Zobrazeno 1 - 10
of 351
pro vyhledávání: '"Ernst, Damien"'
Understanding the variation of the optimal value with respect to change in the data is an old problem of mathematical optimisation. This paper focuses on the linear problem $f(\lambda) = \min c^t x$ such that $(A+\lambda D)x \leq b$, where $\lambda$
Externí odkaz:
http://arxiv.org/abs/2410.14443
Autor:
Vassallo, Maurizio, Bahmanyar, Alireza, Duchesne, Laurine, Leerschool, Adrien, Gerard, Simon, Wehenkel, Thomas, Ernst, Damien
This paper introduces a systematic approach to address the topological path identification (TPI) problem in power distribution networks. Our approach starts by listing the DSO's raw information coming from several sources. The raw information undergo
Externí odkaz:
http://arxiv.org/abs/2409.09075
Autor:
Vassallo, Maurizio, Leerschool, Adrien, Bahmanyar, Alireza, Duchesne, Laurine, Gerard, Simon, Wehenkel, Thomas, Ernst, Damien
A customer topological path represents the sequence of network elements connecting an MV/LV transformer to a customer. Accurate knowledge of these paths is crucial for distribution system operators (DSOs) in digitalization, analysis, and network plan
Externí odkaz:
http://arxiv.org/abs/2409.09073
The increasing adoption of distributed energy resources, particularly photovoltaic (PV) panels, has presented new and complex challenges for power network control. With the significant energy production from PV panels, voltage issues in the network h
Externí odkaz:
http://arxiv.org/abs/2409.09074
The Unit Commitment (UC) problem is a key optimization task in power systems to forecast the generation schedules of power units over a finite time period by minimizing costs while meeting demand and technical constraints. However, many parameters re
Externí odkaz:
http://arxiv.org/abs/2409.03588
Publikováno v:
ICML Workshop on Next Generation of Sequence Modeling Architectures, 2024
Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to th
Externí odkaz:
http://arxiv.org/abs/2407.08415
This study proposes a novel approach based on reinforcement learning (RL) to enhance the sorting efficiency of scrap metal using delta robots and a Pick-and-Place (PaP) process, widely used in the industry. We use three classical model-free RL algori
Externí odkaz:
http://arxiv.org/abs/2406.13453
Policy-gradient algorithms are effective reinforcement learning methods for solving control problems with continuous state and action spaces. To compute near-optimal policies, it is essential in practice to include exploration terms in the learning o
Externí odkaz:
http://arxiv.org/abs/2402.00162
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their
Externí odkaz:
http://arxiv.org/abs/2401.16321
Achieving carbon neutrality is probably one of the most important challenges of the 21st century for our societies. Part of the solution to this challenge is to leverage renewable energies. However, these energy sources are often located far away fro
Externí odkaz:
http://arxiv.org/abs/2310.01964