Grid-integration of electric vehicles: flexibility options ...
Grid Integration of Electric Vehicles and Demand Response with Customer Choices
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Transcript of Grid Integration of Electric Vehicles and Demand Response with Customer Choices
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Grid Integration of Electric Vehicles and Demand Response with Customer Choices
• S. Shao, M. Pipattanasomporn, and S. Rahman, “Grid Integration of Electric Vehicles and Demand Response with Customer Choices,” IEEE Transactions on Smart Grid, vol. 3, no. 1, Mar. 2012.
• S. Shao, M. Pipattanasomporn, and S. Rahman, “Demand Response as a Load Shaping Tool in an Intelligent Grid with Electric Vehicles,” IEEE Transactions on Smart Grid, vol. 2, no. 4, Dec. 2011.
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BBCR Smart Grid Research Group Meeting
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Contributions
• Propose a demand response (DR) strategy to accommodate electric vehicle (EV) charging on a residential distribution circuit
• Goal– Keep the peak demand (before EV penetration)
unchanged• Consumer comfort indices are introduced
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Outline
• Motivations• Modeling of circuit load and EV charge profiles• Demand response strategy design• Consumer comfort indices• Case study• Concluding remarks
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Motivations
• The impact of EVs on electric power systems cannot be overlooked.
• EV penetration may bring higher peak demand• Majority of previous work regarding the
impact of EV penetration on electric power systems focuses at the transmission level
• Recent research started to turn to the distribution level
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Introduction
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This picture is obtained from Wikipedia
Transmission level
Distribution level
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Motivations (cont.)
• Analysis of EV penetration into the distribution network is quite extensive
• There is still a need to – Take into consideration the vehicle driving
patterns– Develop a demand response strategy that will
accommodate EV fleets
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Motivations (cont.)
• Demand response applications in industrial and commercial sectors have been well studied
• Residential demand response strategy taking into account the consumer comfort still needs an in-depth study
• There is the lack of indices to measure the impacts of demand response on consumer convenience
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Modeling of Distribution Circuit Load
• Hourly load curves of an average household are based on the RELOAD [1] database
• Residential loads are classified by nine types:– Space cooling, space heating, water heating, cloth
drying, cooking, refrigeration, freezer, lighting, others
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[1] RELOAD Database Documentation and Evaluation and Use in NEMS [Online]. Available: http://www.onlocationinc.com/LoadShapes-Reload2001.pdf
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Modeling of Distribution Circuit Load (cont.)
• In the papers, the residential loads are classified into two categories:– Controllable: the loads that can be controlled
without noticeable impacts on consumer’s life style• Space cooling/heating, water heating, cloth drying
– Critical: the loads that are either very important or cannot be controlled • Others
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Modeling of EV Fleet Charge
• A survey [2] indicates that the EV plug-in time (coming home time) is close to a normal distribution curve
• The papers use a normal probability distribution function to describe the EV fleet plug-in time
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[2] J. Taylor, A. Maitra, M. Alexander, D. Brooks, and M. Duvall, “Evaluation of the impact of plug-in electric vehicle loading on distribution system operations,” in Proc. IEEE PES Gen. Meet., Jul. 26–30, 2009.
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Modeling of EV Fleet Charge (cont.)
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Mean at 6pm and the variance of 1 h(EV plug in time)
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Modeling of EV Fleet Charge (cont.)
• A study [3] uses the Monte Carlo method to simulate the daily driving distance for each EV in the distribution circuit
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[3] National Household Travel Survey Oakridge National Laboratory, 2001 [Online]. Available: http://nhts.ornl.gov/index.shtml
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Modeling of EV Fleet Charge (cont.)
• Battery usable capacities and charging power requirements (kW)
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* Recommended charging rate
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Demand Response Strategy Design
• The proposed DR strategy is designed in two layers
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Neighborhood Area Network (NAN) Home Area Network (HAN)
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Demand Limit Allocation for Each House at NAN
• Original peak load (before EV penetration) should be set as the demand limit for whole circuit1. NAN control center sorts all reported demand
within a distribution circuit2. Household demand limit (DLi) is set at the point
where the summation of all household to be served is equal to or less than the original peak load
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Demand Limit Allocation for Each House at NAN (cont.)
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(Original peak load)
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Optimization Problem
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Demand Response Strategy in HAN
Step1) Customers set the load priority for each applianceStep2) Customers perform preference settings for each appliance
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(highest)
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Demand Response Strategy in HAN (cont.)
• Step3) HAN control center will compare the total household power consumption (ph,i) with the demand limit (DLi)– If ph,i > DLi, demand response will be performed
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Consumer Comfort Indices
• Evaluate DR impacts on consumer comfort levels
• Indices are defined based on severity, scale, and duration of convenience violations for each controllable appliance
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Severity Indices
• Measure how severely the consumer comfort levels are violated
• Based on the maximum percentage deviation from the original settings
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Severity Indices (cont.)
• Severity indices for HVACs– The largest temperature deviation in percentage
taking into account all homes in a distribution circuit
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Severity Indices (cont.)
• Severity indices for water heater (temperature)
• Severity indices for clothes dryer (time delay)
• Severity indices for electric vehicles (time delay)
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Scale Indices
• Measure the number of consumers whose comfort levels are violated as a percentage of a total household
• Scale indices for HVACs
– nHVAC: the number of homes with the temperature out of preset comfort ranges in each time slot
– N: the total number of consumers– ORAC: ownership rate of HVACs
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Scale Indices (cont.)
• Scale indices for water heaters
• Scale indices for clothes dryers
• Scale indices for electric vehicles
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Duration Indices
• Describe the length of the inconvenient period for HVAC and water heater
• Duration indices for HVACs
• Duration indices for water heaters
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Case Study• Circuit 9 in the Virginia Tech
Electric Service (VTES) area is taken as a case study
• 780 homes• 780*1.9 = 1482 vehicles• EV penetration levels:
– 50 (3.3%) EVs and 100 (6.6%) EVs
• EV charging model– Mix of 40% Chevy Volt, 40%
Nissan LEAF, and 20% Tesla Roadster
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Results
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50 EVs
100 EVs
Summer load profiles
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50 EVs
100 EVs
Winterload profiles
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Consumer Comfort Indices (Summer)
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Consumer Comfort Indices (Winter)
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Concluding Remarks
• EV fleet charge profiles are modeled based on driving distances and battery sizes
• DR strategy provides the utility with unchanged peak demand to avoid distribution circuit upgrade
• Maintain the same peak load with higher EV penetration levels may impact the consumer’s convenience
• Utilities can use the consumer comfort indices to estimate the capability of demand response programs
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Thank you!
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