Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach

Abstract

Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning (RL) agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given twoarea interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error (IAE) of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random loadgeneration disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations.

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

“…The RL techniques discussed in [35,36] were used for comparing the PSS results obtained with the Q-learning algorithm discussed in this paper. The TD3 method was implemented in [37,38] for continuous power disturbances to overcome low-frequency oscillations. In [38], the TD3 method was used to perform parameter estimation and the finetuning of PID controllers and to overcome the problem of low-frequency oscillations caused by load generation variations.…”

“…The TD3 method was implemented in [37,38] for continuous power disturbances to overcome low-frequency oscillations. In [38], the TD3 method was used to perform parameter estimation and the finetuning of PID controllers and to overcome the problem of low-frequency oscillations caused by load generation variations. Deep reinforcement learning methods were implemented for load frequency control of a multi-area power system and battery energy management.…”

Load frequency control in deregulated power system with renewable energy sources: Hybrid GOA‐SNN technique

“…The problems caused by the intermittent nature of renewable resources and the low inertia of these MGs were reduced by the coordination of appropriate virtual‐inertia support (VIS) and DR systems. Khalid et al 15 have used deep reinforcement learning to develop an effective automated load frequency regulation of a hybrid power system. The complexity of the linked power system was increased for a more realistic approach by integrating intermittent RESs, fluctuating demands, and EVs.…”

Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm

“…1 Reinforcement learning surpasses traditional control methods due to its ability to learn and improve through an interactive trial-and-error approach that relies on observations obtained from the dynamic environment. 2 In recent years, there has been a trend toward implementing comprehensive intelligence in industrial production. The surge of cloud computing and communication network connects the related industries, unleashing the full potential of the industrial network orchestrated by machine learning and AI due to their capability to collect and generate large volume of network data.…”

“…Reinforcement learning surpasses traditional control methods due to its ability to learn and improve through an interactive trial‐and‐error approach that relies on observations obtained from the dynamic environment 2 . In recent years, there has been a trend toward implementing comprehensive intelligence in industrial production.…”