A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles

Wu, Chuanshen; Gao, Shan; Liu, Yu; Song, Tiancheng E.; Han, Haiteng

doi:10.1016/j.renene.2020.08.137

Cited by 67 publications

(23 citation statements)

References 27 publications

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“…The upper and lower layer ESSs are used for shifting and smoothing wind energy, respectively. The microgrid is connected to the power grid through a transmission line [24]. The two-layer SMPC controller is used to optimize the charge and discharge power of the hybrid ESS to obtain a suitable power that meets the requirement of the microgrid [25].…”

Section: System Structure Figure 1 Wind Power and Hybrid Ess Sysmentioning

confidence: 99%

Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach

Gao

Liu

et al. 2021

IEEE Access

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The utilization of energy storage system (ESS) is an effective method for dealing with the randomness and variability of wind power. Therefore, improving the coordination between ESS and wind power is a direction worthy of research. This study develops a two-layer stochastic model predictive control (SMPC) method for wind power smoothing on different time-scales. The proposed optimization framework smooths wind energy by adjusting the charge and discharge power of hybrid ESSs; moreover, it combines the SMPC approach and the chance constraints to address the uncertainties of wind energy. Furthermore, the previous states are taken into account, in addition to states within the rolling time horizon, to obtain optimal control of the hybrid ESSs, and capacity planning for hybrid ESSs is conducted simultaneously. The numerical results obtained through amplitude-frequency simulation comparison prove that the proposed method is superior to the conventional method in terms of minimizing the frequency fluctuation of wind power and the size of the ESS.

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Section: System Structure Figure 1 Wind Power and Hybrid Ess Sysmentioning

confidence: 99%

Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach

Gao

Liu

et al. 2021

IEEE Access

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“…A closed-loop distributed model predictive control (DMPC) was designed in [11] to address the intra-hour economic dispatch problem, which can reduce potential variations in the determined generation schedules. In [12], the results of multiple uncertainty sampling were used to simulate the future characteristics of the converged EV, and a two-layer control framework is proposed to optimize it. As a real-time strategy with multi-time scale, the flexibility of MPC provides a framework for combining with multiple methods of optimization.…”

Section: Introductionmentioning

confidence: 99%

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Zhao

Guo

Luo

et al. 2022

IEEE Trans. Smart Grid

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A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multimicrogrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of singlemicrogrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

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“…On the other hand, this electricity increase could create negative impacts on the grid when recharging strategies remain unscheduled, concentrating the electrical consumption in peak demand hours [11][12][13][14]. The use of microgrids with integration of renewable sources to recharge EVs emerges as a solution to cope with the two previously mentioned difficulties [15]. First, the low CI of the renewable sources would decrease the CO2 emissions generated during the electricity generation stage.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations

et al. 2021

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A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles

Cited by 67 publications

References 27 publications

Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach

Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations

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