T H A L E S
Chargepoint Challenge 2: Using Data To Shape Electrification of Transportation
Grid-Level Adaptive EV Charging
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Rio Richardson, Rebecca McCabe, Cindy Wu, Cynthia Garde
Current and Future EV Adoption
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Regulatory�Requirements (increases)
EV fleet size (million)
2010 2030 2050
180
80
0
Low
Medium
High
Uptake scenario:
EV Demand
EV Production & �Variety
EV �Affordability
Grid overload → Unreliability
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Time
Resource
(Source: https://thehub.agl.com.au/articles/2020/03/explainer-the-duck-curve)
Grid Load
Charging Stations
EV �Demand
The Problem
The Solution
As more EVs & charging stations are built, the grid will struggle to keep up with electricity demand.
Scheduling charging will allow charging stations to optimise which cars are charging at any one point.
While there are methods to do this a site-level, there is currently no tool to schedule charging across multiple sites or at the network-level.
Our algorithm looks at a number of individual & grid-level factors and decides the optimal power level and charge timing for a group of EVs across a range of sites.
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How our solution works
Our adaptive charging algorithm would be used by a network of chargers to optimise which cars to charge when, allowing automated demand management across a range of sites.
Each driver would see a smaller impact on their charge time than with site-level adaptive charging, while adding up to a big impact on grid demand at any one time.
Car 1: 13.4 kW 13:07-15:02� 6.2 kW 15:03-15:43� ...
Car 2: 8.3 kW 13:19-14:25� 9.9 kW 14:25-15:02� ...
Car 3: 5.2 kW 12:42-13:06� 1.2 kW 13:07-14:11� ...
Car 4: 4.7 kW 11:57-12:32� 1.9 kW 12:33-14:24� ...
Car 5: 2.3 kW 12:27-13:04� 6.2 kW 13:05-13:07� ...
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Site 1: Public charging station
Sites 2-4: Residential charging station
Our algorithm optimizes charge profiles
Grid power constraints
For each car / site:
Maximize clean energy usage
Minimize power peaks
Power profile in time
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INPUTS
OPTIMISATION
OUTPUT
We created a prototype of the algorithm in MATLAB
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We used several data sources for our prototype - and would add more given availability/time
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Data used:
Data identified to incorporate:
Demand management is useful to both utility providers and charge point networks, among others
Utility providers
Charge point networks
Grid balancing is a critical problem for utility companies; many are already providing incentives to EV owners to charge during off-peak hours
Need to target great customer experience while helping clients (businesses, retailers etc.) optimise electricity costs
Other stakeholders
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T H A L E S
Grid-Level Adaptive EV Charging
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References
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Appendix 1: Energy Demand Over Time
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Source: https://thehub.agl.com.au/articles/2020/03/explainer-the-duck-curve
Appendix 2: Grid Modernization & Energy Continuity
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Source: https://www.windpowerengineering.com/5-ways-ultracapacitors-operate-in-utility-grids-microgrids/
Appendix 3: US Grid
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Source: https://www.eia.gov/realtime_grid/#/status?end=20201107T14