Hybrid Energy Storage Forecasting for ISO Market Bidding
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Solution Overview
Problem
Current energy management systems in power grids lack effective consideration of renewable energy generation uncertainties, particularly under less-than-ideal conditions, leading to market price fluctuations and inefficiencies in energy storage optimization.
Innovation Solution
A hybrid energy storage optimization system that forecasts renewable energy generation and market values using machine-learning models, such as random forest and logistic regression, to dynamically adjust forecasts and optimize energy storage operations within independent system operator (ISO) markets, accounting for uncertainties and variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are used to forecast renewable energy generation and market values, then forecast accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine-learning models as intermediary components between raw renewable energy data and decision-making processes. These models (random forest, logistic regression) serve as mediators that process uncertain renewable generation data and market value data to produce accurate forecasts, thereby resolving the contradiction by embedding complexity within specialized forecasting modules rather than the entire energy management system.
Solution Approach 2:
The system dynamically adjusts forecasting parameters and model configurations based on varying conditions of renewable energy generation and market volatility. By changing parameters such as model selection, forecast horizons, and uncertainty thresholds, the system maintains high forecast accuracy while adapting complexity levels to match operational needs, thus resolving the contradiction between precision and complexity.
2Reliability
If the system accounts for renewable energy uncertainties and market fluctuations, then energy storage optimization is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary forecasting of renewable energy generation and market values before making energy storage optimization decisions. By pre-calculating probability distributions and expected outcomes using machine-learning models, the system reduces real-time computational burden while maintaining optimization reliability, thus resolving the contradiction between reliable optimization and computational requirements.
Solution Approach 2:
The patent implements partial optimization by focusing computational resources on critical decision points and time periods where uncertainty has the greatest impact on energy storage operations. Rather than continuously optimizing at full computational intensity, the system applies optimization selectively based on forecasted uncertainty levels, thereby maintaining reliability while reducing overall computational requirements.
Data Source
AI summary
Methods and systems for renewable energy and storage hybrid resource forecasting and optimization, via an energy storage optimization system, against independent system operator (ISO) market values while accounting for dispatch variabilities in services.