Adaptive EV Charging Platform for Grid Stress Reduction
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Solution Overview
Problem
The increasing adoption of electric vehicles (EVs) puts additional stress on the smart grid, particularly due to the 'Duck curve' phenomenon where solar energy floods the market during the day and electricity demand peaks in the evening, causing inefficiencies in electricity distribution.
Innovation Solution
An adaptive EV charging system that uses Gaussian mixture models (GMMs) to learn and predict EV charging behavior, optimizing charging routines to smooth out electricity demand and match it with available solar energy, thereby controlling the charging of large numbers of EVs to alleviate grid stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging is increased to meet growing EV adoption, then EV service capability is improved, but grid stress and demand peaks worsen
Solution Approach 1:
The system performs preliminary actions by predicting EV arrival times and pre-scheduling charging sessions during off-peak hours when solar energy is abundant. The optimization algorithm proactively assigns charging time slots before EVs actually arrive, ensuring charging occurs during periods of high solar generation rather than waiting for demand to materialize, thus preventing grid stress
Solution Approach 2:
The system dynamically adjusts charging schedules based on real-time solar generation forecasts, actual EV arrivals, and grid conditions. The optimization algorithm continuously re-optimizes charging assignments, shifting charging loads flexibly between time periods and charging stations to balance EV service needs with solar energy availability and grid capacity, thereby managing grid stress while maintaining productivity
2Ease of operation
If EV charging is concentrated during peak demand hours, then user convenience is improved, but the Duck curve effect worsens
Solution Approach 1:
The system provides preliminary information to users about optimal charging time slots that balance convenience with grid benefits. Users receive advance notifications of recommended charging windows during which their vehicles will be charged, allowing them to plan accordingly while ensuring charging occurs during solar-rich periods rather than peak demand hours, thus mitigating the Duck curve effect
Solution Approach 2:
The system implements feedback mechanisms where users are informed about the impact of their charging preferences on grid conditions and solar utilization. The optimization algorithm incorporates user convenience constraints while providing feedback on alternative charging schedules that reduce the Duck curve effect, allowing users to adjust their preferences based on this information to achieve both convenience and grid benefits
3Use of energy by moving object
If solar energy is used to meet EV charging demand, then renewable energy utilization is improved, but charging infrastructure capacity requirements worsen
Solution Approach 1:
The system segments the EV charging load into multiple time-based groups and assigns them to different charging stations or time periods. By dividing the total charging demand into discrete, manageable segments that can be scheduled during specific solar generation windows, the system maximizes solar utilization while preventing any single charging station from requiring excessive capacity to handle concentrated peak loads
Data Source
AI summary
Systems and methods in accordance with embodiments of the invention impalement adaptive electric vehicle (EV) charging. One embodiment includes one or more electric vehicle supply equipment (EVSE); an adaptive EV charging platform, including a processor; a memory containing: an adaptive EV charging application; a plurality of EV charging parameters. In addition, the processor is configured by the adaptive EV charging application to: collect the plurality of EV charging parameters from one or more EVSEs, simulate EV charging control routines and push out updated EV charging control routines to the one or more EVSEs. Additionally, the adaptive EV charging platform is configured to control charging of EVs based upon the plurality of EV charging parameters collected from at least one EVSE.