Power Asset Forecast Scheduling for Grid Delivery Commitments
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Solution Overview
Problem
Power generating assets, particularly those relying on renewable energy sources, face challenges in meeting power delivery obligations to the electrical grid due to unpredictable environmental conditions, leading to potential financial penalties and inefficiencies in energy market participation.
Innovation Solution
A method and system that utilize a controller to predict a future power profile based on environmental data sets and asset health, determining an obligated-power-production schedule, and modifying setpoints to ensure accurate power delivery to the grid, thereby aligning with power production agreements and enhancing asset health management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the power generating asset operates based on predicted environmental conditions, then the power delivery capacity can be optimized, but the reliability of meeting obligated power delivery to the grid deteriorates due to environmental variability
Solution Approach 1:
The system performs preliminary actions by predicting the future power profile over a modeling interval and determining the obligated power production schedule before the delivery period begins. This allows the asset to proactively plan power delivery to meet grid obligations while optimizing for expected environmental conditions, rather than reacting to variability in real-time
Solution Approach 2:
The system dynamically adjusts the modeling interval based on the degree of variability in environmental data. When environmental conditions are highly variable, the modeling interval is shortened to maintain prediction accuracy. This dynamic adaptation allows the system to balance between capturing enough environmental variation for accurate prediction while maintaining reliability in meeting power delivery obligations
2Measurement precision
If the modeling interval is extended to capture more environmental variability, then the prediction accuracy improves, but the loss of time for real-time power delivery adjustment increases
Solution Approach 1:
The modeling interval is made dynamic rather than fixed. The system automatically adjusts the length of the modeling interval based on the degree of variability in environmental data. When variability is low, a longer interval can be used to improve prediction accuracy. When variability is high, the interval is shortened to maintain accuracy while reducing time loss. This dynamic adjustment resolves the contradiction between capturing sufficient variability and maintaining responsive time
Solution Approach 2:
The system changes the parameter of modeling interval length based on environmental conditions. By monitoring the degree of variability in environmental data and adjusting the modeling interval accordingly, the system optimizes the balance between prediction accuracy (which benefits from longer intervals with more data) and response time (which benefits from shorter intervals)
Data Source
AI summary
A system and method are provided for operating a power generating asset coupled to an electrical grid. Accordingly, a controller receives an environmental data set indicative of at least one environmental variable projected to affect the power generating asset over a plurality of potential modeling intervals. The controller then determines the variability of the environmental data set and a corresponding modeling-confidence level at each of the potential modeling intervals based on the variability. A modeling interval is thus selected corresponding to a desired modeling-confidence level. A computer-implemented model is employed to predict a future power profile for the power generating asset over the selected modeling interval. The future power profile is indicative of a power-delivery capacity of the power generating asset at each of a plurality of time intervals of the modeling interval. Based, at least in part, on the future power profile, the controller determines an obligated-power-production schedule for the power generating asset over the modeling interval. The obligated-power-production schedule corresponds to a power production agreement with the electrical grid. In accordance with the obligated-power-production schedule, the controller modifies at least one setpoint of the power generating asset to deliver electrical power to the electrical grid.