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首页> 外文期刊>Smart Grid, IEEE Transactions on >Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique
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Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique

机译：能源市场中的插入式电动车辆行为建模：一种新的基于深入学习技术的聚类技术

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摘要

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate cluster-specific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long- and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.
机译：在运输船队中的插入电动车（PEV）的渗透率延伸及其随后的充电需求引入了电力负荷轮廓的大量间歇性，这对配电网络产生了技术经济挑战。为了解决需求的不确定性，开发了一种具有混合分类任务的新型深度学习方法，可以考虑PEV所有者的旅行特征。分类结构有助于我们通过将特定的预测网络分配给每个旅行行为模式来仔细审查PEV。在我们的混合分类任务中，首先，通过群集它们来识别历史PEVS数据之间的隐藏旅行行为模式;然后，监督分类器将每个新PEV数据指向适当的群集特定的预测网络。基于长的短期内存网络构建了基于深度学习的预测和分类网络，以研究PEV行为中的长期和短期特征。我们所提出的方法的数据驱动结构使我们能够观察和保留PEV旅行数据参数（出发时间，到达时间和行驶距离）之间的相关性，并避免在基于场景的方法中发现的不切实际的旅行样本。为了验证所提出的方法在实际环境中的有效性，我们研究了精确预测PEVS需求在加州独立系统运营商市场的能源市场中的金融利润中的精确预测。数值结果证实了我们提出的基于深度学习的方法的出色性能，以预测PEVS对该领域的基准方法的需求，例如Monte Carlo，Quasi-Monte Carlo和Copula，与实际数据相比只有6.77％。

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