88 citations · 480 across the 47 of their papers we have counts for
23 papers · 1 filter
Scalable Iterative Algorithm for Solving Optimal Transmission Switching with De-energization
Benoît Jeanson, Mathieu Tanneau, Simon Tindemans
Transmission System Operators routinely use transmission switching as a tool to manage congestion and ensure system security. Motivated by sub-transmission operations at RTE, this…
Extreme value distributions of peak loads for non-residential customer segments
Shaohong Shi, Eric A. Cator, Jacco Heres +1
Electrical grid congestion is a growing challenge in Europe, driving the need for accurate prediction of load, particularly of peak load. Non-time-resolved models of peak load offe…
Scalable quantile predictions of peak loads for non-residential customer segments
Shaohong Shi, Jacco Heres, Simon H. Tindemans
Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak…
Risk-based approach to the Optimal Transmission Switching problem
Benoît Jeanson, Simon H. Tindemans
This paper deals with the secure Optimal Transmission Switching (OTS) problem in situations where the TSO is forced to accept the risk that some contingencies may result in the de-…
Impact of Lead Time on Aggregate EV Flexibility for Congestion Management Services
Nanda Kishor Panda, Peter Palensky, Simon H. Tindemans
Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric…
Linear Aggregate Model for Realizable Dispatch of Homogeneous Energy Storage
Mazen Elsaadany, Mads R. Almassalkhi, Simon H. Tindemans
To optimize the dispatch of batteries, a model is required that can predict the state of energy (SOE) trajectory for a chosen open-loop power schedule to ensure admissibility (i.e.…
Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs
Nanda Kishor Panda, Na Li, Simon H. Tindemans
Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable oper…
Stochastic Mean Field Game for Strategic Bidding of Consumers in Congested Distribution Networks
Amirreza Silani, Simon H. Tindemans
The rapid increase of photovoltaic cells, batteries, and Electric Vehicles (EVs) in electric grids can result in congested distribution networks. An alternative to enhancing networ…
Quantifying the Aggregate Flexibility of Electric Vehicles Charging Stations for Dependable Congestion Management Products -- A Dutch Case Study
Nanda Kishor Panda, Simon H. Tindemans
Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, man…
Capturing Chronology and Extreme Values of Representative Days for Planning of Transmission Lines and Long-Term Energy Storage Systems
Mojtaba Moradi-Sepahvand, Simon H. Tindemans
The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming m…
Representative Days and Hours with Piecewise Linear Transitions for Power System Planning
Mojtaba Moradi-Sepahvand, Simon H. Tindemans
Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method t…
Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles
Nanda Kishor Panda, Simon H. Tindemans
Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate…
Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder
Chenguang Wang, Ensieh Sharifnia, Simon H. Tindemans +1
Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical dat…
Generating Multivariate Load States Using a Conditional Variational Autoencoder
Chenguang Wang, Ensieh Sharifnia, Zhi Gao +2
For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the ava…
Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method
Julian N. Betge, Barbera Droste, Jacco Heres +1
Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face. A proven approach for addressing this challe…
Coordination of Heterogeneous Deferrable Loads using the F-MBC Mechanism
Subhitcha Ramkumar, Hazem A. Abdelghany, Simon H. Tindemans
Increasing participation of prosumers in the electricity grid calls for efficient operational strategies for utilizing the flexibility offered by Distributed Energy Resources (DER)…
Low-complexity decentralized algorithm for aggregate load control of thermostatic loads
Simon H. Tindemans, Goran Strbac
Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refr…
Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
Chenguang Wang, Kaikai Pan, Simon Tindemans +1
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can pot…
Distributed Coordination of Deferrable Loads: A Real-time Market with Self-fulfilling Forecasts
Hazem A. Abdelghany, Simon H. Tindemans, Mathijs M. de Weerdt +1
Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficien…
Detection of False Data Injection Attacks Using the Autoencoder Approach
Chenguang Wang, Simon Tindemans, Kaikai Pan +1
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventi…
Low-complexity control algorithm for decentralised demand response using thermostatic loads
Simon Tindemans, Goran Strbac
Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refr…
Sample-Derived Disjunctive Rules for Secure Power System Operation
Jochen L. Cremer, Ioannis Konstantelos, Simon H. Tindemans +1
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the…
Robustly Maximal Utilisation of Energy-Constrained Distributed Resources
Michael Evans, Simon H. Tindemans, David Angeli
We consider the problem of dispatching a fleet of distributed energy reserve devices to collectively meet a sequence of power requests over time. Under the restriction that reserve…