Fleet Load Curve Allocation for Bidirectional EV Energy Trading
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Solution Overview
Problem
Existing methods for integrating electric vehicles with the power grid through bidirectional charging technology are restrictive and not cost-effective, particularly when dealing with a fleet of vehicles.
Innovation Solution
A method that forecasts aggregated load curves for a group of electrical energy stores, trades energy based on these curves, and updates forecasts dynamically to optimize energy distribution, allowing for efficient scaling and price benefits by disaggregating optimized load curves for individual energy stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual electric vehicles are integrated into the power grid through bidirectional charging technology, then grid stabilization capability is achieved, but the integration method is highly restricted and not cost-effective
Solution Approach 1:
The patent merges multiple individual electric vehicles into a unified fleet managed by a central control system. The control system aggregates load curves from individual vehicles to create an overall fleet load curve, enabling coordinated energy management. This merging allows the fleet to operate as a single entity in energy markets, achieving economies of scale and improved adaptability while maintaining grid stabilization capabilities.
Solution Approach 2:
The control system provides multiple functions: it manages individual vehicle charging/discharging, aggregates fleet load curves, trades energy in spot markets and balancing groups, and optimizes energy assignments. This multi-functional approach replaces the need for separate individual vehicle integrations with a single universal fleet management platform, improving both versatility and cost-effectiveness.
2Ease of operation
If individual vehicles are integrated separately into the grid, then each vehicle can be controlled independently, but the overall system is not cost-effective and lacks scaling effects
Solution Approach 1:
The patent combines individual vehicle control with fleet-level aggregation. The control system maintains the ability to manage individual vehicles while simultaneously creating an aggregated fleet load curve for market trading. This dual-level approach preserves individual control ease while achieving the cost-effectiveness and scaling effects of unified fleet management.
Solution Approach 2:
The control system segments the fleet management into distinct functional layers: individual vehicle control layer and fleet aggregation layer. This segmentation allows independent optimization at each level - individual vehicles can be controlled based on specific needs while the fleet as a whole achieves cost-effective energy distribution through aggregated trading.
3Productivity
If aggregated load curves are forecasted for energy trading, then scaling effects improve cost-effectiveness, but the system must handle complex forecasting and dynamic updates
Solution Approach 1:
The control system acts as an intermediary between individual vehicles and energy markets. It handles the complex forecasting and aggregation tasks centrally, transforming individual vehicle data into unified fleet load curves suitable for market trading. This intermediary approach simplifies the overall system architecture by concentrating complexity in a single management layer rather than distributing it across multiple individual vehicle systems.
Data Source
AI summary
A method (S1-S9) is used to assign electrical energy (E) to a group of electrical energy stores (E1-En), in particular a fleet of electric vehicles (E1-En), several of which are intended for bidirectional conduction of current, wherein (a) at least one first instance (INST1), which controls individual load curves of the group of electrical energy stores, forecasts an aggregated load curve (PLG) for these energy stores (S1) and reports said aggregated load curve to a second instance (INST2-1, INST2-2) (S2), (b) the second instance trades an amount of energy corresponding to the forecast load curve (PLG) (S3), (c) the first instance, during the forecast period (T) that has then occurred, forecasts an aggregated load curve band (LGB) for the remaining duration of the forecast period in due consideration of the load curves actually implemented during the forecast period up until then (S4) and reports said aggregated load curve band to the second instance (S5), (d) the second instance determines an optimized load curve (LGO) from the reported load curve band (S6) and requests said optimized load curve from the first instance (S7) and (e) the first instance disaggregates the optimized load curve onto individual load curves for the energy stores controlled by said first instance and controls the energy stores on the basis of the associated individual load curves (S8).