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Predictive Energy Management for Microgrid Using Multi-Agent Deep Deterministic Policy Gradient With Random Sampling
Open accessAbstract
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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“From a communication perspective, FL's key advantage lies in reducing upstream bandwidth demand by transmitting compact gradient updates or weight deltas rather than highfrequency raw telemetry [26].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [26]
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Cites
Microgrids: A review of technologies, key drivers, and outstanding issues
Hirsch, A. I., Parag, Y., & Guerrero, J. M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.040 · Cited by 1,508
Vanadium redox flow batteries: A comprehensive review
Lourenssen, K., Williams, J. L., Ahmadpour, F., Clemmer, R. M. C., & Tasnim, S. H. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100844 · Cited by 727
Deep Reinforcement Learning for Smart Home Energy Management
Yu, L., Xie, W., Xie, D., Zou, Y., Zhang, D., Sun, Z., Zhang, L., Zhang, Y., & Jiang, T. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2957289 · Cited by 483
Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid
Foruzan, E., Soh, L., & Asgarpoor, S. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2823641 · Cited by 304
Nikmehr, N., Ravadanegh, S. N., & Khodaei, A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.071 · Cited by 215
Lv, T., & Ai, Q. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.179 · Cited by 188
Optimization-Based Power and Energy Management System in Shipboard Microgrid: A Review
Xie, P., Guerrero, J. M., Tan, S., Bazmohammadi, N., Vasquez, J. C., Mehrzadi, M., & Al-Turki, Y. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3047673 · Cited by 177
Novel battery degradation cost formulation for optimal scheduling of battery energy storage systems
Lee, J.-O., & Kim, Y.-S. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107795 · Cited by 176
Javaid, N., Hafeez, G., Iqbal, S., Alrajeh, N. A., Alabed, M. S., & Guizani, M. (2018) · IEEE Access · doi:10.1109/access.2018.2866461 · Cited by 172
Electric propulsion system for electric vehicular technology: A review
Kumar, L., & Jain, S. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.09.014 · Cited by 168
Mishra, S. K., Puthal, D., Rodrigues, J. J. P. C., Sahoo, B., & Dutkiewicz, E. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2791619 · Cited by 165
Herrera, V. I., Milo, A., Gaztañaga, H., Etxeberria‐Otadui, I., Villarreal, I., & Camblong, H. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.02.079 · Cited by 162
Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II
Teo, T. T., Logenthiran, T., Woo, W. L., Abidi, K., John, T., Wade, N. S., Greenwood, D., Patsios, C., & Taylor, P. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3031109 · Cited by 130
Jafari, M., Malekjamshidi, Z., Lu, D. D., & Zhu, J. G. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2850852 · Cited by 121
Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2016) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2016.12.006 · Cited by 103
Guo, C., Wang, X., Zheng, Y., & Zhang, F. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107048 · Cited by 93
Goh, H. H., Huang, Y., Lim, C. S., Zhang, D., Liu, H., Dai, W., Kurniawan, T. A., & Rahman, S. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3179567 · Cited by 93
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
Harrold, D. J. B., Cao, J. H., & Fan, Z. (2021) · Energy · doi:10.1016/j.energy.2021.121958 · Cited by 78
Monfaredi, F., Shayeghi, H., & Siano, P. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109292 · Cited by 78
Srithapon, C., Ghosh, P., Siritaratiwat, A., & Chatthaworn, R. (2020) · Energies · doi:10.3390/en13020349 · Cited by 66
Self-citationKaewdornhan, N., Srithapon, C., Liemthong, R., & Chatthaworn, R. (2023) · Energies · doi:10.3390/en16052357 · Cited by 65
Self-citationSalari, A., Ahmadi, S. E., Marzband, M., & Zeinali, M. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104528 · Cited by 60
Salari, A., Zeinali, M., & Marzband, M. (2023) · Energy · doi:10.1016/j.energy.2023.129725 · Cited by 60
Kołodziejczyk, W., Żółtowska, I., & Cichosz, P. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104598 · Cited by 56
Prusty, B. R., & Jena, D. (2016) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2016.00024 · Cited by 51
Srithapon, C., Fuangfoo, P., Ghosh, P. K., Siritaratiwat, A., & Chatthaworn, R. (2021) · IEEE Access · doi:10.1109/access.2021.3061471 · Cited by 50
Self-citationChakraborty, S., Modi, G., & Singh, B. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3232283 · Cited by 39
Kaewdornhan, N., Srithapon, C., & Chatthaworn, R. (2022) · IEEE Access · doi:10.1109/access.2022.3229127 · Cited by 31
Self-citationKyriakou, D. G., Kanellos, F. D., & Ipsakis, D. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101229 · Cited by 29
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Kurukuru, V. S. B., Haque, A., Padmanaban, S., & Khan, M. A. (2021) · IEEE Systems Journal · doi:10.1109/jsyst.2021.3094403 · Cited by 29
Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms
Khan, Z. A., Butt, A. A., Alghamdi, T. A., Fatima, A., Akbar, M., Ramzan, M., & Javaid, N. (2019) · IEEE Access · doi:10.1109/access.2019.2949863 · Cited by 26
Borisoot, K., Liemthong, R., Srithapon, C., & Chatthaworn, R. (2023) · Energies · doi:10.3390/en16062862 · Cited by 22
Self-citationHou, S., Gebreyesus, G., & Fujimura, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122135 · Cited by 19
Kaewdornhan, N., & Chatthaworn, R. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.05.130 · Cited by 18
Self-citationA multi-agent decision approach for optimal energy allocation in microgrid system
Huang, M., Lin, X., Feng, Z., Wu, D., & Shi, Z. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109399 · Cited by 11
Flagged first, then by citations
Research works printed but not linked: 9
- 2019, Electricity Explained
- 2022, Sources of Greenhouse Gas Emissions
- 2020, Provincial Electricity Authority Power Quality Standards B.E.2563 (2020)
- 2009, National Household Travel Survey (NHTS)
- 2023, Load Research of PEA
- 2023, Hourly Solar Irradiation for Khon Kaen, Thailand
- 2023, Hourly Temperature for Khon Kaen
- 2018, Electricity Tariffs for User
- 2022, Review of the Purchase of Electricity From Incremental Renewable Energy Under Natural Gas Management
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Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23
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Sankar, R., Prakash, S. A., Sakhare, N. N., & Kumar, U. A. (2025) · Electrical Engineering · doi:10.1007/s00202-024-02922-7 · Cited by 12
Rodríguez-Aburto, C., Montaño-Pisfil, J., Santos, C., Morcillo-Valdivia, P., Solís-Farfán, R., Curay-Tribeño, J., Morales-Vargas, A., Vara-Sanchez, J., Gutiérrez-Tirado, R., Vigo-Roldán, A., et al. (2025) · Energies · doi:10.3390/en18236291 · Cited by 8
Energy Management Systems for Electric Vehicle Utilizing Solar Integration
Mohamed Fazil, Y., Sudarsan, R., & Vedavalli, S. P. (2025) · International Conference on Emerging Technologies in Engineering Applications (ICETEA) · doi:10.1109/icetea64585.2025.11100071 · Cited by 5
Metadata
- Authors
- Kaewdornhan, N., & Chatthaworn, R.
- Affiliations
- Khon Kaen University (TH)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 95071-95090
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4399800574
- Cited by
- 15
- References
- 36
- Citations by year
- 2026: 9 · 2025: 6
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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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