Day-ahead dispatch of PEV loads in a residential distribution system

Abstract

With the expectation of increasing market share of Plug-in Electric Vehicles (PEVs), utilities expect to see a significant increase in energy demand and system peak as a result of PEVs recharging their batteries from the grid, if the charging is not controlled at the distribution system level. By making the grid "smarter", utilities would be able to maximize utilization of existing assets and defer capital investments, while maintaining system security and reliability. The current research proposes a modeling framework for day-ahead dispatch and dynamic control of PEV loads as well as scheduling of taps and capacitors. The first step of the proposed work, which is presented in this paper, involves the development of a static Genetic Algorithm (GA)-based optimization model that determines the day-ahead schedule for PEV loads, taps and capacitors, with the base load and relevant PEV information provided as inputs to the model. In this case, the objective is to minimize the system peak, while satisfying the physical and operational limits of the distribution system, as a higher system peak translates into higher operational costs for the utility. The proposed approach is tested in an actual distribution feeder, demonstrating its feasibility for realistic applications.

A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network

“…The Genetic algorithm (GA) is debated in study [59,[79][80][81][82] for optimal EV charging. GA is a bio-inspired population-based optimization technique in which the searching of global optimal is executed by selection, recombination, and mutation process.…”

“…The process repeats until the optimal solution is traced [11]. The authors in study [79] developed a static GA model to define day-ahead charging schedule of EVs with other network control actions. In study [80], a multi-objective GA is proposed for the power strategy of hybrid electric vehicle.…”