88 citations · 480 across the 47 of their papers we have counts for
6 papers · 1 filter
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…
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective
Kutay Bölat, Simon H. Tindemans, Peter Palensky
Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this…
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…
Reinforcement Learning by Guided Safe Exploration
Qisong Yang, Thiago D. Simão, Nils Jansen +2
Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them…
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…