M.A. Baherifard; R. Kazemzadeh; A.S. Yazdankhah; M. Marzband
Abstract
With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important ...
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With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.
Power System Operation
A. Niromandfam; A. Sadeghi Yazdankhah; R. Kazemzadeh
Abstract
The regulatory schemes currently used for reliability improvement have weaknesses in the provision of quality services based on the customers’ perspective. These schemes consider the average of the service as a criterion to incentivize or penalize the distribution system operators (DSOs). On the ...
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The regulatory schemes currently used for reliability improvement have weaknesses in the provision of quality services based on the customers’ perspective. These schemes consider the average of the service as a criterion to incentivize or penalize the distribution system operators (DSOs). On the other hand, most DSOs do not differentiate electricity services at the customer level, due to the status of the electricity grid and lack of adequate information about customers’ preferences. This paper proposes a novel reliability insurance scheme (RIS), which enables the electricity consumers to determine their desired reliability levels according to their preferences and pay corresponding premiums to the DSO. The DSO can use the premiums to improve reliability or reimburse consumers. To design efficient insurance contracts, this paper uses utility function to estimate customers’ viewpoints of electricity energy consumption. This function measures the customers’ satisfaction of electricity energy consumption. The proposed utility based reliability insurance scheme (URIS) may create a free-riding opportunity for the DSO, in which low quality service is provided and the collected premiums are used to pay the reimbursements. To prevent free-riding opportunity, this paper incorporates the proposed URIS and reward/penalty schemes (RPSs). The results show that the success of the proposed reliability scheme increases as the grid flexibility increases.
Electric Mechinces & Drive
M. Moazen; R. Kazemzadeh; M. R. Azizian
Abstract
Brushless doubly fed reluctance generator (BDFRG) has been recently suggested as a wind generator. Different control methods are presented in literature for the BDFRG, but there is a gap on control under unbalanced grid voltage condition (UGVC). This paper presents a predictive direct power control (PDPC) ...
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Brushless doubly fed reluctance generator (BDFRG) has been recently suggested as a wind generator. Different control methods are presented in literature for the BDFRG, but there is a gap on control under unbalanced grid voltage condition (UGVC). This paper presents a predictive direct power control (PDPC) method for the BDFRG under UGVC. The proposed PDPC method is based on power compensation strategy, and aims to balance the BDFRG current (strategy I), and to remove the electrical torque pulsation (strategy II). The control objectives are defined using the BDFRG positive sequence (PS) and negative sequence (NS) equations. Then, the active power and reactive power variations are predicted to compute the required voltage for the BDFRG control winding. Finally, the BDFRG is controlled by applying the calculated voltage to the control winding. Simulink toolbox of MATLAB software is used to simulate the system model. Both the proposed PDPC method (with strategies I & II) and the original PDPC method (without a compensation strategy) are applied to control of the BDFRG under UGVC, and the results are compared. The results show the good performance of the proposed PDPC method.
Distribution Systems
O. Eghbali; R. Kazemzadeh; K. Amiri
Abstract
State estimation in the energy management center of active distribution networks has attracted many attentions. Considering an increase in complexity and real-time management of active distribution networks and knowing the network information at each time instant are necessary. This article presents ...
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State estimation in the energy management center of active distribution networks has attracted many attentions. Considering an increase in complexity and real-time management of active distribution networks and knowing the network information at each time instant are necessary. This article presents a two-step multi-area state estimation method in balanced active distribution networks. The proposed method is based on the location of PMU measurements of the network. The network is divided into several sub-areas about PMUs in the first step. A local sate estimation is implemented in each sub-area. The estimated values of the first step along with real measurements are used as measurements for second step estimation. The measurements are located in each sub-area using these values based on the ellipse area method, and the best location of measurements is extracted. Therefore, a second step state estimation including integrated state estimation of the whole network is performed by using the measurements obtained and located from the first step. The estimation results of the first step are used in the second step which improve the estimation accuracy. Simulations are performed on a standard IEEE 69-bus network to validate the proposed method.
Power System Operation
R. Kazemzadeh; M. Moazen
Abstract
Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale ...
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Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time.
Energy Management
A. Hatefi einaddin; A. Sadeghi Yazdankhah; R. Kazemzadeh
Abstract
As an efficient alternative to fossil fuels, renewable energy sources have attained great attention due to their sustainable, cost-effective, and environmentally friendly characteristic. However, as a deficiency, renewable energy sources have low reliability because of their non-deterministic and stochastic ...
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As an efficient alternative to fossil fuels, renewable energy sources have attained great attention due to their sustainable, cost-effective, and environmentally friendly characteristic. However, as a deficiency, renewable energy sources have low reliability because of their non-deterministic and stochastic generation pattern. The use of hybrid renewable generation systems along with the storage units can mitigate the reliability problem. Hence, in this paper, a grid connected hybrid micro-grid is presented, which includes wind and photovoltaic resources as the primary power sources and a hydrogen storage system (including fuel cell and electrolyzer) as a backup. A new power management strategy is proposed to perform a proper load sharing among the micro-grid units. Hybrid (distributed/central) control method is applied for the realization of the control objectives such as DC bus voltage regulation, power factor control, synchronous grid connection, and power fluctuation suppression. Distributed controllers have the task of fulfilling local control objectives such as MPPT implementation and storage unit control. On the other hand, the central control unit is mainly responsible for power management in the micro-grid. Performance and effectiveness of the proposed power management strategy for the presented micro-grid are verified using a simulation study.
R. Kazemzadeh; M. Moazen; R. Ajabi-Farshbaf; M. Vatanpour
Volume 1, Issue 1 , June 2013, , Pages 1-11
Abstract
In this paper, a combinational optimization algorithm is introduced to obtain the best size and location of Static Compensator (STATCOM) in power systems. Its main contribution is considering contingency analysis where lines outages may lead to infeasible solutions especially at peak loads and it commonly ...
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In this paper, a combinational optimization algorithm is introduced to obtain the best size and location of Static Compensator (STATCOM) in power systems. Its main contribution is considering contingency analysis where lines outages may lead to infeasible solutions especially at peak loads and it commonly can be vanished by load-shedding. The objective of the proposed algorithm is firstly to prevent infeasible power flow solutions without undesired load-shedding, which is critical in contingency analysis; and secondly to mitigate overall power losses and costs. Moreover, active and reactive powers generation costs are considered in the proposed objective function. Since there are various constraints such as lines outages number, cost and their duration that must be taken to account, Bacterial Foraging oriented by Particle Swarm Optimization (BF-PSO) algorithm combined with Optimal Power Flow (OPF) is used to solve and overcome the complexity of this combinational nonlinear problem. In order to validate the accuracy of the proposed method, two test systems, including IEEE 30 bus standard system and Azarbaijan regional power system of Iran, are applied in simulation studies. All obtained optimization results show the effectiveness of the suggested combinational method in loss and cost reduction and preventing load-shedding.
