SAE International MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties 2015-01-0305

Description
The increasing number of Plug-in Electric Vehicles (PEVs) impacts the power grid due to their high demand in power and energy, and uncertainties in the charging behavior. Typical PEVs are charged single-phase up to 32 A (7.2-kVA) or tri-phase up to 32 A (22.0- kVA). Both charging technologies have to be discussed in order to determine their impact on planning and operating of low-voltage distribution grids to assure a reliable and stable PEV charging. Traditional grid planning and analysis methods, which average and evenly distribute PEV loads on the distribution grid, fail in providing a realistic answer about the grid capacity to charge PEVs. The question; How many PEVs can be charged simultaneously on a distribution grid remains unanswered. Therefore, this paper describes a novel methodology to realistically evaluate the grid capacity for PEV charging on 3-phase 0.4-kV distribution grids. The proposed methodology is a modified Monte Carlo (MC) simulation technique, which analyzes grid capacity with respect to charging location, technical distribution grid limitations, and PEV charging techniques. The MC technique is numerically optimized to analyze large distribution grids and scaled up PEV charging. Results will be compared to traditional analysis techniques.
Description
The increasing number of Plug-in Electric Vehicles (PEVs) impacts the power grid due to their high demand in power and energy, and uncertainties in the charging behavior. Typical PEVs are charged single-phase up to 32 A (7.2-kVA) or tri-phase up to 32 A (22.0- kVA). Both charging technologies have to be discussed in order to determine their impact on planning and operating of low-voltage distribution grids to assure a reliable and stable PEV charging. Traditional grid planning and analysis methods, which average and evenly distribute PEV loads on the distribution grid, fail in providing a realistic answer about the grid capacity to charge PEVs. The question; How many PEVs can be charged simultaneously on a distribution grid remains unanswered. Therefore, this paper describes a novel methodology to realistically evaluate the grid capacity for PEV charging on 3-phase 0.4-kV distribution grids. The proposed methodology is a modified Monte Carlo (MC) simulation technique, which analyzes grid capacity with respect to charging location, technical distribution grid limitations, and PEV charging techniques. The MC technique is numerically optimized to analyze large distribution grids and scaled up PEV charging. Results will be compared to traditional analysis techniques.

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MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties - 2015-01-0305 - SAE International
Warrendale, PA, United States
MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties
2015-01-0305
MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties 2015-01-0305
The increasing number of Plug-in Electric Vehicles (PEVs) impacts the power grid due to their high demand in power and energy, and uncertainties in the charging behavior. Typical PEVs are charged single-phase up to 32 A (7.2-kVA) or tri-phase up to 32 A (22.0- kVA). Both charging technologies have to be discussed in order to determine their impact on planning and operating of low-voltage distribution grids to assure a reliable and stable PEV charging. Traditional grid planning and analysis methods, which average and evenly distribute PEV loads on the distribution grid, fail in providing a realistic answer about the grid capacity to charge PEVs. The question; How many PEVs can be charged simultaneously on a distribution grid remains unanswered. Therefore, this paper describes a novel methodology to realistically evaluate the grid capacity for PEV charging on 3-phase 0.4-kV distribution grids. The proposed methodology is a modified Monte Carlo (MC) simulation technique, which analyzes grid capacity with respect to charging location, technical distribution grid limitations, and PEV charging techniques. The MC technique is numerically optimized to analyze large distribution grids and scaled up PEV charging. Results will be compared to traditional analysis techniques.

The increasing number of Plug-in Electric Vehicles (PEVs) impacts the power grid due to their high demand in power and energy, and uncertainties in the charging behavior. Typical PEVs are charged single-phase up to 32 A (7.2-kVA) or tri-phase up to 32 A (22.0- kVA). Both charging technologies have to be discussed in order to determine their impact on planning and operating of low-voltage distribution grids to assure a reliable and stable PEV charging. Traditional grid planning and analysis methods, which average and evenly distribute PEV loads on the distribution grid, fail in providing a realistic answer about the grid capacity to charge PEVs. The question; How many PEVs can be charged simultaneously on a distribution grid remains unanswered. Therefore, this paper describes a novel methodology to realistically evaluate the grid capacity for PEV charging on 3-phase 0.4-kV distribution grids. The proposed methodology is a modified Monte Carlo (MC) simulation technique, which analyzes grid capacity with respect to charging location, technical distribution grid limitations, and PEV charging techniques. The MC technique is numerically optimized to analyze large distribution grids and scaled up PEV charging. Results will be compared to traditional analysis techniques.

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  SAE International
Product Category Standards and Technical Documents
Product Number 2015-01-0305
Product Name MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties
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