Search

This paper introduces an enhanced framework for managing Battery Energy Storage Systems (BESS) in residential communities. The non-convex BESS control problem is first addressed using a gradient-based optimizer, providing a benchmark solution. Subsequently, the problem is tackled using multiple Deep Reinforcement Learning (DRL) agents, with a specific emphasis on the off-policy Soft Actor-Critic (SAC) algorithm. This version of SAC incorporates reward refinement based on this non-convex problem, applying logarithmic scaling to enhance convergence rates. Additionally, a safety mechanism selects only feasible actions from the action space, aimed at improving the learning curve, accelerating convergence, and reducing computation times. Moreover, the state representation of this DRL approach now includes uncertainties quantified in the entropy term, enhancing the model's adaptability across various entropy types. This developed system adheres to strict limits on the battery's State of Charge (SOC), thus preventing breaches of SOC boundaries and extending the battery lifespan. The robustness of the model is validated across several Australian states' districts, each characterized by unique uncertainty distributions. By implementing the refined SAC, the SOC consistently surpasses 50 percent by the end of each day, enabling the BESS control to start smoothly for the next day with some reserve. Finally, this proposed DRL method achieves a mean reduction in optimization time by 50 percent and an average cost saving of 40 percent compared to the gradient-based optimization benchmark.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset., Comment: IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, U.S.A, 2024, pp. 1-6

In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to learning-based VVO in expansive and dynamically changing power systems are often hindered by computational complexities. To address this challenge, our research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL), specifically utilizing the Importance Weighted Actor-Learner Architecture (IMPALA) algorithm, executed on the RAY platform. This framework, built upon RLlib-an industry-standard in Reinforcement Learning-ingeniously capitalizes on the distributed computing capabilities and advanced hyperparameter tuning offered by RAY. This design significantly expedites the exploration and exploitation phases in the VVO solution space. Our empirical results demonstrate that our approach not only surpasses existing DRL methods in achieving superior reward outcomes but also manifests a remarkable tenfold reduction in computational requirements. The integration of our DRL agent with the RAY platform facilitates the creation of RLlib-IMPALA, a novel framework that efficiently uses RAY's resources to improve system adaptability and control. RLlib-IMPALA leverages RAY's toolkit to enhance analytical capabilities and significantly speeds up training to become more than 10 times faster than other state-of-the-art DRL methods.

This paper introduces a cyber-physical testbed that integrates the Real-Time Digital Simulator (RTDS) with the Real-Time Automation Controller (RTAC) to enhance cybersecurity in electrical distribution networks. Focused on addressing vulnerabilities to cyber attacks, our testbed employs an advanced control algorithm developed in Python and executed through real-time controllers. Key to our approach is the seamless integration of the host PC machine, RTDS, and RTAC via the Modbus protocol. We present a game theory-based topology control strategy as an effective response to cyber attacks, specifically targeting smart meters. This testbed validates the efficacy of our method in fully eliminating voltage violations due to load altering attacks, showcasing substantial advancements in smart grid cybersecurity via the innovative use of RTDS and RTAC to simulate and counteract complex cyber threats.