Building Equipment Control With Constraint-Modified Load Optimization
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Solution Overview
Problem
Central plant systems face challenges in optimizing thermal energy load distribution across subplants to minimize energy consumption and operating costs, particularly in managing device operation states and constraints effectively.
Innovation Solution
A control system with high and low-level optimization modules, a constraint modifier, and a binary optimization modifier is implemented to determine optimal subplant load allocations and operating states by modifying constraints based on user inputs, minimum on/off schedules, and must-run schedules, using binary optimization and hysteresis adjustments to optimize device selection and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a control system optimizes thermal load distribution across subplants to minimize energy consumption, then energy efficiency improves, but system complexity increases due to multiple optimization modules and constraint modifications
Solution Approach 1:
The control system is divided into distinct functional modules: a high-level optimization module that performs thermal load optimization subject to constraints, a low-level optimization module that determines device operating states, a constraint modifier that adjusts constraints based on minimum on/off schedules, and a binary optimization modifier. This segmentation allows each module to handle specific optimization tasks independently, improving energy efficiency while organizing complexity into manageable components.
Solution Approach 2:
The constraint modifier performs preliminary actions by pre-processing constraints based on minimum on schedules and minimum off schedules before they are applied to the optimization modules. This preliminary constraint modification reduces the computational burden during real-time optimization, allowing the system to achieve energy efficiency without overwhelming complexity during operation.
2Measurement precision
If the system performs high-level and low-level optimization with multiple constraint modifications, then optimization precision improves, but computational time increases
Solution Approach 1:
The optimization process is segmented into two levels: high-level optimization that determines thermal load distribution across subplants subject to modified constraints, and low-level optimization that determines specific device operating states. This segmentation allows each level to focus on specific decisions, improving overall optimization precision while reducing the computational time required for each individual optimization step.
Solution Approach 2:
The constraint modifier performs preliminary constraint adjustments based on minimum on schedules and minimum off schedules before the optimization processes begin. This preliminary action pre-processes the constraint space, reducing the computational complexity that the optimization modules must handle during real-time operation, thereby improving optimization precision without excessive computational time.
3Reliability
If the constraint modifier adjusts constraints based on minimum on/off schedules, then operational reliability improves, but system adaptability decreases due to rigid constraint enforcement
Solution Approach 1:
The constraint modifier dynamically adjusts constraints based on minimum on schedules and minimum off schedules, allowing the system to adapt its operational requirements in real-time. This dynamic constraint modification ensures that devices maintain reliable operation patterns (improving operational reliability) while still allowing flexibility in how the optimization modules respond to changing thermal loads and conditions (maintaining system adaptability).
Data Source
AI summary
A controller is provided for building equipment including a plurality of devices that operate in parallel to affect an environmental condition of a building. The controller includes one or more processing circuits including one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include lowering an upper bound or raising a lower bound of one or more constraints based on a minimum off schedule or a minimum on schedule for the building equipment, performing an optimization of an objective function subject to the one or more constraints to generate control decisions for the building equipment, and operating the building equipment in accordance with the control decisions to affect the environmental condition of the building.