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.

 

Instructions: 

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.

Instructions: 

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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Nowadays, distribution grids are undergoing massive penetration of renewable energy sources (RESs), especially rooftop photovoltaic solar panels (PVs) and small wind turbines (WTs), which lead to a greater ratio of fluctuating generation. As a result, the inherited problems of distribution grids, such as poor voltage profile and high power losses, become even worse. To operate a grid in the optimal mode, we propose a communication- and model-free algorithm which exploits the capabilities of the inverters to control the reactive power output.

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The dataset is supplementary material for the research article 'Techno-economic assessment of grid-level battery energy storage supporting distributed photovoltaic power' published in IEEE Access in October 2021. The dataset corresponds to the annual timeseries at 1-minute resolution (525,600 steps) of the per-unit profiles used for the electric load and the per-unit power output of 8 PV systems.

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Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services.

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These measurements were taken at the point of common coupling using the power quality analyzer PQ-Box 200, where about 30 EV chargers are installed and exploited by the utility. For this reason, this data set only considers the charging behavior of the vehicles employed by the enterprise, namely the Renault Kangoo ZE and Renault Zoe. The period under consideration starts on 5.11.2018 and ends on 07.01.2020. Because of the large amount of data, values with a time interval of 10mins are extracted and used in this data set.

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This is the simulated post fault voltage magnitude transient data from the IEEE 118-bus system. The data is collected from the TSAT software (DSATool). This transient data is used to mimic the PMU voltage measurements. This file has three folders. The first folder 'Train_Test' has the training and testing data. The dimension of the training data 'Pro2_train_v.pkl' and testing data 'Pro2_test_v.pkl' are (8000x25x20) and (2000x25x20), respectively. Here the number of training and testing samples is 8000 (4000 stable, 4000 unstable) and 2000 (1000 stable and 1000 unstable).

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In our study, datasets of two simulators, namely phasor-based simulator and hybrid-type simulator are used. In the hybrid environment, first, the outputs of the phasor-based simulator are converted to instantaneous waveforms, then based on instruction, distortions and noises are added (superimposed) to these waveforms, and finally, the distorted waveforms are fed to the detailed model of PMUs simulated in EMT domain. Outputs of both simulators can be found in the submitted file.

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This file contains one-year measurements of demand (average 11 kWh/day), Electric vehicle charging (3 kW rating), and PV generation (3.3 kWp) for a household in London, UK. 

This dataset is associated with the following paper: 

A. A. R. Mohamed, R. J. Best, X. A. Liu and D. J. Morrow, "A Comprehensive Robust Techno-Economic Analysis and Sizing Tool for the Small-Scale PV and BESS," in IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2021.3107103.

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