Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms: Preprint
Conference
·
OSTI ID:1547236
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- University of Colorado
- University of Minnesota
This paper presents a distributed approach to perform real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approach real-time control/optimization.
- 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:
- 1547236
- Report Number(s):
- NREL/CP-5000-73395
- Conference Information:
- Presented at the IEEE American Control Conference, 10-12 July 2019, Philadelphia, Pennsylvania
- Country of Publication:
- United States
- Language:
- English
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