- This event has passed.
SMART LOCAL ENERGY MARKETS
20/06/2022 | 17:00 - 18:00
Speaker: Dr. Wolfram Rozas, Director of Big Data & Business Analytics, Artificial Intelligence and Smart Energy Executive Programs.
Wolfram Rozas is a quantitative economist and a Ph.D. candidate in Industrial Technologies. He is an expert in the use of all exponential technologies, Big Data & Business Analytics, Artificial Intelligence, Cloud, Internet of Things, Blockchain and Quantum Computing, to achieve strategic business objectives. He has more than 25 years of experience in companies such as PWC or IBM managing Business Intelligence, Business Analytics, Machine Learning, Deep Learning projects, and implementing Cognitive Systems in Financial, Telecommunications, Distribution sectors, Tourism and Transportation, Mass Consumption, Energy and Utilities, Chemicals and Petroleum. He has developed his teaching career since 2003. He has collaborated as an expert in Business Intelligence, Big Data & Business Analytics, Artificial Intelligence (covering Machine Learning, Deep Learning, Reinforcement Learning) in Business Schools and Universities such as CUNEF, Escuela de Organizacion Industrial, Instituto de Empresa, U. Pablo de Olavide, U. Carlos III, UNIR, among others.He has published numerous articles in the field of Data Analysis. Articles are listed Harvard Deusto Business Review, Club De Dirigentes de Madrid, Expansión, Spanish Confederation of Directors and Executives, BIMagazine, ABC, Cinco Días, among others. Appointed by the INE in July 2020, member of the Working Group for Data Stewardship of the Higher Council of Statistics (Instituto Nacional de Estadística). He has been appointed as Coordinator of the Monographic devoted to Data Economy of Revista Industrial, the Ministery of Economy Magazine. He has recently started a line of Digitization Research in the Renewables Energy industry where he has started a thesis on Demand Aggregation and Flexibility based on Bayesian deep neural networks and Game Theory at UNED (Universidad Nacional de Educación a Distancia).
The current climate change situation demands an Energy Transition. The Energy Internet counts on Distributed Energy Resources (DER) to create the Future of Electricity. Unfortunately, these new sources are intermittent, and hence, estimation models perform under uncertainty, which might affect the system’s energy quality. Therefore, there is a need for Flexibility solutions that balance the power flow and optimize financial results. Local Energy Markets (LEM) are a new approach to adopting and extending the flexibility concept. We present a method to develop Smart Local Energy Markets. Its final goal is to boost DERs extension, reduce Greenhouse Gas (GHG) emissions, and make electricity demand more elastic. Furthermore, to optimize the community energy consumption and bill balance, we will show a framework capable of capturing low latency information and transforming it into better social welfare.
Smart Local Energy Markets are sat on cutting-edge technology to derive compelling results for communities that want to reap their beneficial outcomes. The system will build up a memory of low-latency information coming from sensors under an Edge Computing architecture. Containers installed in the Edge Network will run Bayesian deep learning models to estimate load and generation unit levels. With this new information, the system under a Transactive Energy scheme will optimize Community goals automatically.