Reservoir based learning network for control of two-area power system with variable renewable generation

Abstract

The penetration of renewable energy sources into the electric power system is rapidly increasing. Integrating variable renewable energy sources into the transmission grid introduces challenges in real time power system operation. This causes power and frequency fluctuations and raises stability concerns. In this paper, a 200MW photovoltaic (PV) plant is integrated into a two-area four-machine power system. In order to maintain the system frequency, a dynamic tie-line power flow control is implemented using predicted PV power as an input to the automatic generation controller in Area 1, which transfers power to Area 2 with PV generation. The prediction performances of two learning reservoir based networks, an echo state network (ESN) and an extreme learning machine (ELM), are investigated for day and night time operations. The experimental study is performed using actual weather data from Clemson, SC and a real time simulation of a utility-scale PV plant integrated power system. Phasor measurement units (PMUs) are used to provide input signals to automatic generation controllers in the two area power system. Typical tie-line power flow control results based on ESN and ELM models are presented to show the impact of predicting PV power in improved automatic generation control with variable generation. ESN and ELM models provide minimal tie-line power flow deviations from reference power flows during day and night time operations, respectively.