Battery Storage Bidding Lookup Table Monotonicity
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Solution Overview
Problem
The integration of renewable energy sources like wind and solar into the electrical grid is hindered by their intermittent and uncertain nature, requiring effective management of energy storage systems to minimize congestion and maximize revenue through optimal control of power systems, especially in hour-ahead energy markets.
Innovation Solution
A method and system using a processor to generate a lookup table approximating a value function with four dimensions: state of the storage system, price of electricity, prior low bid, and prior high bid, exploiting monotonicity to iteratively update the table and compute optimal bids for hour-ahead energy markets, allowing for efficient battery arbitrage and profit maximization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lookup table approximating a value function with four dimensions is generated to compute optimal bids, then bidding accuracy and revenue maximization are improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent segments the four-dimensional value function computation by exploiting monotonicity properties to divide the state space into regions where the function behaves predictably. This allows the lookup table to be constructed more efficiently by focusing computational effort on boundary regions rather than uniformly across the entire state space, thereby reducing overall computational complexity while maintaining bidding accuracy.
Solution Approach 2:
The patent performs preliminary computation to pre-generate the lookup table approximating the value function before actual bidding operations. By pre-computing and storing value function approximations across the state space, the system transforms complex real-time optimization into simpler table lookups during actual bidding, significantly reducing online computational complexity while preserving bidding accuracy.
2Adaptability or versatility
If the lookup table is updated iteratively by observing subsequent numeric values, then adaptability to market conditions is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements periodic updates to the lookup table rather than continuous real-time updates. The value function approximation is refreshed at scheduled intervals or when significant market condition changes are detected, balancing adaptability to new market conditions with acceptable processing time requirements. This periodic approach prevents excessive computational overhead while maintaining sufficient responsiveness to market dynamics.
Solution Approach 2:
The system uses observed market data and actual bidding outcomes to automatically refine and update the value function approximation without requiring external retraining or manual intervention. The lookup table self-updates by leveraging monotonicity properties and new observations, reducing the need for intensive external processing while improving adaptability over time.
3Productivity
If monotonicity is exploited to update regions of the lookup table, then computational efficiency is improved, but the assumption of monotonicity may limit accuracy in non-monotonic regions
Solution Approach 1:
The patent applies local quality by using monotonicity assumptions specifically in regions where they are valid (such as certain state spaces under specific market conditions) while avoiding their application in regions where the value function may exhibit non-monotonic behavior. This localized use of monotonicity maintains computational efficiency in appropriate regions while preserving accuracy in regions where monotonicity does not hold, effectively balancing the trade-off between efficiency and precision.
Data Source
AI summary
A method for generating bids for a look-ahead, e.g. hour-ahead, energy market is disclosed. The method includes providing a processor configured to generate a lookup table approximating a value function having four dimensions including: state of the storage system, price of electricity, prior low bid and prior high bid. The processor is configured to observe an initial numeric value for a first set of dimensions and exploiting monotonicity to update a first region of the lookup table. The processor is configured to iteratively observe subsequent numeric values for a next set of dimensions and update subsequent regions of the lookup table while exploiting monotonicity to generate the lookup table. The value function is configured with numeric values for all possible sets of dimensions, the numeric values being usable to compute optimal bids.