Energy Storage Bid Optimization via Price Quantile Analysis
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Solution Overview
Problem
In energy wholesale markets, managing the charging and discharging of energy storage devices to optimize energy purchases and sales based on fluctuating market prices is challenging due to the complexity of determining optimal bid prices and capacity thresholds.
Innovation Solution
The system generates distributions of clearing price data points to determine lower and upper quantile price points based on energy storage device capacity and rates, selecting sets of these points to calculate buy and sell bids, and adjusts bids using delta ratios to optimize energy transactions in energy markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If energy storage devices participate in wholesale energy markets to optimize purchases and sales, then profitability is improved, but the complexity of determining optimal bid prices and capacity thresholds increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring clearing price data points from wholesale energy markets and using this information to dynamically adjust bid prices and capacity thresholds. The system analyzes historical and real-time price data, compares it against stored device parameters, and feeds back optimized bid recommendations to the energy storage device operator, resolving the complexity of manual optimization while maximizing profitability
Solution Approach 2:
The system enables self-service by automatically determining optimal bid prices and capacity thresholds using algorithms that process clearing price data points and device capability parameters. The automated system performs the complex calculations and decision-making independently, eliminating the need for manual analysis and reducing operational complexity while maintaining optimal profitability
2Productivity
If bid prices are optimized using clearing price data distributions, then energy purchase and sale efficiency is improved, but the computational processing requirements increase
Solution Approach 1:
The system applies preliminary action by pre-calculating and storing capability parameters such as charge rate, discharge rate, and capacity thresholds before market transactions occur. By preparing these parameters in advance and organizing clearing price data points into distributions beforehand, the system reduces real-time computational requirements while maintaining high transaction efficiency when actual bids are placed
Data Source
AI summary
Techniques for managing the charging and/or discharging of an energy storage device are disclosed. The techniques include obtaining clearing price data points spanning a plurality of dates and for each date: generating a distribution of the clearing price data points corresponding to the date; determining a lower quantile price point of the distribution based on a capacity of the device and a charge rate of the device; and determining an upper quantile price point of the distribution based on the capacity and a discharge rate of the device. The techniques include selecting a first set of the lower quantile price points from the lower quantiles and a second set of the upper quantile price points from the upper quantiles. The techniques include determining a buy bid based on the first set and a sell bid based on the second set and selectively transmitting the buy bid and/or the sell bid.