A Novel Quantum Particle Swarm Optimization for Power Grid with Plug-In Electric Vehicles in Shanghai

This paper studies the plug-in electric vehicles charging/discharging mode under the intelligent power grid in Shanghai with the objective of minimizing the total mean square of load curve of charging and discharging electricity. Considering constrains on battery capacity, electricity power and available time, an electric vehicles charging/discharging optimization model is build for power grid in Shanghai. Based on the parasitic and anti-parasitic behaviors in the nature, we propose a Novel Quantum Particle Swarm Optimization (NQPSO) to solve the problem. Two populations-host group and parasitic group are generated to dynamically changing the population size within the parasitic mechanism so as to improve the population genetic. A quantum particle encoding method is designed according the characteristics of the problem. Finally we apply NQPSO upon instances to explore the performance of our algorithm, and the results have showed the computational evidence for its effectiveness.