Abstract
To overcome the main drawback of the original Artificial Ecosystems Optimization (AEO) algorithm, such as the premature convergence in solving specified and large optimization problems, a new adaptive mechanism search is introduced to create dynamic balance between exploration and exploitation during search process. In the proposed algorithm, the first modification introduced consists of dynamically adjusting the production operator to achieve the global search during the first trial, and then, during the next trials, the production operator adjusted dynamically to guide the other particles to perform local search. The second modification introduced within the standard algorithm is the adaptation of an interactive split technique to enhance the convergence behavior of the algorithm. In this paper the proposed Adaptive Split AEO (ASAEO) is applied to solve the non-smooth economic dispatch considering practical operation constraints, such as the valve point effect, the prohibited zones, the ramp rate limits and the total power losses. Also, and to relieve conflict on many results found in the recent literature using various metaheuristic methods and to demonstrate the particularity of the proposed power system planning strategy, a critical review is presented. In the literature, it is found that many comparative studies have been elaborated based on different databases, and these elaborated comparative studies may be misleading and difficult to judge the real contribution of many proposed optimization methods. Based on a statistical review, it is clearly confirmed that for the six test systems, two technical databases are available, and for the 13 generating units and 40 generating units, two databases are available in the literature. The particularity of the proposed variant named ASAEO has been validated on three standard test systems. The obtained results using the proposed technique compared to many recent methods are competitive for various test systems in terms of solution accuracy and convergence behavior.
Similar content being viewed by others
Availability of Data and Material
All data generated or analyzed during this study are included in this published article.
References
Basu M, Chowdhury A (2013) Cuckoo search algorithm for economic dispatch. Energy 60:1–10
Belkacem M (2019) Solution of non-smooth economic dispatch using interactive grouped adaptive bat algorithm: solving practical economic dispatch. Int J Energy Optim Eng 8(1):88–144
Carpentier J (1962) “Contribution à l’etude du Dispatching Economique” (“contribution to the study of economic dispatch”). Bull Soc Francaise Elect 3:431–447
Coelho LS, Thom Souza RC, Cocco Mariani V (2009) Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems. J Math Comput Simul, Elsevier. https://doi.org/10.1016/j.matcom.2009.03.005
Dhiman G (2019) MOSHEPO: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems. Appl Intell. https://doi.org/10.1007/s10489-019-01522-4
Dhiman G (2020) MOSHEPO: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems. Appl Intell. https://doi.org/10.1007/s10489-019-01522-4
Farag A, Al-Baiyat S, Cheng TC (1995) Economic load dispatch multiobjective optimization procedures using linear programming techniques. IEEE Trans Power Syst 10:731–738
Ghorbani N, Babaei E (2016) Exchange market algorithm for economic load dispatch. Electr Power Energy Syst 75:19–27
Haghrah A, Nekoui MA, Nazari-Heris M, Mohammadi-ivatloo B (2020) An improved real-coded genetic algorithm with random walk based mutation for solving combined heat and power economic dispatch. J Ambient Intell Humanized Comput. https://doi.org/10.1007/s12652-020-02589-5
Hosseinnezhad V, Babaei E (2013) Economic load dispatch using θ-PSO. Electr Power Energy Syst 49:160–169
Irisarri G, Kimball L, Clements K, Bagchi A, Davis P (1998) Economic dispatch with network and ramping constraints via interior point methods. IEEE Trans Power Syst 13:236–242
Kansal V, Dhillon JS (2020) Emended salp swarm algorithm for multiobjective electric power dispatch problem. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020b.106172
Mahdad B (2020) Improvement optimal power flow solution considering SVC and TCSC controllers using new partitioned ant lion algorithm. Electr Eng 102:2655–2672
Mahmoud K, Abdel-Nasser M, Mustafa E, Ali ZM (2020) Improved salp-swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems. Sustainability 12:576. https://doi.org/10.3390/su12020576
Mandal B, Roy PK, Mandal S (2014) Economic load dispatch using krill herd algorithm. Electr Power Energy Syst 57:1–10
Meng K, Wang HG, Dong Z, Wong KP (2010) Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Trans Power Syst 25(1):215–222
Mohammadi F, Abdi H (2018) A modified crow search algorithm (MCSA) for solving economic load dispatch problem. Appl Soft Comput 71:51–65
Niknam T, Mojarrad HD, Meymand HZ (2011) Non-smooth economic dispatch computation by fuzzy and self adaptive particle swarm optimization. Appl Soft Comput 11:2805–2817
Sakthivel VP, Suman M, Sathya PD (2020) Squirrel search algorithm for economic dispatch with valve-point effects and multiple fuels. Energy Sources Part b: Econ Plan Policy 15(6):351–382
Secui DC (2015) A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 89:43–62
Sun J, Palade V, Wu XJ, Fang W, Wang Z (2014) Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization. IEEE Trans Indus Inform 10(1):222–232
Xin-gang Z, Ji L, Jin M, Ying Z (2020) An improved quantum particle swarm optimization algorithm for environmental economic dispatch. Expert Syst Appl 152:11337
Yang X-S, Hosseini SSS, Gandomi AH (2012) Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12:1180–1186
Zakian P, Kaveh A (2018) Economic dispatch of power systems using an adaptive charged system search algorithm. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2018.09.008
Zare M, Narimani MR, Malekpour M, Azizipanah-Abarghooee R, Terzija V (2021) Reserve constrained dynamic economic dispatch in multi-area power systems: an improved fireworks algorithm. Electr Power Energy Syst 126:106579
Zhao W, Wang L, Zhang Z (2019) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl 32:9383–9425. https://doi.org/10.1007/s00521-019-04452-x
Zheng Z-X, Li J-Q, Han Y-Y (2019) An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects. J Exp Theor Artif Intell. https://doi.org/10.1080/0952813X.2019.1673488
Zhongbo Hu, Li Z, Dai C, Xinlin Xu, Xiong Z, Qinghua Su (2020) Multiobjective grey prediction evolution algorithm for environmental/economic dispatch problem. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2992116
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
BM proposed the idea and mainly designed the Matlab program, elaborated the presentation of results and discussion, read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mahdad, B. Adaptive Split Artificial Ecosystem-Based Optimization to Solving Non-smooth Economic Dispatch. Trans Indian Natl. Acad. Eng. 7, 873–895 (2022). https://doi.org/10.1007/s41403-022-00334-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41403-022-00334-2