Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

Energies

Meng Xiong²  Feng Gao¹  Kun Liu²  Siyun Chen²  Jiaojiao Dong² 

[1] State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi’an Jiaotong University, Xi’an 710049, China;

关键词: wind farm;    energy storage;    MPC;    hierarchical control;    power scheduling;    smoothing fluctuation;    coordination of energy storage;    hybrid power system;    optimal energy manage;   

DOI  :  10.3390/en8088020

来源: mdpi

【 摘 要 】

Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

【 授权许可】

CC BY   
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