Flexible selection framework for secondary frequency regulation units based on learning optimisation method

Abstract

Grid connections of new energy sources have increased system frequency fluctuations, which requires secondary frequency regulation(SFR) to ensure the safe and stable operation of the system. SFR balances the power deviation between generation and consumption in real time by changing the setpoints of the units. In this paper, we propose a dynamic area control error (ACE) threshold value-based method to rationalise unit selection for SFR tasks. Each conventional unit or virtual generation unit has an independent threshold to aid dispatchers in determining the response priority of each unit to SFR tasks relative to scheduled tasks. In this manner, power generation units can switch their control mode flexibly and track different setpoints using automatic generation control(AGC) and load control technology. We designed a threshold solver based on a sequential decision framework using the deep reinforcement learning method. The solver can dynamically update thresholds based on the real-time system operational state such that the selected units can better meet the current dispatching needs. In the experiment, we tested the performance of the solver in several typical scenarios; the proposed method was found to be effective in maintaining the frequency stability of the system, with lower operation costs.