A Secured and Trusted Demand Response system based on Blockchain technologies

Tsolakis, Apostolos C.; Moschos, Ioannis; Votis, Konstantinos; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Pandey, Pankai; Katsikas, Sokratis K.; Kotsakis, Evangelos; García‐Castro, Raúl

doi:10.1109/inista.2018.8466303

Cited by 28 publications

(22 citation statements)

References 17 publications

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“…Tsolakis et al [35] presented an approach for the secure exchange of energy data between a cluster of end-users and a Virtual Node. This approach uses a blockchain-based architecture to give a demand response solution.…”

Section: Responsementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Section: Responsementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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“…can be also developed using existing protocols such as OpenADR or IEC 61850 [ 20 ]. A DR framework that assures data security using a blockchain infrastructure, smart contracts, and decentralized applications (dApps) for securing Aggregators-to-Prosumers transactions in the DR context proposed in [ 21 ]. This solution also uses OpenADR for supporting interoperability in terms of information exchange.…”

Section: Related Workmentioning

confidence: 99%

Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy

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et al. 2020

Sensors

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Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer’s energy data using zero-knowledge proofs and validates on the blockchain the prosumer’s activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer’s activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies.

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“…However, the use of Multichain does not support SCs. Other interesting works [6]- [8], propose new systems to provide energy flexibility services to system operators (TSO or DSO) through DR actions based on BC technology, but considering only the interaction between system operators and Balance Service Providers (BSP) without taking into account how prosumers operate the load variations. These works propose to integrate BC solutions in the currently existing DR programs with the aim of reducing information asymmetry for the customers.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

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The use of Distributed Ledger Technologies such as Blockchain for certifying Demand Response services allows for the creation of a distributed system in which customers can communicate with the system operator to provide their flexibility, in a secure, transparent and traceable way. Blockchain technology also supports incentive mechanisms for users taking part in the service through the generation of utility tokens to recognize the user's contribution. This paper presents the experimental test of a novel methodology for Demand Response programs implementation by using the Blockchain technology. The latter is employed for defining a distributed Demand Response service and a new system for its tracing and certification. For this work, a Smart Contract has been conceived and written to execute Demand Response events, calculate users' baseline, compute the support provided by each user towards the fulfilment of the requested load curve modification and remunerate each user with utility tokens proportionally to their contribution. To test the methodology, a Hyperledger Fabric network and a Smart Contract were deployed on four nodes of the Microgrid Laboratory of the Department of Energy Technology at Aalborg University (DK). Subsequently, a realistic scenario comprising two consumer nodes was developed using power electronic converters for generating the household profiles and Smart Meters for the measurement of the consumption profiles. Theoretical and experimental results show the feasibility of Distributed Ledger Technologies in smart grids management with a minimum investment in new hardware while enabling the active participation of customers in Demand Response more transparently and fairly.

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A Secured and Trusted Demand Response system based on Blockchain technologies

Cited by 28 publications

References 17 publications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

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