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Lookup NU author(s): Professor Qiangda Yang, Peng Liu, Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
In power system operation, the combined heat and power economic dispatch (CHPED) is an attractive and momentous optimization problem where the major objective is to find an optimal generation schedule of heat and power to meet the heat and power demands with the minimum cost, while satisfying various practical operation constraints. This paper puts forward an adaptive cuckoo search with differential evolution mutation (ACS-DEM) for solving the CHPED problem. Compared with the basic cuckoo search (CS), there are three main improvements in the proposed ACS-DEM. The first improvement is that adaptive parameters are employed and therefore no parameter adjustment is required. The second is the incorporation of a Gaussian sampling strategy into the global search phase of the algorithm to increase the exploration capability. The third is the introduction of an improved differential evolution mutation strategy into the local search phase to replace the simple biased random walk in the basic CS, thus discouraging the blindness and enhancing the exploitation capability. The outstanding performance of ACS-DEM is first confirmed through the test suite from the 2017 Conference on Evolutionary Computation and then demonstrated on several CHPED problems. The obtained dispatch schedules from ACS-DEM are feasible and in most cases exhibit a distinct improvement over the results offered by six other CS-based algorithms, one state-of-the-art differential evolution algorithm, as well as recent works in this field.
Author(s): Yang Q, Liu P, Zhang J, Dong N
Publication type: Article
Publication status: Published
Journal: Applied Energy
Year: 2022
Volume: 307
Print publication date: 01/02/2022
Online publication date: 10/11/2021
Acceptance date: 11/10/2021
Date deposited: 20/10/2021
ISSN (print): 0306-2619
ISSN (electronic): 1872-9118
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.apenergy.2021.118057
DOI: 10.1016/j.apenergy.2021.118057
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