This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

Battery swapping stations (BSSs) can help shorten the refueling time for electric vehicles (EVs), but their widespread deployment will need decisions on station placement, battery availability, the number of chargers, photovoltaic (PV) capacity, grid import, accessibility, and emissions. This study proposes a decomposed mixed-integer linear programming and geographic information system (MILP–GIS) framework to optimize the planning of BSSs using renewable energy in a Shenzhen GIS–traffic planning case. Each year, the MILP decides on the location and size of the BSS, the PV capacity, battery packs, charging power, aggregate state of charge, reserve inventory, and grid import; a representative post-siting hourly feasibility layer then verifies the PV availability, charging power, aggregate state of charge, reserve inventory, and grid import. The case has 16 demand zones, 24 screened candidate sites and 24 hourly demand slices derived from Shenzhen GIS–traffic inputs that were aggregated into publicly releasable demand-zone and candidate-site tables. A PV investment cost is annualized once, and charging/discharging losses are taken as an electricity charging-input on the grid electricity cost side. The results demonstrate that the six-station $R40$ portfolio can cut the annualized cost from $49.07$ to $46.78$ million CNY/year compared to the case of the grid only, and grid-related CO₂ emissions decrease from $22.05$ to $9.92$ kt/year as the renewable-energy target increases from 0% to 55%, under the base-case techno-economic assumptions. The levelized service cost (LSC) is dominated by the cost of the grid, the cost of the battery, PV capital expenditure (CAPEX), and the demand level, as revealed by the cost audit, station-level, ablation, and sensitivity results. When compared to EV infrastructure planning using benchmarking with GIS-score, p-median and energy-only baselines, the proposed framework provides a better cost–coverage–carbon trade-off in dense urban EV infrastructure planning.