XFC Energy Management Using Forecast-Based Source Selection
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Solution Overview
Problem
Extreme fast-charging (XFC) systems for electric vehicles require high-capacity electric power sources, but conventional methods relying on electric power grids and renewable energy sources are costly and unreliable, making it difficult to support simultaneous charging of multiple vehicles.
Innovation Solution
Energy management systems (EMS) that employ energy arbitrage and stochastic programming to select the most economical combination of electric energy sources, including photovoltaic and grid power, to charge battery energy storage systems, ensuring sufficient energy is available at the lowest cost while optimizing energy source selection based on historical and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional electric power grids and renewable energy sources are used to charge XFC systems, then energy can be provided to charging stations, but the operation becomes costly and unreliable
Solution Approach 1:
The system performs preliminary actions by forecasting energy production and demand before the charging period begins. The EMS generates predictions of renewable energy production and EV charging demand, then uses these forecasts to create an optimal energy source selection plan in advance, allowing the system to secure reliable energy availability while minimizing costs through proactive planning rather than reactive responses
Solution Approach 2:
The system dynamically adjusts energy source selection based on real-time conditions and forecasts. The EMS continuously updates its predictions of renewable energy production and charging demand, then adapts the energy source combination strategy to balance reliability and cost objectives, allowing flexible response to variable renewable generation and unpredictable EV arrival patterns
2Productivity
If multiple electric vehicles are charged simultaneously at XFC stations, then charging capacity increases, but the requirement for high-capacity electric power sources becomes more difficult to support
Solution Approach 1:
The system segments the energy supply strategy by dividing the charging period into time intervals and selecting different energy source combinations for each segment. The EMS forecasts demand and renewable production, then creates a segmented plan that uses renewable energy when available, supplements with grid power during high-demand periods, and employs energy storage systems to bridge gaps, allowing multiple vehicles to be charged simultaneously without requiring a single oversized power source
Solution Approach 2:
The system merges multiple energy sources (renewable energy systems, grid power, and energy storage systems) into a unified energy supply strategy. The EMS combines these diverse sources strategically based on forecasts and real-time conditions, allowing the charging station to support multiple simultaneous XFC sessions by aggregating capacity from various sources rather than relying on a single high-capacity source
3Loss of energy
If energy arbitrage and stochastic programming are used to optimize energy source selection, then operational cost decreases, but the system complexity increases
Solution Approach 1:
The EMS implements self-service by autonomously performing forecasting, optimization, and decision-making without human intervention. The system automatically generates predictions of energy production and demand, applies stochastic programming to evaluate multiple scenarios, and selects optimal energy source combinations based on predefined objectives for reliability and cost, eliminating the need for manual energy management while achieving sophisticated cost optimization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The EMS ensures efficient and cost-effective operation of XFC systems by prioritizing renewable energy sources and strategically using grid power, maintaining energy availability and reducing operational costs, even during variable capacity and demand.
Implementation Method 1
select a most economical electric energy source from two or more electric energy sources, such as an electric power grid and a photovoltaic energy source
Data Source
AI summary
A method implemented by an energy management system (EMS) to optimize energy distribution in an extreme fast-charging (XFC) system includes (1) generating temporal maps for each of a plurality of energy sources, including projected capacity and energy cost over time, (2) generating a temporal map of projected load of one or more EV chargers within the XFC system, and (3) selecting one or more of the energy sources to supply energy to the XFC system over a predetermined time period based on cost, availability, and demand forecasts. This selection, for example, dynamically balances energy costs, ensures charging reliability, and/or optimizes the XFC system to support high-power charging with minimal operational expenses.