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Blockchain and Energy Internet
Emergence of the Energy Internet (EI) demands restructuring of traditional electricity grids to integrate heterogeneous energy sources, distribution network management with grid intelligence and big data management. This paradigm shift is considered to be a breakthrough in the energy industry towards facilitating autonomous and decentralized grid operations while maximizing the utilization of Distributed Generation (DG). Blockchain has been identified as a disruptive technology enabler for the realization of EI to facilitate reliable, self-operated energy delivery.
IoT devices, including smart meters and sensors, communicate real-time measurement data of the large-scale participation of the Distributed Generation (DG) . This is envisaged to facilitate autonomous operation of energy grids, benefiting seamless integration of DG without the involvement of a third party compared to the conventional counterpart. Implementation of EI grids is proposed as an overlay of four layers: namely, physical layer, communication and control layer, application layer and data analysis layer . The former two are comprised of IoT devices and beyond 5G communication technologies, enabled through edge computing respectively. The latter two layers of the novel architecture incorporate the applications of the envisaged EI grid and data analysis technologies supported through big data management . Applications of EI span beyond offering dynamic energy prices to the consumers and obtaining their contribution in DR initiatives. They also exhibit prospects in multi-dimensional aspects, including: (1) Peer-to-Peer (P2P) energy trading; (2) plug-and-play interfacing for DERs; (3) microgeneration; (4) Demand Side Integration (DSI); (5) automation and management of distribution networks; and (6) management of energy data. Figure 1 illustrates the interrelationship of these applications.
2. Energy Sustainability through Heterogeneity
2.1. Key Challenges
Seamless grid integration: How can we facilitate seamless grid integration of DERs in order to achieve a decentralized, customer-centric grid architecture ?
Decentralized marketplace: How is it possible to achieve a decentralized marketplace with dynamic price signaling and maximized consumer satisfaction ?
Secure communication: How can we manage secure communication links and storage for large energy data aggregation arising from the increasing number of grid interconnections in a decentralized and secure platform?
Transient and dynamics: How can we mitigate the transient over voltages and undesirable dynamics resulting from bi-directional energy routing and uncoordinated grid interconnections of DG, ESS and EV ?
Improving interoperability: How can we achieve interoperability of heterogeneous energy sources that adopt different grid interconnection standards?
2.2. Role of Blockchain
2.3. Future Directions
Lessons: Decentralized energy trading is a well-established research area with several approaches proposed and real-world scenarios being implemented . Examples include P2P trading of renewable energy . Communication links are expected to be made secure through proposed blockchain integrated architecture, which, however, has room for improvement with novel Smart Grid 2.0–specific security threats to be addressed . Meanwhile, facilitating seamless grid integration of DGs  and their interoperability  are the challenges with future research prospects considering the existing work, which would require collaborative technological approaches facilitated through blockchain.
3. Improved Trust, Security and Privacy
3.1. Key Challenges
Device tampering: How can we prevent tampering and unauthorized accessing of smart meters and smart sensors to ensure integrity of the obtained energy measurements ?
Man-in-the-Middle attacks in EI grids: How can we establish a secure communication link between the prosumer and the consumer during energy trading and prevent Man-in-the-Middle attacks causing data manipulation?
DDos attacks in EI grids: How will it be possible to detect Distributed Denial of Service (DDoS) attacks causing deliberate traffic of energy requests and depriving the legitimate users from consuming energy ?
Privacy issues: Can a consumer participate in DSI initiatives while preserving the privacy of energy consumption data which can trace back to the behavioral patterns of the user?
Authentication: How can the identity of a node in the energy grid be verified in a decentralized architecture without revealing the connection between the energy signature and the owner’s name and location?
3.2. Role of Blockchain
3.3. Future Directions
Lessons: Blockchain integration with Smart Grid 2.0 has facilitated in mitigating software and network related attacks, including Man-in-the-Middle and DDoS adversaries . Further, the existing work has proposed different user authentication and privacy-preserving approaches, which have been implemented through cryptographic techniques used in blockchain . The most widely adapted approach could be identified as cryptographic encryption–based digital signatures for user verification . However, modern smart grids, which are to incorporate predictive data analytic tools for intelligent decision, are vulnerable to AI and ML–related attacks . These adversaries would overlay the security threats governing Smart Grid 2.0, which would require accelerating the existing research initiatives.
4. Ultimate Reliability and Stability
4.1. Key Challenges
Supply-demand balancing: How can we facilitate seamless integration of DG to achieve dynamic response in power output to rectify supply-demand mismatch ?
Intermittent generation: What will the possibility be of securing stable and reliable grid operation in a decentralized architecture with no third-party involvement and heterogeneous grid interconnections?
Secure communication: How can we facilitate secure communication links to improve the exchange of energy data and control signals between peers to improve stability and reliability of a distributed grid?
Intelligent decision-making: How do we arrive at intelligent decisions for optimal generation allocation to improve grid stability management?
Energy theft: How can we prevent energy theft, ensuring the consumer a reliable energy supply?
Power quality management: How can we mitigate the issues related to non-compliance of power quality standards by the prosumer, DG owner and consumer?
4.2. Role of Blockchain
4.3. Future Directions
Lessons: Elimination of energy theft through double auction and other mechanisms has been presented in the existing literature . However, securing communication channels to offer a reliable electricity supply while addressing the challenges related to intermittent generation of DGs have research prospects for maximization of renewable energy utilization in Smart Grid 2.0 . This would enable matching the supply with the demand; however, maintaining the power quality within the allowable limits  would be another challenge to overcome in the envisaged grid architecture, with little research being carried out related to this aspect.
5. Decentralized Scalability
5.1. Key Challenges
Scalable, decentralized EI grids: How can we offer scalable solutions for decentralized energy grids which would facilitate integration of large numbers of heterogeneous generation sources and extending the customer base ?
Low-latency grid synchronization: How can we achieve low-latency decision-making for the synchronization of large numbers of connected DGs ?
Scalable data management: How can we mange the large energy data set generated by the continuously expanding consumer base of future EI grids?
5.2. Role of Blockchain
5.3. Future Directions
Lessons: Scalability of EI grids are partly addressed through off-chain and side-chain implementations  as well as suitable selection of the blockchain platform . This includes scalable initiatives such as NRG Xchange  and analytical selection of the blockchain platform such as HyperLedger over Ethereum . Energy data management with the increasing number of consumer connections would be a challenge to be addressed, while facilitating low-latency grid synchronization of DERs has not been discussed in the existing literature .
6. Advanced Big Data Management
6.1. Key Challenges
Data silos: How can we overcome data silos and establish trust between prosumers, microgrids and large power plants for better coordination?
Secure communication: How can we achieve secure communication channels between smart meters/smart sensor nodes and the Energy Management System (EMS) ?
Secure data storage: How can we provide secure, privacy-preserving and scalable storage for the aggregated large data sets containing generation and consumption patterns of consumers and prosumers respectively?
Data integrity protection: How can we ensure the integrity of the stored energy data utilized for AI model development, training, validation through ML techniques, testing and deployment ?
Data ownership: How can we ensure ownership of the aggregated energy consumption/production pattern data to prevent privacy-violations arising from unauthorized trading of these sensitive data to a third party?
Scalable grids: How can we facilitate the management of large data volumes while offering scalabilty for grid expansion with numerous grid integration of prosumers, microgrids, EVs and collaborative consumers participating in DSIs?
6.2. Role of Blockchain
6.3. Future Directions
Lessons: Future EI grids will be integrated with AI and ML for predictive data analysis, giving rise to a new set of challenges which were not encountered in previous generations of smart grids . Blockchain integration with EI grids have facilitated data integrity protection through cryptographic hashing  and is well addressed in the existing literature. However, addressing the challenges, including data silos , facilitating secure, scalable data communication and storage with privacy-preserving data ownership , would require further research attention.
7. Grid Intelligence
7.1. Key Challenges
Data manipulation: How can we mitigate manipulation of energy input data (electricity consumption and production data obtained through smart meters) and validate the authenticity of the information?
ML: How can we prevent the model inversion, poisoning pertaining to training and deployment of ML models, used for adaptive decision-making processes in automated generation allocation of EI grids ?
Ethical data aggregation: How can we ensure ethical use of aggregated energy production/consumption data for AI model training and prevent unauthorized data sharing with compliance to privacy preservation?
Transparency: How can we improve transparency in model development, training, testing and deployment, resulting in algorithms that are reliable for diverse applications with grid integration of heterogeneous energy sources?
Automation: How can we assure security in AI-based automation of network control and orchestration with it ?
Trust management: How can we establish trust among stakeholders participating in energy trading in EI grids and improve transparency in process automation through the deployment of AI models ?
Accountability: How do we ensure the accountability of the AI algorithms for automated decision-making processes responsible for generation coordination, distribution network management and fault recovery?
7.2. Role of Blockchain
7.3. Future Directions
Lessons: Grid intelligence would dominate the future autonomous and the challenges arising from AI-integrated smart grids are seldom addressed through the existing literature . Improving transparency to ensure accountability of ML models  and trust management in the decisions arrived through the models  would be EI grid–specific challenges to be addressed in future research.
The entry is from 10.3390/network1020007
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