Measurement-based prediction-correction online distributed optimal power flow algorithm for multi-phase active distribution networks

作者：Xie, Kaijun; Liu, Mingbo; Lu, Wentian^*; Shen, Zhijun; Wu, Jiexuan

来源：JOURNAL OF CLEANER PRODUCTION, 2022, 362: 131935.

DOI：10.1016/j.jclepro.2022.131935

• Distributed energy resource
• Multi-phase active distribution network
• Prediction-correction algorithm
• Time-varying optimization
• Optimal power flow
• Voltage regulation

Online algorithms have shown great potential in solving time-varying optimal power flow (OPF) problems emerging in active distribution networks (ADNs) with numerous power-electronics-interfaced distributed energy resource (DER) integrations. However, existing online algorithms have the following limitations: 1) Correction only algorithms ignore the time-varying characteristics of the optimization problem; 2) Prediction-correction algorithms cannot handle nonlinear OPF problems; 3) Most algorithms only consider the application of single-phase cases. This paper addresses the above limitations by proposing a novel measurement-based prediction correction online distributed OPF algorithm for multi-phase ADNs. This algorithm is based on an improved multiphase linearized alternating current (AC) power flow model that takes advantage of the voltage measurements to improve the approximation accuracy. In each sampling period, the proposed algorithm first performs a limited number of correction steps, iterating the incremental variable based on the current outputs of DERs and the voltage measurements to track the optimal solution. The DERs then execute the resultant incremental output powers. After forecasting the time-varying parameters, the proposed algorithm performs a few prediction steps to seek a better starting point for the correction steps before the next sampling. Numerical test results of the modified IEEE 37-bus and 123-bus distribution systems demonstrated that the proposed algorithm can track the optimal solution of the time-varying nonlinear OPF problem more accurately than existing online algorithms, even in extra-high penetration scenarios (>= 200%).

• 单位
  广州大学