Optimisation and scheduling of Demand Response strategies based on Artificial Hummingbird algorithm

Demand response (DR) has grown as an effective method for enhancing energy system flexibility and reliability, especially with the rise of renewable energy sources. However, uncertainty surrounding Distributed Energy Resources (DERs) presents challenges for Microgrid (MG) operators. Traditional optimization methods often fail to efficiently explore the vast solution space and identify optimal DR schedules within acceptable time frames. This paper proposes the Optimisation-based Scheduling of DR (OSDR) strategy, which leverages a new optimization technique, the SIAHO algorithm. Inspired by the foraging behavior of hummingbirds, the SIAHO algorithm provides a powerful framework for solving complex optimization problems. By mimicking the search patterns of hummingbirds, it efficiently explores the solution space and identifies DR schedules that balance energy costs, load factor, operation costs, and risk. Through extensive simulations and case studies, we evaluate the proposed approach across different scenarios, comparing it with existing methods. The results show that the SIAHO-based approach significantly improves energy cost reduction, peak demand shaving, and grid stability, demonstrating its robustness and effectiveness in optimizing and scheduling DR strategies.