Johnson Controls Tyco IP Holdings LLP

Tyco Fire + Security Gmbh

Johnson Controls Technology Company

Shawn A Schlagenhaft

Ryan C Beaty

Graeme Willmott

Jared W Fread

Matthew J Asmus

Michael J Wenzel

Homero L Noboa

Ryan A Baumgartner

Matthew J Ellis

Steven R Vitullo

Anas W I Alanqar

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.

Control system for building equipment with dynamic capacity constraints

A central plant includes a plurality of subplants and circuitry. The circuitry is configured to obtain an expert model associated with a first subplant of the plurality of subplants. The expert model includes a map of values of independent variables to one or more dependent variables. The circuitry is also configured to receive input data indicating input values for the independent variables and estimate outputs values for the one or more dependent variables associated with the input variables by determining a spatial relationship between the input data and the expert model, reducing the expert model to a reduced model based on the spatial relationship, and performing an interpolation using the reduced model to generate the output values for the one or more dependent variables. The circuitry is also configured to initiate an automated action using the output values.

Central energy system with expert subplant models

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

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

A system for controlling building equipment determines a degradation factor for a first asset of the building equipment by comparing a design curve for the first asset and operational data for the first asset. The design curve includes a plurality of data points that define an operation of the first asset. The system generates an operational curve for the first asset by derating the design curve based on the degradation factor and operates the building equipment based on the operational curve.

Control system for building equipment with equipment model adaptation

A system for controlling a subplant comprising one or more assets includes one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations including generating a design curve for a first asset included in the subplant based on an asset model, the design curve comprising a plurality of data points that define an operation of the first asset, obtaining operational data for the first asset, determining a degradation factor for the first asset by comparing the design curve and the operational data, generating an operational curve for the first asset by derating the design curve based on the degradation factor, and operating the subplant based on the operational curve.

Central plant optimization system with equipment model adaptation

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 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

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

Systems and methods for distributing load of an energy plant are disclosed herein. A target load of at least a first device and a second device of the energy plant is obtained. A first power consumption of the first device and the second device predicted to result from operating the first device and the second device according to a first combination of set points to satisfy the target load is predicted. A second power consumption of the first device and the second device predicted to result from operating the first device and the second device according to a second combination of set points to satisfy the target load is predicted. The first power consumption and the second power consumption are compared, and a combination of set points rendering lower power consumption is selected to operate the energy plant.

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Fond du Lac, WI, United States of America

Shawn A Schlagenhaft