Dr. S.H. (Simon) Tindemans

profile

Biography

Simon Tindemans is an Assistant Professor in the Intelligent Electrical Power Grids group at TU Delft. Previously, he was with the Control and Power group at Imperial College London as a Research Fellow, Marie-Curie Intra-European Fellow and Research Associate. Before switching his research focus to energy systems, he performed his PhD research at AMOLF (Amsterdam, NL), furthering understanding pattern formation in biomolecular systems. He obtained an MSc in theoretical physics from the University of Amsterdam.

Research


The ambition to drastically reduce greenhouse gas emissions is driving unprecedented changes to the way we design and operate the electricity grid. My research targets three challenging areas of interest for current and future electricity grids: data analytics, efficient simulation methods and control of decentralised loads - with a particular interest in the overlaps and interactions between these areas.   
 
In June 2017, I gave a talk at  Tech Foresight 2037  on the future of decentralised electricity grids. You can watch it  here.  
 
Data analytics
In modern electrical power systems, sensors and other sources generate a growing stream of data. Efficient operation of the system requires extracting useful information and actionable insights from this data. My work in this area focuses on statistical learning of predictive models. Examples include the learning of data-driven surrogate models (proxies) that approximate complex elaborate models, and the quantification of uncertainty in predictive models. 
 
Efficient computational methods 
Accurate models of large, intelligent grids are rapidly increasing in complexity. Even if they are accurately validated, exploring the vast space of future events with such models is often unacceptably slow. Moreover, we are often interested in rare high-impact low-probability (HILP) events, where computational bottlenecks are especially prominent. I am working on various Monte Carlo methods that improve sampling efficiency, including importance sampling, active learning and multi-level Monte Carlo.

Control of decentralised flexible loads
Smart appliances and responsive end users provide a significant potential for demand response, but it is not clear what the best approach is for unlocking this potential. Decentralised control with minimal communication requirements is an attractive proposition from the perspective of practical implementation (communication requirements) and privacy (amount of information exchanged; local control decisions). I investigate decentralised aggregate control strategies for smart thermal loads, e.g. refrigerators, air conditioners. 

publications
Robust estimation of risks from small samples

peer reviewed : Y

Royal Society of London. Philosophical Transactions A. Mathematical, Physical and Engineering Sciences (2017) 13 pages , p. 1-13

authors

  • Simon H. Tindemans
  • Goran Strbac
Understanding the aggregate flexibility of thermostatically controlled loads

peer reviewed : Y

2017 IEEE Manchester PowerTech, Powertech 2017 (2017)

authors

  • Vincenzo Trovato
  • Simon Tindemans
  • Goran Strbac
How selective severing by katanin promotes order in the plant cortical microtubule array

peer reviewed : Y

Proceedings of the National Academy of Sciences of the United States of America (2017) 6 pages , p. 6942-6947

authors

  • Eva E. Deinum
  • Simon H. Tindemans
  • Jelmer J. Lindeboom
  • Bela M. Mulder
Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

peer reviewed : Y

IEEE Transactions on Smart Grid (2017) 10 pages , p. 1417-1426

authors

  • Ioannis Konstantelos
  • Geoffroy Jamgotchian
  • Simon H. Tindemans
  • Philippe Duchesne
  • Stijn Cole
  • Christian Merckx
  • Goran Strbac
  • Patrick Panciatici
Impact of high wind penetration on variability of unserved energy in power system adequacy

peer reviewed : Y

2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Proceedings (2016)

authors

  • Sarah Sheehy
  • Gruffudd Edwards
  • Chris J. Dent
  • Behzad Kazemtabrizi
  • Matthias Troffaes
  • Simon Tindemans
Incorporating failures of System Protection Schemes into power system operation

peer reviewed : Y

Sustainable Energy, Grids and Networks (2016) 13 pages , p. 98-110

authors

  • Jose L. Calvo
  • Simon H. Tindemans
  • Goran Strbac
Nondisruptive decentralized control of thermal loads with second order thermal models

peer reviewed : Y

2016 IEEE Power and Energy Society General Meeting, PESGM 2016 (2016)

authors

  • Simon H. Tindemans
  • Goran Strbac
An implicit switching model for distribution network reliability assessment

peer reviewed : Y

19th Power Systems Computation Conference, PSCC 2016 (2016)

authors

  • Yang Yang
  • Simon Tindemans
  • Goran Strbac
Evaluating composite approaches to modelling high-dimensional stochastic variables in power systems

peer reviewed : Y

19th Power Systems Computation Conference, PSCC 2016 (2016)

authors

  • Mingyang Sun
  • Ioannis Konstantelos
  • Simon Tindemans
  • Goran Strbac
Leaky storage model for optimal multi-service allocation of thermostatic loads

peer reviewed : Y

IET Generation, Transmission and Distribution (2016) 9 pages , p. 585-593

authors

  • Vincenzo Trovato
  • Simon H. Tindemans
  • Goran Strbac
ancillary activities
-Geen nevenwerkzaamheden -

2018-01-01 - 2020-01-01