Geospatial Load Growth Modeling for EV and Grid Capacity Forecasting

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Solution Overview

Problem

Existing electrical grid load forecasting techniques fail to accurately predict the impact of emerging electro-technologies like EVs, PVs, and HPs due to their variable and unpredictable consumption patterns, leading to inconsistent modeling outputs and inadequate infrastructure planning.

Innovation Solution

A computer-implemented method that estimates the deployment rate of electro-technologies geospatially, integrates energy consumption profiles with current consumption data, and overlays them onto a digital grid model for precise load forecasting.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If historical averaging techniques are used for load forecasting, then the method is simple and easy to implement, but it cannot accurately predict the impact of emerging electro-technologies with variable consumption patterns

Engineering Contradiction:

Improvesimplicity of forecasting methodVSAvoidaccuracy of load forecasting

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent changes the fundamental parameters of load forecasting by transitioning from historical averaging to a multi-parameter approach that includes deployment rates, geospatial distribution, technology-specific consumption profiles, and infrastructure characteristics. This allows the system to accurately forecast the impact of emerging electro-technologies while maintaining operational simplicity through automated data processing.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If sophisticated econometric and end-use modelling approaches are adopted, then load forecasting accuracy improves, but the process becomes complex and time-consuming, taking years to complete

Engineering Contradiction:

Improveaccuracy of load forecastingVSAvoidtime required for modeling process

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the complex forecasting process into distinct modular components: deployment rate estimation, geospatial disaggregation, technology-specific consumption profiling, and infrastructure impact assessment. Each component processes specific data types and can be executed independently, reducing the overall time required while maintaining comprehensive forecasting accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-processing and validating data, pre-calculating consumption profiles for different technologies, and pre-establishing geospatial distribution models. These preliminary actions prepare the foundation for rapid forecasting execution, reducing the time needed for the main forecasting process.

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If existing load forecasting techniques are used, then the infrastructure planning process remains simple, but the planning becomes inadequate and misaligned with actual power demand needs

Engineering Contradiction:

Improvesimplicity of planning processVSAvoidadequacy of infrastructure planning

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent implements feedback mechanisms that continuously compare forecasted power demand with actual consumption patterns, deployment data, and infrastructure capacity. This feedback loop enables the system to adjust forecasting models and planning recommendations in real-time, ensuring infrastructure planning remains reliable and aligned with actual needs while maintaining process simplicity through automated adjustments.

Inventive Principle:

Principle #23Feedback

4Measurement precision

If geospatially specific deployment data is collected and processed, then forecasting accuracy for specific areas improves, but data processing complexity and computational requirements increase

Engineering Contradiction:

Improvegeospatial forecasting accuracyVSAvoidcomplexity of data processing

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies local quality by tailoring forecasting parameters and consumption profiles to specific geospatial locations, accounting for local characteristics such as demographics, climate, existing infrastructure, and technology adoption patterns. The system processes geospatial data through standardized algorithms that adapt to local conditions, achieving high accuracy without proportionally increasing overall processing complexity.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250378509A1Automated methods and systems for modelling geospatially specific load growth

Publication Date: 2025.12.11 DELOITTE INC

AI summary

Methods and systems are provided for assessing the effects of an adopted technology on power demands on an electrical grid. An estimate of a rate of deployment for the adopted technology in a target utility area is determined over a predetermined time period. An estimate of a total stock of the adopted technology in one or more dissemination areas is determined by geospatially disaggregating the estimated rate of deployment of the adopted technology across the one or more dissemination areas. The estimated total stock of the adopted technology is derived from a forecasted energy consumption profile for one or more subareas within the target utility area. The forecasted energy consumption profiles are integrated with current consumption profiles for display in a graphical user interface.