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计及需求差异的电动汽车并网滚动时域优化
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华北电力大学电气与电子工程学院,北京市 102206

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基金项目:

中央高校基本科研业务费专项资金资助项目(2019QN123);国网浙江省电力有限公司科技项目(5211HZ17000D)。


Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference
Author:
Affiliation:

1.School of Electrical &2.Electronic Engineering, North China Electric Power University, Beijing 102206, China

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This work is supported by Fundamental Research Funds for the Central Universities (No. 2019QN123) and State Grid Zhejiang Electric Power Co., Ltd. (No. 5211HZ17000D).

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    摘要:

    考虑电动汽车(EV)并网后用户的差异性需求,提出一种计及需求差异的EV并网优化控制策略。首先,根据需求差异将并网EV集细化为刚性集、可削减集及灵活集3个子集,并分别建立充放电控制模型。其次,建立用户行为模式模型,描述用户行为模式的分布特征,进而制定计划功率需求。最后,以实际功率与计划功率偏差及功率波动最小为目标,提出并网EV的滚动时域优化策略,每一轮优化将可削减集及灵活集中EV功率增量作为最优控制变量在充放电控制模型约束下求解。实验分析证明,所提优化策略保证了实际功率符合计划功率的同时兼顾了用户的差异性需求,对并网过程中随机动态因素具有一定的鲁棒性。

    Abstract:

    An optimal control strategy of grid-connected electric vehicles (EVs) considering demand difference is proposed, which considers the different demands of EV users. Firstly, the grid-connected EVs are divided into three subsets, including rigid set, reducible set and flexible set according to the demand differences, and the charging-discharging control model is established, respectively. Then, the behavior model of EV users is constructed to describe the distribution characteristics of the user behavior, and then the planned power demand is formulated. Finally, the rolling horizon optimization strategy for grid-connected EVs is proposed with the objective of minimized deviation between actual power and planned power and power fluctuation. The power increments of EVs in reducible set and flexible set are took as the optimal control variables and solved under the constraints of the charging-discharging control model. Experimental analysis shows that the proposed optimization strategy ensures that the actual power matches the planned power while taking into account the different demands of users. Moreover, it is robust to random dynamic factors in the optimization progress.

    表 2 Table 2
    表 3 Table 3
    表 1 Table 1
    图1 控制框架Fig.1 Control framework
    图3 滚动优化结果Fig.3 Results of rolling optimization
    图5 2种计划功率下滚动时域优化结果Fig.5 Results of rolling horizon optimization with two different planned powers
    图 EV并网滚动时域优化流程Fig. Rolling horizon optimization procedure of EV
    图 4类EV行为模式下特征指标在12个区间的概率分布Fig. Probability distribution of characteristic indices in 12 ranges under four types of EV behavior patterns
    图 单一模式下特征指标在12个区间的概率分布Fig. Probability distribution of characteristic indices in 12 ranges under single type of EV behavior pattern
    图 3类EV并网功率及SOC变化Fig. Grid-connected power and SOC changes of three EV types
    图 D2和D3集的EV占比Fig. Proportion of EV in D2 and D3 sets
    图 灵活性裕度与可调参数的关系Fig. Relation between flexibility and adjustable parameters
    图 不同转移概率下的滚动优化结果Fig. Rolling horizon optimization results with different transition probabilities
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引用本文

张丙旭,许刚.计及需求差异的电动汽车并网滚动时域优化[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20191014006.
ZHANG Bingxu,XU Gang.Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191014006.

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  • 收稿日期:2019-10-14
  • 最后修改日期:2020-05-06
  • 录用日期:2020-03-04
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