Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Reinforcement learning and A* search for the unit commitment problem
Previous research has combined model-free reinforcement learning with model-based tree search methods to solve the unit commitment problem with stochastic demand and renewables generation. This approach was limited to shallow search depths and suffered from significant variability in run time across problem instances with varying complexity. To mitigate these issues, we extend this methodology to more advanced search algorithms based on A* search. First, we develop a problem-specific heuristic based on priority list unit commitment methods and apply this in Guided A* search, reducing run time by up to 94% with negligible impact on operating costs. In addition, we address the run time variability issue by employing a novel anytime algorithm, Guided IDA*, replacing the fixed search depth parameter with a time budget constraint. We show that Guided IDA* mitigates the run time variability of previous guided tree search algorithms and enables further operating cost reductions of up to 1%.
Reinforcement learning and A* search for the unit commitment problem
Previous research has combined model-free reinforcement learning with model-based tree search methods to solve the unit commitment problem with stochastic demand and renewables generation. This approach was limited to shallow search depths and suffered from significant variability in run time across problem instances with varying complexity. To mitigate these issues, we extend this methodology to more advanced search algorithms based on A* search. First, we develop a problem-specific heuristic based on priority list unit commitment methods and apply this in Guided A* search, reducing run time by up to 94% with negligible impact on operating costs. In addition, we address the run time variability issue by employing a novel anytime algorithm, Guided IDA*, replacing the fixed search depth parameter with a time budget constraint. We show that Guided IDA* mitigates the run time variability of previous guided tree search algorithms and enables further operating cost reductions of up to 1%.
Reinforcement learning and A* search for the unit commitment problem
Patrick de Mars (Autor:in) / Aidan O’Sullivan (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
An ant colony optimization for unit commitment problem
| British Library Online Contents | 2006
Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System
| DOAJ | 2020
Hybrid component and configuration model for combined-cycle units in unit commitment problem
| DOAJ | 2018
| Europäisches Patentamt | 2019
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
| DataCite | 2024