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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale

Rinchen Zangmo¹, Suresh Kumar Sudabattula¹, Sachin Mishra²

¹School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, Punjab, India.

Scientific Reports

|November 18, 2024

Summary

Optimal placement of renewable distributed generators (RDGs), including solar photovoltaic, wind turbines, and electric vehicles (EVs), in radial distribution systems (RDS) minimizes power loss and enhances grid stability. The Multi-population Evolution Whale Optimization Algorithm (MEWOA) proves effective for these complex optimization tasks.

Keywords:

Distributed generators Electric vehicles Power Loss Voltage Profile Voltage Stability Index Whale optimization algorithm

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Area of Science:

Electrical Engineering
Power Systems Engineering
Renewable Energy Integration

Background:

Radial distribution systems (RDS) face challenges with power loss and voltage stability.
Integrating renewable distributed generators (RDGs) like solar photovoltaic and wind turbines, alongside electric vehicles (EVs), offers significant benefits but requires strategic placement.
Load demand uncertainty adds complexity to grid management.

Purpose of the Study:

To determine the optimal placement of RDGs (solar PV, wind turbines) and EVs in an RDS.
To minimize power loss (PLoss) and improve voltage profile and stability index.
To evaluate the impact of load demand uncertainty on RDG integration.

Main Methods:

Utilized Beta and Weibull distribution functions to model solar irradiance and wind speed.
Employed meta-heuristic algorithms for integrating EVs and optimizing RDG placement.
Tested the proposed methods on an Indian 28-bus test system representing a balanced RDS.
Incorporated load demand uncertainty for 24-hour analysis.

Main Results:

The integration of DGs significantly reduced power loss (PLoss) and improved system efficiency.
Enhanced voltage profiles and improved grid stability were observed after DG integration.
The Multi-population Evolution Whale Optimization Algorithm (MEWOA) demonstrated superior performance compared to existing methods.
The study confirmed the practical value of MEWOA for nonlinear optimization in power systems.

Conclusions:

Optimal placement of RDGs and EVs is crucial for enhancing RDS performance.
MEWOA is a highly effective and practical algorithm for optimizing RDG and EV integration in RDS.
DG integration leads to reduced power loss, increased efficiency, and improved environmental sustainability.