Optimal Design of Electric Vehicle Parking Lot based on Energy Management Considering Hydrogen Storage System and Demand Side Management

Abstract

Basically, a system is associated with uncertainty and it is the existence of such a factor which results in an uncertain planning and designing. In today's power system, these conditions are mostly reasoned through the uncertain performance of a number of parameters e.g. price, so the use of uncertainty modeling becomes necessary. In the present work, an optimization model, based on virus search colony, is used for the optimal performance of intelligent parking of electric vehicles in uncertainty situations caused by the price of the upstream grid in the demand response (DRP) program. It has been used to reduce the daily cost of performance by changing various parts of the load between peak and off-peak intervals. Virus Colony Search (VCS) method is a method based on the random-based population, which is also based on specific behaviors of the virus. Chaos theory has been used to develop this algorithm and improve local and global search. Moreover, the proposed multi-objective algorithm is a model based on non-dominated sorting model, variable detection, memory-based strategy selection and fuzzy theory to select the best Pareto from the answer set. Besides, it has a powerful function in solving the above problem. Regarding the mentioned techniques, the possibility of being in local points is reduced and the speed of convergence to the final response is increased. The proposed method was investigated on a sample system including, intelligent parking, local units, thermal and renewable plants in the proposed upstream grid price uncertainty technique. The obtained results show the efficiency of the suggested model. According to the compared results, under the demand response program, the average value of the smart parking cost is reduced about 4%.