Content area
Abstract
ABSTRACT
Renewable energy sources, such as wind, solar, biomass, hydropower, and geothermal power, have a relatively minor environmental impact compared to nonrenewable sources and are sustainable over the long term. However, the variable nature of renewable energy production and the load demands of plug‐in hybrid electric vehicles (PHEVs) can lead to significant challenges in network performance, including reduced power quality, increased power losses, and voltage fluctuations. Effective integration of these energy sources requires optimal planning that considers various output variables of renewable sources to meet the increased demand from PHEV charging. Furthermore, the development of an efficient energy management strategy for PHEVs poses an optimization challenge that can be addressed using metaheuristic algorithms. In this paper, the multi‐objective particle swarm optimization (MPSO) algorithm is implemented for the optimal placement of the EV charging points, taking into account the surrounding area and the coverage of the stations. The objective function is optimized by the MPSO algorithm with the objective of minimizing the cost of optimizing the locations of the charging points. Finally, the simulated results in standard IEEE 69‐bus distribution systems show that the proposed optimization model led to a reduction in power losses from 268.17 to 229.97 kW in the best charging scenario and to 177.32 kW in the best discharging scenario. Additionally, the minimum bus voltage improved from 0.887 to 0.908 prionits (p.u.) (in charging mode) and 0.917 p.u. (in discharging mode), confirming the effectiveness of the proposed MPSO approach in enhancing network performance
Details
Discharge;
Particle swarm optimization;
Electric power loss;
Marginal pricing;
Electricity distribution;
Voltage;
Electric vehicles;
Optimization;
Smart grid technology;
Electric vehicle charging stations;
Hydroelectric power;
Electric vehicle charging;
Hybrid electric vehicles;
Heuristic methods;
Optimization models;
Placement;
Wind power;
Infrastructure;
Electric potential;
Costs;
Geothermal energy;
Genetic algorithms;
Environmental impact;
Renewable energy sources;
Renewable resources;
Objective function;
Solar energy;
Effectiveness;
Algorithms;
Geothermal power;
Alternative energy sources;
Demand side management;
Energy distribution
1 Thunderbird School of Global Management, Arizona State University Tempe Campus, Phoenix, Arizona, USA
2 Department of Electrical Engineering, Marwadi University Research Center, Faculty of Engineering & Technology Marwadi University, Rajkot, Gujarat, India
3 Department of Electrical and Electronics Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India
4 Department of Computer Engineering and Application, GLA University, Mathura, India
5 Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
6 Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq, Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
7 Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
8 Department of Electrical & Electronics Engineering, Siksha “O” Anusandhan (Deemed to be University), Bhubaneswar, India
9 Graphic Era Hill University, Bhimtal, Uttarakhand, India, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
10 Islamic Azad University, Lahijan Branch, Guilan, Iran