de Mars, Patrick;
O’Sullivan, Aidan;
(2022)
Reinforcement learning and A* search for the unit commitment problem.
Energy and AI
, 9
, Article 100179. 10.1016/j.egyai.2022.100179.
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Abstract
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%.
| Type: | Article |
|---|---|
| Title: | Reinforcement learning and A* search for the unit commitment problem |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.egyai.2022.100179 |
| Publisher version: | https://doi.org/10.1016/j.egyai.2022.100179 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Unit commitment, Reinforcement learning, Tree search, Power systems |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10152355 |
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