OPTIMAL SCHEDULING OF ELECTRIC VEHICLES

Abstract

The conditions of environment are degrading in many countries due to increase in greenhouse gases. The solution of such impacts is to reduce the usage of fossil fuels. The electrification of transport sector is the best way to reduce these emissions. But increase in number of plug in electric vehicle (PEV) arise many problems like scarcity of electricity, increase in power fluctuations when connected to the grid. To deal with such problems, optimal scheduling of electric vehicle is very essential. Two strategies are implemented. First one is profit maximization for PEV user. In this scenario charging of PEV is done when price of electricity is low and discharging of available energy inside the battery during peak hours using Vehicle to grid (V2G) concept. A Particle Swarm Optimization (PSO) algorithm is developed to implement this strategy. This increase the profit for PEV user and also reduces the peak to valley difference of the load profile. A mathematical model is developed having objective of increase the revenue of PEV user by satisfying the essential constraints value. Charging of PEVs when base load is less and discharging of PEVs when base load is high, indirectly helps the grid to satisfy the extra load and thus make the load profile as flat as possible. In Second Strategy, problems like power0transmission0safety0of branches and power fluctuations are taken into account and solved by modifying PSO algorithm. By taking the hourly electricity price data, the charging/discharging profile of particular PEV is found out independently. And also number of PEVs taking part in charging and discharging at each hour in 24 hour period can be obtained. The PSO algorithm runs with random distribution of PEVs i.e. EVs coming with different arrival and departure time. The constraint value such as certain amount of departure state of charge (SOC), maximum power through a particular branch etc. needs to be satisfied. A mathematical model is developed in the second strategy which takes care of benefits of PEV user and also reduces the fluctuations in the load profile after integration of PEVS in the existing system. The PSO algorithm in both strategies find out the optimality of results. In profit maximization strategy, PSO finds the optimal charging and discharging instants of time during single or multiple transactions with the grid on a single day. In power optimization strategy, PSO runs for finding optimal charging and discharging power of PEVs. Simulations results shows the comparison between the uncoordinated charging and coordinated charging after integration of PEVs. The result shows that power optimization strategy maintains balance between PEV user profit and stable operation of grid. Thus on aggregate this proposed algorithm not only tries to flat the load profile but also helps the EV owner to generate revenue leading to more and more individuals shift towards combustion less energy and thus helps to control the pollution