Multi-dimensional big data modelling to ensure long-term power and heat system adequacy


The energy transition to intermittent renewable energy sources (iRES) will pose new challenges to the power and heat systems due to fluctuations of the weather over time scales of days, and months. Weather patterns like the North Atlantic Oscillation cause even major fluctuations over time scales of years. The challenge for transmission system operators (TSOs) is to steer their resources so that they facilitate the energy transition while maintaining system adequacy. However, at present decision-making processes are hampered by the inability of decision support models to use the available datasets because of the quantity and size of the datasets and the computationally intensity of the task. To address this gap, this project brings together expertise in big data analytics, advanced optimization algorithms, climate simulation modelling, and power and heat system modelling. A multi-dimensional modelling framework will be developed consisting of climate simulations, a renewable spatial distribution tool, and a Power and Heat System Model (PHSM) and associated datasets. The framework is complemented with big data analytic tools enabling smart identification of critical conditions for system adequacy. Advanced optimization algorithms will be developed to reduce the computation time of PHSMs. The modelling framework provides detailed insights into how iRES should be complemented with backup options, expansion of interconnectivity and transmission networks, storage and demand response options in order to guarantee long-term adequacy of the power and heat systems. The modelling framework is intended to support especially TSOs, but also other stakeholders in the electricity market with decision-making.


Project number


Main applicant

Dr. ir. M.A. van den Broek

Affiliated with

Universiteit Utrecht, Faculteit Geowetenschappen, Departement Innovatie-, Milieu en Energiewetenschappen


01/01/2018 to 28/02/2022