Patent Description:
A heating, ventilation and air conditioning (HVAC) system 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 may be determined as the set of operating parameters.

One conventional approach of predicting power consumption of a central plant is based on a flow of liquid or gas through a load device. In one example, power consumption of a central plant may be predicted according to a temperature difference between an outlet and an inlet of a load device due to the flow of liquid or gas. However, a status of a device in the energy plant may change thereby rendering a flow through the load device to be difficult to measure. For example, a supply device supplying resources (e.g., gas or liquid) may be turned off or disabled during a time period, thus determining a flow through the load device and determining power consumption due to the flow may not be trivial. Accordingly, assigning load to the energy plant at different time periods may be challenging. An exemplary <CIT> describes a controller for an energy plant configured to obtain thermal energy load of the energy plant, determine an operating state of the energy plant, determine operating parameters of the energy plant according to the determined operating state, and operate the energy plant according to the determined operating parameters.

To solve the problems mentioned above, the present invention defines a controller according to claim <NUM>, a method according to claim <NUM> and a non-transitory computer readable medium according to claim <NUM>. Various claimed and non-claimed embodiments of a controller for an energy plant are disclosed. The controller includes a processing circuit comprising a processor and memory storing instructions executed by the processor. The processing circuit is configured to obtain thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The processing circuit is configured to determine, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data. The processing circuit is configured to determine operating parameters of the energy plant according to the determined operating state. The processing circuit is configured to operate the energy plant according to the determined operating parameters.

In one or more embodiments, the plurality of predefined operating states include a primary state, in which a supply device of the energy plant is enabled and a deferred load is allocated during the time period; a deferred state, in which the supply device of the energy plant is disabled and the deferred load is allocated during the time period; and an off-state, in which no load is allocated during the time period.

In one or more embodiments, the processing circuit is further configured to predict a power consumption of the energy plant according to the determined operating state. The operating parameters of the energy plant may be determined according to the predicted power consumption.

In one or more embodiments, responsive to determining that the operating state is the primary state, the processing circuit is configured to predict the power consumption of the energy plant according to a power consumption of the supply device.

In one or more embodiments, responsive to determining that the operating state is the deferred state, the processing circuit is configured to predict a flow of gas or liquid through a load device to consume the deferred load during the time period; and predict the power consumption according to the predicted flow of gas or liquid.

In one or more embodiments, the processing circuit is configured to generate a schematic data of the energy plant. The schematic data may include a model of a water mass storage in place of the supply device and a model of a load device to consume the deferred load. The processing circuit may be configured to predict a temperature difference in a loop formed by the model of the water mass storage and the model of the load device. The processing circuit may be configured to predict the flow of gas or liquid through the load device according to the predicted temperature difference.

In one or more embodiments, responsive to determining that the operating state is the off-state, the processing circuit is configured to determine that the supply device does not consume power during the time period.

In one or more embodiments, the processing circuit is further configured to determine, for another time period, another operating state of the energy plant from the plurality of predefined operating states based on the thermal energy load allocation data; and predict another power consumption of the energy plant according to the another operating state.

In one or more embodiments, the processing circuit is further configured to compare the power consumption and the another power consumption; and assign the time dependent thermal energy load to the energy plant for one of the time period and the another time period associated with a lower power consumption from the comparison. The operating parameters of the energy plant may be determined according to the assigned time dependent thermal energy load.

Various embodiments of method of operating an energy plant are disclosed herein. The method includes obtaining thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The method further includes determining, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data. The method further includes determining operating parameters of the energy plant according to the determined operating state. The method further includes operating the energy plant according to the determined operating parameters.

In one or more embodiments, the method further includes predicting a power consumption of the energy plant according to the determined operating state. The operating parameters of the energy plant may be determined according to the predicted power consumption.

In one or more embodiments, the method further includes predicting the power consumption of the energy plant according to a power consumption of the supply device, responsive to determining that the operating state is the primary state.

In one or more embodiments, responsive to determining that the operating state is the deferred state, the method further includes predicting a flow of gas or liquid through a load device to consume the deferred load during the time period; and predicting the power consumption according to the predicted flow of gas or liquid.

In one or more embodiments, the method further includes generating a schematic data of the energy plant. The schematic data may include a model of a water mass storage in place of the supply device and a model of a load device to consume the deferred load. The method may further include predicting a temperature difference in a loop formed by the model of the water mass storage and the model of the load device. The method may further include predicting the flow of gas or liquid through the load device according to the predicted temperature difference.

In one or more embodiments, the method further includes, responsive to determining that the operating state is the off-state, determining that the supply device does not consume power during the time period.

In one or more embodiments, the method further includes determining, for another time period, another operating state of the energy plant from the plurality of predefined operating states based on the thermal energy load allocation data, and predicting another power consumption of the energy plant according to the another operating state.

In one or more embodiments, the method further includes comparing the power consumption and the another power consumption; and assigning the time dependent thermal energy load to the energy plant for one of the time period and the another time period associated with a lower power consumption from the comparison. The operating parameters of the energy plant may be determined according to the assigned time dependent thermal energy load.

Various embodiments of a non-transitory computer readable medium comprising instructions for operating an energy plant are disclosed. The instructions when executed by a processor cause the processor to: obtain thermal energy load allocation data indicating time dependent thermal energy load of an energy plant; determine, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data; determine operating parameters of the energy plant according to the determined operating state; and operate the energy plant according to the determined operating parameters.

Referring generally to the FIGURES, disclosed herein are systems and methods for operating the HVAC system based on time dependent deferred load.

Various embodiments of a system, a method, and a non-transitory computer readable medium for operating an energy plant (also referred to as "a central plant") are disclosed herein. In some embodiments, a system includes a controller that operates the HVAC system according to time dependent thermal energy load. In one aspect, the controller obtains thermal energy load allocation data indicating time dependent thermal energy load, and determines, for a time period, an operating state of the energy plant based on the thermal energy load allocation data. The controller may determine operating parameters of the energy plant according to the determined operating state, and operate the energy plant according to the operating parameters.

In some embodiments, the operating state of the energy plant may be one of a primary state, a deferred load state, and an off-state. In the primary state, a primary equipment (also referred to as "a supply device" herein) of the energy plant is enabled during the time period. The primary equipment may be an equipment that produces resource (Chilled Water or Hot Water) of a loop. Hence, the power consumption of the energy plant operating in the primary state can be determined according to the primary equipment in operation. In the deferred load state, the primary equipment is disabled, but a deferred load may be allocated. For the deferred load state, a flow through a loop of the energy plant due to the deferred load can be predicted, and the power consumption of the energy plant can be determined according to the predicted flow. In the off-state, the primary equipment of the energy plant is disabled without any deferred load. Hence, a portion the HVAC system or HVAC devices of the HVAC system coupled to the primary equipment of the energy plant may be determined to be non-operational.

In some embodiments, the disclosed system, method, and non-transitory computer readable medium allow prediction of power consumption of an energy plant even in the deferred load state. In some cases, a status of a device in the energy plant may change thereby rendering a flow through a load device of the energy plant to be difficult to measure. For example, a supply device supplying resources (e.g., gas or liquid) in the deferred load state may be turned off or disabled during a time period. In one aspect, the system generates a schematic data representing schematic relationships of components of the energy plant. The schematic data of the energy plant may include (i) a computer generated model of a dummy device in place of the disabled supply device and (ii) a computer generated model of the load device to consume the deferred load. A flow through the load device may be predicted based on the schematic data including the computer generated model of the dummy device. Hence, power consumption of the energy plant operating in the deferred load state can be predicted based on the predicted flow.

Advantageously, thermal energy load may be assigned to a load device of the energy plant at a time period that allows the energy plant to operate in a power efficient manner. The system compares power consumptions of the energy plant operating according to the thermal energy load assigned at different time periods. Because the disclosed system allows prediction of the power consumptions of the energy plant in different operating states in different time periods, the disclosed system enables the thermal energy load to be assigned to the energy plant at a time period that renders a lower power consumption, and to operate the energy plant accordingly.

Referring now to <FIG>, an exemplary HVAC system in which the systems and methods of the present invention 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, an 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 an 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 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 implementation, the high level optimizer <NUM> determines a time period for allocating a thermal energy load of the HVAC system <NUM>. In one approach, the high level optimizer <NUM> assigns the thermal energy load to the HVAC system <NUM> for a time period, and generates the Q allocation data <NUM> indicating the time period. The high level optimizer <NUM> provides the Q allocation data <NUM> to the low level optimizer <NUM>. In return, the high level optimizer <NUM> receives the operating parameter and power estimation data <NUM> indicating operating parameters of the HVAC devices and predicted power consumption of the HVAC devices operating according to the thermal energy load at the time period. The high level optimizer <NUM> may assign the thermal energy load to the HVAC system <NUM> for different time periods, and obtain power consumptions of HVAC system <NUM> operating at different time periods. The high level optimizer <NUM> may compare power consumptions of the HVAC devices operating at different time periods, and select a time period, during which HVAC system is predicted to operate with a lower power consumption. The high level optimizer <NUM> may operate the HVAC system according to operating parameters of the HVAC devices at the selected time period to achieve power efficiency.

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 Q̇ 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," which is incorporated by reference herein.

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 one or more embodiments, the low level optimizer <NUM> includes an equipment allocator <NUM>, a state predictor <NUM>, and a power estimator <NUM>. Together, these components operate to determine a set of operating parameters, for example, rendering reduced power consumption of the HVAC system <NUM> for a given set of thermal energy loads indicated by the Q allocation data <NUM>, and generate operating parameter data indicating the determined set of operating parameters. 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 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> may repeat 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> may generate 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> determines 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). 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>.

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. 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>.

The 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 one aspect, the state predictor <NUM> and the power estimator <NUM> cannot predict thermodynamic states and power consumption of HVAC devices operating according to time dependent deferred load. For example, during periods of low load, chillers are often cycled in order to meet the cooling loads of the connected buildings. A chiller may be shut off or operate at the minimum load, once the chilled water temperature reaches set point (e.g., <NUM> °F). The chiller may be left off until the return water temperature reaches a certain value (e.g., <NUM> °F). The high level optimizer <NUM> is able to allocate load to be consumed out of the thermal mass of the water loop, thereby shifting load to an earlier or later time in the horizon. However, the state predictor <NUM> may operate independent of time of operation of the HVAC system, and may not accommodate a change in loads during different time periods. Particularly, the state predictor <NUM> may operate independent of time in terms of temperature, and instead utilize a flow, ω, to calculate energy consumptions. The generic equation for this process is shown below. <MAT> where ω is the flow of the water, ρ is density of water, Cp is the specific heat capacity of water, and ΔT is the temperature difference from outlet to inlet. The operating condition of time dependent HVAC devices in the loop results in a change in temperature at different times.

In some embodiments, the equipment allocator <NUM> allows prediction of thermal energy states and power consumption of the HVAC system according to time dependent thermal energy load. In one implementation, the equipment allocator <NUM> obtains the Q allocation data <NUM> indicating time dependent thermal energy load of the energy plant, and determines, for a time period, an operating state of the energy plant based on the Q allocation data <NUM>. For example, the equipment allocator <NUM> determines that the HVAC system is operating in one of predefined operating states including a primary state, a deferred load state, and an off-state. The equipment allocator <NUM> may obtain predicted power consumption of the energy plant according to the determined operating state.

In the primary state, a primary equipment (also referred to as "a supply device") of the energy plant is enabled during a time period. The primary equipment may be an equipment or a supply device that produces resource (Chilled Water, Hot Water) of a loop. In the primary state, the equipment allocator may generate the candidate operating parameter data <NUM> and provide the candidate operating parameter data <NUM> to the state predictor <NUM> to obtain predicted power consumption through the power estimator <NUM>, according to the candidate operating parameter data <NUM>.

In the deferred load state, the primary equipment is disabled, but a deferred load may be allocated. For the deferred load state, the equipment allocator <NUM> may predict an average flow through a loop of the energy plant due to the deferred load, and predict the power consumption of the energy plant according to the predicted average flow. Example process of predicting the power consumption is provided in detail below with respect to <FIG>.

In the off-state, the primary equipment of the energy plant is disabled without any deferred load. Hence, a portion the HVAC system or HVAC devices of the HVAC system coupled to the primary equipment of the energy plant may be determined to be non-operational. Thus, the equipment allocator <NUM> may determine that the power consumption of the portion of the HVAC system or the HVAC devices coupled to the non-operational primary equipment is zero.

Referring to <FIG>, illustrated is an example schematic representation <NUM> of an HVAC system operating in a primary state, according to some embodiments. In the example shown in <FIG>, the primary equipment <NUM> is coupled to the load coil <NUM>. In this configuration, the primary equipment <NUM> supplies resource (e.g., gas or liquid) to the load coil <NUM>. In the primary state, where primary equipment <NUM> is present, the allocation to the Water Mass Storage can be aggregated to the load coil <NUM>, creating either a higher or lower load based on direction of charge. This aggregated load can be solved similarly to a normal plant, and will have a net energy change of '<NUM>,' meaning the production will equal the consumption. In one approach, a power consumption may be calculated by determining a temperature difference between the inlet <NUM> and the outlet <NUM> to determine a flow through the load coil <NUM>. The power consumption may be determined based on the flow through the load coil <NUM>.

Referring to <FIG>, an example schematic representation <NUM> of an HVAC system operating in a deferred load state is shown, according to some embodiments. A deferred load state is when there is a water mass storage or a deferred load allocation with no primary equipment. Since this operating state lacks any primary equipment, there is no source of a temperature difference ΔT in the loop, rendering steady state calculations by the state predictor <NUM> to be impossible. In this operating state, the topology of the plant may be modified to include a computer generated model of a dummy device <NUM> (e.g., water mass storage) in place of the primary equipment <NUM> as shown in <FIG>.

By adding the computer generated model of the dummy device <NUM> as shown in <FIG>, a loop between the model of the dummy device <NUM> and the load coil <NUM> is formed. To calculate a predicted flow through the load coil <NUM>, mCp approximated by, for example, the high level optimizer <NUM> may be applied. An approximation of the equivalence between the dynamic load predicted by the high level optimizer <NUM> and the static load for the low level optimizer <NUM> is shown below.

The time dependent aspect of Ṫ can be added by interpreting the inlet <NUM> temperature of the water mass storage (WMS) <NUM> as the next predicted temperature. This can be shown with the following equations. <MAT> <MAT>.

In Eq. (<NUM>), dt is the time between dispatches. Using this equivalence, the mCp value calculated by the high level optimizer <NUM> can be scaled to a value appropriate for low level optimizer <NUM>. For a regular dispatch, this means it can be treated as a <NUM> minute increment, with off clock dispatches providing less optimal results due to variable time steps. As operational data is collected, the scaling factor can be adjusted to produce results realistic to the plant. This scaled value can be used in the following equation to find an intermediate temperature. <MAT> where TSecondary is the temperature at the outlet <NUM> of the dummy device <NUM> (or inlet of the load coil <NUM>). According to a temperature difference between inlet and outlet or a difference between Tin and TSecondary, an average flow for the loop over the dispatch period can be predicted. Moreover, power consumption of the pump can be predicted based on the average flow. <MAT> where f(ω̇) is a non-linear function dependent on the flow ω̇.

In some embodiments, the low level optimizer <NUM> predicts the flow of gas or liquid according to the following equation: <MAT> In this embodiment, the low level optimizer <NUM> may determine the flow of gas or liquid without determining a temperature difference.

The off-state of a plant is an operating state, in which there is no load allocated to loop storage or primary equipment. In this scenario, the plant may be assumed to be nonoperational, with no flow in any pipes. The power consumption in this scenario may be determined to be zero for HVAC devices coupled to the primary equipment.

Referring to <FIG>, a flow chart illustrating a process <NUM> for deferring load of an HVAC system is shown, according to some embodiments. The process <NUM> may be performed by the high 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 high level optimizer <NUM> assigns a thermal energy load to an energy plant for a first time period (step <NUM>). The high level optimizer <NUM> obtains a first power consumption of the energy plant according to thermal energy load assigned for the first time period (step <NUM>). The high level optimizer <NUM> assigns the thermal energy load to the energy plant for a second time period (step <NUM>). The high level optimizer <NUM> obtains a second power consumption of the energy plant according to thermal energy load assigned for the second time period (step <NUM>). In one approach, the high level optimizer <NUM> generates Q allocation data <NUM> indicating the thermal energy load and the assigned time period, and provides the Q allocation data <NUM> to the low level optimizer <NUM>. In return, the high level optimizer <NUM> receives operating parameter and power estimation data <NUM> indicating operating parameters and predicted power consumption of the energy plant operating according to the operating parameters at the assigned time period.

The high level optimizer <NUM> compares the first power consumption and the second power consumption indicated by the operating parameter and power estimation data <NUM> (step <NUM>). The high level optimizer <NUM> selects a time period for assigning the thermal energy load based on the comparison of the power consumptions (step <NUM>). In one approach, the high level optimizer <NUM> selects a time period that renders a lower power consumption. The high level optimizer <NUM> may operate the HVAC devices at the assigned time period according to operating parameters of the HVAC devices determined by the low level optimizer <NUM> for the assigned time period.

Referring to <FIG>, a flow chart illustrating a process <NUM> for determining power consumption of an HVAC system with time deferred load is shown, 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> receives thermal energy load allocation data <NUM>, for example, from the high level optimizer <NUM> (step <NUM>). The low level optimizer <NUM> determines an operating state of the energy plant (step <NUM>). The low level optimizer <NUM> may determine the operating state according to a time period assigned by the high level optimizer <NUM> as indicated by the thermal energy load allocation data <NUM>. Specifically, the low level optimizer <NUM> determines the operating state according to operating status of availability or cyclic behavior of HVAC devices or load device at the time period assigned. In one approach, the low level optimizer <NUM> determines the operating state from predefined operating states. In one example, the predefined operating states include a primary state, a deferred load state, and an off-state.

In the primary state, the low level optimizer <NUM> determines that a load device is enabled and a supply device (e.g., primary equipment) supplying resource to the load device is enabled (step <NUM>). In the primary state, the low level optimizer <NUM> predicts the flow of gas or liquid through the load device based on the supply device (step <NUM>). For example, the low level optimizer <NUM> obtains a temperature difference between inlet and outlet of the load device and predicts the flow of gas or liquid through the load device based on the temperature difference. The low level optimizer <NUM> predicts power consumption of the predicted flow of gas or liquid (step <NUM>).

In the deferred state, the low level optimizer <NUM> determines that the load device is enabled but the supply device supplying resource to the load device is disabled (step <NUM>). In the primary state, because the supply device supplying resource to the load device is disabled, steady state calculation of states of the devices may not be feasible. The low level optimizer <NUM> obtains a schematic data indicating schematic relationships of components of the energy plant (step <NUM>). The schematic data of the energy plant may include (i) a computer generated model of a dummy device (e.g., water mass storage) in place of the disabled supply device and (ii) a computer generated model of the load device. Based on the schematic data with the computer generated model of the dummy device, the low level optimizer <NUM> predicts the flow of gas or liquid through the load device (step <NUM>). For example, the low level optimizer <NUM> obtains a temperature difference between inlet and outlet of the load device and predicts the flow of gas or liquid through the load device based on the temperature difference. The low level optimizer <NUM> predicts power consumption of the predicted flow of gas or liquid (step <NUM>).

In the deferred state, the low level optimizer <NUM> determines that the load device is enabled but the supply device supplying resource to the load device is disabled (step <NUM>). The low level optimizer <NUM> determines that no power is consumed by the supply device and the load device (step <NUM>).

The low level optimizer <NUM> may predict power consumptions of the HVAC devices operating according to different sets of operating parameters at different operating states, and determine a set of operating parameters rendering a lower power consumption. The low level optimizer <NUM> may generate operating parameter and power estimation data <NUM> and provide it to the high level optimizer <NUM>.

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, as long as they fall within the scope of the present invention which is defined solely by the appended set of claims. 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 invention 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 as long as they fall within the scope of the invention as defined by the appended claims. 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 an energy plant, the controller (<NUM>) comprising:
- a processing circuit (<NUM>) comprising a processor (<NUM>) and memory (<NUM>) storing instructions executed by the processor (<NUM>) that when executed by the processor (<NUM>) cause the processor (<NUM>) to form an equipment allocator (<NUM>) and a state predictor (<NUM>), the controller (<NUM>) being characterized in that the processing circuit (<NUM>) being configured to:
- obtain, by the equipment allocator (<NUM>), thermal energy load allocation data indicating time dependent thermal energy load (Q̇l) of the energy plant;
- determine, by the equipment allocator (<NUM>), for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data, wherein the plurality of predefined operating states include a deferred state, in which a supply device of the energy plant is disabled and the deferred load is allocated during the time period;
- responsive to determining that the operating state is the deferred state:
- use, by the state predictor (<NUM>), a relationship (mCpṪ = Q̇l) between the time dependent thermal load (Q̇l) and a change in temperature (Ṫ) of a fluid within a fluid loop represented by a dummy device (<NUM>) to determine the change in temperature (Ṫ) of the fluid based on the time dependent thermal energy load (Q̇l);
- use an equivalence between the change in temperature (Ṫ) of the fluid within the fluid loop and a change in temperature (ΔT) across the dummy device (<NUM>) and a model (Q̇l = ω̇ρCpΔT) that relates the time dependent thermal energy load (Q̇l) to a flow rate (ω̇) of the fluid within the fluid loop to determine the flow rate (ω̇) of the fluid within the fluid loop;
- determine operating parameters of the energy plant according to the determined operating state and the determined flow rate (ω̇) of the fluid within the fluid loop; and
- operate the energy plant according to the determined operating parameters.