title: Microgrid control for renewable energy sources based on deep reinforcement learning and numerical optimization approaches
metadata.name: dc.contributor.author
metadata.value: Zhadan, Anastasia Yu.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Wu, Haitao
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kudin, Pavel S.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Zhang, Yuyi
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Petrosian, Ovanes L.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-10-23T18:15:29Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-10-23T18:15:29Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Zhadan A. Yu.,Wu H., Kudin P. S., Zhang Y., Petrosian O. L. Microgrid control for renewable energy sources based on deep reinforcement learning and numerical optimization approaches. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2023, vol. 19, iss. 3, pp. 391–402. https://doi.org/10.21638/11701/spbu10.2023.307
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2023.307
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44292
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Optimal scheduling of battery energy storage system plays crucial part in distributed
  energy system. As a data driven method, deep reinforcement learning does not require
  system knowledge of dynamic system, present optimal solution for nonlinear optimization
  problem. In this research, financial cost of energy consumption reduced by scheduling
  battery energy using deep reinforcement learning method (RL). Reinforcement learning can
  adapt to equipment parameter changes and noise in the data, while mixed-integer linear
  programming (MILP) requires high accuracy in forecasting power generation and demand,
  accurate equipment parameters to achieve good performance, and high computational cost
  for large-scale industrial applications. Based on this, it can be assumed that deep RL based
  solution is capable of outperform classic deterministic optimization model MILP. This study
  compares four state-of-the-art RL algorithms for the battery power plant control problem:
  PPO, A2C, SAC, TD3. According to the simulation results, TD3 shows the best results,
  outperforming MILP by 5 % in cost savings, and the time to solve the problem is reduced
  by about a factor of three.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work was supported by St. Petersburg State University (project ID: 94062114).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 19; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: reinforcement learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: energy management system
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: distributed energy system
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: numerical optimization
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Microgrid control for renewable energy sources based on deep reinforcement learning and numerical optimization approaches
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 07.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44292/1/07.pdf
resources.format: Adobe PDF
resources.size: 2,78 MB