Application of Twisting Controller and Modified Pufferfish Optimization Algorithm for Power Management in a Solar PV System with Electric-Vehicle and Load-Demand Integration

Arunesh Kumar Singh, Rohit Kumar, D. K. Chaturvedi, Ibraheem, Gulshan Sharma (), Pitshou N. Bokoro and Rajesh Kumar
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Arunesh Kumar Singh: Department of Electrical Engineering, Jamia Millia Islamia University, New Delhi 110025, India
Rohit Kumar: Department of Electrical Engineering, Jamia Millia Islamia University, New Delhi 110025, India
D. K. Chaturvedi: Dayalbagh Educational Institute, Deemed to be University, Agra 282005, India
Ibraheem: Department of Electrical Engineering, Netaji Subhas University of Technology (NSUT), Dwarka Sector-3, New Delhi 110078, India
Gulshan Sharma: Department of Electrical & Electronics Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa
Pitshou N. Bokoro: Department of Electrical & Electronics Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa
Rajesh Kumar: Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Johannesburg 2094, South Africa

Energies, 2025, vol. 18, issue 14, 1-25

Abstract: To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work.

Keywords: intelligent power management controller; twisting controller; modified pufferfish optimization algorithm; electric vehicle; battery; PV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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