Cloud-Native Big Data Architectures for Smart Grid Energy Optimization
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Abstract
Developing a cloud-native architecture for smart grid energy services leverages big data technologies, microservices-based design, and continuous event-driven processing. Cloud-native systems are designed and built with the cloud in mind, and are typically supported by a microservices architecture, allowing applications to be constructed from independent service components that are managed as a cohesive group by a controller. Big data architectures depend on proven data processing technologies that have been battle tested by the likes of Google and Netflix. These technologies enable elastic data processing at scale, handling of large data volumes with minimal end-to-end latency, and highly fault-tolerant data flows
Energy systems focusing on new-generation energy services, such as demand response or security-constrained optimal power flow, require data management infrastructures for efficient control and reliability verification. Big data technologies make these solutions feasible. Utility-scale data infrastructures have been deployed and operated for years; lessons learned have delivered real-world experience and highlighted remaining challenges. Cloud-native deployments at smaller scales—regional and microgrids—confirm the adaptability of architectures and patterns. The design, governance, and operation of cloud-native data infrastructures make them suitable for privacy, security, and regulatory compliance.
Cloud-native technologies have proved successful for IPv4 and IPv6 application solutions. The adoption of cloud-native technologies and a microservices approach for electric grid security is still an open question. Future work should propose cloud-native data architectures for event-driven energy analytics irrespective of grid size, operator, or technology, and should identify specific service requirements
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