Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization
Effective energy management is crucial in hybrid energy systems for optimal resource utilization and cost savings. This study integrates Deep Deterministic Policy Gradient (DDPG) with Particle Swarm Optimization (PSO) to enhance exploration and exploitation in the optimization process, aiming to imp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Energy Nexus |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772427125000890 |
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| author | Oladimeji Ibrahim Mohd Junaidi Abdul Aziz Razman Ayop Wen Yao Low Nor Zaihar Yahaya Ahmed Tijjani Dahiru Temitope Ibrahim Amosa Shehu Lukman Ayinla |
| author_facet | Oladimeji Ibrahim Mohd Junaidi Abdul Aziz Razman Ayop Wen Yao Low Nor Zaihar Yahaya Ahmed Tijjani Dahiru Temitope Ibrahim Amosa Shehu Lukman Ayinla |
| author_sort | Oladimeji Ibrahim |
| collection | DOAJ |
| description | Effective energy management is crucial in hybrid energy systems for optimal resource utilization and cost savings. This study integrates Deep Deterministic Policy Gradient (DDPG) with Particle Swarm Optimization (PSO) to enhance exploration and exploitation in the optimization process, aiming to improve energy resource utilization and reduce costs in hybrid energy systems. The integrated DDPG-PSO approach leverages DDPG's reinforcement learning and PSO's global search capabilities to enhance optimization solution quality. The PSO optimizes the DDPG actor-network parameters, providing a strong initial policy. DDPG then fine-tunes these parameters by interacting with the energy system, making decisions on battery scheduling and grid usage to maximize cost rewards. The results show that the integrated DDPG-PSO EMS outperforms the traditional DDPG in terms of battery scheduling and grid utilization efficiency. Cost evaluations under critical peak tariffs indicate that both EMS algorithms achieved a 34 % cost saving compared to a grid-only system. Under differential grid tariffs, the proposed DDPG-PSO approach achieved a 28 % cost reduction, outperforming the standalone DDPG, which achieved a 25 % saving. Notably, the DDPG-PSO effectively reduced overall grid dependency, yielding a total operational cost of $665.19, compared to $780.70 for the DDPG. resenting a 14.8 % reduction. The battery charge/discharge profiles further highlight the advantages of the DDPG-PSO strategy. It demonstrated more stable and efficient energy flow behavior, characterized by shallow cycling and partial discharges sustained over several hours. In contrast, the DDPG exhibited more aggressive deep cycling, fluctuating frequently between minimum and maximum charge levels. This improved energy flow management by DDPG-PSO not only reduces wear on the battery system but also promotes long-term sustainability and reliability in hybrid energy management. |
| format | Article |
| id | doaj-art-9f8ac51e5a954897b426f03879c396ce |
| institution | OA Journals |
| issn | 2772-4271 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Nexus |
| spelling | doaj-art-9f8ac51e5a954897b426f03879c396ce2025-08-20T02:07:13ZengElsevierEnergy Nexus2772-42712025-06-011810044810.1016/j.nexus.2025.100448Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilizationOladimeji Ibrahim0Mohd Junaidi Abdul Aziz1Razman Ayop2Wen Yao Low3Nor Zaihar Yahaya4Ahmed Tijjani Dahiru5Temitope Ibrahim Amosa6Shehu Lukman Ayinla7Power Electronics and Drive Research Group (PEDG), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia; Department of Electrical and Electronics Engineering, University of Ilorin, Ilorin 240003, Nigeria; Corresponding authors.Power Electronics and Drive Research Group (PEDG), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia; Corresponding authors.Power Electronics and Drive Research Group (PEDG), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaPower Electronics and Drive Research Group (PEDG), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS 32610, Seri Iskandar, Perak, MalaysiaPower Electronics and Drive Research Group (PEDG), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia; Department of Electrical/Electronics Technology, Federal College of Education (Technical) Bichi, PMB 3473, Kano, NigeriaDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USADepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS 32610, Seri Iskandar, Perak, Malaysia; Department of Computer Engineering, University of Ilorin, Ilorin 240003, NigeriaEffective energy management is crucial in hybrid energy systems for optimal resource utilization and cost savings. This study integrates Deep Deterministic Policy Gradient (DDPG) with Particle Swarm Optimization (PSO) to enhance exploration and exploitation in the optimization process, aiming to improve energy resource utilization and reduce costs in hybrid energy systems. The integrated DDPG-PSO approach leverages DDPG's reinforcement learning and PSO's global search capabilities to enhance optimization solution quality. The PSO optimizes the DDPG actor-network parameters, providing a strong initial policy. DDPG then fine-tunes these parameters by interacting with the energy system, making decisions on battery scheduling and grid usage to maximize cost rewards. The results show that the integrated DDPG-PSO EMS outperforms the traditional DDPG in terms of battery scheduling and grid utilization efficiency. Cost evaluations under critical peak tariffs indicate that both EMS algorithms achieved a 34 % cost saving compared to a grid-only system. Under differential grid tariffs, the proposed DDPG-PSO approach achieved a 28 % cost reduction, outperforming the standalone DDPG, which achieved a 25 % saving. Notably, the DDPG-PSO effectively reduced overall grid dependency, yielding a total operational cost of $665.19, compared to $780.70 for the DDPG. resenting a 14.8 % reduction. The battery charge/discharge profiles further highlight the advantages of the DDPG-PSO strategy. It demonstrated more stable and efficient energy flow behavior, characterized by shallow cycling and partial discharges sustained over several hours. In contrast, the DDPG exhibited more aggressive deep cycling, fluctuating frequently between minimum and maximum charge levels. This improved energy flow management by DDPG-PSO not only reduces wear on the battery system but also promotes long-term sustainability and reliability in hybrid energy management.http://www.sciencedirect.com/science/article/pii/S2772427125000890Hybrid energyParticle swarm optimizationEnergy management systemPolicy gradientGrid efficiencyBattery scheduling |
| spellingShingle | Oladimeji Ibrahim Mohd Junaidi Abdul Aziz Razman Ayop Wen Yao Low Nor Zaihar Yahaya Ahmed Tijjani Dahiru Temitope Ibrahim Amosa Shehu Lukman Ayinla Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization Energy Nexus Hybrid energy Particle swarm optimization Energy management system Policy gradient Grid efficiency Battery scheduling |
| title | Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization |
| title_full | Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization |
| title_fullStr | Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization |
| title_full_unstemmed | Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization |
| title_short | Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization |
| title_sort | integrated ddpg pso energy management systems for enhanced battery cycling and efficient grid utilization |
| topic | Hybrid energy Particle swarm optimization Energy management system Policy gradient Grid efficiency Battery scheduling |
| url | http://www.sciencedirect.com/science/article/pii/S2772427125000890 |
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