Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains

doi:10.11930/j.issn.1004-9649.202306066

Abstract

Abstract:

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.

Key words: electric truck, higher-order Markov chains, load forecasting, two-level discretization, logistics order constraints

Hang LIU, Hao SHEN, Yong YANG, Ling JI, Yang YU. Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains[J]. Electric Power, 2024, 57(5): 61-69.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202306066

https://www.electricpower.com.cn/EN/Y2024/V57/I5/61

Figures/Tables 11

Fig.1 ETA distribution network

Table 1 The adaptive coefficients involved in the road topology map

道路等级	c₁	c₂	c₃	c₄	p₁	p₂	p₃	p₄
快速	0.9526	1	3	3	0.0405	500	3	3
普通	0.9526	1	2	2	0.0405	500	2	2

Fig.2 Dynamic transfer schematic

Fig.3 SOC two-level discretization

Table 2 Parameter Setting

参数名称		参数值
ET电池容量/(kW·h)		300
计划充放电功率/kW		200
充放电效率/%		94

Fig.4 Distribution network topology

Fig.5 ETA charging start time distribution

Fig.6 ETA charging quantity forecast and comparison

Fig.7 ETA with follow-up orders

Fig.8 ETA charging load

Fig.9 Comparison of different sizes of ETA charging loads

References 22

1	彭云, 李相达, 王文渊, 等. 绿色集装箱港口节能减排策略综述[J]. 交通运输工程学报, 2022, 22 (4): 28- 46.
	PENG Yun, LI Xiangda, WANG Wenyuan, et al. Review on energy saving and emission reduction strategies of green container ports[J]. Journal of Traffic and Transportation Engineering, 2022, 22 (4): 28- 46.
2	DUAN X Y, HU Z C, SONG Y H. Bidding strategies in energy and reserve markets for an aggregator of multiple EV fast charging stations with battery storage[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22 (1): 471- 482. DOI
3	LOPEZ K L, GAGNE C, GARDNER M A. Demand-side management using deep learning for smart charging of electric vehicles[J]. IEEE Transactions on Smart Grid, 2019, 10 (3): 2683- 2691. DOI
4	文家燕, 闻海潮, 程洋, 等. 基于GWO-NSGA-Ⅱ混合算法的露天矿低碳运输调度[J]. 工矿自动化, 2023, 49 (2): 94- 101.
	WEN Jiayan, WEN Haichao, CHENG Yang, et al. Low-carbon transportation scheduling of open-pit mine based on GWO-NSGA-Ⅱ hybrid algorithm[J]. Journal of Mine Automation, 2023, 49 (2): 94- 101.
5	陈蓉珺, 何永秀, 陈奋开, 等. 基于系统动力学和蒙特卡洛模拟的电动汽车日负荷远期预测[J]. 中国电力, 2018, 51 (9): 126- 134.
	CHEN Rongjun, HE Yongxiu, CHEN Fenkai, et al. Long-term daily load forecast of electric vehicle based on system dynamics and Monte Carlo simulation[J]. Electric Power, 2018, 51 (9): 126- 134.
6	余军伟, 孙云莲, 张笑迪. 考虑发展不均衡的电动汽车充电负荷预测[J]. 电测与仪表, 2019, 56 (5): 43- 50.
	YU Junwei, SUN Yunlian, ZHANG Xiaodi. Charging load forecasting considering the unbalanced development of EV[J]. Electrical Measurement & Instrumentation, 2019, 56 (5): 43- 50.
7	赵书强, 周靖仁, 李志伟, 等. 基于出行链理论的电动汽车充电需求分析方法[J]. 电力自动化设备, 2017, 37 (8): 105- 112.
	ZHAO Shuqiang, ZHOU Jingren, LI Zhiwei, et al. EV charging demand analysis based on trip chain theory[J]. Electric Power Automation Equipment, 2017, 37 (8): 105- 112.
8	锁军, 李龙, 贺瀚青, 等. 考虑交通路况的电动汽车充电负荷预测[J]. 电网与清洁能源, 2022, 38 (10): 141- 147.
	SUO Jun, LI Long, HE Hanqing, et al. Load forecasting of electric vehicle charging considering traffic conditions[J]. Power System and Clean Energy, 2022, 38 (10): 141- 147.
9	蒋怡静, 于艾清, 黄敏丽. 考虑用户满意度的电动汽车时空双尺度有序充电引导策略[J]. 中国电力, 2020, 53 (4): 122- 130.
	JIANG Yijing, YU Aiqing, HUANG Minli. Coordinated charging guiding strategy for electric vehicles in temporalspatial dimension considering user satisfaction degree[J]. Electric Power, 2020, 53 (4): 122- 130.
10	刘志强, 张谦, 朱熠, 等. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2022, 46 (12): 36- 45.
	LIU Zhiqiang, ZHANG Qian, ZHU Yi, et al. Spatial-temporal distribution prediction of charging loads for electric vehicles considering vehicle-road-station-grid integration[J]. Automation of Electric Power Systems, 2022, 46 (12): 36- 45.
11	PERTL M, CARDUCCI F, TABONE M, et al. An equivalent time-variant storage model to harness EV flexibility: forecast and aggregation[J]. IEEE Transactions on Industrial Informatics, 2019, 15 (4): 1899- 1910. DOI
12	JIN Y W, YU B, SEO M, et al. Optimal aggregation design for massive V2G participation in energy market[J]. IEEE Access, 2020, 8, 211794- 211808. DOI
13	DABBAGHJAMANESH M, MOEINI A, KAVOUSI-FARD A. Reinforcement learning-based load forecasting of electric vehicle charging station using Q-learning technique[J]. IEEE Transactions on Industrial Informatics, 2021, 17 (6): 4229- 4237. DOI
14	ZHANG X, CHAN K W, LI H, et al. Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model[J]. IEEE Trans Cybern, 2021, 51 (6): 3157- 3170. DOI
15	郄朝辉, 李威, 崔晓丹, 等. 基于分层马尔可夫的可修复稳定控制系统可靠性分析[J]. 中国电力, 2020, 53 (3): 101- 109.
	QIE Zhaohui, LI Wei, CUI Xiaodan, et al. Reliability analysis of repairable stability control system based on hierarchical Markov[J]. Electric Power, 2020, 53 (3): 101- 109.
16	ZHANG Q, CHEN X, LIAO S Y. Energy management control strategy for hybrid energy storage systems in electric vehicles[J]. International Journal of Electrochemical Science, 2022, 17 (1): 220121. DOI
17	KIANI S, SHESHYEKANI K, DAGDOUGUI H. An extended state space model for aggregation of large-scale EVs considering fast charging[J]. IEEE Transactions on Transportation Electrification, 2023, 9 (1): 1238- 1251. DOI
18	LIN X Y, ZHANG G J, WEI S S. Velocity prediction using Markov Chain combined with driving pattern recognition and applied to Dual-Motor Electric Vehicle energy consumption evaluation[J]. Applied Soft Computing, 2021, 101, 106998. DOI
19	ALJOHANI TAWFIQ M, AHMED E, OSAMA M. Real-Time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and Markov chain model[J]. Electric Power Systems Research, 2021, 192, 106962. DOI
20	SHEN H R, WANG Z J, ZHOU X Y, et al. Electric vehicle velocity and energy consumption predictions using transformer and markov-chain Monte Carlo[J]. IEEE Transactions on Transportation Electrification, 2022, 8 (3): 3836- 3847. DOI
21	DING T, ZENG Z Y, BAI J W, et al. Optimal electric vehicle charging strategy with Markov decision process and reinforcement learning technique[J]. IEEE Transactions on Industry Applications, 2020, 56 (5): 5811- 5823. DOI
22	董锴, 蔡新雷, 崔艳林, 等. 基于马尔科夫链的电动汽车聚合建模及多模式调频控制策略[J]. 电网技术, 2022, 46 (2): 622- 634.
	DONG Kai, CAI Xinlei, CUI Yanlin, et al. Aggregation modeling based on Markov chain and multi-mode control strategies of aggregated electric vehicles for frequency regulation[J]. Power System Technology, 2022, 46 (2): 622- 634.

[1]	Qingchao SUN, Jialiang LI, Wanli JIANG, Ruoyu WANG, Zhipeng LI, Yarong HU, Jianbin ZHU. Mid-long Term Urban Power Load Forecasting Based on Data-Driven Spatio-temporal Networks [J]. Electric Power, 2025, 58(3): 168-174.
[2]	Yufei WANG, Tong DU, Weiguo BIAN, Zhao ZHANG, Huiting LIU, Lijun YANG. Short-term Load Forecasting Based on DTW K-medoids and VMD Multi-branch Neural Network for Multiple Users [J]. Electric Power, 2024, 57(6): 121-130.
[3]	Dan LI, Shuai HE, Wei YAN, Yue HU, Zeren FANG, Yunyan LIANG. Annual Daily Average Load Curve Prediction Considering Dynamic Time Anchors and Typical Feature Constraints [J]. Electric Power, 2024, 57(11): 36-47.
[4]	Ying ZHOU, Xuefeng BAI, Yang WANG, Min QIU, Chong SUN, Yajie WU, Bin LI. Analysis and Evolution Trend of Temperature-Sensitive Loads for Virtual Power Plant Operation [J]. Electric Power, 2024, 57(1): 9-17.
[5]	TANG Xuchen, CHAO Zhu, DUAN Qinwei, SU Binghong, CHEN Huican. Probabilistic Load Forecasting Method of High Voltage Substation Based on Hierarchical Measurement Data [J]. Electric Power, 2023, 56(8): 143-150.
[6]	JIA Wei, HUANG Yuchun. Method of Load Forecasting in Microgrid Based on Differential Expansion of Small Sample Data [J]. Electric Power, 2023, 56(8): 151-156,165.
[7]	YANG Xihang, HUANG Chun, SHEN Yatao, HU Nianen, WAN Ziheng. Fault Location Method for Distribution Lines Based on Loss Power Matching [J]. Electric Power, 2022, 55(8): 113-120.
[8]	LI Wenwu, SHI Qiang, LI Dan, HU Qunyong, TANG Yun, MEI Jinchao. Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR [J]. Electric Power, 2022, 55(8): 171-177.
[9]	XU Yusong, ZOU Shanhua, LU Xianling. Short-Term Load Forecasting Based on Feature Selection and Combination Model [J]. Electric Power, 2022, 55(7): 121-127.
[10]	LI Dan, ZHANG Yuanhang, LI Huangqiang, TONG Huamin, WANG Lingyun. Design of a Short-Term Load Intelligent Forecasting System for Regional Power Grid Based on Accurate Weather Data [J]. Electric Power, 2022, 55(7): 128-133.
[11]	ZHENG Haofeng, YANG Guohua, KANG Wenjun, LIU Zhiyuan, LIU Shitao, WU Hong, ZHANG Honghao. Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network [J]. Electric Power, 2022, 55(11): 142-148.
[12]	FAN Jiangchuan, YU Haozheng, LIU Huiting, YANG Lijun, AN Jiakun. Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network [J]. Electric Power, 2022, 55(11): 155-162,174.
[13]	YANG Huping, YU Yang, WANG Chao, LI Xiangjun, HU Yitao, RAO Chuchu. Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU [J]. Electric Power, 2022, 55(10): 71-76.
[14]	ZENG Youjun, XIAO Xianyong, XU Fangwei, ZHENG Lin. A Short-Term Load Forecasting Method Based on CNN-BiGRU-NN Model [J]. Electric Power, 2021, 54(9): 17-23.
[15]	PANG Chuanjun, ZHANG Bo, YU Jianming, LIU Yan. Probabilistic Interval Forecasting of Power Load Based on Structured Load model [J]. Electric Power, 2021, 54(9): 89-95.