Distributed energy systems increasingly consist of heterogeneous assets and organizations that must exchange operational data while preserving interoperability, security, and regulatory compliance. Existing integration solutions often rely on syntactic adapters or centralized data hubs, which scale poorly and offer limited transparency or governance. This paper presents a metadata-driven federated monitoring architecture that integrates ontology-based metadata federation, event-driven microservices, and governance-aware provenance tracking to enable secure, scalable, and auditable data sharing across distributed energy infrastructures. The proposed system models all assets and data streams through a unified semantic graph, aligning heterogeneous schemas via automated ontology matching and combined lexical–structural similarity scoring. A microservices pipeline ingests multi-protocol data (OPC-UA, MQTT, REST), applies stream analytics for anomaly detection, and enforces access and compliance policies at the metadata layer. A Web-based interface allows operators to issue GraphQL queries, visualize distributed assets, and monitor real-time alerts linked to provenance records. A prototype implementation demonstrates operational-scale efficiency, achieving low-latency response (≤540 ms for hybrid metadata–telemetry queries over 10,000 assets), near-linear scalability (∼4.5% CPU growth per added node), and high governance accuracy (precision 0.90, recall 0.95, median detection 1.6 s) while maintaining minimal overhead (<8% added latency). These results highlight that the proposed metadata-driven federation delivers both technical performance and governance reliability unmatched by existing Web-based integration frameworks. These results show that metadata federation can be deployed at operational scale while providing explainable compliance and trustworthy data sharing across organizational boundaries. This research advances the state of the art in Web-based system engineering by combining semantic modeling, distributed processing, and security governance into a single deployable framework. Beyond energy systems, the approach offers a foundation for interoperable and auditable monitoring in other critical cyber-physical domains such as industrial IoT, urban infrastructure, and healthcare telemetry.

Ensuring data-flow integrity and rapid threat containment in renewable-integrated, distributed energy systems requires monitoring solutions that are technically rigorous yet lightweight in operation. This paper presents a service-oriented web framework for real-time data-flow tracing and threat propagation analysis in heterogeneous industrial control and energy networks. The framework integrates lightweight provenance tokens embedded in event streams, an incrementally maintained lineage graph with probability-weighted edges, and propagation-aware risk indicators that drive adaptive response orchestration through open web APIs. A progressive web dashboard provides sub-second visualization of dynamic topologies, risk heat maps, and operator controls. Implemented on a Kafka/Flink streaming backbone with a graph database and deployed in an eight-node Kubernetes testbed emulating substations, gateways, and adversarial nodes using OPC UA, MQTT, and REST, the system achieved tracing coverage of 0.96 ± 0.02 and fidelity of 0.92 ± 0.03, with forward propagation prediction reaching precision 0.91 and recall 0.88, outperforming static-topology baselines. Adaptive containment reduced the flow reproduction factor from 1.42 to 0.64, achieved a median containment efficacy of 0.71, and stabilized risk trajectories within two minutes, while operational cost remained low with payload expansion under 12%, CPU overhead below 4%, and service availability above 0.99 for critical assets. User studies showed 38% faster incident response and higher comprehension and confidence compared with static log viewers. These results demonstrate that modern web-engineering practices such as microservices, event-driven streaming, and progressive web interfaces can enable practical, real-time cyber defense for distributed energy infrastructures by bridging static security guidelines with deployable, adaptive situational awareness and containment.