The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

https://www.mdpi.com/2624-6511/4/2/29/pdf

Artificial Intelligence Techniques in Smart Grid: A Survey

The current trend indicates that energy demand and supply will eventually be controlled by autonomous software that optimizes decision-making and energy distribution operations. New state-of-the-art machine learning (ML) technologies are integral in optimizing decision-making in energy distribution networks and systems. This study was conducted on data-driven probabilistic ML techniques and their real-time applications to smart energy systems and networks to highlight the urgency of this area of research. This study focused on two key areas: i) the use of ML in core energy technologies and ii) the use cases of ML for energy distribution utilities. The core energy technologies include the use of ML in advanced energy materials, energy systems and storage devices, energy efficiency, smart energy material manufacturing in the smart grid paradigm, strategic energy planning, integration of renewable energy, and big data analytics in the smart grid environment. The investigated ML area in energy distribution systems includes energy consumption and price forecasting, the merit order of energy price forecasting, and the consumer lifetime value. Cybersecurity topics for power delivery and utilization, grid edge systems and distributed energy resources, power transmission, and distribution systems are also briefly studied. The primary goal of this work was to identify common issues useful in future studies on ML for smooth energy distribution operations. This study was concluded with many energy perspectives on significant opportunities and challenges. It is noted that if the smart ML automation is used in its targeting energy systems, the utility sector and energy industry could potentially save from $237 billion up to $813 billion. • A study on data-driven probabilistic machine learning (ML) in sustainable smart energy/smart energy systems is conducted. • The use of probabilistic ML in core energy technologies are briefly studied. • The ML techniques play a key role in integrating thermal, electric, large-scale renewable energy resources and fuel gird.A variety of tools for implementing ML in energy systems control, efficient management, and operations are discussed. • Recent key developments of ML, its challenges, and state-of-art future research opportunities are briefly described. 

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

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.

/pdf/deep-learning-in-smart-grid-technology-a-review-of-recent-hmqi4c11lh.pdf

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

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

/pdf/detecting-cybersecurity-attacks-in-internet-of-things-using-2lek4rb9.pdf

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Sensors play a vital role in our daily lives and are an essential component for Internet of Things (IoT) based systems as they enable the IoT to collect data to take smart and intelligent decisions. Recent advances in IoT systems, applications, and technologies, including industrial Cyber-Physical Systems (CPSs), are being supported by a wide range of different types of sensors based on artificial intelligence (AI). These smart AI-based sensors are typically characterized by onboard intelligence and have the ability to communicate collaboratively or through the Internet. To achieve the high level of automation required in today’s smart IoT applications, sensors incorporated into nodes must be efficient, intelligent, context-aware, reliable, accurate, and connected. Such sensors must also be robust, safety- and privacy-aware for users interacting with them. Sensors leveraging advanced AI technologies, new capabilities have recently emerged which have the potential to detect, identify, and avoid performance degradation and discover new patterns. Along with knowledge from complex sensor datasets, they can promote product innovation, improve operation level, and open up novel business models. We review sensors, smart data processing, communication protocol, and artificial intelligence which will enable the deployment of AI-based sensors for next-generation IoT applications.

Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system These components are becoming more tightly integrated with IoT They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid

Syed Saqib Ali

Papers

State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review