Building Energy Curtailment Planning for Net Consumption Targets
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Solution Overview
Problem
Existing building management systems (BMS) face challenges in optimally controlling facilities to achieve net zero energy status over a desired time period, especially in adapting to changing conditions.
Innovation Solution
A method and system that provide a net consumption trajectory with targets for subperiods, generate curtailment actions using predictive optimization, and implement these actions to achieve net energy or carbon targets, while also providing a user dashboard for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive optimization is used to generate curtailment actions for net zero energy targets, then energy management effectiveness is improved, but system complexity increases
Solution Approach 1:
The system divides the time period into multiple subperiods and generates curtailment actions for each subperiod separately. This segmentation allows the complex optimization problem to be broken down into manageable chunks, improving energy management effectiveness while keeping computational complexity tractable through incremental processing rather than attempting to optimize the entire time period at once.
2Loss of energy
If curtailment actions are implemented to achieve net consumption targets, then energy efficiency is improved, but operational flexibility is reduced
Solution Approach 1:
The system generates curtailment actions dynamically for each subperiod based on current conditions and net consumption trajectory. This dynamic approach allows the system to adapt curtailment strategies in real-time, maintaining energy efficiency while preserving operational flexibility by adjusting actions based on changing conditions rather than implementing fixed, rigid curtailment schedules.
Solution Approach 2:
The system uses feedback from net consumption measurements and trajectory tracking to adjust curtailment actions. By continuously monitoring actual consumption against targets and modifying subsequent actions accordingly, the system achieves energy efficiency while maintaining flexibility to respond to varying operational conditions and achieve net zero targets.
3Manufacturing precision
If detailed curtailment actions are generated for each subperiod, then control precision is improved, but computational load increases
Solution Approach 1:
The system segments the optimization problem into subperiod-specific curtailment actions rather than computing a single comprehensive plan for the entire time period. This segmentation provides detailed control precision for each subperiod while reducing computational load by processing smaller, incremental optimization problems that can be solved more efficiently with available computing resources.
Data Source
AI summary
A method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.