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.
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