Abstract
In both rural and urban areas, two-wheeler vehicles are the most common means of transportation, contributing to local air pollution and greenhouse gas emissions (GHG). Transitioning to electric two-wheeler vehicles can help reduce GHG emissions while also increasing the socioeconomic status of people in rural Kenya. Renewable energy systems can play a significant role in charging electric two-wheeled vehicles, resulting in lower carbon emissions and increased renewable energy penetration in rural Kenya. As a result, using the Conventional and Renewable Energy Optimization (CARNOT) Toolbox in the MATLAB/Simulink environment, this paper focuses on integrating and modeling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya. Electricity demand data obtained from the Water-Energy Hub was investigated and analyzed. Potential solar energy surplus was identified and the surplus was used to incorporate the electric two-wheeler vehicles. The energy consumption of the electric two-wheeler vehicles was also measured in the field based on the rider’s driving behavior. The modeling results revealed an annual power consumption of 27,267 kWh, a photovoltaic (PV) electricity production of 37,785 kWh, and an electricity deficit of 370 kWh. The annual results show that PV generation exceeds power consumption, implying that there should be no electricity deficit. The results, however, do not represent the results in hourly resolution, ignoring the impact of weather fluctuation on PV production. As a result, in order to comprehend the electricity deficit, hourly resolution results are shown. A load optimization method was designed to efficiently integrate the electric 2-wheeler vehicle into the Water-Energy Hub in order to alleviate the electricity deficit. The yearly electricity deficit was decreased to 1 kWh and the annual electricity consumption was raised by 11% (i.e., 30,767 kWh), which is enough to charge four more electric two-wheeler batteries daily using the load optimization technique.
Highlights
Introduction published maps and institutional affilA worldwide leapfrog to electric cars is already underway in nations such as Norway, China, and others to cut carbon dioxide emissions
Load demands for fishing lanterns and e-bike batteries were generated based on the MATLAB developed Non-linear programming (NLP) load optimization algorithm
The developed load optimization algorithm captures the maximum amount of variable solar generation, which sizes and schedules a finite number of loads (i.e., e-bikes, fishing lanterns) to track available intra-day solar PV power
Summary
A worldwide leapfrog to electric cars is already underway in nations such as Norway, China, and others to cut carbon dioxide emissions. By 2050, when the global number of passenger cars is predicted to more than double, it is expected to account for one-third of all vehicles on the road. This increase is expected to be most pronounced in low-income nations, where automobile emission rules are rarely implemented [1]. EVs are an important part of modern transportation since they incorporate a variety of cutting-edge industrial technology (e.g., an electric motor, a battery, and a charging facility). Electric cars’ limited range and long recharge times are frequently cited as the most important iations
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