Developing artificial hummingbird algorithm with linear controlling strategy and diversified territorial foraging tactics for combined heat and power dispatch

Abstract

Economic Dispatch (ED) in Combined Heat and Power Systems (CHPSs) is critical for optimising fuel costs while managing Power-Only (PO), CHP, and Heat-Only (HO) stations. Robust optimisation strategies are necessary to tackle the growing non-convexity of the ED issue, particularly when considering transmission losses. This research introduces a Modified Artificial Hummingbird Optimisation Algorithm (MAHOA) that integrates a linear controlling strategy and various territorial foraging tactics to enhance global and local search capabilities. These enhancements facilitate coordinated diversification and intensification activities, leading to more effective problem-solving. The MAHOA is compared to the conventional AHOA through testing on 7-station and 24-station CHPS systems. Three distinct power and heat loading levels are investigated. For the 7-station CHPS, the MAHOA showed significant improvements in robustness, with the lowest standard deviations of 0.0017, 3.373, and 0.0233 $/hr, indicating 99.83 %, 35.92 %, and 98.22 % improvements over the AHOA. For the 24-station CHPS, it also shows significant improvements of 0.381 %, 0.45 %, 0.448 %, and 2.815 % in terms of mean, minimum, standard deviation, and maximum values. The MAHOA outperforms the AHOA in 95 % of the simulation runs with faster convergence to optimal solutions. Compared to other methods reported in the literature, the MAHOA offers a viable and superior solution mechanism.

Keywords

• Artificial hummingbird optimisation algorithm
• Economic dispatch
• Different loading conditions
• Combined heat and power units