Danmarks Tekniske Universitet (DTU), Denmark

DTU Compute (the Department of Applied Mathematics and Computer Sciences of the Technical University of Denmark - DTU) is Denmark's largest environment for mathematics and computer science. It consists of 11 research sections with more than 200 staff members and includes the whole innovation chain from fundamental research on mathematics and statistics to the development of new ICT technologies. One of its main focus areas is models and methods for intelligent operation of environmental and energy systems. In particular, DTU Compute hosts the Center for IT-Intelligent Energy Systems in Cities (CITIES), which is currently the largest research project on intelligent and integrated energy systems in Denmark.
As of October 2015 Reuters Top 100 World's Most Innovative University ranked DTU as 1st in the Nordic region and 7th in Europe. According to the Leiden Ranking Citation impact indicator (top 10% problications – all sciences) DTU was ranked 1st in the Nordic region and 48th in Europe.

DTU Compute are involved in several related National and European projects. The largest national project being the Center for IT-Intelligent Energy Systems in CITIES (CITIES), and the currently largest European Smart Grid project is the newly funded H2020 Smart Net project (will start 1. January 2016). DTU Compute is – or has been – involved in a large number of EU FP7 projects (Anemos, SafeWind, NORSEWInD, Anemos+, IRPWind,...) DTU Compute has a key involvement in EERA Smart Grid, EERA JP Smart Cities, EERA JP Wind, and DTU Compute has taken a leading role in forming a new EERA JP Energy Systems Integration. Internationally, DTU Compute is playing a major role in the International Institute for Energy Systems Integration currently hosted by NREL, US. DTU Compute also has a leading role in many IEA Annexes related to energy and buildings like Annex 58, 66 and 67.

Prof. Henrik Madsen is a Professor of Stochastic Dynamical Systems, and he is currently the Head of Center for IT-Intelligent Energy Systems (CITIES). He was appointed Ass. Prof. In Statistics in 1986, Assoc. Prof. In 1989, and Professor in Mathematical Statistics with a special focus on Stochastic Dynamical Systems in 1999. His main research interest is related to analysis and modelling of stochastic dynamical systems, and the application areas are mostly related to Energy Systems, Informatics and Process Modelling. He has authored or co-authored approximately 500 reviewed papers and 12 books.

A subset of relevant projects:

• CITIES – Center for IT-Intelligent Energy Systems in cities. National (Danish) research project on models and methods for intelligent energy systems integration. Budget 10 mill Euros (2014-2019)
• 5s – National (Danish) Research project on Future Electricity Markes. (2013 – 2017)
• SmartNet, a H2020 Research and Innovation Project between 22 leading European Smart Grid research centers and companies that aims to develop optimal market structures and optimal interactions between DSO's and TSO's and leverage flexibility at local level, (2016-2019).
• IRPWind. The aim is to foster better integration of European research activities in the field of wind energy research with the purpose of accelerating the transition to a low-carbon society, (2014-2017).
• NORSEWInD. FP7 research and innovation project focusing on offshore wind power data and planning, (2010-2013).

Facilities (including software packages)
Center for High Performance Computing. Here a number of software packages for forecasting, control and modelling are available.
Toolboxes in R for greybox modelling (CTSM-R) and Model Predictive Control (MPC-R).
An ICT setup for intelligent energy systems integration called 'Smart-Energy Operating Systems / SE-OS'.
Access to a large number of Live Labs (Heat Pumps, Cooling, Combined Heat and Power, Wastewater treatment, …) as well to a number of smart cities project in Denmark and Sweden. This includes access to data.

Key publications

• R. Halvgaard, P. Bacher, B. Perers, E. Andersen, S. Furbo, J.B. Jørgensen, N.K. Poulsen, H. Madsen: Model predictive control for a smart solar tank based on weather and consumption forecasts, Journal energy Procedia, Vol. 30, pp. 270-278, 2013.
• O. Corradi, H. Ochsenfeld, H. Madsen, P. Pinson, Controlling electricity consumption by forecasting its response to varying prices,  IEEE Transactions on Power Systems, Vol. 28, pp. 421-429, 2013.
• P. Meibom, K. Hilger, H. Madsen, D. Vinther: Energy comes together in Denmark: The key to a future fossil-free Danish power system, IEEE Power and Energy Magazin, Vol. 11, pp. 46-55, 2014.
• J.M.M. Gonzáles, A.J. Conejo, H. Madsen, P. Pinson, M.Zugno, Integrating Renewables in Electricity Markets, Operational Problems, Springer, 429 pp., 2014
• M. Zugno, J.M. Morales Gonzales, P. Pinson, H. Madsen: A bilevel model for the electricity retailers' participation in a demand response market, Energy Economics, Vol 36, pp. 182-197, 2014.
• J.E.B. Iversen, J.M. Moralez Gonzales, J.K. Møller, H. Madsen, Probabilistic forecasts of solar irradiance by stochastic differential equations, Environmetrics, Vol. 25, pp. 152-164, 2014.
• N. O'Connell, P. Pinson, H. Madsen, M. O'Malley, Benefits and challenges of electrical demand response; A critical review. Journal of Renewable and Sustainable Energy Reviews, Vol. 39, pp. 686-699, 2014.
• S. Fabrizio, H.W. Bindner, H. Madsen, D. Torregrossa, L. Reyes Chamoro, M. Paolone, A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system, Journal of Electric Power & Energy Systems, Vol. 62, pp. 879-889, 2014.
• J.E.B. Iversen, J.M. Moralez Gonzáles, H. Madsen, Optimal charging of an electric vehicle using a Markov decision process, Applied Energy, Vol. 123, pp. 1-12, 2014.
• E.B. Iversen, J.K. Møller, J.M. Morales, H. Madsen, Inhomogeneous Markov Models for describing driving patters, accepted, IEEE Transactions on Smart Grid, 2015.
• H. Madsen, J. Parvizi, R. Halvgaard, L.E. Sokoler, J.B. Jørgensen, L.H. Hansen, K.B. Hilger: Control of Electricity Loads in Future Electric Energy Systems, in Handbook of Clean Energy Systems, Wiley, 2015.
• E. Lindstrom, H. Madsen, N. Vicke, Consumption management in the Nord Pool region; A stability analysis, Journal of Applied Energy, Vol. 146, pp. 239-246, 2015.
• M. Nielsen, J.M.M. Gonzales, M. Zugno, T.A. Østergaard, H. Madsen, Economic valuation of heat pumps and electric boilers in the Danish energy system; Applied Energy, 2015.
• J.S. Gallego, J.M.M. Gonzales, H. Madsen, Determining reserve requirements in DK1 area of Nord Pool using a probabilistic approach, accepted, Energy, 2015.
• L.B. Rasmussen, P. Bacher, H. Madsen, HA. Nielsen, C. Heerup, T. Green; Load forecasting of supermarket refrigeration; Applied Energy, vol 163, no. Februar 2016, pp. 32-40., 10.1016/j.apenergy.2015.10.046, 2016
• J.K. Møller, M. Zugno, H. Madsen; Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models; Journal of Forecasting., 10.1002/for.2367, 2016
• J. Saez-Gallego, J.M. Morales González, M. Zugno, H. Madsen; A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity. In: IEEE Transactions on Power Systems, 2016