Optimised Scheduling Strategies For Hybrid Renewable Energy Systems With Demand Response Integration

Abstract

Renewable power has grown rapidly to redesign the world energy arena, but its variable nature comes with a need for stability in the grid. This examines a real-time power and demand balance scheduling framework utilising heuristic optimisation techniques, including the Self-Adaptive Differential Evolution Algorithm (SADE), Differential Evolution (DE), and Particle Swarm Optimisation (PSO). The Enhanced Smith-Adaptive Dynamic Environment (ESADE) method maximises convergence and increases scheduling efficiency by maximising profit and retaining grid reliability. They also analyse various demand response strategies for cost savings, such as Critical Peak Pricing (CPP) and Real-Time Pricing (RTP). This research also continues to develop Transactive Energy (TE) frameworks for dynamic pricing and economic operation of prosumers and aggregators. The methods applied in these studies, particularly the application of artificial intelligence, show a substantial improvement in energy management and cost savings, which result in increasing grid stability and optimisation in overall performance.