The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.

This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.

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The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.

1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)): * MPPT_IncCond=Simulink.Variant('MPPT_MODE==1') MPPT_PandO=Simulink.Variant('MPPT_MODE==2') MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3') MPPT_MODE=1 Without_FAULT=Simulink.Variant('FAULT_MODE==1') Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2') Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3') Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4') Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5') Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6') FAULT_MODE=1* 3. For more details about the Variant Subsystems see the Matlab Documentation Center: https://www.mathworks.com/help/simulink/variant-systems.html or https://www.mathworks.com/help/simulink/examples/variant-subsystems.html

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The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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Multiwinding-Transfomer-based (MTB) DC-DC converter did emerge in the last 25 years as an interesting possibility to connect several energy systems and/or to offer higher power density because of the reduction of transformer core material and reduction of power converter stages. MTB DC-DC converters can be considered as an interesting compromise between non-modular and a modular DC-DC converter since they are themselves modular in the construction.

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This provides the code and data used in the paper "Optimal EV Scheduling in Residential Distribution Networks Considering Customer Charging Preferences" by Mailys Le Cam and Barry Hayes.

Some material has been adapated from the OpenDSS help files: http://smartgrid.epri.com/SimulationTool.aspx Some data has been taken from the IEEE test feeders archive: http://sites.ieee.org/pes-testfeeders/

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Wide area control (WAC) in smart grid requires low-latency communications across the grid to transfer various control commands and Phasor Measurement Unit (PMU) measurements. Smart grid communications are generally implemented between multiple control centers, transformer substations and control centers, and power plants and control centers.

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The power flow is usually formulated by nonlinear equations and may present multiple solutions. However, most of these solutions do not represent a practical situation but are mathematical findings. Remarkably, in unbalanced multiphase systems with impedance-grounded loads, a phenomenon can occur where two or more solutions may especially show practical significance. These solutions are called operationally-stable solutions (solutions which for a given loading level the nodal voltages, currents, and losses are feasible) and may be obtained in Distribution Systems (DS).

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An emission rate-based carbon tax is applied to fossil-fueled generators along with a Smart Grid resource allocation (SGRA) approach. The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid.

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*This datasheet is being updated progressively to provide more details.

**This datasheet provides the phasor measurement data in actual power systems.**

These PMU data were recorded during a Low Frequency Oscillation incident and a Short Circuit Incident, respectively.

These PMU data are used for the studies in wide-area control systems (WACS) and PMU data compressions.

**Please cite this datasheet and the papers in your work if they help.**

## Refer to the documentation file for detailed information.

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The uploaded data file is a part of data used or generated by a model proposed in a paper entitled: “A New Dynamic Stochastic EV Model for Power System Planning Applications”. The proposed model in this paper consists of two sub-models which are the travel behavior and the battery depletion sub-models. The proposed model is taken into consideration the different trip purposes, starting and ending trip times, as well as the corresponding battery depletion. The outcomes from the travel behavior sub-model are the starting times, the ending times and the trips distances.

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