Distributed HVAC system cost optimization
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Solution Overview
Problem
Existing solutions for reducing HVAC system energy consumption are not scalable for larger systems, often suffer from excessive complexity, high computational load, and iterative negotiations between controllers, leading to suboptimal energy-saving results.
Innovation Solution
A distributed HVAC system cost optimization method where a master controller evaluates incremental modifications to system variables, communicates with slave controllers to assess cost savings, and implements changes independently, leveraging existing hardware and reducing communication load, allowing for robust and scalable energy optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If distributed HVAC systems implement complex iterative negotiation protocols between controllers to optimize energy consumption, then energy-saving potential is improved, but system complexity and computational load increase excessively
Solution Approach 1:
The system divides the HVAC control architecture into hierarchical segments: a master controller that performs complex optimization calculations and slave controllers that execute simplified control actions. This segmentation allows the master controller to handle the computational burden of energy optimization while slave controllers maintain simple operational status, resolving the contradiction between achieving energy savings and maintaining system simplicity.
Solution Approach 2:
The master controller acts as an intermediary between the optimization algorithm and the distributed slave controllers. It translates complex energy optimization decisions into simple control commands for slave controllers, eliminating the need for iterative negotiations between controllers while still achieving energy-saving goals through centralized optimization.
2Loss of energy
If existing HVAC optimization solutions are applied to larger systems, then energy-saving coverage is improved, but scalability is reduced due to excessive complexity
Solution Approach 1:
The hierarchical control structure with one master controller and multiple slave controllers enables the system to scale to larger HVAC installations. The master controller manages the optimization for the entire system while slave controllers handle local execution, allowing the architecture to accommodate increasing system size without proportionally increasing overall complexity.
Solution Approach 2:
The master controller performs comprehensive optimization calculations for the entire HVAC system, potentially evaluating more control variables and scenarios than any single slave controller would need. This excessive action at the master level ensures optimal energy savings across the whole system while keeping individual slave controller actions simple and executable.
3Loss of energy
If iterative negotiation protocols are implemented between HVAC controllers to achieve energy optimization, then energy-saving precision is improved, but communication load and time consumption increase
Solution Approach 1:
The master controller performs all necessary optimization calculations and determines the optimal control strategy in advance, before issuing commands to slave controllers. This preliminary action eliminates the need for iterative negotiations and back-and-forth communications, achieving energy-saving precision through upfront optimization while minimizing communication time to simple command transmission.
Solution Approach 2:
The system implements feedback from slave controllers to the master controller regarding their operational status and local conditions. The master controller uses this feedback to refine its optimization decisions and issue updated control commands, achieving precise energy savings through informed centralized control without requiring iterative negotiations between controllers.
Data Source
AI summary
Various embodiments herein include at least one of systems, devices, methods, and methods for distributed HVAC system cost optimization. Such embodiments are generally implemented within a controller of HVAC system component, such as within boiler, cooler, air handling unit, and rooftop unit controllers. In some embodiments, multiple controllers exchange data to control various components of an HVAC system. One of the controllers, such as a primary plant of the system for heating or cooling, is designated as a master controller and the other component controllers are designated as slave controllers. Each controller, both master and slave controllers, includes at least one model that models variable settings of the component or components for which the respective controller is responsible. The model is utilized by the respective controller to both adjust the modeled variable component settings and to determine a cost-variable of operation.