Data Mining in Nonlinear Probabilistic Load Flow Based on Monte Carlo Simulation

Li, Junfang; Zhang, Buhan; Liu, Yifang

doi:10.1109/icise.2009.448

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…3The weight, w, of the probability variable is calculated according to Equation (22). (4) The estimated point, P i,j , is calculated according to Equation (18). (5) The OIPOPF, P Li,j , is calculated according to Equation (17) and its constraints.…”

Section: Algorithm Calculation Stepsmentioning

confidence: 99%

Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

Chen

Guo

et al. 2020

Sustainability

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With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

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Section: Algorithm Calculation Stepsmentioning

confidence: 99%

Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

Chen

Guo

et al. 2020

Sustainability

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show abstract

“…However, PPF is difficult to solve because of the complex relationship between various uncertainties and the corresponding power flow solution. Traditional methods of solving PPF can be divided into three types: the analytical method , the approximation method and the simulation method . The solution procedure of these methods is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The probability characteristic of these solutions is regarded as the PPF results. A typical method is Monte‐Carlo simulation (MCS) . Compared with the analytical method and the approximation method, the simulation method is more accurate.…”

Section: Introductionmentioning

confidence: 99%

Improving the scalability of deep neural network for probabilistic power flow

Xiang

Feng

Dai

et al. 2020

Int Trans Electr Energ Syst

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Probabilistic power flow (PPF) is an effective tool to address the increasing uncertainties in power systems. However, the high computational burden restricts the practical application of PPF. Deep neural network (DNN) can achieve the fast calculation of PPF. However, the structure of DNN should match the size of the system. With the development of renewable energy and power demand, new buses or branches would be added to the system. Under this circumstance, the trained‐DNN for the original system cannot be applied to the extended system. To improve the scalability of DNN for the extended system, a knowledge transfer method is proposed in this paper, which transfers the knowledge acquired by the original trained‐DNN to the new DNN. The following two main features are contained: 1) parameters of input and output layers are transferred to initialize parts of parameters in the new DNN according to the variable with which they are connected; 2) remaining parameters of input and output layers are initialized based on the non‐parametric estimation. Simulation tests are performed on the modified IEEE 39‐bus system and 118‐bus system and the results show that the proposed method can improve the training efficiency of new DNN and reduce the dependence on training samples.

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