Building Load Curtailment Using Gradient-Based Constraint Optimization
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Solution Overview
Problem
Central plants face challenges in efficiently managing energy loads during peak periods, leading to uncomfortable conditions and increased costs due to the inability to meet load demands with existing equipment, particularly in large buildings.
Innovation Solution
A controller system that determines gradients of objective functions related to carbon emissions, disease transmission risk, occupant comfort, and monetary costs to optimize the operation of equipment by modifying constraint variables such as ventilation rates and load requirements, allowing for optimized energy distribution across subplants during curtailment periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing building equipment is used to meet large building load demands, then equipment capacity is maintained, but the load cannot be met resulting in uncomfortable conditions
Solution Approach 1:
The system dynamically modifies constraint variables (ventilation rates, temperature setpoints, load requirements) based on real-time gradient calculations of the objective function. This allows the system to adapt equipment operation parameters to meet varying load demands while maintaining occupant comfort within acceptable ranges defined by the modified constraints.
Solution Approach 2:
The controller continuously updates constraint variables based on gradient-based optimization, transforming the static equipment capacity into a dynamic system that can adapt to changing load conditions. The system dynamically adjusts ventilation rates, temperature setpoints, and other operational parameters to respond to real-time building conditions and load variations.
2Use of energy by moving object
If load curtailment is implemented to meet equipment capacity limits, then energy consumption is reduced, but building conditions become uncomfortable
Solution Approach 1:
The system changes operational parameters within dynamically modified constraint boundaries. By adjusting constraint variables such as temperature setpoints and ventilation rates based on gradient calculations, the system enables load curtailment that reduces energy consumption while maintaining building conditions within acceptable comfort ranges, rather than using fixed conservative constraints.
Solution Approach 2:
The gradient-based optimization provides continuous feedback on how changes in constraint variables affect the objective function (carbon emissions, cost, comfort). This feedback mechanism allows the system to make informed adjustments to load curtailment strategies, reducing energy consumption while monitoring and maintaining acceptable building comfort levels.
3Reliability
If constraint variables are kept fixed to ensure safety margins, then risk mitigation is maintained, but optimization opportunities are lost
Solution Approach 1:
The system transforms fixed safety margin constraints into dynamic constraint variables that adapt based on real-time gradient calculations. The controller continuously adjusts constraint variables within safe operating boundaries, allowing the system to capture optimization opportunities while maintaining reliability through gradient-based risk assessment and constraint modification.
Solution Approach 2:
The system dynamically modifies constraint parameters based on gradient information, allowing safety margins to be optimized rather than fixed. By changing constraint variables in response to gradient calculations, the system maintains reliability through controlled parameter adjustments while improving optimization efficiency by utilizing previously conservative constraint boundaries.
Data Source
AI summary
A controller includes a processing circuit comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include determining a gradient of an objective function with respect to a constraint variable used to define a constraint on a control process that uses the objective function and modifying the constraint variable to have a modified value in response to determining that a trigger condition is satisfied. The trigger condition is based on the gradient. The operations include performing the control process subject to the constraint using the modified value of the constraint variable and operating equipment in accordance with a result of the control process.