Optimizing Economic Load Dispatch in a 10-Unit System Using the SSA Method

Year : 2024 | Volume : 14 | Issue : 03 | Page : 1 13
    By

    Tejaswita Khobaragade,

  • K. T. Chaturvedi,

  1. PhD Scholar, Department of Electrical and Electronics Engineering,University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya,, Madhya Pradesh,, India
  2. Associate professor, Department of Electrical and Electronics Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya,, Madhya Pradesh,, India

Abstract

The optimization of economic load dispatch (ELD) in a 10-unit power system employing the social spider algorithm (SSA) method is reported. ELD is a critical aspect of power system operation, aiming to allocate the power generation among multiple units efficiently while meeting the demand at the lowest possible cost. The SSA method, inspired by the collaborative behavior of social spiders, demonstrates its efficacy in solving optimization problems. In this research, the 10-unit power system is modeled, considering various constraints and objectives inherent to ELD problems. The SSA method is applied to search for the optimal combination of power generation levels for each unit, minimizing the overall cost and ensuring the fulfillment of demand. The results obtained through the SSA method are compared with traditional methods to assess its effectiveness in achieving a more economical and efficient load distribution. The study provides insights into the applicability and advantages of SSA in addressing ELD challenges in power systems with a specific focus on 10-unit configurations. The findings contribute to the ongoing efforts in utilizing nature-inspired algorithms for power system optimization, offering a promising approach for enhancing operational efficiency and cost-effectiveness.

Keywords: SSA, PEV, V2G, Thermal Units, ELD

[This article belongs to Trends in Electrical Engineering ]

How to cite this article:
Tejaswita Khobaragade, K. T. Chaturvedi. Optimizing Economic Load Dispatch in a 10-Unit System Using the SSA Method. Trends in Electrical Engineering. 2024; 14(03):1-13.
How to cite this URL:
Tejaswita Khobaragade, K. T. Chaturvedi. Optimizing Economic Load Dispatch in a 10-Unit System Using the SSA Method. Trends in Electrical Engineering. 2024; 14(03):1-13. Available from: https://journals.stmjournals.com/tee/article=2024/view=176300


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References

  1. Adhvaryyu PK, Chattopadhyay PK, Bhattacharjya Dynamic economic emission load dispatch of hybrid power system using bio-inspired social spider algorithm. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, July 4–6, 2016. pp. 1–6. doi: 10.1109/ICPEICES.2016.7853448.
  2. Yang Z, Li K, Niu Q, Xue Y, Foley, A. A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. J Mod Power Syst Clean Energy. 2014; 2: 298–307.
  3. Behera S, Behera S, Barisal AK, Pradhan Economic load dispatch with renewable energy resources and plug-in electric vehicles. In: Proceedings of the 2020 International Conference on Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG), Bhubaneswar, India, February 14–15, 2020. pp. 22–26.
  4. Baş E, Ülker Improved social spider algorithm for large scale optimization. Artif Intell Rev. 2021; 54: 3539–3574.
  5. Yang Z, Li K, Niu Q, Zhang C, Foley Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads. In: Proceedings of the 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), Orlando, FL, USA, December 9–12, 2014. pp. 1–7.
  6. Mortazi A, Saeed S, Akbari H. Optimizing operation scheduling in a microgrid considering probabilistic uncertainty and demand response using social spider algorithm. Int J Smart Electr Eng. 2023; 12 (2): 113–
  7. Hosseinalipour A, Gharehchopogh FS, Masdari M, Khademi A. Toward text psychology analysis using social spider optimization algorithm. Concurr Comput Pract Experience. 2021; 33 (17): e6325. doi: 1002/cpe.6325.

 

  1. Yao M, Molzahn DK, Mathieu J An optimal power-flow approach to improve power system voltage stability using demand response. IEEE Trans Control Netw Syst. 2019; 6: 1015–1025. /doi: 10.1109/TCNS.2019.2910455.
  2. Hao W-K, Li Y-P, Wang J-S, Zhu Solving economic load dispatch problem of power system based on differential evolution algorithm with different mutation strategies. IAENG Int J Comput Sci. 2022; 49: 156–165.
  3. Dubey HM, Pandit M, Panigrahi B Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. Int J Electr Power Energy Syst. 2016; 83: 158–174.
  4. Al-Betar MA, Awadallah MA, Makhadmeh SN, Abu Doush I, Abu Zitar R, Alshathri S, Elaziz M A hybrid Harris Hawks optimizer for economic load dispatch problems. Alexandria Eng J. 2023; 64: 365–389.
  5. Yang W, Cheng T, Guo Y, Yang Z, Feng A modified social spider optimization for economic dispatch with valve-point effects. Complexity. 2020; 2020: 2865929.
  6. Yu JJQ, Li VOK. A social spider algorithm for global optimization. Appl Soft Comput. 2015; 30: 614–627.
  7. Banerjee S, Maity D, Chanda CK. Teaching learning-based optimization for economic load dispatch problem considering valve point loading effect. Int. J. Electr Power Energy Syst. 2015; 73: 456–464.
  8. Yuan X, Su A, Yuan Y, Nie H, Wang An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy. 2009; 34: 67–74.
  9. Maharana HS, Dash S Quantum behaved artificial bee colony based conventional controller for optimum dispatch. Int J Electr Comput Eng. 2023; 13: 1260.
  10. Ma H, Yang Z, You P, Fei Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging. Energy. 2017; 135: 101–111.
  11. Benalcazar P, Samper ME, Vargas Short-term economic dispatch of smart distribution grids considering the active role of plug-in electric vehicles. Electr Power Syst Res. 2019; 177: 105932.
  12. Wu D, Aliprantis DC, Ying Load scheduling and dispatch for aggregators of plug-in electric vehicles. IEEE Trans Smart Grid. 2011; 3: 368–376.
  13. Scarabaggio P, Carli R, Dotoli Noncooperative equilibrium-seeking in distributed energy systems under AC power flow nonlinear constraints. IEEE Trans Control Netw Syst. 2022; 9: 1731–1742. doi: 10.1109/TCNS.2022.3181527.
  14. Mignoni N, Scarabaggio P, Carli R, Dotoli Control frameworks for transactive energy storage services in energy communities. Control Eng Pract. 2023; 130: 105364.
  15. Venkatesan K, Govindarajan Optimal power flow control of hybrid renewable energy system with energy storage: a WOANN strategy. J Renew Sustain Energy. 2019; 11: 015501.
  16. Ahangar HG, Yew WK. Flynn D. Smart local energy systems: optimal planning of stand-alone hybrid green power systems for on-line charging of electric vehicles. IEEE Access. 2023; 11: 7398– doi: 10.1109/ACCESS.2023.3237326.
  17. Yang Z, Yang F, Min H, Tian H, Hu W, Liu J, Eghbalian Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources. Energy. 2023; 263 (Part A): 125695.

Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 31/07/2024
Accepted 13/08/2024
Published 30/09/2024


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