Impacts of Distributed Renewable Energy Source Integration on the Reliability of Distribution Networks: A Bibliometric Review

Traditional reliability indicators, such as SAIDI, SAIFI, DEC, and FEC, remain essential benchmarks, but they have proven insufficient to capture recovery capacity and vulnerability under extreme events. This bibliometric review clarifies these limitations while mapping how advanced control solutions—such as deep reinforcement learning (DRL), model predictive control (MPC), and graph neural networks (GNNs)—are being employed to enhance network restoration, voltage regulation, and outage management. By integrating discussions of conventional indices with the emerging role of artificial intelligence and storage technologies, this study provides a dual contribution: (i) identifying how resilience and reliability are being redefined in the literature, and (ii) highlighting research gaps in the standardization of event-based metrics, such as restoration time and customer minutes lost. The results aim to support regulators and operators in adopting intelligent, secure, and sustainable strategies for distribution networks, ensuring that technological advances are aligned with energy justice and real operational challenges.