A new hybrid algorithm combining Ant Lion optimization and particle swarm optimization to solve an economic dispatch problem with non-smooth cost function

• Autorzy: Takeang Chiraphon , Aurasopon Apinan

Identyfikatory

DOI

10.15199/48.2021.09.24

Warianty tytułu

PL

Nowy algorytm hybrydowy łączący optymalizację Ant Lion i optymalizację roju cząstek w celu rozwiązania ekonomicznego problemu dystrybucji z funkcją kosztów nierównomiernych

• Języki publikacji: EN

Abstrakty

EN

This paper presents a new hybrid algorithm which is a combination of ant lion optimization (ALO) and particle swarm optimization (PSO) to solve an economic dispatch (ED) problem with non-smooth cost function characteristic. In the proposed algorithm, HALO-PSO, ALO method is used to find the initial value and PSO is used to find the best solutions causing it provides faster and more accurate results compared to conventional methods. To show its effectiveness, the HALO-PSO was applied to test two systems consisting of either 6 or 13 power generating units. Results confirm that the proposed HALO-PSO algorithm is capable of obtaining rapid convergence and a high quality solution efficiently.

PL

W artykule przedstawiono nowy algorytm hybrydowy, który jest kombinacją optymalizacji Ant Lion (ALO) i optymalizacji roju cząstek (PSO) w celu rozwiązania problemu ekonomicznej dystrybucji (ED) z niegładką charakterystyką funkcji kosztu. W proponowanym algorytmie HALOPSO, metoda ALO służy do znalezienia wartości początkowej, a PSO służy do znalezienia najlepszych rozwiązań, dzięki czemu zapewnia szybsze i dokładniejsze wyniki w porównaniu do metod konwencjonalnych. Aby wykazać jego skuteczność, HALO-PSO został zastosowany do przetestowania dwóch systemów składających się z 6 lub 13 jednostek wytwórczych. Wyniki potwierdzają, że proponowany algorytm HALO-PSO jest w stanie skutecznie uzyskać szybką konwergencję i wysokiej jakości rozwiązanie.

Słowa kluczowe

EN

Ant Lion optimization; particle swarm optimization; hybrid algorithm; economic dispatch

PL

dystrybucja energii ekonomiczna; algorytm rojowy; algorytm hybrydowy

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 9
• Strony: 115--122
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Takeang Chiraphon

• Faculty of Engineering, Rajamangala University of Technology Lanna, Lampang, Thailand
• Faculty of Engineering, Mahasarakham University, Mahasarakham 44150, Thailand

• https://orcid.org/0000-0001-6700-9454

autor

Aurasopon Apinan

• Faculty of Engineering, Rajamangala University of Technology Lanna, Lampang, Thailand
• Faculty of Engineering, Mahasarakham University, Mahasarakham 44150, Thailand

• https://orcid.org/0000-0002-4898-303X

Bibliografia

• [1] R.J. Ringlee and D.D. Williams. “Economic dispatch operation considering valve throttling losses, II-distribution of system loads by the method of dynamic programming,” IEEE Trans. Power Appar. Syst., (1963), pp. 615-620.
• [2] A. Farag, S. Al-Baiyat and T. C. Cheng, “Economic Load Dispatch Multi objective Optimization Procedures Using Linear Programming Techniques,” IEEE Transactions On Power Systems, 10(1995), No. 2, pp. 731-738.
• [3] A. A. El-Keib, H. Ma, and J.L. Hart. “Environmentally Constrained Economic Dispatch Using the LaGrangian Relaxation Method,” IEEE Transactions on Power Systems, 9(1994), no. 4, pp. 1723-1729.
• [4] Vijay Kumar Joshi, “Optimization of Economic Load Dispatch Problem by using Tabu Search Algorithm,” International Journal of Latest Trends in Engineering and Technology, 8 (2017), Issue 4-1, pp. 182-187
• [5] M. N. Nwohu and Osaremwinda Osarobo Paul, “Evaluation of Economic Load Dispatch Problem in Power Generating Stations by the Use of Ant Colony Search Algorithms,” International Journal of Research Studies in Electrical and Electronics Engineering (IJRSEEE), 3(2017), Issue 1, pp. 20- 29.
• [6] Vishnu Suresh, Przemyslaw Janik and Michal Jasinski, “Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization,” ARCHIVES OF ELECTRICAL ENGINEERING, 69(2020), Issue 2, pp. 335-348.
• [7] S. A. Oluwadare, G. B. Iwasokun, O. Olabode, O. Olusi, and A. E. Akinwonmi, “Genetic Algorithm-based Cost Optimization Model for Power Economic Dispatch Problem,” British Journal of Applied Science & Technology, 15(2016), Issue 6, pp.1-10.
• [8] Dexuan Zou, Steven Li, Xiangyong Kong, Haibin Ouyang and Zongyan Li. “Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy,” Applied Energy, ELSEVIER, 237(2019), pp.646-670.
• [9] Roberto Ponciroli, Nicolas E. Stauff, Jackson Ramsey, Francesco Ganda and Richard B. Vilim, “An Improved Genetic Algorithm Approach to the Unit Commitment/Economic Dispatch Problem,” IEEE TRANSACTIONS ON POWER SYSTEMS, 35(2020), No. 5, pp. 4005-4013.
• [10] G. Mehta, R. P. Singh, and V. K. Yada, “Optimization of Combined Economic Emission Dispatch Problem using Artificial Bee Colony Method,” International Journal on Cybernetics & Informatics (IJCI), 6(2017), no. 1/2, pp.107-118.
• [11] Rabiee A, Jamadi M, Mohammadi-Ivatloo B and Ahmadian A. “Optimal Non-Convex Combined Heat and Power Economic Dispatch via Improved Artificial Bee Colony Algorithm,” Processes, 8(2020), Issue 9, pp. 1-22.
• [12] Apinan Aurasopon and Chiraphon Takeang. “Multiple Hybrid of Lambda Iteration and Bee Colony Optimization Method for Solving Economic Dispatch Problem,” International Journal on Electrical Engineering and Informatics, 13(2021), Number 1, pp. 57-72.
• [13] Ziane Ismail, Benhamida Farid and Graa Amel, “Simulated Annealing Optimization for Generation Scheduling with Cubic Fuel Cost Function”, WSEAS Transactions on Information Science and Applications, 14(2017), pp. 64-69.
• [14] Riyadh BOUDDOU, Farid BENHAMIDA, Amine ZEGGAI, Nanda Kishore Ray, Binod Kumar Prusty and Subhransu Sekhar Dash. “The Dynamic Economic Dispatch in an Integrated Wind-Thermal Electricity Market Using Simulated Annealing Algorithm,” PRZEGLĄD ELEKTROTECHNICZNY, 96(2020), no. 11, pp. 55-60.
• [15] Vikram Kumar Kamboj, Ashutosh Bhadoria and S. K. Bath, “Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer,” Neural Comput & Applic, 28(2017), pp. 2181-2192.
• [16] Faisal Z. Alazemi and Ahmed Y. Hatata, “Ant Lion Optimizer for Optimum Economic Dispatch Considering Demand Response as a Visual Power Plant,” Electric Power Components and Systems, 47(2019), Issue 6-7, pp. 629-643.
• [17] Hardiansyah Hardiansyah, “Dynamic economic emission dispatch using ant lion optimization,” Bulletin of Electrical Engineering and Informatics, 9(2020), No. 1, pp. 12-20.
• [18] Hardiansyah Hardiansyah and Junaidi Junaidi, “Multi-Objective Ant Lion Optimizer for Solving Environmental/Economic Dispatch,” PRZEGLĄD ELEKTROTECHNICZNY, 97(2021), Issue 2, pp. 153-158.
• [19] K. Selvakumar, K. Vijayakumar, D. Sattianadan, and C. S. Boopathi, “Shuffled Frog Leaping Algorithm (SFLA) for Short Term Optimal Scheduling of Thermal Units with Emission Limitation and Prohibited Operational Zone (POZ) Constraints,” Indian Journal of Science and Technology, 9(2016), No. 42, pp. 1-6.
• [20] Majid Khalili, Javad Nikoukar and Mostafa Sedighizadeh, “Combined Heat and Power Economic Dispatch using Improved Shuffled Frog Leaping Algorithm,” Journal of Advances in Computer Research, 12(2021), No. 1, pp.1-11.
• [21] M. Mohammadian, A. LorestaniM and M. Ardehali, “Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm”, Energy, Elsevier, 161(2018), pp. 710-724.
• [22] Soudamini Behera, Sasmita Behera, Ajit Kumar Barisal and Pratikhya Sahu, "Dynamic economic emission dispatch of thermal-wind-solar system with constriction factor-based particle swarm optimization algorithm", World Journal of Engineering, 18(2020), No. 2, pp. 217-227.
• [23] Gong Wenjie, Yu Litao, Han Aoyang and Li Zhengjie. “Optimal Dispatch Model of Active Distribution Network Based on Particle Swarm Optimization Algorithm with Random Weight,” International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2021), IEEE Xplore, April(2021), pp. 482-485.
• [24] Seyedali Mirjalili, “The Ant Lion Optimizer,” Advances in Engineering Software, 83(2015), pp. 80-98.
• [25] Kennedy J, Eberhart R., “Particle swarm optimization,” IEEE International Conference Neural Networks, 4(1995), pp. 1942– 1948.
• [26] Taher Niknam, “A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and nonconvex economic dispatch problem,” Applied Energy, 87(2010), pp.327–339.
• [27] Taher Niknam, Hasan Doagou Mojarrad and Hamed Zeinoddini Meymand’ “A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects,” Energy Conversion and Management, 52(2011). pp. 1800–1809.
• [28] Shanhe Jiang, Zhicheng Ji and Yanxia Shen, “A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints,” Electrical Power and Energy Systems, 55(2014), pp. 628–644.
• [29] Mehdi Mehdinejad, Behnam Mohammadi-Ivatloo, Reza Dadashzadeh-Bonab and Kazem Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms,” Electrical Power and Energy Systems, 83(2016), pp. 104–116.
• [30] R. Rahmani, M. F. Othman, R. Yusof, and M. Khalid, “Solving Economic Dispatch Problem using Particle Swarm Optimization by an Evolutionary Technique for Initializing Particles”, Journal of Theoretical and Applied Information Technology, 46(2012), No. 2, pp. 526-536.
• [31] E. Bijami and M. M. Farsangi, “An Improved Adaptive Shuffled Frog Leaping Algorithm to Solve Various Non-smooth Economic Dispatch Problems in Power Systems,” Iranian Conference on Intelligent Systems (ICIS), (2014), pp. 1-6.
• [32] Venkatakrishnan GR., Rengaraj R. and Salivahanan S., “Grey wolf optimizer to real power dispatch with non-linear constraints,” Comput. Model Eng. Sci., 115(2018), No.1, pp. 25–45.
• [33] Jun Chen and Hashem Imani Marrani, An Efcient New Hybrid ICA‑PSO Approach for Solving Large Scale Non‑convex Multi Area Economic Dispatch Problems, “An Efcient New Hybrid ICA‑PSO Approach for Solving Large Scale Non‑convex Multi Area Economic Dispatch Problems,”
• [34] S. Nagaraju, et al., “Economic Load Dispatch Considering Valve Point Loading using Cuckoo Search Algorithm,” International journal of Science & Engineering Development Research, (2016), vol. 1, pp. 225-229.
• [35] Mohammad Reza Gholami Dehbalaee, Gholam hossein Shaeisi and majid valizadeh, “A novel exclusive binary search algorithm to solve the nonlinear economic dispatch problem,” Journal of Energy Management and Technology (JEMT), 4(2020), Issue 3, pp. 48-56.
• [36] Wanchai Khamsen, Chiraphon Takeang and Patiphat Aunban, “Hybrid method for solving the non smooth cost function economic dispatch problem,” International Journal of Electrical and Computer Engineering (IJECE), 10(2020), No.1, pp. 609- 616.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).