Facility Energy Hedge Control for Real-Time Load Scheduling
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Solution Overview
Problem
Facilities face challenges in managing energy procurement due to volatile energy markets, leading to inefficiencies and inaccuracies in forecasting energy needs, manual or heuristic block energy purchasing, and a lack of integration with real-time operational risks and facility operations.
Innovation Solution
A dynamic optimization model using machine learning and statistical methods to forecast energy market conditions, quantify risk, and recommend block energy hedges, integrated with facility control systems to adjust operational schedules and component control for energy and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or heuristic block energy purchasing is used, then ease of operation is maintained, but measurement precision and forecasting accuracy deteriorate
Solution Approach 1:
The patent introduces an optimization tool as an intermediary between manual energy purchasing decisions and actual energy procurement. This tool processes market data, facility load information, and hedge parameters to generate recommended block energy hedge amounts, improving forecasting accuracy without requiring full automation of the purchasing process.
Solution Approach 2:
The system enables self-service through automated calculation of optimal hedge amounts based on input parameters. The optimization tool automatically processes data and generates recommendations, reducing the need for manual analysis while maintaining operational control and ease of use through a user-friendly interface.
2Reliability
If block energy hedges are purchased to protect against price volatility, then reliability of energy supply is improved, but loss of energy (in financial terms) increases due to hedge premiums
Solution Approach 1:
The optimization tool calculates partial block energy hedge amounts rather than requiring full coverage. It determines the optimal hedge ratio based on risk tolerance parameters, facility load characteristics, and market conditions, allowing facilities to purchase only the necessary portion of energy hedges to achieve desired protection levels while minimizing premium costs.
Solution Approach 2:
The system allows dynamic adjustment of risk tolerance parameters and hedge parameters to optimize the balance between reliability and cost. By changing these parameters, facilities can adjust their exposure to price volatility and hedge premium costs according to their specific needs and market conditions.
3Adaptability or versatility
If energy procurement is decoupled from real-time operations, then ease of operation is maintained, but adaptability to changing market conditions and operational risks deteriorates
Solution Approach 1:
The optimization tool incorporates feedback mechanisms by continuously receiving updated facility load information, market data, and operational parameters. This feedback loop enables the system to adjust block energy hedge recommendations in real-time based on changing conditions, improving adaptability while managing complexity through structured data integration.
Solution Approach 2:
The system integrates multiple functions including market data analysis, load forecasting, risk assessment, and hedge optimization into a single unified tool. This multi-functionality allows the system to adapt to various market conditions and operational scenarios while presenting a consistent interface, reducing perceived complexity for users.
4Productivity
If traditional energy procurement methods are used, then device complexity is minimized, but productivity in energy cost management deteriorates
Solution Approach 1:
The optimization tool performs preliminary analysis and calculation of optimal block energy hedge amounts before actual purchasing decisions are made. By pre-processing market data, facility load information, and risk parameters, the system improves energy cost management efficiency by providing actionable recommendations in advance, reducing the need for complex real-time decision-making.
Data Source
AI summary
Disclosed are techniques for system for controlling components in a facility based on energy hedges. The system can include a controller to control the components in the facility. The controller can perform a process including: receiving pre-purchased energy hedge information for a period of time, monitoring real-time energy market conditions based on real-time energy information from an energy grid data source, generating component control instructions based on current operating conditions, energy capacity of the facility, the pre-purchased energy hedge information, and the monitored real-time energy market conditions, and executing the component control instructions to cause the components in the facility to perform the operational tasks. The pre-purchased energy hedge information can be determined based on predicting block energy hedges for the period of time and purchasing at least a portion of the predicted block energy hedges for that time.