The long-term analyses of implementation pathways supporting EV expansion in Thailand using adaptive neuro fuzzy inference system

Generally, road transport is a major energy-consuming sector. Electric Vehicle (EV) technology is one of the most promising solutions to reduce dependence on fossil fuels and greenhouse gas (GHG) emissions in the transportation sector. However, a large increase of EVs raises concerns about negative impacts on electricity generation, transmission, and distribution systems. This dissertation aims to evaluate the impacts of EV expansion in Thai road transport, consisting of three main aspects: energy impacts, environmental impacts, and EV charging demand impacts, considering fuel consumption models developed by using Adaptive Neuro-Fuzzy Inference System (ANFIS) approach. Then, the implementation pathways supporting these impacts were introduced.Fuel consumption of individual vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The ANFIS approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square Error (RMSE) and Mean Average Error (MAE) which related to the setting of ANFIS parameters. The experimental results indicated that the training data sample, number, and type of membership functions are the most important factor affecting the performance of the ANFIS network. However, the number of epochs does not necessarily significantly improve the system performance, too many the number of epochs setting may not provide the best results and lead to excessive responding time. The results also demonstrate that three factors, consisted of the engine size, driving speed, and the number of passengers, are important factors that influence the change of vehicle fuel consumption. The selected ANFIS models with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.Then the fuel consumption models were fully completed, these developed models were used to evaluate the impact of EV charging according to different EV expansion scenarios. This evaluation analyzes the benefits and trade-offs for EV penetration in Thai road transport based on EV penetration scenarios from 2019 to 2036. Two charging strategies are considered to assess the impact of EV charging: free charging and off-peak charging. Uncertainty variables are considered by a stochastic approach based on Monte-Carlo simulation (MCS). The simulation results shown that the adoption of EVs can reduce both energy consumption and GHG emissions. The results also indicate that the increased load due to EV charging demand in all scenarios is still within the buffer level, compared to the installed generation capacity in the Power Development Plan 2018 revision 1 (PDP2018r1), and the off-peak charging strategy is more beneficial than the free-charging strategy. However, the increased load demand caused by all EV charging strategies has a direct impact on the power generating schedule, and also decreases the system reliability level.According to the results of the analysis of implementation pathways to support EV expansion, it was indicated that the Thai government should establish policies and measures to motivate people who use internal combustion engine vehicles (ICEVs) to replace them with EVs. Tax reduction measures, such as registration tax exemption, VAT, reduction of annual vehicle tax, buyer subsidy and rebate measures, etc., should be considered a priority, which will effectively reduce energy consumption in the transport sector. In addition, the introduction of a policy to provide subsidies to individual ICEV owners who occupy the vehicle for more than ten years and are interested in switching to EVs can be a great incentive. Using alternative energy sources such as solar energy can reduce CO2 emissions generated by thermal power plants to meet EV charging demand. Furthermore, the energy storage system (ESS) can also reduce the impact of EV charging during peak hours, resulting in higher system stability and reliability. However, the implementation pathways in this study are only a preliminary assessment, and further research is needed to determine the suitability of the investment cost and installation capacity of both the solar and ESS, which is an optimization study.