Energy Management
A.Y. Dewi; M.Y. Arabi; Z.F. Al-lami; M.M. Abdulhasan; A.S. Ibrahim; R. Sattar; D.A. Lafta; B.A. Usmanovich; D. Abdullah; Y. Yerkin
Abstract
Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ...
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Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ability to quickly recover from faults. Intelligent distribution networks can self-heal, which speeds up restoration and ensures energy delivery. This paper proposes a comprehensive strategy for intelligent distribution network self-healing after flaws. Restoration involves identifying and isolating the damaged area using offline and online methods. Online approaches, notably islanding, have helped restore services in the affected region. This paper presents a novel linear mathematical approach to optimize online islanding. The model estimates the boundaries of islanded microgrids and the appropriate number of microgrids for faults, enabling quick restoration. This analysis also seeks to determine the fault-affected area's system layout. A mathematical model defines the ideal arrangement in the first layer of the two-layered approach. The next layer analyzes unit participation in the intelligent distribution system, focusing on rescheduling, allocation, and organization. Additionally, the study identifies the best energy storage solutions to aid restoration. The recommended strategy uses adaptive load reduction and demand response to maximize system recovery. The mathematical model benefits from various strategies, including faster execution and better outcomes. This research advances intelligent distribution networks by combining advanced mathematical modeling, self-healing, and smart load control. These upgrades boost distribution networks' effectiveness.
