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Dergiler / Balkan Journal of Electrical and Computer Engineering / 2022 / Cilt: 10 - Sayı: 3

Dynamic Economic Dispatch with Valve-Point Effect Using Crow Search Algorithm

Cenk ANDİÇ
Ali ÖZTÜRK
Salih TOSUN
Sayfa
237–244
Yayın Tarihi
2022-07-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This paper presents a method based on meta-heuristic to solve Dynamic Economic Dispatch (DED) problem in a power system.
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Temel Bulgular & Veri:In this study, line losses, generation limit values of generators, generation-consumption balance, valve-point effect and ramp rate limits of generator are included as constraints.
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Sonuç & Katkı:Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Dynamic economic dispatch · Thermal power units · Crow search algorithm

Abstract

This paper presents a method based on meta-heuristic to solve Dynamic Economic Dispatch (DED) problem in a power system. In this paper, Crow Search Algorithm (CSA), which is one of the heuristic methods is proposed to solve the DED problem in a power system. In this study, line losses, generation limit values of generators, generation-consumption balance, valve-point effect and ramp rate limits of generator are included as constraints. The proposed algorithm was implemented on two different test cases. Finally, the CSA results were compared with the results of well-known heuristics in the literature such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Symbiotic Organism Search (SOS) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA), Imperial Competitive Algorithm (ICA), Modified Ant Colony Optimization (MACO) algorithm. The results show that the proposed algorithm has a better operating cost. With the results of the algorithm proposed in the test system 1, a profit of $2,056,5931 per day and $751,751,4815 per year is obtained. It is seen that with the results of the algorithm proposed in the test system 2, a daily profit of $12,279,7328 and a yearly profit of $4,482,102,472 are obtained. Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.

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Bu Makaleye Atıf Yap

ANDİÇ, C., ÖZTÜRK, A., TOSUN, S. (2022). Dynamic Economic Dispatch with Valve-Point Effect Using Crow Search Algorithm. Balkan Journal of Electrical and Computer Engineering, Cilt: 10 - Sayı: 3, 237–244. https://ajindex.com/articles/473136

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Kaynakça (11)

  1. Vlachogiannis, J. G., & Lee, K. Y. (2009). Economic load dispatch—A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. IEEE Transactions on Power Systems, 24(2), 991-1001.
  2. Jayabarathi, T., Jayaprakash, K., Jeyakumar, D. N., & Raghunathan, T. (2005). Evolutionary programming techniques for different kinds of economic dispatch problems. Electric power systems research, 73(2), 169-176.
  3. Mandal, B., & Roy, P. K. (2021). Dynamic economic dispatch problem in hybrid wind based power systems using oppositional based chaotic grasshopper optimization algorithm. Journal of Renewable and Sustainable Energy, 13(1),013306. Xiong, G., & Shi, D. (2018). Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy, 157, 424-435.
  4. Zheng, Z., Li, J., & Han, Y. (2020). An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects. Journal of Experimental & Theoretical Artificial Intelligence, 32(5), 805-829.
  5. Dai, C., Hu, Z., & Su, Q. (2021). An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects. Energy, 122461. Younes, Z., Alhamrouni, I., Mekhilef, S., & Reyasudin, M. (2021). A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Engineering Journal, 12(2), 1985-1994.
  6. Yalcinöz, T., & Altun, H. (2000). Comparison of simulation algorithms for the Hopfield neural network: an application of economic dispatch. Turkish Journal of Electrical Engineering & Computer Sciences, 8(1), 67-80.
  7. Zou, D., Li, S., Kong, X., Ouyang, H., & Li, Z. (2019). Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy. Applied energy, 237, 646-670.
  8. Mahor, A., Prasad, V., & Rangnekar, S. (2009). Economic dispatch using particle swarm optimization: A review. Renewable and sustainable energy reviews, 13(8), 2134-2141.
  9. Sonmez, Y. (2011). Multi-objective environmental/economic dispatch solution with penalty factor using Artificial Bee Colony algorithm. Scientific Research and Essays, 6(13), 2824-2831.
  10. Andic, C., Ozturk, A., & Tosun, S. (2020). Türkiye’deki güc sisteminde karga arama algoritması kullanilarak ekonomik yük dagitimi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(1), 428-436.
  11. Pradhan, M., Roy, P. K., & Pal, T.