Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Tyco Fire + Security Gmbh

Graeme Willmott

Matthew J Asmus

Jared W Fread

Ryan C Beaty

Shawn A Schlagenhaft

Michael J Wenzel

Mohammad N ElBsat

Matthew J Ellis

John H Burroughs

Bradley J Ridder

Andrew J Przybylski

Steven R Vitullo

Kirk H Drees

Robert D Turney

Ryan A Baumgartner

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.

Control systems and methods for building equipment with optimization modification

Disclosed herein are a system, method, and non-transitory computer readable medium for operating an energy plant. In one aspect, a system determines a ratio of flow rates between devices of the energy plant connected in parallel with each other in a branch. The system generates a candidate set of operating parameters of the devices according to the ratio of flow rates. The system predicts thermodynamic states of the devices operating according to the candidate set of operating parameters. The system determines whether the predicted thermodynamic states satisfy constraints of the devices. The system determines whether the predicted thermodynamic states satisfy a target thermal energy load of the branch based on the ratio of the flow rates. The system operates the energy plant according to the candidate set of operating parameters, in response to determining that the predicted thermodynamic states satisfy the constraints and the target thermal energy load.

Central plant control system with setpoints modification based on physical constraints

A central plant includes a plurality of subplants including a chiller configured to output supply water at a supply water temperature, a sensor configured obtain a measurement of the supply water temperature, and a control system. The control system is configured to calculate an additional load factor based on the measurement of the supply water temperature and a supply water temperature setpoint, obtain an actual load for the chiller, calculate an effective load based on the additional load factor and the actual load, generate load allocations for the plurality of subplants based on the effective load, and control the plurality of subplants to operate in accordance with the load allocations.

Central plant with secondary strong prevention

Disclosed herein are related to a system, a method, and a non-transitory computer readable storing instructions for operating an energy plant comprised of heating, ventilation and air conditioning (HVAC) devices. In one aspect, the system generates gradient data indicating a gradient of operating performance of the energy plant with respect to values of a plurality of control variables of HVAC devices. The system determines, from the plurality of control variables, a reduced group of control variables of the HVAC devices based on the gradient data. The system determines a set of values of the reduced group of control variables. The system operates the energy plant according to the determined set of values of the reduced group of control variables.

Central plant control system with computation reduction based on sensitivity analysis

A controller for a central plant having subplants operating to produce resources consumed by a building. The controller includes a processing circuit including a processor and memory storing instructions executed by the processor. The memory includes an offline rank generator that receives historical subplant allocation data and generates subplant ranks based on the historical subplant allocation data, each of the subplant ranks is associated with one of the subplants and defines a priority of each subplant with respect to production of a resource relative to other subplants that produce the resource. The memory also includes a high level optimizer that uses the subplant ranks associated with each of the subplants to determine resource allocation of the subplants according to the subplant ranks. The processing circuit is operates the subplants according to the resource allocation.

Central plant control system with subplant rank generator

A controller for a plurality of heating, ventilation, or air conditioning (HVAC) devices includes a processing circuit that includes one or more processors and memory. The controller detects a change in condition that affects an operating status of a first HVAC device of the plurality of HVAC devices. The controller uses schematic relationships between the plurality of HVAC devices to determine a reduced subset of the plurality of HVAC devices for which operating parameters are to be generated based on the operating status of the first HVAC device. The controller generates operating parameters for the reduced subset of the plurality of HVAC devices and operates the plurality of HVAC devices using the operating parameters.

Central plant control system with dynamic computation reduction

A control system for a central plant having devices serving energy loads of a building. The control system includes a central plant optimizer wizard generator that receives user input and generates a central plant model for the control system. The central plant optimizer wizard generator includes an equipment model generator that receives user input and generates equipment models for the devices in the central plant, a device layer generator that generates device layers, an asset layer generator that generates asset layers, and a scaled load profile generator that generates a scaled building load profile of the building using the asset layers and the user data. The central plant optimizer wizard generator generates the central plant model using the asset layers and the scaled building load profile. A demand response optimizer uses the central plant model to determine control decisions for the devices included in the central plant.

Building control system with central plant model generation

Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

Central plant control system based on load prediction through mass storage model

A control system for a central plant having subplants including devices operating to serve energy loads of a building. The system includes a high level optimization module that performs high level optimization of thermal loads subject to constraints to generate subplant load allocations. The control system includes a low level optimization module that performs low level optimization of the subplant load allocations to determine operating states for the devices. The control system includes a constraint modifier that modifies the constraints for the high level optimization module based on equipment schedules. The control system also includes a binary optimization modifier including a pruner module that receives the minimum off schedule to determine adjusted branches and a seeder module that receives the minimum on schedule to determine a starting node for use in binary optimization performed by the low optimization module.

Central plant optimization with optimization modification

Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant having a plurality of subplants that operate to produce one or more resources consumed by a building based on ranks. In one aspect, the system obtains rank identifiers indicating ranks of the plurality of subplants. In one aspect, the ranks indicate a priority of each subplant with respect to production of a resource relative to other subplants that produce the resource. In one aspect, the system determines resource allocation of the plurality of subplants according to the ranks of the plurality of subplants. The system may operate the plurality of subplants according to the resource allocation.

Central plant control system with rank-based asset allocator

Disclosed herein are related to a method, a system, and a non-transitory computer readable medium storing instructions for operating a group of central plant equipment to serve thermal energy loads of a building or building system. In one approach, a base capacity of one or more devices of the group of central plant equipment is identified. A change in requested load allocated to the one or more devices crossing the base capacity at a crossover time may be detected. In one approach, an adjusted capacity of the one or more devices is set such that the adjusted capacity is offset from the base capacity before the crossover time and decays toward the base capacity during a decay period after the crossover time. The requested load allocated may be compared with the adjusted capacity after the crossover time. The group of central plant equipment may be operated according to the comparison.

Central plant control system with decaying capacity adjustment

A control system for a central energy facility with distributed energy storage includes a high level coordinator, a low level airside controller, a central plant controller, and a battery controller. The high level coordinator is configured to perform a high level optimization to generate an airside load profile for an airside system, a subplant load profile for a central plant, and a battery power profile for a battery. The low level airside controller is configured to use the airside load profile to operate airside HVAC equipment of the airside subsystem. The central plant controller is configured to use the subplant load profile to operate central plant equipment of the central plant. The battery controller is configured to use the battery power profile to control an amount of electric energy stored in the battery or discharged from the battery at each of a plurality of time steps in an optimization period.

Control system for central energy facility with distributed energy storage

A controller for an energy plant includes a processing circuit having a processor and memory which stores instructions executed by the processor. The processing circuit is configured to identify, from a plurality of thermodynamic states affected by a plurality of heat, ventilation, and air conditioning (HVAC) devices, a reduced subset of the plurality of thermodynamic states to be predicted based on connections between the plurality of HVAC devices. The processing circuit is configured to predict values of the reduced subset of the plurality of thermodynamic states and operate the plurality of HVAC devices based on the predicted values of the reduced subset of the plurality of thermodynamic states.

Central plant control system with computation reduction based on graph theory

Predictor variables that affect production or consumption of a resource are sampled at a plurality of times within a time period and aggregated to generate an aggregated value for each predictor variable over the time period. A model is generated which estimates the production or consumption in terms of the predictor variables. A regression process is performed to generate values for a plurality of regression coefficients in the model based on a cumulative production or consumption of the resource for the time period and the aggregated values. The sampled values of the predictor variables are then applied as inputs to the model to estimate productions or consumptions of the resource at each of the plurality of times. The estimated productions or consumptions may be used as inputs to a controller that operates equipment.

Central plant control system with building energy load estimation

Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, a system determines schematic relationships of a plurality of heat, ventilation, and air conditioning (HVAC) devices of the energy plant based on connections of the plurality of HVAC devices. Each HVAC device is configured to operate according to a corresponding operating parameter. The system determines, from a plurality of HVAC devices, a reduced subset of the HVAC devices based on the schematic relationships. The system predicts thermodynamic states of the reduced subset. The system determines a set of operating parameters of the plurality of HVAC devices based on the thermodynamic states. The system operates the plurality of HVAC devices according to the set of operating parameters.

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West Milwaukee, WI, United States of America

Graeme Willmott