Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Wood, David A.

doi:10.1007/s40866-019-0074-0

Cited by 8 publications

(3 citation statements)

References 55 publications

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“…The cost sensitivity and reaction times of power producers and consumers also influence the stability factor. Wood [33] propose a model called decentral smart grid control (DSGC) to render the demand-side control of distributed power grids by associating the electricity price to variations in grid frequency upon the time gauge of a few seconds. The authors simulate the power demand-side consumption/production on analogous time gauges.…”

Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…e cost of power also plays an important role in ensuring the stability of the distributed power systems. e authors in [24] have proposed a decentralized SG control model to ensure demand-side management in the grid by analyzing the electricity price versus grid frequency deviation. e authors have also implemented an optimized data matching ML technique and the transparent open box learning model to realize dynamic SG stability.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction

Mishra

Nayak

Naik

et al. 2022

International Transactions on Electrical Energy Systems

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The smart grid is considered a conventional application domain of cyber-physical system (CPS) tools in the electrical utility industry. The physical system dynamics of SG with the assistance of CPS are generally controlled by connected sensors and controllers via a communication link. These CPSs, which rely heavily on an expansive communication network and intelligent computing algorithms, are susceptible to cyber-physical attacks and are also sensitive to various technical, economical, and social factors compromising their stability. Assessment and prediction of the stability of CPSs are very vital in this context. In this work, a novel optimized (memetic algorithm-based) extreme learning machine model for smart grid-CPS stability prediction has been proposed. Here, the teaching-learning-based optimization and simulated annealing techniques are used to design the memetic algorithm. The experimental result regarding the proposed model is then compared with other contemporary machine learning and deep learning models.

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“…This study tests the hypothesis that ML algorithms combined with resampling techniques can provide highly accurate predictions for the stability of decentralized electricity grids. ML algorithms can detect trends and anomalies in datasets and thus help grid system operators to make real-time decisions for better distribution of available electricity [38]. Different approaches were used for the stability prediction of power grids, but effective results were not achieved [11,[31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Ibrar

Hassan

Shaukat

et al. 2022

Wireless Communications and Mobile Computing

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A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.

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Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Cited by 8 publications

References 55 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

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