CN105929686B

CN105929686B - Low level central facility optimization

Info

Publication number: CN105929686B
Application number: CN201610104416.7A
Authority: CN
Inventors: 马修·J·阿斯穆斯; 罗伯特·D·特尼
Original assignee: Johnson Controls Technology Co
Current assignee: Johnson Controls Tyco IP Holdings LLP
Priority date: 2015-02-27
Filing date: 2016-02-25
Publication date: 2020-02-07
Anticipated expiration: 2036-02-25
Also published as: CN105929686A

Abstract

Systems and methods for low-level central facility optimization are provided. The controller of the central facility uses binary optimization to determine one or more possible on/off configurations of the equipment of the central facility that satisfy the operating constraints and satisfy the thermal energy load set points. The controller determines an optimal operating set point for each possible on/off configuration and generates an operating parameter including at least one of the possible on/off configuration and the optimal operating set point. The operating parameter optimizes an amount of energy consumed by the central facility device. The controller outputs the generated operating parameters via the communication interface for controlling the central facility device.

Description

Low level central facility optimization

Cross reference to related patent applications

This application claims the benefit and priority of U.S. patent application No.14/634,615, filed on day 27/2/2015, which is U.S. patent application No.14/634,615 claims the benefit and priority of U.S. provisional patent application No.61/987,361, filed on day 1/5/2014. U.S. patent application No.14/634,615 and U.S. provisional patent application No.61/987,361 are both incorporated herein by reference in their entirety.

Technical Field

The present disclosure relates generally to the operation of central facilities that serve thermal energy loads (thermal energy loads) of buildings. The present disclosure more particularly relates to systems and methods for optimizing the operation of one or more sub-facilities of a central facility.

Background

The central facility may include various types of equipment configured to service the thermal energy load of a building or building campus (i.e., building systems). For example, the central facility may include a heater, cooler, heat recovery cooler, cooling tower, or other type of equipment configured to provide heating or cooling for a building. Some central facilities include thermal energy storage configured to store thermal energy generated by the central facility for future use.

The central facility may consume resources (e.g., electricity, water, natural gas, etc.) from the utility to heat or cool a working fluid (e.g., water, glycol, etc.) that is circulated to the building or stored for future heating or cooling of the building. The fluid lines typically deliver heated or cooled fluid to an air handler located on the roof of the building or to various floors or areas of the building. The air handler pushes the air through a heat exchanger (e.g., a heating coil or a cooling coil) through which a working fluid flows to provide heating or cooling of the air. The working fluid then returns to the central facility to receive further heating or cooling, and the cycle continues.

As variables such as temperature and humidity change, the central facility may cycle on or off multiple coolers, heaters, pumps or cooling towers to provide varying thermal energy loads to the building. High efficiency devices may help reduce energy consumed by a central facility. However, the effectiveness of such a device depends to a large extent on the control technique used. It is challenging and difficult to efficiently control the central facilities of a building.

Disclosure of Invention

One embodiment of the present disclosure is a controller for a central facility. The central facility has a plurality of sub-facilities that serve the thermal energy load of the building or building system. The controller includes a processing circuit having a processor and a memory. The processing circuit is configured to identify a thermal energy load set point for a sub-facility of the central facility. The processing circuit includes: a binary optimization module configured to determine one or more possible on/off configurations of equipment of a sub-facility using binary optimization based on the identified thermal energy load set points; a setpoint evaluator module configured to determine an optimal operating setpoint for each available on/off configuration of sub-facility devices; and a sub-facility control module configured to generate operating parameters for the sub-facility devices. The generated operating parameters include at least one of an operational on/off configuration and an optimal operating set point. The controller further includes a communication interface coupled to the processing circuit and configured to output the generated operating parameters for controlling the sub-facility devices.

In some embodiments, the controller further comprises a constraint evaluator module configured to identify applicable constraints of the sub-facility device. Each feasible on/off configuration can satisfy the applicable constraints and can be evaluated to yield sub-facility devices that meet the thermal energy load set point.

In some embodiments, the binary optimization module uses a branch-and-bound approach to determine one or more possible on/off configurations.

In some embodiments, the setpoint evaluator module determines the optimal operating setpoint using a non-linear optimization that minimizes the amount of power consumed by the sub-facility devices.

In some embodiments, the setpoint evaluator module is configured to: the amount of power consumed by each of the possible on/off configurations of the sub-facility devices at the optimal operating set point is estimated, and which of the possible on/off configurations is estimated to minimize the amount of power consumed by the sub-facility devices at the optimal operating set point.

In some embodiments, identifying the thermal energy load set point comprises: receiving thermal energy load set points from a high-level optimization that uses predicted thermal energy loads for the building to determine optimal thermal energy load set points for the plurality of sub-facilities.

In some embodiments, the processing circuit comprises a low-level optimization module configured to generate resource consumption curves for the sub-facilities. The resource consumption curve may indicate a minimum amount of resources consumed by the sub-facility as a function of thermal energy load on the sub-facility.

Another embodiment of the present disclosure is a method of controlling a central facility. The central facility has a plurality of sub-facilities that serve the thermal energy load of the building or building system. The method comprises the following steps: identifying, by processing circuitry of a controller of a central facility, a thermal energy load set point for a sub-facility of the central facility; and determining, by a binary optimization module of the processing circuit, one or more available on/off configurations of the equipment of the sub-facility using binary optimization based on the identified thermal energy load set point. The method further comprises the following steps: determining, by a setpoint evaluator module of the processing circuit, an optimal operating setpoint for each available on/off configuration of the sub-facility device; and generating operating parameters of the sub-facility device by a sub-facility control module of the processing circuit. The operating parameter includes at least one of an enabled on/off configuration and an optimal operating set point. The method further includes outputting the generated operating parameters for controlling the sub-facility devices via a communication interface coupled to the processing circuit.

In some embodiments, the method includes identifying applicable constraints for the sub-facility devices. Each feasible on/off configuration can satisfy the applicable constraints and be evaluated to yield sub-facility devices that meet the thermal energy load set point.

In some embodiments, determining one or more available on/off configurations includes using a branch-and-bound approach.

In some embodiments, determining the optimal operating set point includes using a non-linear optimization that minimizes the amount of power consumed by the sub-facility devices.

In some embodiments, the method comprises: estimating an amount of power consumed by each possible on/off configuration of the sub-facility device at the optimal operational set point; and identifying which of the possible on/off configurations is estimated to minimize the amount of power consumed by the sub-facility devices at the optimal operating set point.

In some embodiments, the method comprises: resource consumption curves are generated for the sub-facilities. The resource consumption curve may indicate a minimum amount of resources consumed by the sub-facility as a function of the thermal energy load on the sub-facility.

Another embodiment of the present disclosure is a method of determining optimal operating parameters of equipment of a central facility that services a thermal energy load of a building or building system. The method may be performed by a controller of a central facility. The method comprises the following steps: initializing a database of possible schemes and a database of feasible schemes; and retrieving a branch from the possible solution database. The retrieved branch includes a specified operating state for one or more devices of the central facility apparatus. The method further comprises the following steps: determining whether the branch satisfies an applicable constraint of a central facility device using the specified operating state; and adding the branch to the feasible solution database in response to a determination that the branch satisfies the applicable constraint. The method further comprises the following steps: operating parameters of the central facility equipment are generated. The operating parameters include operating states specified by branches in the feasibility database.

In some embodiments, the branch includes a designated operating state for a first device of the central facility apparatus and an unspecified operating state for a second device of the central facility apparatus.

In some embodiments, the method further comprises: the first extended branch is created by specifying a first operational state of the second device. The determining, adding, and generating steps may be performed for a first extended branch. The method may further comprise: creating a second extended branch by specifying a second operational state of a second device; and a second extension branch is added to the potential solution database to be evaluated in subsequent iterations of the method.

In some embodiments, the method further comprises: in response to a determination that the first extension branch does not satisfy the applicable constraint, determining whether the first extension branch is likely to satisfy the applicable constraint if an unspecified operating state in the first extension branch is subsequently specified; and in response to a determination that the first extended branch is likely to satisfy the applicable constraints, adding the first extended branch to the potential solution database for evaluation in a subsequent iteration of the method.

In some embodiments, the method further comprises: the amount of power consumed by each branch in the possible solution database is estimated. Retrieving the branch from the database of possible solutions may include retrieving the branch having the lowest estimated power consumption.

In some embodiments, the method comprises: the amount of power consumed by each branch in the feasible solution database is estimated. The generated operating parameters may include the operating state specified by the branch in the feasible solution database having the lowest estimated power consumption.

Those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, novel features, and advantages of the devices and/or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth below when taken in conjunction with the drawings.

Drawings

FIG. 1 is a diagram of a central facility including a plurality of sub-facilities operable to service a thermal energy load of a building or building system, according to an exemplary embodiment.

Fig. 2 is a block diagram of a central facility system including a central facility controller configured to generate on/off decisions and operating set points for the central facility device of fig. 1, according to an example embodiment.

FIG. 3 is a block diagram illustrating a low-level optimization module of the central facility controller of FIG. 2 in greater detail according to an exemplary embodiment.

Fig. 4 is a block diagram illustrating the central facility controller of fig. 2 receiving operating conditions (operating conditions) from the plurality of sub-facilities of fig. 1 and providing the resulting on/off states and operating set points as operating commands for the central facility device, according to an exemplary embodiment.

FIG. 5 is a flow diagram of a low-level optimization process that may be performed by the central facility controller of FIG. 2 to determine feasible on/off configurations of the central facility device and operational set points that optimize energy consumed by the plurality of sub-facilities, according to an example embodiment.

FIG. 6 is a flow chart of another low-level optimization process that may be performed by the central facility controller of FIG. 2 as a particular implementation of the process of FIG. 5 to determine the feasible on/off configurations and operational set points of the central facility device in accordance with an exemplary embodiment.

FIG. 7 is a flowchart of a binary optimization process that may be performed by the central facility controller of FIG. 2 to determine a feasible on/off configuration of the central facility device, according to an example embodiment.

Fig. 8 a-8 d are branch-and-bound diagrams illustrating several potential branches (i.e., combinations of on/off states of central facility devices) that may be considered possible scenarios in the binary optimization process of fig. 7, according to an example embodiment.

Detailed Description

Referring generally to the drawings, systems and methods for low-level central facility optimization according to exemplary embodiments are shown. The systems and methods described herein may be used to control equipment of a central facility that provides heating and/or cooling for a building or building campus (i.e., building systems). The central facility may include a plurality of sub-facilities, each configured to perform a particular function. For example, the central facility may include a heater sub-facility, a cooler sub-facility, a heat recovery cooler sub-facility, a hot (hot) thermal energy storage sub-facility, a cold (cold) thermal energy storage sub-facility, and so forth.

According to an exemplary embodiment, each sub-facility receives a setpoint thermal energy load (e.g., thermal energy per unit time) to be serviced by the sub-facility and generates a plant dispatch status (equipment dispatch status) and/or an operational setpoint for various devices of the sub-facility to service the setpoint thermal energy load. For example, each sub-facility may include a plurality of individual devices (e.g., heaters, coolers, pumps, valves, etc.) configured to facilitate the functionality of the sub-facility. The plant schedule state may control which devices of the sub-facility are to be utilized to service the thermal energy load (e.g., on/off state). The operational set point may determine a respective set point for each active device (active device) of the sub-facility that is capable of operating at variable capacity (e.g., operating a chiller at 10% capacity, 60% capacity, etc.).

The low-level central facility optimization described herein may be used in conjunction with high-level central facility optimization. The high-level optimization may determine an optimal distribution of thermal energy loads across multiple sub-facilities of the central facility over a time horizon to minimize the cost of operating the central facility. For example, the high level optimization may determine the optimal sub-facility load for each sub-facilityAnd provides the optimal sub-facility load as a set point for each sub-facility. Can be applied to each sonThe facility performs low-level optimization. The low-level optimization may determine which devices of the sub-facility are to be utilized (e.g., on/off states) and/or determine operational setpoints (e.g., temperature setpoints, flow setpoints, etc.) of the individual devices of the sub-facility to load the sub-facility for

The energy consumed to make the service is minimized.

In some embodiments, a low-level optimization is performed on several different combinations of load and weather conditions to produce multiple data points (i.e., minimum energy consumption values for various sub-facility loads). Sub-facility curves can be fitted to the low-level optimization data for each sub-facility to determine utility usage (e.g., power consumption, resource consumption, etc.) as a function of the load serviced by the sub-facility. High-level optimization may use sub-facility curves for multiple sub-facilities to determine the optimal distribution of thermal energy loads across the sub-facilities.

Low-level optimization may use thermal models (thermal models) of the sub-facility devices and/or sub-facility networks to determine the minimum energy consumption for a given thermal energy load. The equipment profile (e.g., thermal load versus resource loss) for each device or combination of devices may be used to determine the best device selection for a given load condition. For example, operating two coolers at 50% capacity may consume less energy than operating a single cooler at 100% capacity while producing the same thermal energy load. The plant curve may determine the optimal switching point for switching off or on the respective device. A thermal network may be used to establish the feasibility of device selection and to ensure that the required thermal energy load can be met. For example, the operational capacity of one device may depend on the operational set points of other upstream, downstream devices or component devices. Low-level optimization may take into account current operating conditions (e.g., weather conditions, environmental conditions, etc.) in determining the optimal plant selection and/or set point to service a given thermal energy load.

In some embodiments, the low-level optimization uses hybrid binary optimization (e.g., branch-and-bound) and/or non-linear optimization (e.g., sequential quadratic programming) to make the sub-facilities dependentThe energy consumption is minimized. Non-exhaustive binary optimization and non-linear optimization contribute to energy consumption minimization. Determining to satisfy sub-facility loads using binary optimization

The optimum combination of devices of (1). Non-linear optimization may be used to determine an optimal operating set point (e.g., a set point expected to produce minimal or near minimal power consumption for a determined combination of devices). The system or method of the present invention with hybrid binary optimization and/or non-linear optimization may advantageously result in lower central facility operating costs. In some embodiments, hybrid optimization may be advantageously adapted to improve real-time (i.e., near real-time) optimization and automation of a central facility. Hybrid optimization may be implemented in a planning tool to predict the performance of a central facility over a future period of time.

Referring now to FIG. 1, a diagram of a central facility 10 is shown, according to an exemplary embodiment. The central facility 10 is illustrated as including a plurality of sub-facilities including a heater sub-facility 12, a heat recovery chiller sub-facility 14, a chiller sub-facility 16, a cooling tower sub-facility 18, a Thermal Energy Storage (TES) sub-facility 20, and a cold Thermal Energy Storage (TES) sub-facility 22. The sub-facilities 12-22 consume resources (e.g., water, natural gas, electricity, etc.) from a utility to service the thermal energy load (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, the heater sub-facility 12 may be configured to heat water in a hot water loop 24, the hot water loop 24 circulating hot water between the central facility 10 and a building (not shown). The chiller sub-facility 16 may be configured to cool water in a cold water loop 26, which cold water loop 26 circulates cold water between the central facility 10 and the building. The heat recovery chiller sub-facility 14 may be configured to transfer heat from the cold water loop 26 to the hot water loop 24 to provide additional heating of the hot water and additional cooling of the cold water. The condensate loop 28 may absorb heat from the cold water in the chiller sub-facility 16 and reject the absorbed heat to the cooling tower sub-facility 18 or transfer the absorbed heat to the hot water loop 24. The hot TES sub-facility 20 and the cold TES sub-facility 22 store hot and cold thermal energy, respectively, for later use.

The hot water loop 24 and the cold water loop 26 may deliver heated and/or cooled water to an air handler located on the roof of the building, or to various floors or zones of the building. The air handler pushes the air through a heat exchanger (e.g., a heating coil or a cooling coil) through which the water flows to provide heating or cooling of the air. The heated or cooled air may be delivered to various areas of the building to service the thermal energy load of the building. The water is then returned to the central facility 10 to receive further heating or cooling in the sub-facilities 12-22.

Although the central facility 10 is illustrated and described as heating and cooling water for circulation to the building, it should be understood that any other type of working fluid (e.g., ethylene glycol, CO2, etc.) may be used in place of or in addition to water to service the thermal energy load. In other embodiments, the central facility 10 may provide heating and/or cooling directly to a building or campus without the need for an intermediate heat transfer fluid. The central facility 10 may be physically separate from or physically integrated with (e.g., located within) the buildings serviced by the sub-facilities 12-22.

Each of the sub-facilities 12-22 may include a plurality of devices configured to facilitate the functionality of the sub-facility. For example, the heater sub-facility 12 is illustrated as including a plurality of heating elements 30 (e.g., boilers, electric heaters, etc.), the heating elements 30 being configured to add heat to the hot water in the hot water loop 24. The heater sub-assembly 12 is also illustrated as including a number of

pumps

32, 34 configured to circulate hot water in the hot water loop 24 and control the flow rate of the hot water through each heating element 30. The heat recovery chiller sub-facility 14 is illustrated as including a plurality of heat recovery heat exchangers 36 (e.g., refrigeration circuits), the heat recovery heat exchangers 36 configured to transfer heat from the cold water loop 26 to the hot water loop 24. The heat recovery chiller sub-facility 14 is also illustrated as including a number of

pumps

38, 40 configured to circulate hot and/or cold water through the heat recovery heat exchangers 36 and control the flow rate of the water through each heat recovery heat exchanger 36.

The chiller sub-facility 16 is illustrated as including a plurality of chillers 42, the plurality of chillers 42 being configured to remove heat from the cold water in the cold water loop 26. The chiller sub-facility 16 is also illustrated as including a number of

pumps

44, 46 configured to circulate cold water in the cold water loop 26 and control the flow rate of the cold water through each chiller 42. The cooling tower sub-facility 18 is illustrated as including a plurality of cooling towers 48, the plurality of cooling towers 48 configured to remove heat from the condensate in the condensate loop 28. The cooling tower sub-facility 18 is also illustrated as including a number of pumps 50, the pumps 50 being configured to circulate the condensate in the condensate loop 28 and to control the flow rate of the condensate through each cooling tower 48.

The hot TES sub-facility 20 is illustrated as including a hot TES water tank (tank)52, the hot TES water tank 52 configured to store hot water for later use. The hot TES sub-facility 20 may also include one or more pumps or valves configured to control the flow rate of hot water into or out of the hot TES water tank 52. The cold TES sub-facility 22 is illustrated as including a cold TES water tank 54, the cold TES water tank 54 configured to store cold water for later use. The cold TES sub-facility 22 may also include one or more pumps or valves configured to control the flow rate of cold water into or out of the cold TES water tank 54. In some embodiments, one or more pumps (e.g., pumps 32, 34, 38, 40, 44, 46, and/or 50) in the central facility 10 or a pipeline in the central facility 10 includes an isolation valve associated therewith. In various embodiments, isolation valves may be integrated with or located upstream or downstream of the pumps to control fluid flow in the central facility 10. In other embodiments, more, fewer, or different types of devices may be included in central facility 10.

Referring now to FIG. 2, a block diagram illustrating a central facility system 100 according to an exemplary embodiment is shown. The system 100 is illustrated as including a central facility controller 102, a building automation system 108, and a plurality of sub-facilities 12-22. The sub-facilities 12-22 may be the same as previously described with reference to fig. 1. For example, the sub-facilities 12-22 are illustrated as including a heater sub-facility 12, a heat recovery chiller sub-facility 14, a chiller sub-facility 16, a hot TES sub-facility 20, and a cold TES sub-facility 22.

Each of the sub-facilities 12-22 is illustrated as including a device 60, the device 60 being controllable by the central facility controller 102 and/or the building automation system 108 to optimize performance of the central facility 10. The apparatus 60 may include, for example, a heating device 30, a cooler 42, a heat recovery heat exchanger 36, a cooling tower 48, thermal energy storage devices 52-54, pumps 32, 34, 38, 44, 46, 50, valves, and/or other devices of the sub-facilities 12-22. The various devices of the apparatus 60 may be switched on or off to adjust the thermal energy load serviced by each of the sub-facilities 12-22. In some embodiments, various devices of the plant 60 may be operated at variable capacities (e.g., operating the chiller at 10% capacity or 60% capacity) according to the operating set points received from the central facility controller 102.

In some embodiments, one or more of the sub-facilities 12-22 includes a sub-facility level controller configured to control the equipment 60 of the respective sub-facility. For example, the central facility controller 102 may determine the on/off configuration and global operational set points for the devices 60. In response to the on/off configuration and the received global operational set point, the sub-facility controller may turn various devices of the apparatus 60 on or off and implement a particular operational set point (e.g., damper position, damper (vane) position, fan speed, pump speed, etc.) to achieve or maintain the global operational set point.

A Building Automation System (BAS)108 may be configured to monitor conditions within a controlled building. For example, the BAS108 can receive input from a plurality of sensors (e.g., temperature sensors, humidity sensors, airflow sensors, voltage sensors, etc.) distributed throughout the building and report building conditions to the central facility controller 102. Building conditions may include, for example, a temperature of a building or a region of a building, a power consumption of a building (e.g., an electrical load), a state of one or more actuators configured to affect a controlled state within a building, or other types of information related to a controlled building. The BAS108 can operate the sub-facilities 12-22 to affect monitored conditions within the building and to service the thermal energy load of the building.

The BAS108 can receive control from the central facility controller 102Control signals specifying the on/off state and/or set point of the device 60. The BAS108 may control the equipment 60 (e.g., via actuators, power relays, etc.) according to control signals provided by the central facility controller 102. For example, the BAS108 can use closed loop control to operate the plant 60 to achieve a set point specified by the central facility controller 102. In various embodiments, the BAS108 may be integrated with the central facility controller 102 or may be part of a separate building management system. According to an exemplary embodiment, the BAS108 is sold by Johnson Controls, Inc

A brand building management system.

The central facility controller 102 may use information received from the BAS108 to monitor the status of the controlled building. The central facility controller 102 may be configured to predict a thermal energy load (e.g., a heating load, a cooling load, etc.) of the building for a plurality of time steps within a prediction window (e.g., using a weather forecast from a weather service). The central facility controller 102 may generate on/off decisions and/or set points for the equipment 60 to minimize the energy costs consumed by the sub-facilities 12-22 to service the predicted heating and/or cooling loads for the duration of the prediction window. Central facility controller 102 may be configured to perform process 500 (fig. 5), process 600 (fig. 6), process 700 (fig. 7), and other processes described herein. According to an exemplary embodiment, the central facility controller 102 is integrated within a single computer (e.g., one server, one housing, etc.). In various other exemplary embodiments, the central facility controller 102 may be distributed across multiple servers or computers (e.g., may exist in distributed locations). In another exemplary embodiment, the central facility controller 102 may be integrated with a smart building manager that manages multiple building systems and/or integrated with the BAS 108.

The central facility controller 102 is illustrated as including a communication interface 104 and a processing circuit 106. The communication interface 104 may include a wired or wireless interface (e.g., socket, antenna, transmitter, receiver, transceiver, wire connection, etc.) for data communication with various systems, devices, or networks. For example, the communication interface 104 may include an ethernet card and port for sending and receiving data via an ethernet-based communication network and/or a WiFi transceiver for communicating via a wireless communication network. Communication interface 104 may be configured to communicate via a local or wide area network (e.g., the internet, building WAN, etc.) and may use a variety of communication protocols (e.g., BACnet, IP, LON, etc.).

The communication interface 104 may be a network interface configured to facilitate electronic data communication between the central facility controller 102 and various external systems or devices (e.g., the BAS108, the sub-facilities 12-22, etc.). For example, the central facility controller 102 may receive information from the BAS108 indicating one or more measured conditions of the controlled building (e.g., temperature, humidity, electrical load, etc.) and one or more conditions of the sub-facilities 12-22 (e.g., equipment conditions, power consumption, equipment availability, etc.). The communication interface 104 may receive input from the BAS108 and/or the sub-facilities 12-22 and may provide operating parameters (e.g., on/off decisions, set points, etc.) to the sub-facilities 12-22 via the BAS 108. The operating parameters may cause the sub-facilities 12-22 to be activated, deactivated, or adjust set points of various devices of the apparatus 60.

Still referring to fig. 2, the processing circuit 106 is illustrated as including a processor 110 and a memory 112. Processor 110 may be a general or special purpose processor, an Application Specific Integrated Circuit (ASIC), one or more Field Programmable Gate Arrays (FPGAs), a set of processing components, or other suitable processing components. The processor 110 may be configured to execute computer code or instructions stored in the memory 112 or received from other computer readable media (e.g., CDROM, network storage, remote server, etc.).

Memory 112 may include one or more devices (e.g., memory units, storage devices, etc.) for storing data and/or computer code to perform and/or facilitate the various processes described in this disclosure. Memory 112 may include Random Access Memory (RAM), Read Only Memory (ROM), hard drive memory, temporary memory, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory 112 may include database components, object code components, script components, or any other type of information structure that supports the various activities and information structures described in this disclosure. Memory 112 may be communicatively connected to processor 110 via processing circuitry 106 and may include computer code for performing (e.g., by the processor) one or more processes described herein.

Still referring to fig. 2, the memory 112 is illustrated as including a building status monitor 134. The central facility controller 102 may receive data via the building status monitor 134 regarding the entire building or building space to be heated or cooled with the central facility 10. In an exemplary embodiment, the building status monitor 134 may include a graphical user interface component configured to provide a graphical user interface to a user for selecting building requirements (e.g., selecting overall temperature parameters, selecting a schedule (schedules) for a building, selecting different temperature levels for different building zones, etc.).

The central facility controller 102 may determine an on/off configuration and an operating set point to meet the building demand received from the building status monitor 134. In some embodiments, the building status monitor 134 receives, collects, stores, and/or transmits cooling load demands, building temperature set points, occupancy data, weather data, energy data, schedule data, and other building parameters. In some embodiments, building status monitor 134 stores data regarding energy costs, such as pricing information (energy charge, demand charge, etc.) obtained from utilities 126.

Still referring to fig. 2, memory 112 is illustrated as including a load/rate prediction module 122. Load/rate prediction module 122 may be configured to predict the thermal energy load of a building or campus at each time step k (e.g., k-1 … n) of a prediction period

Load/rate prediction module 122 is illustrated as receivingWeather forecast from weather service 124. In some embodiments, load/rate prediction module 122 predicts thermal energy load as a function of weather forecast

In some embodiments, the load/rate prediction module 122 uses feedback from the BAS108 to predict the load

Feedback from the BAS108 can include various types of sensory input (e.g., temperature, flow, humidity, enthalpy, etc.) or other data related to the controlled building (e.g., input from HVAC systems, lighting control systems, security systems, water systems, etc.).

In some embodiments, the load/rate prediction module 122 receives measured electrical loads and/or previously measured load data from the BAS108 (e.g., via the building status monitor 134). Load/rate prediction module 122 may be a function of a given weather forecastDay type (day), time of day (t) and last measured load data (Y)_k-1) To predict the load

This relationship is expressed in the following equation:

in some embodiments, load/tariff prediction module 122 predicts the load using a deterministic plus stochastic model (deterministic plusstalk model)

The deterministic plus stochastic model is trained from historical load data. Load/rate prediction module 122 may use any of a variety of prediction methods to predict load

(e.g., using linear regression for the deterministic portion and AR models for the stochastic portion).

Load/rate prediction module 122 is illustrated as receiving utility rates from utility 126. The utility rate may indicate a cost or price per unit of resource (e.g., electricity, natural gas, water, etc.) provided by utility 126 in each time step k within the prediction window. In some embodiments, the utility rate is a time-varying rate. For example, the price of electricity may be higher at certain times of the day or days of the week (e.g., during high demand periods), while it may be lower at other times of the day or days of the week (e.g., during low demand periods). The utility rate may define a number of periods and a cost per unit of resource in each period. The utility rate may be an actual rate received from utility 126 or a predicted utility rate estimated by load/rate prediction module 122.

In some embodiments, the utility rates include rates charged for demand of one or more resources provided by utility 126. The demand charge may define a separate cost imposed by the utility 126 based on the maximum usage (e.g., maximum energy consumption) of a particular resource during the demand charge period. The utility rate may define a plurality of demand charge periods and one or more demand charges associated with each demand charge period. In some cases, the demand charge periods may partially or completely overlap each other and/or the prediction window. Advantageously, the optimization module 128 may be configured to take into account demand charges in a high-level optimization process performed by the high-level optimization module 130.

Load/rate prediction module 122 may predict the load

And utility rates are stored in memory 112 and/or predicted loads

And utility rates are provided to the optimization module 128. The optimization module 128 may use the predicted loadAnd utility rates to determine the optimal load distribution of the sub-facilities 12-22 and to generate on/off decisions and set points for the equipment 60.

Still referring to fig. 2, the memory 112 is illustrated as including an optimization module 128. The optimization module 128 may perform a cascaded optimization process to optimize the performance of the central facility 10. For example, the optimization module 128 is illustrated as including a high-level optimization module 130 and a low-level optimization module 132. The high-level optimization module 130 may determine how to distribute the thermal energy load across the sub-facilities 12-22 for each time step within the prediction window to minimize the energy cost consumed by the sub-facilities 12-22. The low-level optimization module 132 may determine how best to operate each sub-facility at the load set point determined by the high-level optimization module 130.

Advantageously, the cascaded optimization process performed by the optimization module 128 allows the optimization process to be performed in a time-efficient manner. Due to faster system dynamics, low-level optimization may use a relatively short timeframe or no timeframe at all, compared to the time to re-optimize the facility load. High-level optimization may use a relatively long time horizon when the dynamics and capabilities of thermal energy storage allow the load to be postponed for a long period of time.

The low-level optimization module 132 may generate and provide the sub-facility power curves to the high-level optimization module 130. The sub-facility power curve may indicate utility usage (e.g., measured in power units such as kW) of each sub-facility 12-22 as a function of the load serviced by the sub-facility. In some embodiments, the low-level optimization module 132 generates a sub-facility power curve based on the equipment model 120 (e.g., by incorporating the equipment model 120 for each device into the aggregate power curve of the sub-facility). The low-level optimization module 132 may generate a sub-facility power curve by running a low-level optimization process (described in more detail with reference to figures 5-7) for several different load and weather conditions,to generate a plurality of data points

The low-level optimization module 132 may fit a curve to these data points to generate a sub-facility power curve.

High-level optimization module 130 may receive load and rate predictions from load/rate prediction module 122 and sub-utility power curves from low-level optimization module 132. The high-level optimization module 130 may determine an optimal load distribution for the sub-facilities 12-22 (e.g., for each sub-facility) within a prediction window

) And provides the optimal load distribution to the low-level optimization module 132. In some embodiments, the high-level optimization module 130 determines the optimal load distribution by minimizing the total operating cost of the central facility 10 within the prediction window. In other words, the predicted load from load/rate prediction module 122 is given

And utility rate information, the high-level optimization module 130 may distribute the predicted load across the sub-facilities 12-22

To minimize costs.

In some cases, optimizing load distribution may include using TES sub-facilities 20 and/or 22 to store thermal energy for a first time step for use in a later time step. Thermal energy storage may advantageously allow thermal energy to be generated and stored during a first period of time when the energy price is relatively low and subsequently to be taken out and used during a second period of time when the energy price is relatively high. The high-level optimization may differ from the low-level optimization in that the high-level optimization has a longer time constant due to the thermal energy storage provided by the TES sub-facilities 20-22. The high-level optimization can be expressed by the following equation:

whereinIncluding an optimal high-level decision for the entire prediction period (e.g., optimal load for each sub-facility 12-22)

) And J_HLIs a high level cost function.

To find the best high-level decision

The high-level optimization module 132 may minimize the high-level cost function J_HL. High level cost function J_HLMay be the sum of the economic cost of each utility consumed by each sub-facility 12-22 for the duration of the prediction period. E.g. a high level cost function J_HLThe following equation can be used for expression:

wherein n is_hIs the number of time steps k within the prediction period, n_sIs the number of sub-facilities, t_sIs the duration of the time step, c_jkIs the economic cost of utility j at time step k of the prediction period, and u_jikIs the usage of utility j by sub-facility i at time step k. In some embodiments, the cost function J_HLIncluding additional demand charges such as:

wherein w_dIs a weighted term, c_demandIs the cost of the demand, and the max () item is selected at the applicable demandPeak power usage during the charging period.

In some embodiments, the high-level optimization performed by the high-level optimization module 130 is the same as or similar to the high-level optimization process described in U.S. patent application No.14/634,609 (attorney docket No.081445-0655), entitled "high-level central facility optimization" and filed on even date herewith. The entire disclosure of U.S. patent application No.14/634,609 is incorporated herein by reference.

In some embodiments, the high-level optimization module 130 provides the optimal load profile for each time step to the low-level optimization module 132 at the beginning of the time step. The optimal load profile for a subsequent time step may be updated by the higher-level optimization module 130 and provided to the lower-level optimization module 132 at the beginning of the subsequent time step.

The low-level optimization module 132 may use the sub-facility loads determined by the high-level optimization module 130 to determine the optimal low-level decision for the plant 60(e.g., binary on/off decision, flow set point, temperature set point, etc.). A low-level optimization process may be performed for each sub-facility 12-22. The low-level optimization module 132 may be responsible for determining which devices in each sub-facility to use and their setpoints such that energy consumption is minimized while sub-facility load setpoints are reached. The low-level optimization can be expressed using the following equation:

wherein

Including the best low-level decision_LLIs a low-level cost function.

To find the best low-level decisionThe low-level optimization module 132 may minimize low-level costsFunction J_LL. Low level cost function J_LLMay represent the total energy consumption of all devices of the equipment 60 in the applicable sub-facility. Low level cost function J_LLThe following equation can be used for expression:

where N is the number of devices of the apparatus 60 in the sub-facility, t_sIs the duration of the time step, b_jIs a binary on/off decision (e.g., 0 off, 1 on), and u_jIs a function of the set point theta_LLThe energy source used by device j. Each device may have a continuous variable that may be varied to determine the lowest possible energy consumption for the total input conditions.

The low-level optimization module 132 may minimize a low-level cost function J_LLThe low-level cost function J_LLSubject to inequality constraints based on the capabilities of the device 60 and equality constraints based on energy and mass balances. In some embodiments, optimal low-level decision

Limited by switching constraints that define a short time range for maintaining the device in an on or off state after a binary on/off switch. Switching constraints may prevent the device from cycling quickly between on and off. In some embodiments, the low-level optimization module 132 performs device-level optimization without regard to system dynamics. The optimization process may be slow enough to safely assume that the plant control has reached its steady state. Thus, the low-level optimization module 132 may determine the best low-level decision at a time instance rather than over a long time horizon

The low-level optimization module 132 may determine the best operating state (e.g., on or off) of the devices for the plurality of apparatuses 60. The low-level optimization module 132 may store code that may be executed by the processor 110To perform operations described later in this application, including binary optimization operations and/or quadratic compensation operations. According to an exemplary embodiment, the on/off combination may be determined using binary optimization and quadratic compensation. Binary optimization may minimize a cost function representing the power consumption of devices in the applicable sub-facility. In some embodiments, non-exhaustive (i.e., not all potential combinations of devices are considered) binary optimization is used. Quadratic compensation can be used when considering devices with quadratic (rather than linear) power consumption. The low-level optimization module 132 may also use non-linear optimization to determine the optimal operating set point for the plant. The non-linear optimization may identify a further low-level cost function J_LLMinimized operating set point. The low-level optimization module 132 will be described in more detail with reference to fig. 3.

Still referring to fig. 2, the memory 112 is illustrated as including a sub-facility control module 138. The sub-facility control module 138 may store historical data regarding past operating conditions, past operating set points, and instructions for calculating and/or executing control parameters of the sub-facilities 12-22. The sub-facility control module 138 may also receive/store and/or transmit data regarding the condition of various devices of the equipment 60, such as work efficiency, equipment degradation, date of last use (date of last service), life parameters, condition ratings, or other device-specific data. The sub-facility control module 138 may receive data from the sub-facilities 12-22 and/or the BAS108 via the communication interface 104. The sub-facility control module 138 may also receive and store on/off status and operational set points from the low-level optimization module 132.

Data and processing results originating from the optimization module 128, the sub-facility control modules 138, or other modules of the central facility controller 102 may be accessed by the monitoring and reporting application 136 (or pushed to the monitoring and reporting application 136). The monitoring and reporting application 136 may be configured to generate a real-time "system health" dashboard for viewing and manipulation (navigator) by a user (e.g., a central facility engineer). For example, the monitoring and reporting application 136 may include a web-based monitoring application having a number of Graphical User Interface (GUI) elements (e.g., widgets, dashboard controls, windows, etc.) for displaying Key Performance Indicators (KPIs) or other information to a user of the GUI. In addition, GUI elements may summarize relative energy usage and intensity across central facilities in different buildings (real or modeled), different parks, or the like. Other GUI elements or reports may be generated and displayed based on the available data to allow a user to conduct performance evaluations across one or more central facilities through one screen. The user interface or report (or underlying data engine) may be configured to centralize and categorize the operating conditions by building, building type, equipment type, and the like. The GUI element may comprise a chart or histogram that allows a user to visually analyze the operating parameters and power consumption of the cooling water system's equipment.

Still referring to FIG. 2, the central facility controller 102 may include one or more GUI servers, web services 114, or GUI engines 116 to support the monitoring and reporting application 136. In various embodiments, the application 136, web service 114, and GUI engine 116 may be provided outside the central facility controller 102 as separate components (e.g., as part of an intelligent building manager). The central facility controller 102 may be configured to maintain a detailed historical database (e.g., a relational database, an XML database, etc.) of relevant data and the central facility controller 102 includes computer code modules that continuously, frequently, or infrequently query, centralize, convert, search, or otherwise process the data maintained in the detailed database. The central facility controller 102 may be configured to provide the results of any such processing to other databases, tables, XML files, or other data structures for further querying, calculation, or access by, for example, external monitoring and reporting applications.

The central facility controller 102 is illustrated as including a configuration tool 118. The configuration tool 118 may allow a user to define (e.g., via a graphical user interface, via prompt-triggered "wizards," etc.) how the central facility controller 102 should react to changing conditions in the central facility subsystem. In an exemplary embodiment, the configuration tool 118 allows a user to build and store a condition response scenario that can span multiple central facility devices, multiple building systems, and multiple enterprise control applications (e.g., work order management system applications, entity resource planning applications, etc.). For example, the configuration tool 118 may provide a user with the ability to combine data (e.g., from subsystems, from event histories) using a variety of conditional logic. In different exemplary embodiments, the conditional logic may range from simple logical operators (e.g., AND, OR, exclusive OR (XOR), etc.) between multiple conditions to pseudo code structures OR complex programming language functions (allowing more complex interactions, conditional statements, loops, etc.). The configuration tool 118 may present a user interface to build these conditional logics. The user interface may allow a user to graphically define policies and responses. In some embodiments, the user interface may allow the user to select a previously stored or previously constructed policy and adjust it or enable it for its system.

Referring now to FIG. 3, a block diagram illustrating the low-level optimization module 132 in greater detail is shown, according to an exemplary embodiment. The low-level optimization module 132 may store computer code (e.g., a set of executable computer code instructions stored in a non-transitory computer-readable medium) that may be executed by the processor 110. The low-level optimization module 132 may be configured to generate and output operational status commands and set points for the device 60 via, for example, the communication interface 104. The operating status commands (e.g., on/off) and set points output to the devices 60 of a particular sub-facility may be estimated (e.g., by the low-level optimization module 132) to minimize the power consumption of the sub-facility.

The low-level optimization module 132 is illustrated as including an operating state evaluator 154. The operational status evaluator 154 may examine a plurality of potential device on/off combinations to select one combination for use by the central facility 10. For example, if the chiller sub-facility 16 has four chillers (i.e., chiller 1, chiller 2, chiller 3, and chiller 4), a potential on/off combination may be [1, 0,0, 1] indicating that chiller 1 is on, chiller 2 is off, chiller 3 is off, and chiller 4 is on. In some cases, the operational state of a particular device may be unspecified (e.g., corresponding to a node that has not yet been evaluated) and may be represented by a question mark. For example, the combination [1, 0,? Is it? Cooler 1 may be instructed to be on, cooler 2 off, and coolers 3-4 have unspecified operating conditions. The unspecified operating state does not necessarily indicate that the operating state is unknown, but indicates that the operating state for the respective device has not been evaluated and is not fixed by such a combination. For example, the combination [1, 0,0,? The operating states of cooler 1, cooler 2 and cooler 3 are fixed, but the indicator cooler 4 may be on or off and still satisfy the combination. In an exemplary embodiment, the operating state evaluator 154 uses non-exhaustive binary optimization and quadratic compensation to determine which combination of on/off states to use to select. Modules 156, 158 (described in more detail below) provide instructions for implementing non-exhaustive binary optimization and quadratic compensation, respectively.

The low-level optimization module 132 is illustrated as including a possible solution database 168, a feasible solution database 164, and a discard database (discard database) 166. The possible solution database 168 may contain on/off combinations estimated to be able to satisfy the thermal energy load and optimization constraints of the sub-facility. When any unspecified operating state is considered to be a wildcard, if a combination can potentially satisfy all applicable constraints (e.g., sub-facility load constraints, device capability constraints, etc.), the combination can be stored in a potential solution database. For example, if combination [1,1,0,1] satisfies the constraint but combination [1,1,0,0] does not, then combination [1,1,0,? The constraint will be satisfied because the unspecified operating state can be either on (1) or off (0). The possible scenarios database 168 stores possible combinations (i.e., combinations that meet facility load requirements and system constraints) that may or may not result in the lowest power consumption.

Drop database 166 may contain combinations of sub-facility load and/or system constraint requirements that are currently known or estimated to be unsatisfied. The drop database 166 may store infeasible combinations of devices (i.e., combinations that are unlikely to meet facility load requirements and/or system constraints) regardless of the operating state of any device having an unspecified operating state. For example, if neither combination [1,1,0,1] nor [1,1,0,0] satisfies the constraint, then combination [1,1,0,? The constraints will not be satisfied either, as none of the values that specify an operating state will cause the combination to satisfy the constraints.

The viable solution database 164 may contain potential combinations that can meet, and additionally can meet, the sub-facility load and system constraint requirements with minimal energy consumption. In some embodiments, the viable solution database 164 stores the best combination of on/off states (i.e., the combination that results in the lowest energy consumption). In some embodiments, the feasibility solution database 164 stores combinations that satisfy all applicable constraints, regardless of the value of any unspecified operating state. For example, if both combinations [1,1,0,1] and [1,1,0,0] satisfy the constraint, then the combination [1,1,0,? Can be stored as a feasible combination because any value that does not specify an operating state will cause such a combination to satisfy the constraint.

Databases

164, 166, and 168 may store potential combinations in any suitable data structure or data structures, including linked lists, trees, arrays, relational database structures, object-based structures, or other data structures.

The operating state evaluator 154 may receive possible combinations of on/off states from the possible scenarios database 168. The operating state evaluator 154 may evaluate combinations in the possible solution database 168 in view of currently applicable constraints (e.g., sub-facility loads, device capabilities, etc.) and store various combinations in the possible solution database 168, the possible solution database 164, and/or the discard database 166 based on the evaluation results. In some embodiments, the health evaluator 154 periodically evaluates new combinations (e.g., those combinations that have not been recently evaluated as a potentially best solution) from the feasible solutions database 164 and/or the discard database 166 for further evaluation. Further, when new devices are purchased online, these new devices and new combinations including new devices may be added to the viable solution database 164 for consideration by the operational state evaluator 154.

The operating state evaluator 154 may receive constraints for the low-level optimization process from the constraint evaluator 150. Constraints may include, for example, maximum device capabilities, energy or mass balance constraints, minimum device capabilities, and the like. The constraints may establish minimum and/or maximum parameters for the device 60. In some embodiments, the constraints are quantities that can be automatically generated based on, for example, historical data. In other embodiments, an operator of the central facility system may set and/or modify the constraints. Constraints include, for example: each device of the sub-facility system operates at a minimum load (e.g., 30%). This requirement advantageously ensures that power is consumed efficiently (i.e., that the work performed by the device is sufficient to justify the power required to operate the device). Constraints may also include: the total power of the chiller plant is less than a maximum value. This requirement may advantageously prevent the sub-facility from becoming overloaded.

The operating state evaluator 154 may use the constraints to identify the feasible on/off configuration. The operating state evaluator 154 may provide the potential on/off combinations to the constraint evaluator 150, which constraint evaluator 150 may be configured to check the potential combinations against the current constraint. If a potential combination fails to satisfy the current constraints, the health evaluator 154 may move the potential combination to the discard database 166 and/or remove the potential combination from the viable solution database 164.

Still referring to FIG. 3, the operating state estimator 154 is illustrated as including a non-exhaustive binary optimization module 156 and a quadratic compensation module 158. The binary optimization module 156 can include computer code instructions for optimizing (e.g., minimizing) a low-level cost function J representing sub-facility energy consumption_LL. According to an exemplary embodiment, the binary optimization module 156 uses a branch-and-bound approach to perform binary optimization. The binary optimization process is described in more detail in the discussion of FIG. 7. In some embodiments, the binary optimization module 156 performs branch-and-bound methods so that not all possible combinations of devices 60 are considered in any given situation. This may advantageously reduce the computational time required by the operating condition evaluator 154.

The secondary compensation module 158 may include computer code instructions configured to compensate for the non-linear nature of the system. For example, the quadratic compensation module 158 may take into account the power consumption of some devices of the apparatus 60 having a quadratic form (i.e., a non-linear form). The secondary compensation module 158 may be selectively utilized when the power consumption of the device being considered by the operating state estimator 154 is secondary.

The secondary compensation module 158 may advantageously take into account the fact that: i.e., the binary optimization performed by the non-exhaustive binary optimization module 156 is intended for linear systems, but the power consumption of a particular device is a quadratic function (quadratic function). For example, in a purely linear system, binary optimization will generally return the minimum devices needed to meet the facility load. If switching on both devices will meet the facility load, it is possible to disregard other combinations, even if the power consumption of the other combinations is lower. However, in an exemplary embodiment, with the aid of the quadratic compensation module 158 (or another non-linear compensation), alternative embodiments are identified and then compared.

Since the chiller power is not linear, a quadratic compensation can be performed for each device with a non-linear or quadratic power curve, advantageously checking whether the lowest power combination of these devices is achieved by adding another device. For example, the binary optimization module 156 may identify a combination of devices that meet the facility load (e.g., both devices are on). The binary search may continue by looking back at the combination of devices when the next device is also activated, rather than deactivated. For example, even if two devices are on to meet the facility load, a binary search may use the secondary power curve of each device to account for expected power variations when three devices are on. The power consumption of a single device may be reduced as additional devices are turned on because one or more of these devices may operate more efficiently at a lower capacity than at a higher capacity. The net power consumption (netpower consumption) is reduced as a result. If three devices are on resulting in lower power consumption, it is a more desirable solution than two devices are on. On the other hand, the increased headroom energy consumption (overhead energy consumption) by turning on an add-on device, regardless of the efficiency gain in the device that was initially turned "on" by turning on another device, may lead to a determination that the add-on device should not be turned on.

Still referring to fig. 3, the low-level optimization module 132 is illustrated as including a setpoint evaluator 160. The set point evaluator 160 may be configured to examine one or more combinations of activated (e.g., "on") devices to determine an optimal operating set point. The optimal operating setpoint may be estimated to reach the sub-facility load setpoint while minimizing power consumption and to meet other constraints on the low-level optimization process. According to an exemplary embodiment, the setpoint evaluator 160 estimates an optimal temperature setpoint (e.g., a hot water temperature setpoint, a condensate water temperature setpoint, a cooling water temperature setpoint, etc.), a flow rate setpoint (e.g., a flow rate through the respective heating element 30, cooler 42, heat recovery cooler 36, cooling tower 48, etc.), etc., and/or a pressure setpoint (e.g., a hot water differential pressure, a cooling water differential pressure, etc.) for a given combination of activated devices of the plant 60. In other embodiments, more, fewer, or different set points may be determined.

Set point evaluator 160 may receive one or more potential on/off combinations from operating state evaluator 154 and/or potential schedule database 168. When these combinations are determined to be infeasible or when one potential combination is repeatedly identified as being inefficient relative to the other scenarios, setpoint evaluator 160 may move the potential combinations to discard database 166. In some cases, the setpoint assessor 160 may also move the potential combinations to the viable solution database 164 (e.g., when one combination is estimated to minimize power consumption compared to the other combinations).

Still referring to fig. 3, the setpoint evaluator 160 is illustrated as including a non-linear optimization module 162. The non-linear optimization module 162 may include computer code for optimizing (e.g., minimizing) a low-level cost function J for a set of activated central facility devices (e.g., devices that are "turned on")_LL. The operating state (e.g., on/off) of the device may have been previously determined using the operating state evaluator 154. According to various embodiments, the non-linear optimization module 162 performs non-linear optimization using a direct or indirect search method. For example, the nonlinear optimization module 162 may use the Nelder-Mead or downhill simplex method, generalized gradient-generalized-gradient (GRG), Sequential Quadratic Programming (SQP), steepest descent (Cauchy method), conjugate gradient (Fletcher-Reeves method), or other nonlinear optimization methods.

The low-level optimization module 132 is illustrated as including a GUI service 152. The GUI service 152 may be configured to generate a graphical user interface for the central facility controller 102 or another server to provide input devices (e.g., displays, mobile phones, client computers, etc.) to a user. The graphical user interface may present or interpret valid device combinations, system efficiencies, system set points, system constraints, or other system information. The GUI service 152 may facilitate a user's (e.g., a central facility engineer) ability to track energy usage and operating status of central facility devices via, for example, a web-based monitoring application. The GUI service 152 may additionally allow the user to manually set and update system constraints, available devices, certain thresholds (e.g., for moving one combination to a discard group), optimal on/off operating conditions, and optimal operating set points.

Referring now to FIG. 4, a block diagram illustrating a central facility controller 102 and sub-facilities 12-22 according to an exemplary embodiment is shown. The central facility controller 102 is configured to send the determined on/off status and operating set points to the sub-facilities 12-22 and/or the devices 60 in each sub-facility. Communication between the central facility controller 102 and the devices 60 may be direct (as shown in fig. 4) or via one or more intermediaries (e.g., BAS108, sub-facility level controllers, etc.). The central facility controller 102 and the sub-facilities 12-22 may automatically transmit and receive data without user intervention. In other embodiments, the user may additionally provide manual input or permissions to the central facility controller 102 and/or the sub-facilities 12-22.

The number of devices activated for the apparatus 60 in each sub-facility may depend on the load on the sub-facility. In some embodiments, the device 60 may be coupled to a local controller that receives and executes operating conditions and set points from the central facility controller 102. The local controller may be configured to transmit operating conditions for the device 60 back to the central facility controller 102. For example, the local controller of a particular device may report or acknowledge back to the central facility controller 102 the current operating status (on/off), current workload, device energy consumption, device on/run time, device operating efficiency, fault status, or other information for processing or storage. The performance of the device 60 may be evaluated using a coefficient of performance (COP), a power consumption value (kw/ton) per facility load, or another value indicative of power efficiency or power consumption.

Referring now to FIG. 5, a flowchart of a process 500 for low-level central facility optimization is shown, according to an example embodiment. The process 500 may be performed by the central facility controller 102, as described with reference to fig. 2-4. In some embodiments, the process 500 is performed by the processing circuitry 106 of the central facility controller 102 according to instructions stored in the low-level optimization module 132. Process 500 may be performed for each sub-facility of central facility 102 to determine the feasible on/off configurations of the devices of the sub-facility and identify which of these feasible on/off configurations is the best (i.e., results in the lowest energy consumption). The optimal configuration may be provided as a work order to the devices of the sub-facility.

The process 500 is illustrated as including identifying thermal energy load set points for sub-facilities of a central facility (step 502). In some embodiments, step 502 includes receiving a thermal energy load set point from a high-level optimization module or process. For example, the high-level optimization module 130 may be configured to perform a high-level optimization process that determines the thermal energy load set-point for each sub-facility 12-22. The thermal energy load set point may be based on a predicted thermal energy load for the building or campus and/or a utility rate that defines a cost of one or more resources consumed by the sub-facilities 12-22 to provide service to the thermal energy load. The high-level optimization module 130 may determine an optimal distribution of thermal energy loads across the sub-facilities 12-22, as described with reference to FIG. 2. In some embodiments, the optimal distribution of thermal energy loads results in the lowest cost to meet the predicted thermal energy demand of a building or campus over a time horizon. In various embodiments, the thermal energy load set points identified in step 502 may be received from the high-level optimization module 130, retrieved from memory, or otherwise obtained from any other data source (e.g., specified by a user, received from an external system or process via a communication network, etc.).

Still referring to FIG. 5, a process 500 is illustrated that includes determining one or more possible on/off configurations of devices of a sub-facility using non-exhaustive binary optimization (step 504). Step 504 may utilize binary optimization to determine one or more feasible combinations of devices that will meet the sub-facility load set point under a set of actual or expected conditions (e.g., load conditions, weather conditions, etc.). The feasible combination may also satisfy any constraints on the system (e.g., maximum total power, minimum power for a single device, etc.).

According to an exemplary embodiment, the binary optimization performed at step 504 is accomplished using a branch-and-bound approach. The branch-and-bound method recursively identifies the satisfaction of the cost function J_LLThe solution of optimization constraints (i.e., feasible solution). For example, the cost function J_LLMay be subject to load constraints based on the thermal energy load set point received in step 502 (i.e., to ensure that the energy load set point is met), capacity constraints defining minimum and/or maximum capacities of each device represented in the cost function, timeout constraints preventing devices from being activated or deactivated within a certain threshold period after a change in operating state, or a cost function J_LLAny other optimization constraints applicable. A scenario (or "branch") is a combination of on/off states of sub-facility devices.

In some embodiments, step 504 includes determining a cost function J given the identification scheme_LLUpper and lower boundaries of (a). Optimization attempts to minimize the cost function, while branches that do not meet the requirements are discarded or eliminated. If the lower bound of the cost function of the first solution is greater than the upper bound of the cost function of the second solution, then the first solution will be removed as a non-viable solution. This is because the best case (i.e., lowest power consumption) of the first scheme is still higher than the worst or highest power consumption of the second scheme. Other solutions are retained and compared to each other to determine the best solution.

According to an exemplary embodiment, binary optimization is non-exhaustive. That is, non-exhaustive optimization does not search every possible combination of devices to find the best solution. This advantageously improves computational efficiency. For example, for 8 coolers, there is 2⁸Or 256 possible combinations. In some embodiments, non-exhaustive optimization finds the best solution in half or fewer searches. A binary optimization procedure will be described with reference to FIG. 7As described in more detail.

The on/off configuration determined in step 504 may be used as a variable in a cost function representing the total energy consumption of the sub-facility. Cost function J_LLThe power consumption of a particular sub-facility is described as a function of binary on/off decisions and device set points. The cost function may be of the form:

where N is the number of devices in the sub-facility, t_sIs the duration of the time step, b_jIs a binary on/off decision (e.g., 0 off, 1 on), and u_jIs a function of the device set point θ_LLThe energy source used by device j. Each device may have a continuous variable that may be varied to determine the lowest possible energy consumption for the total input conditions.

In some cases, the sub-facility device is a chiller (e.g., for chiller sub-facility 16). Chiller power is generally a quadratic function of load and boost and it is time varying. As the additional coolers are energized, the load on the individual coolers is reduced, thus changing the energy consumption u of each cooler in the cost function_j. According to an exemplary embodiment, the binary optimization may be configured to update with the energy consumption value. In other cases, the sub-facility devices may include heating elements, heat exchangers, pumps, valves, cooling towers, or other equipment (e.g., equipment 60) that facilitate the sub-facility functions. Energy consumption value u per device_jMay be different and may vary according to different plant models (e.g., linear, quadratic, time-varying or invariant, etc.) for different types of equipment. The various components of the low-level optimization module 132 (fig. 3) may be used in step 504 based on the different devices represented in the cost function. For example, the quadratic compensation module 158 may be used when the chiller is part of a cost function to account for the quadratic nature of the chiller power.

Still referring to fig. 5, the process 500 is illustrated as including using non-linear optimization to determine the optimal operating set point for each possible on/off configuration (step 506). Step 506 may beMinimizing cost function J using non-linear optimization_LL. According to various embodiments, the nonlinear optimization is performed using direct and/or indirect search methods (e.g., a downhill simplex (Nelder-Mead) method, GRG, SQP, Cauchy method, Fletcher-Reeves method, etc.). The set points determined in step 506 may define the capabilities of each activated sub-facility device that is capable of operating at a plurality of different capabilities. The set point may affect the power consumption of each possible combination. The non-linear optimization determines one or more set points that will further minimize power consumption.

The process 500 is illustrated as including estimating the energy consumption from each possible configuration at the optimal operating set point (step 508) and identifying which of the possible on/off configurations has the lowest energy consumption at the optimal operating point (step 510). In some embodiments, more than one combination may be possible. For example, several different on/off configurations may meet facility load requirements and meet constraints on the system. However, each possible combination may have a different resulting energy consumption (resultant energy consumption).

At step 508, each of the possible on/off configurations identified in step 504 are evaluated at the optimal operating set points identified in step 506 to determine the energy consumption resulting from each of the possible combinations. The optimum operating set point may be different for each possible configuration. For example, the optimal operating capacity of a chiller when three chillers are active may be lower than the optimal operating capacity of a chiller when only two chillers are active to service the same thermal energy load. At step 510, the on/off configuration with the lowest energy consumption at the optimal set point is identified. The configuration and set points identified in step 510 may represent the best (i.e., lowest energy consumption) solution that satisfies all constraints on the cost function.

Still referring to FIG. 5, a process 500 is illustrated that includes issuing work orders to devices of the sub-facility according to the identified on/off configuration (step 512). Step 510 may include sending the work order to a building automation system (e.g., the BAS108) configured to control the sub-facility device, the sub-facility level controller, or a device level controller of the sub-facility device. The work order may specify which sub-facility devices are active (i.e., "on"), which are inactive (i.e., "off), and in some embodiments specify a set point for each active sub-facility device. The on/off state and setpoint provided in step 512 may override (override) any previous control decisions that indicate which devices are active and/or the setpoint for each active device.

Referring now to FIG. 6, a flowchart of a process 600 for low-level central facility optimization is shown, according to an exemplary embodiment. The process 600 may be performed by the central facility controller 102, as described with reference to fig. 2-4, and may be a specific embodiment of the process 500. In some embodiments, the process 600 is performed by the processing circuitry 106 of the central facility controller 102 according to instructions stored in the low-level optimization module 132. The process 600 may be used to determine an optimal operating point for a central facility and issue work orders to various devices of the central facility at the optimal operating point.

The process 600 is illustrated as including receiving sub-facility thermal energy load set points for a plurality of sub-facilities of a central facility from a high-level optimization (step 602). In some embodiments, step 602 includes receiving a thermal energy load set point from a high-level optimization module or process. For example, the high-level optimization module 130 may be configured to perform a high-level optimization process that determines the thermal energy load set-point for each sub-facility 12-22. The thermal energy load set point may be based on a predicted thermal energy load for the building or campus and/or a utility rate that defines a cost of one or more resources consumed by the sub-facilities 12-22. The high-level optimization module 130 may determine an optimal distribution of thermal energy loads across the sub-facilities 12-22, as described in conjunction with FIG. 2. In some embodiments, the optimal distribution of thermal energy loads across sub-facilities 12-22 results in the lowest cost of meeting the predicted thermal energy demand of a building or campus over a certain time horizon.

Still referring to fig. 6, process 600 is illustrated as including identifying a previous operating point of the central facility (step 604). The previous operating point may include an on/off status and/or an operating set point for each device of the central facility at a previous time step. In some embodiments, step 604 includes retrieving the previous operating point from the sub-facility control module 138. The sub-facility control module 138 may store historical data regarding past operating conditions, past operating set points, and instructions for calculating and/or executing control parameters of the sub-facilities 12-22. The sub-facility control module 138 may also receive, store, and/or transmit data regarding the condition of the various devices of the central facility, such as operating efficiency, equipment degradation, date of last use, service life parameters, condition ratings, or other device-specific data. The sub-facility control module 138 may also receive and store on/off status and operational set points from the low-level optimization module 132. Step 604 may include retrieving such data from the sub-facility control module 138 and using the retrieved data to identify the previous operating point.

The process 600 is illustrated as including using non-exhaustive binary optimization to select a feasible on/off configuration of one or more coolers and one or more cooling towers of a central facility (step 606), to select a feasible on/off configuration of one or more heat recovery coolers and one or more heating elements of a central facility (step 608), and to select a feasible on/off configuration of one or more pumps of a central facility (step 610). Steps 606-610 may be the same as or similar to step 504 described with reference to FIG. 5. For example, step 606-. The feasible combination also satisfies any constraints on the system (e.g., maximum total power, minimum power of a single device, etc.).

In some embodiments, step 606-. The energy consumption of a sub-facility can be represented by a cost function as follows:

where N is the device in the sub-facilityNumber of (2), t_sIs the duration of the time step, b_jIs a binary on/off decision (e.g., 0 off, 1 on), and u_jIs a function of the device set point θ_LLThe energy source used by device j. The selected feasible combination minimizes the cost function J_LLSaid cost function J_LLSubject to sub-facility load constraints and any other constraints on optimization.

Still referring to fig. 6, process 600 is illustrated as including determining an optimal operating point for the central facility based on the selected on/off configuration using non-linear optimization (step 612). Step 612 may be the same as or similar to step 506 described with reference to fig. 5. For example, step 612 may utilize non-linear optimization to determine the setpoint vector θ_LLSaid set point vector θ_LLMaking a cost function J subject to optimization constraints_LLAnd (4) minimizing. According to various embodiments, the nonlinear optimization is performed using direct and/or indirect search methods (e.g., a downhill simplex (Nelder-Mead) method, GRG, SQP, Cauchy method, Fletcher-Reeves method, etc.).

The set points determined at step 612 may define the capabilities of each active device capable of operating at a plurality of different capabilities and may affect the power consumption of each possible combination. The non-linear optimization performed in step 612 determines one or more set points that minimize the power consumption for each possible combination. In some embodiments, the combination of the optimal on/off configuration and the optimal device set point defines an optimal operating point.

Process 600 is illustrated as including determining whether the percentage change in the optimal operating point is less than a threshold (step 614). Step 614 may include comparing the optimal operating point determined in step 612 with the previous operating point identified in step 604. The comparison may include comparing on/off states and/or set points of the various devices of the central facility. In embodiments, the comparison may be performed individually for each device or for a group of devices (e.g., in each sub-facility, in each category identified individually in

step

606 and 610, in each device type, etc.). For example, the operating point may be an operating point specific to the device, an operating point specific to the sub-facility, an operating point specific to the type of device, or an overall (e.g., average) operating point for all devices of the central facility.

If the percentage change between the previous operating point identified in step 604 and the optimal operating point determined in step 612 is less than a threshold value (i.e., "yes" from step 614), then it may be determined that the optimization process converged (converting) at the optimal value. If the percentage change determined in step 614 is less than the threshold, process 600 may continue with issuing a work command at the optimal work point (step 616). Step 616 may include sending the work order to a building automation system (e.g., the BAS108) configured to control the sub-facility device, the sub-facility level controller, or a device level controller of the sub-facility device. The work order may specify which sub-facility devices are active (i.e., "on"), which are inactive (i.e., "off), and in some embodiments specify a set point for each active sub-facility device. The on/off states and setpoints provided in step 616 may override any previous control decisions that indicate which devices are active and/or the setpoints for each active device.

If the percentage change between the previous operating point identified in step 604 and the optimal operating point determined in step 612 is not less than the threshold (i.e., "no" to the outcome of step 614), then it may be determined that the optimization process has not converged on the optimal value or that convergence has not sufficiently stabilized. If the percentage change determined in step 614 is not less than the threshold, process 600 will return to step 606. Steps 606-614 may be repeated until the optimal operating point converges on a particular value (i.e., the percentage change determined in step 614 is less than the threshold).

Referring now to FIG. 7, a flow diagram of a non-exhaustive binary optimization process 700 is shown in accordance with an exemplary embodiment. The process 700 may be performed by the central facility controller 102 as described with reference to fig. 2-4 and may be used to determine one or more possible on/off configurations of devices of a particular sub-facility. In some embodiments, the process 700 may be used to complete step 504 of process 500 and/or step 606 of process 600 as well as 610. The process 700 may be performed by the processing circuitry 106 of the central facility controller 102 according to instructions stored in the non-exhaustive binary optimization module 156.

Process 700 is illustrated as including initializing a possible scenarios database and a possible scenarios database (step 702). The potential solution database may contain on/off combinations estimated to be able to satisfy the thermal energy load and optimization constraints of the sub-facility. The combination may be characterized as a "branch", as graphically represented in fig. 8 a-8 d. A branch corresponds to a particular combination of on/off states of one or more devices (or portions thereof). Each on/off state can be described as a "bud" (bud). For example, in branch 852 of FIG. 8a, the "on" state of chiller 1 and the "on" state of chiller 2 are each separate "buds".

The combination of on/off states may alternatively be represented by a string, vector, array or other set of binary variables b_iTo describe. For example, branch 858 of FIG. 8D may be described as [1,1,0,1]](i.e., cooler 1 on, cooler 2 on, cooler 3 off, and cooler 4 on). In general, branching (i.e., combinations of shoots) is described as [ a, …, a, B, c, …, c ]]In the form of (1). "B" indicates the bud or device currently being considered (i.e., for determining whether the device should be turned on or off). B may be characterized as a "working bud". a, …, a denotes the combination of devices previously considered (i.e. devices that are switched on/off before the device corresponding to the working bud). Each device in group a, …, a may have a specified on/off state (e.g., a-0 or a-1). c …, c denotes the combination of devices to be considered after the working bud device has been determined to be on or off. Each device in the group c, …, c may have an unspecified on/off state (e.g., c.

The general description [ a, …, a, B, c, …, c ] may represent any number and/or type of devices. For example, a, …, a may include zero or more devices, B may include one or more devices, and c, …, c may include zero or more devices. According to an exemplary embodiment, the a, …, a device has lower power consumption than the working bud device, and therefore the a, …, a device is considered before the working bud device. Similarly, the working bud device may have lower power consumption than the c, …, c device, which is considered behind the working bud device. In other words, combinations of sub-facility devices may be considered in order of increasing power consumption.

In the case where any unspecified operating state is considered to be a wildcard, if a combination can potentially satisfy all applicable constraints (e.g., sub-facility load constraints, device capability constraints, etc.), the combination can be stored in a potential solution database. For example, if combination [1,1,0,1] satisfies the constraint but combination [1,1,0,0] does not, then combination [1,1,0,? The constraint will be satisfied because the unspecified operating state can be either on (1) or off (0).

According to an exemplary embodiment, determining one or more optimal on/off combinations is an iterative process. For example, a portion of a combination of devices may be considered to determine whether the combination has a chance to meet load requirements and/or system constraints. If the portfolio has a certain probability of meeting the load requirements and/or system constraints, the portfolio can be returned to the potential solution database. The viable solution database may contain on/off combinations that have been determined to be able to satisfy applicable constraints. If a combination is determined to satisfy the applicable constraints, the combination may be moved from the potential solution database to the viable solution database.

Still referring to FIG. 7, process 700 is illustrated as including identifying applicable constraints (step 704). The applicable constraints may define one or more requirements that must be met in order for the combination of on/off states to be feasible. In other words, the feasible combinations may be those that satisfy the applicable constraints. Step 704 may include determining the sub-facility load set points to be met, the number and type of sub-facility devices, the maximum and minimum operating capabilities of the devices, the operating conditions and estimated power consumption of each device, and the like. The applicable constraints may be received at the low-level optimization module 132 from the high-level optimization module 130 (e.g., sub-facility load constraints), from the equipment model 120 (e.g., equipment capability constraints), from the BAS108 (e.g., user-defined constraints, timeout constraints, etc.), or from any other data source.

Process 700 is illustrated as including retrieving a branch from a potential regimen database (step 706) and determining whether the branch has an unspecified bud (step 708). Each branch corresponds to a particular combination of on/off states (or portions thereof) of one or more devices. In some embodiments, the potential solution database stores a plurality of branches and an estimated power consumption associated with each branch. Branches in the potential solution database may be sorted by power consumption associated with each branch (e.g., in ascending order). In some embodiments, step 706 includes retrieving the branch with the lowest associated power consumption. If the branch has one or more unassigned buds, the associated power consumption may be the estimated power consumption of the device represented only by the assigned buds. In some embodiments, step 708 includes identifying the first unassigned bud as a working bud.

If the branch has an unspecified bud (i.e., "yes" as a result of step 708), the process 700 may continue with generating a first expanded branch by specifying a first state of the bud (B1) and a second expanded branch by specifying a second state of the bud (B2) (step 710). The state of an unspecified bud may be designated as "on" or "off," which may be indicated by various binary indicators (e.g., 0 off, 1 on, etc.). In some embodiments, the first expansion branch designates the state of the shoot as "on" and the second expansion branch designates the state of the shoot as "off. In other embodiments, the first extended branch designates the state of the bud as "off" and the second extended branch designates the state of the bud as "on". The first expanded branch B1 may be retained for further evaluation in

steps

714 and 724, while the second expanded branch B2 may be returned to the potential solution database (step 712). If the branch retrieved in step 706 does not have an unspecified bud (i.e., "no" to the outcome of step 708), the process 700 may identify the branch as B1 and proceed to step 714.

Still referring to fig. 7, process 700 is illustrated as including determining whether branch B1 satisfies the applicable constraints (step 714). The branch B1 evaluated in step 714 may be the first expanded branch generated in step 710 (e.g., if step 710-. Step 714 may include determining whether the combination of activated devices specified by the branch satisfies the constraints identified in step 704. If branch B1 includes one or more unspecified buds, the determination in step 714 may assume that any unspecified bud has an "open" status. If branch B1 satisfies the applicable constraints (i.e., "yes" as a result of step 714), process 700 may continue with the addition of branch B1 to the viable solutions database (step 716) and proceed to step 724. Adding branch B1 to the viable solution database may indicate that the device designated "on" in branch B1 may be operated to meet the applicable constraints without any additional devices.

If branch B1 does not satisfy the applicable constraints (i.e., "NO" outcome of step 714), process 700 may continue with a determination of whether branch B1 likely satisfies the applicable constraints (step 718). Step 718 may include determining whether the branch can satisfy the applicable constraints if one or more additional devices are used. Additional devices may be selected from a group of devices in branch B1 that have an unspecified status. For example, if branch B1 includes one or more unspecified buds, step 718 may include determining whether the branch is capable of satisfying the applicable constraints if one or more of the unspecified buds are then designated as "on".

If branch B1 is unlikely to satisfy the applicable constraints (i.e., "NO" at the outcome of step 718), process 700 may continue with branch B1 being dropped (step 720) and proceed to step 724. Dropping a branch may include adding the branch to a drop database (e.g., drop database 166) and/or excluding the branch from further consideration as a potential solution. If the branch does not have any unspecified buds, the process 700 may automatically proceed from step 718 to step 720 because no additional devices may be added. If branch B1 may be able to satisfy the applicable constraints (i.e., "yes" as a result of step 718), process 700 may continue with branch B1 being added to the potential solutions database (step 722) and proceed to step 724. Adding branch B1 to the potential solution database may allow the branch to be further evaluated in subsequent iterations of process 700.

Still referring to FIG. 7, process 700 is illustrated as including determining whether the potential solution database is empty (step 724). Once all branches in the possible solution database are either moved to the feasible solution database or discarded as infeasible solutions, the possible solution database will be empty. Not every potential combination is considered in the non-exhaustive optimization. For example, the entire branch may be discarded without evaluating each individual combination based on the branch. Advantageously, this allows binary optimization to identify the best solution without having to process or evaluate each possible combination. If any branches remain in the potential solution database, the process 700 may return to step 706 and repeat steps 706-724. Otherwise, process 700 may end.

Referring now to fig. 8 a-8 d, a graphical representation of a combination of devices is shown, according to an exemplary embodiment. These combinations are represented in a tree structure, with branches representing combinations of devices and buds representing the on/off status of each device. Fig. 8 a-8 d represent four coolers. According to an exemplary embodiment, the cooler 1 results in the lowest power consumption. The remaining coolers lead to an increasing power consumption, with the cooler 4 leading to the comparatively largest power consumption. Fig. 8 a-8 d are illustrated as including branches starting with the cooler 1 switched on. A tree structure (i.e., database, array, data object, etc.) may be created, maintained, updated, processed, and/or stored in possible solution database 168, feasible solution database 164, and/or discard database 166. The calculations (e.g., on/off states) reflected in the tree structures of fig. 8 a-8 d may be performed by the central facility controller 102.

The tree structure of fig. 8 a-8 d may be exported to at least one of a storage device, a user device, or other device on the building management system. The output may be a graphical user interface (e.g., on a client device, on a mobile device, a graphical user interface generated by a web server, etc.). For example, the tree structure of FIG. 8A may be output to the monitoring and reporting application 136 (FIG. 2) via the GUI engine 116. According to this embodiment, the tree structure may include a portion or all of the branches and buds of the data shown in FIGS. 8 a-8 d. In some embodiments, the user can select which branches and buds should be visible via the user interface. The user can also add branches and buds of data not shown in fig. 8A-D, including for example branches where the cooler 1 is disconnected. In other embodiments, the processes described herein will be performed without displaying a combined graphical representation. Although the tree structures of FIGS. 8 a-8 d are illustrated as two-dimensional graphs, another information structure suitable for representing and storing graph data may be used. For example, a relational database having one or more association tables may be used.

Referring to FIG. 8a, a graphical representation of a combination of devices is shown, according to an exemplary embodiment. Bud 802 may represent the working bud of an activated branch (active branch) (i.e., the device being considered in the possible or feasible combination of devices). Combination 852 includes chiller 1 on and chiller 2 on. The on/off status of the additional devices (e.g., chiller 3 and chiller 4) may be added to combination 852 as part of determining an alternate device combination that will also meet the facility load.

Referring to FIG. 8b, a graphical representation of another device combination is shown according to an exemplary embodiment. Bud 802 can be a working bud. Combination 854 includes cooler 1 on, cooler 2 off, and cooler 3 on. The on/off status of an additional device (e.g., chiller 4) may be added to the combination 854 as part of determining an alternate device combination that will also meet the facility load. In another embodiment, bud 806 can be a working bud.

Referring to FIG. 8c, a graphical representation of another device combination is shown according to an exemplary embodiment. Bud 804 may represent the working bud of the active branch. The combination 856 includes cooler 1 on, cooler 2 on and cooler 3 on. The "on" state of the cooler 1 is representative of the previous devices in that branch. The on/off status of an additional device (e.g., chiller 4) may be added to the combination 856 as part of determining an alternate device combination that will also meet the facility load.

Referring to FIG. 8d, a graphical representation of yet another apparatus combination is shown, according to an exemplary embodiment. Bud 804 can be a working bud. Combination 858 includes cooler 1 on, cooler 2 on, cooler 3 off, and cooler 4 on. A combination 858 may indicate that the working bud device is on, the next device is off, and the device is on again later. The "on" state of the cooler 1 is representative of the previous devices in that branch. In an embodiment of the chiller plant having four devices, the bud 808 may represent the last bud.

The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, orientations, etc.). For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions thereof may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.

The present disclosure contemplates various methods, systems, and program products on memory or other machine-readable media for performing various operations. Embodiments of the present disclosure may be implemented using an existing computer processor, or by a special purpose computer processor for a suitable system (incorporated for this or other purposes), or by a hardwired system. Embodiments within the scope of the present disclosure include program products or memory including machine-readable media having machine-executable instructions or data structures carried or stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

Although the figures may show a particular order of method steps, the order of the steps may differ from that shown. In addition, two or more steps may be performed simultaneously or partially simultaneously. Such variations will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the present disclosure. Likewise, a software implementation can be achieved through standard programming techniques with rule based logic and other logic to achieve the various connection steps, processing steps, comparison steps and decision steps.

Claims

1. A controller for a central facility having a plurality of sub-facilities serving thermal energy loads of a building or building system, the controller comprising:

processing circuitry comprising a processor and a memory, wherein the processing circuitry comprises:

a high-level optimization module configured to generate thermal energy load set points for sub-facilities of the central facility by performing a high-level optimization that determines an optimal thermal energy load set point for each of the plurality of sub-facilities by optimally distributing predicted thermal energy loads for the building across the plurality of sub-facilities;

a low-level optimization module comprising a binary optimization module configured to determine one or more available on/off configurations of equipment of the sub-facility using binary optimization to achieve a thermal energy load set point resulting from the high-level optimization;

the low-level optimization module comprises a setpoint evaluator module configured to determine an optimal operating setpoint for equipment of the sub-facility for each possible on/off configuration;

wherein the low-level optimization module is configured to generate a resource consumption profile for the sub-facility by optimizing resource consumption of the sub-facility under a number of different combinations of thermal energy load and weather conditions, the resource consumption profile indicating a minimum amount of resources consumed by the sub-facility as a function of thermal energy load on the sub-facility, and wherein the high-level optimization module is configured to receive the resource consumption profile for the sub-facility from the low-level optimization module; and

a sub-facility control module configured to generate operating parameters for equipment of the sub-facility, the operating parameters including at least one of the available on/off configurations and the optimal operating set point; and

a communication interface coupled to the processing circuit and configured to output the generated operating parameters for controlling equipment of the sub-facility.

2. The controller of claim 1, further comprising a constraint evaluator module configured to identify applicable constraints for equipment of the sub-facility;

wherein each available on/off configuration satisfies the applicable constraints and is estimated to result in equipment of the sub-facility that satisfies the thermal energy load set point.

3. The controller of claim 1, wherein the binary optimization module uses a branch-and-bound method to determine the one or more available on/off configurations.

4. The controller of claim 1, wherein the setpoint evaluator module determines the optimal operating setpoint using a non-linear optimization that minimizes an amount of power consumed by equipment of the sub-facility.

5. The controller of claim 1, wherein the setpoint evaluator module is configured to:

estimating an amount of power consumed by each possible on/off configuration of equipment of the sub-facility at the optimal operational set point; and

identifying which of the possible on/off configurations is estimated to minimize an amount of power consumed by equipment of the sub-facility at the optimal operating set point.

6. The controller of claim 1, wherein the low-level optimization module generates the resource consumption profile by:

optimizing resource consumption by the sub-facility for a number of different combinations of thermal energy loads and weather conditions to produce a plurality of data points including minimum energy consumption values for various sub-facility loads; and

fitting the resource consumption curve to the plurality of data points.

7. A method of controlling a central facility having a plurality of sub-facilities serving a thermal energy load of a building or building system, the method comprising:

generating, by processing circuitry of a controller of the central facility, thermal energy load set points for sub-facilities of the central facility, wherein the thermal energy load set points are generated by a high-level optimization module of the processing circuitry by performing a high-level optimization that determines an optimal thermal energy load set point for each of the plurality of sub-facilities by distributing predicted thermal energy loads for the building across the plurality of sub-facility optimizations;

determining, by a binary optimization module of a low-level optimization module of the processing circuit, one or more available on/off configurations of equipment of the sub-facility using binary optimization to achieve a thermal energy load set point resulting from the high-level optimization;

determining, by a set point evaluator module of the low-level optimization module of the processing circuit, an optimal operating set point for equipment of the sub-facility for each feasible on/off configuration;

generating, by the low-level optimization module of the processing circuit, a resource consumption profile of the sub-facility at a number of different combinations of thermal energy load and weather condition, the resource consumption profile indicating a least amount of resources consumed by the sub-facility as a function of thermal energy load on the sub-facility, and wherein the high-level optimization module is configured to receive the resource consumption profile of the sub-facility from the low-level optimization module;

generating, by a sub-facility control module of the processing circuit, operating parameters of equipment of the sub-facility, the operating parameters including at least one of the feasible on/off configuration and the optimal operating set point; and

outputting the generated operating parameters for controlling equipment of the sub-facility via a communication interface coupled to the processing circuit.

8. The method of claim 7, further comprising:

identifying applicable constraints for devices of the sub-facility;

9. The method of claim 7, wherein determining the one or more available on/off configurations comprises using a branch and bound method.

10. The method of claim 7, wherein determining the optimal operating set point comprises using a non-linear optimization that minimizes an amount of power consumed by equipment of the sub-facility.

11. The method of claim 7, further comprising:

12. The method of claim 8, wherein generating the resource consumption profile comprises:

fitting the resource consumption curve to the plurality of data points.

13. The method of claim 7, further comprising:

providing a possible plan database and a feasible plan database;

retrieving, by the controller, a branch from the potential solution database, the branch including a specified operating state of one or more devices in a central facility;

determining, by the controller, whether the branch satisfies an applicable constraint of the central facility using the specified operating state;

adding, by the controller, the branch to the viable solution database in response to determining that the branch satisfies the applicable constraint; and

generating, by the controller, operating parameters of the central facility device, the operating parameters including the operating states in the viable solution database specified by the branch.