Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains
Compared with ordinary electric vehicles, electric trucks have higher charging power, larger battery capacity and more considerable dispatching potential, while their charging load presents greater randomness due to many factors such as cargo weight, logistics characteristics and driving path. To th...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-05-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306066 |
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| author | Hang LIU Hao SHEN Yong YANG Ling JI Yang YU |
| author_facet | Hang LIU Hao SHEN Yong YANG Ling JI Yang YU |
| author_sort | Hang LIU |
| collection | DOAJ |
| description | Compared with ordinary electric vehicles, electric trucks have higher charging power, larger battery capacity and more considerable dispatching potential, while their charging load presents greater randomness due to many factors such as cargo weight, logistics characteristics and driving path. To this end, this paper proposes a electric trucks aggregation load prediction method based on higher-order Markov chain considering logistics characteristics. Firstly, on the basis of considering the soft time window constraint to realize the path planning of the electric trucks, the charging time of the electric trucks is predicted by analyzing their driving characteristics to obtain the charging quantity of the electric trucks at each moment. Secondly, the charge state interval of the electric trucks is partitioned with fuzzy two-level discretization, and each large interval is further subdivided into n small intervals so as to improve the prediction accuracy. And then, after obtaining the charge state multi-step transfer probability of the electric trucks, a aggregation load prediction model is established by using high-order Markov chain to achieve more accurate load prediction. Finally, the actual electric truck data of a logistics park is used for simulation verification, and the results show that the proposed load prediction model accurately predicts the aggregation power of electric trucks and reduces the prediction error of the ordinary Markov chain method. |
| format | Article |
| id | doaj-art-d4b33a1aaecc4d948cdf5373e024834d |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-05-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-d4b33a1aaecc4d948cdf5373e024834d2025-08-20T02:47:33ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-05-01575616910.11930/j.issn.1004-9649.202306066zgdl-11-liuhangLoad Forecast of Electric Trucks Aggregation Based on Higher-order Markov ChainsHang LIU0Hao SHEN1Yong YANG2Ling JI3Yang YU4State Grid Handan Electric Power Supply Company, Handan 056000, ChinaState Grid Handan Electric Power Supply Company, Handan 056000, ChinaState Grid Handan Electric Power Supply Company, Handan 056000, ChinaGuodian Nanjing Automation Co., Ltd., Nanjing 210032, ChinaState Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources (North China Electric Power University), Baoding 071003, ChinaCompared with ordinary electric vehicles, electric trucks have higher charging power, larger battery capacity and more considerable dispatching potential, while their charging load presents greater randomness due to many factors such as cargo weight, logistics characteristics and driving path. To this end, this paper proposes a electric trucks aggregation load prediction method based on higher-order Markov chain considering logistics characteristics. Firstly, on the basis of considering the soft time window constraint to realize the path planning of the electric trucks, the charging time of the electric trucks is predicted by analyzing their driving characteristics to obtain the charging quantity of the electric trucks at each moment. Secondly, the charge state interval of the electric trucks is partitioned with fuzzy two-level discretization, and each large interval is further subdivided into n small intervals so as to improve the prediction accuracy. And then, after obtaining the charge state multi-step transfer probability of the electric trucks, a aggregation load prediction model is established by using high-order Markov chain to achieve more accurate load prediction. Finally, the actual electric truck data of a logistics park is used for simulation verification, and the results show that the proposed load prediction model accurately predicts the aggregation power of electric trucks and reduces the prediction error of the ordinary Markov chain method.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306066electric truckhigher-order markov chainsload forecastingtwo-level discretizationlogistics order constraints |
| spellingShingle | Hang LIU Hao SHEN Yong YANG Ling JI Yang YU Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains Zhongguo dianli electric truck higher-order markov chains load forecasting two-level discretization logistics order constraints |
| title | Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains |
| title_full | Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains |
| title_fullStr | Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains |
| title_full_unstemmed | Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains |
| title_short | Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains |
| title_sort | load forecast of electric trucks aggregation based on higher order markov chains |
| topic | electric truck higher-order markov chains load forecasting two-level discretization logistics order constraints |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306066 |
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