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The integration of renewable energy sources, such as rooftop solar panels, into smart grids poses significant challenges for managing customer-side battery storage. In response, this paper introduces a novel reinforcement learning (RL) approach aimed at optimizing the coordination of these batteries. Our approach utilizes a single-agent, multi-environment RL system designed to balance power saving, customer satisfaction, and fairness in power distribution. The RL agent dynamically allocates charging power while accounting for individual battery levels and grid constraints, employing an actor–critic algorithm. The actor determines the optimal charging power based on real-time conditions, while the critic iteratively refines the policy to enhance overall performance. The key advantages of our approach include: (1) Adaptive Power Allocation: The RL agent effectively reduces overall power consumption by optimizing grid power allocation, leading to more efficient energy use. (2) Enhanced Customer Satisfaction: By increasing the total available power from the grid, our approach significantly reduces instances of battery levels falling below the critical state of charge (SoC), thereby improving customer satisfaction. (3) Fair Power Distribution: Fairness improvements are notable, with the highest fair reward rising by 173.7% across different scenarios, demonstrating the effectiveness of our method in minimizing discrepancies in power distribution. (4) Improved Total Reward: The total reward also shows a significant increase, up by 94.1%, highlighting the efficiency of our RL-based approach. Experimental results using a real-world dataset confirm that our RL approach markedly improves fairness, power efficiency, and customer satisfaction, underscoring its potential for optimizing smart grid operations and energy management systems. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]