. Juan M. Morales’s expertise lies in the fields of Data Analytics and Optimization, with particular focus on their applications to Energy Engineering and Economics, to which he has contributed a number of technical publications, including two monographs on the challenges of a fossil-free energy sector. He is a recipient of a Starting Grant awarded by the European Research Council for his project “Advanced Analytics to Empower the Small Flexible Consumers of Electricity,” a Senior Member of IEEE, and a current member of the editorial boards of IEEE Transactions on Power Systems and the Springer journal TOP (the official journal of the Spanish Society of Statistics and Operations Research).
Data-driven power systems
Thrilling yet challenging times lie ahead for the electrical power industry. The development of microgrids, the growing contribution of weather-driven renewable energy sources, the greater involvement of power consumers, and the increasing exchange of electricity among neighboring regions, all demand solid innovations in the way we plan and operate what is, most likely, the most colossal infrastructure ever built by Homo sapiens: The power grid. Furthermore, the extensive monitoring of this grid, together with the proliferation of technologies for intelligent control and computations, is creating countless opportunities for the exploitation of the massive amount of data that power systems generate in pursuit of a long-overdue modernization of ageing infrastructure and ambitious sustainability goals.
In this talk, we will show examples of how ideas from the fields of machine learning, optimization and decision-making can be put to work together to develop novel data-driven operational and planning methods that facilitate the imperative transformation of current power grids into sustainable, reliable, secure and cost-efficient cyber-physical systems. We will discuss a variety of data-driven methods of different complexity, some of which are surprising for their apparent simplicity but remarkable benefits in terms of computational costs and/or social welfare, proving the huge potential value in the data still to be unleashed. Moreover, we will show that all agents involved in the power sector, namely, producers, consumers, retailers, operators, planners, etc. can profit from the smart use of data.