Abstract
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.
Highlights
In recent years, renewable energy resources such as wind power have begun to connect to the power grid on a large scale
The occurrence of wind ramp events [2]–[6] will adversely affect the power balance of the system, resulting in the tie lines of interconnected power systems having difficulty in accurately tracking the scheduling plan, increasing the difficulty of tie line power control. It must be determined whether automatic generation control (AGC) [7]–[9] can reduce the impact of wind ramp events on the power of tie lines, and evaluations of the
In terms of AGC simulation model research, reference [10] successively introduced an AGC model built with the power system simulation programs PSS/E, Eurostag and PSD-FDS
Summary
Renewable energy resources such as wind power have begun to connect to the power grid on a large scale. Deep reinforcement learning has yielded remarkable achievements related to many aspects of power systems, such as the coordinated control of the hybrid energy storage in microgrids [20], the generation unit tripping strategy under emergency circumstances [21] and AGC strategy research [22], thereby displaying its effectiveness in solving decision-making problems. This paper proposes a data-driven method for AGC parameter fitting based on deep reinforcement learning to evaluate the control performance of AGC for wind power ramping. It is difficult to accurately use a mathematical model to express the regulation effect of AGC on the tie line when wind power output changes in multiple scenarios, and this needs to be analysed in combination with the nonlinear characteristic fitting ability of deep learning. The strategy with the largest value function is called the optimal strategy
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