Smart Grid

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.

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Smart grid technologies are deepening the interdependence of electric power and communication systems, but that interdependence is difficult to quantify. In the case of microgrids, communication systems can be essential to maintaining stability during islanded operations. Though many power system studies assume the presence of perfect communication networks, detailed modelling of power and communication systems for dynamic studies of microgrids is rare.

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992 Views

Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 

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1299 Views

An optimization model with heuristic algorithm is implemented to optimize the virtual resistances of droop control for the grid-connected converters of dispatchable units, such that the power flow can be regulated. The performances of the proposed strategy are evaluated by the case studies of a 12-bus 380 V DC microogrid using matlab and a 32-bus 380 V DC microgrid using a real-time digital simulator.

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516 Views

We introduce a novel dataset containing a total of 61 distinct HEAs. The proposed appliances (e.g. fans, fridges, washers, etc.) are of different kinds, ages, brands and power
levels. They have been recorded in steady-state conditions in a French 50 Hz electrical grid. The measurement setup consists of an AC current probe (E3N Chauvin Arnoux) with a 10 mV/A sensitivity and a differential voltage probe with

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1239 Views

This dataset includes high-resolution (1 s) power and reactive power profiles of household appliances. The dataset consists of ground truth data from a European household, laboratory measurements and few artificial created data. Specifically, the dataset includes data for TV, washing machine, toaster, iron, hairdryer, dish washer, PC, refrigerator, air-conditioner unit, range, dryer, heat pump (different modes of operation), BEV, water heater, light bulb and always-on load profiles.

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