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.

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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

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