Economic–Reliability Optimization of Energy Systems Toward Reducing Unserved Energy Using the Seagull Optimization Algorithm
Subject Areas : electrical and computer engineering
Sina Samadi Gharehveran
1
*
,
Kimia Shirini
2
,
MirReza SeyedGhoreyshi
3
,
Sadra Amirzehni
4
1 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
2 - Tabriz Islamic Art University, Multimedia Faculty, Tabriz, Iran
3 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
4 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Keywords: Seagull Optimization Algorithm, Demand Response Program, Microgrid, Electric Vehicle, Distribution Locational Marginal Prices,
Abstract :
Given the growing concerns about the increase in environmental pollutants from fossil-fuel power plants, numerous economic challenges related to optimizing generation and consumption costs in energy distribution networks have emerged. This paper presents a multi-objective optimization model for the operational management of microgrids considering the growing demand for hybrid electric vehicles. The objective of the proposed model is to reduce total operating costs and enhance network reliability by minimizing unserved energy. To achieve these goals, a multi-objective Seagull Optimization Algorithm, inspired by the search behavior of seagulls, is employed to obtain optimal solutions. Electric vehicles and demand response programs are incorporated into the model to reduce carbon dioxide emissions and smooth distribution locational prices. The main contribution of this research is the development of a hybrid optimization framework for coordinating electric vehicles, renewable energy resources, and demand response management. Performance evaluation using the 69-bus distribution network demonstrates that the proposed method reduces operating costs by 12.5%, decreases unserved energy from 300 kW to 150 kW, reduces network power losses by 15%, and improves the voltage profile within stability limits. Results confirm the effectiveness of the proposed approach in optimizing network performance and reducing operational costs.
[1] S. S. Gharehveran, K. Shirini, S. C. Khavar, S. H. Mousavi, and A. Abdolahi, "Deep learning-based demand response for short-term operation of renewable-based microgrids," The Journal of Supercomputing, vol. 80, no. 18, pp. 26002-26035, 2024.
[2] م. ظهیری، ک. شیرینی و س. صمدی قره¬ورن، "بهره¬برداری بهینه ریزشبکه¬های چند حامله انرژی تاب¬آور تخت رویدادهای با احتمال کم و تأثیر بالا،" علوم و فناوری¬های پدافند نوین، سال 14، ش. 4، صص. 60-51، زمستان 1402.
[3] S. S. Gharehveran, S. G. Zadeh, and N. Rostami, "Resilience-oriented planning and pre-positioning of vehicle-mounted energy storage facilities in community microgrids," Journal of Energy Storage, vol. 72, pt. A, Article ID: 108263, 2023.
[4] X. Xiaoluan, H. Farajian, X. Xifeng, and K. Ohshima, "Scheduling of renewable energy and plug-in hybrid electric vehicles based microgrid using hybrid crow—pattern search method," Journal of Energy Storage, vol. 47, Article ID: 103605, Mar. 2022.
[5] K. Shirini, M. B. Kordan, and S. S. Gharehveran, "Impact of learning rate and epochs on LSTM model performance: A study of chlorophyll-a concentrations in the marmara sea," The Journal of Supercomputing, vol. 81, Article ID: 265, 2025.
[6] S. M. Farshbaf, K. Shirini, S. S. Gharehveran, and A. Abdollahi, "Reducing phase lag and noise in residential load forecasting with a hybrid residual-attention model," Algorithms, vol. 19, no. 8, Article ID: 661, 2026.
[7] M. Ahrari, et al., "A security-constrained robust optimization for energy management of active distribution networks with presence of energy storage and demand flexibility," Journal of Energy Storage, vol. 84, pt. B, Article ID: 111024, 2024.
[8] N. S. S. Singh, et al. ,"Artificial neural network-based adaptive voltage control of a hybrid zeta DC/DC converter using multilayer perceptron and radial basis function architectures with back-propagation pearning," IEEE Access, vol. 14, pp. 105596-105622, 2026.
[9] S. S. Gharehveran, S. Ghassemzadeh, and N. Rostami, "Two-stage resilience-constrained planning of coupled multi
-energy microgrids in the presence of battery energy storages," Sustainable Cities and Society, vol. 83, , Article 103952, 2022. [10] ر. غفارپور، س. سعدی، س. زمانیان و م. محمودیان، "ارتقای تاب¬آوری سامانه¬های توزیع الکتریکیاز طریق عملکرد بهینه و تطبیق¬پذیری در حضور منابع تولید سیار،" نشریه علمی پدافند غیر عامل، سال 14، ش. 3، صص. 114-105، پاییز 1402.
[11] H. Moradi CheshmehBeigi and M. Geravandi, "Unit commitment risk evaluation considering load uncertainty and wind power," Journal of Energy Management and Technology, vol. 7, no. 3, pp. 134-141, Summer 2023.
[12] س. صمدی قره¬ورن، م. نصیری و س. قاسم¬زاده، "بهره¬برداری بهینه ریزشبکه¬های چند حامله انرژی تاب¬آور تخت رویدادهای با احتمال کم و تأثیر بالا،" علوم و فناوری¬های پدافند نوین، سال 16، ش. 1، صص. 60-51، بهار 1404.
[13] Y. Mei, B. Li, H. Wang, X. Wang, and M. Negnevitsky, "Multi-objective optimal scheduling of microgrid with electric vehicles," Energy Reports, vol. 8, pp. 4512-4524, Nov. 2022.
[14] S. Samadi Gharehveran, K. Shirini, M. Sarhangzadeh, and S. Gholinavaz, "A review of game theory-based approaches for demand side management in smart energy grids," Power, Control, and Data Processing Systems, vol. 3, no. 1, pp. 28-37, Winter 2026.
[15] م. عابدینی و ح. معظمی گودرزی، "بازآرایی شبکه¬های توزیع برق جهت افزایش تاب¬آوری و قابلیت اطمینان با استفاده از توابع هدف فازی و نظریه بازی¬،" نشریه علمی پدافند غیر عامل، سال 15، ش. 3، صص. 38-27، پاییز 1403.
[16] K. Shirini, H. S. Aghdasi, and S. Saeedvand, "Modified imperialist competitive algorithm for aircraft landing scheduling problem," The Journal of Supercomputing,, vol. 80, no. 10, pp. 13782-13812, 2024.
[17] ع. کریمی سعید آبادی، س. ب. مظفری، س. سلیمانی و ح. محمدنژاد شورکائی، "بهره¬برداری بهینه از سیستم تامین آب-انرژی یک ریزشبکه جزیره¬ای با چند حامل انرژی با هدف ارتقاء تاب¬آوری در مقابل حملات سایبری¬،" نشریه علمی پدافند غیر عامل، سال 13، ش. 4، صص. 10-1، زمستان 1401.
[18] G. Balram and P. S. Kumar, "Harmonics reduction and balanced transition in hybrid renewable energy sources in a micro grid power system," Journal of New Materials for Electrochemical Systems, vol. 26, no. 4, pp. 233-15, 2023.
[19] م. میرصادقی، ف. موذن و ر. غفارپور، "بهبود تاب¬آوری شبکه قدرت در مواجهه با حملات متمرکز با استفاده از آنالیز پیشامد،" نشریه علمی پدافند غیر عامل، سال 13، ش. 3، صص. 10-1، پاییز 1401.
[20] A. Aldosary, et al., "Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm," Neural Computing and Applications, vol. 33, pp. 10005-10020, 2021.
[21] S. S. Gharehveran, K. Shirini, and A. Abdolahi, "Optimizing energy storage solutions for grid resilience: A comprehensive overview," In A. Y. Abdelaziz, M. A. Mossa, and M. Bajaj (eds.) Energy Storage Devices - A Comprehensive Overview. Intech Open, 2025.
[22] G. Li and X. P. Zhang, "Modeling of Plug-in Hybrid Electric Vehicle Charging Demand in Probabilistic Power Flow Calculations," IEEE Trans. on Smart Grid, vol. 3, no. 1, pp. 492-499, Mar. 2012.
[23] S. Samadi Gharehveran, "A review of energy management of multi-microgrid power systems in the presence of uncertainty of distributed generation gesources," Power, Control, and Data Processing Systems, vol. 2, no. 4, pp. 1-13, 2025.
[24] K. Shirini, H. A. Hayati, S. S. Gharehveran, and K. Shirini, "Electricity price forecasting with ensemble meta-models and SHAP explainers: A PCA-driven approach," Scientific Reports, vol. 16, Article Id: 6466, 2026.
[25] A. Taherihajivand, K. Shirini, and S. Samadi Gharehveran, "An overview of product performance prediction using artificial algorithms," Journal of Agricultural Mechanization, vol. 9, no. 3, pp. 1-14, 2024.
[26] A. Nekahdari, E. D. Kouzehkonani, N. Saeedi, K. Shirini, and S. S. Gharehveran, "Multi-task procedural content generation with reinforcement learning," Scientific Reports, vol. 16, Article ID: 18389, 2026.
[27] K. Shirini, A. T. Hajivand, and S. S. Ghareveran, "A novel deep learning-based method for potato leaf disease classification," in Proc. 9th Adv. Eng. Days, vol. 9, pp. 462-464, Tabriz, Iran, Jul. 9-10, 2024.
[28] A. Taherihajivand, K. Shirini, and S. Samadi Gharehveran, "Weed detection in fields using convolutional neural network based on deep learning," Agric. Eng., vol. 47, no. 1, pp. 129-142, Apr. 2024.
[29] س. صمدی قره¬ورن، "بهینه¬سازی باتری و چیدمان آنها در وسایل نقلیه الکتریکی بر اساس بهینه¬سازی چند هدفه،" نشریه علمی پدافند غیر عامل، سال 16، ش. 4، صص. 33-17، زمستان 1404.
[30] J. D. Knowles and D. W. Corne, "Approximating the nondominated front using the pareto archived evolution strategy," Evolutionary Computation, vol. 8, no. 2, pp. 149-172, Jun. 2000.
[31] R. Nemati, K. Shirini, and S. S. Gharehveran, "FER-HA: A hybrid attention model for facial emotion recognition," The Journal of Supercomputing, vol. 81, Article ID: 1485, 2025.
[32] T. Hai, A. K. Alazzawi, J. M. Zain, and H. Oikawa, "A stochastic optimal scheduling of distributed energy resources with electric vehicles based microgrid considering electricity price," Sustainable Energy Technologies and Assessments, vol. 55, Article ID: 102879, 2023.
[33] S. Samadi Gharehveran, K. Shirini, M. Sarhangzadeh, and S. Gholinavaz, "A review of game theory-based approaches for demand side management in smart energy grids," Power, Control, and Data Processing Systems, vol. 3, no.1, pp. 28-37, Winter 2025.
[34] H. Farzin and M. Monadi, "Reliability enhancement of active distribution grids via emergency V2G programs: An analytical cost/worth evaluation framework," Scientia Iranica, Trans. on Computer Science & Engineering and Electrical Engineering, vol. 26, pp. 3635-3645, Nov./Dec. 2019.