Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

Feng, Chunhua; Li, Chaoshun; Li, Chang; Mai, Zijun; Wu, Chunwang

doi:10.1155/2021/5692621

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…However, with the increasing capacity of wind and solar power, the issue of abandoning wind and solar energy is unavoidable, and conventional hydropower cannot effectively store the electricity generated from abandoned wind and solar power (Jin et al, 2023). Pumped storage, as a crucial technology for enhancing the absorption level of new energy, has developed rapidly in China (Garcia-Gonzalez et al, 2008;Feng et al, 2021;Huang et al, 2023;Liu et al, 2023;Wang et al, 2023). How to use pumped storage technology efficiently and economically to promote the absorption of new energy is an important direction of current research.…”

Section: Introductionmentioning

confidence: 99%

Research on joint dispatch of wind, solar, hydro, and thermal power based on pumped storage power stations

Jia,

Zhang,

Zhou

et al. 2024

Front. Energy Res.

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In the context of energy conservation and emission reduction, the integration and consumption of large-scale wind and solar resources is an inevitable trend in future energy development. However, with the increase of wind and solar grid-connected capacity, the power system also requires more flexible resources to ensure safe operation. To enhance the economic efficiency of the complementary operation of wind, solar, hydro, and thermal sources, considering the peak regulation characteristics of different types of power sources, the study of the joint dispatch model of complementary utilization of various generation methods like wind, solar, hydro, thermal, and storage is of great significance for the economic dispatch of the power system. Existing studies mainly focus on traditional thermal power units or hydropower units, with few studies investigating the impact of pumped-storage power stations on the absorption of renewable energy. Firstly, this paper introduces the composition and function of each unit under the research framework and establishes a joint dispatch model for wind, solar, hydro, and thermal power. Secondly, the paper elaborates on the objective function within the model, mainly covering the operating costs of thermal power units, hydropower units, pumped storage, wind and solar units, the cost of discarding new energy, and the cost of load shedding. Subsequently, the paper presents the constraints of the system model, mainly the feasible boundaries for the operation of each unit within the system. Finally, The results of the calculations show that the proposed model reduces the total operating cost by 12% and the power abandonment rate by 82% compared to the conventional model. It is shown that the proposed model can not only significantly improve the economic efficiency of the system operation but also reduce the level of energy waste and load shedding, effectively enhancing the degree of energy utilization within the system.

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Section: Introductionmentioning

confidence: 99%

Research on joint dispatch of wind, solar, hydro, and thermal power based on pumped storage power stations

Jia,

Zhang,

Zhou

et al. 2024

Front. Energy Res.

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“…The carbon neutrality target by countries worldwide has raised the demand in combining the power system with energy storage units, in order to buffer the system instability brought by high penetration of the renewable energy system (Hunt et al, 2020;Feng et al, 2021). With the ability of quick responding to changes in the amount of power running through the grid, pumped storage hydropower (PSH) is considered as the primary choice for ensuring grid reliability.…”

Section: Introductionmentioning

confidence: 99%

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

Lu¹,

Wei²,

Zhu³

et al. 2022

Front. Energy Res.

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Hydroelectric energy storage, that is, pumped storage hydropower (PSH) is considered as the essential solution for grid reliability with high penetration of renewable power, due to its advantages of cost-effectiveness for grid energy storage as well as supporting ancillary services. However, the operation modes of the main transformer unit in PSH are way more complex than the conventional power transformer, which makes the condition monitoring and fault detection of PSH becoming a technical challenge. In this article, an operation status recognition model of main transformers in PSH based on artificial visualization of mechanical vibration signals and deep learning is proposed. The vibration signals on a series of 500 kV/360 MVA main transformers of PSH are monitored periodically by contacting sensor arrays. These vibration signals are processed into nephograms by using linear interpolation fitting and 1D to 2D data mapping. A deep learning method based on the convolutional neural network (CNN) is used to classify nephograms obtained under different operation modes, that is, no load, full load, DC bias, and short circuit. The proposed status prediction algorithm was trained and tested through 150 sets of vibration nephogram samples, which ensures the feasibility of the nephogram generation method and the performance of the classifier. The testing results show that the overall status prediction accuracy for the proposed algorithm achieves 89.7% when the network structure is optimized. It is indicated that the mechanical vibration of the main transformer has a pattern matching relationship with the operating state of PSH. In practice, the operating status of PSH can be diagnosed remotely by embedded IoT sensors; the health index of PSH can also be estimated by weighed analysis of the changing trend of vibration data obtained in the life cycle.

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“…Liang et al proposed an MPC strategy based on enumeration to determine the optimal switching time of a PSS with the objectives of increasing operational flexibility and promoting frequency regulation (Liang et al, 2019). Feng et al proposed a new adaptive MPC strategy and conducted a simulation experiment to verify the superiority of this strategy in terms of adjusting the voltage and load and suppressing frequency oscillation (Chen Feng et al, 2021). Although MPC has the above advantages, it still has certain disadvantages when compared to traditional control methods.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Model Predictive Control Method for Pumped Storage Systems

et al. 2021

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Integrated systems required for renewable energy use are under development. These systems impose more stringent control requirements. It is quite challenging to control a pumped storage system (PSS), which is a key component of such power systems. Because of the S-characteristic area of the PSS pump turbine, traditional proportional-integral-derivative (PID) control induces considerable speed oscillation under medium and low water heads. PSSs are difficult to model because of their nonlinear characteristics. Therefore, we propose a machine learning (ML)-based model predictive control (MPC) method. The ML algorithm is based on Koopman theory and experimental data that includes PSS state variables, and is used to establish linear relationships between the variables in high-dimensional space. Subsequently, a simple, accurate mathematical PSS model is obtained. This mathematical model is used via the MPC method to obtain the predicted control quantity value quickly and accurately. The feasibility and effectiveness of this method are simulated and tested under various operating conditions. The results demonstrate that the proposed MPC method is feasible. The MPC method can reduce the speed oscillation amplitude and improve the system response speed more effectively than PID control.

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Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

Cited by 9 publications

References 38 publications

Research on joint dispatch of wind, solar, hydro, and thermal power based on pumped storage power stations

Research on joint dispatch of wind, solar, hydro, and thermal power based on pumped storage power stations

Operation Risk Assessment of Hydroelectric Energy Storage Based on Data Visualization and Convolutional Neural Network

A Machine Learning-Based Model Predictive Control Method for Pumped Storage Systems

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