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Existing metaheuristic approaches treat every bus as equally viable\u2014producing prohibitively large search spaces\u2014and most existing frameworks lack post-optimization structural resilience assessment. This paper proposes a three-stage planning framework addressing both gaps. Stage\u00a01 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\u2013265\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (monotonically increasing with system size). Stage\u00a02 applies five metaheuristics\u2014a proposed hybrid Grasshopper\u2013Grey Wolf Optimizer (GOA-GWO), Standard GOA, Fuzzy-GOA, PSO, and WOA\u2014with ELLS-biased initialization under a six-objective fitness function. Stage\u00a03 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\u201314% (GOA variants: 11\u201314%) and reduces variance by 33\u201351%. 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\u2014PSO yields the worst resilience (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$R_{\\textrm{NSBA}}{=}0.45$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) despite competitive fitness. Friedman rank tests (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$p{&lt;}3{\\times }10^{-5}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) with Holm\u2013Bonferroni-corrected Wilcoxon comparisons (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$n{=}10$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ; Wilcoxon-based power analysis justifies the sample size for\n                    <jats:inline-formula>\n                      <jats:tex-math>$$d{\\ge }0.6$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) confirm the Hybrid significantly outperforms Standard GOA, Fuzzy-GOA, and WOA, and is statistically indistinguishable from PSO on IEEE-33.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-52179-2","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:35:55Z","timestamp":1786667755000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ELLS\u2013NSBA three-stage framework for optimal DG\/SC placement and EV integration in distribution networks"],"prefix":"10.1038","volume":"16","author":[{"given":"Aamir","family":"Nawaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Altamimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Javid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Galeela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"52179_CR1","doi-asserted-by":"publisher","first-page":"9948","DOI":"10.1038\/s41598-025-93817-5","volume":"15","author":"S Panda","year":"2025","unstructured":"Panda, S. et al. 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