Smart Contract Design in Distributed Energy Systems: Comparison
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Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Sara Rouhani.

Blockchain technology and, in particular, smart contracts based on it, offers a new, decentralized mechanism for entering into and fulfilling contracts in diverse markets. Energy markets are no exception, and indeed, the decentralized nature of the blockchain may be particularly important for them as the penetration of residential prosumers offering microgeneration to the grid grows. 

  • energy systems
  • distributed
  • BC
  • smart contract
  • smart grid
  • energy market

1. Introduction

Energy systems today are undergoing tremendous change, as the energy demands of a growing population conflict with the need to decarbonize generation and transmission. The Smart Grid, which leverages Information Technology (IT) to improve energy system efficiencies, is a key response to these needs. Blockchain (BC) technologies, and especially Smart Contracts (SCs), are important frontiers in designing the IT infrastructure of the Smart Grid. A number of studies indicate that they may be suitable for decentralization and distributed management of system resources at multiple layers of the Smart Grid. A BC is a distributed shared ledger replicated across all nodes in a network, which communicate through a peer-to-peer protocol. Data are attached to this ledger as a group of transactions called blocks, with each block cryptographically linked to the previous one. BCs thus offer immutability, tamper resistance, and data provenance. BCs employ decentralized consensus algorithms to validate transactions and add blocks to the chain. Within a BC, SCs are executable code that implements the logic behind each transaction [1,2][1][2]. SCs enable the design of distributed applications based on BCs; they specify how data are read from, analyzed, and written to the BC. In particular, SCs for decentralized energy systems can verify and prepare energy data for the ledger and then analyze the stored data. BC-based management of energy systems may offer several advantages. Data are protected against loss, tampering, and single points of failure. Security and privacy are enhanced through business rules in the SCs and the inherent features of BCs [3,4][3][4], while costs are simultaneously reduced [1]. BCs can allow parties with no history of trusting each other to transact safely without needing guarantors [5]. They can also promote social cohesion and a sense of community, preference satisfaction, and uncertainty reduction [6].

2. Smart Contract Design in Energy Systems

This literature breaks down into four categories: energy trading markets, ancillary services, auditing and monitoring, and cybersecurity, which are shown in Figure 31.
Figure 31. Semantic network for the energy market [1,3,4,5,11,12,14,29,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55].
Semantic network for the energy market [1][3][4][5][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].

2.1. Energy Trading Markets

Energy trading is the most common application of BCs in energy systems. Multiple studies have investigated BC approaches to energy markets, which eliminate third parties in establishing, regulating, or operating them [5]. Such approaches can make energy system operations more efficient while reducing the peak load [3], increase market efficiency by reducing the threshold of participation for small retailers [1], reduce the cost of energy for residential users [32][11], and encourage investment in renewable generation plants [33][12]. A radically decentralized ledger can save and process the necessary trading information, which can improve the efficiency and privacy of energy trading in the smart grid [34][13]. Based on this theoretical research, energy trading platforms based on BC have been developed [35][14]. 

2.1.1. User Registration and Data Collection

For permissioned BCs, market participants must be authenticated and authorized to interact with the BC. Kang et al. [13][35] implemented user registration SCs, which also monitor energy usage and energy remaining in real-time. Prosumers are registered along with their conditions and sell price and are then matched with buyers.
SCs can also collect and record necessary data in the ledger. Such data are commonly generated off-chain and may come from prosumers, utility companies, etc. The data may include selling price and amounts of energy, auction bids, closed bid data [4[4][5],5], user energy profiles [9][36], user satisfaction [36][15], generation information, demand information [37[16][17],38], and smart-meter readings [39,40][18][19].

2.1.2. Contract Formation, Fulfillment, and Settlement

Multiple studies use SCs to collect user buy and sell prices for a given time window, which are entered into a selected auction format. These include double auctions [4[4][20][21],41,42], open English auctions [3[3][22],43], Vickrey second price auctions [5,44,45][5][23][24], Merit Order [56][37], and several other formats [11,46,47,48,49,50,57,58][7][25][26][27][28][29][38][39]. Auction winners form power purchase contracts. The process then repeats for the next time window (which is potentially as short as five minutes). Comparative studies of SC-based auction models have examined market dynamics (e.g., market efficiency or bidding strategies) [4,44][4][23] or BC network performance (e.g transaction latency or throughput) [44][23]. Both involve simulating the auction on a chosen BC network.
Contract fulfillment can be automated if the BC is integrated with metering infrastructure. The source of energy consumed can be tracked, proving fulfillment and enabling automated billing. Munising et al. [12][8] presented a decentralized optimal power-flow model for batteries, shapeable loads, and deferrable loads in a distribution network. This can also track the generation source of consumed energy (renewables, fossil fuel, etc.) and its cost [39][18]. Using this tracked information, it is also possible for the SC to dynamically change the power sources dispatched to fulfill contracts for a given consumer [59][40]. Individual prosumers in the energy trading market can be dynamically selected as validators of the BC in [51][30].
Contract settlement via SCs is another frequent topic of research [4,41,46][4][20][25]. Using BCs improves data privacy, identity management, and resilience toward cyber threats. In addition, the atomicity of data transactions in computing results, release, and payment can be assured [7,60][41][42]. Some utilities also accept cryptocurrencies for energy and electricity payments. PowerLedger operates a BC-based energy market using the POWER token [61][43]. Numerous researchers either employ existing cryptocurrencies or create new tokens, e.g., Han et al. [4], El-Syed et al. [62][44], and Muzumdar et al. [44][23].

2.1.3. Reducing Energy Cost

SC-based energy markets reduce costs by eliminating third parties [63][45], providing market access for smaller-scale, low-cost providers, and matching producers and consumers with congruent production and demand patterns. This enables cost reduction, peak load reduction, and enhanced market efficiency [1,32,52,53,54][1][11][31][32][33]. Jiang et al. [55][34] proposed an SC that can respond to energy demand based on the Stackelberg game, which can reduce the energy loss while maximizing the trading revenue considering the randomness of various energy sources. Gao et al. [64][46] achieved a 20% reduction in energy transmission cost with a BC based on a fog-computing paradigm. Large-scale energy auctions in this framework can be processed efficiently and with low latency even under conditions of high participation. Knirsch et al. [52][31] proposed an SC that matches utility generation with forecast consumer loads for optimal tariff selection. Saxena et al. [63][45] proposed a BC-based energy market for residential communities that reduced peak demand by 46% and energy costs overall by 6%. Bouachir et al. [57][38] proposed a federated learning method to predict demand and consumption in an auction round, reducing costs by 17.8%. Suther et al. [15][47] and Kang et al. [13][35] also proposed an SC-based local energy market. SC-based optimization in a time-of-use tariff model is proposed in [65][48]. SCs can also react to demand–response pricing signals [66][49].

2.1.4. Energy Transfer

SCs can schedule energy transfers between consumers and producers. This is distinct from contract fulfillment in that energy transfer requires the physical dispatch of power along a power grid. SCs can define business rules, e.g., scheduled or event-based delivery, or respond to particular situations [67,68,69][50][51][52]. Each SC can invoke another one, carrying out subsequent delivery processes [62][44]. Utz et al. [40][19] connected metering points via SCs, linking each one to a balancing group for settling energy delivery and consumption. In [38][17], energy trading among multiple regions can be interconnected by soft open points through SCs. This improves flexibility, while the voltage stability and power quality in the system are maintained. Figure 42 showed the semantic network for ancillary services.
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Figure 42. Semantic network for ancillary services [9,12,35,43,53,60,61,62,63].
Semantic network for ancillary services [8][14][22][32][36][42][43][44][45].

2.2. Ancillary Services

Ancillary services ensure the electric system provides acceptable power quality while ensuring reliability and security. Electric System Operators (ESOs) are responsible for organizing these services to ensure reliability. For example, The Alberta Electric System Operator (AESO) in Alberta, Canada [70][53], procures ancillary services including Operating Reserve (OR), Transmission Must-Run (TMR), Black Start, and Load Shed Services for imports (LSSi).
1
Operating Reserve: These reserves provide additional capacity or frequency support to the system when the energy supply is not adequate for the present demand. Regulating reserves are immediately available whenever a momentary imbalance in supply and demand would cause a voltage sag. Spinning reserve refers to generators that are synchronized to the grid but not yet delivering power; they can quickly inject substantial additional energy into the grid when needed. Supplemental (“standby”) reserve refers to generators that are available but not yet synchronized to the grid.
2
Transmission Must-Run (TMR): TMR generation compensates for insufficient local transmission infrastructure relative to local demand.
3
Black Start Service: Some generators need start-up power provided to them. Generators that do not are contracted to provide this power in the event of a total system blackout.
4
Load Shed Services for imports (LSSi): LSSi contracts permit AESO to shut down power flow to selected high-demand consumers when necessary to balance energy demand with available supply.
SC implementations of decentralized control systems can be robust, reliable, transparent, traceable, and secure. Energy supply can be optimized by tracking production and consumption and managing and prioritizing energy delivery [9,12,38][8][17][36]. SCs enable secure transactions, can monitor users’ behavior and data, define rules, and provide decision support, all of which are particularly important for renewables in microgrids. The intermittency of, e.g., solar or wind power, becomes a larger problem when a grid has a smaller power capacity, covers a smaller physical area, and serves a more homogeneous user base. Danzi et al. [71][54] proposed an SC-based control scheme using a subset of Distributed Energy Resources (DERs) to act as voltage regulators. Decentralized energy trading allows prosumers to sell excess power [8][55]. Figure 53 showed the semantic diagram for auditing and monitoring.
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Figure 53. Semantic diagram for auditing and monitoring [8,12,13,14,15,54,72].
Semantic diagram for auditing and monitoring [8][9][33][35][47][55][56].
Energy storage units (ESUs) are essential for integrating renewables into the power grid and need to be integrated into energy markets. Yang et al. [10][57] introduce an Automated Demand Response framework using SCs for local scheduling and trading of energy among ESUs. It is also essential to coordinate ESU charging/discharging, or else the grid could be destabilized. Baza et al. [73][58] proposed an SC for scheduling ESU charging. The ESU’s power demand, time-to-complete-charging (TCC), and battery state-of-charge (SoC) determine which ESU(s) are charged in the current time window. Liu et al. [74][59] proposed an SC for electric vehicle (EV) charging to minimize grid fluctuations and charging costs for EV owners. Silvestre et al. [75][60] present a BC-based management framework for microgrids covering ancillary operations, particularly voltage regulation. [76][61] developed an SC-based decentralized voltage stability algorithm. They evaluated the performance of SCs in real-time control and investigated sharding mechanisms to improve scalability. Thomas et al. [47][26] and Mhaisen et al. [72][56] used SCs to implement security control strategies between different components of the Smart Grid.

2.3. Auditing and Monitoring

SCs can monitor devices, users, and data to detect malicious behavior or prevent threats to the system. Auditing refers to SCs tracking energy usage in real-time, initiating contracts, or auditing user preferences such as price, time, etc. Monitoring applications include Gao et al.’s [14][9] proposal to identify malicious usage of electrical power and data. Li et al. [77][62] design a distributed renewable energy system for residential, commercial, and industrial users; SCs are used to maintain stability even under such different load profiles. Yang et al. [16][63] allow residential consumers to track power usage and interior air temperature and to purchase additional power when needed. Yuhong Li et al. [9][36] allow consumers to trace the generation of consumed energy and form SCs to select generation sources of their choosing. Monitoring often involves Distributed State Estimation (DSE) using asynchronous data; delays in data transfer can negatively affect DSE. Asefi et al. [78][64] utilize an SC to improve delayed-delivery tolerance in DSE.

2.4. Cybersecurity

The cybersecurity literature on BCs in the Smart Grid usually focuses on two threats: unauthorized use of personal data or the security of energy transactions (either consumer-to-utility or peer-to-peer). Numerous studies have shown that patterns of energy usage, even just observed on the main feeder, can often be mapped to the identities and activities of persons in a private home [79][65]. The security of energy transactions, meanwhile, addresses necessary characteristics such as authorization, integrity, non-repudiation, and auditable fulfillment of a contract. The main difference between these two strands of research appears to be the presence or absence of a middleware layer that protects data privacy.
Studies focused on privacy protection rely on ensuring that a data requestor can only access data that the owner explicitly permits them to access, and that, furthermore, the requestor only receives anonymized summarized reports. Access to the raw data itself is not provided, ensuring that the owner’s privacy cannot be breached by accident, malicious intent, or external penetration of the requestor’s IT systems. Figure 64 is a good example of this approach [7][41].
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Figure 64. Privacy-preserving data access via Smart Contracts [7].
Privacy-preserving data access via Smart Contracts [41].
In Figure 64, a utility wishes to access consumer data. The consumer will securely store his/her data in an off-chain database (assumed to be cloud-based). The utility and consumer will negotiate an SC defining the data to be provided and the compensation to the consumer. The consumer deploys this contract to the BC. The utility can invoke the SC, triggering a (third-party) cloud-based off-chain computation involving the consumer’s data. Only the result of this computation is returned to the utility, and the consumer is paid [7][41]. Another approach to securing raw data is to deliberately add noise to the data, as in Gai et al. [80][66].
Dorri et al. [60][42] presented a framework for negotiating an energy price using BCs by using a routing method based on the destination public key. A two-phase commit protocol ensures contract fulfillment and settlement. They evaluate a Proof-of-Concept (PoC) implementation in a second paper [81][67]. They also investigated removable BC networks to improve scalability, throughput, latency, and privacy [82][68]. J. Wang et al. [83][69] proposed an anonymous authentication and key agreement protocol for SCs.
The second research theme, securing energy transactions, is very much a classic application of BCs. The literature on energy trading, in general, is vast, and the readers can refer to those sources for further discussion. The basic approach of [84][70] illustrates several key concepts. In Figure 75, consumers and generators negotiate SCs for energy delivery. Each generator creates at least one Authorized Node, which can form contracts and interact with the underlying BC as a peer. In this example, an Ethereum BC is used, using Ethereum’s Proof of Stake consensus algorithm. That algorithm requires the Ether coin, and so [84][70] settlement is also carried out in Ether coins.
Figure 75. Blockchain-based energy trading [84].
Blockchain-based energy trading [70].
Gai et al. [85][71] proposed a permissioned BC to counter fraudulent energy usage, communication interference, and data-center attacks. Lombardi et al. [86][72] implement auctions with SCs, which can also detect and isolate insecure smart meters. Gao et al. [14][9] used SCs to detect fraudulent power usage. W. Wang et al. [87][73] suggested a framework for identification and authentication in the smart grid. Hao et al. [88][74] propose a BC-based anonymous authentication scheme to facilitate fast and privacy-aware roaming and protect private information. SCs are used for user registration, signature verification, and user logout. Aung et al. [89][75] focus on the privacy of homeowners’ data, but they also developed an access control mechanism giving service personnel elevated privileges during emergencies. Guan et al. [90][76] aim to protect the privacy of both the data owner and receiver. An SC with a novel data obfuscation method based on ring signatures is proposed.

References

  1. Wang, X.; Yang, W.; Noor, S.; Chen, C.; Guo, M.; van Dam, K.H. Blockchain-based smart contract for energy demand management. Energy Procedia 2019, 158, 2719–2724.
  2. Rouhani, S.; Deters, R. Security, performance, and applications of smart contracts: A systematic survey. IEEE Access 2019, 7, 50759–50779.
  3. Kumari, A.; Shukla, A.; Gupta, R.; Tanwar, S.; Tyagi, S.; Kumar, N. ET-DeaL: A P2P Smart Contract-based Secure Energy Trading Scheme for Smart Grid Systems. In Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada, 6–9 July 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1051–1056.
  4. Han, D.; Zhang, C.; Ping, J.; Yan, Z. Smart contract architecture for decentralized energy trading and management based on blockchains. Energy 2020, 199, 117417.
  5. Hahn, A.; Singh, R.; Liu, C.C.; Chen, S. Smart contract-based campus demonstration of decentralized transactive energy auctions. In Proceedings of the 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 23–26 April 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–5.
  6. Monroe, J.G.; Hansen, P.; Sorell, M.; Berglund, E.Z. Agent-Based Model of a Blockchain Enabled Peer-to-Peer Energy Market: Application for a Neighborhood Trial in Perth, Australia. Smart Cities 2020, 3, 1072–1099.
  7. Myung, S.; Lee, J.H. Ethereum smart contract-based automated power trading algorithm in a microgrid environment. J. Supercomput. 2020, 76, 4904–4914.
  8. Münsing, E.; Mather, J.; Moura, S. Blockchains for decentralized optimization of energy resources in microgrid networks. In Proceedings of the 2017 IEEE Conference on Control Technology and Applications (CCTA), Kohala Coast, HI, USA, 27–30 August 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 2164–2171.
  9. Gao, J.; Asamoah, K.O.; Sifah, E.B.; Smahi, A.; Xia, Q.; Xia, H.; Zhang, X.; Dong, G. GridMonitoring: Secured sovereign blockchain based monitoring on smart grid. IEEE Access 2018, 6, 9917–9925.
  10. Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system. Decent. Bus. Rev. 2008, 21260.
  11. Khalid, R.; Javaid, N.; Almogren, A.; Javed, M.U.; Javaid, S.; Zuair, M. A blockchain-based load balancing in decentralized hybrid P2P energy trading market in smart grid. IEEE Access 2020, 8, 47047–47062.
  12. Mengelkamp, E.; Gärttner, J.; Rock, K.; Kessler, S.; Orsini, L.; Weinhardt, C. Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Appl. Energy 2018, 210, 870–880.
  13. Zhou, Y.; Manea, A.N.; Hua, W.; Wu, J.; Zhou, W.; Yu, J.; Rahman, S. Application of Distributed Ledger Technology in Distribution Networks. Proc. IEEE 2022, 110, 1963–1975.
  14. Lin, Y.J.; Chen, Y.C.; Zheng, J.Y.; Chu, D.; Shao, D.W.; Yang, H.T. Blockchain Power Trading and Energy Management Platform. IEEE Access 2022, 10, 75932–75948.
  15. Patrizi, N.; LaTouf, S.K.; Tsiropoulou, E.E.; Papavassiliou, S. Prosumer-Centric Self-Sustained Smart Grid Systems. IEEE Syst. J. 2022, 16, 6042–6053.
  16. Nakayama, K.; Moslemi, R.; Sharma, R. Transactive energy management with blockchain smart contracts for P2P multi-settlement markets. In Proceedings of the 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 17–20 February, 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–5.
  17. Ji, H.; Jian, J.; Yu, H.; Ji, J.; Wei, M.; Zhang, X.; Li, P.; Yan, J.; Wang, C. Peer-to-Peer Electricity Trading of Interconnected Flexible Distribution Networks Based on Distributed Ledger. IEEE Trans. Ind. Inform. 2022, 18, 5949–5960.
  18. Kounelis, I.; Steri, G.; Giuliani, R.; Geneiatakis, D.; Neisse, R.; Nai-Fovino, I. Fostering consumers’ energy market through smart contracts. In Proceedings of the 2017 International Conference in Energy and Sustainability in Small Developing Economies (ES2DE), Funchal, Portugal, 10–12 July 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–6.
  19. Utz, M.; Albrecht, S.; Zoerner, T.; Strüker, J. Blockchain-based management of shared energy assets using a smart contract ecosystem. In Proceedings of the International Conference on Business Information Systems, Berlin, Germany, 18–20 July 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 217–222.
  20. Mengelkamp, E.; Notheisen, B.; Beer, C.; Dauer, D.; Weinhardt, C. A blockchain-based smart grid: Towards sustainable local energy markets. Comput.-Sci.-Res. Dev. 2018, 33, 207–214.
  21. Brousmichc, K.L.; Anoaica, A.; Dib, O.; Abdellatif, T.; Deleuze, G. Blockchain energy market place evaluation: An agent-based approach. In Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 1–3 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 321–327.
  22. Seven, S.; Yao, G.; Soran, A.; Onen, A.; Muyeen, S. Peer-to-peer energy trading in virtual power plant based on blockchain smart contracts. IEEE Access 2020, 8, 175713–175726.
  23. Muzumdar, A.; Modi, C.; Madhu, G.; Vyjayanthi, C. A trustworthy and incentivized smart grid energy trading framework using distributed ledger and smart contracts. J. Netw. Comput. Appl. 2021, 183, 103074.
  24. Amanbek, Y.; Tabarak, Y.; Nunna, H.K.; Doolla, S. Decentralized transactive energy management system for distribution systems with prosumer microgrids. In Proceedings of the 2018 19th International Carpathian Control Conference (ICCC), Szilvasvarad, Hungary, 28–31 May 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 553–558.
  25. Sabounchi, M.; Wei, J. Towards resilient networked microgrids: Blockchain-enabled peer-to-peer electricity trading mechanism. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 26–28 November 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–5.
  26. Thomas, L.; Zhou, Y.; Long, C.; Wu, J.; Jenkins, N. A general form of smart contract for decentralized energy systems management. Nat. Energy 2019, 4, 140–149.
  27. Khattak, H.A.; Tehreem, K.; Almogren, A.; Ameer, Z.; Din, I.U.; Adnan, M. Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities. J. Inf. Secur. Appl. 2020, 55, 102615.
  28. Liu, N.; Tan, L.; Sun, H.; Zhou, Z.; Guo, B. Bilevel Heat–Electricity Energy Sharing for Integrated Energy Systems With Energy Hubs and Prosumers. IEEE Trans. Ind. Inform. 2022, 18, 3754–3765.
  29. Zhang, M.; Eliassen, F.; Taherkordi, A.; Jacobsen, H.A.; Chung, H.M.; Zhang, Y. Demand–Response Games for Peer-to-Peer Energy Trading With the Hyperledger Blockchain. IEEE Trans. Syst. Man Cybern. Syst. 2022, 52, 19–31.
  30. Abdelsalam, H.A.; Srivastava, A.K.; Eldosouky, A. Blockchain-Based Privacy Preserving and Energy Saving Mechanism for Electricity Prosumers. IEEE Trans. Sustain. Energy 2022, 13, 302–314.
  31. Knirsch, F.; Unterweger, A.; Eibl, G.; Engel, D. Privacy-preserving smart grid tariff decisions with blockchain-based smart contracts. In Sustainable Cloud and Energy Services; Springer: Berlin/Heidelberg, Germany, 2018; pp. 85–116.
  32. Gough, M.; Santos, S.F.; Almeida, A.; Lotfi, M.; Javadi, M.S.; Fitiwi, D.Z.; Osório, G.J.; Castro, R.; Catalão, J.P.S. Blockchain-Based Transactive Energy Framework for Connected Virtual Power Plants. IEEE Trans. Ind. Appl. 2022, 58, 986–995.
  33. AlSkaif, T.; Crespo-Vazquez, J.L.; Sekuloski, M.; van Leeuwen, G.; Catalão, J.P.S. Blockchain-Based Fully Peer-to-Peer Energy Trading Strategies for Residential Energy Systems. IEEE Trans. Ind. Inform. 2022, 18, 231–241.
  34. Jiang, T.; Chung, C.Y.; Ju, P.; Gong, Y. A Multi-Timescale Allocation Algorithm of Energy and Power for Demand Response in Smart Grids: A Stackelberg Game Approach. IEEE Trans. Sustain. Energy 2022, 13, 1580–1593.
  35. Kang, E.S.; Pee, S.J.; Song, J.G.; Jang, J.W. A blockchain-based energy trading platform for smart homes in a microgrid. In Proceedings of the 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, Japan, 27–30 April 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 472–476.
  36. Li, Y.; Rahmani, R.; Fouassier, N.; Stenlund, P.; Ouyang, K. A blockchain-based architecture for stable and trustworthy smart grid. Procedia Comput. Sci. 2019, 155, 410–416.
  37. Heck, K.; Mengelkamp, E.; Weinhardt, C. Blockchain-based local energy markets: Decentralized trading on single-board computers. Energy Syst. 2020, 12, 603–618.
  38. Bouachir, O.; Aloqaily, M.; Özkasap, Ö.; Ali, F. FederatedGrids: Federated Learning and Blockchain-assisted P2P Energy Sharing. IEEE Trans. Green Commun. Netw. 2022, 6, 424–436.
  39. Dimobi, I.; Pipattanasomporn, M.; Rahman, S. A transactive grid with microgrids using blockchain for the energy Internet. In Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 17–20 February 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–5.
  40. Mhaisen, N.; Allahham, M.S.; Mohamed, A.; Erbad, A.; Guizani, M. On Designing Smart Agents for Service Provisioning in Blockchain-Powered Systems. IEEE Trans. Netw. Sci. Eng. 2022, 9, 401–415.
  41. Wang, Y.; Su, Z.; Zhang, N.; Chen, J.; Sun, X.; Ye, Z.; Zhou, Z. SPDS: A Secure and Auditable Private Data Sharing Scheme for Smart Grid Based on Blockchain and Smart Contract. IEEE Trans. Ind. Inform. 2020, 17, 7688–7699.
  42. Dorri, A.; Luo, F.; Kanhere, S.S.; Jurdak, R.; Dong, Z.Y. SPB: A secure private blockchain-based solution for distributed energy trading. IEEE Commun. Mag. 2019, 57, 120–126.
  43. Ledger, P. Power Ledger White Paper; Power Ledger Pty Ltd.: Perth, Australia, 2017; Volume 8.
  44. El-Sayed, I.; Khan, K.; Dominguez, X.; Arboleya, P. A real pilot-platform implementation for blockchain-based peer-to-peer energy trading. In Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, 2–6 August 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–5.
  45. Saxena, S.; Farag, H.; Brookson, A.; Turesson, H.; Kim, H. Design and field implementation of blockchain based renewable energy trading in residential communities. In Proceedings of the 2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE), Doha, Qatar, 19–21 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–6.
  46. Gao, G.; Song, C.; Bandara, T.G.T.A.; Shen, M.; Yang, F.; Posdorfer, W.; Tao, D.; Wen, Y. FogChain: A Blockchain-Based Peer-to-Peer Solar Power Trading System Powered by Fog AI. IEEE Internet Things J. 2022, 9, 5200–5215.
  47. Suthar, S.; Pindoriya, N.M. Blockchain and smart contract based decentralized energy trading platform. In Proceedings of the 2020 21st National Power Systems Conference (NPSC), Gandhinagar, India, 17–19 December 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–5.
  48. Afzal, M.; Huang, Q.; Amin, W.; Umer, K.; Raza, A.; Naeem, M. Blockchain enabled distributed demand side management in community energy system with smart homes. IEEE Access 2020, 8, 37428–37439.
  49. You, H.; Hua, H.; Cao, J. A Smart Contract-based Energy Trading Strategy in Energy Internet. In Proceedings of the 2019 IEEE International Conference on Energy Internet (ICEI), Nanjing, China, 20–24 May 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 478–483.
  50. Zhang, X.; Fan, M. Blockchain-based secure equipment diagnosis mechanism of smart grid. IEEE Access 2018, 6, 66165–66177.
  51. Wen, Z.; Zheng, Y.; Li, Y. Analysis of Decentralized Energy Transactions Based on Smart Contract. In Proceedings of the 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 6–8 November 2020; IEEE: Piscataway, NJ, USA, 2020; Volume 1, pp. 819–824.
  52. Alao, O.; Cuffe, P. Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace. IEEE Trans. Smart Grid 2022, 13, 4730–4746.
  53. The Alberta Electric System Operator. Available online: https://www.aeso.ca/ (accessed on 30 September 2021).
  54. Danzi, P.; Angjelichinoski, M.; Stefanović, Č.; Popovski, P. Distributed proportional-fairness control in microgrids via blockchain smart contracts. In Proceedings of the 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, 23–26 October 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 45–51.
  55. Hu, W.; Hu, Y.; Yao, W.; Lu, W.; Li, H.; Lv, Z. A blockchain-based smart contract trading mechanism for energy power supply and demand network. Adv. Prod. Eng. Manag. 2019, 14, 284–296.
  56. Mhaisen, N.; Fetais, N.; Massoud, A. Secure smart contract-enabled control of battery energy storage systems against cyber-attacks. Alex. Eng. J. 2019, 58, 1291–1300.
  57. Yang, X.; Wang, G.; He, H.; Lu, J.; Zhang, Y. Automated demand response framework in ELNs: Decentralized scheduling and smart contract. IEEE Trans. Syst. Man Cybern. Syst. 2019, 50, 58–72.
  58. Baza, M.; Nabil, M.; Ismail, M.; Mahmoud, M.; Serpedin, E.; Rahman, M.A. Blockchain-based charging coordination mechanism for smart grid energy storage units. In Proceedings of the 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA, 14–17 July 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 504–509.
  59. Liu, C.; Chai, K.K.; Zhang, X.; Lau, E.T.; Chen, Y. Adaptive blockchain-based electric vehicle participation scheme in smart grid platform. IEEE Access 2018, 6, 25657–25665.
  60. Di Silvestre, M.L.; Gallo, P.; Ippolito, M.G.; Musca, R.; Sanseverino, E.R.; Tran, Q.T.T.; Zizzo, G. Ancillary services in the energy blockchain for microgrids. IEEE Trans. Ind. Appl. 2019, 55, 7310–7319.
  61. Honari, K.; Zhou, X.; Rouhani, S.; Dick, S.; Liang, H.; Li, Y.; Miller, J. A Scalable Blockchain-based Smart Contract Model for Decentralized Voltage Stability Using Sharding Technique. arXiv 2022, arXiv:2206.13776.
  62. Li, Y.; Yang, W.; He, P.; Chen, C.; Wang, X. Design and management of a distributed hybrid energy system through smart contract and blockchain. Appl. Energy 2019, 248, 390–405.
  63. Yang, Y.; Liu, M.; Zhou, Q.; Zhou, H.; Wang, R. A Blockchain Based Data Monitoring and Sharing Approach for Smart Grids. IEEE Access 2019.
  64. Asefi, S.; Madhwal, Y.; Yanovich, Y.; Gryazina, E. Application of Blockchain for Secure Data Transmission in Distributed State Estimation. IEEE Trans. Control Netw. Syst. 2022, 9, 1611–1621.
  65. Wang, H.; Zhang, J.; Lu, C.; Wu, C. Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective. IEEE Trans. Smart Grid 2020, 12, 2529–2543.
  66. Gai, K.; Wu, Y.; Zhu, L.; Qiu, M.; Shen, M. Privacy-preserving energy trading using consortium blockchain in smart grid. IEEE Trans. Ind. Inform. 2019, 15, 3548–3558.
  67. Dorri, A.; Hill, A.; Kanhere, S.; Jurdak, R.; Luo, F.; Dong, Z.Y. Peer-to-peer energytrade: A distributed private energy trading platform. In Proceedings of the 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Seoul, Republic of Korea, 14–17 May 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 61–64.
  68. Dorri, A.; Luo, F.; Karumba, S.; Kanhere, S.; Jurdak, R.; Dong, Z.Y. Temporary immutability: A removable blockchain solution for prosumer-side energy trading. J. Netw. Comput. Appl. 2021, 180, 103018.
  69. Wang, J.; Wu, L.; Choo, K.K.R.; He, D. Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure. IEEE Trans. Ind. Inform. 2019, 16, 1984–1992.
  70. Zheng, D.; Deng, K.; Zhang, Y.; Zhao, J.; Zheng, X.; Ma, X. Smart grid power trading based on consortium blockchain in Internet of Things. In Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, Guangzhou, China, 15–17 November 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 453–459.
  71. Gai, K.; Wu, Y.; Zhu, L.; Xu, L.; Zhang, Y. Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet Things J. 2019, 6, 7992–8004.
  72. Lombardi, F.; Aniello, L.; De Angelis, S.; Margheri, A.; Sassone, V. A blockchain-based infrastructure for reliable and cost-effective IoT-aided smart grids. In Proceedings of the Living in the Internet of Things: Cybersecurity of the IoT—2018, London, UK, 28–29 March 2018; pp. 1–6.
  73. Wang, W.; Huang, H.; Zhang, L.; Su, C. Secure and efficient mutual authentication protocol for smart grid under blockchain. Peer-Peer Netw. Appl. 2021, 14, 2681–2693.
  74. Hao, X.; Ren, W.; Choo, K.K.R.; Xiong, N.N. A Self-Trading and Authenticated Roaming Scheme Based on Blockchain for Smart Grids. IEEE Trans. Ind. Inform. 2022, 18, 4097–4106.
  75. Aung, Y.N.; Tantidham, T. Ethereum-based Emergency Service for Smart Home System: Smart Contract Implementation. In Proceedings of the 2019 21st International Conference on Advanced Communication Technology (ICACT), Pyeong Chang, Republic of Korea, 17–20 February 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 147–152.
  76. Guan, Z.; Zhou, X.; Liu, P.; Wu, L.; Yang, W. A Blockchain-Based Dual-Side Privacy-Preserving Multiparty Computation Scheme for Edge-Enabled Smart Grid. IEEE Internet Things J. 2022, 9, 14287–14299.
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