Diminishing Energy Consumption Cost and Optimal Energy Management of Photovoltaic Aided Electric Vehicle (PV-EV) By GFO-VITG Approach

ABSTRACT

This paper proposes a hybrid GFO-VITG approach for the energy management system (EMS) of the photovoltaic (PV) aided electric vehicle (EV). The proposed system is the combination of Ground water flow optimization (GFO) and Vascular Invasive Tumor Growth optimization algorithm (VITG), and hence, it is known as the GFO-VITG method. The main aim of this paper is optimal energy management for diminishing the system cost with power loss of the system. Additionally, based on the enhancement of EMS for optimal control of PV-aided EVCS, the GFO-VITG method is proposed. The GFO-VITG model enhances the vehicle-to-grid (V2G) method for producing complementary functions and takes dynamic cost of electricity. The proposed technique is stimulated under MATLAB/Simulink, and efficiency is compared with that of existing techniques. Consequently, the output shows that the GFO-VITG model is efficient for getting better solution through minimal computation and also lessens the difficulty of requisite algorithms. The simulation outcomes demonstrate that the energy management system can decrease the overall expenses more than 55% in summer, 29% in winter related to typical charging policy, when ensuring the fulfillment index of electric vehicle-charging demands without knowing the departure time of electric vehicles. In the number of iterations of 100, 250, 500, and 1000, the simulation times of the proposed technique are 14.8 seconds, 29.2 seconds, 69.9 seconds, and 77.1 seconds.

KEYWORDS:

• Energy management system
• photovoltaic
• Vascular Invasive Tumor Growth
• ground water flow optimization
• electric vehicle

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing does not apply to this article because no data sets were generated during the current study.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

P. Rajesh

Mr. Paulthurai Rajesh graduated from Anna University, India. He has more than seven years of IT experience. His current research interests include artificial intelligence, power system, smart grid technologies and soft computing.

Francis H Shajin

Mr. Francis H Shajin graduated from Anna University, India. He has more than 7 years of IT experience. His current research interests include very-large-scale integration, soft computing, image processing, machine learning and networking.

Balapanur Mouli Chandra

Dr. Balapanur Mouli Chandra, is a Professor at QIS College of Engineering & Technology, Ongole, Andhra Pradesh, India.  He received his Ph. D from faculty of Electrical & Electronics Engineering, Jawaharlal Nehru Technological University (JNTUH), Hyderabad in 2015 and M. Tech (Power Electronics) from Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous), Nandyal, Kurnool Dist., A.P in the year 2007.  He received his B. Tech in Electrical & Electronics Engineering from Jaya Prakash Narayan College of Engineering (Autonomous), Mahabub Nagar, Telangana, in the year 2004.  He has more than 15 years of experience in teaching, research.  His research interests include Power electronic converters, Power electronics control of AC & DC Drives, Power Grid integration & Electric vehicles.

Bapayya Naidu Kommula

Dr. Bapayya Naidu Kommula, has obtained Bachelor of Technology (B.Tech.) and Master of Technology (M.Tech.) from Jawaharlal Nehru Technological University College of Engineering, Kakinada (JNTUCEK) in the years 2006 and 2012 respectively. He obtained his Ph.D. from Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, AP, India in the year 2019.He is presently working as Assistant Professor at Electrical and Electronics Engineering (EEE) Department, Aditya Engineering College, AP, India.  His areas of interests are Special Electrical Machines, Smart Grid, Renewable Energy Sources, Optimization Techniques and Multilevel Inverters.