HVAC Self-Optimizing Control Using Non-Optimal Operating Data
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Solution Overview
Problem
Building management systems face challenges in optimizing energy costs due to the complexity of HVAC systems, where conventional feedback control structures often fail to efficiently manage trade-offs between fan and chiller energy consumption, especially when optimal data for optimal plant behavior are not available.
Innovation Solution
A self-optimizing control system that calculates a self-optimizing control variable by multiplying the measured state of building equipment by a matrix and adding an offset vector, using non-optimal reference data to adjust inputs and drive the system towards optimal operation, thereby optimizing energy usage without requiring optimal operating data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedback control structures are used to maintain setpoints, then control objectives are satisfied, but optimization cost function measures vary with system operating points and disturbances leading to increased energy consumption
Solution Approach 1:
The patent transforms the control approach by changing from direct control of physical parameters (temperature, flow rates) to control of optimized parameters (cost function derivatives). The controller calculates optimized setpoints by solving the cost function derivative equation, allowing the system to adapt parameters dynamically to minimize energy consumption while maintaining comfort. This is evident in the optimization layer that adjusts supply air temperature and fan speed based on real-time cost function evaluation.
Solution Approach 2:
The patent implements a two-layer feedback structure: the control layer provides fast feedback to maintain comfort setpoints, while the optimization layer provides slower feedback by adjusting setpoints based on cost function derivatives. This nested feedback mechanism allows the system to respond to disturbances while continuously optimizing energy consumption, resolving the contradiction between reliable control and energy efficiency.
2Use of energy by moving object
If an optimization method is used to adjust setpoints to minimize total cost, then energy costs are reduced, but computational requirements and system complexity increase
Solution Approach 1:
The patent divides the control system into distinct functional layers: the control layer handles fast comfort maintenance, while the optimization layer handles slower cost minimization. This segmentation allows each layer to operate independently with appropriate computational complexity, reducing overall system complexity while achieving both comfort and cost optimization goals.
Solution Approach 2:
The patent introduces an intermediary optimization layer that translates between the control layer's comfort objectives and the building's cost minimization goals. This intermediary computes optimized setpoints using cost function derivatives and passes them to the control layer, acting as a mediator that reconciles the conflicting objectives without requiring direct complex optimization in the fast control loop.
3Ease of operation
If self-optimizing control is designed to maintain optimization targets at constant setpoints, then both control and optimization objectives are satisfied, but optimal data corresponding to optimal plant behavior is not available
Solution Approach 1:
The patent performs preliminary computation of the cost function derivative and its relationship with measured variables before implementing control. By pre-calculating the optimization mapping (how cost function derivatives relate to measurable system states), the system can directly compute optimized setpoints without requiring real-time optimal data during operation. This preliminary analysis enables the self-optimizing control to function with readily available measurements.
Solution Approach 2:
The patent replaces the need for physical optimal data collection with a mathematical model-based approach. Instead of requiring actual optimal operating data from the plant, the system uses a mathematical model to compute the cost function derivative and its relationship with measured variables, substituting physical data requirements with computational relationships that can be derived from standard measurements.
Data Source
AI summary
A building management system includes building equipment configured to operate in accordance with an input to alter a variable state or condition of a building as a process function of the input while incurring a cost of operating the equipment as a cost function of the input. The building management system also includes a controller configured to calculate a value of a self-optimizing control variable as a function of a measured state of the building equipment and drive the value of the self-optimizing control variable towards a setpoint value by generating the input based on the self-optimizing control variable and providing the input to the building equipment. The function comprises multiplying the measured state by a matrix and adding an offset vector. Values of elements of the matrix and the offset vector are determined using a non-optimal reference.