Power and Energy
This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the independent per-phase control capability of PV three-phase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered. This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks.
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We provide here a simple but fully functional platform for making peer to peer energy trading using the tobalaba network with block chain
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This is the datasheet for the examined 30-unit test system including 14 DG (indexed from 1 to 14), 4 IG (indexed from 15 to 18), 4 FD (indexed from 19 to 22), 4 ID (indexed from 23 to 26) and 4 ES units (indexed from 27 to 30), and the datasheet for the plug-and-play test system with 4 new plugged in DER including 2 DG (indexed from 31 to 32) and 2 FD (indexed from 33 to 34) .
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Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.
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This simulation model is a transmitting system for electromagnetic sounding based on tri-state boost converter.
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Please see the files.
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Please see the document.
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Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility.
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This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.
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