Alternative Current Injection Newton and Fast Decoupled Power Flow

Oliveira, Cristina Coutinho de; Neto, Alfredo Bonini; Alves, Dilson Amancio; Minussi, Carlos R.; Castro, Carlos A.

doi:10.3390/en16062548

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…While it is computationally less intensive than iterative methods, it may converge slowly or fail to converge for certain system configurations, particularly those with high levels of nonlinearity or ill-conditioned matrices [26]. Fast-decoupled methods decouple the real and reactive power equations, allowing for faster convergence by solving each equation separately [27,28]. However, they may lack accuracy in highly meshed systems and can be sensitive to initial conditions, leading to divergence.…”

Section: Related Workmentioning

confidence: 99%

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Tang,

Zhang,

et al. 2024

Energies

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Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

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Section: Related Workmentioning

confidence: 99%

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Tang,

Zhang,

et al. 2024

Energies

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“…Instead of relying on current state-of-the-art load-flow analysis tools and methods, e.g., the current iteration (Gauss-Seidel) or the Newton-Raphson approaches presented in [11][12][13], which are applied in varying research topics, as illustrated by contributions. e.g., [14][15][16][17], a novel load-flow calculation method, based on a statespace approach, is introduced and applied to calculate the system-defining flows. The aforementioned novel algorithm differs from the established state-of-the-art algorithms and uses the mathematical state-space representation to directly calculate a system's load flow, as shown by the overview given by Blenk in [18].…”

Section: Planned Res Expansion Inmentioning

confidence: 99%

State-Space Load Flow Calculation of an Energy System with Sector-Coupling Technologies

Bottler

Weindl

2023

Energies

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This paper addresses the sector-coupling principle, highlights each associated sector’s technologies and showcases their future development, according to the German grid development plan. Furthermore, the research project ESM-Regio, and its goals in terms of simulatively analyzing the sector-coupling approach for a specific model region and future scenarios, is introduced. In this context, the key methods for modeling the electricity sector’s loading behavior are showcased. Most importantly, the state-space load flow calculation, load modeling (including the integration of the power demands of the sector-coupling technologies) and an assessment of grid operating equipment, based on thermal aging models, are described.

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Technique for Reactive Loss Reduction and Loading Margin Enhancement Using the Curves of Losses versus Voltage Magnitude

et al. 2023

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This paper presents an alternative methodology to obtain the precise amount of shunt reactive power compensation in order to simultaneously reduce the total reactive power losses, improve the voltage profile and increase the loading margin of power systems. The amount of shunt reactive power compensation to be allocated is determined based on the curve of the total reactive power losses versus the voltage magnitude of a chosen bus. The best places for shunt reactive compensation are defined by the load bus participation factors of the critical mode provided by the static modal analysis. Simulation results employing shunt capacitors obtained with the new approach for the IEEE test systems (14, 57 and 300 buses) show that the procedure leads to a reduction in total reactive and active power losses and simultaneously improves the voltage profile and loading margin.

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Alternative Current Injection Newton and Fast Decoupled Power Flow

Cited by 3 publications

References 25 publications

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

State-Space Load Flow Calculation of an Energy System with Sector-Coupling Technologies

Technique for Reactive Loss Reduction and Loading Margin Enhancement Using the Curves of Losses versus Voltage Magnitude

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