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Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems

Hafiz Majid Hussain, Ashfaq Ahmad. Pedro H. J. Nardelli

TL;DR

The paper tackles local energy system management under distributed energy resources by deploying a hierarchical multi-agent system. It introduces crBPSO, a hybrid optimization that injects GA-style crossover into BPSO velocity updates to enhance exploration. The MAS comprises simple reflex agents, model-based reflex agents, and a utility agent to coordinate energy packets and energy transactions. Empirical results on a 10-prosumer, 24-hour micro-grid show crBPSO yields up to about a 21% reduction in the average aggregated system cost and shifts consumption to lower-price periods, demonstrating the method's practicality and scalability.

Abstract

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.

Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems

TL;DR

The paper tackles local energy system management under distributed energy resources by deploying a hierarchical multi-agent system. It introduces crBPSO, a hybrid optimization that injects GA-style crossover into BPSO velocity updates to enhance exploration. The MAS comprises simple reflex agents, model-based reflex agents, and a utility agent to coordinate energy packets and energy transactions. Empirical results on a 10-prosumer, 24-hour micro-grid show crBPSO yields up to about a 21% reduction in the average aggregated system cost and shifts consumption to lower-price periods, demonstrating the method's practicality and scalability.

Abstract

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.
Paper Structure (8 sections, 7 equations, 3 figures, 1 table)

This paper contains 8 sections, 7 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Multi-Agent System Model
  3. Simple-Reflex Agents
  4. Model-based Reflex Agents
  5. Utility Agent
  6. The crBPSO Algorithm
  7. Results and Discussions
  8. Conclusion

Figures (3)

  • Figure 1: MA-based LES architecture involving SRAs (loads and PV source), MRAs (battery storage and ESP), UA (energy router) and the external power grid.
  • Figure 2: Profiles of energy demand (SRA), PV generation (SRA) and price signal (MRA).
  • Figure 3: Optimal energy allocation plans of the MA-based architecture under GA, BPSO and crBPSO algorithms: (a) for SRAs, and (b) for MRAs.