Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Abstract

The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

“…Recently, DL has become a research hotspot for its excellent ability to handle nonlinear Time Series (TS) energy data [21]. Thus, the marriage of PVPF and DL gives an impetus to build more sustainable and robust energy management paradigms [20]. DL methods have been successfully used in solar irradiance and solar power production forecasting.…”

“…DL independently extracts features as an efficient big data-driven analytic scheme to process insufficient quality data that contains noise, heterogeneous data. DL models can efficiently handle the complexity, diversity, and integrity data conundrums that encounter meteorological data integration to improve the steadiness and security of power dispatch [20].…”

“…AEs are loosely inspired by the way the human brain works. Typical components of the approach are the encoder and the decoder [20]. By minimizing the reconstruction error between the input data at the encoding layer and its reconstruction at the decoding layer.…”

“…This eliminates the need for domain expertise and hard core feature extraction adopted in handed engineering-and shallow learning-based models. More specifically, feature extraction-based DL is deducted automatically and optimally configured using an end-to-end pipeline to promote faster learning without being told to do so explicitly [20]. Secondly, traditional ML techniques such as Random Forest (RF) and Decision Trees (DT) might not handle multidimensional data [19].…”

Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble

“…Neural networks have shown to be remarkably successful in a wide range of pattern recognition tasks [18]. Their effectiveness has been clearly demonstrated in a variety of power grid applications [19][20][21].…”