Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint

Conference · Wed Aug 15 04:00:00 EDT 2018

OSTI ID:1465640

Annoni, Jennifer ^[1]; Johnson, Kathryn E ^[1]; Bay, Christopher ^[2]; Pao, Lucy ^[3]; Taylor, Timothy ^[2]

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1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2. Colorado School of Mines
3. University of Colorado

Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1465640
• Report Number(s): NREL/CP-5000-70936
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
distributed control
optimization
wind farm control