scholarly journals Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

2020 ◽  
Vol 12 (5) ◽  
pp. 1709
Author(s):  
Ziqiang Zhou ◽  
Fei Tang ◽  
Dichen Liu ◽  
Chenxu Wang ◽  
Xin Gao
Keyword(s):  
Distribution Systems ◽  
Distribution Networks ◽  
Load Flow ◽  
Renewable Energy Resources ◽  
Probabilistic Load Flow ◽  
Distributed Generators ◽  
High Penetration ◽  
Potential Risks ◽  
Probabilistic Load ◽  
Bus System

Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.

scholarly journals Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4234 ◽  
Author(s):  
Zhou ◽  
Yang ◽  
Yang ◽  
Yang ◽  
Littler ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Energy Distribution ◽  
Flow Analysis ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Load Flow ◽  
Probabilistic Load Flow ◽  
Load Flow Analysis ◽  
Distributed Generators ◽  
Probabilistic Load

Probabilistic Load Flow (PLF) calculations are important tools for analysis of the steady-state operation of electrical energy networks, especially for electrical energy distribution networks with large-scale distributed generators (DGs) and electric vehicle (EV) integration. Traditional PLF has used the Cumulant Method (CM) and Latin Hypercube Sampling (LHS) method. However, traditional CM requires that each input variable be independent of one another, and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices. To solve these problems, taking into account the Q-MCS theory of LHS, this paper proposes a CM PLF algorithm based on improved LHS (ILHS-CM). The cumulants of the input variables are obtained based on sampling results. The probability distribution of the output variables is obtained according to the Gram-Charlier series expansion. Moreover, DGs, such as wind turbines, photovoltaic (PV) arrays, and EVs integrated into the electrical energy distribution networks are comprehensively considered, including correlation analysis and dynamic load flow analysis for EV-coordinated charging. Four scenarios are analyzed based on the IEEE-30 node network, including with/without DGs and EVs, error analysis and performance evaluation of the proposed algorithm, correlation analysis of DGs and EVs, and dynamic load flow analysis with EV integration. The results presented in this paper demonstrate the effectiveness, accuracy, and practicability of the proposed algorithm.

Planning of Distribution System with High Penetration Level for Distributed Generation in Smart Grid

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Reza Tajik
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Distribution Networks ◽  
Planning Problem ◽  
Renewable Energy Resources ◽  
Power Distribution System ◽  
High Penetration

Nowadays, the utilization of renewable energy resources in distribution systems (DSs) has been rapidly increased. Since distribution generation (DG) use renewable resources (i.e., biomass, wind and solar) are emerging as proper solutions for electricity generation. Regarding the tremendous deployment of DG, common distribution networks are undergoing a transition to DSs, and the common planning methods have become traditional in the high penetration level. Indeed, in conformity with the voltage violation challenge of these resources, this problem must be dealt with too. So, due to the high penetration of DG resources and nonlinear nature of most industrial loads, the planning of DG installation has become an important issue in power systems. The goal of this paper is to determine the planning of DG in distribution systems through smart grid to minimize losses and control grid factors. In this regard, the present work intending to propose a suitable method for the planning of DSs, the key properties of DS planning problem are evaluated from the various aspects, such as the allocation of DGs, and planning, and high-level uncertainties. Also depending on these analyses, this universal literature review addressed the updated study associated with DS planning. In this work, an operational design has been prepared for a higher performance of the power distribution system in the presence of DG. Artificial neural network (ANN) has been used as a method for voltage monitoring and generation output optimization. The findings of the study show that the proposed method can be utilized as a technique to improve the process of the distribution system under various penetration levels and in the presence of DG. Also, the findings revealed that the optimal use of ANN method leads to more controllable and apparent DS.

Probabilistic load flow computation using saddle-point approximation

2017 ◽  
Vol 36 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Meghdad Tourandaz Kenari ◽  
Mohammad Sadegh Sepasian ◽  
Mehrdad Setayesh Nazar
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Distribution Networks ◽  
Load Flow ◽  
Cumulative Distribution ◽  
Wind Generation ◽  
Saddle Point Approximation ◽  
Content Type ◽  
Probabilistic Load Flow ◽  
Probabilistic Load

Purpose The purpose of this paper is to present a new cumulant-based method, based on the properties of saddle-point approximation (SPA), to solve the probabilistic load flow (PLF) problem for distribution networks with wind generation. Design/methodology/approach This technique combines cumulant properties with the SPA to improve the analytical approach of PLF calculation. The proposed approach takes into account the load demand and wind generation uncertainties in distribution networks, where a suitable probabilistic model of wind turbine (WT) is used. Findings The proposed procedure is applied to IEEE 33-bus distribution test system, and the results are discussed. The output variables, with and without WT connection, are presented for normal and gamma random variables (RVs). The case studies demonstrate that the proposed method gives accurate results with relatively low computational burden even for non-Gaussian probability density functions. Originality/value The main contribution of this paper is the use of SPA for the reconstruction of probability density function or cumulative distribution function in the PLF problem. To confirm the validity of the method, results are compared with Monte Carlo simulation and Gram–Charlier expansion results. From the viewpoint of accuracy and computational cost, SPA almost surpasses other approximations for obtaining the cumulative distribution function of the output RVs.

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