Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan

Ahl, Amanda; Yarime, Masaru; Goto, Mika; Chopra, Shauhrat S.; Kumar, Nallapaneni Manoj; Tanaka, Kenji; Sagawa, Daishi

doi:10.1016/j.rser.2019.109488

Cited by 105 publications

(45 citation statements)

References 41 publications

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“…The peers are the participants involved in the electricity trades (typically, the consumer, prosumer, regulatory authority, etc.) [162,163]. For several years, the centralized model has performed well, but with the rise in the volume of data shared during the trading process, there is a possibility that the servers encounter bottlenecks and a single point of failure, making them vulnerable to attack [10].…”

Section: Application Of Blockchain In Smart Gridsmentioning

confidence: 99%

“…In a permission-less or public BC (system without access limitation), the introduction of new information on to block must be allied with specific resources like fuel cost and computational tools. For example, the consensus proof-of-work (PoW) mechanism entails participating nodes to solve the hash code and to validate the node [10,162,163]. Hence, the PoW consensus creates computational expenses due to adding new information (the next block) [164].…”

Section: Application Of Blockchain In Smart Gridsmentioning

confidence: 99%

“…Security issues that lie with peer communication in the ESN can be addressed using IoT and BC systems [158]. The BC's trust and transparent features ensure that information flow between the peers is always secure and happens through a specific channel based on the defined agreement on the smart contracts [162,163]. Additionally, the BC traceability feature allows us to quickly identify the destruction of information and manipulation of energy consumption data.…”

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confidence: 99%

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Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

et al. 2020

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Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.

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Section: Application Of Blockchain In Smart Gridsmentioning

confidence: 99%

Section: Application Of Blockchain In Smart Gridsmentioning

confidence: 99%

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confidence: 99%

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Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

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“…This project is led by Digital Grid Corporation in collaboration with the University of Tokyo, Tateyama Kagaku group, Kansai Electric Co., Ltd., TEPCO Holdings, Hitachi IE system, NTT Data, Tessera Technology, and US-Design, with financial support provided by the Ministry of the Environment through the Low Carbon Technology Research, Development and Demonstration Program [75]. Peer-to-peer electricity exchanges are implemented among individual households including prosumers as well as consumers and a large shopping center [76]. Each prosumer has a DGR of 5 kW photovoltaics, a battery of 12 kWh, sub-grid selling and buying legs of 10 kW, and a digital grid controller (DGC) that communicates with buying and selling smart meters and DG net, whereas each prosumer has only a DGC [75].…”

Section: Urawa-misono Districtmentioning

confidence: 99%

Energy Transitions Towards Low Carbon Resilience: Evaluation of Disaster-Triggered Local and Regional Cases

Barrett

Copping

et al. 2019

Sustainability

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Following numerous global scientific studies and major international agreements, the decarbonization of energy systems is an apparent and pressing concern. The consequence of continued emission growth tied to rising global average temperatures is difficult to predict, but against a background of other natural and human-induced disasters, may create a situation, from a positive perspective, where each disaster event triggers "build back better" responses designed to speed the transition toward low carbon, resilience-oriented energy systems. This article examines the potential for disaster-triggered responses in communities, at various local and regional levels, in four industrial economies in the Asia Pacific region: Japan, China, Australia, and the USA. Seven case studies were evaluated against a set of criteria that exemplify the key aspects of resilient energy systems. The research results suggest that a new space of innovation does emerge in post-disaster situations at a range of local and regional scales. The greatest potential benefit and opportunity for significant gains, however, appears to manifest at the small community level, and the ultimate challenge relates to how to mainstream local innovations into state and national level transformation on energy systems so as to enhance resilience and promote rapid decarbonization.

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“…In some cases, multiple RERs-based power generation facilities called microgrids are linked and are referred to as hybrid RERs-based microgrids [3]. The use of electricity from such DPG is commercially and socially favorable in some cases for the prosumers [3,4]. In some cases, depending upon the benefits provided by the electric utility in terms of power selling and, incentives for supplied power during peak hours, favorable selling prices etc., the DPG systems are linked to the electric power grid, which has led to considerable progress in DPG-linked electrical power grids in different countries.…”

Section: Introductionmentioning

confidence: 99%

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

et al. 2020

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Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

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Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan

Cited by 105 publications

References 41 publications

Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

Energy Transitions Towards Low Carbon Resilience: Evaluation of Disaster-Triggered Local and Regional Cases

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

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