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An optimized GRT model with blockchain digital smart contracts for power generation enterprises
Energy Economics, Volume: 128, Start page: 107153
Swansea University Author: Mohammad Abedin
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DOI (Published version): 10.1016/j.eneco.2023.107153
Abstract
The traditional power generation rights trading (GRT) market is faced with the problems of weak interconnection of electricity-carbon market and low security. Using smart contracts in the blockchain, the idea of establishing a weakly centralized GRT structure is proposed in this paper. The carbon em...
| Published in: | Energy Economics |
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| ISSN: | 0140-9883 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64913 |
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| Abstract: |
The traditional power generation rights trading (GRT) market is faced with the problems of weak interconnection of electricity-carbon market and low security. Using smart contracts in the blockchain, the idea of establishing a weakly centralized GRT structure is proposed in this paper. The carbon emission factor was introduced to improve the GRT model, and carbon emission market is used to further stimulate the emission reduction vitality of generating units. The empirical results show that compared with the benchmark model and improved model 1, the improved GRT model proposed by us has the best emission reduction effect. The contribution of this paper is to make up for the existing research that cannot fully consider the impact of carbon peak and carbon neutralization on the GRT market, as well as the information security issues brought by big data trading on the GRT platform. This paper puts forward some policy implications for the decarbonization and green development of the electricity market advocated by the Chinese government. |
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| Keywords: |
big data; blockchain; digital smart contract; generation rights transaction (GRT); power generation enterprises |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
This work was supported by the National Social Science Foundation of China (No. 20BJL058), the National Natural Science Foundation of China (No. 72001191, 72104021) and the Natural Science Foundation of Shandong Province (No. ZR2023MG042, ZR2020MG074). |
| Start Page: |
107153 |