基于高斯过程回归的电动汽车集群灵活性的概率预测

Flexibility probabilistic prediction of electric vehicle fleet based on Gaussian process regression

中文关键词: 电动汽车  充电灵活性  累积电量包络线  高斯过程回归  机会约束

英文关键词: electric vehicle  charging flexibility  cumulative energy envelope  Gaussian process regression  chance constraint

中文摘要:

      电动汽车（electric vehicle，EV）是重要的新型可调节负荷资源，预测其灵活性是实施优化调控的重要前提。首先，提出基于EV正常充电会话数据推断集群充电可行域的方法，形成关于EV集群调节灵活性的历史数据集。随后，针对充电负荷的高随机性特点，提出基于高斯过程回归的EV集群灵活性的概率预测方法。所得到的概率预测结果可用于建立EV集群运行优化问题的机会约束，并转换为特定置信度下的确定性约束。最后，利用实际充电数据进行仿真验证，结果表明，所提方法能从电量和功率两个方面比较准确的预测EV集群的充电灵活性；通过调整置信度，能够在对EV集群优化调度时权衡经济性和优化结果的可实施性。

英文摘要:

      Electric vehicles（EVs）are important new adjustable load resources,and predicting their flexibility is an essential prerequisite for implementing optimized dispatch. A method to infer the fleet charging feasibility domain based on normal charging session data of EVs is proposed,forming a historical dataset on the flexibility of EV fleets. Subsequently,considering the high stochasticity of charging load,a probability prediction method for the flexibility of EV fleets based on Gaussian process regression is proposed. The obtained probability prediction results can be used to establish chance constraints for the optimization problem of EV fleet and convert them into deterministic constraints under specific confidence levels. Lastly,simulation verification is conducted using actual charging data. The results show that the proposed method can accurately predict the charging flexibility of EV fleets from both energy and power aspects. By adjusting the confidence level,it is possible to balance the economy and the implementability when optimizing the EV fleets.