ArticleScientific reports2026
ELLS-NSBA three-stage framework for optimal DG/SC placement and EV integration in distribution networks.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The growing penetration of distributed generation (DG), shunt capacitors (SC), and electric vehicles (EVs) in distribution networks demands optimal siting strategies that balance operational efficiency and structural resilience. Existing metaheuristic approaches treat every bus as equally viable-producing prohibitively large search spaces-and most existing frameworks lack post-optimization structural resilience assessment. This paper proposes a three-stage planning framework addressing both gaps. Stage 1 introduces the Enhanced Laplacian Loss Sensitivity (ELLS) index, which fuses loss sensitivity factors, Fiedler-vector spectral centrality, and betweenness centrality to shortlist candidate buses, reducing the combinatorial search space by 45-265× (monotonically increasing with system size). Stage 2 applies five metaheuristics-a proposed hybrid Grasshopper-Grey Wolf Optimizer (GOA-GWO), Standard GOA, Fuzzy-GOA, PSO, and WOA-with ELLS-biased initialization under a six-objective fitness function. Stage 3 evaluates placement structural quality through a Network Structural Bus Analysis (NSBA) framework comprising three topology-aware metrics (EDDI, VSC, CBC). Validation on IEEE 33-bus, 69-bus, and 123-node systems shows that ELLS guidance improves mean fitness by 6-14% (GOA variants: 11-14%) and reduces variance by 33-51%. The Hybrid GOA-GWO achieves the best composite fitness (0.392) and 78.2% loss reduction on the 33-bus system. NSBA reveals that operationally optimal placements do not necessarily maximize structural resilience-PSO yields the worst resilience ([Formula: see text]) despite competitive fitness. Friedman rank tests ([Formula: see text]) with Holm-Bonferroni-corrected Wilcoxon comparisons ([Formula: see text]; Wilcoxon-based power analysis justifies the sample size for [Formula: see text]) confirm the Hybrid significantly outperforms Standard GOA, Fuzzy-GOA, and WOA, and is statistically indistinguishable from PSO on IEEE-33.
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