Guest Editorial: Knowledge-and Data-Driven Smart Energy Management in Distribution Networks

Abstract

Distribution networks (DN) are gradually transformed into their active form due to increasing penetration of distributed generation and fast development of use-side flexible resources. Due to limited measurements and communication capability, conventional power system analysis methods based on analytical formulation become inadequate for the management of DNs with high uncertainties and complex interactions. The advancement of the Internet of Things and artificial intelligence (AI) technologies enables data-driven approaches for the forecasting, modeling, operation, and control of DNs. To address challenges in practical industrial applications, such as interpretability, reliability, security, portability, and lack of high-quality training data, the nexus of data-driven and knowledge-based analysis methods have attracted growing research interest. The objective of this special issue is to identify and disseminate cutting-edge research focusing on integrating data-driven and knowledge-based technologies to tackle emerging challenges in smart management of active distribution systems.