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Adaptive hybrid optimization for integrated distribution network planning with distributed generation and electric vehicle charging
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DOI：10.1080/17509653.2026.2638175.png)
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Abstract
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This paper presents an Adaptive Elite Hybrid Binary Particle Swarm–Grey Wolf Optimizer (AE-BPSO-BGWO) for the simultaneous optimization of distribution system reconfiguration (DSR), distributed generator (DG) sizing and placement, and electric vehicle (EV) charger allocation. The algorithm combines the exploration capability of Binary Particle Swarm Optimization with the exploitation strength of Binary Grey Wolf Optimizer and introduces an elite-adaptive mechanism that dynamically adjusts the search process in binary solution spaces. The performance of the proposed method is evaluated in two stages. First, benchmark tests are conducted on Sphere, Ackley, Griewank, Rosenbrock, Rastrigin, Schwefel, Zakharov, Levy, Michalewicz, and Bent Cigar functions, showing superior convergence speed and robustness compared with conventional and hybrid metaheuristic algorithms. Second, the algorithm is applied to IEEE 33- and 69-bus distribution systems under six scenarios: base case, reconfiguration only, DG allocation only, DG allocation after reconfiguration, reconfiguration after DG allocation, and simultaneous reconfiguration with DG allocation. In the 33-bus system, the reconfiguration-after-DG scenario achieves the lowest power loss of 38.28 kW and a minimum voltage of 0.9861 p.u. In the 69-bus system, the simultaneous optimization scenario reduces power loss to 27.16 kW with a minimum voltage of 0.9796 p.u., confirming the effectiveness of the proposed method.
Keywords:
Radial topology reconfiguration
hybrid binary optimization
adaptive elite BPSO–BGWO
optimal DG deployment
EV charging placement
C61
Q41
Q42
L94
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