A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment
A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment
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DOI：
10.1109/tpwrs.2018.2890714
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发表时间：
2019
影响因子：
6.6
通讯作者：
Sadra Babaei;Chaoyue Zhao;Lei Fan
中科院分区：
文献类型：
--
作者：
Sadra Babaei;Chaoyue Zhao;Lei Fan
由于分布式能源（DER）的渗透率不断提高，电力系统运营商面临着确保分布式能源有效整合的重大挑战。虚拟发电厂 (VPP) 使分布式能源能够通过聚合分布式能源并作为单一实体参与批发市场来提供有价值的服务。然而，VPP 的可用容量取决于其 DER 输出，该输出是随时间变化的，并且当独立系统运营商运行日前单位承诺引擎时无法准确得知。在本研究中，我们开发了一个模型来评估 VPP 的物理特性，即考虑到风电输出和负载消耗的不确定性，其最大容量和爬坡能力。所提出的模型基于分布式鲁棒优化方法，该方法利用未知参数的矩信息（例如均值和协方差）。我们将模型重新表述为二进制二阶圆锥曲线程序，并开发了一个分离框架来解决它。我们首先解决一个两阶段问题，然后用多阶段案例对其进行基准测试。进行案例研究以显示所提出方法的性能。
Due to the increasing penetration of distributed energy resources (DERs), power system operators face significant challenges of ensuring the effective integration of DERs. The virtual power plant (VPP) enables DERs to provide their valuable services by aggregating them and participating in the wholesale market as a single entity. However, the available capacity of VPP depends on its DER outputs, which is time varying and not exactly known when the independent system operator runs the day-ahead unit commitment engine. In this study, we develop a model to evaluate the physical characteristics of the VPP, i.e., its maximum capacity and ramping capabilities, given the uncertainty in wind power output and load consumption. The proposed model is based on a distributionally robust optimization approach that utilizes moment information (e.g., mean and covariance) of the unknown parameter. We reformulate the model as a binary second-order conic program and develop a separation framework to address it. We first solve a two-stage problem and then benchmark it with a multi-stage case. Case studies are conducted to show the performance of the proposed approach.