Multi-Residential Energy Scheduling Under Time-of-Use and Demand Charge Tariffs With Federated Reinforcement Learning

Lee, Jae‐Hoon; Park, Jiyoon; Sim, Hoon-Seong; Lee, Hyun-Suk

doi:10.1109/tsg.2023.3251956

Cited by 4 publications

(1 citation statement)

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“…The software, written in Python, uses simple vehicle (mobility + specifications) and charging station (location + specifications) information as input and produces simulated electric vehicle energy usage and grid load profiles as output. This tool is called 'Grid-Sim' and is publicly available [13]. The application for this contribution is to allow stakeholders to make informed decisions regarding electrical upgrades for electrifying a vehicle fleet.…”

Section: Contributionmentioning

confidence: 99%

Grid-Sim: Simulating Electric Fleet Charging with Renewable Generation and Battery Storage

Giliomee,

Booysen

2023

WEVJ

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The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.

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Section: Contributionmentioning

confidence: 99%