Repetitive Learning Frequency Control for Energy Intensive Corporate Microgrids subject to Cyclic Batch Loads

Ramlal, Craig J.; Singh, Arvind; Rocke, Sean

doi:10.1109/isgt-europe47291.2020.9248968

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In this paper, the discrete adjustable load is analyzed and studied [26]. Without losing generality, the mathematical model of a smelting furnace is used to represent the discrete adjustable load [27].…”

Section: Model Of Energy-intensive Load Dispatchingmentioning

confidence: 99%

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

Zhang

et al. 2021

Processes

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Due to the uncertainty of wind power output, the congestion of wind power has become prominent. Exactly how to improve the capacity of wind power consumption has become a problem that needs to be studied urgently. In this paper, an energy storage system and energy-extensive load with adjustable characteristics are used as an important means of consuming wind power. Firstly, we analyze the reasons for the congestion according to the characteristics of wind power output, and establish a model of the grid’s ability to integrate wind power based on the concept of a wind power admissible interval. Secondly, we analyze the energy-extensive load regulation characteristics and establish an energy-extensive load dispatch model. Thirdly, on the basis of considering the energy-extensive load and energy storage system adjustment constraints, a bi-level optimization model is established. The upper level determines the configured capacity of the energy storage system with the goal of minimizing the total economic investment of the energy storage system, and the lower level coordinates the dispatching with the goal of maximizing wind power consumption and minimizing system operating costs. Finally, a certain region is taken as an example to verify the validity of the proposed method.

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Section: Model Of Energy-intensive Load Dispatchingmentioning

confidence: 99%

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

Zhang

et al. 2021

Processes

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“…One of the methods used to control the load frequency is the robust control method [16,17], H-Infinite Control (H ∞ ) [18]. In [19,20], some methods have been presented based on the evolutionary algorithm and the iteration method.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

et al. 2022

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In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results.

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Repetitive Learning Frequency Control for Energy Intensive Corporate Microgrids subject to Cyclic Batch Loads

Cited by 2 publications

References 18 publications

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

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