Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration

Yang, Lin; Zhang, Cihang; Tang, Yong; Zhong, Wuzhi; Zhao, Yongning; Lu, Peng; Zhai, Bingxu; Haibo, Lan; Li, Zhi

doi:10.1109/tpwrs.2019.2914277

Cited by 88 publications

(17 citation statements)

References 33 publications

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“…Furthermore, the ADMM algorithm with fast convergence speed was adopted to solve the optimization problem, which effectively reduces the amount of computation. The active power scheduling strategy with multiple time scales, which provides good prediction accuracy was studied in Ye et al's work [76,77].…”

Section: Hierarchical Mpcmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

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Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

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Section: Hierarchical Mpcmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

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“…In a power system for a large area, for example, a power transmission system, the typical objective of their management may be the implementation of load frequency control, which is realized by appropriately operating the generators [134,135]. Other objectives of the supply-side EMS in a broad sense include the reduction of operational cost, for example, in generator dispatch task [136,137] or in the operation planning of multicarrier energy system consisting of integrated electricity and natural gas systems [138] and grid code compliance of variable renewable energy sources [139].…”

Section: Control Targets Of Emss An Objective Of Emssmentioning

confidence: 99%

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Fujimoto

Ishii

Hayashi

2020

IEEJ Transactions Elec Engng

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Smart cities will be an important component for the construction of a sustainable future society. This paper focuses on the functionalities of smart cities from the perspective of electric power systems and reviews recent works on the various types of energy management systems (EMSs), particularly those focusing on the interaction among systems distributed in a city. Furthermore, this paper describes the idea of a platform for evaluating cooperative EMS technologies distributed in a city. The related research and demonstration of the EMS technology in real cities that were evaluated using the proposed platform are presented. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…In [14], the proposed predictive scheme can be easily integrated with a complementary control block to provide grid frequency support. However, there are still challenges in the accuracy and coordination of wind power forecasting and active power and frequency control strategies [15][16][17][18][19]. Some nonlinear control algorithms have been applied to the qZSI system, including neural network control, fuzzy control, and sliding mode control of the BHO and PSO methods to adjust PI-MR controller parameters.…”

Section: Introductionmentioning

confidence: 99%

Variable weight coefficient MPC control strategy for qZSI‐VSG wind power grid‐connected system

Zhang

Liu

Luo³

et al. 2023

IET Renewable Power Gen

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Quasi‐Z source inverters (qZSI) can overcome the disadvantages of conventional wind power systems, because of achieving the balanced DC‐link voltage by using its boost ability. The majority of currently used control methods for conventional inverters are susceptible to parameter changes, and the qZSI are controlled by finite switching sequence model predictive control (FSS‐MPC). In this paper, a Variable weight coefficient Model Predictive Control (V‐MPC) strategy is proposed. First, based on the optimal scheduling algorithm, according to the priority of the controlled object. The proportioning weights of the global feasible solution and the local optimal solution in the algorithm are coordinated. Then, using the equality‐constraints quadratic programming method, according to the relationship between the system frequency and the reference value, increase the weight of the qZSI algorithm or increase the proportion of VSG control to improve the ability to suppress frequency fluctuations. It can give full play to the short‐term power support role of the virtual synchronous wind turbine. The modeling and testing findings validate the suggested control strategy. When compared to the conventional finite control set model predictive control (FCS‐MPC), the V‐MPC can minimize the big store inductance current ripple and have lower THD in the load currents waveform.

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Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration

Cited by 88 publications

References 33 publications

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Variable weight coefficient MPC control strategy for qZSI‐VSG wind power grid‐connected system

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