Price-Incentive Distribution Grid Load Scheduling Using Hybrid Grey Wolf Optimization

DOI: https://doi.org/10.33650/jeecom.v8i2.16815
Authors

(1) * Biobele Alexander Wokoma   (Rivers State University, Port Harcourt, Nigeria.)  
        Nigeria
(2)  Kinba Queen Blue-Jack   (Rivers State University, Port Harcourt, Nigeria.)  
        Nigeria
(*) Corresponding Author

Abstract


Distribution feeders increasingly require load schedules that respond to time-varying prices while preserving operational limits. This paper presents a Hybrid Grey Wolf Optimizer (HGWO) for 24-hour distribution grid load scheduling underprice incentives. The method augments the leader-guided position update of the conventional Grey Wolf Optimizer with a momentum-based directional term and evaluates candidate schedules through a penalty-aware objective that combines energy price, incentive reward, and constraint violations. The model was implemented in MATLAB and assessed using hourly price, base-load, and incentive data obtained for a representative 11 kV feeder of the Port Harcourt Electricity Distribution Company. Thirty independent runs were conducted and compared with classical GWO. HGWO attained a best objective value of 38,910 ¢, approximately 2.3% below the GWO result, and reached a stable solution in 58 iterations compared with 82 iterations. Its standard deviation decreased from 760 ¢ to 412 ¢, indicating more consistent search performance. Sensitivity tests on penalty coefficient and population size further showed lower and flatter objective responses. The results demonstrate that HGWO can improve convergence speed, robustness, and price-responsive peak-load redistribution for day-ahead feeder scheduling.

Keywords

Demand response; Distribution grid; Grey wolf optimization; Load scheduling; Price incentive; Swarm intelligence



Full Text: PDF



References


P. Siano, “Demand response and smart grids—a survey,” Renew. Sustain. Energy Rev., vol. 30, pp. 461–478, 2014, doi: 10.1016/j.rser.2013.10.022.

L. I. Wong, M. H. Sulaiman, M. R. Mohamed, and M. S. Hong, “Grey Wolf Optimizer for solving economic dispatch problems,” in Proc. 2014 IEEE Int. Conf. Power Energy (PECon), Dec. 2014, pp. 150–154.

X.-S. Yang, Engineering Optimization: An Introduction with Metaheuristics, 3rd ed. Wiley, 2010.

A. Mirjalili and A. H. Gandomi, Eds., Comprehensive Metaheuristics: Algorithms and Applications. Elsevier, 2023.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, 1995, doi: 10.1109/ICNN.1995.488968.

K. Nainar, J. R. Pillai, and B. Bak-Jensen, “Incentive price-based demand response in active distribution grids,” Appl. Sci., vol. 11, no. 1, Art. no. 180, Dec. 2020, doi: 10.3390/app11010180.

B. Xu, J. Wang, M. Guo, J. Lu, G. Li, and L. Han, “A hybrid demand response mechanism based on real-time incentive and real-time pricing,” Energy, 2021, doi: 10.1016/j.energy.2021.120940.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, doi: 10.1016/j.advengsoft.2013.12.007.

B. Dey, B. Bhattacharyya, and F. P. G. Márquez, “A hybrid optimization-based approach to solve environment constrained economic dispatch problem on microgrid system,” J. Clean. Prod., vol. 307, Art. no. 127196, Jul. 2021.

A. U. R. Adib, W. B. I. Rashid, M. A. R. Jahin, and H. J. Apon, “Hybrid GWOPSO algorithm based load scheduling approach for PV integrated households,” in Proc. 12th Int. Conf. Smart Grid (icSmartGrid), May 2024, pp. 400–405, doi: 10.1109/icSmartGrid61814.2024.10565860.

J. Hu, Z. Song, Y. Tan, and M. Tan, “Optimizing integrated energy systems using a hybrid approach blending grey wolf optimization with local search heuristics,” J. Energy Storage, vol. 87, Art. no. 111384, 2024, doi: 10.1016/j.est.2024.111384.

R. K. Yadav, P. N. Hrisheekesha, and V. S. Bhadoria, “Grey Wolf Optimization based demand side management in solar PV integrated smart grid environment,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3241856.

J. Yang, G. Zhang, and K. Ma, “Real-time pricing-based scheduling strategy in smart grids: A hierarchical game approach,” J. Appl. Math., 2014, doi: 10.1155/2014/329656.

M. H. Mahmoodian, H. Gharibvand, S. Y. Tabrizi, G. B. Gharehpetian, and H. Rastegar, “Optimization of residential PV and battery storage systems in Iran using Grey Wolf Optimizer under the latest tariff structure,” in Proc. 10th Int. Conf. Technol. Energy Manage. (ICTEM), 2025, pp. 1–6.

E. N. Osegi and B. A. Wokoma, “A concurrent power distribution systems model for demand-supply scheduling using the Grey Wolf Optimizer,” in Grey Wolf Optimizer: A Pack of Solutions for Your Optimization Problems, S. Mirjalili, Ed. Morgan Kaufmann, 2026.

X. Zhang, J. Yang, W. Wang, T. Jing, and M. Zhang, “Optimal operation analysis of the distribution network comprising a micro energy grid based on an improved Grey Wolf Optimization algorithm,” Appl. Sci., vol. 8, no. 6, Art. no. 923, Jun. 2018.

S. Sharma, B. Dey, S. Misra, and J. Das, “Enhancing economic performance of a grid-connected microgrid through incentive-driven demand response program,” Arab. J. Sci. Eng., pp. 1–25, 2026.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v8i2.16815


Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Biobele Alexander Wokoma

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.