Combined control and power hardware in‐the‐loop simulation for testing smart grid control algorithms

Μανιατόπουλος, Μάριος; Lagos, Dimitris T.; Kotsampopoulos, Panos; Hatziargyriou, Nikos D.

doi:10.1049/iet-gtd.2016.1341

Cited by 66 publications

(46 citation statements)

References 30 publications

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“…The controller parameters and the power‐sharing coefficients are optimized through particle swarm optimization (PSO) solver to enhance the stability of the microgrid. Testing the chain of control algorithms is discussed in Maniatopoulos and Lagos and shown in Figure . As shown in Figure , testing of the microgrids usually starts with pure software simulation to analyze general control algorithm and logics.…”

Section: Real‐time Modeling and Simulationmentioning

confidence: 99%

A review on real‐time simulation and analysis methods of microgrids

Ahmadiahangar

Rosin

Niaki

et al. 2019

Int Trans Electr Energ Syst

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Summary Microgrid is a recently developed concept for future power systems. The main characteristics of the microgrid are the capability of integration of renewable energy sources and the ability to operate in two grid‐connected and islanded modes. A significant challenge of microgrid implementation is developing comprehensive control methods to ensure efficient, stable, and reliable operation. Real‐time studies are a promising approach in this case. In this paper, various real‐time energy management approaches have been thoroughly explained following a new categorization of them. A significant literature review of real‐time simulation and modeling methods has also been presented. A review of different applications of hardware‐in‐the‐loop testing of the microgrid is included in the present study. Finally, a discussion on demand for further research has been made. The presented work is organized to allow a reader to understand the importance of real‐time studies of microgrids and highlight trends in literary works without delving deeply into each one.

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Section: Real‐time Modeling and Simulationmentioning

confidence: 99%

A review on real‐time simulation and analysis methods of microgrids

Ahmadiahangar

Rosin

Niaki

et al. 2019

Int Trans Electr Energ Syst

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“…Additionally, EM lies within the focus of this institute as well as low‐voltage grids operated by multiagent systems . Smart grid controllers and other hardware are tested using their power‐hardware‐in‐the‐loop (PHIL) simulation environment and their controller‐hardware‐in‐the‐loop (CHIL) simulation environment …”

Section: Smart Grid Labs Descriptionmentioning

confidence: 99%

Smart grid lab research in Europe and beyond

et al. 2019

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Summary In this survey, the objective is to identify current trends in the smart grid research by exploring the work carried out in numerous smart grid labs worldwide. For this purpose, a large number of smart grid labs are identified, and a short description of their activities is given. Fifty‐eight out of the 75 identified labs are located in Europe. Smart grid research is divided into categories, which represent popular topics of research in the field. The predominant category of research is identified to be generation and distributed energy resources (Gen & DER) with 91% of the labs conducting research in this field. Aggregated information is presented regarding the labs, providing a clear idea of the topics of research carried out. Connections between different topics of research are presented, which reveal synergies or collaboration gaps among various smart grid topics. Grid management and Gen & DER and energy storage and Gen & DER have been found to be popular combinations of topics with 55 labs active in both, respectively. In addition, we provide insights on the entities at which research is targeted and consider the evolution of publications produced by the labs on the different categories. An overall increase in publications was observed over the past 11 years in virtually all categories of smart grid research with the most published scientific papers in Gen & DER and electromobility. Collaborations between research institutes have been analyzed, pointing out existing joint research conducted and the huge potential to explore synergies between institutes further. Our work is useful in order to identify the smart grid areas where research is focusing on. This gives a clear picture of potential synergies between labs for knowledge sharing and enhancing their research efforts.

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“…A comprehensive summary of PHIL is presented in [26], and a couple of examples of the recent use of PHIL are provided in [27,28]. A representation of the laboratory setup used is depicted in Figure 11.…”

Section: Laboratory Setupmentioning

confidence: 99%

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

Abbood

Benigni

2018

Modelling and Simulation in Engineering

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We present a data-driven modeling (DDM) approach for static modeling of commercial photovoltaic (PV) microinverters. The proposed modeling approach handles all possible microinverter operating modes, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. The approach is based on Artificial Neural Network (ANN) and Fast Fourier Transform (FFT). To generate the data used to train the model, a Power Hardware in the Loop (PHIL) approach is applied. Instantaneous inputs-outputs data are collected from the terminals of a commercial PV microinverter at time domain. Then, the collected data are converted to the frequency domain using Fast Fourier Transform (FFT). The ANNs that are the core of the DDM are developed in frequency domain. The outputs of the ANNs are then converted back to time domain for validation and use in system level simulation. The comparison between measured and simulated data validates the performance of the presented approach.

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Combined control and power hardware in‐the‐loop simulation for testing smart grid control algorithms

Cited by 66 publications

References 30 publications

A review on real‐time simulation and analysis methods of microgrids

A review on real‐time simulation and analysis methods of microgrids

Smart grid lab research in Europe and beyond

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

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