EV Charging Flexibility Control for Peak Load Shifting
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Solution Overview
Problem
The integration of electric vehicles (EVs) into power systems poses challenges such as peak demand spikes, infrastructure stress, and limited supply-demand balancing capabilities due to the limitations of existing energy storage systems (ESSs).
Innovation Solution
The proposed system utilizes EVs as a distributed, virtual Energy Storage System (ESS) by intelligently scheduling their charging and discharging to improve power supply-demand balancing and peak shaving or shifting in the power system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EVs charge simultaneously during peak demand periods, then individual EV charging needs are met, but power grid infrastructure experiences excessive stress and peak demand spikes
Solution Approach 1:
The system schedules EV charging in advance during off-peak periods when grid demand is lower. The optimization controller determines charging schedules before peak demand periods occur, pre-coordinating charging times to avoid simultaneous charging during high-demand periods, thus reducing infrastructure stress while meeting EV charging needs
Solution Approach 2:
The system dynamically adjusts EV charging schedules based on real-time grid conditions, demand predictions, and renewable energy availability. The optimization controller continuously modifies charging times and rates to balance individual EV charging requirements with overall grid stress constraints, enabling flexible response to changing conditions
2Reliability
If traditional ESSs are used for supply-demand balancing, then some peak shaving capability is achieved, but the system lacks sufficient flexibility and scalability
Solution Approach 1:
The system segments the energy storage function across multiple distributed EVs rather than relying on a single centralized ESS. Each EV battery becomes an independent energy storage unit that can be individually scheduled and controlled, providing modular scalability and enhanced system flexibility while maintaining supply-demand balancing capabilities
Solution Approach 2:
The system enables EV batteries to serve dual functions: their primary function for vehicle propulsion and a secondary function for grid energy storage. This multi-functionality allows the same physical batteries to provide both transportation and energy storage services, significantly increasing system versatility and flexibility without requiring dedicated ESS infrastructure
3Stress or pressure
If EV charging is scheduled to reduce peak demand, then grid infrastructure stress is reduced, but charging flexibility for individual EVs may be limited
Solution Approach 1:
The optimization controller incorporates feedback from multiple sources including EV driver preferences, vehicle usage patterns, and real-time grid conditions. The system continuously monitors these factors and adjusts charging schedules accordingly, ensuring that peak demand reduction goals are met while respecting individual EV charging flexibility requirements and adapting to changing conditions
Data Source
AI summary
Systems and methods are provided relating to power systems, such as a power grid, including for providing control to a power system by utilizing available flexibility in charging electric vehicles (EVs). The system generates control information for controlling the power system based on predicted power demand in the system during a target time period and based on predicted EV charging curtailment information, which relates to a predicted flexibility in charging EVs while meeting charging goals of the EVs during a target time period. The generated control information includes EV charging scheduling information that utilizes the predicted flexibility in charging EVs by scheduling charging of EVs to curtail or to increase an aggregate charging load of the EVs during the target time period.