Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910787839197184 |
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| author | Hussain, Hafiz Majid Nardelli, Ashfaq Ahmad. Pedro H. J. |
| author_facet | Hussain, Hafiz Majid Nardelli, Ashfaq Ahmad. Pedro H. J. |
| contents | This article presents a new hybrid algorithm, crossover binary particle swarm optimization (crBPSO), for allocating resources in local energy systems via multi-agent (MA) technology. Initially, a hierarchical MA-based architecture in a grid-connected local energy setup is presented. In this architecture, task specific agents operate in a master-slave manner. Where, the master runs a well-formulated optimization routine aiming at minimizing costs of energy procurement, battery degradation, and load scheduling delay. The slaves update the master on their current status and receive optimal action plans accordingly. Simulation results demonstrate that the proposed algorithm outperforms selected existing ones by 21\% in terms average energy system costs while satisfying customers' energy demand and maintaining the required quality of service. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems Hussain, Hafiz Majid Nardelli, Ashfaq Ahmad. Pedro H. J. Systems and Control This article presents a new hybrid algorithm, crossover binary particle swarm optimization (crBPSO), for allocating resources in local energy systems via multi-agent (MA) technology. Initially, a hierarchical MA-based architecture in a grid-connected local energy setup is presented. In this architecture, task specific agents operate in a master-slave manner. Where, the master runs a well-formulated optimization routine aiming at minimizing costs of energy procurement, battery degradation, and load scheduling delay. The slaves update the master on their current status and receive optimal action plans accordingly. Simulation results demonstrate that the proposed algorithm outperforms selected existing ones by 21\% in terms average energy system costs while satisfying customers' energy demand and maintaining the required quality of service. |
| title | Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2501.09832 |