Ad-hoc Networks and Cloud Databases for Renewable Energy Systems

The growing heterogeneity and decentralization in renewable energy infrastructures have resulted in a need for adaptive, scalable, and intelligent communication and data management solutions. On the one hand, centralized systems have limitations in terms of latency, scalability, and fault resilience, whereas purely decentralized systems can encounter challenges with data integration and long-term analytics. In this article, a hybrid architecture using mobile ad hoc networks and cloud databases to improve the collaborative operation of distributed renewable energy systems is introduced. The architecture leverages latency-aware routing protocols for hard real-time communication among edge devices, solar panels, wind turbines, and battery storage. At the same time, it uses cloud-based predictive analytics to enable more powerful capabilities, such as failure diagnostics and power scheduling. In extensive simulations, we demonstrated improvements of several orders of magnitude across key operational metrics, including latency reduction, throughput gains, energy efficiency, and scaling. Furthermore, introduced machine learning applications, a BiLSTM-CNN hybrid for fault prediction, and a reinforcement learning agent for energy dispatch, improving system flexibility and the ability to make informed decisions. The results demonstrate the potential of hybrid communication and analytics systems to enable next-generation smart grid applications by improving reliability, responsiveness, and resource allocation. This study adds to the existing knowledge base on intelligent energy by providing a design that can be easily replicated and scaled, while accounting for operational and long-term sustainability performance.