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Showing 6 results for Zakariazadeh

A. Zakariazadeh, Sh. Jadid,
Volume 10, Issue 2 (June 2014)

Microgrid (MG) is one of the important blocks in the future smart distribution systems. The scheduling pattern of MGs affects distribution system operation. Also, the optimal scheduling of MGs will be result in reliable and economical operation of distribution system. In this paper, an operational planning model of a MG which considers multiple demand response (DR) programs is proposed. In the proposed approach, all types of loads can participate in demand response programs which will be considered in either energy or reserve scheduling. Also, the renewable distributed generation uncertainty is covered by reserve prepared by both DGs and loads. The novelty of this paper is the demand side participation in energy and reserve scheduling, simultaneously. Furthermore the energy and reserve scheduling is proposed for day-ahead and real-time. The proposed model was tested on a typical MG system in connected mode and the results show that running demand response programs will reduce total operation cost of MG and cause more efficient use of resources.
S. G. M. Rokni, M. Radmehr, A. Zakariazadeh,
Volume 15, Issue 1 (March 2019)

In this paper, a new energy management method is proposed for residential consumers based on a distributed algorithm. Consumers could participate in demand response programs by managing their schedulable and deferrable loads as well as using of photovoltaic (PV) systems. In the proposed method, the Alternating Direction Method of Multiplier (ADMM) is used to model the distributed management and scheduling of buildings electricity consumption. By implementing the distributed algorithm, a large number of residential consumers can update their consumption parameters by online communication with the central controller in parallel. The results confirm that residential customers are able to reduce their electricity bill by modifying their electricity consumption patterns without reducing their welfare.

A. Hassannejad Marzouni, A. Zakariazadeh,
Volume 16, Issue 3 (September 2020)

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and pseudo measurements data. This study presents a new approach to model errors for the distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of the artificial neural network method. In the proposed method, the radial basis function network with the Gaussian kernel is also implemented to decompose pseudo measurements into several components. The robustness of the proposed error modeling method is assessed on IEEE 123-bus distribution test system where the problem is optimized by the imperialist competitive algorithm. The results evidence that the proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in the distribution state estimation.

M. Ajoudani, A. Sheikholeslami, A. Zakariazadeh,
Volume 16, Issue 4 (December 2020)

The development of communications and telecommunications infrastructure, followed by the extension of a new generation of smart distribution grids, has brought real-time control of distribution systems to electrical industry professionals’ attention. Also, the increasing use of distributed generation (DG) resources and the need for participation in the system voltage control, which is possible only with central control of the distribution system, has increased the importance of the real-time operation of distribution systems. In real-time operation of a power system, what is important is that since the grid information is limited, the overall grid status such as the voltage phasor in the buses, current in branches, the values of loads, etc. are specified to the grid operators. This can occur with an active distribution system state estimation (ADSSE) method. The conventional method in the state estimation of an active distribution system is the weighted least squares (WLS) method. This paper presents a new method to modify the error modeling in the WLS method and improve the accuracy SVs estimations by including load variations (LVs) during measurement intervals, transmission time of data to the information collection center, and calculation time of the state variables (SVs), as well as by adjusting the variance in the smart meters (SM). The proposed method is tested on an IEEE 34-bus standard distribution system, and the results are compared with the conventional method. The simulation results reveal that the proposed approach is robust and reduces the estimation error, thereby improving ADSSE accuracy compared with the conventional methods.

T. Agheb, I. Ahmadi, A. Zakariazadeh,
Volume 17, Issue 3 (September 2021)

Optimal placement and sizing of distributed renewable energy resources (DER) in distribution networks can remarkably influence voltage profile improvement, amending of congestions, increasing the reliability and emission reduction.  However, there is a challenge with renewable resources due to the intermittent nature of their output power. This paper presents a new viewpoint at the uncertainties associated with output powers of wind turbines and load demands by considering the correlation between them. In the proposed method, considering the simultaneous occurrence of real load demands and wind generation data, they are clustered by use of the k-means method. At first, the wind generation data are clustered in some levels, and then the associated load data of each generation level are clustered in several levels. The number of load levels in each generation level may differ from each other. By doing so the unrealistic generation-load scenarios are omitted from the process of wind turbine sizing and placement. Then, the optimum sizing and placement of distributed generation units aiming at loss reduction are carried out using the obtained generation-load scenarios. Integer-based Particle Swarm Optimization (IPSO) is used to solve the problem. The simulation result, which is carried out using MATLAB 2016 software, shows that the proposed approach causes to reduce annual energy losses more than the one in other methods. Moreover, the computational burden of the problem is decreased due to ignore some unrealistic scenarios of wind and load combinations.

Pardis Asghari, Alireza Zakariazadeh,
Volume 19, Issue 4 (December 2023)

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

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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.