Data-driven recursive multivariable modeling, operation, and control of active distribution networks with distributed generation

This paper addresses the challenges arising from the increasing integration of distributed generation into active distribution networks (ADNs), focusing on their modeling, operation, and control. Data-driven recursive multivariable modeling, capable of capturing both static and dynamic interactions in real time, has emerged as a promising solution. By utilizing the extensive data generated by modern grid infrastructure, this approach enhances network model accuracy and improves operational efficiency and control strategies. This paper strengthens the connection between Process Systems Engineering (PSE) and power systems, traditionally underexplored in this domain. By integrating PSE principles, particularly data-driven and control allocation methodologies, into the modeling, operation, and control of ADNs, this work optimizes power system performance. Three Recursive Partial Least Squares (RPLS) methodologies—sample-wise, block-wise, and moving-window—are rigorously compared regarding estimation/prediction characteristics and convergence speed. This novel analysis challenges the assumption of instantaneous model adaptation, emphasizing the importance of carefully considering convergence periods for effective monitoring, control, and optimization. The paper proposes and analyzes three control structures integrated into an RPLS-based supervisory strategy for voltage regulation at ADN nodes: (1) decentralized control, (2) control allocation with measurement combination, and (3) optimization-based centralized control. Different integration formats are evaluated based on the controller technology used: (a) simple setpoint updates, (b) full ADN model adaptation to recalculate controller matrices, and (c) full model adaptation for updating the optimization formulation. Simulation results were obtained using the IEEE 33-bus test system. The results reveal a trade-off between the complexity and performance benefits of each control strategy. Although no strategy proves definitively superior, the latter two show more promising overall prospects.