Nanogrids, Microgrids, and the Internet of Things (IoT)

Energies, 2019

I started hearing a lot about energy digitization a little over a year ago, talking to my colleagues during conferences and meetings [...]

Energies

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers ...

IEEE Access, 2021

The Internet of Things (IoT) is a rapidly emerging field of technologies that delivers numerous cutting-edge solutions in various domains including the critical infrastructures. Thanks to the IoT, the conventional power system network can be transformed into an effective and smarter energy grid. In this article, we review the architecture and functionalities of IoT-enabled smart energy grid systems. Specifically, we focus on different IoT technologies including sensing, communication, computing technologies, and their standards in relation to smart energy grid. This article also presents a comprehensive overview of existing studies on IoT applications to the smart grid system. Based on recent surveys and literature, we observe that the security vulnerabilities related to IoT technologies have been attributed as one of the major concerns of IoT-enabled energy systems. Therefore, we review the existing threat and attack models for IoT-enabled energy systems and summarize mitigation techniques for those security vulnerabilities. Finally, we highlight how advanced technologies (e.g., blockchain, machine learning, and artificial intelligence) can complement IoT-enabled energy systems to be more resilient and secure and overcome the existing difficulties so that they become more effective, robust, and reliable in operation. Precisely, this article will help understand the framework for IoT-enabled smart energy system, associated security vulnerabilities, and prospects of advanced technologies to improve the effectiveness of smart energy systems.

Energy Conversion and Management: X, 2024

Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain's role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.

International Journal of Advanced Engineering and Nano Technology (IJAENT), 2015

Our nation's infrastructure for generating, transmitting, and distributing electricity-"The Grid"-is a relic based in many respects on century-old technology. It consists of expensive, centralized generation via large plants, and a massive transmission and distribution system. It strives to deliver high-quality power to all subscribers simultaneously-no matter what their demand-and must therefore be sized to the peak aggregate demand at each distribution point. In this paper we describe what the electricity grid could look like in 10 years, and specifically how Federal investment in data analytics approaches is critical to realizing this vision.

2017

By 2019, Norway will complete the national rollout of advanced metering systems (AMS) for all customers. Beyond near-time monitoring of voltage quality and frictionless billing of customers, such a rollout opens a host of possibilities. However, a full-scale rollout is not without challenges. For instance, throughput limitations of radio-mesh networks, privacy considerations, and bounds on compute and storage infrastructure limit the cardinality of metering data to levels below that of which established techniques (for example non-intrusive load disaggregation) require. Pilot projects are now exploring how to mitigate these challenges as well as seeking novel opportunities that open up through data fusion and recent advances in machine learning. In this contribution, we outline the capabilities of the Norwegian AMS system and describe established use-cases and non-intrusive load monitoring. We then discuss a pilot on detection of electric vehicles. Based on preliminary findings, we ...

Integration of IoT in Energy Sector, 2024

The incorporation of renewable energy sources and the efficient utilization of energy are crucial factors in facilitating sustainable energy transitions and addressing the issue of climate change. The Internet of Things (IoT) is a modern technology that has numerous applications in the energy sector. These applications include energy supply, transmission and distribution, as well as demand management. The utilization of IoT can enhance energy efficiency, augment the proportion of renewable energy, and mitigate the environmental consequences of energy consumption. This study examines the current body of literature about the use of Internet of Things (IoT) technology in energy systems, with a specific focus on its application in smart grids. In addition, we explore the enabling technologies of the Internet of Things (IoT), such as cloud computing and other platforms for data analysis.

Journal of Intelligent Systems and Internet of Things (JISIoT), 2021

In the 21 st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020

The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

Journal of International Studies, 2018

With integration between machine automation and data being the hallmark of industrial revolution 4.0, the resilience of energy infrastructure in modern economy has taken a new significance. The study aims at discussing the potential and examining the impacts of the Internet of Things (IoT), which are smart devices with embedded sensors and connectivity, enabling data exchange to the energy sector. This technology contributes towards enhancing industry's sustainable practices through the industrial internet of things. With data from the "edge of the grid," these sensors assist in efficient energy consumption, providing constant monitoring for the regulatory authority, particularly on pollutant emissions. IoT technology may complement the national electric smart-grid, enhancing its reliability by feeding these raw data into machine learning neural network for the optimal operation. All these technologies shall complement one another, as Malaysia transform from a net energy exporter into an energy importer. Practicing efficient energy consumption can reduce this external dependency, and enhance national energy security. This paper derives statistical data sourced from the Energy Commission and technical data from publications of other scholars. On smaller scale, IoT implementation in manufacturing plants may resulted in 15% operating cost reduction. The benefits on national level implementation however remains unknown.