This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end.

A demand response management (DRM) system is proposed here, in which a service provider determines a mutual optimal solution for the utility and the customers in a microgrid setting. Such a system may find use with a service provider interacting with the respective customers and utilities under the existence of some DRM agreements. The service provider is an entity which acts at different levels of the electrical grid and carry out the optimization. The lowest level controls one ‘neighborhood’ while higher levels of service providers control other lower level service providers. A microgrid consisting of a smart neighborhood of twelve customers was used as experimental case study and an advanced metering infrastructure (AMI) was implemented. Based on the formulation of an optimization problem which exploits price-responsive demand flexibility and the AMI infrastructure, a win-win-win strategy is presented. The interior point method was used to solve the objective function and the application of particle swarm optimization and artificial immune systems for demand response was explored. Results for a range of typical scenarios were presented to demonstrate the effectiveness of the proposed demand-response management framework.