Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties

Nikmehr, Nima; Ravadanegh, Sajad Najafi

doi:10.1016/j.ijepes.2015.11.094

Cited by 120 publications

(60 citation statements)

References 27 publications

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“…To conclude, the properties of the MGC are complex multiple objective, multi-time scale, multi-constraint and high-dimension in nature, hence, the management schemes to obtain optimal operational modes is still one of important research directions in the future [212].…”

Section: Energy Management and Economic Schedulingmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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Section: Energy Management and Economic Schedulingmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

View full text Add to dashboard Cite

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“…where i dre f 1 and i d1 are the reference and measured currents of MG 1 , respectively; k pc and k ic are the proportional-integral (PI) parameters of current control loop; ω 1 is the angular frequency of MG 1 .…”

Section: Control Diagram Of Convertermentioning

confidence: 99%

“…A stand-alone multi-microgrid (MMG) system where several microgrids (MGs) are interconnected has gained more attention recently [1][2][3][4][5]. Interconnecting multiple stand-alone MG systems can bring the economic benefit due to the ability of sharing surplus power in each MG.…”

Section: Introductionmentioning

confidence: 99%

“…The power of the DG should be generated rapidly to compensate for the load disturbance. Figure 11 shows the DG output power of two MGs in Case 1 where the load disturbance is only in the MG 1 . In the case of the MMG system with the single frequency control in the BTB converter, the DG 1 compensates fully for the load change whereas the DG 2 output power is maintained constant during the load disturbance in MG 1 .…”

mentioning

confidence: 99%

“…Figure 11 shows the DG output power of two MGs in Case 1 where the load disturbance is only in the MG 1 . In the case of the MMG system with the single frequency control in the BTB converter, the DG 1 compensates fully for the load change whereas the DG 2 output power is maintained constant during the load disturbance in MG 1 . It is observed that the MG 2 does not support the load disturbance in the MG 1 system if the single frequency control scheme is used in the BTB converter.…”

mentioning

confidence: 99%

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Multi-Frequency Control in a Stand-Alone Multi-Microgrid System Using a Back-To-Back Converter

2017

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Abstract:A stand-alone multi-microgrid (MMG) system can be formed by connecting multiple stand-alone microgrids (MGs). In the stand-alone MMG system where the frequencies of each MG system are different, a back-to-back (BTB) converter can be used for interconnecting the adjacent MG system. The frequency control performance of the MMG system can be improved by designing the suitable controller of the BTB converter. This study proposes a multi-frequency control in the BTB converter to improve the performance of frequency regulation in the MMG system. Autonomous power sharing between each MG system is achieved by using the proposed multi-frequency control. The stand-alone MMG system where two stand-alone MG systems with different nominal frequencies are interconnected using the BTB converter is simulated in this study to show the feasibility of the proposed multi-frequency controller. Each stand-alone MG system consists of an inverter-based distributed generator (DG) that uses a grid-forming converter with a conventional frequency droop controller. The inverter-based DG is responsible for the primary frequency control in each MG system. To show the effectiveness of the proposed multi-frequency control, a comparison study of the multi-frequency control and the single frequency control is presented in this study. Simulation results show that the system stability can be improved by using the proposed multi-frequency controller.

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Reliability‐constrained optimal design of multicarrier microgrid

Amir

Azimian

Razavizadeh

2019

Int Trans Electr Energ Syst

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Summary The unprecedented penetration of distributed generation in distribution energy networks provides utilities with a unique opportunity to manage portions of networks as microgrids (MGs). The implementation of an MG may offer many benefits, such as capital investments deferral, reduction of greenhouse gas emissions, improvement in reliability, and reduction in network losses. Future energy networks will contain various forms of energy which are acquired by mixing several sources and energy storages in the concept called multicarrier microgrid (MCMG). In order to draw the most effective performance from MCMG systems, appropriate design and operation are essential. This paper represents a compound co‐optimization strategy to find the best type and size of components and the associated optimum dispatch in a grid‐tied community MCMG implementing reliability criteria. Here, the required level of reliability is handled within the optimization process to fulfill multiple demands. The mixed‐integer nonlinear programming (MINLP) technique of general algebraic modeling system (GAMS) and the genetic algorithm of MATLAB software are utilized to solve the co‐optimization problem. Additionally, a contemporary time‐based demand response program is modeled to reshape the load curve, as well as prevent the undue use of energy in peak hours. Eventually, the proposed strategy is applied to a test case to select the best components while respecting reliability restrictions. Numerical simulations prove the effectiveness of the proposed expansion planning.

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Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties

Cited by 120 publications

References 27 publications

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Multi-Frequency Control in a Stand-Alone Multi-Microgrid System Using a Back-To-Back Converter

Reliability‐constrained optimal design of multicarrier microgrid

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