Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine
In this paper, the structural characteristics of the perceptron neural network and the calculation method of the hierarchical relationship of the MLP neural network model are first studied. Then the aspects of two-part settlement, generation-side settlement, and customer-side settlement in the elect...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0311 |
| _version_ | 1797303088798760960 |
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| author | Duan Ruiqin Zhu Xinchun Liu Shuangquan Li Xiufeng Shao Qizhuan Wu Yang |
| author_facet | Duan Ruiqin Zhu Xinchun Liu Shuangquan Li Xiufeng Shao Qizhuan Wu Yang |
| author_sort | Duan Ruiqin |
| collection | DOAJ |
| description | In this paper, the structural characteristics of the perceptron neural network and the calculation method of the hierarchical relationship of the MLP neural network model are first studied. Then the aspects of two-part settlement, generation-side settlement, and customer-side settlement in the electricity spot settlement mechanism are studied, and the importance of these mechanisms for the operation of the electricity market and risk identification is pointed out. Following that, the effectiveness of the risk identification model is assessed and analyzed. This paper examines market performance indicators, the impact of the dual-track mechanism, and the time characteristics of the price index to characterize risk. The results show that in Guangdong, for example, the price in the day-ahead market is much higher than the supply-demand equilibrium price most of the time, and the maximum difference can be as high as 0.662 yuan/(kW-h). For the entire month, the real-time market’s average price is RMB 0.546/(kW-h) and it is RMB 0.063/(kW-h) higher than the day-ahead market. The importance of this study lies in its role in managing and responding to risks for electricity market operators and participants. |
| first_indexed | 2024-03-07T23:47:57Z |
| format | Article |
| id | doaj.art-2dd976aab7484a4f858cedd61497bbbd |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T23:47:57Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-2dd976aab7484a4f858cedd61497bbbd2024-02-19T09:03:37ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0311Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron MachineDuan Ruiqin0Zhu Xinchun1Liu Shuangquan2Li Xiufeng3Shao Qizhuan4Wu Yang51Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.1Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.1Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.1Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.1Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.1Yunnan Power Dispatch and Controlling Center, Kunming, Yunnan, 650011, China.In this paper, the structural characteristics of the perceptron neural network and the calculation method of the hierarchical relationship of the MLP neural network model are first studied. Then the aspects of two-part settlement, generation-side settlement, and customer-side settlement in the electricity spot settlement mechanism are studied, and the importance of these mechanisms for the operation of the electricity market and risk identification is pointed out. Following that, the effectiveness of the risk identification model is assessed and analyzed. This paper examines market performance indicators, the impact of the dual-track mechanism, and the time characteristics of the price index to characterize risk. The results show that in Guangdong, for example, the price in the day-ahead market is much higher than the supply-demand equilibrium price most of the time, and the maximum difference can be as high as 0.662 yuan/(kW-h). For the entire month, the real-time market’s average price is RMB 0.546/(kW-h) and it is RMB 0.063/(kW-h) higher than the day-ahead market. The importance of this study lies in its role in managing and responding to risks for electricity market operators and participants.https://doi.org/10.2478/amns-2024-0311multilayer perceptronsettlement mechanismelectricity spot settlementrisk identificationmarket performance00a73 |
| spellingShingle | Duan Ruiqin Zhu Xinchun Liu Shuangquan Li Xiufeng Shao Qizhuan Wu Yang Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine Applied Mathematics and Nonlinear Sciences multilayer perceptron settlement mechanism electricity spot settlement risk identification market performance 00a73 |
| title | Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine |
| title_full | Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine |
| title_fullStr | Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine |
| title_full_unstemmed | Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine |
| title_short | Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine |
| title_sort | analysis of risk characterization and identification techniques for electricity spot settlement based on multilayer perceptron machine |
| topic | multilayer perceptron settlement mechanism electricity spot settlement risk identification market performance 00a73 |
| url | https://doi.org/10.2478/amns-2024-0311 |
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