Building Equipment Capacity Control Under Dynamic Production Constraints
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Solution Overview
Problem
Optimally allocating energy loads across various subplants in a central plant is challenging, especially with real-time pricing from utilities, where producing resources at low cost and storing them for use during higher costs is advantageous.
Innovation Solution
A control algorithm is implemented that constrains production below maximum values for multiple time steps and dynamically adjusts these constraints based on predicted values of dynamic variables, such as weather forecasts, to optimize energy distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If production is constrained below maximum values to take advantage of real-time pricing and store resources during low-cost periods, then economic cost is reduced, but production capacity utilization decreases
Solution Approach 1:
The control algorithm performs preliminary actions by producing and storing energy resources during low-cost periods before peak demand occurs. The system anticipates future high-cost periods and prepares by accumulating resources in storage tanks during economically favorable times, thereby reducing overall energy costs while maintaining production capacity utilization through strategic timing.
Solution Approach 2:
The system dynamically adjusts production constraints based on real-time pricing signals and predicted demand patterns. Rather than maintaining static maximum production limits, the control algorithm continuously modifies production targets to align with varying economic conditions, enabling flexible optimization of both cost and productivity across different time periods.
2Productivity
If production is dynamically adjusted based on predicted weather conditions and lift variations, then operational efficiency is improved, but control system complexity increases
Solution Approach 1:
The control algorithm incorporates feedback mechanisms that continuously monitor actual weather conditions, lift variations, and production outcomes. This feedback loop enables the system to learn from past performance and refine its predictions, improving operational efficiency through data-driven adjustments while managing control complexity through iterative optimization rather than overly complex upfront modeling.
Solution Approach 2:
The system optimizes operational efficiency by dynamically changing key parameters such as production targets, storage rates, and equipment operating points based on predicted weather conditions and lift variations. This parameter-based approach allows flexible adaptation to changing conditions without requiring complex structural modifications to the control system architecture.
3Measurement precision
If maximum production values are updated as a function of dynamic variables like outdoor temperature, then production accuracy is improved, but measurement and prediction requirements increase
Solution Approach 1:
The control algorithm uses universal relationships between outdoor temperature and equipment performance that apply across different operating conditions. By establishing general functional relationships rather than requiring equipment-specific calibration curves, the system achieves accurate production predictions while reducing the need for extensive measurement and prediction infrastructure.
Data Source
AI summary
A method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.