Long-term forecasting of the impact of EV home charging at different adoption rates on the Egyptian load profile

Predicting the impact of electric vehicle (EV) fleet charging load on the grid load profile is essential for policymakers during grid planning. A systematic three-stage framework is proposed to forecast the long-term impact of EV home charging on national grids. The framework incorporates: (1) forecasting baseline grid load growth excluding EVs, (2) projecting EV market development, and (3) modeling EV charging behavior uncertainties (plug-in time & rate at home). For the first stage, five models (the autoregressive integrated moving average model, the artificial neural network model based on economic parameters, the nonlinear autoregressive exogenous neural network model, the long short-term memory network, and the convolutional neural network) are evaluated to select the most suitable model. The second stage is investigated using the Bass diffusion model in five penetration scenarios (10%-50%). The third stage is assessed using a probabilistic model based on data acquired by a public survey. The study applied Egypt as a case study, and the results are analyzed using peak load and load factor. Results revealed that 50% market penetration will increase peak load by 20.36% and reduce the load factor by 14.34% by 2040. However, the 10% market penetration limits these impacts to 3.16% and 2.46%, respectively. The study recommends applying demand-side management programs or controlling market expansion to balance the grid demand profile and EV adoption as policy implications. The framework is designed to accommodate a specific area, a city, or a country, as a scalable tool for policymakers addressing the energy-transport nexus in developing economies.