Energy Asset Scheduling via Predictive Baseline Optimization
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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 incentivize artificial energy usage and decrease participation over time, leading to inaccurate revenue calculations and reduced participation in demand response programs.
Innovation Solution
A mathematical optimization process is used to determine a suggested operating schedule for energy assets, employing a predictive customer baseline energy profile and objective cost functions to minimize net energy-related costs, allowing energy customers to optimize energy usage and generation based on real-time and forecasted prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical actual-use-based customer baselines are used to determine revenue, then revenue calculations are simple to implement, but accuracy of revenue calculations deteriorates and participation in demand response programs decreases
Solution Approach 1:
The system performs preliminary actions by determining customer baselines and generating candidate operating schedules before the actual trading period. The optimization process pre-calculates suggested schedules based on forecasted prices and customer baseline energy profiles, allowing participants to prepare in advance rather than reacting to actual prices in real-time.
Solution Approach 2:
The system dynamically adjusts operating schedules based on forecasted wholesale electricity prices and customer-specific baseline energy profiles. The optimization process considers time-varying parameters including forecasted prices, baseline profiles, and operational constraints to generate adaptive schedules that respond to changing market conditions.
2Adaptability or versatility
If historical actual-use-based customer baselines are used, then implementation is straightforward, but participation in demand response programs decreases over time
Solution Approach 1:
The system changes key parameters by replacing fixed historical baselines with dynamically determined customer baseline energy profiles. These profiles are specific to each customer's operational characteristics and are used in conjunction with forecasted prices to generate optimized schedules, allowing the system to adapt to diverse customer needs and market conditions.
Solution Approach 2:
The system creates idealized copies of customer operational patterns through baseline energy profiles that represent typical usage without market incentives. These copied patterns serve as reference points for optimization, allowing the system to suggest deviations from normal operation that capture demand response value while maintaining operational realism.
3Measurement precision
If mathematical optimization processes are implemented, then revenue generation accuracy improves, but computational complexity increases
Solution Approach 1:
The optimization process is segmented into distinct components: determining customer baselines, generating candidate operating schedules, evaluating projected net costs for each candidate, and selecting the optimal schedule. This segmentation allows the complex problem to be broken into manageable steps that can be implemented systematically.
Solution Approach 2:
The system replaces manual or rule-based scheduling mechanisms with automated mathematical optimization. Instead of using simple thresholds or heuristic rules, the system employs objective cost functions and optimization algorithms to automatically determine schedules that minimize net energy-related costs based on forecasted prices and customer baselines.
Data Source
AI summary
The apparatuses and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier, for a system that includes an energy storage asset. The apparatuses and methods herein can be used to generate operating schedules for a controller of the energy storage asset. When implemented, the generated operating schedules facilitates derivation of the energy-related revenue, over a time period T, associated with operation of the at least one energy storage asset according to the generated operating schedule. The energy-related revenue available to the energy customer over the time period T is based at least in part on a wholesale electricity market.