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Abstract The transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

In the past decade, solar photovoltaic (PV) modules have emerged as promising energy sources worldwide. The only limitation associated with PV modules is the efficiency with which they can generate electricity. The dust is the prime ingredient whose accumulation on the surface of PV impacts negatively over its efficiency at a greater rate. This research aims to explore the effects of dust accumulation on the energy output and operating temperature of polycrystalline silicon PV panels situated in two different climatic regions of Pakistan, i.e., Islamabad and Bahawalpur. In both the regions, one PV module is kept in ambient environment for six weeks to allow dust to deposit over its surface and perform experimental analysis with one clean module as reference for performance comparison. After six weeks of atmospheric exposure, dusty modules displayed significantly smaller efficiency as a function of different dust densities in the two regions. Dust samples from both cities are collected and analyzed to evaluate their structural attributes and composition. The PV module in Islamabad region had a dust layer over its surface with a density of 6.388 g/m2 and its efficiency was reduced by 15.08%. In Bahawalpur region, the dust density was observed to be 10.254 g/m2 which caused the output power to be slashed by 25.42%. Temperature analysis of modules shows that dust increases their temperatures which is also a quantity responsible for lower PV power generation with same amount of irradiance. The research findings are crucial for determining and predicting PV power degradation in two different atmospheres and determining the schedule of cleaning cycle.