Load Estimation of Complex Power Networks from Transformer Measurements and Forecasted Loads

Abstract

This paper presents a load estimation method applicable to complex power networks (namely, heavily meshed secondary networks) based on available network transformer measurements. The method consists of three steps: network reduction, load forecasting, and state estimation. The network is first mathematically reduced to the terminals of loads and measurement points. A load forecasting approach based on temperature is proposed to solve the network unobservability. The relationship between outdoor temperature and power consumption is studied. A power-temperature curve, a nonlinear function, is obtained to forecast loads as the temperature varies. An “effective temperature” reflecting complex weather conditions (sun irradiation, humidity, rain, etc.) is introduced to properly consider the effect on the power consumption of cooling and heating devices. State estimation is adopted to compute loads using network transformer measurements and forecasted loads. Experiments conducted on a real secondary network in New York City with 1040 buses verify the effectiveness of the proposed method.

A Short‐Term Load Forecasting Model of LSTM Neural Network considering Demand Response

“…is method improved the robustness of short-term load forecasting [23]. Rong and de León [24] proposed a load estimation method suitable for complex power networks. In order to solve the problem of network unobservability, Rong and de León proposed a nonlinear power-temperature curve to predict the load that varies with temperature.…”