Patent Description:
The present disclosure relates generally to the operation of a central plant for serving building thermal energy loads. The present disclosure relates more particularly to systems and methods for optimizing the operation of one or more subplants of a central plant.

A heating, ventilation and air conditioning (HVAC) system (also referred to as "a central plant" or "an energy plant" herein) may include various types of equipment configured to serve the thermal energy loads of a building or building campus. For example, a central plant may include HVAC devices such as heaters, chillers, heat recovery chillers, cooling towers, or other types of equipment configured to provide heating or cooling for the building. Some central plants include thermal energy storage configured to store the thermal energy produced by the central plant for later use.

A central plant may consume resources from a utility (e.g., electricity, water, natural gas, etc.) to heat or cool a working fluid (e.g., water, glycol, etc.) that is circulated to the building or stored for later use to provide heating or cooling for the building. Fluid conduits typically deliver the heated or chilled fluid to air handlers located on the rooftop of the building or to individual floors or zones of the building. The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the working fluid flows to provide heating or cooling for the air. The working fluid then returns to the central plant to receive further heating or cooling and the cycle continues.

Controlling the central plant includes determining a set of operating parameters of the HVAC devices. In particular, some HVAC device operates according to a selected operating parameter from a range of operating parameters. Examples of the operating parameters include operating capacity (e.g., <NUM>% capacity) of corresponding HVAC devices. Determining a set of operating parameters includes, for a candidate set of operating parameters, predicting thermodynamic states (e.g., pressure values, temperatures values, mass flow values, etc.) of different HVAC devices in operation together, and predicting power consumption of the central plant based on the predicted thermodynamic states. By comparing power consumptions of different candidate sets of operating parameters, a candidate set with the lowest power consumption is determined as the set of operating parameters.

One conventional approach of predicting thermodynamic states of a central plant for a candidate set of operating parameters includes computing the full thermodynamic states by a non-linear solver. However, predicting thermodynamic states of the central plant in a complex arrangement by the non-linear solver is inefficient in terms of computational resources (e.g., processor usage and memory used). Furthermore, predicting thermodynamic states for multiple sets of operating parameters, and comparing power consumptions for multiple sets of operating parameters to determine a set of thermodynamic states rendering lower power consumption through a conventional approach are inefficient and computationally exhaustive.

<CIT> discloses a HVAC system continuity when controllers and other parts of HVAC system are replaced where on detection of a fault a specific subset of heating or cooling equipment is identified and analytics algorithms are looked up for the specific subset of heating or cooling equipment.

<CIT> discloses that a reduced subset of a HVAC system is determined during implementation. Relationships between HVAC devices are modeled by connections defined within each instance of a thermodynamic block. A thermodynamic data model is used to calculate performance metrics such as power usage and loads. A thermodynamic block is an instance of the thermodynamic data model and defines e.g. measured or calculated data values, control parameters, device setpoints.

One implementation of the present disclosure is a controller for a plurality of heating, ventilation, or air conditioning (HVAC) devices according to claim <NUM>.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes identifying a second HVAC device of the plurality of HVAC devices that is schematically dependent on the first HVAC device based on the schematic relationships and excluding the second HVAC device from the reduced subset of the plurality of HVAC devices.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes identifying a second HVAC device of the plurality of HVAC devices that is arranged in series with the first HVAC device based on the schematic relationships and excluding the second HVAC device from the reduced subset of the plurality of HVAC devices in response to the change in condition causing the first HVAC device to become inactive.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes identifying a second HVAC device of the plurality of HVAC devices that is arranged in parallel with the first HVAC device based on the schematic relationships and setting an operating status of the second HVAC device to provide a flow path through the second HVAC device in response to the change in condition causing a flow path through the first HVAC device to become closed.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes determining that the first HVAC device is a linking device that couples a first group of the plurality of HVAC devices with a second group of the plurality of HVAC devices based on the schematic relationships and excluding the second group of the plurality of HVAC devices from the reduced subset of the plurality of HVAC devices in response to the change in condition causing the first HVAC device to decouple the first group from the second group.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes identifying a stranded node coupled to the first HVAC device in response to the change in condition causing the operating status of the first HVAC device to become inactive. Using the schematic relationships further includes using the schematic relationships to identify a second HVAC device of the plurality of HVAC devices coupled to the stranded node and excluding the second HVAC device from the reduced subset of the plurality of HVAC devices.

Another implementation of the present disclosure is a method for operating a plurality of heating, ventilation, or air conditioning (HVAC) devices according to claim <NUM>.

In some embodiments, using the schematic relationships between the plurality of HVAC devices to determine the reduced subset of the plurality of HVAC devices includes identifying a stranded node coupled to the first HVAC device in response to the change in condition causing the operating status of the first HVAC device to become inactive, using the schematic relationships to identify a second HVAC device of the plurality of HVAC devices coupled to the stranded node, and excluding the second HVAC device from the reduced subset of the plurality of HVAC devices.

In some embodiments, the change in condition is a change in a monitored variable that causes the operating status of the first HVAC device to become inactive and determining the reduced subset of the plurality of HVAC devices includes excluding the first HVAC device from the reduced subset of the plurality of HVAC devices.

Referring generally to the FIGURES, disclosed herein are systems and methods for determining a set of operating parameters for operating a HVAC system through disclosed dynamic computation reduction.

In some embodiments, a central plant controller disclosed herein dynamically performs computation reduction for operating the HVAC system. In one approach, the central plant controller obtains an incidence matrix based on connections of HVAC devices of the HVAC system. An incidence matrix is a matrix indicating schematic connections of HVAC devices of the HVAC system. The central plant controller may detect a change in condition of a HVAC device of the HVAC devices, and determine a modified subset of the HVAC devices based on the incidence matrix according to the changed condition of the HVAC device. Examples of the changed condition of the HVAC device include change in internal or external characteristic (e.g., temperature, pressure, flow rate, etc.) of the HVAC device. In one approach, the central plant controller obtains a reduced number of HVAC devices from all or a subset of the HVAC devices based on the incidence matrix according to the changed condition of the HVAC device, and determines a set of operating parameters of the reduced number of HVAC devices.

Beneficially, the central plant controller improves an operation efficiency of the HVAC system by dynamically reducing computation resource for determining operating parameters of the HVAC system. In one aspect, the central plant controller detects a change in condition of a HVAC device, and automatically determines one or more HVAC devices schematically dependent on the HVAC device. The central plant controller may dynamically modify a number of operating parameters of HVAC devices to be determined, according to the changed condition of the HVAC device and schematic dependencies of other HVAC devices. As a result, the HVAC system may omit or isolate determining operating parameters of inoperable HVAC devices that are schematically independent from operable HVAC devices. Inoperable HVAC devices may be disabled or turned off, and operable HVAC devices may be enabled or turned on during operation. Hence, the central plant controller may identify operating parameters rendering an improved performance of the HVAC system in a computationally efficient manner, and operate the HVAC system according to the determined operating parameters.

Referring now to <FIG>, an exemplary HVAC system in which the systems and methods of the present disclosure can be implemented are shown, according to an exemplary embodiment. While the systems and methods of the present disclosure are described primarily in the context of a building HVAC system, it should be understood that the control strategies described herein may be generally applicable to any type of control system.

Referring particularly to <FIG>, a perspective view of a building <NUM> is shown. Building <NUM> is served by a building management system (BMS). A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof.

The BMS that serves building <NUM> includes a HVAC system <NUM>. HVAC system <NUM> can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building <NUM>. For example, HVAC system <NUM> is shown to include a waterside system <NUM> and an airside system <NUM>. Waterside system <NUM> can provide a heated or chilled fluid to an air handling unit of airside system <NUM>. Airside system <NUM> can use the heated or chilled fluid to heat or cool an airflow provided to building <NUM>. An exemplary waterside system and airside system which can be used in HVAC system <NUM> are described in greater detail with reference to <FIG>.

HVAC system <NUM> is shown to include a chiller <NUM>, a boiler <NUM>, and a rooftop air handling unit (AHU) <NUM>. Waterside system <NUM> can use boiler <NUM> and chiller <NUM> to heat or cool a working fluid (e.g., water, glycol, etc.) and can circulate the working fluid to AHU <NUM>. In various embodiments, the HVAC devices of waterside system <NUM> can be located in or around building <NUM> (as shown in <FIG>) or at an offsite location such as a central plant (e.g., a chiller plant, a steam plant, a heat plant, etc.). The working fluid can be heated in boiler <NUM> or cooled in chiller <NUM>, depending on whether heating or cooling is required in building <NUM>. Boiler <NUM> can add heat to the circulated fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. Chiller <NUM> can place the circulated fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulated fluid. The working fluid from chiller <NUM> and/or boiler <NUM> can be transported to AHU <NUM> via piping <NUM>.

AHU <NUM> can place the working fluid in a heat exchange relationship with an airflow passing through AHU <NUM> (e.g., via one or more stages of cooling coils and/or heating coils). The airflow can be, for example, outside air, return air from within building <NUM>, or a combination of both. AHU <NUM> can transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU <NUM> can include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid can then return to chiller <NUM> or boiler <NUM> via piping <NUM>.

Airside system <NUM> can deliver the airflow supplied by AHU <NUM> (i.e., the supply airflow) to building <NUM> via air supply ducts <NUM> and can provide return air from building <NUM> to AHU <NUM> via air return ducts <NUM>. In some embodiments, airside system <NUM> includes multiple variable air volume (VAV) units <NUM>. For example, airside system <NUM> is shown to include a separate VAV unit <NUM> on each floor or zone of building <NUM>. VAV units <NUM> can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building <NUM>. In other embodiments, airside system <NUM> delivers the supply airflow into one or more zones of building <NUM> (e.g., via supply ducts <NUM>) without using intermediate VAV units <NUM> or other flow control elements. AHU <NUM> can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU <NUM> can receive input from sensors located within AHU <NUM> and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU <NUM> to achieve set-point conditions for the building zone.

Referring now to <FIG>, a block diagram of a waterside system <NUM> is shown, according to an exemplary embodiment. In various embodiments, waterside system <NUM> can supplement or replace waterside system <NUM> in HVAC system <NUM> or can be implemented separate from HVAC system <NUM>. When implemented in HVAC system <NUM>, waterside system <NUM> can include a subset of the HVAC devices in HVAC system <NUM> (e.g., boiler <NUM>, chiller <NUM>, pumps, valves, etc.) and can operate to supply a heated or chilled fluid to AHU <NUM>. The HVAC devices of waterside system <NUM> can be located within building <NUM> (e.g., as components of waterside system <NUM>) or at an offsite location such as a central plant.

In <FIG>, waterside system <NUM> is shown as a central plant having a plurality of subplants <NUM>-<NUM>. Subplants <NUM>-<NUM> are shown to include a heater subplant <NUM>, a heat recovery chiller subplant <NUM>, a chiller subplant <NUM>, a cooling tower subplant <NUM>, a hot thermal energy storage (TES) subplant <NUM>, and a cold thermal energy storage (TES) subplant <NUM>. Subplants <NUM>-<NUM> consume resources (e.g., water, natural gas, electricity, etc.) from utilities to serve the thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, heater subplant <NUM> can be configured to heat water in a hot water loop <NUM> that circulates the hot water between heater subplant <NUM> and building <NUM>. Chiller subplant <NUM> can be configured to chill water in a cold water loop <NUM> that circulates the cold water between chiller subplant <NUM> and the building <NUM>. Heat recovery chiller subplant <NUM> can be configured to transfer heat from cold water loop <NUM> to hot water loop <NUM> to provide additional heating for the hot water and additional cooling for the cold water. Condenser water loop <NUM> can absorb heat from the cold water in chiller subplant <NUM> and reject the absorbed heat in cooling tower subplant <NUM> or transfer the absorbed heat to hot water loop <NUM>. Hot TES subplant <NUM> and cold TES subplant <NUM> can store hot and cold thermal energy, respectively, for subsequent use.

Hot water loop <NUM> and cold water loop <NUM> can deliver the heated and/or chilled water to air handlers located on the rooftop of building <NUM> (e.g., AHU <NUM>) or to individual floors or zones of building <NUM> (e.g., VAV units <NUM>). The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the water flows to provide heating or cooling for the air. The heated or cooled air can be delivered to individual zones of building <NUM> to serve the thermal energy loads of building <NUM>. The water then returns to subplants <NUM>-<NUM> to receive further heating or cooling.

Although subplants <NUM>-<NUM> are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to serve the thermal energy loads. In other embodiments, subplants <NUM>-<NUM> can provide heating and/or cooling directly to the building or campus without requiring an intermediate heat transfer fluid. These and other variations to waterside system <NUM> are within the teachings of the present invention.

Each of subplants <NUM>-<NUM> can include a variety of equipment's configured to facilitate the functions of the subplant. For example, heater subplant <NUM> is shown to include a plurality of heating elements <NUM> (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in hot water loop <NUM>. Heater subplant <NUM> is also shown to include several pumps <NUM> and <NUM> configured to circulate the hot water in hot water loop <NUM> and to control the flow rate of the hot water through individual heating elements <NUM>. Chiller subplant <NUM> is shown to include a plurality of chillers <NUM> configured to remove heat from the cold water in cold water loop <NUM>. Chiller subplant <NUM> is also shown to include several pumps <NUM> and <NUM> configured to circulate the cold water in cold water loop <NUM> and to control the flow rate of the cold water through individual chillers <NUM>.

Heat recovery chiller subplant <NUM> is shown to include a plurality of heat recovery heat exchangers <NUM> (e.g., refrigeration circuits) configured to transfer heat from cold water loop <NUM> to hot water loop <NUM>. Heat recovery chiller subplant <NUM> is also shown to include several pumps <NUM> and <NUM> configured to circulate the hot water and/or cold water through heat recovery heat exchangers <NUM> and to control the flow rate of the water through individual heat recovery heat exchangers <NUM>. Cooling tower subplant <NUM> is shown to include a plurality of cooling towers <NUM> configured to remove heat from the condenser water in condenser water loop <NUM>. Cooling tower subplant <NUM> is also shown to include several pumps <NUM> configured to circulate the condenser water in condenser water loop <NUM> and to control the flow rate of the condenser water through individual cooling towers <NUM>.

Hot TES subplant <NUM> is shown to include a hot TES tank <NUM> configured to store the hot water for later use. Hot TES subplant <NUM> can also include one or more pumps or valves configured to control the flow rate of the hot water into or out of hot TES tank <NUM>. Cold TES subplant <NUM> is shown to include cold TES tanks <NUM> configured to store the cold water for later use. Cold TES subplant <NUM> can also include one or more pumps or valves configured to control the flow rate of the cold water into or out of cold TES tanks <NUM>.

In some embodiments, one or more of the pumps in waterside system <NUM> (e.g., pumps <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and/or <NUM>) or pipelines in waterside system <NUM> include an isolation valve associated therewith. Isolation valves can be integrated with the pumps or positioned upstream or downstream of the pumps to control the fluid flows in waterside system <NUM>. In various embodiments, waterside system <NUM> can include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system <NUM> and the types of loads served by waterside system <NUM>.

Referring now to <FIG>, a block diagram of an airside system <NUM> is shown, according to an exemplary embodiment. In various embodiments, airside system <NUM> can supplement or replace airside system <NUM> in HVAC system <NUM> or can be implemented separate from HVAC system <NUM>. When implemented in HVAC system <NUM>, airside system <NUM> can include a subset of the HVAC devices in HVAC system <NUM> (e.g., AHU <NUM>, VAV units <NUM>, ducts <NUM>-<NUM>, fans, dampers, etc.) and can be located in or around building <NUM>. Airside system <NUM> can operate to heat or cool an airflow provided to building <NUM> using a heated or chilled fluid provided by waterside system <NUM>.

In <FIG>, airside system <NUM> is shown to include an economizer-type air handling unit (AHU) <NUM>. Economizer-type AHUs vary the amount of outside air and return air used by the air handling unit for heating or cooling. For example, AHU <NUM> can receive return air <NUM> from building zone <NUM> via return air duct <NUM> and can deliver supply air <NUM> to building zone <NUM> via supply air duct <NUM>. In some embodiments, AHU <NUM> is a rooftop unit located on the roof of building <NUM> (e.g., AHU <NUM> as shown in <FIG>) or otherwise positioned to receive return air <NUM> and outside air <NUM>. AHU <NUM> can be configured to operate an exhaust air damper <NUM>, mixing damper <NUM>, and outside air damper <NUM> to control an amount of outside air <NUM> and return air <NUM> that combine to form supply air <NUM>. Any return air <NUM> that does not pass through mixing damper <NUM> can be exhausted from AHU <NUM> through exhaust air damper <NUM> as exhaust air <NUM>.

Each of dampers <NUM>-<NUM> can be operated by an actuator. For example, exhaust air damper <NUM> can be operated by actuator <NUM>, mixing damper <NUM> can be operated by actuator <NUM>, and outside air damper <NUM> can be operated by actuator <NUM>. Actuators <NUM>-<NUM> can communicate with an AHU controller <NUM> via a communications link <NUM>. Actuators <NUM>-<NUM> can receive control signals from AHU controller <NUM> and can provide feedback signals to AHU controller <NUM>. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by actuators <NUM>-<NUM>), status information, commissioning information, configuration settings, calibration data, and/or other types of information or data that can be collected, stored, or used by actuators <NUM>-<NUM>. AHU controller <NUM> can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control actuators <NUM>-<NUM>.

Still referring to <FIG>, AHU <NUM> is shown to include a cooling coil <NUM>, a heating coil <NUM>, and a fan <NUM> positioned within supply air duct <NUM>. Fan <NUM> can be configured to force supply air <NUM> through cooling coil <NUM> and/or heating coil <NUM> and provide supply air <NUM> to building zone <NUM>. AHU controller <NUM> can communicate with fan <NUM> via communications link <NUM> to control a flow rate of supply air <NUM>. In some embodiments, AHU controller <NUM> controls an amount of heating or cooling applied to supply air <NUM> by modulating a speed of fan <NUM>.

Cooling coil <NUM> can receive a chilled fluid from waterside system <NUM> (e.g., from cold water loop <NUM>) via piping <NUM> and can return the chilled fluid to waterside system <NUM> via piping <NUM>. Valve <NUM> can be positioned along piping <NUM> or piping <NUM> to control a flow rate of the chilled fluid through cooling coil <NUM>. In some embodiments, cooling coil <NUM> includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller <NUM>, by BMS controller <NUM>, etc.) to modulate an amount of cooling applied to supply air <NUM>.

Heating coil <NUM> can receive a heated fluid from waterside system <NUM> (e.g., from hot water loop <NUM>) via piping <NUM> and can return the heated fluid to waterside system <NUM> via piping <NUM>. Valve <NUM> can be positioned along piping <NUM> or piping <NUM> to control a flow rate of the heated fluid through heating coil <NUM>. In some embodiments, heating coil <NUM> includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller <NUM>, BMS controller <NUM>, etc.) to modulate an amount of heating applied to supply air <NUM>.

Each of valves <NUM> and <NUM> can be controlled by an actuator. For example, valve <NUM> can be controlled by actuator <NUM> and valve <NUM> can be controlled by actuator <NUM>. Actuators <NUM>-<NUM> can communicate with AHU controller <NUM> via communications links <NUM>-<NUM>. Actuators <NUM>-<NUM> can receive control signals from AHU controller <NUM> and can provide feedback signals to AHU controller <NUM>. In some embodiments, AHU controller <NUM> receives a measurement of the supply air temperature from a temperature sensor <NUM> positioned in supply air duct <NUM> (e.g., downstream of cooling coil <NUM> and/or heating coil <NUM>). AHU controller <NUM> can also receive a measurement of the temperature of building zone <NUM> from a temperature sensor <NUM> located in building zone <NUM>.

In some embodiments, AHU controller <NUM> operates valves <NUM> and <NUM> via actuators <NUM>-<NUM> to modulate an amount of heating or cooling provided to supply air <NUM> (e.g., to achieve a set-point temperature for supply air <NUM> or to maintain the temperature of supply air <NUM> within a set-point temperature range). The positions of valves <NUM> and <NUM> affect the amount of heating or cooling provided to supply air <NUM> by heating coil <NUM> or cooling coil <NUM> and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU controller <NUM> can control the temperature of supply air <NUM> and/or building zone <NUM> by activating or deactivating coils <NUM>-<NUM>, adjusting a speed of fan <NUM>, or a combination thereof.

Still referring to <FIG>, airside system <NUM> is shown to include a BMS controller <NUM> and a client device <NUM>. BMS controller <NUM> can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system <NUM>, waterside system <NUM>, HVAC system <NUM>, and/or other controllable systems that serve building <NUM>. BMS controller <NUM> can communicate with multiple downstream building systems or subsystems (e.g., HVAC system <NUM>, a security system, a lighting system, waterside system <NUM>, etc.) via a communications link <NUM> according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller <NUM> and BMS controller <NUM> can be separate (as shown in <FIG>) or integrated. The AHU controller <NUM> may be a hardware module, a software module configured for execution by a processor of BMS controller <NUM>, or both.

In some embodiments, AHU controller <NUM> receives information (e.g., commands, set points, operating boundaries, etc.) from BMS controller <NUM> and provides information (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.) to BMS controller <NUM>. For example, AHU controller <NUM> can provide BMS controller <NUM> with temperature measurements from temperature sensors <NUM>-<NUM>, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller <NUM> to monitor or control a variable state or condition within building zone <NUM>.

Client device <NUM> can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system <NUM>, its subsystems, and/or devices. Client device <NUM> can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device <NUM> can be a stationary terminal or a mobile device. For example, client device <NUM> can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device <NUM> can communicate with BMS controller <NUM> and/or AHU controller <NUM> via communications link <NUM>.

Referring to <FIG>, illustrated is a block diagram of a central plant controller <NUM>, according to some embodiments. In some embodiments, the central plant controller <NUM> is part of the HVAC system <NUM> of <FIG>. Alternatively, the central plant controller <NUM> is coupled to the HVAC system <NUM> through a communication link. The central plant controller <NUM> may be the AHU controller <NUM> of <FIG>, or a combination of the BMS controller <NUM> and the AHU controller <NUM> of <FIG>. In one configuration, the central plant controller <NUM> includes a communication interface <NUM>, and a processing circuit <NUM>. These components operate together to determine a set of operating parameters for operating various HVAC devices of the HVAC system <NUM>. In some embodiments, the central plant controller <NUM> includes additional, fewer, or different components than shown in <FIG>.

The communication interface <NUM> facilitates communication of the central plant controller <NUM> with other HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.). The communication interface <NUM> can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.). In various embodiments, communications via the communication interface <NUM> can be direct (e.g., local wired or wireless communications) or via a communications network (e.g., a WAN, the Internet, a cellular network, etc.). For example, the communication interface <NUM> can include an Ethernet/USB/RS232/RS485 card and port for sending and receiving data through a network. In another example, the communication interface <NUM> can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, the communication interface <NUM> can include cellular or mobile phone communication transceivers.

The processing circuit <NUM> is a hardware circuit executing instructions to determine a set of parameters for operating HVAC devices of the HVAC system <NUM>. In one embodiment, the processing circuit <NUM> includes a processor <NUM>, and memory <NUM> storing instructions (or program code) executable by the processor <NUM>. The memory <NUM> may be any non-transitory computer readable medium. In one embodiment, the instructions executed by the processor <NUM> cause the processor <NUM> to form software modules including a high level optimizer <NUM> and a low level optimizer <NUM>. The high level optimizer <NUM> may determine how to distribute thermal energy loads across HVAC devices (e.g., subplants, chillers, heaters, valves, etc.) for each time step in the prediction window, for example, to minimize the cost of energy consumed by the HVAC devices. The low level optimizer <NUM> may determine how to operate each subplant according to the thermal energy loads determined by the high level optimizer <NUM>. In other embodiments, the processor <NUM> and the memory <NUM> may be omitted, and the high level optimizer <NUM> and the low level optimizer <NUM> may be implemented as hardware modules by a reconfigurable circuit (e.g., field programmable gate array (FPGA)), an application specific integrated circuit (ASIC), or any circuitries, or a combination of software modules and hardware modules.

In one implementation, the high level optimizer <NUM> determines thermal energy loads of HVAC devices of the HVAC system <NUM>, and generates Q allocation data <NUM> indicating the determined thermal energy loads. The Q allocation data for a particular HVAC device or subplant may indicate the amount of thermal energy (e.g., heating energy, cooling energy) or other resource (e.g., hot water, cold water, steam, electricity, etc.) to be produced by the particular HVAC device or subplant. The high level optimizer <NUM> may provide the Q allocation data <NUM> to the low level optimizer <NUM>. In return, the high level optimizer <NUM> may receive, from the low level optimizer <NUM>, operating parameter and power estimation data <NUM> indicating a set of operating parameters to operate HVAC devices of the HVAC system <NUM>, predicted power consumptions when operating the HVAC system <NUM> according to the set of operating parameters, or both. Based on the operating parameter and power estimation data <NUM>, the high level optimizer <NUM> can operate the HVAC system <NUM> accordingly or generate different Q allocation data <NUM> for further optimization. The high level optimizer <NUM> and the low level optimizer <NUM> may operate together online in real time, or offline at different times.

In one or more embodiments, the high level optimizer <NUM> includes an asset allocator <NUM> that determines a distribution of thermal energy loads of the HVAC devices of the HVAC system <NUM> based on a predicted thermal energy load of the HVAC system <NUM>. In some embodiments, the asset allocator <NUM> determines the optimal load distribution by minimizing the total operating cost of HVAC system <NUM> over the prediction time window. In one aspect, given a predicted thermal energy load ℓ̂k and utility rate information received through a user input or automatically determined by a scheduler (not shown), the asset allocator <NUM> may determine a distribution of the predicted thermal energy load ℓ̂k across subplants to minimize the cost. The asset allocator <NUM> generates the Q allocation data <NUM> indicating the predicted loads ℓ̂k of different HVAC devices of the HVAC system <NUM> and provides the Q allocation data <NUM> to the low level optimizer <NUM>.

In some embodiments, distributing thermal energy load includes causing TES subplants to store thermal energy during a first time step for use during a later time step. Thermal energy storage may advantageously allow thermal energy to be produced and stored during a first time period when energy prices are relatively low and subsequently retrieved and used during a second time period when energy prices are relatively high. The high level optimization may be different from the low level optimization in that the high level optimization has a longer time constant due to the thermal energy storage provided by TES subplants. The high level optimization may be described by the following equation: <MAT> where <MAT> contains the optimal high level decisions (e.g., the optimal load Q for each of subplants) for the entire prediction period and JHL is the high level cost function.

To find the optimal high level decisions <MAT>, the asset allocator <NUM> may minimize the high level cost function JHL. The high level cost function JHL may be the sum of the economic costs of each utility consumed by each of subplants for the duration of the prediction time period. For example, the high level cost function JHL may be described using the following equation: <MAT> where nh is the number of time steps k in the prediction time period, ns is the number of subplants, ts is the duration of a time step, cjk is the economic cost of utility j at a time step k of the prediction period, and ujik is the rate of use of utility j by subplant i at time step k. In some embodiments, the cost function JHL includes an additional demand charge term such as: <MAT> where wd is a weighting term, cdemand is the demand cost, and the max() term selects the peak electricity use during the applicable demand charge period.

In some embodiments, the high level optimization performed by the high level optimizer <NUM> is the same or similar to the high level optimization process described in <CIT> and titled "High Level Central Plant Optimization,".

The low level optimizer <NUM> receives the Q allocation data <NUM> from the high level optimizer <NUM>, and determines operating parameters (e.g., capacities) of the HVAC devices of the HVAC system <NUM>. In some embodiments, the low level optimization performed by low level optimizer <NUM> is the same or similar to the low level optimization process described in <CIT>. In one or more embodiments, the low level optimizer <NUM> includes an equipment allocator <NUM>, a state predictor <NUM>, and a power estimator <NUM>. In some embodiments, the low level optimizer <NUM> includes different, more, or fewer components, or includes components in different arrangements than shown in <FIG>.

In a general embodiment, the low level optimizer <NUM> works in conjunction with high level optimizer <NUM>. High level optimizer <NUM> may determine optimal thermal energy loads such that, when distributed to various subplants within HVAC system <NUM>, the overall cost and/or power consumption of operating HVAC system <NUM> is minimized. Particularly, these optimal thermal energy loads are provided to low level optimizer <NUM> as various thermal energy setpoints, and low level optimizer <NUM> is configured to reach these thermal energy setpoints. The low level optimization may determine which devices of a particular subplant to utilize (e.g., on/off states) and/or operating setpoints (e.g., temperature setpoints, flow setpoints, etc.) for individual devices of the subplant such that energy consumption is minimized while serving the subplant load.

In one configuration, the equipment allocator <NUM> receives the Q allocation data <NUM> from the high level optimizer <NUM>, and generates candidate operating parameter data <NUM> indicating a set of candidate operating parameters of HVAC devices of the HVAC system <NUM>. The state predictor <NUM> receives the candidate operating parameter data <NUM> and predicts thermodynamic states of the HVAC system <NUM> at various locations for the set of candidate operating parameters. The state predictor <NUM> generates state data <NUM> indicating the predicted thermodynamic states, and provides the state data <NUM> to the power estimator <NUM>. The power estimator <NUM> predicts, based on the state data <NUM>, total power consumed by the HVAC system <NUM> operating according to the set of candidate operating parameters, and generates the power estimation data <NUM> indicating the predicted power consumption. The equipment allocator <NUM> repeats the process with different sets of candidate operating parameters to obtain predicted power consumptions of the HVAC system <NUM> operating according to different sets of candidate operating parameters, and select a set of operating parameters rendering lower power consumption. The equipment allocator <NUM> generates the operating parameter and power estimation data <NUM> indicating (i) the selected set of operating parameters and (ii) predicted power consumption of the power plant when operating according to the selected set of operating parameters, and provide the operating parameter and power estimation data <NUM> to the high level optimizer <NUM>.

The equipment allocator <NUM> is a component that interfaces with the high level optimizer <NUM>. In one aspect, the equipment allocator <NUM> receives the Q allocation data, and determines a candidate set of operating parameters of HVAC devices of the HVAC system <NUM>. For example, the equipment allocator <NUM> may determine that a first chiller is assigned to operate with a first range of thermal energy load and a second chiller is assigned to operate with a second range of thermal energy load based on the Q allocation data. In this example, the equipment allocator <NUM> may determine that operating parameters (e.g., between <NUM>% to <NUM>% capacity) of the first chiller can achieve the first range of thermal energy load and operating parameters (e.g., between <NUM>~<NUM>% capacity) of the second chiller can achieve the second range of thermal energy load. From different combinations of operating parameters of the first chiller and the second chiller, the equipment allocator <NUM> selects a candidate set of operating parameters (e.g., <NUM>% capacity of the first chiller and <NUM>% capacity of the second chiller) satisfying loads specified by the Q allocation data <NUM>. Additionally, the equipment allocator <NUM> generates the candidate operating parameter data <NUM> indicating the selected candidate set of operating parameters, and provides the candidate operating parameter data <NUM> to the state predictor <NUM>. Equipment allocator <NUM> may include various components (e.g., stranded node analyzer, solver simplifier, non-linear solver, etc.) responsible for reducing the computational processing required by non-linear calculations during optimization.

Various methods for computationally reducing the nonlinear calculations determined by the non-linear solver. The non-linear solver may be a component of equipment allocator <NUM>. In some embodiments, state predictor <NUM> reduces the number of thermodynamic states, as described above. In other embodiments, computations of nonlinear equations are accomplished through implementation of graph theory. For example, the non-linear may analyze the nodes and edges of a particular subplant (e.g., graph) in HVAC system <NUM> and generate a system of equations based on the analyzed data. The non-linear solver may then generate a system of equations based on this data in the form of a matrix (e.g., incidence matrix). By taking various derivatives of the generated matrix, such as the psedoinverse and the laplacian, linear algebra can be performed to computationally reduce the calculations required by the non-linear solver. In some embodiments, the process of implementing graph theory to computationally reduce calculation for a non-linear solver is the same or similar to the process described in <CIT>, the entire disclosure of which is incorporated by reference herein.

In some embodiments, nonlinear calculations are reduced by implementation of sequential quadratic programming (SQP). SQP may be implemented as an iterative method of determining an optimized equation by monitoring the final solution of an equation and perturbing the inputs to determine the change in the output. Monitoring this change in the output (i.e., output gradient), a high gradient can be indicative of a sensitive variable, while a small gradient can be indicative of an insensitive variable, with reference to the output. Implementing this in the non-linear solver can allow for several variables in HVAC system <NUM> to be optimized using SQP, resulting in computationally faster calculations with the non-linear solver. In some embodiments, the reduction process through implementation of SQP is the same or similar to the process described in <CIT>. The various embodiments for decreasing computation time in the non-linear solver may be performed by various components within central plant controller <NUM>. For example, computation reduction may be performed by equipment allocator <NUM> as described with reference to <FIG> below.

In some embodiments, nonlinear calculations are reduced by implementation of stranded node analyses. A stranded node analysis may incorporate aspects of graph theory as described above, such that the non-linear solver may need to determine how HVAC equipment are related to one another. This process is described in greater detail with reference to <FIG> below. In some embodiments, the low level optimization and stranded node analysis performed by the low level optimizer <NUM> are the same or similar to the those described in <CIT> and titled "Central Plant Control System With Computation Reduction Based On Stranded Node Analysis,".

The state predictor <NUM> predicts an operating condition of the HVAC system <NUM> based on a set of operating parameters of the HVAC system <NUM> as indicated by the candidate operating parameter data <NUM>. The operating condition of the HVAC system <NUM> includes thermodynamic states at various locations of the HVAC system <NUM>. Examples of thermodynamic states include input pressure value, output pressure value, input mass flow value, output mass flow value, input enthalpy value, output enthalpy value, etc. In one approach, predicting thermodynamic states of the HVAC system <NUM> includes applying the set of operating parameters to a linear solver and a non-linear solver. Generally, the non-linear solver consumes a large amount of resources (e.g., processor threads and storage capacity) to obtain a solution. In one or more embodiments, the state predictor <NUM> reduces a number of unknown thermodynamic states to be predicted based on schematic arrangements of HVAC devices of the HVAC system <NUM>, and may further reduce the number of unknown thermodynamic states to be predicted by propagating known thermodynamic states based on the operating parameters using the linear solver, as described in detail below with respect to <FIG>. In some embodiments, the reduction of thermodynamic states is the same or similar to the process described in <CIT>. Advantageously, a fewer number of unknown thermodynamic states can be determined by the non-linear solver, thereby improving efficiency of predicting the thermodynamic states for the set of operating parameters. The state predictor <NUM> generates state data <NUM> indicating the predicted thermodynamic states for the candidate set of operating parameters, and provides the state data <NUM> to the power estimator <NUM>.

Still referring to <FIG>, power estimator <NUM> predicts power consumed by the HVAC system <NUM> based on the state data <NUM>. In one approach, the power estimator <NUM> determines, for each HVAC device, a predicted power consumption based on thermodynamic states (e.g., pressure values, mass flow values, enthalpy values, etc.) and an operating parameter (e.g., capacity) of the HVAC device. In addition, the power estimator <NUM> may add power consumptions of the HVAC devices of the HVAC system <NUM> to obtain a total power consumption of the HVAC system <NUM>. The power estimator <NUM> generates the power estimation data <NUM> indicating the total power consumption of the HVAC system <NUM>, power consumption of each HVAC device, or any combination of them, and provides the power estimation data <NUM> to the equipment allocator <NUM>.

In some embodiments, the equipment allocator <NUM> compares predicted power consumptions of the HVAC system <NUM> for multiple sets of operating parameters, and selects a set of operating parameters for operating the HVAC system <NUM>. In one approach, the equipment allocator <NUM> selects, from the multiple sets of operating parameters, the set of operating parameters rendering the lowest power consumption. Hence, the HVAC system <NUM> operating based on the set of operating parameters determined by the equipment allocator <NUM> benefits from reduced power consumption. The equipment allocator <NUM> may generate the operating parameter and power estimation data <NUM> indicating the set of operating parameters to operate HVAC devices of the HVAC system <NUM>, predicted power consumptions when operating the HVAC system <NUM> according to the set of operating parameters, or any combination of them, and provide the operating parameter and power estimation data <NUM> to the high level optimizer <NUM>.

In some embodiments, the equipment allocator <NUM> performs a stranded node analysis to remove variables within low level optimizer <NUM> to reduce computation time during nonlinear calculations. The stranded node analysis includes determining schematic dependencies of HVAC devices of the HVAC system by removing or adding a device and determining any stranded node after removing or adding the device from a list of devices to be evaluated. In one aspect, a device may be removed from the list if it is determined to be disabled or turned off. Conversely, a device may be added to the list if it is determined to be enabled or turned on. For example, after removing a first HVAC device, if a second HVAC device is coupled to a stranded node, then the equipment allocator <NUM> determines that the second HVAC device is schematically dependent on the first HVAC device (e.g., connected in series with the first HVAC device such that disabling the first HVAC device effectively disables the second HVAC device as well). For another example, if a third HVAC device is not coupled to any stranded node after removing the first HVAC device, then the equipment allocator <NUM> determines that the third HVAC device is schematically independent from the first HVAC device (e.g., not connected in series with the first HVAC device such that the third HVAC device and first HVAC device can operate independently). Based on the stranded node analysis, the equipment allocator <NUM> allows the state predictor <NUM> to omit predicting operating states of inoperable HVAC devices and additional HVAC devices schematically dependent on the inoperable HVAC device. Hence, the state predictor <NUM> may perform computation for a fewer number of unknowns.

Referring to <FIG>, illustrated is a block diagram of the equipment allocator <NUM>, according to some embodiments. In one configuration, the equipment allocator <NUM> includes a stranded node analyzer <NUM>, a candidate operating parameter generator <NUM>, an output operating parameter generator <NUM>, a dynamic equipment controller <NUM>, and a group generator <NUM>. These components operate together to dynamically determine a set of operating parameters rendering an improved performance of the HVAC system for a reduced number of HVAC devices according to change in condition of one or more HVAC devices, and generate operating parameter and power estimation data <NUM> indicating the determined set of operating parameters and corresponding power consumption of the HVAC system. In some embodiments, the equipment allocator <NUM> includes additional, fewer, or different components than shown in <FIG>.

The stranded node analyzer <NUM> is a component that performs stranded node analysis to determine schematic dependencies of HVAC devices. A stranded node analysis may be performed to determine how various HVAC equipment are related to one another. In the event that an HVAC device (e.g., a chiller) is communicably connected (e.g., wired connection, wireless connection, etc.) to another HVAC device (e.g., a pump), the connection between the two devices (e.g., the node) may then be considered "stranded," as the connection to the subsequent HVAC device has been severed. In one example, after removing a first HVAC device, if a second HVAC device is coupled to a stranded node, then the stranded node analyzer <NUM> determines that the second HVAC device is schematically dependent on the first HVAC device. For another example, if a third HVAC device is not coupled to any stranded node after removing the first HVAC device, then the stranded node analyzer <NUM> determines that the third HVAC device is schematically independent from the first HVAC device. Stranded node analysis disclosed herein allows the central plant controller <NUM> to reduce a number of HVAC devices or operating states of the HVAC to be predicted. In one implementation, the stranded node analyzer <NUM> includes a system identifier <NUM>, a solver simplifier <NUM>, and an equipment selector <NUM>. In this configuration, the stranded node analyzer <NUM> determines schematic dependencies of the HVAC devices, and determines a reduced number of operating states of the HVAC devices to be predicted. In some embodiments, the stranded node analyzer <NUM> includes additional, fewer, or different components than shown in <FIG>.

In some embodiments, stranded node analyzer <NUM> may eliminate variables from schematic representation. For example, upon determining the chiller C1 is inoperable, stranded node analyzer <NUM> may remove chiller C1 from schematic representation and update the incidence matrix according. In other embodiments, stranded node analyzer <NUM> may fix the value of a variable or device control signal such that the device does not have to be solved for. In other words, various non-linear calculations (e.g., non-linear calculations solved by equipment allocator <NUM> for optimization) require calculations of several variables. It may reduce computational complexity to change a variable to a constant rather than removing the device entirely. In various embodiments, these processes are performed by various components within equipment allocator <NUM> and are not limited to stranded node analyzer <NUM>.

The system identifier <NUM> is a component that obtains plant netlist data indicating schematic arrangement of the HVAC devices, and performs stranded node analysis based on the plant netlist data to determine dependencies of the HVAC devices. In some embodiments, the plant netlist data may be converted into an index matrix as described below. As described in the present disclosure, schematic representations may be generated by netlists, incidence matrices, or both. In some embodiments, a netlist defines what HVAC equipment is present in the circuit (e.g., subplant) and the respective nodes of which the HVAC equipment is connected to. In some embodiments, an incidence matrix is a matrix that shows the relationship between two objects. In various embodiments, an incidence matrix is generated from a netlist. For example, subplant X has seven devices and <NUM> nodes. An incidence matrix is generated that is representative of subplant X, wherein the matrix has <NUM> rows, per each device, and <NUM> columns, per each node. A "<NUM>" may be generated in the locations of the matrix of which the respective node and device are connected in the netlist. This allows for a mathematical representation of a physical schematic of an HVAC system. An example of an incidence matrix is shown in Eq. (<NUM>) below.

The plant netlist data describe a plurality of HVAC devices (e.g., chillers, boilers, pumps, fans, valves, etc.) of the HVAC system and schematic connections thereof. For example, the schematic arrangement of the HVAC devices of the HVAC system can be represented by plant netlist data as shown below.

The example plant netlist above details several attributes of various HVAC devices: device type, device name, and nodal connections (input node and output node). The device type may refer to a code used my equipment allocator <NUM> a reference to a particular type of device within the netlist. The device name may be the name of a particular device of that general type (e.g., Chiller1 is a type of Chiller (CHLR)). The nodal connections represent the nodes to which each device is connected, the input node being shown first, followed by the output node.

Referring now to <FIG>, an exemplary embodiment of a schematic representation of the example pant netlist above is shown, according to some embodiments. System <NUM> of <FIG> is shown to include PCHWP1 <NUM>, CH1 <NUM>, PCHWP2 <NUM>, CH2 <NUM>, and cold load <NUM>. The first line (row) of the example plant netlist above represents a chilled water pump (type CHWP) named PCHWP1 connected to a first node (N1) as an input node and a second node (N2) as an output node. This is shown in <FIG> where PCHWP1 <NUM> is connected to first node (N1) as an input node and a second node (N2) as an output node. Similarly, the third line (row) of the netlist shows a first chiller (CH1 <NUM>) connected to node <NUM> as an input node and node <NUM> as an output node.

The plant netlist data may be automatically generated based on a graphical user interface allowing a user to schematically define connections of the plurality of HVAC devices. Alternatively, the plant netlist data may be manually entered by a user through a text editor. Schematically representing arrangements of the HVAC devices of the HVAC system enables the state predictor <NUM> to reduce a number of unknown thermodynamic states to be determined. For example, the HVAC device may determine dependencies of the plurality of HVAC devices, and determine to omit prediction of operating states of one or more HVAC devices that do not contribute to the operation of the HVAC system or do not contribute to a change in the operation of the HVAC system.

In one approach, the system identifier <NUM> obtains an incidence matrix (e.g., incidence matrix 800A) representing schematic connections of HVAC devices of the HVAC system in a matrix format based on the netlist data. In various embodiments, the incidence matrix A may be an n by m (i.e., n×m) matrix. In one embodiment, each row is associated with a corresponding HVAC device and each column is associated with a corresponding node, such as represented in incidence matrix 800A. In this embodiment, n represents the number of HVAC devices, and m represents the number of nodes. In another embodiment, each row is associated with a corresponding node and each column is associated with a corresponding HVAC device. In this embodiment, n represents the number of nodes, and m represents the number of HVAC devices. Although following descriptions are provided with the incidence matrix with rows corresponding to HVAC devices and columns corresponding to nodes, principles disclosed herein may be applied to an incidence matrix with rows corresponding to nodes and columns corresponding to HVAC devices.

In some embodiments, the incidence matrix A can be modified to adjust updated nodal connections for the various HVAC devices. Modifying the incidence matrix A may include the equipment allocator <NUM> receiving information that an HVAC device has gone offline (e.g., inoperable, loss of power, broken, etc.). Equipment allocator <NUM> may remove the offline HVAC device from a schematic representation, resulting in a broken connection from the input node of the removed HVAC device to the output node of the removed HVAC device. System identifier <NUM> may then determine the various other HVAC devices that are schematically dependent on the removed HVAC device. An example of this is shown in <FIG>. Referring to <FIG> a first HVAC device C1 is connected to a node at the input (e.g., Node N4 and a node at its output (e.g., N2). A second HVAC device P1 is connected to Node N2 at its input and Node N1 at its output. In the event that HVAC device C1 goes offline, equipment allocator <NUM> may remove the second HVAC device from a schematic representation, resulting in Node N2 only being connected to HVAC device. This is shown in <FIG>, where device C1 is removed from schematic representation. Therefore, Node N2, having only a single device connection, may be considered a stranded node. The process of stranded node analyzer <NUM> updating schematic representations of HVAC systems, determining which nodal connections between HVAC devices are stranded, and optimizing the nonlinear calculations based on which nodal connections between HVAC devices are stranded may be referred to stranded node analysis. <FIG> are described in greater detail below.

In an exemplary embodiment, <FIG> displays an incidence matrix for devices within HVAC system <NUM>. <FIG> is described in greater detail below, but may described herein at a general level for exemplifying an incidence matrix. In the columns under "devices" of <FIG>, device type, device name, and device number are shown. These attributes may, alone or in combination, allow equipment allocator <NUM> to determine the data which device is being analyzed and the operating parameters for the respective device. The columns under "nodes" may refer to the various nodal connections for each device. In the exemplified embodiment, a "-<NUM>" represents the device connection to that node at the input of the device. A "<NUM>" represents the device connection to that node at the output of the device. A "<NUM>" no direct nodal connection from the device to that node. For example, in the first row of <FIG>, Chiller C1 is not connected to Node <NUM>, the input of Chiller C1 is connected to Node <NUM>, Chiller C1 is not connected to Node <NUM>, the output of Chiller C1 is connected to Node <NUM>, and Chiller C1 is not connected to Node <NUM>. A schematic representation of the incidence matrix shown in <FIG> is shown in <FIG> and <FIG> are described in greater detail below.

Referring back to <FIG>, solver simplifier <NUM> generates a simplified list of HVAC devices based on schematic dependencies of the HVAC devices. For example, the solver simplifier <NUM> obtains a list of HVAC devices that are operable or inoperable from the high level optimizer <NUM>. The solver simplifier <NUM> may remove or exclude devices schematically dependent on inoperable HVAC devices from the list of HVAC devices. The solver simplifier <NUM> may add devices schematically dependent on operable HVAC devices to the list of HVAC devices. The solver simplifier <NUM> may perform the process of modifying the incident matrix A as illustrated above to obtain the simplified list of HVAC devices. A detailed process of solver simplifier generated a simplified list of HVAC devices based on schematic dependencies of the HVAC devices is disclosed with reference to <FIG> in conjunction with <FIG> below.

The equipment selector <NUM> is a component that receives the Q allocation data <NUM> from the high level optimizer <NUM>, and determines a set of operating parameters of HVAC system <NUM> according to the Q allocation data <NUM>. In one implementation, the equipment selector <NUM> stores a look up table (i.e., LUT) indicating a relationship between thermal energy loads and corresponding sets or ranges of operating parameters of the HVAC system. For example, the equipment selector <NUM> receives the Q allocation data <NUM> indicating a target thermal energy load of a heater and a target thermal energy load of a cooler. In this example, the equipment selector <NUM> may determine that a first range of the operating parameter of the heater corresponds to the target thermal energy load of the heater and a second range of the operating parameter of the heater corresponds to the target thermal energy load of the cooler based on the look up table. In one aspect, the equipment selector <NUM> determines a set of operating parameters of HVAC devices in the simplified list from the solver simplifier <NUM>.

The dynamic equipment controller <NUM> is a component that detects a change in various conditions (e.g., conditions of a HVAC devices, outdoor air temperature, operating schedule for the dive, measurements form the system, change in required load, etc.), and modifies a list of HVAC devices to determine operating parameters. Various changes and the effect of detecting the various changes are described in greater detail with reference to <FIG>. In some embodiments, the dynamic equipment controller <NUM> includes an equipment condition detector <NUM> and a device list modifier <NUM>. In some embodiments, the components of the dynamic equipment controller <NUM> operate together with the components of the stranded node analyzer <NUM> or are implemented by the stranded node analyzer <NUM>. In some embodiments, the dynamic equipment controller <NUM> includes more, fewer, or different components than shown in <FIG>.

The equipment condition detector <NUM> is a component that detects an operating condition of HVAC devices. In one approach, the equipment condition detector <NUM> receives sensor values indicating internal or external characteristic (e.g., temperature, pressure, flow rate, etc.) of a HVAC device, and compares the characteristic of the HVAC device against a corresponding threshold. In one example, if the characteristic of the HVAC device exceeds the corresponding threshold, the equipment condition detector <NUM> may determine that the HVAC device is inoperable, hence should be turned off or disabled. Alternatively, if the characteristic of the HVAC device exceeds the corresponding threshold, the equipment condition detector <NUM> may determine that the HVAC device is operable, hence should be turned on or enabled. If the characteristic of the HVAC device is below the corresponding threshold, the equipment condition detector <NUM> may determine that the HVAC device is operable, hence should be turned on or enabled. Alternatively, if the characteristic of the HVAC device exceeds the corresponding threshold, the equipment condition detector <NUM> may determine that the HVAC device is inoperable, hence should be turned off or disabled. In some embodiments, if the characteristic of the HVAC device exceeds the corresponding threshold, equipment condition detector <NUM> may determine that the HVAC device is operable. In one approach, the equipment condition detector <NUM> receives data indicating internal or external characteristic (e.g., temperature, pressure, flow rate, etc.) of a HVAC device, and compares an amount of change in the characteristic of the HVAC device against a corresponding threshold amount. The threshold amount may be predetermined or dynamically adjusted. In one example, if the amount of change in the characteristic of the HVAC device exceeds the corresponding threshold for a predetermined time period (e.g., <NUM> minutes), the equipment condition detector <NUM> may determine that the HVAC device is inoperable. Similarly, if the amount of change in the characteristic of the HVAC device is below the corresponding threshold for a predetermined time period (e.g., <NUM> minutes), the equipment condition detector <NUM> may determine that the HVAC device is operable. In some embodiments, if the characteristic of the HVAC device exceeds the corresponding threshold, equipment condition detector <NUM> may determine that the HVAC device is operable. Alternatively, if the characteristic of the HVAC device drops below the corresponding threshold, equipment condition detector <NUM> may determine that the HVAC device is inoperable. Several more examples regarding operating conditions of devices within HVAC system <NUM> are described in greater detail with reference to <FIG> below.

The device list modifier <NUM> is a component that modifies a list of HVAC devices to determine operating parameters according to the determined operating condition of one or more HVAC devices. In one approach, the device list modifier <NUM> configures the stranded node analyzer <NUM> to modify a list of HVAC devices to determine the operating parameters according to operable or inoperable condition of HVAC device. Hence, operating parameters of the modified list of HVAC devices can be dynamically determined according to changed conditions (e.g., external temperature) of HVAC devices, and HVAC devices can be operated in a computationally efficient manner.

The group generator <NUM> is a component that groups HVAC devices. In some embodiments, the functionality of group generator <NUM> is similar to the processes described in <CIT>. In one aspect, the group generator <NUM> groups HVAC devices that are schematically connected in parallel, and stores group data indicating grouped HVAC devices. In some embodiments, the group generator <NUM> operates together with the stranded node analyzer <NUM>, the dynamic equipment controller <NUM>, other components of the equipment allocator <NUM>, or any combination of them. In one approach, the group generator <NUM> obtains netlist data representing schematic connections of HVAC devices, and determines a group of HVAC devices that operates independently or do not overlap with another group of HVAC devices. In one aspect, each group of HVAC devices operates independently or does not schematically overlap with the other groups of HVAC devices. In some embodiments, the group generator <NUM> generates group data, in response to detecting a change in condition of one or more HVAC devices. For example, the group generator <NUM> detects that a HVAC device is disabled during operation of the central plant, and determines a largest group of HVAC devices that includes one or more nodes associated with the disabled HVAC device and operates independently from other groups of HVAC devices. For another example, the group generator <NUM> detects that a HVAC device is enabled during operation of the central plant, and determines a largest group of HVAC devices that includes one or more nodes associated with the enabled HVAC device and operates independently from other groups of HVAC devices. In some embodiments, the group generator <NUM> divides a group of HVAC devices into cycles, where a cycle is a set of HVAC devices that are coupled to each other in parallel. In one aspect, the group generator <NUM> determines a subsystem including same type of HVAC devices that are connected to each other in parallel. By dynamically grouping and identifying subsystems in response to a change in condition of one or more HVAC devices, workload may be dynamically distributed among the HVAC devices in the subsystem.

The candidate operating parameter generator <NUM> is a component that interfaces with the state predictor <NUM>, and generates candidate operating parameter data <NUM> based on the operating parameters of the HVAC system. The candidate operating parameter generator <NUM> may generate the candidate operating parameter data <NUM> based on operating parameters of the HVAC devices in the simplified list from the equipment allocator <NUM>. The candidate operating parameter generator <NUM> may provide the candidate operating parameter data <NUM> to the state predictor <NUM>. Because the candidate operating parameter data <NUM> indicate operating parameters of the HVAC devices in the simplified list, rather than operating parameters of full HVAC devices of the HVAC system, computation resources for predicting operating states of the HVAC devices by the state predictor <NUM> may be conserved.

The output operating parameter generator <NUM> is a component that determines a set of operating parameters for operating the HVAC system, and provides the operating parameter and power estimation data <NUM> indicating the set of operating parameters and predicted power consumption. In one example, the output operating parameter generator <NUM> determines, from different sets of operating parameters, the set of operating parameters rendering the lowest power consumption.

Referring to <FIG> illustrated is an example schematic representation of a HVAC system <NUM>, according to some embodiments. The example HVAC system <NUM> includes pumps P1, P2, P3, P4, and chillers C1, C2. The pump P1 is coupled to the chiller C1 at node N2 in series between nodes N1, N4, and the pump P2 is coupled to the chiller C2 at node N3 in series between nodes N1, N4. Hence, the pump P1 and the chiller C1 are coupled to the pump P2 and the chiller C2 in parallel. The pump P3 is coupled to the pump P4 in parallel between nodes N1 and N4.

In one example, the central plant controller <NUM> dynamically detects a volatile change in pump pressure within pump P2 via one or more pressure sensors and determines that the pump P2 is inoperable. Assuming that the pump P2 and the chiller C2 were both enabled or operable, and after detecting volatile change in pump pressure within pump P2, the central plant controller <NUM> determines that the chiller C2 is schematically dependent on the pump P2 through a stranded node analysis, as after removing or disabling the pump P2, the node N3 becomes stranded. Hence, the central plant controller <NUM> generates a list of HVAC devices including the pumps P1, P3, P4 and the chiller C1 for determining operating parameters without the pump P2 and the chiller C2 that are schematically dependent on each other. Moreover, the central plant controller <NUM> dynamically determines operating parameters of the pumps P1, P3, P4 and the chiller C1 according to the change in condition of the pump P2. Various examples of schematic updates based on various conditions are described in greater detail with reference to <FIG> below.

Referring to <FIG>, illustrated is an example schematic representation 700A of a HVAC system, according to some embodiments. Referring to <FIG>, illustrated is an example incidence matrix 800A of the HVAC system representing schematic connections of the HVAC system of <FIG>, according to some embodiments. In one example, C1 represents the Office of Statewide Health Planning and Development (OSHPD) chiller plant with P1 being its primary pump, C2 represents the heat recovery chiller (HRC) Chiller Plant with P2 being its primary pump, and P3 represents the secondary pump. If the high level optimizer <NUM> indicates that the OSHPD plant is inoperable or should be disabled, the low level optimizer <NUM> may remove the OSHPD plant from the incidence matrix 800B, as shown below in <FIG> or <FIG>. For example, the value '-<NUM>' in an element <NUM> is substituted by '<NUM>', and the value '<NUM>' in an element <NUM> is substituted by '<NUM>'.

Referring to <FIG>, illustrated is an example schematic representation 700B of the HVAC system with a device removed from the schematic representation shown in <FIG>, according to some embodiments. Referring to <FIG>, illustrated is an example incidence matrix 800B of the HVAC system representing schematic connections of the HVAC system of <FIG>, according to some embodiments. Removal of C1 leaves the P1 connected to a stranded node N2, meaning P1 can also be removed. The node N4 connected to C2 is not left stranded, as there are still inlets (C2 and the Tank), and outlets (P3 and the Tank). In one approach, a column <NUM> of the incidence matrix having a value '<NUM>' or '-<NUM>' but not having a pair of '<NUM>' and '-<NUM>' is detected. Such column <NUM> represents a stranded node. Node N4 is not a stranded node, because column <NUM> has at least a pair of '-<NUM>' and '<NUM>'. If a stranded node is detected, a row <NUM> containing a nonzero value of the detected column <NUM> is identified. Such row <NUM> corresponds to a HVAC device P1 schematically dependent on the removed HVAC device C1.

Referring to <FIG>, illustrated is an example schematic representation 700C of the HVAC system after removing another device dependent on the removed device of <FIG>, according to some embodiments. Referring to <FIG>, illustrated is an example incidence matrix 800C of the HVAC system representing schematic connections of the HVAC system of <FIG>, according to some embodiments. After removing P1, the simplified schematic representation of HVAC system can be obtained as shown in <FIG> or <FIG>. For example, the value '-<NUM>' in an element <NUM> is substituted by '<NUM>', and the value '<NUM>' in an element <NUM> is substituted by '<NUM>'.

<FIG> is a flow chart illustrating a process <NUM> of dynamically determining a set of operating parameters of HVAC devices, according to some embodiments. The process <NUM> may be performed by the low level optimizer <NUM> of <FIG>. In some embodiments, the process <NUM> may be performed by other entities. In some embodiments, the process <NUM> may include additional, fewer, or different steps than shown in <FIG>.

The low level optimizer <NUM> obtains an incidence matrix indicating schematic relationships of HVAC devices (step <NUM>). The low level optimizer <NUM> may obtain plant netlist data representing schematic connections of HVAC devices, and generate the incidence matrix according to the plant netlist data. In one example, the incidence matrix A may be an n by m matrix. In one embodiment, each row is associated with a corresponding HVAC device and each column is associated with a corresponding node. In this embodiment, n represents the number of HVAC devices, and m represents the number of nodes. A HVAC device coupled to a node through an input of the HVAC device may have a value of -<NUM>, and a HVAC device coupled to the node through an output of the HVAC device may have a value of <NUM>.

The low level optimizer <NUM> detects a change in condition of a HVAC device (step <NUM>). The low level optimizer <NUM> may obtain sensor values indicating internal or external characteristic (e.g., temperature, pressure, flow rate, etc.) of the HVAC device as the operating condition of the HVAC device. The low level optimizer <NUM> may obtain the sensor values through the communication interface <NUM>, and determine the operating condition of the HVAC device according to the sensor data. In one approach, the low level optimizer <NUM> compares the sensed value against a threshold value, and determines whether the HVAC device is operable or inoperable according to the comparison. In another approach, the low level optimizer <NUM> determines an operating state, from a plurality of operating states, of the HVAC device according to the comparison. For example, if the external temperature of the HVAC device exceeds a threshold value, the low level optimizer <NUM> determines that the HVAC device is inoperable. For another example, if the external temperature of the HVAC device drops below the threshold value, the low level optimizer <NUM> determines that the HVAC device is operable.

The low level optimizer <NUM> determines a modified subset of the HVAC devices based on the schematic relationships and the detected changed condition of the HVAC device (step <NUM>). In one approach, the low level optimizer <NUM> may generate a list of a subset of HVAC devices, of which thermodynamic states are to be determined. The low level optimizer <NUM> may identify an inoperable device and exclude HVAC devices that are schematically dependent on the inoperable device from the list. In one approach, the low level optimizer <NUM> performs a stranded node analysis based on the incidence matrix, and determines a modified subset of the HVAC devices. In one example, the low level optimizer <NUM> generates or substitutes values of an inoperable HVAC device (or disabled device) with '<NUM>'. In one example, the low level optimizer <NUM> generates or substitutes a value of operable HVAC devices (or enabled device) with '<NUM>' or '-<NUM>' depending on a flow direction. If a column contains a nonzero value but no longer contains a pair of '<NUM>' and '-<NUM>', then one or more rows in the incidence matrix A that contain nonzero values in that column indicate that corresponding HVAC devices are schematically dependent on the inoperable HVAC device. The low level optimizer <NUM> may iteratively identify additional schematically dependent HVAC devices by further substituting values of schematically dependent HVAC devices with '<NUM>', and determining whether any column has either '<NUM>' or '-<NUM>' but no longer contains a pair of '<NUM>' and a '-<NUM>'. Similarly, the low level optimizer <NUM> may identify an operable device and include HVAC devices that are schematically dependent on the operable device to the list.

The low level optimizer <NUM> predicts thermodynamic states of the reduced subset (step <NUM>), and determines a set of operating parameters of the HVAC device (step <NUM>). In one approach, the low level optimizer <NUM> determines thermodynamic states of HVAC devices in the modified list, for example, through a non-linear solver, and determines a set of operating parameters of the HVAC devices that render an optimal performance (e.g., lowest power consumption). The controller <NUM> operates HVAC devices according to the determined set of operating parameters.

<FIG> is an example schematic representation of the HVAC system <NUM> including HVAC devices in separate groups, according to some embodiments. The example HVAC system <NUM> includes two groups <NUM> and <NUM>. In one aspect, the HVAC devices of the group <NUM> are schematically independent from, or schematically do not overlap with the HVAC devices of the group <NUM>. The groups <NUM>, <NUM> may be schematically independent from each other because of a change in condition of one or more HVAC devices that are coupled between HVAC devices in different groups <NUM>, <NUM>.

In <FIG>, the group <NUM> includes pumps P1, P2, P3, P4, P5 and chillers C1, C2. In one example, the pumps P1, P2, P3 are coupled to each other in parallel between the nodes N1, N2, the chillers C1, C2 are coupled to each other in parallel between the nodes N2, N3, and the pumps P4, P5 are coupled to each other in parallel between the nodes N3, N1. The group <NUM> includes pumps P6, P7, P8, P9, P10 and chillers C3, C4. Moreover, the pumps P6, P7, P8 are coupled to each other in parallel between the nodes N4, N5, the chillers C3, C4 are coupled to each other in parallel between the nodes N5, N6, and the pumps P9, P10 are coupled to each other in parallel between the nodes N6, N4. The HVAC devices of the HVAC system <NUM> may be grouped into the groups <NUM>, <NUM>, according to a change in condition of one or more HVAC devices of the HVAC system <NUM>. An example approach of grouping HVAC devices is described below with respect to <FIG>.

<FIG> is a flow chart illustrating a process <NUM> of dynamically grouping HVAC devices, according to some embodiments. The process <NUM> may be performed by one or more components of the low level optimizer <NUM> of <FIG> including, for example, the group generator <NUM>. The process <NUM> may include performing any of the functions descried with reference to the group generator <NUM>. In some embodiments, the process <NUM> may be performed by other entities. In some embodiments, the process <NUM> may include additional, fewer, or different steps than shown in <FIG>.

In one example, the low level optimizer <NUM> obtains netlist data indicating schematic relationships of the HVAC devices of the HVAC system <NUM> (step <NUM>), and determines independent groups of HVAC devices based on the netlist data (step <NUM>). For example, the low level optimizer <NUM> determines groups <NUM>, <NUM> of HVAC devices that do not schematically overlap with each other. In one approach, the low level optimizer <NUM> dynamically determines independent groups of HVAC devices, in response to detecting a change in condition of one or more HVAC devices. Referring to <FIG>, for example, the low level optimizer <NUM> detects that a HVAC device connected between nodes N1, N4 is removed or disabled during operation of the central plant, and determines largest groups <NUM>, <NUM> of HVAC devices that include nodes N1, N4 associated with the removed or disabled HVAC device. For another example, the low level optimizer <NUM> detects that one or more HVAC devices are added or enabled between nodes N1, N4 during operation of the central plant, and determines a largest group of HVAC devices that include nodes N1, N4 associated with the added or enabled HVAC devices.

Moreover, the low level optimizer <NUM> may divide groups <NUM>, <NUM> of HVAC devices into cycles (step <NUM>). A cycle is a set of HVAC devices that are coupled to each other in parallel. Referring to <FIG>, for example, pumps P1, P2, P3 form a first cycle, chillers C1, C2 form a second cycle, and pumps P4, P5 form a third cycle in the group <NUM>. Additionally, pumps P6, P7, P8 form a first cycle, chillers C3, C4 form a second cycle, and pumps P9, P10 form a third cycle in the group <NUM>.

The low level optimizer <NUM> determines a subsystem including same type of HVAC devices that are connected to each other in parallel (step <NUM>). In one aspect, same type of HVAC devices that are connected in parallel that were not identified by the high level optimizer <NUM> may be identified by the low level optimizer <NUM>. In another aspect, HVAC devices of the same type that are connected to each other in parallel can be identified in response to a change in condition of one or more HVAC devices, and workload may be dynamically distributed among the HVAC devices in the subsystem.

<FIG> variously disclose different embodiments of utilizing a central plant controller to implement high level and low level optimization. For example, <FIG> disclose embodiments of implementing a stranded node analysis via equipment allocator <NUM> in low level optimizer <NUM>, according to some embodiments. In another example, <FIG> discloses a process <NUM> of dynamically determining groups and/or cycles for HVAC devices that may be performed by the low level optimizer <NUM>, according to some embodiments. Although these components and processes are described separately for ease of explanation, it should be understood that control circuity for HVAC system <NUM> (e.g., central plant controller <NUM>) may implement various device grouping/removal processes (e.g., stranded node analyses, device removal from netlists, stranded subplant analyses, dynamically grouping HVAC devices, etc.) and/or the components that perform these processes either alone or combination with each other. For example, central plant controller <NUM> may both determine one or more subsystems including the same type of HVAC devices that are connected to each other in parallel (as shown in <FIG>) and implement a stranded node analysis via equipment allocator <NUM> (as shown in <FIG>) on the one or more subsystem. Various embodiments of different processes are described below.

Referring now to <FIG>, a process <NUM> for optimizing an HVAC system is shown, according to some embodiments. Process <NUM> can be performed by various control circuity in HVAC system <NUM>, such as low level optimizer <NUM>, according to some embodiments. In some embodiments, various steps or combinations of steps in process <NUM> may be referred to as "stages" in the present disclosure. For example, process <NUM> is shown to include removing devices based on one or more conditions (step <NUM>), which may be referred to as the second stage (e.g., <NUM>nd Stage, Stage Two, etc.). Process <NUM> may represent a general optimization process for low level optimizer <NUM>.

Process <NUM> is shown to include receiving cycle/group information from asset allocator (step <NUM>). In some embodiments, equipment allocator <NUM> may provide group/cycle data via group generator <NUM> as described above. In various embodiments, asset allocator <NUM> may decide to exclude one or more subplants from HVAC system <NUM> and remove the excluded subplant from an incidence matrix representation. This may prevent low level optimizer <NUM> from providing control signals to the various equipment within the excluded subplant. In other embodiments, asset allocator <NUM> may choose to include a subplant periodically and exclude a subplant periodically. This may result in the actuation of one or more valves within HVAC system <NUM> that can include or isolate a particular subsystem based on instructions from asset allocator <NUM>. Various examples of the embodiments shown above are described in greater detail with reference to <FIG>.

Process <NUM> is shown to include implementing a stranded node analysis (step <NUM>). The stranded node analysis may be performed by the low level optimizer <NUM> and is the same or similar to those described in <CIT> and titled "Central Plant Control System With Computation Reduction Based On Stranded Node Analysis,". Step <NUM> may be referred to as the Stage <NUM> in process <NUM> and be implemented after Stage <NUM>, after Stage <NUM>, or both (as shown in <FIG>). In step <NUM>, low level optimizer <NUM> may monitor the high level groupings of HVAC system <NUM> (e.g., subplants, etc.) to determine if any stranded nodes are present.

Process <NUM> is shown to include removing devices based on one or more conditions (step <NUM>). "Devices," as used herein may refer to any HVAC component or other building device capable of altering an environmental condition either directly or indirectly. In some embodiments, devices may include chillers, boilers, pumps valves, heating coils, and various other HVAC devices. In other embodiments, devices may include external HVAC devices such as an outdoor air temperature sensor. In some embodiments, devices include the various devices as shown in <FIG>. Step <NUM> may be performed by various components within equipment allocator <NUM>, such as dynamic equipment controller <NUM>. Step <NUM> may include removing devices from an incident matrix (e.g., matrix 8A) such that equipment allocator <NUM> in low level optimizer <NUM> is restricted from providing the device with control signals during optimization.

For example, asset allocator <NUM> determines that subplant A is not required to perform optimization of HVAC system <NUM>, but subplant B is required. Equipment allocator <NUM> may then evaluate various devices within subplant B on a device-by-device basis to determine if the devices are required in the optimization of system <NUM>. Various examples of conditions within step <NUM> are described below.

In some embodiments, step <NUM> is implemented in schematic representation 700A as shown in <FIG>. Asset allocator <NUM> determines that the thermal energy needs during optimization does not require chiller C1 for operation. Equipment allocator <NUM> may then implement a stranded node analysis (e.g., <NUM>rd stage) to determine schematic dependencies of HVAC devices based on chiller C1 and, as a result, remove pump P1from the representation. Equipment allocator <NUM> may then implement the <NUM>nd stage of process <NUM> to determine if any devices require removal with respect to the schematic representation generated by equipment allocator <NUM>, resulting in an updated incidence matrix. In some embodiments, removal of a device may be performed when a device is no longer capable of causing further change in a measured variable. For example chiller C1 may attempt to chiller <NUM> (as shown in <FIG>) may attempt to reach a water temperature setpoint of <NUM>. At a 700kW capacity at full-load, chiller <NUM> is incapable of lowering the water temperature, which is currently at <NUM>, any lower. Accordingly, asset allocator <NUM> removes chiller <NUM> schematic representation and updates the incidence matrix accordingly.

In some embodiments, removal of a device may be performed when a device may operate outside of normal operating parameters required by hysteresis. For example, valve <NUM> (as shown in <FIG>) may allow or restrict fluid from entering cooling coil <NUM>. Operational parameters may have been implemented such that valve <NUM> is required to allow chilled fluid to enter cooling coil <NUM> when building zone temperatures rise above <NUM>. Additionally, valve <NUM> may be required to remain open for a predetermined period of time (e.g., <NUM> minute, <NUM> minutes, etc.) to mitigate potential piping issues (e.g., water hammer). Equipment allocator <NUM> may determine that valve <NUM> must allow and restrict cold fluid within an interval lower than the predetermined period of time. As this is outside of the operational parameters required by hysteresis, equipment allocator <NUM><NUM> removes valve <NUM> from schematic representation and updates the incidence matrix accordingly.

In some embodiments, removal of a device may be performed when a device is inoperable. For example, pump <NUM> has incurred a mechanical issue (e.g., water sediment buildup, broken pump vanes, cracked rotor, etc.). This may render pump <NUM> inoperable for pumping chilled fluid to chiller <NUM>. Accordingly, equipment allocator <NUM> removes pump <NUM> from the incidence matrix representation. In some embodiments, removal of a device may be performed when a device in providing unreliable outputs. For example, pump <NUM> may be providing volatile measurements that increase and decrease significantly in substantially short periods of time. In such an embodiment, equipment allocator <NUM> may remove pump <NUM> from schematic representation and updates the incidence matrix accordingly.

In some embodiments, removal of a device may be performed when an external device receives measurements that are indicative or abnormal operating conditions. For example, an outdoor air temperature (OAT) sensor may receive temperature measurements that are significantly higher that standard operating temperatures (e.g., <NUM>). In such an embodiment, equipment allocator <NUM> may determine that various devices (e.g., chiller C1) may generate too much power consumption to be operable during optimization processes. Equipment allocator <NUM> may then remove chiller C1 from schematic representation and update the incidence matrix accordingly.

In some embodiments, removal of a device may be performed based on various uncontrollable external variables (e.g., season changes, extreme weather conditions, etc.). For example, building <NUM> may be located in an environment in which a severe ice storm occurs. Temperatures may drop below -<NUM> and render HVAC devices and/or external devices (e.g., OAT sensors) inoperable. In such an embodiment, equipment allocator <NUM> may determine that the various devices are not in a condition to operate. This may be due to too high of power consumption to operate under normal operating parameters, mechanical issues due to temperature, and various other device-related issues. Equipment allocator <NUM> may then remove chiller C1 from schematic representation and update the incidence matrix accordingly. In some embodiments, measurements received from OAT sensors may lead equipment allocator <NUM> to remove entire groups/subplants from schematic representation and update the incidence matrix accordingly. In various embodiments, external factors are not limited to temperature and can include other factors such as sediment buildup in pipes, flooding, and/or mechanical issues with the devices.

In some embodiments, removal of a device may be performed based on changes in required load that cause devices to be turned on/off based on operating sequences. For example, equipment allocator <NUM> may determine that, of various chillers A-F within HVAC system <NUM>, chillers A-F generate chilled fluid in a non-linear relationship with respect to power consumption. In the exemplified embodiment, the chillers A-F consume less power when operating a low capacity (e.g., chilling fluid at <NUM>% total load capacity for the chillers) and consume more power when operating at a high capacity (e.g., chilling fluid at <NUM>% total load capacity for the chillers). Accordingly, asset allocator <NUM> may determine that it is optimal in terms of power consumption to operate all chillers A-F at a lower capacity rather than some chillers at a higher capacity. For example, if equipment allocator <NUM> determines that <NUM> units of chilled fluid must be generated, equipment allocator <NUM> may determine that controlling chillers A-F to generate <NUM> units of chilled fluid each is more optimal than controlling chillers A-B to generate <NUM> units of chilled fluid each. However if asset allocator did determine that controlling chillers A-B to generate <NUM> units of chilled fluid each was more optimal, equipment allocator <NUM> may ay then remove chillers C-F from schematic representation and update the incidence matrix accordingly, as they would no longer be implemented in the optimization process.

Process <NUM> is shown to include implementing a stranded node analysis (step <NUM>). Step <NUM> may be substantially similar to step <NUM>. Process <NUM> may be configured to implement a stranded node analysis at any point during optimization, include after stage one, after stage two, or both. As shown in <FIG>, step <NUM> is performed after stage two and may implement stranded node analysis with regards to HVAC devices rather than other higher-level schematic representations (e.g., subplants).

Process <NUM> is shown to include updating the high level optimizer with operating parameter data or power estimation data (step <NUM>). In some embodiments, equipment allocator <NUM> may provide high level optimizer <NUM> with various information that was retrieved during various stages of low level optimization. For example, power estimator <NUM> as shown in <FIG> may provide power estimation data back to equipment allocator <NUM>. Data regarding power estimation and operational parameters regarding various HVAC devices may be provided to high level optimizer <NUM>.

Referring now to <FIG>, various embodiments of schematic representations of subplants within HVAC system <NUM> are shown, according to exemplary embodiments. Referring particularly to <FIG>, a first exemplary embodiment of system <NUM> is shown. System <NUM> is shown to include first subplant <NUM>, first coil <NUM>, valve <NUM>, <NUM>, second coil <NUM>, second subplant <NUM>. The various coils and valves as shown in <FIG> may be identical or substantially similar to the coils and valves disclosed with reference to <FIG>. First subplant <NUM> and second subplant <NUM> may be similar to heat recover chiller subplant <NUM>, chiller subplant <NUM>, heater subplant <NUM>, or any other subplant within HVAC system <NUM> disclosed herein.

In some embodiments, system <NUM> is configured to alter certain parameters based on decisions provided by high level optimizer <NUM>. For example, high level optimizer <NUM> may determine not to use first subplant <NUM> during optimization. Equipment allocator <NUM> may then remove first subplant <NUM> from the schematic representation generated by equipment allocator <NUM>. Equipment allocator <NUM> may then update an incidence matrix accordingly. This is may result in valves <NUM>,<NUM> receiving control signals to remain closed, as equipment allocator <NUM> will be restricted from providing fluid to first supplant <NUM> and first coil <NUM>.

In some embodiments, high level optimizer <NUM> may determine to use first subplant <NUM> during certain periods during optimization, but not during other periods. In such an embodiment, first subplant <NUM> would remain in the incidence matrix representation while valves <NUM>, <NUM> receiving control signals corresponding to the decisions processed by equipment allocator <NUM>. For example, asset allocator <NUM> may determine that first subplant <NUM> must engage in operation during time period A. Before time period A, valves <NUM>, <NUM> remain closed and restrict fluid from entering first subplant <NUM> or first coil <NUM>. During time period A, valves <NUM>, <NUM> receive control signals to open and allow first subplant <NUM> and first coil <NUM> to engage in operation. In some embodiments, these decisions are performed by asset allocator <NUM>, equipment allocator <NUM>, or a combination of both.

Referring particularly to <FIG>, a second exemplary embodiment of system <NUM> is shown. System <NUM>, as shown in <FIG>, is shown to include first subplant <NUM>, coil <NUM>, valve <NUM>, and thermal energy storage (TES) <NUM>. TES may be identical or substantially similar to any thermal energy storage component disclosed herein (e.g., hot TES <NUM>, cold TES <NUM>, etc.). In some embodiments, system <NUM> is configured to alter certain parameters based on decisions provided by high level optimizer <NUM>.

For example, asset allocator <NUM> determines that TES <NUM> requires an increase in energy storage (e.g., an increase in stored chilled fluid). Equipment allocator <NUM> may then close valve <NUM> to restrict fluid from bypassing TES <NUM> and forcing fluid to enter TES <NUM>. In some embodiments, first coil <NUM> is further restricted from receiving fluid so all fluid must enter TES <NUM>. In various embodiments, the various control decisions are made by asset allocator <NUM>, equipment allocator <NUM>, or a combination of both. In some embodiments, TES may receive control signals to discharge stored thermal energy. During discharge, equipment allocator <NUM> may provide a control signal to valve <NUM> to remain closed during discharging.

Referring now to <FIG>, a process <NUM> for optimizing an HVAC system is shown, according to some embodiments. Process <NUM> may be implemented by asset allocator <NUM>. In some embodiments, process <NUM> may implemented by a combination of processing components within central plant controller. For example, process <NUM> may implemented by a combination of asset allocator <NUM> and equipment allocator <NUM>.

Process <NUM> is shown to include receiving cycle/group information from asset allocator (step <NUM>). In some embodiments, asset allocator <NUM>, equipment allocator <NUM>, or a combination of both may provide group/cycle data via group generator <NUM> as shown above with reference to <FIG>. In various embodiments, asset allocator <NUM> may decide to exclude one or more subplants from HVAC system <NUM> and remove the excluded subplant from an incident matrix representation. This may prevent control equipment within low level optimizer <NUM> from providing control signals to the various equipment within the excluded subplant. In other embodiments, asset allocator <NUM> may choose to include a subplant periodically and exclude a subplant periodically. This may result in the actuation of one or more valves within HVAC system <NUM> that can include or isolate a particular subsystem based on instructions from asset allocator <NUM>.

Process <NUM> is shown to include splitting the system into two separate subsystems (step <NUM>). In some embodiments, step <NUM> includes splitting up an HVAC system into two or more distinct subsystems for purposes of decreasing latency during optimization and increasing efficiency. For example, asset allocator <NUM> may decide to split system <NUM> into two distinct subsystems, with a first subsystem on the left side of valves <NUM>, <NUM> (i.e., first subplant <NUM> and first coil <NUM>) and the second subsystem on the right side of valves <NUM>, <NUM> (i.e., second subplant <NUM> and second coil <NUM>).

Process <NUM> is shown to include disabling the linking device (step <NUM>). In some embodiments, a linking device (e.g., valve, damper, etc.) may be located between two split subsystems such that opening of the linking device may allow the subsystems to connect. Step <NUM> may include disabling (e.g., closing) valves <NUM>, <NUM> to create two independent subsystems within system <NUM>, as shown in <FIG>. In some embodiments, only a single valve connects two split subsystems. In other embodiments, more than two valves can be used to connect two split subsystems.

Process <NUM> is shown to include implementing stranded node analyses on each subsystem independently and remove devices within each independent subsystem based on one or more conditions (step <NUM>). After the linking device (e.g., valves <NUM>, <NUM>) have been disabled, system <NUM> may now be analyzed as two distinct subsystems to asset allocator <NUM>. Implementation of a stranded node analysis may be identical or similar to that of the stranded node analysis as described with reference to <FIG>. In some embodiments, implementation of the stranded node analysis in each subsystem by asset allocator <NUM> may be performed on a device-by-device basis. For example, asset allocator <NUM> may implement stranded node analysis as shown with reference to <FIG> and remove HVAC devices rather than other higher-level schematic representations (e.g., removing subplants).

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, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present 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 methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures 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 include 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 include, 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.

Claim 1:
A controller (<NUM>) for a plurality of heating, ventilation, or air conditioning, HVAC, devices, the controller (<NUM>) comprising:
- a processing circuit (<NUM>) comprising one or more processors (<NUM>) and memory (<NUM>) storing instructions that, when executed by the one or more processors (<NUM>), cause the one or more processors (<NUM>) to:
- detect a change in condition that affects an operating status of a first HVAC device of the plurality of HVAC devices;
- use schematic relationships between the plurality of HVAC devices to determine a reduced subset of the plurality of HVAC devices for which operating parameters are to be generated based on the operating status of the first HVAC device; characterized in that the instructions further cause the one or more processors (<NUM>) to:
- predict thermodynamic states of the reduced subset of the plurality of HVAC devices;
- determine a set of operating parameters for the reduced subset of the plurality of HVAC devices based on the predicted thermodynamic states that render a lowest power consumption, wherein power consumption is predicted based on the predicted thermodynamic states of the reduced subset of the plurality of HVAC devices and a candidate set that renders the lowest power consumption is determined as the set of operating parameters for the reduced subset of the plurality of HVAC devices by comparing power consumptions of different candidate sets of operating parameters; and
- operate the reduced subset of the plurality of HVAC devices using the determined set of operating parameters.