N. Afsari; S.J. SeyedShenava; H. Shayeghi
Abstract
The inevitable emergence of intelligent distribution networks has introduced new features in these networks. According to most experts, self-healing is one of the main abilities of smart distribution networks. This feature increases the reliability and resiliency of networks by reacting fast and restoring ...
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The inevitable emergence of intelligent distribution networks has introduced new features in these networks. According to most experts, self-healing is one of the main abilities of smart distribution networks. This feature increases the reliability and resiliency of networks by reacting fast and restoring the critical loads (CLs) during a fault. Nevertheless, the stochastic nature of the components in a power system imposes significant computational risk in enabling the system to self-heal. In this paper, a mathematical model is introduced for the self-healing operation of networked Microgrids (MGs) to assess the risk in the optimal service restoration (SR) problem. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) and their stochastic nature besides the distributed generation units (DGs), the ability to reconfiguration, and demand response program are considered simultaneously. The objective function is designed to maximize the restored loads and minimize the risk. The Conditional Value-at-Risk (CVaR) is used to calculate the risk of the SR as one of the most efficient and famous risk indices. In the general case study and considering $\beta $ equal to the 0, 1, 2, 3, and 4, expected values of SR for the risk-averse problem is 21.2, 20, 19.3, 19.1, and 19\% less than the risk-neutral problem, respectively. The formulation of the problem is mixed-integer linear programming (MILP), and the model is tested in the modified Civanlar test system. The analysis of several case studies has proved the performance of the proposed model and the importance of risk management in the problem.
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.
