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

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Power enhanced solar PV array configuration based on calcudoku puzzle pattern for partial shaded PV system.

The power output of solar photovoltaic systems can be affected by environmental factors, such as partial shading. This can lead to a decrease in the power conversion rate of the system. Although existing solutions for this issue are cost-effective and efficient, new solutions could further improve the system's performance by increasing consistency, power generation, and reducing mismatch loss and costs. To address this, a new method for configuring PV arrays was proposed using the calcudoku puzzle pattern. The performance of this new array configuration was evaluated in MATLAB/Simulink® for a 9 × 9 PV array and compared to conventional methods like Series-parallel, Total Cross Tied (TCT), and Sudoku array configurations. The performance was evaluated under eight different shading patterns based on power conversion rate and mismatch losses between the PV rows. The proposed array configuration resulted in 3.9%-13.3% of mismatch losses across the different shading patterns, while other configurations had a minimum of 13.8% to a maximum of 51.9% of mismatch losses. This reduction in mismatch losses directly improved the power conversion rate of the PV array.