On machine learning-based techniques for future sustainable and resilient energy systems
Permanently increasing penetration of converter-interfaced generation and renewable
energy sources (RESs) makes modern electrical power systems more vulnerable to low …
energy sources (RESs) makes modern electrical power systems more vulnerable to low …
Learning to run a power network challenge: a retrospective analysis
Power networks, responsible for transporting electricity across large geographical regions,
are complex infrastructures on which modern life critically depend. Variations in demand …
are complex infrastructures on which modern life critically depend. Variations in demand …
Winning the l2rpn challenge: Power grid management via semi-markov afterstate actor-critic
Safe and reliable electricity transmission in power grids is crucial for modern society. It is
thus quite natural that there has been a growing interest in the automatic management of …
thus quite natural that there has been a growing interest in the automatic management of …
Power grid congestion management via topology optimization with AlphaZero
M Dorfer, AR Fuxjäger, K Kozak, PM Blies… - arXiv preprint arXiv …, 2022 - arxiv.org
The energy sector is facing rapid changes in the transition towards clean renewable
sources. However, the growing share of volatile, fluctuating renewable generation such as …
sources. However, the growing share of volatile, fluctuating renewable generation such as …
[HTML][HTML] Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
The operation of electricity grids has become increasingly complex due to the current
upheaval and the increase in renewable energy production. As a consequence, active grid …
upheaval and the increase in renewable energy production. As a consequence, active grid …
[HTML][HTML] Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network
Abstract Solving AC-Optimal Power Flow (OPF) problems is an essential task for grid
operators to keep the power system safe for the use cases such as minimization of total …
operators to keep the power system safe for the use cases such as minimization of total …
[HTML][HTML] Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm
The massive integration of renewable-based distributed energy resources (DERs) inherently
increases the energy system's complexity, especially when it comes to defining its …
increases the energy system's complexity, especially when it comes to defining its …
Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation
Decarbonisation is driving dramatic growth in renewable power generation. This increases
uncertainty in the load to be served by power plants and makes their efficient scheduling …
uncertainty in the load to be served by power plants and makes their efficient scheduling …
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
M Subramanian, J Viebahn… - 2021 IEEE Madrid …, 2021 - ieeexplore.ieee.org
System operators are faced with increasingly volatile operating conditions. In order to
manage system reliability in a cost-effective manner, control room operators are turning to …
manage system reliability in a cost-effective manner, control room operators are turning to …
[HTML][HTML] Power balance control of RES integrated power system by deep reinforcement learning with optimized utilization rate of renewable energy
T Wei, X Chu, D Yang, H Ma - Energy Reports, 2022 - Elsevier
A power balance control method is proposed for renewable energy source (RES) integrated
power systems based on deep reinforcement learning (DRL), with the reasonable utilization …
power systems based on deep reinforcement learning (DRL), with the reasonable utilization …