Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications

Dudek, Grzegorz; Piotrowski, Paweł; Baczyński, D.

doi:10.3390/en16073024

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Forecasting represents one of the critical components of this grid which plays a vital role in achieving efficient grid operation and management. Forecasting involves predicting future electric load and renewable power generation, which are necessary for the efficient scheduling of power generation, transmission, and distribution [179,180]. Electric load and price forecasting are the two primary types of forecasting used in the smart grid.…”

Section: Forecasting In Smart Gridsmentioning

confidence: 99%

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

et al. 2023

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The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

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Section: Forecasting In Smart Gridsmentioning

confidence: 99%

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

et al. 2023

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A novel AI-assisted forecasting strategy reveals the energy imbalance sign for the day-ahead electricity market

Carnevale,

Cavaiola,

Mazzino

2024

Energy Reports

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An overview of Artificial Intelligence applications to electrical power systems and DC microgrids

Rajitha,

Raghu Ram

2024

E3S Web of Conf.

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Microgrids are composed of distributed energy resources such as energy storage devices, photovoltaic (PV) systems, backup generators, and wind energy conversion systems. Because renewable energy sources are intermittent, modern power networks must overcome the stochastic problem of increasing the penetration of renewable energy, which necessitates precise demand forecasting to deliver the best possible power supply. Technologies based on artificial intelligence (AI) have become a viable means of implementing and optimizing microgrid energy management. Owing to the sporadic nature of renewable energy sources, artificial intelligence offers a range of solutions based on the growth in sensor data and compute capacity to create sustainable and dependable power. Artificial intelligence (AI) techniques continue to evolve in DC Microgrids with the aim of perfect voltage profile, minimum distribution losses, optimal schedule of power, planning and controlling of grid parameters and lowering unit price. AI methods can improve DC Micro grid performance by monitoring and controlling the grid parameters by reducing the computational and processing time. This paper offers a comprehensive summary of some of the most recent research on artificial intelligence techniques used to DC Micro grids and electrical power system networks.

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Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications

Abstract: A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner [...]

Cited by 5 publications

References 18 publications

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

A novel AI-assisted forecasting strategy reveals the energy imbalance sign for the day-ahead electricity market

An overview of Artificial Intelligence applications to electrical power systems and DC microgrids

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