An elitist cuckoo search algorithm for combined heat and power economic dispatch

Abstract

Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results.

KEYWORDS:

• Combined heat and power
• cuckoo search
• economic dispatch
• optimisation
• swarm intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available at https://doi.org/10.1016/j.epsr.2012.08.005 and https://doi.org/10.1007/s00521-017-3074-9 references (Mohammadi-Ivatloo, Moradi-Dalvand, and Rabiee 2013; Nazari-Heris et al. 2017). The data generated and/or analysed during the current study are available from the corresponding author ([email protected]) on reasonable request.

Additional information

Funding

This study was supported by the National Key Research and Development Program of China [grant number 2017YFA0700300] and the Fundamental Research Funds for the Central Universities [grant number N2025032].

Notes on contributors

Qiangda Yang

Qiangda Yang received the B.S., M.S., and Ph.D. degrees from Northeastern University, Shenyang, China, in 2003, 2006, and 2009, respectively. He is currently an Associate Professor at Northeastern University, Shenyang, China. His current research interests include evolutionary computation, swarm intelligence, energy system management, and intelligent manufacturing.

Hongbo Gao

Hongbo Gao received the B.S., M.S., and Ph.D. degrees from Northeastern University, Shenyang, China, in 2001, 2004, and 2017, respectively. She is a lecturer at Liaoning Provincial College of Communications, Shenyang, China. Her current research interests include intelligent manufacturing and intelligent fault diagnosis.

Ning Dong

Ning Dong received the B.S. degree in aircraft engineering from Zhengzhou University of Aeronautics, Zhengzhou, China, in 2019, and the M.S. degree in power engineering from Northeastern University, Shenyang, China, in 2021. His research interests include energy system management and swarm intelligence.

Peng Liu

Peng Liu received the B.S. degree in energy and power engineering from Ludong University, Yantai, China, in 2019. Now he is studying for the M.S. degree in power engineering from Northeastern University, Shenyang, China. His research interests include swarm intelligence and energy internet scheduling.