Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Zhou, Zijing; Tang, Fei; Liu, Dichen; Wang, Chenxu; Gao, Xin

doi:10.3390/su12051709

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…The PG&E data set is provided by Pacific Gas and Electric Company and includes data for the California electric system. The dataset is large in size and includes a large amount of power flow data, load data and renewable energy data, covering a multi-year time span and covering a variety of power system operations (Zhou et al, 2020). The PG&E data set includes multiple data features such as current, voltage, load, solar and wind energy of the power system.…”

Section: Experimental Datasetsmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

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The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

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Section: Experimental Datasetsmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

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“…To address the issue of allocating overvoltage responsibility in the distribution network, various methods have been proposed, including the Shapley value method, the core solution method, and the Raiffa solution method. The Shapley value method stands out for its fairness and additivity, making it widely applicable in the power industry for quantifying cost allocation in areas such as electrical and wind energy [6][7] . In [8], a Shapley value-based method for distributing overvoltage responsibility in distribution networks is introduced.…”

Section: Introductionmentioning

confidence: 99%

The responsibility allocation method for overvoltage problem in distribution network with large-scale distributed photovoltaics integration based on the improved Shapley value

Li,

Yang,

et al. 2024

J. Phys.: Conf. Ser.

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Aiming at the node overvoltage problem due to distributed PV generation large-scale integrated into the distribution network, an improved overvoltage apportionment method is proposed to determine the overvoltage responsibility of each PV generation system. Based on the node Shapley value calculation with the voltage loss function, the influence factor of the distribution network is derived, and the improved Shapley value correction factor is obtained by using the gray correlation analysis method. The improved Shapley value is used to calculate the overvoltage responsibility. The nodes with a similar voltage change trend, in the calculation example of the PG&E-69 node distribution system, can be combined to reduce the calculation burden. The simulation results show that the improved Shapley value is more fair and reasonable than the Shapley value, considering the influence of all factors comprehensively.

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“…Among many existing clustering algorithms, Kmeans is one of the most popular one (Gan et al 2007). In solving PPF and also probabilistic optimal power flow, Deng (Deng et al 2017;Deng et al 2019) and Zhou (Zhou et al 2020) tried to handle large variations of input random variables using the method of combined cumulant and the conventional K-means. K-means is easy to execute; however, it has a number of drawbacks: it only converges to arbitrary local optima and does not guarantee to find the global optimum solution for clustering; it is difficult to predict the number of clusters; random selection initial cluster centers has a strong impact on the final results.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Assessment of Power Systems with Renewable Energy Sources based on an Improved Analytical Approach

Ngo

Nguyen

et al. 2021

Int. J. Renew. Energy Dev.

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The increasing penetration of renewable energy sources has introduced great uncertainties and challenges into computation and analysis of electric power systems. To deal with uncertainties, probabilistic approaches need to be used. In this paper, we propose a new framework for probabilistic assessment of power systems taking into account uncertainties from input random variables such as load demands and renewable energy sources. It is based on the cumulant-based Probabilistic Power Flow (PPF) in combination with an improved clustering technique. The improved clustering technique is also developed in this study by making use of Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) to reduce the range of variation in the input data, thus increasing the accuracy of the traditional cumulant-based PPF (TCPPF) method. In addition, thanks to adopting PCA for dimensionality reduction, the improved clustering technique can be effectively dealt with a large number of input random variables so that the proposed framework for probabilistic assessment can be applied for large power systems. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Uncertainties from input random variables are represented by appropriate probabilistic models. Extensive testing on the test system indicates good performance of the proposed approach in comparison to the traditional cumulant-based PPF and Monte Carlo simulation. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Extensive testing on the test system, using Matlab (R2015a) on an Intel Core i5 CPU 2.53 GHz/4.00 GB RAM PC, indicates good performance of the proposed approach (PPPF) in comparison to the TCPPF and Monte Carlo simulation (MCS): In terms of computation time, PPPF needs 4.54 seconds, while TCPPF and MCS require 2.63 and 251 seconds, respectively; ARMS errors are calculated for methods using benchmark MCS and their values clearly demonstrate the higher accuracy of PPPF in estimating probability distributions compared to TCPPF, i.e., the maximum (Max) and mean (Mean) values of ARMS errors of all output random variables are: ARMSPPPFmax = 0.11%, ARMSTCPPFmax = 0.55%, and ARMSPPPFmean = 0.06%, ARMSTCPPFmean = 0.35%.

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Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Cited by 6 publications

References 36 publications

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

The responsibility allocation method for overvoltage problem in distribution network with large-scale distributed photovoltaics integration based on the improved Shapley value

Probabilistic Assessment of Power Systems with Renewable Energy Sources based on an Improved Analytical Approach

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