A self-interested distributed economic model predictive control approach to battery energy storage networks

2019 ◽  
Vol 73 ◽  
pp. 9-18 ◽  
Author(s):  
Ruigang Wang ◽  
Xinan Zhang ◽  
Jie Bao
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Economic Model ◽  
Battery Energy Storage ◽  
Control Approach ◽  
Economic Model Predictive Control ◽  
Storage Networks ◽  
Battery Energy

scholarly journals Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2285 ◽  
Author(s):  
Yantao Liao ◽  
Jun You ◽  
Jun Yang ◽  
Zuo Wang ◽  
Long Jin
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Current Control ◽  
Multilevel Converters ◽  
Battery Energy Storage ◽  
Circulating Current ◽  
Modular Multilevel Converters ◽  
Ac Current ◽  
Battery Energy

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Mohamed M. Alhneaish ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control

Abstract Wind power intermittency represents one of the major challenges facing the future growth of grid-connected wind energy projects. The integration of wind turbines and energy storage systems (ESS) provides a viable approach to mitigate the unfavorable impact on grid stability and power quality. In this study, an economic model predictive control (MPC) framework is presented for an integrated wind turbine and flywheel energy storage system (FESS). The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a baseline wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is investigated. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load with negligible effect on the wind energy harvested.

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