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Showing 3 results for Hajiabadi
Determining Appropriate Buses and Networks for Applying Demand Side Management Programs by Structural Analysis of EENS
B. Adineh, H. Rajabi Mashhadi, M. E. Hajiabadi,
Volume 10, Issue 2 (June 2014)
Abstract
The main goal of this paper is to structurally analyze impact of DSM programs on reliability indices. A new approach is presented to structurally decompose reliability index Expected Energy Not Supplied (EENS) by using Monte Carlo simulation. EENS is decomposed into two terms. The first term indicates EENS which is caused by generation contingencies. The second term indicates EENS which is caused by transmission and generation contingencies. The proposed approach can be used to indicate appropriate buses for applying DSM. Furthermore, networks are studied at two levels HLI and HLII. Studies show that in some networks reliability indices are affected mostly at the HLI level. While in some other networks, reliability indices are influenced mostly at the HLII level. It means that in these networks, reliability indices are affected by transmission contingencies. Then, it is shown that the implementation of load shifting is effective in some networks and buses. These are the ones which their EENS is more influenced by generation contingencies. However it is not effective in the ones which their EENS is more influenced by transmission contingencies. The simulation results on the IEEE-RTS and Khorasan network show the efficiency of the proposed approach.
Evaluation of Price-Sensitive Loads' Impacts on LMP and Market Power using LMP Decomposition
S. M. Sadr, H. Rajabi Mashhadi, M. Ebrahim Hajiabadi,
Volume 12, Issue 2 (June 2016)
Abstract

This paper presents a novel approach for evaluating impacts of price-sensitive loads on electricity price and market power. To accomplish this aim an analytical method along with agent-based computational economics are used. At first, Nash equilibrium is achieved by computational approach of Q-learning then based on the optimal bidding strategies of GenCos, which are figured out by Q-learning, ISO's social welfare maximization is restated considering demand side bidding. In this research, it was demonstrated that Locational Marginal Price (LMP) at each node of system can be decomposed into five components. The first constitutive part is a constant value for the respective bus, while the next two components are related to GenCos and the last two parts are associated to Load Serving Entities (LSEs). Market regulators can acquire valuable information from the proposed LMP decomposition. First, sensitivity of electricity price at each bus and Lerner index of GenCos to the bidding strategies and maximum pricesensitive demand of LSEs are revealed through weighting coefficients of the last two terms in the decomposed LMP. Moreover, the decomposition of LMP expresses contribution of LSEs to the electricity price. The simulation results on two test systems confirm the capability of the proposed approach.


Integrated Preventive and Predictive Maintenance Markov Model for Circuit Breakers Equipped With Condition Monitoring
A. Karimabadi, M. E. Hajiabadi, E. Kamyab, A. A. Shojaei,
Volume 16, Issue 2 (June 2020)
Abstract
The Circuit Breaker (CB) is one of the most important equipment in power systems. CB must operate reliably to protect power systems as well as to perform tasks such as load disconnection, normal interruption, and fault current interruption. Therefore, the reliable operation of CB can affect the security and stability of power network. In this paper, effects of Condition Monitoring (CM) of CB on the maintenance process and related costs are analyzed. For this, A mathematical formulation to categorize and model equipment failures based on their severity is developed. By CM, some of the high severity failures, named major failures, can be early detected and be corrected as a minor failure. This formulation quantifies the effect of CM on the outage rate and Predictive Maintenance (PDM) rate of equipment. Also, by combining the predictive maintenance to preventive maintenance, the Integrated Preventive and Predictive Maintenance Markov model is presented to analyze the effect of CM on the maintenance process. Finally, the optimal inspection rates of CBs based on the minimum maintenance cost in the traditional and the proposed Markov model are determined. To verify the effectiveness and applicability of the method, the proposed approach is applied to the CBs of KREC in Iran.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.