Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability

Abstract

By 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, forecasting, power flow analysis, and optimization of feeders for operational decision-making by individually modeling each of these numerous renewables in the absence of complete information are operationally challenging and technically impractical. In response, we introduce a method to accurately estimate the aggregate capacities of the connected DERs on distribution feeders and a near-term forecasting method. Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. This step of estimation of the aggregate capacities of connected DERs, is a sequel to solving feeder hosting capacity problem. The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs.