I am a Research Fellow working on statistical learning and control for electricity grids. My research focuses on the development of analytical and computational methods for reliability analysis (including Monte Carlo sampling methods, learning of proxy models, quantification of uncertainty) and the decentralised control of smart appliances using mean field approaches.
For the CDT Future Power Networks and Smart Grids (joint between Imperial and University of Strathclyde) I teach part of the module Managing Risk and Uncertainty in Grid Operation (Strathclyde-EE807).
Occasionally, I lead physics activities for visiting schools in the Wohl Reach Out Lab.
Calvo JL, Tindemans SH, Strbac G, 2016, Incorporating failures of System Protection Schemes into power system operation, Sustainable Energy Grids & Networks, Vol:8, ISSN:2352-4677, Pages:98-110
Trovato V, Tindemans SH, Strbac G, 2016, Leaky storage model for optimal multi-service allocation of thermostatic loads, IET Generation Transmission & Distribution, Vol:10, ISSN:1751-8687, Pages:585-593
Tindemans SH, Trovato V, Strbac G, 2015, Decentralized Control of Thermostatic Loads for Flexible Demand Response, IEEE Transactions on Control Systems Technology, Vol:23, ISSN:1063-6536, Pages:1685-1700
Tindemans SH, Strbac G, 2017, Robust estimation of risks from small samples, Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, Vol:375, ISSN:1471-2962
Tindemans SH, Strbac G, 2015, Visualising risk in generating capacity adequacy studies using clustering and prototypes, General Meeting of the IEEE-Power-and-Energy-Society, IEEE, ISSN:1944-9925
et al., 2014, Resilience performance of smart distribution networks, Report D4 for the “Low Carbon London” LCNF project
et al., 2014, Residential consumer responsiveness to time-varying pricing, Report A3 for the “Low Carbon London” LCNF project