Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth, Gary Hahn · 7 authors
The electrical substation-grid testbed was created to integrate the GOOSE and/or DNP (Distributed Network Protocol) messages with time synchronized sources and Distributed Ledger Technology (DLT). The objective was to study the impact of faults and cyber-events at an electrical substation with inside (protective relays) and outside (power meters) substation devices. The electrical substation-grid testbed was based on the design of a 34.5/ 12.47 kV electrical substation (sectionalized bus configuration) with two power transformers, connected to radial power lines and load feeders. The electrical substation-grid testbed was installed at 252 lab space (Advanced Power System Protection), Grid Research Integration and Deployment Center (GRID-C), Oak Ridge National Laboratory. This testbed was created for Task 5, DarkNet project.The electrical substation-grid testbed was created to simulate fault and/or cyber events that could potentially result in damage to the electrical infrastructure. In addition, tests were run that are usually not allowed to be performed in an operational electrical power grid, because these test scenarios could trip breakers and/or generate fault situations that could potentially damage equipment. The number of tests performed in the electrical substation-grid testbed were executed in a better way than in a real electrical substation and/or power grid, because multiple tests could be run in a short period of time, and complex permits, and safety/ schedule restrictions like in a real electrical substation environment were not needed.The electrical substation-grid testbed was created using real measurement, communication, and protection devices that are used by electrical utilities, to have same conditions that we could observe in a real power grid or electrical substation. The electrical substation-grid testbed was based on using a real time simulator and expansion box with amplifiers that were wired to electrical substation-grid devices. This hardware-in-the-loop (HIL) was provided by protective relays, power meters, ethernet switches, remote terminal units, synchronized timing network clock, DLT devices, workstations, and servers.This report includes the design, installation, and assessment of the electrical substation-grid testbed that was similar to an operational electrical substation, integrating the power system protection, communication, and control systems. The results for the electrical substation-grid testbed were based on:• verifying the analog signals for protective relays and power meters, • observing the synchronized time source frame at devices, • authenticating the GOOSE (IEC 61850) and DNP messages from power meters and protective relays, and • verifying the trip conditions of protective relays at fault tests with the power system fault event detection, using DLT devices.For future work, the electrical substation-grid testbed with protective relays and power meters, using DLT and synchronized time source from DarkNet, will be used to study the impact of cyber-events at inside and outside substation devices. Advanced algorithms for detecting cyber-events produced by non-desired protective relay settings will be studied, to improve the detection and reliability of protection, control, and communication systems at power grids.
This paper introduces a blockchain-based P2P energy trading platform, where prosumers can trade energy autonomously with no central authority interference. Multiple prosumers can collaborate in producing energy to form a single provider. Clients’ power consumption is monitored using a smart meter that interfaces with an IoT node connected to a blockchain private network. The smart contracts, invoked on the blockchain, enable the autonomous trading interactions between parties and govern accounts behavior within the Ethereum state. The decentralized P2P trading platform utilizes autonomous pay-per-use billing and energy routing, monitored by a smart contract. A Gated Recurrent Unit (GRU) deep learning-based model, predicts future consumption based on past data aggregated to the blockchain. Predictions are then used to set Time of Use (ToU) ranges using the K-mean clustering. The data used to train the GRU model are shared between all parties within the network, making the predictions transparent and verifiable. Implementing the K-mean clustering in a smart contract on the blockchain allows the set of ToU to be independent and incontestable. To secure the validity of the data uploaded to the blockchain, a consensus algorithm is suggested to detect fraudulent nodes along with a Proof of Location (PoL), ensuring that the data are uploaded from the expected nodes. The paper explains the proposed platform architecture, functioning as well as implementation in vivid details. Results are presented in terms of smart contract gas consumption and transaction latency under different loads.
Dhanya Therese Jose, Jørgen Holme, Antorweep Chakravorty, Chunming Rong
The demand for electricity is increasing exponentially day by day, especially with the arrival of electric vehicles. In the smart community neighborhood project, electricity should be produced at the household or community level and sold or bought according to the demands. Since the actors can produce, sell, and buy according to the demands, thus the name prosumers. ICT solutions can contribute to this in several ways, such as machine learning for analyzing the household data for customer demand and peak hours for the usage of electricity, blockchain as a trustworthy platform for selling or buying, data hub, and ensuring data security and privacy of prosumers. TOTEM: Token for controlled computation is a framework that allows users to analyze the data without moving the data from the data owner's environment. It also ensures the data security and privacy of the data. Here, in this article, we will show the importance of the TOTEM architecture in the EnergiX project and how the extended version of TOTEM can be efficiently merged with the demands of the current and similar projects.
Kisung Park, Joonyoung Lee, Ashok Kumar Das, Youngho Park
With the ongoing revolutionary growth of the industrial Internet of Things and smart grid networks, smart grid (SG) communication has been acknowledged as a next-generation network for intelligent and efficient electric power transmission. In SG networks, smart meters (SMs) generally send requests for electricity demand to service providers (SPs), which deal with the requests for efficient energy distribution. However, SGs experience many security issues with the deployed SMs and untrusted wireless communication. To tackle these security issues, we propose a privacy-preserving authentication scheme for demand response management in SGs, called BPPS. It can resist various attacks and achieve secure mutual authentication with key agreement; moreover, it provides integrity of demand-response data using blockchain. Moreover, we perform the informal and formal (mathematical) security analysis to confirm that BPPS is secure against various attacks and achieves session key security, respectively. Furthermore, we conduct the performance and simulation analysis for SGs using NS3 and Ethereum testnet. Consequently, BPPS provides high-level security and can be applied to actual SG networks.
Although the traditional P2P botnet has significant resilience against termination, its dependence on neighbor lists (NL) has left it vulnerable to infiltration and destruction. In addition, it is not sufficient in protecting the botmaster’s identity. To overcome these weaknesses, we proposed BlockchainBot, a botnet model that leveraged IoT devices as maintainers, and integrated blockchain, also known as distributed ledger technology (DLT). The BlockchainBot was able to fully deploy bots on public blockchains. It was versatile for multiple botnet applications and eliminated the dependence on NL. In addition, we further introduced a novel method, the forking of a channel, to kick out spy nodes that infiltrate a botnet. To further enforce the resistance against a single point of failure (SPoF), we introduced bot-cluster dispersing to prevent clustering around full nodes and more evenly scatter bots to prevent hostile takeovers. The analysis of the security of BlockchainBot indicated that it had strong resilience against DDoS attacks, Sybil attacks, and forensic investigations. Furthermore, the security of the forking of the channel and bot-cluster dispersing were also shown to be effective. The robustness of the BlockchainBot against the Sybil attack was also briefly discussed. Experimental results authenticated the effectiveness and performance of the BlockchainBot, as compared to previous models.
We present a prototype of a decentralized power trading system based on the use of distributed ledger technology. This sort of efficient, decentralized marketplace is needed to empower prosumers and make them first-class members of a smart, decentralized power grid in order to drive further renewable energy adoption. Unlike the bulk of previous work in this field, we focus on private permissioned distributed ledgers rather than conventional blockchains. The proposed solution is entirely independent of cryptocurrency, with an explicit design capability of being adapted piecemeal without any fundamental changes to the present regulatory environment. To be economical, efficient, and scalable, our prototype is based on a lean, Corda-based private permissioned distributed ledger. It allows for instant, automatic bidding on and trading of ‘power promises’ and the robust implementation of short-term, small-scale liquid electrical power futures. We demonstrate that the prototype performs well and presents several clear advantages over existing solutions based on conventional blockchains. Therefore, the proposed approach represents a promising, robust solution to the smart grid decentralized power trading problem.
Mohammed Amin Almaiah, Aitizaz Ali, Fahima Hajjej, Muhammad Fermi Pasha · 5 authors
The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.
Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems due to the natures of automation, standardization, and selfenforcement. This paper proposes a Blockchain smart contracts based platform to manage the grid connection for both large scale generation companies and individual prosumers (both producers and consumers). Through evaluating the capacity margin and carbon intensity for each substation or feeder in power networks, the incurred connection fee and low carbon incentive are formulated for incentivizing the local energy balance and connection of renewable energy sources. Case studies testify the effectiveness for encouraging the low carbon grid connection.
In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. Since blockchain-based energy exchanges can automate transactions between their members and provide increased levels of security thanks to smart contracts, these new initiatives may eventually revolutionize how customers interact with utility companies. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. The first three prototypes were developed in Solidity and deployed using Ethereum. Heavyweight is fully on-chain, whereas Featherweight and Lightweight showcase various levels of hybridization. Weightless, in turn, was deployed using Quorum, a gas-free alternative to Ethereum. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.
In future power systems with the characteristics of high-elastic, demand response (DR) is considered to be an essential way for improving system stability and awakening demand-side resources. However, all the DR programs in China are implemented in a centralized model, which is easier for supervision, but hard to deal with the challenges of data credibility, privacy protection, transaction efficiency, and so on. Therefore, a novel blockchain-based framework for the DR program is proposed to deal with the problems. On this basis, the bidding transaction process based on the repeated verification mechanism is designed, and the effectiveness of blockchain in DR is analyzed. Then, the smart contract functions involved in DR bidding transactions and subsidy settlement are customized. The feasibility of the DR bidding mechanism is illustrated by the simulation results on the platform of Remix IDE.
Blockchain technology is recognized as a suitable tool to secure the energy trading because it could perfectly match the distributed structure of peer-to-peer (P2P) energy market. But its usage is stuck on the transaction level. Control systems are significant to the microgrid as they ensure a stable power delivery system and regulate the performance of parameters such as active power and frequency. This paper proves that the blockchain technology is also effective in securing the distributed control systems against the false data injection attack. A six-prosumer microgrid is tested with the implementation of the hierarchical blockchain system. The security of both the control system and energy trading system of the microgrid is ensured. Smart contracts are created to calculate the feedback measurements for the control system and execute the energy transactions. According to the hierarchical structure, the private blockchain with static nodes is implemented for the distributed control to match the sampling rate. A Proof-of-Authority based blockchain is utilized to support the energy trading. In addition, a double auction based simple iteration (DA-SI) pricing scheme is designed to improve the social welfare of the microgrid. Finally, case studies are presented to verify the proposed hierarchical blockchain system as an effective method to safeguard the control system and maximize the benefits of prosumers. Numerical results show the effectiveness and feasibility of the proposed approach.
As we all know, the behavior of stealing electric energy governance is always the difficulty and key point in the management of electric power enterprises. In recent years, as bitcoin’s value continued to climb, the theft of electricity by bitcoin mining user began to appear. In order to solve the power theft problem of bitcoin mining users, we conducted an in-depth study on the power consumption behavior of such users based on the power data analysis technology. This paper analyzes the power consumption characteristics of bitcoin mining users and uses electric data acquire system to monitor power consumption behavior. The paper makes comparative analysis on the massive data such as voltage and power of the electric energy acquisition system, analyzes and calculates the Pearson correlation coefficient between the electricity consumption of each customer and the line loss statistics of the power station by using Pearson correlation algorithm combined with the power loss of the power station, and analyzes the suspected users of stealing electric energy by taking an actual example. Through our research, we found a total of 16 bitcoin miners suspected of stealing electricity. After on-site investigation and evidence collection, we found that 10 of the users did have abnormal power consumption, and the accuracy rate reached 62.50%. Therefore, the economic benefits of this research are very significant.
Tuukka Mustapää, H. Tunkkari, Jaan Taponen, L. Immonen · 8 authors
Digitalization and the rapid development of IoT systems has posed challenges for metrology because it has been comparatively slow in adapting to the new demands. That is why the digital transformation of metrology has become a key research and development topic all over the world including the development of machine-readable formats for digital SI (D-SI) and digital calibration certificates (DCCs). In this paper, we present a method for using these digital formats for metrological data to enhance the trustworthiness of data and propose how to use digital signatures and distributed ledger technology (DLT) alongside DCCs and D-SI to ensure integrity, authenticity, and non-repudiation of measurement data and DCCs. The implementation of these technologies in industrial applications is demonstrated with a use case of data exchange in a smart overhead crane. The presented system was tested and validated in providing security against data tampering attacks.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Networked control systems (NCSs) are widely used in practical applications because of their flexibility in deployment. However, due to the dependence on the communication network, NCSs could be vulnerable to malicious cyberattacks. To address this problem, a novel blockchain technology-assisted networked predictive secure control approach is presented for the first time in this article. First, the introduction of blockchain technology brings a significant boost to the inherent resilience of the NCS in an active manner without relying on any prior knowledge of the system or potential attacks. However, blockchain technology would induce time delays unfavorable to the NCS, which could result in the low real-time performance of the control system. Subsequently, a networked Kalman filter-based predictive control is specially designed to compensate for the low real-time property of blockchain technology. A detailed analysis of the security and stability of the closed-loop NCS with the developed networked predictive secure controller is also presented, while sufficient conditions for the closed-loop system to be simultaneously stable and safe in a probabilistic sense are given. Finally, to verify the performance of the proposed approach in terms of practicality, an experimental prototype of a photovoltaic (PV)-based power generation system subjected to random cyber-attacks is built for voltage regulation.
Given the ongoing transition towards a more decentralised and adaptive energy system, the potential of blockchain-enabled smart contracts for the energy sector is being increasingly recognised. Due to their self-executing, customisable and tamper-proof nature, they are seen as a key technology for enabling the transition to a more efficient, transparent and transactive energy market. The applications of smart contracts include coordination of smart electric vehicle charging, automated demand-side response, peer-to-peer energy trading and allocation of the control duties amongst the network operators. Nevertheless, their use in the energy sector is still in its early stages as there are many open challenges related to security, privacy, scalability and billing. In this paper, we systematically review 178 peer-reviewed publications and 13 innovation projects, providing a thorough analysis of the strengths and weaknesses of smart contracts used in the energy sector. This work offers a broad perspective on the opportunities and challenges that stakeholders using this technology face, in both current and emergent markets, such as peer-to-peer energy trading platforms. To provide a roadmap for researchers and practitioners interested in the technology, we propose a systematic model of the smart contracting process, by developing a novel 6-layer architecture, as well as presenting a sample energy contract in pseudocode form and as open-source code. Our analysis focuses on the two mainstream application areas we identify for smart contract use in this area: energy and flexibility trading, and distributed control. The paper concludes with a comprehensive, critical discussion of the advantages and challenges that must be addressed in the area of smart contracts and blockchains in energy, and a set of recommendations that researchers and developers should consider when applying smart contracts to energy system settings.
Control systems are significant to the microgrid as they regulate performance parameters such as frequency, active power, and voltage. Distributed control systems allow direct communication between the secondary controllers and controls the parameters efficiently. To secure each distributed control process and ensure a good quality of control results, a proof-of-authority private blockchain is applied in this article to defend the distributed control system against various types of cyber-attacks such as false data injection. A four-distributed generation islanded microgrid is tested with the implementation of the blockchain. Smart contracts are created to calculate the control feedback and return the value to corresponding secondary controllers. All of the four nodes are initially assigned as the authority nodes to share the mining burden, but according to the proof-of-authority consensus protocol, the authority role could be excluded if the node behaves illegally and causes damage to the control system. In addition, different attacking scenarios are categorized and analyzed with their respective solutions. Finally, a case study is introduced to verify the corresponding solutions and proves that the proposed method is able to secure the distributed control system while ensuring the control quality. Numerical results show the effectiveness and feasibility of the proposed approach.
Adamu Sani Yahaya, Nadeem Javaid, Muhammad Umar Javed, Ahmad Almogren · 5 authors
This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.
Interoperable and secure data management techniques are fundamental for most of large-scale Structural Health Monitoring (SHM) systems. Indeed, given the relevance of SHM critical measurements, data integrity must be protected against tampering or falsifications. In this paper, we propose a four-layer SHM architecture that allows to build an effective data pipeline from sensors to consumer applications, passing through the cloud. The architecture is built on top of the MODRON platform and exploits the recent advances of the W3C Web of Things (WoT) standard for interoperability. We then discuss how third-party services can take benefit of the W3C WoT architecture to retrieve the SHM critical data and to publish them on the Ethereum Blockchain through an SHM-specific Smart Contract, for data protection and traceability purposes. We test the effectiveness of the Smart Contract implementation in terms of latency and costs under simulated workloads.
Ouns Bouachir, Moayad Aloqaily, Öznur Özkasap, Faizan Safdar Ali
Peer-to-Peer (P2P) energy trading platforms envisioned energy sectors to satisfy the increasing demand for energy. The vision of this paper is not only to trade energy but also to have part of it being shared. Therefore, this paper presentsFederatedGridswhich is a P2P energy trading and sharing platform inside and across microgrids. Energy sharing allows exchanging energy between the categories of consumers and prosumers in return for future benefits.FederatedGridsplatform uses blockchain and federated learning to enable autonomous activities while providing trust and privacy among all participants. Indeed, based on various smart contracts using federated learning,FederatedGridscalculates a prediction of the future energy production and demand allowing the system to autonomously switch between trading and sharing, and enabling the prosumers to make decisions related to their participation in the energy sharing process. Up to our knowledge, this work is the first attempt to create a hybrid energy trading and sharing platform, with the real sharing meaning, and that uses federated learning over the smart contract for energy demand prediction. The experimental results showed a 17.8% decrease in energy cost for consumers and a 76.4% decrease in load over utility grids.