{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T21:31:49Z","timestamp":1768253509236,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, to balance power supplement from the solar energy\u2019s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum\u2019s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained.<\/jats:p>","DOI":"10.3390\/s22062410","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"2410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["EggBlock: Design and Implementation of Solar Energy Generation and Trading Platform in Edge-Based IoT Systems with Blockchain"],"prefix":"10.3390","volume":"22","author":[{"given":"Subin","family":"Kwak","sequence":"first","affiliation":[{"name":"Samsung Electronics, Yeongtong-Gu, Suwon 10285, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1102-3905","authenticated-orcid":false,"given":"Joohyung","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computing, Gachon University, Seongnam 13120, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2548-5214","authenticated-orcid":false,"given":"Jangkyum","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, KAIST, Daejeon 34141, Korea"}]},{"given":"Hyeontaek","family":"Oh","sequence":"additional","affiliation":[{"name":"Institute for Information Technology Convergence, KAIST, Daejeon 34141, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","unstructured":"SEIA (2017). Solar Energy Introduction, SEIA. Technical Report."},{"key":"ref_2","unstructured":"Leeton, U., Uthitsunthorn, D., Kwannetr, U., Sinsuphun, N., and Kulworawanichpong, T. (2010, January 19\u201321). Power loss minimization using optimal power flow based on particle swarm optimization. Proceedings of the ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Detyniecki, M., Marsala, C., Krishnan, A., and Siegel, M. (2012, January 10\u201315). Weather-based solar energy prediction. Proceedings of the 2012 IEEE International Conference on Fuzzy Systems, Brisbane, QLD, Australia.","DOI":"10.1109\/FUZZ-IEEE.2012.6251145"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.renene.2018.03.070","article-title":"Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control","volume":"126","author":"Fleetwood","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3524","DOI":"10.1109\/TIE.2014.2387340","article-title":"Distributed energy trading in microgrids: A game-theoretic model and its equilibrium analysis","volume":"62","author":"Lee","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apenergy.2018.03.010","article-title":"Peer-to-Peer energy trading in a Microgrid","volume":"220","author":"Zhang","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TII.2016.2522641","article-title":"Optimal bidding strategy and intramarket mechanism of microgrid aggregator in real-time balancing market","volume":"12","author":"Pei","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_8","unstructured":"Mihaylov, M., Jurado, S., and Moffaert, K. (2014, January 3\u20134). NRG-X-change. Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems, Barcelona, Spain."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s00450-017-0360-9","article-title":"A blockchain-based smart grid: Towards sustainable local energy markets","volume":"33","author":"Mengelkamp","year":"2018","journal-title":"Comput. Sci. Res. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.apenergy.2018.06.025","article-title":"Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency","volume":"226","author":"Zhang","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_11","unstructured":"Murdock, H.E., Gibb, D., Andr\u00e9, T., Appavou, F., Brown, A., Epp, B., Kondev, B., McCrone, A., Musolino, E., and Ranalder, L. (2022, March 18). Renewables 2019 Global Status Report. Available online: https:\/\/repository.usp.ac.fj\/11648\/1\/gsr_2019_full_report_en.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.renene.2018.07.046","article-title":"Competitive advantage in the renewable energy industry: Evidence from a gravity model","volume":"131","author":"Kuik","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.apenergy.2018.06.074","article-title":"Multi objective unit commitment with voltage stability and PV uncertainty","volume":"228","author":"Furukakoi","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cole, W.J., and Frazier, A. (2019). Cost Projections for Utility-Scale Battery Storage, Technical Report.","DOI":"10.2172\/1529218"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1109\/TVT.2020.2967052","article-title":"A blockchain-based framework for lightweight data sharing and energy trading in V2G network","volume":"69","author":"Hassija","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","first-page":"396","article-title":"Blockchain-based electric vehicle incentive system for renewable energy consumption","volume":"68","author":"Chen","year":"2020","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_17","first-page":"263","article-title":"DEAL: Differentially private auction for blockchain-based microgrids energy trading","volume":"13","author":"Hassan","year":"2019","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.future.2017.09.023","article-title":"From blockchain consensus back to Byzantine consensus","volume":"107","author":"Gramoli","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101595","DOI":"10.1016\/j.resourpol.2020.101595","article-title":"The development of energy blockchain and its implications for China\u2019s energy sector","volume":"66","author":"Zhu","year":"2020","journal-title":"Resour. Policy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100081","DOI":"10.1016\/j.iot.2019.100081","article-title":"Blockchain for the IoT and industrial IoT: A review","volume":"10","author":"Wang","year":"2020","journal-title":"Internet Things"},{"key":"ref_22","unstructured":"Levin, J. (2022, March 18). Auction Theory. Available online: http:\/\/www.mohamedelafrit.com\/education\/CNAM\/ESD208-Incitations-et-Design-Economique\/Lectures\/MOOCs\/Stanford-Econ286\/Stanford-Auction_Theory-Jonathan_Levin.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, H., Lee, G.H., Choi, J.K., and Heo, Y. (2020, January 21\u201323). Seal-bid renewable energy certification trading in power system using blockchain technology. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Islan, Korea.","DOI":"10.1109\/ICTC49870.2020.9289395"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4140","DOI":"10.1109\/TII.2018.2883655","article-title":"Battery-wear-model-based energy trading in electric vehicles: A naive auction model and a market analysis","volume":"15","author":"Kim","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","unstructured":"Google (2020). Introduction of Metamask, Google Inc.. Technical Report."},{"key":"ref_26","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2410\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:40:18Z","timestamp":1760136018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,21]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062410"],"URL":"https:\/\/doi.org\/10.3390\/s22062410","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,21]]}}}