Group EV Charging Orchestration Under Grid Capacity Limits
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Solution Overview
Problem
Current infrastructure struggles to manage the increasing demand for electric vehicle charging, leading to infrastructure limitations, such as undersized breakers and transformers, and creates challenges like the 'duck-curve' problem, where peak demand coincides with renewable energy low usage, making generation capacity difficult to anticipate and deliver.
Innovation Solution
An active demand management system that dynamically optimizes EV charging by using smart EV chargers, an orchestration service, and real-time data integration from sources like Eagle and AMI, to coordinate charging sessions based on user preferences, grid conditions, and pricing, ensuring efficient energy use and adherence to grid constraints without requiring hardware upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging demand is increased to meet user needs, then user satisfaction and charging accessibility are improved, but infrastructure capacity and grid stability deteriorate due to undersized breakers and transformers
Solution Approach 1:
The system dynamically adjusts EV charging rates based on real-time grid conditions, transformer capacity, and demand patterns. The orchestration service continuously monitors grid state and modulates charging power delivery, transforming static infrastructure into a dynamically adaptable system that can handle variable demand without requiring permanent capacity upgrades
Solution Approach 2:
The system changes operational parameters such as charging power levels, voltage, and current based on grid conditions and transformer capacity. By adjusting these parameters in real-time, the system optimizes charging efficiency while preventing overload conditions that would compromise grid stability
2Ease of operation
If EV charging is concentrated during peak hours to meet user schedules, then user convenience is improved, but peak demand and infrastructure strain worsen
Solution Approach 1:
The system performs preliminary actions by pre-charging EV batteries during off-peak hours when grid demand is low, ensuring vehicles are ready for use during peak periods without requiring peak-time charging. This anticipatory charging approach maintains user convenience while avoiding peak demand concentration
Solution Approach 2:
The system implements periodic charging cycles that distribute charging loads across different time periods rather than concentrating them during peak hours. By alternating between charging and non-charging periods based on grid conditions, the system maintains user needs while smoothing out peak demand patterns
3Productivity
If infrastructure capacity is increased through hardware upgrades to handle EV demand, then charging capacity is improved, but cost and implementation complexity worsen
Solution Approach 1:
The system replaces physical infrastructure expansion (mechanical/electrical upgrades) with a software-based orchestration service that intelligently manages existing charging assets. Instead of adding more breakers, transformers, or charging stations, the system uses algorithms to optimize the utilization of existing infrastructure, achieving capacity improvements without physical expansion
Data Source
AI summary
Active demand management systems and methods are disclosed herein. An example method includes determining one or more conditions necessary to compute a rule set, determining a current state of one or more devices, receiving user inputs and overrides, if any, via the one or more devices, determining both a forecasted demand and a demand threshold, based on the rule set, the current state of each of the one or more devices, and the user inputs and overrides, when the forecasted demand is greater than the demand threshold, generating a plan to power off the one or more networked devices, one by one, in an order from the least important device to the most important device, until the forecasted demand no longer exceeds the demand threshold; and delivering energy-related device commands for the one or more devices, based on the generated plan.