Efficient power management optimization based on whale optimization algorithm and enhanced differential evolution

• Published in: Alexandria engineering journal Vol. 79; pp. 652 - 670
• Main Authors: Zaman, Khalid, Zhaoyun, Sun, Shah, Babar, Hussain, Altaf, Hussain, Tariq, Khan, Umer Sadiq, Ali, Farman, Sarra, Boukansous
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.09.2023; Elsevier
• Subjects: Cost of electricity; EDE; Energy optimization; Megawatt-hours; Scheduling optimizations; WOA; Scheduling optimizations; WOA; Megawatt-hours; Cost of electricity; EDE; Energy optimization
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2023.08.045

Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required Real-Time Price (RTP) and Critical Peak Price (CPP) loads, WOA and EDE capabilities can save costs and ensure the device receives sufficient voltage. The three most recent performance indicators are kWh per charge, energy usage, and the maximum-average ratio. Existing models are evaluated according to their expenses (in rupees), energy consumption, cost per kilowatt-hour, and total cost. All simulation results indicate that HWDE is the optimal solution in every circumstance. In MATLAB simulations, HWDE consistently outperforms its rivals.