Abstract
This article proposes a distributed hierarchical automatic generation control (AGC) framework with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control algorithm and AGC dispatch algorithm and adapt to the development trend of AGC from centralized framework to centralized-decentralized framework. The framework also involves a multi agent distributed multiple improved deep deterministic policy gradient (MADMI-TD3) algorithm that is characterized by excellent global search capability and optimizing speed. The algorithm can help create an optimal AGC strategy in a randomization environment so as to obtain an optimal cooperative control of AGC. According to a simulation verification on the LFC model for an interconnected power grid of a province, the algorithm is superior to the current algorithms and conventional engineering methods in terms of control performance and economic benefits. In other words, the algorithm can improve control performance and reduce the regulation mileage payment.
Highlights
The ever-increasing innovation of renewable energy makes the power grid more dispersed, diverse, and random [1]–[3]
To conclude: 1) In the performance-based frequency regulation market, the VGA-automatic generation control (AGC) that is based on the proposed MADMITD3 can help build a concentrated-decentralized autonomous framework in order to solve the problem concerning the collaboration of conventional control algorithm and dispatch algorithm
Compared to the conventional combination algorithm, the virtual generation alliance automatic generation control (VGA-AGC) framework can realize the comprehensive optimization of control performance and economic benefits in the process of secondary frequency regulation of a power grid with large random disturbance
Summary
The ever-increasing innovation of renewable energy makes the power grid more dispersed, diverse, and random [1]–[3]. The first category is the control algorithm such as the conventional PID algorithm [7], [8], sliding mode control (SMC) [9], active disturbance rejection control (ADRC) [10], fractional Order PID (FOPID) [11], fuzzy control [8], and reinforcement learning series such as Q learning algorithm [12], [13], Q learning algorithm [14], R(λ) learning algorithm [15], (Deep Q-Network) DQN [16], and (Double Deep Q-Network)DDQN [17] Speaking, these algorithms take the entire power grid as a single area for calculation of generation command, which is proportionally distributed to AGC regulation units. 2) The proposed MADMI-TD3 algorithm employs different parameters of multiple actor networks and critic networks for distributed optimizing, in addition, several techniques like classified experience replay, variable noise models, warm boot of experience pool are utilized to obtain an adaptive reinforcement learning control algorithm with superior global search ability and optimizing speed.
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