Energy Load Matching Using Actionable Allocation Models
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Solution Overview
Problem
Utilities face challenges in accurately matching energy production from various sources with consumption, leading to inefficiencies, increased costs, and suboptimal utilization of renewable energy resources due to the complexity of managing energy generation, allocation, and customer preferences for renewable energy.
Innovation Solution
A system and method for automating the matching of energy load with generation by using a processor to collect and analyze energy datasets from load centers and generation sources, applying a matching engine to align load centers with generation sources based on constraints, and generating an allocation report to adjust energy distribution across the grid network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching methods are used to align energy production with consumption, then operational control flexibility is maintained, but matching accuracy and efficiency deteriorate due to complexity of managing energy generation, allocation, and customer preferences
Solution Approach 1:
The patent replaces manual matching operations with an automated computing system that uses processors to execute matching algorithms. The system automatically aligns energy production with consumption by processing energy datasets, evaluating constraints, and determining optimal matches between generation sources and load centers, thereby eliminating manual intervention while improving matching accuracy.
Solution Approach 2:
The system enables self-service by allowing the automated matching process to independently evaluate energy datasets, determine constraints, and generate matches without external intervention. The processor automatically processes energy data from multiple generation sources and load centers, evaluates constraints such as renewable energy preferences and regulatory requirements, and determines optimal matching configurations autonomously.
2Productivity
If automated matching systems are implemented to improve energy allocation efficiency, then productivity and operational efficiency improve, but device complexity and implementation costs increase
Solution Approach 1:
The automated matching system is designed to perform multiple functions within a single integrated platform. It processes energy datasets from various generation sources (renewable and non-renewable), evaluates multiple types of constraints (regulatory, customer preference, operational), generates matching configurations, and produces allocation reports. This multi-functionality consolidates what would otherwise require multiple separate systems into one unified solution.
Solution Approach 2:
The system segments the complex energy matching problem into distinct processing stages: data collection from generation sources and load centers, constraint evaluation, matching algorithm execution, and report generation. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity.
3Measurement precision
If comprehensive energy datasets are collected from multiple generation sources and load centers to improve matching accuracy, then measurement precision improves, but data processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing energy datasets as they are collected from generation sources and load centers. Energy datasets are structured and validated in advance, with constraints identified and stored in ready-to-use formats. This preliminary preparation reduces the computational burden during the actual matching process, thereby reducing overall processing time while maintaining comprehensive data analysis.
Data Source
AI summary
A system for automating matching of energy load with energy generation for energy consumers, utilities, and gird operators, comprising a processor and a memory containing instructions configuring the processor to collect, from a grid network connecting load centers and generation sources, energy datasets including energy attribute records and energy load data, match each load center to the generation sources based on the energy datasets, matching further comprises generating actionable energy models, each one of the actionable energy models representing a configurable allocation of the energy attribute records, based on the energy datasets, and matching the load centers and generation sources as a function of at least one actionable energy model, populate an allocation report for each load center, and transmit the allocation report to an external device in communication with the processor.