EV Bidirectional Charging Scheduling for Fleet-Level Grid Stability
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Solution Overview
Problem
Power supply entities face challenges in managing the unpredictable behavior of electric vehicles (EVs) connected to the grid, leading to increased costs from purchasing energy in intraday markets due to fluctuating prices and difficulty in meeting customer demand, especially when dealing with large fleets of EVs.
Innovation Solution
A system utilizing a covariant quantum kernel-based quantum algorithm to dynamically schedule bidirectional charging of EVs, disaggregating fleet-level solutions to optimize charging and discharging, thereby reducing reliance on intraday markets and improving power grid stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum algorithms with covariant quantum kernels are used for dynamic scheduling, then scheduling accuracy and power grid stability are improved, but device complexity increases
Solution Approach 1:
The patent introduces a quantum computing intermediary system that acts as a mediator between the power grid scheduling problem and the optimization solution. The quantum system processes complex scheduling calculations that are difficult for classical computers, providing accurate bidirectional charging schedules while isolating the complexity within the quantum computing layer rather than the overall power grid system.
2Stability of the object's composition
If bidirectional charging scheduling is implemented for large EV fleets, then power grid stability is improved, but difficulty in managing unpredictable EV behavior increases
Solution Approach 1:
The patent implements feedback mechanisms where the quantum-based scheduling system continuously monitors EV charging/discharging behavior and adjusts schedules in real-time. The system learns from actual EV behavior patterns and incorporates this feedback into subsequent scheduling decisions, enabling effective management of unpredictable EV fleet behavior while maintaining power grid stability.
Solution Approach 2:
The scheduling system is designed to be dynamic rather than static, adapting to changing EV behavior patterns, power grid conditions, and market prices in real-time. The quantum algorithm processes dynamic inputs from EVs and generates adaptive scheduling decisions that respond to unpredictable behavior changes, transforming the system from rigid to flexible and responsive.
3Adaptability or versatility
If intraday market trading is used to meet customer demand, then customer demand can be satisfied, but expenses increase due to fluctuating prices
Solution Approach 1:
The patent employs preliminary action by using quantum algorithms to predict optimal charging and discharging schedules in advance based on forecasted power grid conditions and market prices. EVs are scheduled to charge when electricity is cheap and discharge when prices are high, proactively managing energy transactions before intraday market fluctuations occur, thereby reducing the need for expensive reactive purchases and lowering overall energy expenses while still meeting customer demand.
Data Source
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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.