Simulated Customer Baseline Energy Profile for Wholesale Electricity Revenue
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Solution Overview
Problem
Current methods for determining revenue in wholesale electricity markets are limited by historical actual-use-based customer baselines, which can lead to inaccurate estimates and discourage participation due to disincentives and artificially inflated usage, undermining the goal of reducing electricity consumption.
Innovation Solution
A mathematical optimization process using a simulated and predictive customer baseline energy profile, based on physical attributes of energy assets and real-time feedback, to determine suggested operating schedules for energy assets, facilitating revenue generation from wholesale electricity markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical actual-use-based customer baselines are used to determine revenue in wholesale electricity markets, then revenue calculations are based on simple historical data, but revenue accuracy deteriorates and participation is discouraged due to artificially inflated electricity usage and decreased revenue over time
Solution Approach 1:
The patent transforms the revenue calculation approach by changing the fundamental parameters used: instead of relying on historical actual-use data, the system uses simulated customer baseline energy profiles generated through mathematical optimization. This parameter change enables accurate representation of energy asset behavior under various operating conditions, resolving the contradiction between calculation simplicity and accuracy.
Solution Approach 2:
The patent creates a simulated copy of the customer baseline energy profile that replicates how energy assets would actually behave based on their physical attributes and operating conditions. This simulated profile serves as a more accurate surrogate for actual usage patterns, eliminating the need for complex historical data analysis while improving revenue calculation accuracy.
2Ease of operation
If historical actual-use-based customer baselines are used, then the method is easy to implement, but energy customers are discouraged from participating in demand response programs due to artificially inflated electricity usage and decreased revenue over time
Solution Approach 1:
The system enables energy assets to effectively self-report their baseline energy consumption through the simulated customer baseline profile. The mathematical optimization process automatically determines how assets would behave under normal operating conditions without requiring manual historical data collection or complex adjustments, making the system easy to implement while accurately reflecting actual usage patterns.
Solution Approach 2:
The patent incorporates real-time feedback mechanisms where the simulated baseline profile continuously adapts to actual operating conditions and physical changes in energy assets. This feedback loop ensures that revenue calculations remain accurate and reflective of current asset behavior, maintaining customer incentive to participate in demand response programs.
3Measurement precision
If simulated customer baseline energy profile based on physical attributes and real-time feedback is used, then revenue calculation accuracy is improved, but the complexity of the method increases
Solution Approach 1:
The patent replaces complex mechanical data collection and historical analysis systems with a computational mathematical optimization model. By substituting physical data gathering methods with algorithm-based simulation, the system achieves high accuracy in revenue calculation while maintaining operational simplicity through automated computation rather than manual processes.
4Adaptability or versatility
If simulated customer baseline energy profile based on physical attributes and real-time feedback is used, then the method adapts to current conditions and physical changes, but the computational requirements increase
Solution Approach 1:
The system performs preliminary computational work by establishing the simulated customer baseline energy profile in advance, incorporating physical attributes and expected operating conditions. This preliminary action enables the system to adapt to current conditions efficiently during actual demand response events, reducing real-time computational energy requirements while maintaining high adaptability.
Data Source
AI summary
The disclosure facilitates generation of energy-related revenue for an energy customer of an electricity supplier. The disclosure herein can be implemented to generate suggested operating schedules for energy assets that include 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 at least in part on a physical model of the thermodynamic property of the at least one energy asset and at least in part on data representative of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and/or 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.