Contemplating the alarming increase in emission of greenhouse gases and global energy demand, electric vehicles (EVs) powered by both the grid and solar photovoltaic (PV) systems pose an effective solution. PV arrays reduce the dependence of EVs on the utility grid for charging. As EVs store energy, they can apportion power to fulfill load demands during peak hours. This paper evaluates the impacts of integrating EVs and PV arrays in distribution networks by evaluating their effects in a given system, during a pre-characterized period with controlled charging and discharging strategy. It simulates EVs’ movement in a geographic region by considering a case study-based EV travelling pattern. Adaptive modified multi-objective whale optimization algorithm (A-MWOA) is utilized not exclusively to limit the effect of EV charging/releasing on the network yet, in addition, to reduce the expenses borne by both the EV proprietor and the service provider. It estimates the maximum number of EVs and PV arrays that can be securely incorporated in a given system and the progressions incited by EVs in the load diagrams, voltage profiles, line loading and energy losses are analyzed. The results validate the proposed model from the perspective of practical applications.

考虑到温室气体排放和全球能源需求的惊人增长，由电网和太阳能光伏（PV）系统供电的电动汽车（EV）构成了有效的解决方案。光伏阵列减少了电动汽车对公用电网充电的依赖。当电动汽车存储能量时，它们可以分配功率以满足高峰时段的负载需求。本文通过在给定系统的预表征时段内采用可控的充电和放电策略，通过评估给定系统中的电动汽车和光伏阵列的集成效果来评估它们。通过考虑基于案例研究的EV行驶模式，它可以模拟EV在地理区域内的运动。自适应改进的多目标鲸鱼优化算法（A-MWOA）不仅用于限制EV充电/释放对网络的影响，而且还可以减少EV所有者和服务提供商承担的费用。它估计了可以安全地合并到给定系统中的EV和PV阵列的最大数量，并分析了EV在负载图中，电压曲线，线路负载和能量损耗中引起的变化。结果从实际应用的角度验证了所提出的模型。分析线路负荷和能量损耗。结果从实际应用的角度验证了所提出的模型。分析线路负荷和能量损耗。结果从实际应用的角度验证了所提出的模型。