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Reinforcement Learning-Adaptive Hybrid Metaheuristic for Optimal Distributed Generation and Shunt Capacitor Placement with Embedded V2G Dispatch in Distribution Networks

Authors

  • Abdul Rehman Imtiaz

    Department of Electrical Engineering and Technology, Government College University, Faisalabad, 38000, Pakistan
    Author

  • Umer Ijaz

    Department of Electrical Engineering and Technology, Government College University, Faisalabad, 38000, Pakistan
    Author

  • Muhammad Farhan

    Department of Electrical Engineering and Technology, Government College University, Faisalabad, 38000, Pakistan
    Author

DOI:

https://doi.org/10.22581/0844

Keywords:

Adaptive Operator Selection , Distributed Generation Placement, Hybrid GOA-GWO, Multi-algorithm Benchmarking, Non-parametric Statistical Validation, Vehicle to Grid Integration

Abstract

This paper benchmarks six metaheuristics—including a reinforcement-learning- adaptive Grasshopper– Grey Wolf optimiser (RL-GOA-GWO) with Q-learning- based adaptive operator selection—and a four-layer multi-layer perceptron (MLP) surrogate for joint placement of distributed generation (DG) and shunt capacitor (SC)   banks   with   embedded   rule-based   vehicle-to-grid   (V2G)   dispatch.   A   six- objective weighted-sum formulation is evaluated on five systems (IEEE 33, 69, 123-bus   balanced   equivalent,   MV   Oberrhein   179,   Custom   567-bus)   under identical budgets (30 agents, 100 iterations, n=5 runs). The RL-adaptive advantage is topology-dependent: marginal on small radial feeders but producing a large effect on the rugged IEEE-123 landscape (mean improvement 6.8%, Cohen’s d= − 1.12, 95% confidence interval (CI) [−2.43,0.19]). Particle Swarm Optimization (PSO)   is   competitive   throughout,   consistent   with   No-Free-Lunch   theory. Friedman rank tests and Cohen’s d with 95% CIs provide exploratory validation; confirmatory studies with n≥25 are recommended.

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References

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Published

2026-10-01

Issue

Vol. 45 No. 4 (2026): October Issue

Section

Articles

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Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Reinforcement Learning-Adaptive Hybrid Metaheuristic for Optimal Distributed Generation and Shunt Capacitor Placement with Embedded V2G Dispatch in Distribution Networks. (2026). Mehran University Research Journal of Engineering and Technology, 45(4), 183-194. https://doi.org/10.22581/0844

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