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Cited by 17Microgrid Control and OptimizationSmart Grid Energy ManagementOptimal Power Flow Distribution

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Predictive Energy Management for Microgrid Using Multi-Agent Deep Deterministic Policy Gradient With Random Sampling

Open access
Kaewdornhan, Niphon, Chatthaworn, Rongrit·2024·IEEE Access
DOI 10.1109/access.2024.3416706
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Abstract

In a MicroGrid (MG) equipped with a Battery Energy Storage System (BESS), an Energy Management System (EMS) plays a crucial role in predictive controlling BESS operations for optimal power flow among uncertainties from renewable energy resources and heavy loads, such as solar photovoltaic systems and electric vehicles, respectively. State-of-the-art EMS designs have integrated Deep Reinforcement Learning (DRL) for EMS development and Probabilistic Power Flow (PPF) for preventing the violation of power system constraints while accounting for all uncertainties. However, using PPF to handle uncertainties alongside training a single-agent DRL provides the optimal solution for addressing all uncertain scenarios, but not the best solution for each scenario. Moreover, employing a single-agent DRL yields a low performance in predictive controlling of BESS operations. To address these challenges, a multi-agent DRL based on Deep Deterministic Policy Gradient (DDPG) is proposed. This method divides the roles of each agent for predicting 24-hour-ahead actions in BESS control based on changing 24-hour-ahead MG behavior every hour. Furthermore, MG parameters are randomly sampled to retain MG uncertainties instead of relying on PPF for uncertainty mitigation. Consequently, multi-agent DDPG with random sampling can directly learn from the MG environment and provide the best solution for each scenario. Simulation results demonstrate that the proposed method can reduce training computational time by 92.84%, provide a higher value of the summed mean of 24-hours-ahead reward by 1.50% to 28.37%, and achieve a lower mean daily total related cost by 9.22% compared to applying the state-of-the-art method.

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Research works printed but not linked: 9

  1. 2019, Electricity Explained
  2. 2022, Sources of Greenhouse Gas Emissions
  3. 2020, Provincial Electricity Authority Power Quality Standards B.E.2563 (2020)
  4. 2009, National Household Travel Survey (NHTS)
  5. 2023, Load Research of PEA
  6. 2023, Hourly Solar Irradiation for Khon Kaen, Thailand
  7. 2023, Hourly Temperature for Khon Kaen
  8. 2018, Electricity Tariffs for User
  9. 2022, Review of the Purchase of Electricity From Incremental Renewable Energy Under Natural Gas Management

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Metadata

Authors
Kaewdornhan, N., & Chatthaworn, R.
Affiliations
Khon Kaen University (TH)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
12
Pages
95071-95090
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2024.3416706
OpenAlex
W4399800574
Cited by
15
References
36
Citations by year
2026: 9 · 2025: 6
FWCI
2.35
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Research and Graduate Studies, Khon Kaen University
  • National Research Council of Thailand (N41A661146)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
Keywords
microgrid energy management, battery energy storage, MADDPG, deep reinforcement learning, probabilistic load flow, renewable energy uncertainty, electric vehicle charging, random sampling, optimal power flow

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