Blockchain-Based Smart Contracts for Power Purchase Agreements: Trading Solar Energy with Fuzzy Pricing

Document Type : Research Paper

Authors

1 Ph.D student, Department of Electrical Engineering, Shahid Bahonar University of Kerman

2 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

Solar energy as a renewable energy, is widely used by many solar power plants to generate electricity. As production in this sector grows, conducting business in this area becomes increasingly important. Solar energy trading is facilitated through power purchase agreements (PPA), which outline the key elements for building a solar power plant and managing related transactions. However, the current trading system has several drawbacks. These challenges include the absence of transaction tracking and payment mechanisms, limited user access to transaction data, and low transparency, trust, and consistency in solar energy pricing. A new technology that has recently emerged and can be utilized for trading is blockchain technology. It is a continuously growing list of recordable items, called blocks that are linked together using encryption. By leveraging blockchain for solar energy trading, many existing challenges can be addressed. Essentially, blockchain functions as a decentralized shared ledger, enabling members to collaborate for specific purposes without depending on a central authority. One of the notable features of some blockchain networks, including Ethereum, is the ability to create smart contracts. A smart contract is a set of computer codes that automatically execute on a blockchain platform under predefined conditions, without the central authority need. Blockchain, through the use of smart contracts, can offer a secure, transparent, peer-to-peer, distributed, decentralized, verifiable, reliable, and traceable platform for solar energy transactions. Additionally, by defining national cryptocurrencies within this localized context, it is possible to prevent the outflow of currency from the country during international transactions. To address the issue of solar energy pricing, which depends on the inflation rate factor and the dollar rate factor, the use of a fuzzy system can be effective. Fuzzy logic is a computational approach used to model uncertainties and complexities in real-world systems. In the context of PPA, a fuzzy system can serve as an effective tool for pricing electricity generated by solar power plants. As a result, designing a platform with blockchain features for solar energy trading, implementing PPA contracts on the blockchain, and using a fuzzy system for solar energy pricing are extensive areas of focus for this research.

Keywords

Main Subjects

References

[1] N. Z. Aitzhan, D. Svetinovic, Security and privacy in decentralized energy trading through multi-signatures,
blockchain and anonymous messaging streams, IEEE Transactions on Dependable and Secure Computing, 15(5)
(2016), 840-852. https://doi.org/10.1109/TDSC.2016.2616861
[2] A. M. Antonopoulos, G. Wood, Mastering ethereum: Building smart contracts and dapps, O’reilly Media, 2018.
[3] L. A. Q. Aranibar, Learning fuzzy logic from examples, Master’s thesis, Ohio University, 1994.
[4] J. Bao, D. He, M. Luo, K. K. R. Choo, A survey of blockchain applications in the energy sector, IEEE Systems
Journal, 15(3) (2020), 3370-3381. https://doi.org/10.1109/JSYST.2020.2998791
[5] Y. Besanger, Q. T. Tran, M. T. Le, On the applicability of distributed ledger architectures to peer-to-peer energy
trading framework, In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018
IEEE Industrial and Commercial Power Systems Europe, (2018), 1-5. https://doi.org/10.1109/EEEIC.2018.
8494446
[6] P. Chapman, D. Xu, L. Deng, Y. Xiong, Deviant: A mutation testing tool for solidity smart contracts, In 2019 IEEE
International Conference on Blockchain (Blockchain), (2019), 319-324. https://doi.org/10.1109/Blockchain.
2019.00050
[7] J. Chiu, T. V. Koeppl, Blockchain-based settlement for asset trading, The Review of Financial Studies, 32(5) (2019),
1716-1753. https://doi.org/10.1093/rfs/hhy122
[8] V. C¸ oban, S. C¸ evik Onar, Strategic analysis of solar energy pricing process with hesitant fuzzy cognitive map,
Energy Management-Collective and Computational Intelligence with Theory and Applications, (2018), 195-227.
https://doi.org/10.1007/978-3-319-75690-5_10
[9] T. T. A. Dinh, J. Wang, G. Chen, R. Liu, B. C. Ooi, K. L. Tan, Blockbench: A framework for analyzing private
blockchains, In Proceedings of the 2017 ACM International Conference on Management of Data, (2017), 1085-1100.
https://doi.org/10.1145/3035918.3064033
[10] A. Dorri, F. Luo, S. S. Kanhere, R. Jurdak, Z. Y. Dong, SPB: A secure private blockchain-based solution for
distributed energy trading, IEEE Communications Magazine, 57(7) (2019), 120-126. https://doi.org/10.1109/
MCOM.2019.1800577
[11] M. B. Dowlatshahi, V. Derhami, H. Nezamabadi-Pour, Fuzzy particle swarm optimization with nearest-better
neighborhood for multimodal optimization, Iranian Journal of Fuzzy Systems, 17(4) (2020), 7-24. https://doi.
org/10.22111/ijfs.2020.5403
[12] M. Eftekhari, M. Eftekhari, M. Majidi, H. Nezamabadi Pour, Securing interpretability of fuzzy models for modeling
nonlinear MIMO systems using a hybrid of evolutionary algorithms, Iranian Journal of Fuzzy Systems, 9(1) (2012),
61-77. https://doi.org/10.22111/IJFS.2012.226
[13] K. Gai, Y. Wu, L. Zhu, M. Qiu, M. Shen, Privacy-preserving energy trading using consortium blockchain in smart
grid, IEEE Transactions on Industrial Informatics, 15(6) (2019), 3548-3558. https://doi.org/10.1109/TII.2019.
2893433
[14] A. M. Gil-Lafuente, Fuzzy logic in financial analysis, Berlin: Springer, 175 (2005). https://doi.org/10.1007/
3-540-32368-6
[15] Y. Gong, H. Yao, Z. Xiong, C. P. Chen, D. Niyato, Blockchain-aided digital twin offloading mechanism in spaceair-ground networks, IEEE Transactions on Mobile Computing, 24(1) (2025), 183-197. https://doi.org/10.1109/
TMC.2024.3455417
[16] M. Grincalaitis, Mastering Ethereum: Implement advanced blockchain applications using Ethereum-supported tools,
services, and protocols, Packt Publishing Ltd, 2019.
[17] K. Hojckova, H. Ahlborg, G. M. Morrison, B. Sand´en, Entrepreneurial use of context for technological system
creation and expansion: The case of blockchain-based peer-to-peer electricity trading, Research Policy, 49(8) (2020),
104046. https://doi.org/10.1016/j.respol.2020.104046
[18] J. Horta, D. Kofman, D. Menga, A. Silva, Novel market approach for locally balancing renewable energy production
and flexible demand, In 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm),
(2017), 533-539. https://doi.org/10.1109/SmartGridComm.2017.8340728
[19] E. Hossain, I. Khan, F. Un-Noor, S. S. Sikander, M. S. H. Sunny, Application of big data and machine learning in
smart grid, and associated security concerns: A review, IEEE Access, 7 (2019), 13960-13988. https://doi.org/
10.1109/ACCESS.2019.2894819
[20] W. Hua, H. Sun, A blockchain-based peer-to-peer trading scheme coupling energy and carbon markets, In 2019
International Conference on Smart Energy Systems and Technologies (SEST), (2019), 1-6. https://doi.org/10.
1109/SEST.2019.8849111
[21] Z. Hussein, M. A. Salama, S. A. El-Rahman, Evolution of blockchain consensus algorithms: A review on the
latest milestones of blockchain consensus algorithms, Cybersecurity, 6(1) (2023), 30. https://doi.org/10.1186/
s42400-023-00163-y
[22] K. Iyer, C. Dannen, Building games with ethereum smart contracts, New York: Apress., (2018), 19-36.
[23] J. S. R. Jang, C. T. Sun, E. Mizutani, Neuro-fuzzy and soft computing-a computational approach to learning
and machine intelligence, [Book Review], IEEE Transactions on Automatic Control, 42(10) (1997), 1482-1484.
https://doi.org/10.1109/TAC.1997.633847
[24] Y. Jo, C. Park, Enhancing ethereum PoA clique network with DAG-based BFT consensus, In 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), (2024). https://doi.org/10.1109/ICBC59979.
2024.10634410
[25] E. S. Kang, S. J. Pee, J. G. Song, J. W. Jang, A blockchain-based energy trading platform for smart homes in
a microgrid, In 2018 3rd International Conference on Computer and Communication Systems (ICCCS), (2018),
472-476. https://doi.org/10.1109/CCOMS.2018.8463317
[26] R. K. Kodali, S. Yerroju, B. Y. K. Yogi, Blockchain based energy trading, In TENCON 2018-2018 IEEE Region 10
Conference, (2018), 1778-1783. https://doi.org/10.1109/TENCON.2018.8650447
[27] K. G. H. Kong, J. Y. Lim, W. D. Leong, W. P. Q. Ng, S. Y. Teng, J. Sunarso, B. S. How, Fuzzy optimization
for peer-to-peer (P2P) multi-period renewable energy trading planning, Journal of Cleaner Production, 368 (2022),
133122. https://doi.org/10.1016/j.jclepro.2022.133122
[28] N. Kshetri, J. Voas, Blockchain-enabled e-voting, IEEE Software, 35(4) (2018), 95-99. https://doi.org/10.1109/
MS.2018.2801546
[29] A. Kumari, R. Gupta, S. Tanwar, S. Tyagi, N. Kumar, When blockchain meets smart grid: Secure energy trading
in demand response management, IEEE Network, 34(5) (2020), 299-305. https://doi.org/10.1109/MNET.001.
1900660
[30] A. Laszka, S. Eisele, A. Dubey, G. Karsai, K. Kvaternik, TRANSAX: A blockchain-based decentralized forwardtrading energy exchanged for transactive microgrids, In 2018 IEEE 24th International Conference on Parallel and
Distributed Systems (ICPADS), 15(3) (2018), 918-927. https://doi.org/10.1109/PADSW.2018.8645001
[31] H. Lopez, A. Zilouchian, Peer-to-Peer energy trading for PV prosumers using fuzzy logic inference systems, In
2023 IEEE 50th Photovoltaic Specialists Conference (PVSC)), (2023), 1-4. https://doi.org/10.1109/PVSC48320.
2023.10359770
[32] X. Lu, Z. Guan, X. Zhou, X. Du, L. Wu, M. Guizani, A secure and efficient renewable energy trading scheme based
on blockchain in smart grid, In 2019 IEEE 21st International Conference on High Performance Computing and
Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data
Science and Systems (HPCC/SmartCity/DSS), (2019), 1839-1844. https://doi.org/10.1109/HPCC/SmartCity/
DSS.2019.00253
[33] H. Luo, Q. Zhang, G. Sun, H. Yu, D. Niyato, Symbiotic blockchain consensus: Cognitive backscatter
communications-enabled wireless blockchain consensus, IEEE/ACM Transactions on Networking, 32(6) (2024),
5372-5387. https://doi.org/10.1109/TNET.2024.3462539
[34] B. K. Mohanta, S. S. Panda, D. Jena, An overview of smart contract and use cases in blockchain technology, In
2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), (2018),
1-4. https://doi.org/10.1109/ICCCNT.2018.8494045
[35] H. Nezamabadi-Pour, The fundamental of blockchain and cryptocurrencies, Shahid Bahonar University of Kerman
Press. [In Farsi], 2023.
[36] H. Nezamabadi-Pour, Advanced concepts of blockchain and smart contracts, Shahid Bahonar University of Kerman
Press. [In Farsi], 2023.
[37] H. Nezamabadi-Pour, S. Yazdani, M. M. Farsangi, M. Neyestani, A solution to an economic dispatch problem by a
fuzzy adaptive genetic algorithm, Iranian Journal of Fuzzy Systems, 8(5) (2011), 1-21. https://doi.org/10.22111/
IJFS.2011.283
[38] S. C. Oh, M. S. Kim, Y. Park, G. T. Roh, C. W. Lee, Implementation of blockchain-based energy trading system, Asia Pacific Journal of Innovation and Entrepreneurship, 11(3) (2017), 322-334. https://doi.org/10.1108/
APJIE-12-2017-037
[39] H. Omrane, M. S. Masmoudi, M. Masmoudi, Fuzzy logic based control for autonomous mobile robot navigation,
Computational Intelligence and Neuroscience, 2016(1) (2016), 9548482. https://doi.org/10.1155/2016/9548482
[40] C. D. Parameswari, V. Mandadi, Healthcare data protection based on blockchain using solidity, In 2020 Fourth
World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), (2020), 577-580. https:
//doi.org/10.1109/WorldS450073.2020.9210296
[41] W. Pedrycz, Fuzzy control and fuzzy systems, Research Studies Press Ltd, 1993. https://doi.org/10.5555/
534143
[42] S. J. Pee, E. S. Kang, J. G. Song, J. W. Jang, Blockchain-based smart energy trading platform using smart
contract, In 2019 International Conference on Artificial Intelligence in Information and Communication, (2019),
322-325. https://doi.org/10.1109/ICAIIC.2019.8668978
[43] M. Pipattanasomporn, M. Kuzlu, S. Rahman, A blockchain-based platform for exchange of solar energy: Laboratoryscale implementation, In 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), (2018), 1-9. https://doi.org/10.23919/ICUE-GESD.2018.8635679
[44] M. R. Rezaeifar, M. R. Zare Mehrjerdi, H. Nezamabadi-pour, H. Mehrabi Boshr Abadi, Designing a sustainable
development model for agricultural sector under critical circumstances (COVID-19 Pandemic): A fuzzy approach,
Iranian Journal of Fuzzy Systems, 20(2) (2023), 173-200. https://doi.org/10.22111/ijfs.2023.7565
[45] T. J. Ross, Fuzzy logic with engineering applications, John Wiley and Sons, 2005.
[46] M. Sabounchi, J. Wei, Towards resilient networked microgrids: Blockchain-enabled peer-to-peer electricity trading
mechanism, In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), (2017), 1-5. https:
//doi.org/10.1109/EI2.2017.8245449
[47] A. Salian, S. Shah, J. Shah, K. Samdani, Review of blockchain enabled decentralized energy trading mechanisms,
In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), (2019),
1-7. https://doi.org/10.1109/ICSCAN.2019.8878731
[48] L. Suganthi, S. Iniyan, A. A. Samuel, Applications of fuzzy logic in renewable energy systems-a review, Renewable
and Sustainable Energy Reviews, 48 (2015), 585-607. https://doi.org/10.1016/j.rser.2015.04.037
[49] N. Szabo, Smart contracts: Building blocks for digital markets, EXTROPY: The Journal of Transhumanist Thought
(16), 18(2) (1996), 28.
[50] Y. C. Tsao, V. V. Thanh, Toward sustainable microgrids with blockchain technology-based peer-to-peer energy
trading mechanism: A fuzzy meta-heuristic approach, Renewable and Sustainable Energy Reviews, 136 (2021),
110452. https://doi.org/10.1016/j.rser.2020.110452
[51] W. Viriyasitavat, D. Hoonsopon, Blockchain characteristics and consensus in modern business processes, Journal
of Industrial Information Integration, 13 (2019), 32-39. https://doi.org/10.1016/j.jii.2018.07.004
[52] S. Wang, L. Ouyang, Y. Yuan, X. Ni, X. Han, F. Y. Wang, Blockchain-enabled smart contracts: Architecture,
applications, and future trends, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(11) (2019),
2266-2277. https://doi.org/10.1109/TSMC.2019.2895123
[53] W. Warutai, P. S. W. Thongrod, C. Charoenlarpnopparut, Optimal energy transaction ledger model for microgrid
energy trading, In 2019 IEEE 3rd International Conference on Circuits, Systems and Devices (ICCSD), (2019),
106-110. https://doi.org/10.1109/ICCSD.2019.8842999
[54] G. Wood, Ethereum: A secure decentralised generalised transaction ledger, Ethereum Project Yellow Paper,
151(2014) (2014), 1-32.
[55] G. Wood, C. Reitwiessner, A. Beregszaszi, L. Husikyan, Y. Hirai, Ethereum: A secure decentralised generalised
transaction ledger, Solidity Documentation. Ethereum Corp, 2016.
[56] P. Xie, W. Yan, P. Xuan, J. Zhu, Y. Wu, X. Li, J. Zou, Conceptual framework of blockchain-based electricity
trading for neighborhood renewable energy, In 2018 2nd IEEE Conference on Energy Internet and Energy System
Integration, (2023), 1-5. https://doi.org/10.1109/EI2.2018.8581887
[57] J. Yang, K. Yang, Z. Xiao, H. Jiang, S. Xu, S. Dustdar, Improving commute experience for private car users
via blockchain-enabled multitask learning, IEEE Internet of Things Journal, 10(24) (2023), 21656-21669. https:
//doi.org/10.1109/JIOT.2023.3317639
[58] A. Yazdani-Chamzini, S. H. Yakhchali, D. Volungevi˘cien´e, E. K. Zavadskas, Forecasting gold price changes by using
adaptive network fuzzy inference system, Journal of Business Economics and Management, 13(5) (2012), 994-1010.
https://doi.org/10.3846/16111699.2012.683808
[59] S. Yu, S. Yang, Y. Li, J. Geng, Distributed energy transaction mechanism design based on smart contract, In
2018 China International Conference on Electricity Distribution (CICED), (2018), 3370-3381. https://doi.org/
10.1109/CICED.2018.8592130
[60] R. Zhang, V. E. Sathishkumar, R. D. Jackson Samuel, Fuzzy efficient energy smart home management system for
renewable energy resources, Sustainability, 12(8) (2020), 3115. https://doi.org/10.3390/su12083115
[61] Z. Zhou, L. Tan, G. Xu, Blockchain and edge computing based vehicle-to-grid energy trading in energy internet, In
2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), (2018), 1-5. https://doi.
org/10.1109/EI2.2018.8582652
Iranian Journal of Fuzzy Systems
Volume 21, Issue 6
November and December 2024
Pages 173-193

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