An Energy Efficient Load Management in Solar Integrated Power Network using Novel Metaheuristic Optimization

Abstract

The increasing demand for power driven by rapid urbanization poses significant challenges to modern power systems. To ensure a sustainable and stable energy supply, adopting effective energy management and Demand Side Management (DSM) strategies is crucial. This study introduces a novel energy optimization framework that combines DSM with the Wolverine Optimization Algorithm enhanced by a Time-Varying Exponential Coefficient (WoOA-TVEC). The adaptive nature of the exponential coefficient enables a dynamic balance between exploration and exploitation, resulting in faster convergence and improved optimization results. The proposed framework, integrated with time-dependent cost functions for Economic Load Dispatch (ELD) that support DSM, optimizes generator operations while adhering to system constraints, aiming for substantial cost reductions and enhanced system stability. To manage the intermittency of renewable energy sources and dynamic load conditions, the algorithm uses normalized solar photovoltaic (PV) generation profiles and real-time load factors. This enables the optimization model to efficiently respond to temporal changes in generation and demand while maintaining overall energy balance. Benchmark testing on standard mathematical functions showed that WoOA-TVEC outperformed other metaheuristic algorithms, including WoOA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Butterfly Optimization Algorithm (BOA), in terms of convergence speed, robustness, and solution quality. For the DSM application on the IEEE 14-bus system, WoOA-TVEC was compared with PSO, demonstrating significant improvements in total generation cost reduction and peak load shaving. These results confirm the scalability and practical relevance of the proposed framework for smart grid environments with renewable energy integration.