V2G Demand Flexibility Control for Grid Load Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional approaches for balancing energy supply and demand in power grids using vehicle-to-grid (V2G) technology are limited in optimizing overall efficiency, as they primarily focus on scheduling charging cycles and returning energy to the grid without effectively managing the overall power distribution from electric vehicles (EVs).
Innovation Solution
A demand-side flexibility (DSF) system that utilizes machine learning (ML) models to determine patterns for operating a power grid over a given time interval, optimizing the proportion of power to be sourced from EVs and power generation systems, and adjusts variables to optimize efficiency, including incentives for EV owners to plug their vehicles into the grid during peak demand times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling approaches are used for V2G charging cycles, then charging schedules can be provided for EVs, but overall power distribution efficiency cannot be optimized
Solution Approach 1:
The patent introduces a demand-side flexibility (DSF) system as an intermediary between EVs and the power grid. This DSF system aggregates multiple EV batteries and manages them collectively, optimizing power distribution at the system level rather than individually scheduling each EV. The DSF system acts as a mediator that coordinates between the grid operator and individual EVs, enabling overall efficiency optimization without requiring complex individual EV scheduling.
Solution Approach 2:
The patent combines multiple EV batteries into a unified demand-side flexibility system, merging their capabilities to collectively optimize power distribution. By aggregating EV resources and managing them as a unified system rather than individual units, the patent achieves better overall efficiency while reducing the complexity of managing each EV separately.
2Productivity
If ML models are used to predict market loads and optimize charging schedules, then charging optimization can be achieved, but overall grid efficiency balancing is not accomplished
Solution Approach 1:
The patent creates a universal demand-side flexibility system that performs multiple functions: it aggregates EV batteries, predicts power needs using ML models, optimizes charging schedules, and manages power distribution collectively. This multi-functional system achieves both charging optimization and overall grid efficiency balancing, replacing the need for separate individual EV optimization systems.
Solution Approach 2:
The patent implements feedback mechanisms where the DSF system continuously monitors grid conditions, EV battery states, and power distribution patterns. This feedback enables the system to dynamically adjust charging schedules and power distribution strategies, achieving optimal grid efficiency while simplifying the optimization process through centralized control rather than complex distributed optimization.
3Reliability
If EVs are incentivized to plug into the grid during peak demand, then power shortages can be smoothed, but procurement costs may increase
Solution Approach 1:
The patent uses ML models to predict future power needs and proactively schedules EV charging during off-peak hours when electricity is cheaper. By taking preliminary action to charge EVs when power is abundant and inexpensive, the system reduces the need for expensive peak-hour power procurement, thereby smoothing power shortages while actually reducing procurement costs.
Solution Approach 2:
The patent dynamically adjusts charging parameters such as charging rates, timing, and duration based on real-time and predicted grid conditions. By changing these parameters optimally, the system achieves reliable power supply stability while minimizing procurement costs, avoiding the need to simply incentivize peak-hour charging that would increase costs.
Data Source
AI summary
Implementations for receiving, by DSF system, data representative of a set of constants, determining, by the DSF system, data representative of a set of predictions, at least a portion of predictions being determined from a set of ML models, optimizing, by the DSF system, a value of an objective function subject to a set of constraints, the value of the object function being optimized for a time interval based on a set of constants, the set of predictions, and a set of variables, providing, by the DSF system, the set of variables as output of optimizing the value of the objective function, and transmitting, by the DSF system, instructions to a set of assets of the power grid to provision power based on values of at least a sub-set of variables in the set of variables, the set of assets at least partially comprising a set of EVs.