Combining model-based and model-free methods for stochastic control of distributed energy resources

• Published in: Applied energy Vol. 283; p. 116204
• Main Authors: Chen, Yue, Lin, Yashen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2021
• Subjects: Combining model-based and model-free methods; Distributed energy resources; energy; operating costs; Spatial–temporal noise; Stochastic control; uncertainty; Stochastic control; Combining model-based and model-free methods; Spatial–temporal noise; Distributed energy resources
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2020.116204

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. In the paper, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder. •Stochastic control of distributed energy resources in a distribution system under spatial–temporal uncertainty.•A novel hierarchical control framework combines model-based and model-free methods to take the advantages of both methods.•The model-based chance-constrained optimal-power-flow problem minimizes the operational cost and optimizes the operating reserve.•The distributed model-free extremum seeking controllers absorb real-time system uncertainty to achieve grid objectives.