Review of microgrid technology

Bs, Hartono; Budiyanto, Yan; Setiabudy, Rudy

doi:10.1109/qir.2013.6632550

Cited by 148 publications

(54 citation statements)

References 1 publication

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“…This paper follows the lumped load on a common transmission grid approach shown in [13], [15], [18]. However, this model differs in dividing the system load into two parts critical and controllable loads [19].…”

Section: Micro-grid Modelmentioning

confidence: 99%

A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Karim

Currie

Lie

2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

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Abstract-Balancing the active and the reactive power in a stand-alone micro-grid is a critical task. A micro-grid without energy storage capability is even more vulnerable to stability issues. This paper investigates a distributed secondary control to maintain the rated voltage in a stand-alone micro-grid. Here multiple machine learning algorithms have been implemented to provide the secondary control where a primary control scheme is insufficient to maintain a stable voltage after a sudden change in the load. The performance of the secondary control is monitored by a centralized system and in most of the cases it does not interfere. Based on different contingencies the proposed method would suggest different machine learning algorithms which are previously trained with similar data. The contingencies are based on an imbalance either in the active or in the reactive power in the system. It is considered that the distributed generators such as the wind and solar plants as well as the residential loads have some degree of randomness. The secondary control is invoked only in the events when primary droop control is insufficient to address the stability issue and maintain a desired voltage in the system.

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Section: Micro-grid Modelmentioning

confidence: 99%

A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Karim

Currie

Lie

2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

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“…As RESs are naturally uncertain and non-dispatchable, distributed energy storage devices (DESDs) connect to the MG networks along with RESs in most MG cases [1]. To make full use of distributed sources and increase operating efficiency, active power dispatch optimization for MGs has received considerable attention [2][3].…”

Section: Mg and Active-power Dispatch Optimizationmentioning

confidence: 99%

Active Power Dispatch Optimization for a Grid-Connected Microgrid with Uncertain Multi-Type Loads

Tang²,

et al. 2016

Preprint

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An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented. Keywords

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“…Micro-grids can build up grid flexibility and help alleviate grid disturbances as well as function as a grid resource for faster system response and recovery [1][2][3][4][5][6]. Micro-grids support a flexible and efficient electric grid by enabling the integration of growing deployments of distributed energy resources such as renewable like solar as shown in Figure1.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Resonant DC-DC converter for Microgrid Applications

Jaisudha¹,

Srinivasan²,

Gunasekaran³

2017

IJPEDS

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This paper proposes a non-isolated soft-switching bidirectional DC/DC converter for interfacing energy storage in DC microgrid. The proposed converter employs a half-bridge boost converter at input port followed by a LCC resonant tank to assist in soft-switching of switches and diodes, and finally a voltage doubler circuit at the output port to enhance the voltage gain by two times. The LCC resonant circuit also adds a suitable voltage gain to the converter. Therefore, overall high voltage gain of the converter is obtained without a transformer or large number of multiplier circuit. For operation in buck mode, the high side voltage is divided by half with capacitive divider to gain higher step-down ratio. The converter is operated at high frequency to obtain low output voltage ripple, reduced magnetics and filters. Zero voltage turn-on is achieved for all switches and zero current turn-on and turn-off is achieved for all diodes in both modes i.e., buck/boost operation. Voltage stress across switches and diode is clamped naturally without external snubber circuit. An experimental prototype has been designed, built and tested in the laboratory to verify the performance of the proposed converter.

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Review of microgrid technology

Cited by 148 publications

References 1 publication

A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Active Power Dispatch Optimization for a Grid-Connected Microgrid with Uncertain Multi-Type Loads

Bidirectional Resonant DC-DC converter for Microgrid Applications

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