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Harris Hawks Optimization Algorithm for Energy-Efficient Load Dispatch in Multi-Source Renewable Power Grids.

Najaad OubeBlika
Volume 1

Abstract

Energy-efficient load dispatch is a fundamental optimization problem in modern renewable power grids, particularly as power systems increasingly integrate multiple generation sources such as photovoltaic arrays, wind turbines, hydropower units, biomass generators, battery energy storage systems, and conventional backup units. The variability and uncertainty of renewable energy generation introduce significant operational challenges related to power balance, generation cost, transmission losses, reserve allocation, voltage stability, emission reduction, and reliable demand satisfaction. Conventional load dispatch methods often rely on deterministic assumptions, linearized models, or gradient-based optimization techniques, which may become less effective when dealing with nonlinear, non-convex, multi-source, and constraint-rich renewable power grid environments. To address these challenges, this paper proposes a Harris Hawks Optimization (HHO)-based framework for energy-efficient load dispatch in multi-source renewable power grids.

The proposed framework formulates the load dispatch problem as a constrained optimization task aimed at determining the optimal power contribution of each available energy source while satisfying system demand and operational limits. The objective function is designed to minimize total generation cost, fuel consumption of backup units, active power losses, carbon emissions, renewable energy curtailment, and battery degradation cost, while maximizing renewable energy utilization and overall energy efficiency. Operational constraints include power balance, generator capacity limits, ramp-rate limits, spinning reserve requirements, battery state-of-charge boundaries, charging and discharging limits, transmission line capacity, voltage stability margins, and renewable generation availability. By incorporating these technical and economic constraints, the proposed model provides a realistic representation of dispatch decisions in renewable-rich power grids.

The Harris Hawks Optimization algorithm is employed as the core metaheuristic search strategy due to its ability to balance exploration and exploitation through cooperative hunting behavior inspired by Harris hawks. In the proposed dispatch framework, candidate solutions represent power allocation schedules among renewable sources, storage systems, and dispatchable generators. The HHO algorithm iteratively updates these candidate solutions to identify near-optimal dispatch patterns under uncertain load demand and renewable output conditions. To improve robustness, scenario-based modeling is incorporated using historical solar irradiance, wind speed, hydrological flow, biomass availability, electricity demand, and weather forecasting data. In addition, uncertainty handling strategies such as Monte Carlo simulation, probabilistic renewable modeling, and reserve-aware dispatch are integrated to improve decision reliability under fluctuating generation conditions.

A comprehensive simulation environment is developed to evaluate the proposed framework across different operating scenarios, including peak demand periods, low renewable generation intervals, high renewable penetration, grid-connected operation, islanded microgrid operation, and storage-supported dispatch. Performance is assessed using total operating cost, energy efficiency, power loss reduction, renewable utilization rate, emission reduction, load satisfaction ratio, convergence speed, computational time, and constraint violation rate. Comparative experiments are conducted against conventional economic dispatch methods, Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, Differential Evolution, and rule-based dispatch strategies. Experimental evaluation is expected to demonstrate that the proposed HHO-based approach achieves superior dispatch efficiency, lower operating cost, reduced emissions, and improved convergence behavior compared with existing optimization techniques.

The proposed framework can be integrated into smart grid energy management systems, microgrid controllers, and renewable dispatch platforms to support real-time and day-ahead operational planning. Its flexible optimization structure enables adaptation to different grid scales, energy resource portfolios, and operational priorities. Furthermore, sensitivity analysis is incorporated to examine the influence of renewable penetration level, storage capacity, demand uncertainty, and penalty factor selection on dispatch performance. These analytical components provide valuable insights for grid operators and planners seeking to improve renewable energy integration while maintaining economic and technical reliability.

The proposed Harris Hawks Optimization-based load dispatch framework offers an efficient, scalable, and robust solution for multi-source renewable power grids. By optimizing power allocation across heterogeneous energy resources while accounting for cost, emissions, losses, and operational constraints, the system supports cleaner, more reliable, and more economically efficient power system operation. Its ability to manage uncertainty and nonlinear dispatch behavior makes it a promising tool for next-generation smart grids, renewable microgrids, and sustainable energy management applications.

 

 

Keywords

References

  • [1] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Abdelhamid, A. A., Eid, M. M., & Ibrahim, A. (2024). Greylag goose optimizati on:
  • [2] Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., Gharehchopogh, F. S., El -kenawy, E. S. M., ... & Mirjalili, S. (2024).
  • [3] El-Kenawy, E. S. M., Ibrahim, A., Mirjalili, S., Eid, M. M., & Hussein, S. E. (2020). Novel feature selection and voting classif ier
  • [4] El-Kenawy, E. S. M., Eid, M. M., Saber, M., & Ibrahim, A. (2020). MbGWO -SFS: Modified binary grey wolf optimizer based on
  • [5] El-Kenawy, E. S., & Eid, M. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. Int. J. Innov. Compu t. Inf.
  • [6] El-Kenawy, E. S. M., Mirjalili, S., Ibrahim, A., Alrahmawy, M., El -Said, M., Zaki, R. M., & Eid, M. M. (2021). Advanced meta -
  • [7] Khodadadi, N., Abualigah, L., El -Kenawy, E. S. M., Snasel, V., & Mirjalili, S. (2022). An archive -based multi -objective arithmetic
  • [8] Ibrahim, A., Mirjalili, S., El -Said, M., Ghoneim, S. S., Al -Harthi, M. M., Ibrahim, T. F., & El -Kenawy, E. S. M. (2021). Wind speed
  • [9] Abdelhamid, A. A., El -Kenawy, E. S. M., Alotaibi, B., Amer, G. M., Abdelkader, M. Y., Ibrahim, A., & Eid, M. M. (2022). Robust
  • [10] El-Kenawy, E. S. M., Mirjalili, S., Alassery, F., Zhang, Y. D., Eid, M. M., El -Mashad, S. Y., ... & Abdelhamid, A. A. (2022). Novel
  • [11] Hassib, E. M., El -Desouky, A. I., Labib, L. M., & El -Kenawy, E. S. M. (2020). WOA+ BRNN: An imbalanced big data classification
  • [12] Eid, M. M., El -kenawy, E. S. M., & Ibrahim, A. (2021, March). A binary sine cosine -modified whale optimization algorithm for feature
  • [13] El-Sayed Towfek, M. (2018). El -kenawy. Trust Model for Dependable File Exchange in Cloud Computing. International Journal of
  • [14] Abdelhamid, A. A., Towfek, S. K., Khodadadi, N., Alhussan, A. A., Khafaga, D. S., Eid, M. M., & Ibrahim, A. (2023). Waterwhee l plant
  • [15] El-Kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Mirjalili, S., Khodadad, N., Alduailij, M. A., ... & Khafaga, D. S. (2023). Al-
  • [16] Alhussan, A. A., Abdelhamid, A. A., El -Kenawy, E. S. M., Ibrahim, A., Eid, M. M., Khafaga, D. S., & Ahmed, A. E. (2023). A binary
  • [17] Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El -Kenawy, E. S. M. (2024). Global Potato Production Forecasting Based
  • [18] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [19] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
  • [20] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
  • [21] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
  • [22] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
  • [23] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
  • [24] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [25] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
  • [26] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
  • [27] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
  • [28] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
  • [29] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [30] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
  • [31] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
  • [32] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [33] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [34] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
  • [35] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
  • [36] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
  • [37] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
  • [38] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [39] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
  • [40] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
  • [41] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
  • [42] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
  • [43] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
  • [44] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
  • [45] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
  • [46] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
  • [47] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
  • [48] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
  • [49] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
  • [50] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
  • [51] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
  • [52] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
  • [53] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
  • [54] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
  • [55] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
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