Perne Mallikarjun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Perne Mallikarjun has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
Muhammad Ammirrul Atiqi Mohd Zainuri
Department of Electrical, Electronic and Systems Engineering, Faculty Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
1 shared publication- Praveen Kumar Balachandran
Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, India
1 shared publication Sundar Rajan Giri Thulasiraman
Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India
1 shared publication
Latest Publications
- Open Access
- 23 March 2025
- Published by Springer Nature in Scientific Reports
- Vol. 15 (1) , 1-30
Economic energy optimization in microgrid with PV/wind/battery integrated wireless electric vehicle battery charging system using improved Harris Hawk Optimization
This paper investigates the economic energy management of a wireless electric vehicle charging stations (EVCS) connected to hybrid renewable energy system comprising photovoltaic (PV), wind, battery storage, and the main grid. The study adopts an Improved Harris Hawk Optimization (IHHO) algorithm to optimize energy management and minimize operational costs under varying scenarios. Three distinct wireless EV charging load profiles are considered to evaluate the performance of the proposed optimization technique. Simulation results demonstrate that the IHHO algorithm achieves significant cost reductions and improves energy utilization efficiency compared to other state-of-the-art optimization algorithms such as Improved Quantum Particle Swarm Optimization (IQPSO), Honeybee Mating Optimization (HBMO), and Enhanced Exploratory Whale Optimization Algorithm (EEWOA). For scenarios with renewable energies, the IHHO algorithm reduced electricity costs by up to 36.41%, achieving a per-unit cost as low as 3.17 INR for the most demanding EV charging profile. Under scenarios of renewable generation disconnection, the IHHO algorithm maintained its superiority, reducing costs by up to 37.89% compared to unoptimized dispatch strategies. The integration of battery storage further enhanced the system’s resilience and cost-effectiveness, particularly during periods of renewable unavailability. The IHHO algorithm’s robust performance, reflected in its ability to handle dynamic and challenging operational conditions, demonstrates its potential for practical deployment in real-world wireless EVCS powered by hybrid renewable energy systems. The findings highlight the IHHO algorithm as a reliable and efficient tool for optimizing energy dispatch, promoting the integration of renewable energy, and supporting sustainable wireless EVCS infrastructure development. Simulation results demonstrate that IHHO outperforms all benchmark algorithms, achieving electricity cost reductions of up to 35.82% in EV Profile 3, with a minimum per-unit electricity cost of 3.11 INR/kWh across all scenarios. Specifically, IHHO achieved the lowest electricity cost of 6479.72 INR/day for EV Profile 1, 10,893.23 INR/day for EV Profile 2, and 20,821.63 INR/day for EV Profile 3, consistently outperforming IQPSO, HBMO, and EEWOA.
- 23 March 2025
- Published by Springer Nature in Scientific Reports
- Vol. 15 (1) , 1-30
Open AccessEconomic energy optimization in microgrid with PV/wind/battery integrated wireless electric vehicle battery charging system using improved Harris Hawk Optimization
This paper investigates the economic energy management of a wireless electric vehicle charging stations (EVCS) connected to hybrid renewable energy system comprising photovoltaic (PV), wind, battery storage, and the main grid. The study adopts an Improved Harris Hawk Optimization (IHHO) algorithm to optimize energy management and minimize operational costs under varying scenarios. Three distinct wireless EV charging load profiles are considered to evaluate the performance of the proposed optimization technique. Simulation results demonstrate that the IHHO algorithm achieves significant cost reductions and improves energy utilization efficiency compared to other state-of-the-art optimization algorithms such as Improved Quantum Particle Swarm Optimization (IQPSO), Honeybee Mating Optimization (HBMO), and Enhanced Exploratory Whale Optimization Algorithm (EEWOA). For scenarios with renewable energies, the IHHO algorithm reduced electricity costs by up to 36.41%, achieving a per-unit cost as low as 3.17 INR for the most demanding EV charging profile. Under scenarios of renewable generation disconnection, the IHHO algorithm maintained its superiority, reducing costs by up to 37.89% compared to unoptimized dispatch strategies. The integration of battery storage further enhanced the system’s resilience and cost-effectiveness, particularly during periods of renewable unavailability. The IHHO algorithm’s robust performance, reflected in its ability to handle dynamic and challenging operational conditions, demonstrates its potential for practical deployment in real-world wireless EVCS powered by hybrid renewable energy systems. The findings highlight the IHHO algorithm as a reliable and efficient tool for optimizing energy dispatch, promoting the integration of renewable energy, and supporting sustainable wireless EVCS infrastructure development. Simulation results demonstrate that IHHO outperforms all benchmark algorithms, achieving electricity cost reductions of up to 35.82% in EV Profile 3, with a minimum per-unit electricity cost of 3.11 INR/kWh across all scenarios. Specifically, IHHO achieved the lowest electricity cost of 6479.72 INR/day for EV Profile 1, 10,893.23 INR/day for EV Profile 2, and 20,821.63 INR/day for EV Profile 3, consistently outperforming IQPSO, HBMO, and EEWOA.