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The mass production of combustion engines has caused numerous problems such as air pollution, the limitation of fossil fuel reserves and inconsistent prices. Therefore, much attention has been paid for research and design of electric vehicles (EVs) in developed countries, among which charging and discharging stations are of great significance in order to the promote EVs. This paper provides insights into the optimization of locating EVs charging and discharging stations equipped with solar panels. To this end, we determine the solar power generation capacity as well as the optimum charging/discharging time for EVs using the combination of multi-objective non-dominated genetic algorithm (NSGA-II) and fuzzy decision maker. In order to reasonably simulate the actual system conditions, increasing the applicability of results, we include the uncertainty of network consuming load, along with uncertainty of power generation using solar panels as well as the uncertainty of those EVs that return to the station considerably before the full discharge. These uncertainties are taken into account using the non-sequential Monte Carlo probabilistic method. [ABSTRACT FROM AUTHOR]

The work presented in this paper deals with developing a charge scheduling strategy for electric vehicles in a predefined geographical region. Charging stations in the geographical region are considered to provide multiple charging levels with separate piles with an individual queue for each charging level. Assigning a charging station to each electric vehicle is considered as an optimization problem to minimize travel time, queue time, recharging time, and cost of energy for battery recharging. The objective function is constrained to the reachability of the electric vehicle with the available state of charge of the battery to the allotted charging station without violating the maximum permissible depth of discharge limit and allowable charging rate. The optimization model empowers the users to prioritize the function variable based on travel requirements and battery specifications in different case studies using the Opposition-Based Marine Predator Algorithm. This proposed algorithm is an improved version of the recently reported Marine Predator Algorithm in which the opposition-based learning mechanism is included to improve the solution accuracy. The solution obtained and the analysis of results show that the proposed strategy significantly reduces travel time, queue time, recharging time, and energy cost while fulfilling the constraints imposed. [ABSTRACT FROM AUTHOR]

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The large-scale integration of electric vehicles (EVs) into modern power grid brings both challenges and opportunities to the performance of the systems. This paper presents an optimal static (when EV is stationary) charging scheduling scheme of EVs to minimize the charging cost while complying with the constraints related to the status of the charging station. The proposed systematic charging scheme is based on "Particle Swarm Optimization (PSO)". It is compared with well-established algorithms such as "Arrival Time-Based priority (ATP) algorithm" and "SOC-Based Priority (SBP) algorithm". In addition, a microgrid scenario is further considered for reducing the consumption of energy from the grid and also, reducing the charging cost by properly shifting the EV load. Based on the study carried out for a sample test cases considered, it is found that the proposed scheme has better performance compared to the existing schemes. [ABSTRACT FROM AUTHOR]

In the context of the global green and low-carbon transformation, microgrids containing renewable energy have been widely developed. At present, renewable energy generation has the disadvantages of instability and low energy density. In addition, the high proportion of electric vehicles (EVs) connected to the state grid will cause different degrees of disturbance to its safe operation. Therefore, a coordinated operation strategy of EV and photovoltaic (PV)-energy-storage charging stations induced by dynamic electricity price considering carbon reduction benefit is proposed. On the power generation side, a dual-axis PV tracking control method with "fixed frequency + variable frequency" control is proposed. One day is used as a period to divide the time segments, and the same time segment uses the fixed frequency tracking method, while different time segments use the variable frequency tracking method to improve the power generation efficiency. On the electricity consumption side, a dynamic electricity price strategy is adopted, using the minimum carbon reduction cost as the reward function, optimizing the dynamic electricity price under the minimum carbon reduction cost using the deep deterministic policy gradient (DDPG) algorithm to promote the shifting of EV charging load to the effective hours of PV generation. In conclusion, the simulation analysis is carried out in Zibo City, and the generation capacity of the proposed tracking method on the power generation side is improved by about 32% compared with the fixed PV generation capacity. Compared with the time-of-use electricity price, the optimized dynamic electricity price under the minimum carbon reduction cost can better promote the load transfer and photoelectric consumption of EVs and reduce the carbon reduction cost. [ABSTRACT FROM AUTHOR]

Transport sector in India accounts for 20 % of total commercial energy demand of the country, of which a considerable amount is consumed in the form of liquid and gaseous fuel. A major part of these fuels are imported by the Government. Apart from the import expenditure, Government of India has subsidized these fuels to make it available at affordable prices. To check the financial burden and achieve environmental benefits, technical advancement in present system or alternative infrastructure is required. The present study examines the possible impacts on economy and environment by the implementation of battery electric vehicles (BEVs) along with the conventional road transport system in metropolitans with a case study of Kolkata. The major impact has been observed in controlling the vehicular emission with a decrease in CO level by 26.27 t per day, on replacement of only 2 % of the present public transport by suitable BEVs. Maintaining similar service for the passengers the electrical energy required by the alternative vehicles has been estimated to be 41,766 kWh per day. This energy has been proposed to be supplied by remodeled fuel stations equipped with solar photovoltaic systems, if charging strategy is based on renewable sources. In case of fuel economy, the infrastructure has shown the potential in reducing the consumption of diesel and autogas (LPG) by 11,654 and 3,256 liter per day, respectively. [ABSTRACT FROM AUTHOR]