RoGANDER: A Robust Generative Adversarial Network for Distributed Energy Resources

The swift expansion of integrated renewable energy within the electrical grid underscores the essential role of distributed energy resources (DERs) in shaping the future of power transmission and distribution networks. This increasing incorporation of DERs has prompted power utility providers to enhance their systematic awareness and deploy advanced load control techniques. Nonetheless, a persistent challenge revolves around optimizing energy dispatching amidst substantial fluctuations originating from DERs. These fluctuations primarily stem from long-term data stochasticity and the inherent unpredictability of integrated DERs. Existing methods fall short in addressing the intricate nature of energy distribution from utilities to households, especially when dealing with exogenous loads beyond smart meters. To address this issue, this paper proposes RoGANDER, a Robust Generative Adversarial Network (GAN) for DERs. It overcomes challenges associated with high spatial and temporal data granularity, along with significant time consumption. The RoGANDER approach leverages generative sequential data to mitigate data unpredictability and stochasticity. Our evaluation results confirm a notable reduction in fluctuations and an enhanced ability to anticipate households’ energy needs.