Multi-Agent Deep Reinforcement Learning for Sectional AGC Dispatch

Jiawen Li,Zhuohuan Li,Tao Yu,Dan Lin,Hanxin Zhu,Fusheng Li

doi:10.1109/access.2020.3019929

Abstract

Aiming at the problem of coordinating system economy, security and control performance in secondary frequency regulation of the power grid, a sectional automatic generation control (AGC) dispatch framework is proposed. The dispatch of AGC is classified as three sections with the sectional dispatch method. Besides, a hierarchical multi-agent deep deterministic policy gradient (HMA-DDPG) algorithm is proposed for the framework in this paper. This algorithm, considering economy and security of the system in AGC dispatch, can ensure the control performance of AGC. Furthermore, through simulation, the control effect of the sectional dispatch method and several AGC dispatch methods on the Guangdong province power grid system and the IEEE 39 bus system is compared. The result shows that the best effect can be achieved with the sectional dispatch method.

Highlights

Automatic generation control (AGC) is an important operation task of interconnected power grid, which can maintain the system frequency and tie-line exchange power to the expected values [1]
The short-term random fluctuation and corresponding system power deficiency are relatively small in the interconnected power grid with traditional hydropower power and coal-fired power as the main power supply, the small power regulation variation of the AGC unit has little effect to the security and generation cost of the power grid
SETTINGS FOR SECTIONAL AGC DISPATCH ALGORITHM As the HMA-DDPG has the advantage of fast response and being not easy to get into a local optimum in the online testing operation after pre-training, this algorithm needs to be used in the dispatch processes of the first section and the second section that require fast response

Summary

INTRODUCTION

Automatic generation control (AGC) is an important operation task of interconnected power grid, which can maintain the system frequency and tie-line exchange power to the expected values [1]. The main motivations and novelties of this paper are given as follows: 1) The previous studies of AGC dispatch don’t consider the coordination of AGC control performance, generation cost and security constraints, especially for collaborative optimization in a power grid that has large-scale of new energy and distributed energy. To fill up this gap, a sectional AGC dispatch is proposed to simultaneously to solve the coordination problem, when it dispatches the real-time total power regulation command to all the controllable regulation resources. Assumption 3: during training, the agents do not communicate with each other, or the communication content is only a component of their respective observation

MADDPG TRAINING METHOD

SECTIONAL AGC DISPATCH ALGORITHM-HMA-DDPG

SETTINGS FOR SECTIONAL AGC DISPATCH ALGORITHM

FIRST SECTION DISPATCH AND SECOND SECTION DISPATCH ALGORITHM-HMA-DDPG

THIRD-SECTION DISPATCH ALGORITHM-PRIMAL-DUAL INTERIOR POINT METHOD

CONCLUSION