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Mohsin Shahzad
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This paper presents the advantages of the optimal DG placement during the operation stage of the radial distribution system. The method taken for optimal DG placement (ODGP) is considered from our previous work, which gives optimal locations based on Load Concentration Factor (LCF). Various cases of random DG placement (RDGP) are considered which are divided into two main categories. In first, the locations are varied and in second DG sizes are rotated on same locations found by LCF method. In all these cases, the total active power delivered by the DGs is kept same as for the case of ODGP. The load growth in network is considered to correspond to the future operational stage. A novel Voltage Quality Index (VQI) is proposed in this work which provides a straightforward method for quantifying the system's voltage quality status. Furthermore, two different scenarios, with and without a centralized voltage control algorithm, are considered for the comparison purpose. The advantages of ODGP in all these cases are highlighted by comparing the active power loss reduction, and voltage profile and quality improvement. The results showed that, in case of voltage problem, algorithm could improve voltage to a better limit, while keeping the losses reduced, in case of ODGP than RDGP cases.
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This paper presents the advantages of the optimal DG placement during the operation stage of the radial distribution system. The method taken for optimal DG placement (ODGP) is considered from our previous work, which gives optimal locations based on Load Concentration Factor (LCF). Various cases of random DG placement (RDGP) are considered which are divided into two main categories. In first, the locations are varied and in second DG sizes are rotated on same locations found by LCF method. In all these cases, the total active power delivered by the DGs is kept same as for the case of ODGP. The load growth in network is considered to correspond to the future operational stage. A novel Voltage Quality Index (VQI) is proposed in this work which provides a straightforward method for quantifying the system's voltage quality status. Furthermore, two different scenarios, with and without a centralized voltage control algorithm, are considered for the comparison purpose. The advantages of ODGP in all these cases are highlighted by comparing the active power loss reduction, and voltage profile and quality improvement. The results showed that, in case of voltage problem, algorithm could improve voltage to a better limit, while keeping the losses reduced, in case of ODGP than RDGP cases.
A method of reducing active power losses in distribution networks by simultaneous placement of optimally sized DGs is presented in this paper. Optimal DG sizes are calculated using analytical expressions in Python environment. IEEE 37 node test system, developed in DigSILENT PowerFactory, is used for the simulations. Three different use cases are presented in this work for validation and comparison. The active power loss reduction is compared with the base case (without any DGs). Different DG types, based on active and reactive power output, are considered during this work. The analytical expressions for simultaneous sizing proved their usefulness for loss minimization in radial networks. The complete study is done for the cases of three and four DGs placement in the system. To highlight the expanded capabilities of proposed method, it is applied to both the static and time varying load conditions.
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A method of reducing active power losses in distribution networks by simultaneous placement of optimally sized DGs is presented in this paper. Optimal DG sizes are calculated using analytical expressions in Python environment. IEEE 37 node test system, developed in DigSILENT PowerFactory, is used for the simulations. Three different use cases are presented in this work for validation and comparison. The active power loss reduction is compared with the base case (without any DGs). Different DG types, based on active and reactive power output, are considered during this work. The analytical expressions for simultaneous sizing proved their usefulness for loss minimization in radial networks. The complete study is done for the cases of three and four DGs placement in the system. To highlight the expanded capabilities of proposed method, it is applied to both the static and time varying load conditions.
The potential of demand side as a frequency reserve proposes interesting opportunity in handling imbalances due to intermittent renewable energy sources. This paper proposes a novel approach for computing the parameters of a stochastic battery model representing the aggregation of Thermostatically Controlled Loads (TCLs). A hysteresis based non-disruptive control is used using priority stack algorithm to track the reference regulation signal. The parameters of admissible ramp-rate and the charge limits of the battery are dynamically calculated using the information from TCLs that is the status (on/off), availability and relative temperature distance till the switching boundary. The approach builds on and improves on the existing research work by providing a straight-forward mechanism for calculation of stochastic parameters of equivalent battery model. The effectiveness of proposed approach is demonstrated by a test case having a large number of residential TCLs tracking a scaled down real frequency regulation signal.
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The potential of demand side as a frequency reserve proposes interesting opportunity in handling imbalances due to intermittent renewable energy sources. This paper proposes a novel approach for computing the parameters of a stochastic battery model representing the aggregation of Thermostatically Controlled Loads (TCLs). A hysteresis based non-disruptive control is used using priority stack algorithm to track the reference regulation signal. The parameters of admissible ramp-rate and the charge limits of the battery are dynamically calculated using the information from TCLs that is the status (on/off), availability and relative temperature distance till the switching boundary. The approach builds on and improves on the existing research work by providing a straight-forward mechanism for calculation of stochastic parameters of equivalent battery model. The effectiveness of proposed approach is demonstrated by a test case having a large number of residential TCLs tracking a scaled down real frequency regulation signal.
This paper investigates the usefulness of analytical expressions for finding optimal sizes of Distributed Generators (DGs). Results in this study revealed some interesting and useful features of analytical expression in regard to voltage profile improvement. Method was applied on IEEE 37 node radial test system and simulations were performed in DigSILENT PowerFactory coupled with Python. The validation of results was performed by comparing the voltage profiles of proposed method with two study cases; one without any DG while other with DGs of sizes equal to the cumulative load. DGs of different types, based on their output power, have been tested and results are presented. Moreover, results proved that the proposed method is better as compared to the methods in other study cases for static and time varying loads.
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This paper investigates the usefulness of analytical expressions for finding optimal sizes of Distributed Generators (DGs). Results in this study revealed some interesting and useful features of analytical expression in regard to voltage profile improvement. Method was applied on IEEE 37 node radial test system and simulations were performed in DigSILENT PowerFactory coupled with Python. The validation of results was performed by comparing the voltage profiles of proposed method with two study cases; one without any DG while other with DGs of sizes equal to the cumulative load. DGs of different types, based on their output power, have been tested and results are presented. Moreover, results proved that the proposed method is better as compared to the methods in other study cases for static and time varying loads.
This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented.
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This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented.
Conference paper
(2015)
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Aadil Latif, Mohsin Shahzad, Peter Palensky, Wolfgang Gawlik
PowerFactory is a powerful power system analysis tool used for simulating the electrical grid. 'Smart Grid' envisions a modernized electrical grid that uses communication to gather and act on information. The ever increasing communication and controls in power systems increases the complexity of the system. Co-simulation becomes essential to couple system simulators from different domains. This paper gives an overview of possible PowerFactory / Matlab coupling approaches. It further discusses the advantages and limitations of each of these. Additionally, an alternate coupling approach is suggested, its pros and cons are discussed. Two test cases have been implemented that highlight different advantages of the proposed method.
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PowerFactory is a powerful power system analysis tool used for simulating the electrical grid. 'Smart Grid' envisions a modernized electrical grid that uses communication to gather and act on information. The ever increasing communication and controls in power systems increases the complexity of the system. Co-simulation becomes essential to couple system simulators from different domains. This paper gives an overview of possible PowerFactory / Matlab coupling approaches. It further discusses the advantages and limitations of each of these. Additionally, an alternate coupling approach is suggested, its pros and cons are discussed. Two test cases have been implemented that highlight different advantages of the proposed method.