Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

Gruosso, Giambattista; Gajani, G. Storti; Zhang, Zheng; Daniel, Luca; Maffezzoni, P.

doi:10.1109/access.2019.2891699

Cited by 37 publications

(10 citation statements)

References 16 publications

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“…This can help utility and stakeholders to understand the effects of renewables' variability and unhealthiness of the network. It is to state that Monte Carlo simulation is used to observe the stochastic behavior of the network because we keep only two or three parameters of DGs as varying so simple Monte Carlo method holds, however, if the number of parameters is large, other methods are preferable for analyzing the uncertain behavior [36][37][38][39].…”

Section: Application: Dgs and Stochastic Analysismentioning

confidence: 99%

An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain

et al. 2020

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This paper presents an alternative solution for the power-flow analysis of power systems with distributed generation provided by heterogeneous sources. The proposed simulation approach relies on a suitable interpretation of the power network in terms of a nonlinear circuit in the phasor domain. The above circuit interpretation can be solved directly in the frequency-domain via the combination of a standard tool for circuit analysis with an iterative numerical scheme, providing directly the steady-state solution of the power-flow of a generic distribution network. At each iteration, the resulting circuit turns out to be composed by two decoupled subnetworks, a large linear part and a set of smaller nonlinear pieces accounting for the load characteristics, with evident benefits in terms of the computational time. The feasibility and strength of the proposed simulation scheme have been verified on a large benchmark consisting of the IEEE 8500-node test feeder. Then it is applied to the statistical simulation of a power network accounting for the variability effects of renewable generators. According to the results, the proposed tool provides an effective alternative to the state-of-the-art approaches for power-flow analysis further highlighting the benefits of the application of well-established tools for circuit analysis to power-flow problems.

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Section: Application: Dgs and Stochastic Analysismentioning

confidence: 99%

An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain

et al. 2020

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“…Modeling residential EV charging can be used in many studies, such as load flow analysis, optimal power flow, demand response, energy management, power system stability, power system protection, frequency regulation, and household electric consumption management [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Model in the Urban Residential Sector

et al. 2022

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Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

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“…The polynomial approximation is a method that has recently been proposed to overcome the shortcomings of numerical and analytical methods in the study of random phenomena in the power systems and has yielded acceptable results. In [15] and [16], the probabilistic load flow calculations of a distribution network under conditions of random load changes have been performed based on polynomial approximation, and the efficiency of this method has been investigated by comparison with the Monte Carlo method. The polynomial approximation method has also been used in the planning [17] and dynamic analysis [18] of power systems, which has acceptable results and good computational speed.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic small-signal stability analysis of power systems based on Hermite polynomial approximation

Tabrizchi

Rezaei

2021

SN Appl. Sci.

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This paper proposes a probabilistic small-signal stability analysis method based on the polynomial approximation approach. Since the correct determination of unknown coefficients has a direct effect on the accuracy of the polynomial approximation method, this paper presents a method for determining these coefficients, with high coverage on the probabilistic input domain of the problem. In this method, by increasing the number of random input variables, the proposed method can continue to maintain its efficiency. After determining the unknown coefficients, the load flow results and the system state matrix are determined for random changes of all loads based on the Hermite polynomial approximation. Then, the small-signal stability of the system is probabilistically evaluated based on stochastic analysis of the system eigenvalues. The consistency and validity of the proposed method are demonstrated based on the simulation studies in the MATLAB® software environment. In the simulation studies, the performance of the proposed method is examined by comparison with Point Estimation, Cumulant, Monte Carlo, and Chebyshev polynomial-based methods, for IEEE 14-bus and IEEE 39-bus test systems. Article highlights Probabilistic small-signal stability analysis of power systems Modeling of governing equations of power system based on Hermite polynomial approximation Forming the Collocation matrix based on a robust method

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Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

Cited by 37 publications

References 16 publications

An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain

An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain

Electric Vehicle Charging Model in the Urban Residential Sector

Probabilistic small-signal stability analysis of power systems based on Hermite polynomial approximation

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