Research on AGC Performance During Wind Power Ramping Based on Deep Reinforcement Learning

With the increase in wind power penetration, wind power ramping events have increasingly influenced tie line power control in the power grid. Large power changes during ramping events make it difficult to accurately track the scheduling plans of tie lines and can even lead to overrun. Determining how to evaluate the control performance of automatic generation control (AGC) for wind power ramping has become an urgent problem. In this context, this paper studies the control performance of AGC for wind power ramping based on deep reinforcement learning. First, a tie line power control model of a power system with an AGC module is established. Then, measured data, which include thermal power, wind power, hydropower output and tie line power data, and a deep reinforcement learning method are combined for AGC parameter estimation based on the deep Q-network (DQN) algorithm. Next, the AGC parameter in different scenarios are fit by using measured phasor measurement unit (PMU) data, and on the basis of the fitted model, AGC performance evaluation is performed for wind power ramping events. Finally, the simulation results verify the feasibility and effectiveness of analysing the relationship between wind power ramping and AGC performance based on the AGC parameter fitting model.

Abstract licence: CC BY-NC-ND

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