- Egypt, Aswan
- 00201069386771
Karar Al-Nagar
Aswan University, Electrical Engineering, Faculty Member
- Electrical Engineering, Renewable Energy, Power System, Smart Grid, Scholarships for Master and Phd Degree, Power System analysis, and 9 moreWind Energy, Wind Power, Electrical Power Distribution, Wind turbine, Power transformers, Induction Generators, Three Phase Distribution System, Power flow, and Distribution Power Flowedit
- I am working as an Assistant Professor at Electrical Engineering department, Aswan Faculty of Engineering, Aswan University.edit
Research Interests:
Research Interests:
Abstract Voltage deviation (VD) and voltage flicker (VF) are considered common operational problems associated with high photovoltaic (PV) penetrated distribution systems. In this paper, an optimal control strategy is proposed for... more
Abstract Voltage deviation (VD) and voltage flicker (VF) are considered common operational problems associated with high photovoltaic (PV) penetrated distribution systems. In this paper, an optimal control strategy is proposed for minimizing VD and VF in PV-rich distribution systems. The control strategy is based on proposed analytical expressions that minimize both voltage problems by optimizing the smart functions of the PV inverters and control devices simultaneously. The proposed analytical expressions are formulated based on voltage sensitivities with respect to the active and reactive power injections of PV. Specifically, a three-level control strategy with different time resolutions is proposed to significantly alleviate voltage deviation/flicker while minimizing PV active power curtailments and tap movements for transformers. These control levels are (1) local control (LC), (2) area control (AC), and (3) coordinated control (CC). LC provides rapid local control actions to minimize VD and VF, AC minimizes VD within the corresponding area individually, and CC plays a vital role to coordinate between the various control units. The proposed control strategy is assessed using high PV penetration with realistic high-resolution very-variable solar radiation datasets (10 ms). To demonstrate the accuracy and efficiency of the proposed analytical expressions, the calculated results have been compared with existing methods. Results demonstrate that the proposed control strategy effectively coordinates between the various voltage control units while minimizing VD and VF.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Voltage fluctuation and voltage rise are common issues associated with the massive integration of photovoltaic (PV) technologies in distribution systems. In this paper, sensitivity‐based and optimization‐based methods are proposed for... more
Voltage fluctuation and voltage rise are common issues associated with the massive integration of photovoltaic (PV) technologies in distribution systems. In this paper, sensitivity‐based and optimization‐based methods are proposed for mitigating voltage fluctuation and rise in the presence of plug‐in hybrid electric vehicles (PHEVs). The concept of these methods is to optimize the reactive power of PV inverter and active power from charging station of PHEVs for matching a target voltage profile. The sensitivity‐based method is based on the first‐order power flow sensitivities around the desired voltage profile. An optimization model is used in optimization‐based to minimize the mismatches between the fluctuating and required voltage profiles. The charging/ discharging operation of PHEVs and reactive power of PV inverter are simultaneously controlled for compensating power fluctuation during cloud transients and load fluctuations. The results demonstrate the effectiveness of the proposed methods compared with existing methods.
Research Interests:
The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These... more
The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965$\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.
Research Interests:
Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use... more
Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.
Research Interests:
The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization... more
The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization method to optimally schedule electric vehicles (EV) with considering the uncertainty of RES generation and loads. For this purpose, the RES generation (including photovoltaic and wind power) and loads are considered as interval parameters, and the charging/discharging power of EV is expressed as an interval variable to be optimally computed. The capability of RES inverters to regulate voltages is also considered in the interval optimization model. The objective function is to minimize the network active power losses and total voltage magnitude deviation with considering overall system constraints. The proposed method is tested on a 33-bus distribution system with uncertain RESs and loads, and the optimal day-ahead scheduling of EV is performed. Different case studies are carried out to test the effectiveness of the proposed method. It is demonstrated that the proposed interval optimization method can accurately represent the uncertain problem, and it provides further information compared with the deterministic optimization.
Research Interests:
This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more... more
This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.
Research Interests:
Research Interests:
Research Interests: Engineering, Computer Science, Optimal Control, Power Electronics, Distributed System, and 15 moreGenetic Algorithm, Control system, Global Warming, Numerical Simulation, Fossil Fuels, IEEE, Connection, Inverter, Photovoltaic system, Distributed Generators, Electrical And Electronic Engineering, Fossil Fuel, on load tap changer, IEEE Systems, and Distributed Networks
Research Interests:
Research Interests:
Abstract This paper presents a steady state analysis of allocation of photo-voltaic and wind generation units in electrical distribution networks. A complete steady state models for PV energy power generation systems for power flow... more
Abstract This paper presents a steady state analysis of allocation of photo-voltaic and wind generation units in electrical distribution networks. A complete steady state models for PV energy power generation systems for power flow applications is applied. In addition, a new model for the induction generator for wind generation unit will be presented. These models are driven without any assumption and by taking into consideration complete generation system equivalent circuits parameters. It is noticeable that the input data for these driven ...
Research Interests:
Abstract The paper presents an accurate procedure for modeling induction generator in steady state unbalanced distribution power-flow analysis. The proposed induction generator model is based on the steady-state model of the induction... more
Abstract The paper presents an accurate procedure for modeling induction generator in steady state unbalanced distribution power-flow analysis. The proposed induction generator model is based on the steady-state model of the induction machine. The model is developed without any assumptions. The unbalanced factor of the voltage at the machine bus is fully exploited. The model is integrated into the forward-backward sweep power-flow algorithm. Calculated results of induction generator connected to unbalanced radial feeders show ...
Research Interests:
Abstract The paper presents analysis of distribution system connected with distributed generations. The study addresses aspects related to optimal sizing and location of DG units for losses minimization and voltage stability improvements.... more
Abstract The paper presents analysis of distribution system connected with distributed generations. The study addresses aspects related to optimal sizing and location of DG units for losses minimization and voltage stability improvements. Many cases have investigated to highlight the relationship between the optimum size and location for losses minimization and the optimum size and location for stability improvements. The student version of the AMPL software is used in the proposed study. The objective function is formulated with full ...
Research Interests:
Research Interests: Engineering, Genetics, Computer Science, Environmental Economics, Genetic Algorithms, and 15 moreDistributed System, Genetic Algorithm, Economic Forecasting, Electrical Machines, Artificial Intelligent, Energy Saving, Degradation, Electric Power System, Environmental Impact, ANT COLONY, Electric Power, Distributed Generators, Exact solution methods, convergence time, and Distributed Networks
Research Interests:
Research Interests:
In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale... more
In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.
Research Interests:
Accommodating a high penetration of intermittent photovoltaic (PV) in distribution systems can potentially cause several operational problems, most importantly, voltage violations. An optimal probabilistic approach is proposed in this... more
Accommodating a high penetration of intermittent photovoltaic (PV) in distribution systems can potentially cause several operational problems, most importantly, voltage violations. An optimal probabilistic approach is proposed in this article to optimally host high penetrations of PV units considering their stochastic nature. A benefit of the proposed approach is that it provides wider planning options since it optimizes the interfacing inverter oversize with smart watt-var functionalities. These smart functionalities include: 1) active power curtailment and 2) inverter reactive power. The utilization of these functionalities in the optimization model yields an optimal PV hosting that maximizes the benefits to distribution systems. The optimal probabilistic model of PV incorporates the probabilities of the PV power output and load while optimizing the inverter oversize and the two functionalities simultaneously. The proposed approach complies with the recently released IEEE 1547:2018 standard which regulates the reactive power support via the interfacing PV inverters. The efficacy of the proposed approach is demonstrated by comparisons with existing approaches. The results confirm the superiority of the proposed approach to optimally accommodate high PV penetration at single or multiple locations while minimizing voltage violations. The proposed approach is also applied to maximize the hosting capacity of PV.
Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have... more
Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.
The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements... more
The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf opti-mizer integrated with a Lévy mutation operator (GWO-Lévy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Lévy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV. Index Terms-Distribution systems, grey wolf optimizer (GWO), Lévy operator, photovoltaic (PV), voltage drop, voltage rise.
The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation... more
The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation devices and causes voltage violation problems. This paper proposes an effective control strategy for voltage regulators in the DS based on the voltage sensitivity using a multi-agent system (MAS) architecture. The features of the unbalanced distribution system (UDS) with the PV and different types and configurations of voltage regulators are considered in the proposed strategy. The novelty of the proposed method lies in realizing both the control optimality of minimizing voltage violations and the flexibility to accommodate changes in the DS topology using an MAS scheme. An advantageous feature of using the MAS scheme is the robust control performance in normal operation and against system failure. Simulation studies have been conducted using IEEE 34-node and 123-node distribution test feeders considering high PV penetration and different sun profiles. The results show that the proposed voltage control strategy can optimally and effectively manage the voltage regulators in the UDS, which decrease their operation stresses and minimize the overall voltage deviation.
Research Interests:
The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These... more
The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster or quantize the full data to reduce the computational time on the expensive of the accuracy. In this paper, we propose a fast yet accurate energy loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-sec time resolution is 28691 (99.9965% reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.
Research Interests:
Voltage fluctuation and voltage rise are common issues associated with the massive integration of photovoltaic (PV) technologies in distribution systems. In this paper, sensitivity‐based and optimization‐based methods are proposed for... more
Voltage fluctuation and voltage rise are common issues associated with the massive integration of photovoltaic (PV) technologies in distribution systems. In this paper, sensitivity‐based and optimization‐based methods are proposed for mitigating voltage fluctuation and rise in the presence of plug‐in hybrid electric vehicles (PHEVs). The concept of these methods is to optimize the reactive power of PV inverter and active power from charging station of PHEVs for matching a target voltage profile. The sensitivity‐based method is based on the first‐order power flow sensitivities around the desired voltage profile. An optimization model is used in optimization‐based to minimize the mismatches between the fluctuating and required voltage profiles. The charging/ discharging operation of PHEVs and reactive power of PV inverter are simultaneously controlled for compensating power fluctuation during cloud transients and load fluctuations. The results demonstrate the effectiveness of the proposed methods compared with existing methods.
Research Interests:
The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization... more
The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization method to optimally schedule electric vehicles (EV) with considering the uncertainty of RES generation and loads. For this purpose, the RES generation (including photovoltaic and wind power) and loads are considered as interval parameters, and the charging/discharging power of EV is expressed as an interval variable to be optimally computed. The capability of RES inverters to regulate voltages is also considered in the interval optimization model. The objective function is to minimize the network active power losses and total voltage magnitude deviation with considering overall system constraints. The proposed method is tested on a 33-bus distribution system with uncertain RESs and loads, and the optimal day-ahead scheduling of EV is performed. Different case studies are carried out to test the effectiveness of the proposed method. It is demonstrated that the proposed interval optimization method can accurately represent the uncertain problem, and it provides further information compared with the deterministic optimization.
Research Interests:
The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs).... more
The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.
Research Interests:
This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more... more
This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.
Research Interests:
The paper presents an unbalanced three-phase power-flow model for wind turbine generating systems (WTGSs). The model takes into account voltage unbalance factor which exists at the point of common coupling. The developed model is... more
The paper presents an unbalanced three-phase power-flow model for wind turbine generating systems (WTGSs). The model takes into account voltage unbalance factor which exists at the point of common coupling. The developed model is integrated with the unbalanced forward/backward sweep power-flow method. The model comprises of three main components: they are the wind turbine, induction generator, and interface transformer to the grid. Due to their design symmetry, the generator and the transformer are modeled using symmetrical sequence networks. The results show that the developed model has robust convergence characteristics. The solution of the IEEE unbalanced radial feeders shows that the injected powers per phase due to the WTGS are not equal and strongly dependent on the voltage unbalance factor. The results also show that the simplified models based on positive sequence network lead to inaccurate overall power-flow solution.
Research Interests:
This paper presents an efficient approach for developing three-phase transformer admittance matrices in the radial power-flow analysis. The proposed transformer model overcomes the singularity problem of the nodal admittance submatrices... more
This paper presents an efficient approach for developing three-phase transformer admittance matrices in the radial power-flow analysis. The proposed transformer model overcomes the singularity problem of the nodal admittance submatrices of ungrounded transformer configurations. This has been achieved by applying symmetrical components modeling. The classical (6 × 6) transformer nodal admittance matrix written in phase components is converted to sequence components instead of the (3 × 3) admittance submatrices. In this model, the phase shifts accompanied with special transformer connections are included in the radial power-flow solution process without any convergence problems. The final model of the transformer is represented by a generalized power-flow equation written in phase components. The developed equation is applicable for all transformer connections. The transformer model is integrated into the radial power-flow and tested using the IEEE radial feeders. The results have shown that the developed transformer model is very efficient and the radial power-flow has robust convergence characteristics.
Research Interests:
The paper presents analysis of distribution system connected with distributed generations. The study addresses aspects related to optimal sizing and location of DG units for losses minimization and voltage stability improvements. Many... more
The paper presents analysis of distribution system connected with distributed generations. The study addresses aspects related to optimal sizing and location of DG units for losses minimization and voltage stability improvements. Many cases have investigated to highlight the relationship between the optimum size and location for losses minimization and the optimum size and location for stability improvements. The student version of the AMPL software is used in the proposed study. The objective function is formulated with full consideration of both quality and inequality constraints. On the other hand, the stability index criterion is used for calculating the best location and size for system stability improvements. The 90 bus test system from the literature is used for the different studied cases. The results show that calculating minimum system losses is not necessary to achieve coherence improvement for the voltage stability problem.
Research Interests:
This paper presents an efficient three-phase power flow algorithm for distribution network analysis. A new transformer model with various connections is implemented in the forward/backward sweep power flow method. The developed method... more
This paper presents an efficient three-phase power flow algorithm for distribution network analysis. A new transformer model with various connections is implemented in the forward/backward sweep power flow method. The developed method provides an effective solution to the singularity problem of the nodal admittance submatrices appeared in some transformer configurations. Different load models and capacitor banks are also implemented with any number of phases and any connection. The proposed load flow has been tested using both the IEEE 4 and 34 node test feeders. The obtained results show that the proposed load flow is very efficient and the numerical solution is identical to that provided with the IEEE data.
Research Interests:
The paper presents an accurate procedure for modeling induction generator in steady state unbalanced distribution power-flow analysis. The proposed induction generator model is based on the steady-state model of the induction machine. The... more
The paper presents an accurate procedure for modeling induction generator in steady state unbalanced distribution power-flow analysis. The proposed induction generator model is based on the steady-state model of the induction machine. The model is developed without any assumptions. The unbalanced factor of the voltage at the machine bus is fully exploited. The model is integrated into the forward-backward sweep power-flow algorithm. Calculated results of induction generator connected to unbalanced radial feeders show that the developed induction generator model is accurate.
Research Interests:
This paper presents a steady state analysis of allocation of photo-voltaic and wind generation units in electrical distribution networks. A complete steady state models for PV energy power generation systems for power flow applications is... more
This paper presents a steady state analysis of allocation of photo-voltaic and wind generation units in electrical distribution networks. A complete steady state models for PV energy power generation systems for power flow applications is applied. In addition, a new model for the induction generator for wind generation unit will be presented. These models are driven without any assumption and by taking into consideration complete generation system equivalent circuits parameters. It is noticeable that the input data for these driven models are only the environmental conditions. In addition, different load models, capacitor banks, distribution transformer and voltage regulators are also implemented with any number of phases and connection. This Analysis is performed to identify issues that will be most relevant to engineers working in planning and operations of distribution systems with installed distribution generation. Comprehensive tests are applied on 123 node IEEE distribution test system.