Demand side management using optimization strategies for efficient electric vehicle load management in modern power...

Area of Science:

• Electrical Engineering
• Computer Science
• Optimization Algorithms

Background:

• The rapid growth of Electric Vehicles (EVs) presents challenges for grid stability and efficiency.
• Demand Side Management (DSM) is crucial for integrating high EV loads without compromising grid performance.
• Existing grid management strategies require enhancement to accommodate dynamic energy demands.

Purpose of the Study:

• To develop and evaluate a novel DSM algorithm for managing EV charging loads.
• To integrate distributed generation (Solar PV) and Battery Energy Storage Systems (BESS) within the DSM framework.
• To compare the efficacy of various optimization algorithms in achieving DSM objectives.

Main Methods:

• Development of a DSM algorithm with tailored objective functions and constraints.
• Integration of EV load, Solar PV generation, and BESS into the optimization model.
• Simulation using MATLAB/Simulink across diverse load scenarios (residential, IT sector).
• Application of multiple optimization algorithms: Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS), and Slime Mould Algorithm (SMA).

Main Results:

• Significant reduction in peak load from 4.5 MW to 2.6 MW.
• Increase in minimum load from 0.5 MW to 1.2 MW, narrowing the peak-to-valley load difference.
• Comparative analysis of algorithms based on peak-to-valley reduction, computation time, and convergence rate.

Conclusions:

• The developed DSM algorithm effectively manages EV loads, enhancing grid stability and efficiency.
• Optimization algorithms, particularly [mention best performing if specified, otherwise generalize], demonstrate strong performance in achieving DSM goals.
• The integration of EVs, Solar PV, and BESS through advanced DSM is a viable strategy for future smart grids.