Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment
The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on syst...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/12/4819 |
| _version_ | 1797595042804662272 |
|---|---|
| author | Wei Liao Yi Yang Qingwei Wang Ruoyu Wang Xieli Fu Yinghua Xie Junhua Zhao |
| author_facet | Wei Liao Yi Yang Qingwei Wang Ruoyu Wang Xieli Fu Yinghua Xie Junhua Zhao |
| author_sort | Wei Liao |
| collection | DOAJ |
| description | The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy. |
| first_indexed | 2024-03-11T02:30:50Z |
| format | Article |
| id | doaj.art-7b0a4ea30aaa4a2a9bc2a9a1b9d64288 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T02:30:50Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7b0a4ea30aaa4a2a9bc2a9a1b9d642882023-11-18T10:14:31ZengMDPI AGEnergies1996-10732023-06-011612481910.3390/en16124819Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market EnvironmentWei Liao0Yi Yang1Qingwei Wang2Ruoyu Wang3Xieli Fu4Yinghua Xie5Junhua Zhao6Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, ChinaShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, ChinaShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, ChinaThe electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy.https://www.mdpi.com/1996-1073/16/12/4819electricity spot marketsystem planningsocial welfarehybrid experimental learningdata-driven |
| spellingShingle | Wei Liao Yi Yang Qingwei Wang Ruoyu Wang Xieli Fu Yinghua Xie Junhua Zhao Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment Energies electricity spot market system planning social welfare hybrid experimental learning data-driven |
| title | Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment |
| title_full | Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment |
| title_fullStr | Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment |
| title_full_unstemmed | Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment |
| title_short | Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment |
| title_sort | interpretable hybrid experiment learning based simulation analysis of power system planning under the spot market environment |
| topic | electricity spot market system planning social welfare hybrid experimental learning data-driven |
| url | https://www.mdpi.com/1996-1073/16/12/4819 |
| work_keys_str_mv | AT weiliao interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT yiyang interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT qingweiwang interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT ruoyuwang interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT xielifu interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT yinghuaxie interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment AT junhuazhao interpretablehybridexperimentlearningbasedsimulationanalysisofpowersystemplanningunderthespotmarketenvironment |