Plugged-in electric vehicle-assisted demand response strategy for residential energy management

Abstract

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.

Energy Management Scheme for Optimizing Multiple Smart Homes Equipped with Electric Vehicles

“…In [23], a DR program that included the integration of RESs, BESSs, and EVs is presented with a comparison between an existing technique and a new technique based on a heuristic programmable controller. Even more DR strategies based on the integration of RESs, BESSs, and EVs were also presented in [24] where the researchers used a fuzzy logic controller to solve the optimization. Further, in [25], the cooperation of an EV and BESS in reactive power compensation is presented with the aim of minimizing electricity costs while maximizing the power factor for a system including solar PV, a BESS, and EVs.…”