ABSTRACT Peak-load management is an important process that allows energy providers to reshape load profiles, increase energy efficiency, and reduce overall operational costs and carbon emissions. This paper presents an improved decision-tree-based algorithm to reduce the peak load in residential distribution networks by coordinated control of electric vehicles (EVs), photovoltaic (PV) units, and battery energystorage systems (BESSs). The peak-load reduction is achieved by reading the domestic load in real time through a smart meter and taking appropriate coordinated action by a controller using the proposed algorithm. The proposed control algorithm was tested on a real distribution network using real load patterns and load dynamics, and validated in a laboratory experiment. Two types of EVs with fast and flexible charging capability, a PV unit, and BESSs were used to test the performance of the proposed control algorithm, which is compared with that of an artificial-neural-network technique. The results show that using the proposed method, the peak demand on the distribution grid can be reduced significantly, thereby greatly improving the load factor. INDEX TERMS Electric vehicle, energy management, EV management, peak load management, peak shaving. NOMENCLATURE Available energy of EV to shave the peak Off-peak-hour electricity price C T p Peak-hour electricity price SOC max Maximum state of charge (SOC) of the EV SOC 2 The SOC level when EV can start vehicle-togrid (V2G) SOC EV SOC of the EV at a particular time The rate of charging in V2G (0 ≤ U (t) ≤ P av i ) V (t) The rate of discharging in V2G P i Available power from a PV unit S pv Capacity of the installed PV module V Bt
The authors in REF present an algorithm for peak load reduction with coordinated response using Electricity Storage System (ESS), Photovoltaics (PV) and electric vehicle.
Peak-Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles
IEEE Access
IEEE Access
Computer Science