Journal of Applied Science and Engineering

Published by Tamkang University Press


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Ngo Minh KhoaThis email address is being protected from spambots. You need JavaScript enabled to view it., Tran Tien Dat, and Nguyen Vu Hoa

Faculty of Engineering and Technology, Quy Nhon University, Binh Dinh, Vietnam



Received: May 2, 2023
Accepted: November 12, 2023
Publication Date: December 29, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The demand side management (DSM) is one of the most vital techniques which electric utilities have applied to control their electricity demand by incentivizing customers to modify their energy consumption patterns during peak hours or reduce their overall energy consumption. This paper studied the application of genetic algorithm (GA) to optimize the DSM problem to bring economic efficiency to electric customers. The objective function of the optimal problem was the maximum loading factor or the minimum energy cost. In addition, the equality and inequality constraints of DSM techniques were also set so that the GA can be applied to find the optimal solution for customers. The optimization toolbox in the Matlab software was applied to establish the mathematical model of the optimization problem. The test system including a synthetic load and three large-capacity boilers was carried out to validate the method. The optimal simulation results of the proposed method indicated clearly that the loading profiles of the loads in the test system were flexibly adjusted to maximize the loading factor as well as to minimize the energy cost of the whole system.

Keywords: Demand side management, loading profile, loading factor, objective function, optimization tools

  1. [1] B. Dey, F. P. G. Márquez, and A. Bhattacharya, (2022) “Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems" Sustainable Energy Technologies and Assessments 54: 102903.
  2. [2] A. T. Dahiru, D. Daud, C. W. Tan, Z. T. Jagun, S. Samsudin, and A. M. Dobi, (2023) “A comprehensive review of demand side management in distributed grids based on real estate perspectives" Environmental Science and Pollution Research: 1–30.
  3. [3] B. Koul, K. Singh, and Y. Brar, (2021) “An introduction to smart grid and demand-side management with its integration with renewable energy" Advances in Smart Grid Power System: 73–101.
  4. [4] P. Kumar, I. Ali, and D. V. Thanki. “Demand-side management: Energy efficiency and demand response”. In: Handbook of Research on Power and Energy System Optimization. IGI Global, 2018, 453–479.
  5. [5] P. Gupta. Demand Side Management: An approach to peak load smoothing. 2012.
  6. [6] K. Gyanwali and T. R. Bajracharya. “Demand side management in industrial sector of nepal”. In: Proceedings of IOE Graduate Conference. 1. 2013, 108–116.
  7. [7] A. Glazunova, (2022) “Development of a Day-Ahead Demand Side Management Strategy to Improve the Microgrid Efficiency" IFAC-PapersOnLine 55(9): 256–261.
  8. [8] M. Izadi, F. Razavi, et al., (2017) “Energy Loss Reduction in a 20-kV Distribution Network Considering Available Budget" Journal of Applied Science and Engineering 20(1): 21–30.
  9. [9] C. Tao, Q. Zhuo, X. Guo, W. Wang, T. Li, and X. Bai, (2023) “Research On Voltage Coordinated Control Of Active Distribution Network Considering Demand Response" Journal of Applied Science and Engineering 26(8): 1083–1094.
  10. [10] S. N. Bragagnolo, J. C. Vaschetti, and F. Magnago, (2021) “A technical and economic criteria comparison on demand side management with multi-level optimization model" IEEE Latin America Transactions 19(9): 1494–1501.
  11. [11] R. Torkan, A. Ilinca, and M. Ghorbanzadeh, (2022) “A genetic algorithm optimization approach for smart energy management of microgrids" Renewable Energy 197: 852–863.
  12. [12] M. Awais, N. Javaid, N. Shaheen, Z. Iqbal, G. Rehman, K. Muhammad, and I. Ahmad. “An efficient genetic algorithm based demand side management scheme for smart grid”. In: 2015 18th international conference on network-based information systems. IEEE. 2015, 351–356.
  13. [13] L. Mellouk, M. Boulmalf, A. Aaroud, K. Zine-Dine, and D. Benhaddou, (2018) “Genetic algorithm to solve demand side management and economic dispatch problem" Procedia computer science 130: 611–618.
  14. [14] R. H. Saloom and H. K. Khafaji, “A New Technique for Genetic Mutations Detection and Classification Using Deep Learning":
  15. [15] Z. Garroussi, R. Ellaia, E.-G. Talbi, and J.-Y. Lucas, (2020) “A hybrid non-dominated sorting genetic algorithm for a multi-objective demand-side management problem in a smart building" International Journal of Electrical & Computer Engineering (2088-8708) 10(1):
  16. [16] Y. Barradi, N. Khaldi, K. Zazi, and M. Zazi, (2019) “A novel genetic approach applied for power loss reduction and improved bus voltage profile in distribution network system" International Journal of Intelligent Engineering and Systems 12(6): 91–99.
  17. [17] J. Pono´cko. Data analytics-based demand profiling and advanced demand side management for flexible operation of sustainable power networks. Springer Nature, 2020.
  18. [18] B. Mota, P. Faria, and Z. Vale, (2022) “Residential load shifting in demand response events for bill reduction using a genetic algorithm" Energy 260: 124978.
  19. [19] D. Javor and A. Janjic, (2016) “Application of demand side management techniques in successive optimization procedures" Communications in Dependability and quality management 19(4): 40–51.
  20. [20] M. Gunasekaran, H. Mohamed Ismail, B. Chokkalingam, L. Mihet-Popa, and S. Padmanaban, (2018) “Energy management strategy for rural communities’ DC micro grid power system structure with maximum penetration of renewable energy sources" Applied Sciences 8(4): 585.
  21. [21] A. Tavakoli, S. Saha, M. T. Arif, M. E. Haque, N. Mendis, and A. M. Oo, (2020) “Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review" IET Energy Systems Integration 2(3): 243–260.
  22. [22] S. Ponrekha A, M. Subathra, C. Bharatiraja, N. Manoj Kumar, and H. Haes Alhelou, (2022) “A topology review and comparative analysis on transformerless gridconnected photovoltaic inverters and leakage current reduction techniques" IET Renewable Power Generation:
  23. [23] K. Parvin, M. Hannan, L. H. Mun, M. H. Lipu, M. G. Abdolrasol, P. J. Ker, K. M. Muttaqi, and Z. Dong, (2022) “The future energy internet for utility energy service and demand-side management in smart grid: Current practices, challenges and future directions" Sustainable Energy Technologies and Assessments 53: 102648.
  24. [24] F. Rabee, I. Jazaery, and K. Kumar, (2023) “QuaternaryChild Crossover for Genetic Algorithm in Real-Time Scheduling Optimization" International Journal of Intelligent Engineering & Systems 16(2):
  25. [25] G. Strbac, (2008) “Demand side management: Benefits and challenges" Energy policy 36(12): 4419–4426.
  26. [26] L. A. Alnabi, A. K. Dhaher, and M. B. Essa, (2022) “Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing" International Journal of Intelligent Engineering & Systems 15(1):
  27. [27] M. U. Saleem, M. R. Usman, M. A. Usman, and C. Politis, (2022) “Design, deployment and performance evaluation of an IoT based smart energy management system for demand side management in smart grid" IEEE Access 10: 15261–15278.
  28. [28] C. M. Affonso and R. V. da Silva, (2015) “Demand side management of a residential system using simulated annealing" IEEE Latin America Transactions 13(5): 1355–1360.