Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm Based on Alternating Direction Method of Multipliers

Kou

IET Renewable Power Gen

et al. 2022

From technical and economical viewpoints, an all-dc offshore wind farm (OWF) with a high-voltage direct-current (HVDC) connection, which is also known as HVDCconnected all-dc OWF, is the future trend for offshore wind energy applications. However, due to the decoupling effect of power converters, all-dc OWF cannot directly provide short-term frequency support for the onshore ac gird, that is, primary frequency response and inertia support. To address this issue, this paper presents a distributed model predictive control (DMPC) scheme for all-dc OWF. By directly suppressing the voltage deviation and the rate of change of voltage of the offshore dc collection network, this scheme indirectly decreases the frequency deviation and the rate of change of frequency (RoCoF) of the onshore ac grid. Meanwhile, the scheme ensures the stability of wind turbines in alldc OWF during the frequency events. Considering a large number of wind turbines in the OWF, the corresponding optimization problem is solved in a distributed way using the alternating direction method of multipliers (ADMM), thus reducing the computational burden. Simulations including the performance comparison with droop control validate the effectiveness of this scheme.

Section: Control Principlementioning

confidence: 99%

Distributed model predictive control of all‐dc offshore wind farm for short‐term frequency support

Kou

IET Renewable Power Gen

et al. 2022

“…Measuring the farm-specific wind characteristics including mean wind speed, wind speed distribution (diurnal, seasonal, annual patterns), distribution of wind direction, short-term fluctuations, long-term fluctuations and wind shear profile are essential for determining the location of farm and turbines. This can strongly influence the performance of the wind turbines and thus the power generated by the wind farms 2 . Moreover, interactions among multiple turbines change the power generation efficiency of turbines.…”

Section: Introductionmentioning

confidence: 99%

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Rad

Bekker

Arashi

2022

Sci Rep

Wind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the empirical evidence as to whether the data are skew or multimodal or not. So far mixtures of von Mises within the directional statistics domain, are used for modeling wind direction to capture the multimodality nature present in the data. In this paper, due to the skewed and multimodal patterns of wind direction on different sites of the locations understudy, a mixture of multimodal skewed von Mises is proposed for wind direction. Furthermore, a Bayesian analysis is presented to take into account the uncertainty inherent in the proposed wind direction model. A simulation study is conducted to evaluate the performance of the proposed Bayesian model. This proposed model is fitted to datasets of wind direction of Marion island and two wind farms in South Africa and show the superiority of the approach. The posterior predictive distribution is applied to forecast the wind direction on a wind farm. It is concluded that the proposed model offers an accurate prediction by means of credible intervals. The mean wind direction of Marion island in 2017 obtained from 1079 observations was 5.0242 (in radian) while using our proposed method the predicted mean wind direction and its corresponding $$95\%$$ 95 % credible interval based on 100 generated samples from the posterior predictive distribution are obtained 5.0171 and (4.7442, 5.2900). Therefore, our results open a new approach for accurate prediction of wind direction implementing a Bayesian approach via mixture of skew circular distributions.

“…Particularly in large-scale wind power plants that require a wide area, SCADA is the basic configuration for operational reliability. With the enhancement of a communication capacity in SCADA, as in [3], recent studies, such as [4,5], have developed active response plans that include the Transmission System Operator (TSO) in terms of both real/reactive power. As for real power control, improving the response to limiting signals is a primary concern for the hierarchical control structure [6].…”

Section: Introductionmentioning

confidence: 99%

Development of Reactive Power Allocation Method for Radial Structure Wind Farm Considering Multiple Connections

et al. 2022

In recent years, the number of wind farms consisting of type 3 and type 4 wind turbines located within the distribution system has been growing rapidly. Wind turbines can be utilized as a continuous reactive power source to support the system voltage by taking advantage of their reactive power control capability. This paper aims to further develop the reactive power assignment strategy in order to minimize losses in wind farms described in the published paper. We introduce the method of reconfiguration and numbering to apply the algorithm to the wind farm structure and develop the previously-defined allocation ratio into two types of allocation ratios. The goal is to apply the loss minimization algorithm to a wind farm configuration with up to two wind turbines connected to one ring main unit (RMU). The proposed strategy reduces power loss and increases the real power flow in the wind farm by allocating reactive power to connected wind turbines taking into account the resistance value. The proposed allocation technique is validated in a Real Time Digital Simulator (RTDS)-based Hardware-in-the-loop Simulation (HILS) environment considering the Dongbok wind farm configuration in Jeju, South Korea. In the simulation, a Raspberry Pi acts as a wind farm controller sending a reactive power dispatch signal to each wind turbine via Modbus TCP/IP protocol. The simulation results mean that, applying the proposed algorithm, we can expect loss reduction effects in the wind farm.