Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

Blockchain technology in the renewable energy sector: A co-word analysis of academic discourse

The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.

Research on digital transformation strategy of the energy industry based on differential game under the dual-carbon background in China

Digital transformation of the energy industry is at the vanguard of promoting green and low-carbon development of energy, and the transformation and upgrading of the energy industry is a critical path to achieve the goal of "dual carbon." The study takes energy enterprises, digital technology providers, and local governments in China as the research objects, then by constructing a three-party dynamic differential game model, four scenarios are considered respectively. The optimal digitization effort, digitization level, carbon emission reduction expectations, and optimal benefits of each subject are studied. The results show that: (1) Government subsidies function as an effective regulating mechanism, helping regulate the profit distribution of energy enterprises. With government subsidies, cost-sharing contracts can improve the profits of energy enterprises and digital service providers and enhance industry digitization and carbon emission reduction to some extent. (2) When there are government subsidies, the optimal digitalization efforts of energy enterprises and the optimal subsidy coefficient of government under the three decisions are independent of time. The extent to which subsidies improve system members' income is correlated to its game structure and status. (3) In the centralized decision-making state, digitalization level, product emission reduction, and total system profit are higher than in the decentralized decision-making state, which can better help the overall revenue system composed of energy enterprises, digital service providers, and local governments to achieve economic and environmental optimization at the same time and reach the Pareto optimal state. (4) With government subsidies and after the introduction of contracts, when certain conditions are met, the followers' optimal digital effort level, product emission reduction, digitization level, demand and profits of each member are all improved except for the leader's optimal effort level, Pareto improvement of profits of all participating members in the system is realized.

Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT

At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques [...].

Research on the evolution and driving factors of digitalization of energy in China-A new perspective based on coupling coordination

The world is experiencing the tide of digital transformation. Promoting the digitalization of energy is an important measure to realize energy sustainability. The digitalization of energy is a product of the coupled and coordinated development of the energy industry and the digital economy. This paper takes 30 provinces in China from 2011 to 2020 as research objects. The Entropy Weight Method is used to measure the development level of China's energy industry and digital economy, and the Coupling Coordination Degree Model is used to measure the digitalization of energy (DE). Then, the evolution characteristics and driving factors of China's DE are studied by using Grey Prediction Model, Kernel Density Analysis Method and Geodetector Model. The conclusions are mainly as follows: (1) Though the development level of energy industry and digital economy in China are both growing over time, there are distinct features in their spatial distribution. (2) The DE is in a disorderly state as a whole, but it will be upgraded to the stage of barely coordinated during China's 14th Five-Year Plan period (2021-2025). From the evolutionary process, the distribution pattern and trend changes of DE are characterized by obvious polarization distribution and increasing absolute differences, and this feature is more obvious in the central and western regions. (3) Government support, FDI, and pollution governance have an increasing impact on DE, but this impact varies considerably between the east and central-west regions. Therefore, this paper suggests that local governments should formulate policies to promote the DE according to local conditions. The central government should focus on accelerating the construction of the "East Digital West Computing" project and the development of the energy Internet to solve the problem of uneven regional development.

The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.

Internet of Things for smart energy systems: A review on its applications, challenges and future trends

<abstract> <p>Internet of Things (IoT) is a terminology used for a mixed connection of heterogeneous objects to the internet and to each other with the employment of recent technological and communication infrastructures. Its incorporation into engineering systems have gradually become very popular in recent times as it promises to transform and ease the life of end users. The use of IoT in smart energy systems (SES) facilitates an ample offer of variety of applications that transverses through a wide range of areas in energy systems. With the numerous benefits that includes unmatched fast communication between subsystems, the maximization of energy use, the decrease in environmental impacts and a boost in the dividends of renewable energies, IoT has grown into an emerging innovative technology to be integrated into smart energy systems. In this work, we have provided an overview of the link between SES, IoT and Internet of Energy (IoE). The main applications of IoT in smart energy systems consisting of smart industries, smart homes and buildings, and smart cities are explored and analyzed. The paper also explores the challenges limiting the employment of IoT technologies in SES and the possible remedies to these challenges. In addition, the future trends of this technology, its research direction and reasons why industry should adopt it are also addressed. The aim of this work is to furnish researchers in this field, decision and energy policy makers, energy economist and energy administrators with a possible literature outline on the roles and impacts of IoT technology in smart energy systems.</p> </abstract>