Efficient Scheduling of Power Generating Units Using Grasshopper Optimization Algorithm

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Muhammad Aidil Adha Aziz, Zuhaila Mat Yasin, Zuhaina Zakaria, Elia Erwani Hassan

10.14445/23488379/IJEEE-V11I8P113

How to Cite?

Muhammad Aidil Adha Aziz, Zuhaila Mat Yasin, Zuhaina Zakaria, Elia Erwani Hassan, "Efficient Scheduling of Power Generating Units Using Grasshopper Optimization Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 8, pp. 145-151, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P113

Abstract:

This article introduces a novel method for addressing the unit commitment problem in power systems by utilizing the Grasshopper Optimization Algorithm (GOA). Unit commitment is a crucial technique used in electric power systems for operational planning, aiming to schedule power generating units efficiently to meet load demand while minimizing total operational costs. This involves considering various operational constraints such as power balance, generation capacity, start-up expenses, and minimum durations for both starting up and shutting down. GOA is a mathematical model that accurately replicates the distinct characteristics of grasshopper behavior during both the nymph and adulthood phases, specifically their foraging behavior in search of food sources in the natural environment. The aim of this study is to identify the most efficient unit commitment for producing scheduling, with the goal of minimizing the total operating cost while considering various limitations. The proposed technique is applied to a test system consisting of 5 generating units over a time horizon of 24 hours. The numerical outcomes of the GOA are being compared to those of the Dynamic Programming (DP) technique in terms of the total operating cost. The findings revealed that GOA offers the most economical total operating cost in comparison to DP.

Keywords:

Total operational costs, Unit commitment, Generator scheduling, Cost minimization, Dynamic programming.

References:

[1] Ce Shang et. al., “Robust Planning Upon Unit Commitment,” International Journal of Electrical Power & Energy Systems, vol. 157, pp. 1-4, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Soraphon Kigsirisin, and Hajime Miyauchi, “Short-Term Operation Schedulling of Unit Commitment Using Binary Alternative Moth-Flame Optimization,” IEEE Access, vol. 9, pp. 12267-12281, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] G.W. Chang et al., “A Practical Mixed Integer Linear Programming Based Approach for Unit Commitment,” IEEE Power Engineering Society General Meeting, vol. 1, pp. 221-225, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Neha Thakur, and L.S. Titare, “Determination of Unit Commitment Problem Using Dynamic Programming,” International Journal of Novel Research in Electrical and Mechanical Engineering, vol. 3, no. 1, pp. 24-28, 2016.
[Google Scholar] [Publisher Link]
[5] Prateek Kumar Singhal, and R. Naresh Sharma, “Dynamic Programming Approach for Solving Power Generating Unit Commitment Problem,” 2nd International Conference on Computer and Communication Technology, pp. 298-303, 2011. [CrossRef] [Google Scholar] [Publisher Link]
[6] Tomonobu Senjyu et al., “Emerging Solution of Large-Scale Unit Commitment Problem by Stochastic Priority List,” Electric Power Systems Research, vol. 76, no. 5, pp. 283-292, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiang Yu, and Xueqing Zhang, “Unit Commitment Using Lagrangian Relaxation and Particle Swarm Optimization,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 510-522, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] C.C.A. Rajan, “An Evolutionary Programming Based Tabu Search Method for Solving the Unit Commitment Problem in Utility System,” IEEE Region 10 Conference TENCON 2004, vol. 3, no. 1, pp. 472-475, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[9] V. Senthil Kumar, and M.R. Mohan, “Solution to Security Constrained Unit Commitment Problem Using Genetic Algorithm,” International Journal of Electrical Power Energy System, vol. 32, no. 2, pp. 117-125, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tolga Karabas, and Sedef Meral, “An Exact Solution Method and a Genetic Algorithm-Based Approach for the Unit Commitment Problem in Conventional Power Generation Systems,” Computers & Industrial Engineering, vol. 176, 2023. [CrossRef] [Google Scholar] [Publisher Link]
[11] I. Jacob Raglend et al., “Solution to Profit Based Unit Commitment Problem Using Particle Swarm Optimization,” Applied Soft Computing, vol. 10, no. 4, pp. 1247-1256, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[12] K. Vaisakh, and L.R. Srinivas, “Evolving Ant Colony Optimization Based Unit Commitment,” Applied Soft Computing, vol. 11, no. 2, pp. 2863-2870, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] E.S. Ali, S.M. Abd Elazim, and A.S. Balobaid, “Implementation of Coyote Optimization Algorithm for Solving Unit Commitment Problem in Power Systems,” Energy, vol. 263, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Pravin G. Dhawale et al., “Chaotic Arithmetic Optimization Algorithm for Optimal Sizing of Security Constrained Unit Commitment Problem in Integrated Power System,” IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), pp. 366-371, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Xiaodong Zheng, Jianhui Wang, and Meng Yue, “A Fast Quantum Algorithm for Searching the Quasi-Optimal Solutions of Unit Commitment,” IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4755-4758, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Paulos Bekana, Archana Sarangi, and Shubhendu Kumar Sarangi, “Analysis of Crossover Techniques in Modification of Grasshopper Optimization Algorithm,” International Conference in Advances in Power, Signal, and Information Technology (APSIT), pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ayman Alhejji et al., “Optimal Power Flow Solution with an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm,” IEEE Access, vol. 8, pp. 119020-119037, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Pratap Chandra Nayak, Ramesh Chandra Prusty, and Sidhartha Panda, “Grasshopper Optimization Algorithm Optimized Multistage Controller for Automatic Generation Control of a Power System with FACTS Devices,” Protection and Control of Modern Power Systems, vol. 6, no. 1, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Suresh Velamuri et al., “Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation,” IEEE Systems Journal, vol. 16, no. 2, pp. 3284-3295, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ahmed Hussain Elmetwaly, Azza Ahmed Eldesouky, and Abdelhay Ahmed Sallam, “An Adaptive D-FACTS for Power Quality Enhancement in an Isolated Microgrid,” IEEE Access, vol. 8, pp. 57923-57942, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Yeganeh Sharifian, and Hamdi Abdi, “Solving Multi-Area Economic Dispatch Problem Using Hybrid Exchange Market Algorithm with Grasshopper Optimization Algorithm,” Energy, vol. 267, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Masoud Ahmadipour et al., “Optimal Load Shedding Scheme Using Grasshopper Optimization Algorithm for Islanded Power System with Distributed Energy Resources,” Ain Shams Engineering Journal, vol. 14, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] A. Turgeon, “Optimal Unit Commitment,” IEEE Transaction on Automatic Control, vol. 22, no. 2, pp. 223-227, 1977.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shahrzad Saremi, Seyedali Mirjalili, and Andrew Lewis, “Grasshopper Optimisation Algorithm: Theory and Application,” Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
[CrossRef] [Google Scholar] [Publisher Link]