Energy Asset Operating Schedule Optimization
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Solution Overview
Problem
Current methods for determining revenue generation in wholesale electricity markets are limited by reliance on historical actual electricity usage, which can lead to inaccurate revenue calculations and discourage participation due to artificially inflated usage and decreased revenue over time.
Innovation Solution
A mathematical optimization process is used to determine a suggested operating schedule for energy assets, employing a predictive customer baseline energy profile based on physical attributes and real-time feedback, rather than historical data, to maximize revenue generation from wholesale electricity markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical actual electricity usage data is used to determine revenue generation, then revenue calculations can be performed using available data, but the accuracy of revenue calculations deteriorates over time due to artificially inflated usage and decreased revenue
Solution Approach 1:
The patent applies preliminary action by establishing a predictive customer baseline energy profile before actual usage patterns can distort historical data. This predictive profile is created using physical attributes and real-time feedback mechanisms, allowing the system to proactively determine operating schedules that maximize revenue before the problem of inflated historical usage occurs. The optimization process uses this pre-established baseline to guide energy asset operation, preventing the deterioration of calculation accuracy over time.
Solution Approach 2:
The patent implements feedback by using real-time energy usage data and actual revenue outcomes to continuously refine and update the predictive customer baseline energy profile. This closed-loop feedback mechanism allows the system to adapt to changing conditions while maintaining accurate revenue calculations. The real-time feedback prevents the accumulation of errors that would otherwise occur with static historical data, thereby resolving the contradiction between maintaining accuracy over time and using available historical information.
2Quantity of substance
If historical actual electricity usage is relied upon, then data availability is improved, but participation encouragement deteriorates due to artificially inflated usage and decreased revenue
Solution Approach 1:
The patent applies the taking out principle by extracting only the essential physical attributes from historical data to create the predictive baseline, while excluding the distorted accumulated usage patterns. This selective extraction removes the harmful element (artificially inflated usage) while preserving the useful information (physical attributes that determine energy consumption). The system uses this purified baseline to generate operating schedules that reflect true operational needs rather than historical anomalies, thereby maintaining participant encouragement while still utilizing available data.
Solution Approach 2:
The patent creates a predictive customer baseline energy profile that serves as an idealized copy of expected usage patterns, rather than directly using the flawed historical actual usage data. This predictive copy is generated based on physical attributes and optimized for revenue maximization, providing a clean reference model that doesn't suffer from the accumulation of inflated usage. Participants are encouraged because the system uses this optimized copy rather than their distorted historical performance, allowing them to achieve better results than their historical data would suggest.
3Measurement precision
If predictive customer baseline energy profile based on physical attributes is used, then revenue calculation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a predictive customer baseline energy profile that serves multiple functions simultaneously: it establishes an accurate reference for revenue calculation, provides an optimized operating schedule for energy assets, and acts as a dynamic target that adapts to changing conditions. This single multi-functional predictive model replaces what would otherwise require separate systems for historical data analysis, real-time monitoring, and optimization, thereby reducing overall system complexity while maintaining high accuracy. The mathematical optimization process integrates these functions into a unified approach that determines operating schedules directly from the predictive baseline.
Data Source
AI summary
Apparatus, systems, and methods are described that can be used to generate an operating schedule for a controller of an energy storage asset that is in communication with a transport vehicle, based on an optimization process. The operating schedule is generated based on an operation characteristic of the energy storage asset, an energy-generating capacity of the transport vehicle in communication with the energy storage asset based on a motion of the transport vehicle, and a price associated with a market (including a regulation market and/or an energy market). Operation of the energy storage asset according to the generated operating schedule facilitates derivation of energy-related revenue, over a time period T. The energy-related revenue available to the energy customer over the time period T is based at least in part on the regulation market and/or the energy market.