Peak-Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles

• Published in: IEEE access Vol. 6; pp. 29353 - 29365
• Main Authors: Mahmud, Khizir, Hossain, M. Jahangir, Town, Graham E.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Control algorithms; Control theory; Decision trees; Electric vehicle; Electric vehicles; Electrical loads; energy management; Energy storage; EV management; Heuristic algorithms; Neural networks; Peak load; peak load management; peak shaving; Photovoltaic cells; Real-time systems; Reduction; State of charge; Storage systems; Stress concentration; Uncertainty; Vehicle-to-grid
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2837144

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 energy-storage 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.