Electric Vehicle Route Optimization And Price Response Analysis

With the rapid development of our economy and the constant improvement of transportation infrastructure, the flow of material and personnel in the area of increasingly frequent, the logistics industry gradually become an important part of social economy. Logistics distribution is a logistics terminal, distribution vehicle routing optimization is an important means to improve the transportation speed, reduce the transportation cost. At present, the common fuel vehicle is the main tool of logistic distribution, but as the oil crisis, energy security and environmental protection issues highlighted, the electric vehicle as a new economic and environmental protection of transportation, has a tendency to replace the ordinary fuel vehicle, which led to the electric vehicle routing problem. Electric vehicles use electricity, and have obvious difference with common fuel vehicle on the way of energy supply and use characteristics, that make the electric vehicle routing problem more complex than the traditional vehicle routing problem. Therefore, the study of the problem in both theory and practical application has important significance.Firstly, constructed the single electric vehicle route optimization model under constant electricity price. The model taking into account the rapid charging of electric vehicle in the distribution process, load impact on the unit power consumption, and the influence of rapid charge to battery life loss, achieving the minimum distribution costs while meeting the battery capacity constraints. The model expression difficulties caused by fast-charging at one same charge station many times, be solved by introducing a charging station virtual nodes. Article solved the model using genetic algorithm, and in the population initialization phase, and constructed chromosome standardization operation, making the generated chromosomes suitable for crossover operation of genetic algorithms. Algorithm was programmed by MATLAB, a 32-node distribution system numerical simulation showed a cost advantage of electric vehicles in use, and verified the validity of the model.Secondly, established electric vehicles route optimization model under time-of-use (TOU) electricity price. The model taking into account the rapid charging behavior in the distribution process, conventional charging optimization after return to the distribution center and the influence of rapid charge to battery life loss, achieving minimum distribution costs while meeting the maximum load constraint, battery capacity constraints, and the deadline to return distribution center constraints. Article presented learning partheno-genetic algorithm to solve the model, formulated rules to generating initial chromosome in the chromosome population initialization phase to overcome the difficult to generate feasible initial solution under complex constraint conditions; In the mutation phase, constructed node delete operator and node add operator for charging station node, to guarantee the global convergence of the algorithm; we constructed the knowledge model that includes elite individual knowledge and expert experience, and improved algorithm efficiency through individual learning of knowledge. Based on the distribution system with 62 nodes and 112 nodes for numerical simulation, compared with genetic algorithm(GA) and tabu search algorithm(TS) optimization results, showed the learning partheno genetic advantage in solving speed, quality and stability. Based on 62 nodes distribution system, comparative analysis of the price response characteristics under TOU electricity price, fixed price and discount price. Based on a small distribution network, three types of electricity price under the large-scale electric vehicle charging load effects on electrical loads for power distribution network is studied.