Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.