scholarly journals Wind farms providing secondary frequency regulation: Evaluating the performance of model-based receding horizon control

2017 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Dynamic Model ◽  
Wind Farm ◽  
Wind Farms ◽  
Static Model ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Control Approach ◽  
Model Based ◽  
Model Control ◽  
Wake Model

Abstract. We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, "RegA'" and "RegD", which are used by PJM, an independent system operator in the Eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation. Our results demonstrate that the dynamic-model controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower time scales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.

scholarly journals Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control

2018 ◽  
Vol 3 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Dynamic Model ◽  
Wind Farm ◽  
Wind Farms ◽  
Static Model ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Control Approach ◽  
Model Based ◽  
Model Control ◽  
Wake Model

Abstract. This paper is an extended version of our paper presented at the 2016 TORQUE conference (Shapiro et al., 2016). We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large-eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, “RegA” and “RegD”, which are used by PJM, an independent system operator in the eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation services or markets. Our results demonstrate that the dynamic-model-controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower timescales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.

scholarly journals Model-based receding horizon control of wind farms for secondary frequency regulation

Wind Energy ◽  
2017 ◽  
Vol 20 (7) ◽  
pp. 1261-1275 ◽  
Author(s):  
Carl R. Shapiro ◽  
Pieter Bauweraerts ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Wind Farms ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Receding Horizon ◽  
Model Based

A Comparative Study of the Influence of Different Wake Models on Wind Farm Layout Optimization

2021 ◽  
Author(s):  
Puyi Yang ◽  
Hamidreza Najafi
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Vital Role ◽  
Layout Optimization ◽  
Zone Model ◽  
Wind Farm Layout ◽  
Yaw Control ◽  
Wind Farm Layout Optimization ◽  
Wake Model ◽  
Large Wind

Abstract The accuracy of analytical wake models applied in wind farm layout optimization (WFLO) problems plays a vital role in the present era that the high-fidelity methods such as LES and RANS are still not able to handle an optimization problem for large wind farms. Based on a verity of analytical wake models developed in the past decades, FLOw Redirection and Induction in Steady State (FLORIS) has been published as a tool integrated several widely used wake models and the expansions for them. This paper compares four wake models selected from FLORIS by applying three classical WFLO scenarios. The results illustrate that the Jensen wake model is the fastest one but the defect of underestimation of velocity deficit is obvious. The Multi Zone model needs to be applied additional tunning on the parameters inside the model to fit specific wind turbines. The Gaussian-Curl wake model as an advanced expansion of the Gaussian wake model does not perform an observable improvement in the current study that the yaw control is not included. The default Gaussian wake model is recommended to be used in the WFLO projects which implemented under the FLROIS framework and has similar wind conditions with the present work.

scholarly journals Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 544 ◽  
Author(s):  
Tanvir Ahmad ◽  
Abdul Basit ◽  
Juveria Anwar ◽  
Olivier Coupiac ◽  
Behzad Kazemtabrizi ◽  
...  
Keyword(s):  
Turbulence Intensity ◽  
Wind Farm ◽  
Control Strategies ◽  
Wind Farms ◽  
Coordinated Control ◽  
Medium Size ◽  
Farm Production ◽  
Fast Processing ◽  
Farm Efficiency ◽  
Wake Model

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( C P ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

scholarly journals Theory and Verification of a new 3D RANS Wake Model

2020 ◽  
Author(s):  
Philip Bradstock ◽  
Wolfgang Schlez
Keyword(s):  
Wind Farm ◽  
Three Dimensional ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Ambient Flow ◽  
Turbulence Decay ◽  
Local Shear ◽  
Time Lagged ◽  
Wake Model

Abstract. This paper details the background to the WakeBlaster model: a purpose built, parabolic three-dimensional RANS solver, developed by ProPlanEn. WakeBlaster is a field model, rather than a single turbine model; it therefore eliminates the need for an empirical wake superposition model. It belongs to a class of very fast (a few core seconds, per flow case) mid-fidelity models, which are designed for industrial application in wind farm design, operation and control. The domain is a three-dimensional structured grid, with approximately 80 nodes covering the rotor disk, by default. WakeBlaster uses eddy viscosity turbulence closure, which is parameterized by the local shear, time-lagged turbulence development, and stability corrections for ambient shear and turbulence decay. The model prescribes a profile at the end of the near-wake, and the spatial variation of ambient flow, by using output from an external flow model. The WakeBlaster model is verified, calibrated and validated using a large volume of data from multiple onshore and offshore wind farms. This paper presents example simulations for one offshore wind farm.

scholarly journals FLOWSTAR-Energy: a high resolution wind farm wake model

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Measured Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Model ◽  
Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

Sign in / Sign up

Export Citation Format

Share Document