EV Bidirectional Charging Schedule for Intraday Market Volatility
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Solution Overview
Problem
Power supply entities face challenges in managing fluctuating electricity prices in intraday markets due to unpredictable EV behavior, leading to increased costs from purchasing energy to meet demand, and existing V2G systems struggle to scale with site constraints and real-time adjustments.
Innovation Solution
A system utilizing a quantum algorithm with covariant quantum kernels to dynamically schedule EV bidirectional charging, disaggregating fleet-level solutions for EVs, optimizing charge/discharge to align with day-ahead market commitments and reduce reliance on intraday markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum algorithms with covariant quantum kernels are used for dynamic scheduling, then classification accuracy and scheduling efficiency are improved, but device complexity increases
Solution Approach 1:
The patent introduces quantum algorithms with covariant quantum kernels as an intermediary computational tool between the scheduling system and the classification problem. This intermediary leverages quantum computing capabilities to achieve high-accuracy multi-class classification of EV charging states while managing the complexity through specialized quantum software implementations.
2Loss of energy
If EV bidirectional charging is dynamically scheduled to meet day-ahead commitments, then reliance on intraday markets is reduced, but real-time market fluctuations and EV behavior unpredictability increase system complexity
Solution Approach 1:
The system performs preliminary scheduling actions by using quantum algorithms to predict and classify EV charging states in advance. This allows the system to proactively plan bidirectional charging schedules that align with day-ahead market commitments, reducing the need for reactive intraday market purchases and managing complexity through advance computational planning.
Solution Approach 2:
The patent implements feedback mechanisms where the quantum classification system continuously monitors EV states and market conditions, adjusting scheduling decisions in real-time. This feedback loop enables the system to adapt to unpredictable EV behavior and market fluctuations while maintaining optimization goals.
3Manufacturing precision
If fleet-level solutions are disaggregated to individual EV level, then scheduling precision is improved, but computational burden increases
Solution Approach 1:
The patent applies segmentation by dividing the fleet-level scheduling problem into individual EV-level classification tasks. The quantum algorithm processes each EV's state separately, enabling precise individual scheduling decisions while distributing the computational burden across multiple independent classification operations rather than one monolithic computation.
Data Source
AI summary
An exemplary system comprises a memory that stores and a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise an optimizing component that generates a fleet-level solution for maintaining a vehicle-to-grid (V2G) system by a fleet of electric vehicles (EVs), and a scheduling component that constructs a schedule for bidirectional charging of a portion of the fleet by disaggregating the fleet-level solution based on a multi-class classification resulting from an execution of a quantum algorithm, based on a covariant quantum kernel, on a quantum system. In one or more embodiments, the multi-class classification comprises classes of charging, discharging, and no bidirectional charging.