On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Refaat, Shady S.; Abu‐Rub, Haitham

doi:10.1002/9781119422464.ch15

Cited by 7 publications

(4 citation statements)

References 222 publications

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“…In contrast, we limit this holistic review to the DL-based PVPF, leaving aside shallow ML and physical methods-based PVPF. Therefore, this study provides insights not previously fully covered or evaluated by other reviews [33].…”

Section: B Review Novelty and Contributionsmentioning

confidence: 87%

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

et al. 2021

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Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. Firstly, we draw connections between PVPF and DL approaches and show how this relation might cross-fertilize or extend both directions. Then, fruitful discussions are conducted based on three classes: discriminative learning, generative learning, and deep reinforcement learning. In addition, this review analyzes recent automatic architecture optimization algorithms for DL-based PVPF. Next, the notable DL technologies are thoroughly described. These technologies include federated learning, deep transfer learning, incremental learning, and big data DL. After that, DL methods are taxonomized into deterministic and probabilistic PVPF. Finally, this review concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to PVPF applications. By compiling this study, we expect to help aspiring stakeholders widen their knowledge of the staggering potential of DL for PVPF.

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Section: B Review Novelty and Contributionsmentioning

confidence: 87%

“…Deterministic PVPF provides accurate and specific future values [136], [137]. Further, these methods are easy-to-use, deploy and evaluate using score metrics such as RMSE, MAE, R, and MAPE [33]. Unsteady PVPG threatens energy generation.…”

Section: A Point Pv Power Forecastingmentioning

confidence: 99%

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

et al. 2021

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“…AI also optimizes the operation of these systems by predicting energy generation, managing energy storage and minimizing downtime. Moreover, flexibility usage is optimized by AI means, adjusting demand and supply accordingly [10]. AI enhances the management and control of the power system.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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“…The latter may struggle with low sampling efficiency when simulating various system operating scenarios. The adaptive and robust control mechanism of RL can respond to changing system conditions in real-time [12], [13]. Unfortunately, traditional RL algorithms, while powerful, are often hampered by their inherent storage, and high complexity which becomes even more pronounced in intricate and multifaceted systems.…”

Section: Introductionmentioning

confidence: 99%

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Massaoudi,

Abu-Rub,

Ghrayeb

2023

IEEE Access

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The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies.INDEX TERMS Deep reinforcement learning, dynamic security control, electric power systems, power system stability, smart grids. Nomenclature

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On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Cited by 7 publications

References 222 publications

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

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

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

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