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Practice and associated factors of Covid-19 prevention among health professionals in Ethiopia: a systematic review and meta-analysis
Sponge-based environmental DNA detection as a useful tool in monitoring Mycobacterium tuberculosis complex markers in European bison (Bison bonasus)
Development of a self-assembling multimeric Bann-RBD fusion protein in Pichia pastoris as a potential COVID-19 vaccine candidate
Assessment of arsenite removal efficiency, resistance, and biotransformation by Microbacterium hydroxycarbonoxydans isolated from contaminated sites
Reply to Tauzin: How to implement novelty in theory of mind tasks
Paxilline derived from an endophytic fungus of Baphicacanthus cusia alleviates hepatocellular carcinoma through autophagy-mediated apoptosis
Empirical evidence and role mechanisms of big data enabling corporate green development
Reply to Füller et al.: Exome-wide genetic associations with socioeconomic status and their pleiotropy among health outcomes
A morphometric anthropometric analysis of the upper lip in adult Western Romanian population- a retrospective study
Application of metal oxides thin-film catalysts in structured catalytic ozonation reactor for dye and by-product detoxification
Investigation and risk assessment of fluoride concentration in drinking water, soil and food products in Hamedan rural areas
Millennial paleoclimate variations on the Central Tibetan Plateau during MIS4-MIS2 inferred from a sediment core based RPI chronology
An optimized method for directed differentiation of hypothalamic neural stem cells in a 3D culture system
Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning
Fault detection for Li-ion batteries of electric vehicles with feature-augmented attentional autoencoder
Communicative mentalization is limited in nonhuman great apes
Reduction of tar, sulfur, chlorine and CO2 in syngas produced by gasification of refuse-derived fuel pellets
Differential gene expression in trabecular bone osteocytes is related to the local strain and strain gradient
Demand side management with electric vehicles and optimal renewable resources integration under system uncertainties
Abstract The rapid growth of integrating electrical vehicles (EVs) into the distribution network has introduced complexities and power flow inefficiencies. To address these challenges, optimal renewable energy resources (RERs) integration along with applied demand-side management (DSM) contribute to managing load profiles and generation thus reducing costs. This should be smartly attained through selecting efficient optimization techniques to improve power quality, voltage profile, and reliability. This paper aims to investigate the effect of integrating EVs and applying peak load shifting (PLS) as a DSM strategy with the optimal allocation of distributed energy resources, specifically wind and photovoltaic (PV) systems, as distributed generators (DGs) on distribution networks. Taking into consideration the stochastic behavior of RERs, EVs demand elasticity of charging and discharging scenarios and load variance. The main objective of this work focuses on power loss reduction and implementing PLS to flatten the load profile and form a new loadability to reduce costs. The study is demonstrated on a typical IEEE 69-bus system, considering the load, EVs, and RERs profiles during weekdays in winter and summer seasons. The study examines the optimal size and location of combining two DGs (wind and PV), in addition to incorporating bidirectional plug-in hybrid electric vehicles into the system. The study utilizes the Zebra optimization algorithm (ZOA), in comparison with the Whale optimization algorithm (WOA), Grey wolf optimization algorithm (GWO), and Genetic algorithm (GA). The latter is employed only as a reference for comparison. For each season, the simulation is divided into two parts, each part consists of four cases. Part (1) is simulated assuming constant power integration for the RERs while part (2) considers their stochastic behavior. Also, optimal charging strategies for EVs are examined for cost-effectiveness during high penetration levels for the IEEE 123-bus system. The results demonstrated the effectiveness of the proposed algorithm in reducing power loss. Moreover, shifting peak hours flattens the load profile, thereby reducing costs and power loss across the distribution network. Furthermore, the performance of the ZOA dominates the WOA, GWO, and GA.