Cooperative control for self-organizing microgrids and game strategies for optimal dispatch of distributed renewable generations

Maknouninejad, Ali; Lin, Wei; Harno, Hendra G.; Qu, Zhihua; Simaan, Marwan A.

doi:10.1007/s12667-011-0048-3

Cited by 33 publications

(16 citation statements)

References 26 publications

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“…4 and Table 1. Data exchange rate (so-called communication frequency, [11,20,22]) is 5 [Hz], and communication links are assumed to be ideal [5,10]. In this case study, it is assumed that the SMG is working in grid-connected mode and already reached its steady-state in normal condition.…”

Section: Case Study and Simulation Resultsmentioning

confidence: 99%

“…• Power sharing: The adopted power sharing method in this work is the Fair Utilization method in which all DGs generate power at the same ratio [10][11][12]. The Fair Utilization method is mathematically stated as…”

Section: Control System Objectivesmentioning

confidence: 99%

“…Voltage Source Inverters (VSIs) are widely used for connecting EI-DGs to the grid [5,8,9]. Each inverter is equipped with a control system, which pursues the grid control strategy [10][11][12]. The control strategies presented in the literature are broadly divided into two main categories based on their communication requirements:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed cooperative control system for smart microgrids

Hamidi

Livani

Hosseinian

et al. 2016

Electric Power Systems Research

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a b s t r a c tThis paper presents a decentralized control method for Electronically Interfaced Distributed Generations (EI-DGs) that controls their output frequencies, power generations, and voltages during grid-connected, islanding, and synchronizing modes. The proposed control system utilizes droop control to quickly balance generation and demand after sudden disturbances. It utilizes distributed cooperative control to stabilize the system frequency and voltage, and also to distribute the generation among DGs. Furthermore, the proposed control system adjusts the frequency and voltage to reconnect the microgrid to the main grid by utilizing only local measurements from the neighboring DGs. The proposed control system is verified on a modified IEEE Standard 399-1997 test system using MATLAB/SIMULINK and the advantages and disadvantages of the proposed method are discussed according to the results.

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Section: Case Study and Simulation Resultsmentioning

confidence: 99%

Section: Control System Objectivesmentioning

confidence: 99%

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Distributed cooperative control system for smart microgrids

Hamidi

Livani

Hosseinian

et al. 2016

Electric Power Systems Research

View full text Add to dashboard Cite

show abstract

“…In this case, power flows between the grid and the microgrid should be regulated by tertiary control at the point of common coupling (PCC). This control is used in the grid-connected mode to adjust the power flow of a microgrid [22].…”

Section: -mentioning

confidence: 99%

Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages

et al. 2017

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Development of renewable energies and DC loads have led microgrids toward the creation of DC networks. The predictions show that the hybrid microgrids will be used widely in the future. This article has studied the voltage stability in the presence of sources of energy storage in AC/DC hybrid networks. However, because the different dynamics of hybrid networks applying centralized and distributed controllers will be faced with different problems, in this study, a multi-agent control for the microgrid has been used. A new structure referred to here as an event-driven microgrid control management (EDMCM) has been developed to control the microgrid. This method increases response speed and accuracy of decision making. Hybrid Network Simulation results confirm the validity of the developed model.

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“…However, each of the aforementioned techniques has its own drawbacks. Nevertheless, hybrid energy systems are used for overcoming intermittency, uncertainty and low availability of each renewable energy source which makes the system more reliable [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

2015

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Microgrid is a low voltage electrical network with distributed generations, energy storage devices and controllable loads. This paper utilizes artificial neural network (ANN) to predict the optimum voltages in order to extract the maximum power and increment the efficiency of photovoltaic system. In this regard, the optimum voltages are achieved by the genetic algorithm (GA). Then these optimum values are used in ANN method. The results of ANN-GA is compared with the other methods that verified the proposed method with high accuracy which can track the maximum power point (MPP) under different insolation and temperature circumstances and also, meet the load demand with less fluctuation around the MPP.; also it can increase the convergence speed to achieve the MPP. As well as, the evaluation of fuzzy logic controller (FLC) in comparison with the PI controller in pitch angle of wind turbine (WT) is carried out. In order to control the output power of wind turbine, by implementing the wind speed and active power as inputs of FLC, it has faster responses, smoother power curves, less oscillation than aforementioned methods which lead to improve the dynamic responses of WT. The models are developed and applied in the Matlab/Simulink program.

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Cooperative control for self-organizing microgrids and game strategies for optimal dispatch of distributed renewable generations

Cited by 33 publications

References 26 publications

Distributed cooperative control system for smart microgrids

Distributed cooperative control system for smart microgrids

Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages

Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

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