Self-Tuning Energy Asset Model for Wholesale Market Revenue
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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 calculations and disincentivize participation, as they do not account for changes in energy asset characteristics and do not provide real-time adjustments.
Innovation Solution
A mathematical optimization process using a self-tuning energy asset model that determines a suggested operating schedule for energy assets, such as building assets, based on physical attributes and real-time feedback, to optimize energy-related costs and revenues, incorporating forecasted wholesale electricity prices and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical actual electricity usage is used to determine revenue generation, then the calculation method is simple, but the accuracy of revenue calculation deteriorates and participation is disincentivized
Solution Approach 1:
The patent implements a self-tuning model that dynamically adjusts to changing energy asset characteristics in real-time, transitioning from static historical data to dynamic adaptive modeling. This resolves the contradiction by maintaining calculation accuracy through continuous adaptation while managing complexity through automated tuning mechanisms.
Solution Approach 2:
The system incorporates real-time feedback loops where actual performance data is continuously fed back to refine and update the self-tuning model parameters. This feedback mechanism enables the system to automatically improve revenue calculation accuracy without requiring manual intervention, thus managing complexity while enhancing precision.
2Adaptability or versatility
If historical actual electricity usage is used, then the method is easy to implement, but it does not account for changes in energy asset characteristics
Solution Approach 1:
The self-tuning model is designed to automatically adapt to changes in energy asset characteristics without requiring external reconfiguration or manual updates. The model self-adjusts its parameters based on incoming data, enabling the system to account for asset changes autonomously while keeping the implementation approach relatively simple.
Solution Approach 2:
The system dynamically changes model parameters in response to detected changes in energy asset characteristics. By automatically adjusting parameters rather than requiring complete model redesign, the system achieves high adaptability while controlling implementation complexity through parameter-based flexibility.
3Measurement precision
If real-time adjustments are implemented, then revenue calculation accuracy improves, but computational requirements increase
Solution Approach 1:
The self-tuning model implements real-time adjustments selectively, focusing computational resources on the most critical parameters and time periods that have the greatest impact on revenue calculation accuracy. This partial action approach maintains high accuracy while reducing overall computational power requirements by not unnecessarily optimizing all parameters at all times.
Data Source
AI summary
The apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier. The apparatuses and methods herein can be used to generate suggested operating schedules for the energy assets that including a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based on a parametric estimation using at least one model parameter. The model parameter is at least one of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.