Patent Description:
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 may be 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> relates to a building management system including one or more sensors that measure a variable state or condition in the building management system and a plurality of building management system devices that operate to affect the variable state or condition measured by the one or more sensors. Each of the building management system devices stores a thermodynamic block that models the building management system device. Each of the thermodynamic blocks includes a list of connections and a list of stats. The connections define one or more inputs to the thermodynamic block and one or more outputs from the thermodynamic block. The stats define one or more relationships between the inputs and the outputs. Each of the building management system devices includes a solver configured to perform calculations using the stats and connections defined by the thermodynamic block stored within the building management system device.

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

According to the invention, which is solely defined by the appended claims, a central plant controller disclosed herein determines operating states of the HVAC system based on schematic dependencies of a plurality of HVAC devices of the HVAC system. The central plant controller determines dependencies of the HVAC devices based on a stranded node analysis. A stranded node analysis herein refers to determining schematic dependencies of HVAC devices of the HVAC system by removing a HVAC device and determining any stranded node after removing the HVAC device. A stranded node is a node that is having only one or of inlets or outlets but not having a pair of inlet and outlet. If a node has one or more inlets to receive input gas or liquid without any outlet, then the node is considered a stranded node. Similarly, if a node has one or more outlets to output gas or liquid without any inlet, then the node is considered a stranded node, then the node is considered a stranded node. After removing a first HVAC device, if a second HVAC device is coupled to a stranded node, then the central plant controller determines that the second HVAC device is schematically dependent on the first HVAC device. If a third HVAC device is not coupled to any stranded node after removing the first HVAC device, then the central plant controller determines that the third HVAC device is schematically independent from the first HVAC device. Stranded node analysis disclosed herein allows the central plant controller to reduce a number of HVAC devices or operating states of the HVAC to be predicted.

Beneficially, the central plant controller improves an operation efficiency of the HVAC system by reducing computation resource for determining operating parameters of the HVAC system through stranded node analysis disclosed herein. The central plant controller predicts operating states of a reduced number of HVAC devices operating according to operating parameters of the reduced number of HVAC devices rather than the full HVAC devices of the HVAC system. As a result, the HVAC system omits or isolates predicting operating states of disabled or turned off HVAC devices that are schematically independent from enabled or turned on HVAC devices. Consequently, the central plant controller identifies operating parameters rendering an improved performance of the HVAC system in a computationally efficient manner, and operates 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. 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> includes 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. 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>.

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>. 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. 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>. AHU <NUM> may be 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>. AHU controller <NUM> can control 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>. Cooling coil <NUM> can include 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>. Heating coil <NUM> can include 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>. 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>.

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.). 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.

AHU controller <NUM> can receive 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, clientfacing 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 the invention. The central plant controller <NUM> can be 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>. 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.). 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>. According to the invention, 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 a not claimed example, 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 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 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. Particularly, the low level optimizer <NUM> determines the set of operating parameters based on schematic dependence of the operating parameters on a performance of the HVAC system <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 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) 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>.

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>. 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> generates 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>.

The equipment allocator <NUM> performs stranded node analysis to identify a reduced group of the operating parameters for determining a set of operating parameters rendering an improved performance (e.g., lower power consumption) of the HVAC system. The stranded node analysis includes determining schematic dependencies of HVAC devices of the HVAC system by removing a device and determining any stranded node after removing the device. According to the invention, 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. 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. Based on the stranded node analysis, the equipment allocator <NUM> allows the state predictors to omit predicting operating states of a disabled or turned off HVAC device and additional HVAC devices schematically dependent on the disabled or turned off HVAC device. Hence, the state predictor <NUM> performs computation for a fewer number of unknowns.

Referring to <FIG>, illustrated is a block diagram of the equipment allocator <NUM>. The equipment allocator <NUM> includes a stranded node analyzer <NUM>, a candidate operating parameter generator <NUM>, and an output operating parameter generator <NUM>. These components operate together to determine a set of operating parameters rendering an improved performance of the HVAC system for a reduced number of HVAC devices according to stranded node analysis, 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. 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>.

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. 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 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 A representing schematic connections of HVAC devices of the HVAC system in a matrix format based on the netlist data. 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. 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 rows corresponding to HVAC devices. 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>. For example, the incidence matrix generator <NUM> obtains the following incidence matrix A for the example schematic representation <NUM> shown in <FIG>.

By modifying the incidence matrix A, the system identifier <NUM> may remove an HVAC device, and determine whether other devices are schematically dependent on the removed HVAC device. For example, after removing a first HVAC device, a second HVAC device coupled to a stranded node (or a floating node) is determined to be schematically dependent on the first HVAC device. On the other hand, after removing the first HVAC device, a third HVAC device not coupled to any stranded node (or any floating node) is determined to be schematically independent from the first HVAC device. In one approach, the system identifier <NUM> substitutes values of a row of the incidence matrix A corresponding to a selected HVAC device with zero, and analyzes each column of the incidence matrix A. 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 indicates that corresponding HVAC devices are schematically dependent on the selected HVAC device. The system identifier <NUM> iteratively identifies additional schematically dependent HVAC devices by further removing schematically dependent HVAC devices, and determining whether any column has either '<NUM>' or '-<NUM>' but no longer contains a pair of '<NUM>' and a '-<NUM>' value. The solver simplifier <NUM> may generate a look up table indicating dependencies of the HVAC devices. A detailed example process of stranded node analysis is provided below with respect to <FIG>, and <FIG>. Accordingly, the system identifier <NUM> identifies schematically dependent and independent HVAC devices through the incident matrix A.

The 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 enabled (turned on) or disabled (turned off) from the high level optimizer <NUM>. The solver simplifier <NUM> removes or excludes devices schematically dependent on disabled HVAC devices from the list of HVAC devices. The solver simplifier <NUM> includes devices schematically dependent on enabled HVAC devices from the list of HVAC devices. The solver simplifier <NUM> performs the process of modifying the incident matrix A as illustrated above to obtain the simplified list of HVAC devices.

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 the HVAC system according to the Q allocation data <NUM>. In one implementation, the equipment selector <NUM> stores a look up table 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 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> indicates 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 600A of an HVAC system, according to some embodiments. <FIG> illustrates an example schematic representation 600B of the HVAC system with a primary pump P1 removed from the schematic representation shown in <FIG> for performing stranded node analysis, according to some embodiments. <FIG> illustrates an example schematic representation 600C of the HVAC system with a second pump P2 removed from the schematic representation shown in <FIG> for performing stranded node analysis, according to some embodiments. Through a stranded node analysis, the low level optimizer <NUM> may determine whether an HVAC device is a primary device or a secondary device. Distinguishing between the primary device or the secondary device may be critical during equipment selection. Lack of information on whether a pump subsystem feeds the load or a chiller subsystem may complicate the process. To distinguish between the primary pump P1 and the secondary pump P2, simply removing the device from the incidence matrix enables identification of the equipment with which the device is associated. For example, removal of primary pump P1 will uncover a stranded node Na between the pump P1 and chiller C1, which will establish the association or dependencies between the pump P1 and chiller C1 as shown in <FIG>. However, removal of secondary pump P2 will not result in any of the nodes Nb, Nc, Nd becoming stranded, because of the decouple line that allows a flow of gas or liquid through the secondary pump P2 to bypass the chiller C1 and the pump P1, meaning there will be no association or dependencies between the pump P2 and chiller C1.

Referring to <FIG>, illustrated is an example schematic representation 700A of an 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 to 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 detected. 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 determining a set of operating parameters of HVAC devices through a stranded node analysis, according to the invention. 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 steps than shown in <FIG>.

The low level optimizer <NUM> determines schematic relationships of HVAC devices through a stranded node analysis (step <NUM>). The low level optimizer <NUM> may obtain plant netlist data representing schematic connections of HVAC devices, and generate an incidence matrix according to the plant netlist data. The low level optimizer <NUM> may replace nonzero values of a row of the incidence matrix with zero, then identify whether a stranded node exists by detecting whether a column of the incidence matrix has a nonzero value but not a pair of '-<NUM>' and '<NUM>'. Another row of the incidence matrix having the nonzero value of the column is detected. A HVAC device associated with the another row is determined to be dependent on a HVAC device associated with the row.

The low level optimizer <NUM> determines a reduced subset of the HVAC devices based on the schematic relationships (step <NUM>). In one approach, the low level optimizer <NUM> determines which device is to be enabled or disabled based on the Q allocation data <NUM>. For example, if no load is assigned to a device, the low level optimizer <NUM> determines that the device is disabled or turned off. For another example, if any load is assigned to a device, the low level optimizer <NUM> determines that the device is enabled or turned on. The low level optimizer <NUM> generates a subset of HVAC devices, of which thermodynamic states are to be determined. The low level optimizer <NUM> may identify a device to be disabled and exclude HVAC devices that are schematically dependent on the disabled device from the list. Similarly, the low level optimizer <NUM> may identify a device to be enabled and include HVAC devices that are schematically dependent on an enabled 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>). The controller <NUM> operates HVAC devices according to the determined set of operating parameters.

<FIG> is a flow chart illustrating a process <NUM> of performing a stranded node analysis. The process <NUM> may be performed by the stranded node analyzer <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 stranded node analyzer <NUM> obtains plant netlist data schematically representing schematic connections of HVAC devices of a HVAC system (step <NUM>). 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.

The stranded node analyzer <NUM> selects a HVAC device from a plurality of HVAC devices in the HVAC system (step <NUM>). The HVAC device may be randomly selected or selected according to a user instruction. The stranded node analyzer <NUM> removes the selected HVAC device from the plant netlist data (step <NUM>), and determines whether any stranded node exists after the removing the selected HVAC device (step <NUM>). If a stranded node having either an inlet or an outlet but not having a pair of inlet and outlet exists, then the stranded node analyzer <NUM> determines that, from the remaining HVAC devices, HVAC device connected to the stranded node is dependent on the selected HVAC device (step <NUM>). The stranded node analyzer <NUM> may select the dependent HVAC device, and return to step <NUM>. If no stranded node is detected, then the stranded node analyzer <NUM> determines that the selected HVAC device is not dependent on other HVAC devices (step <NUM>). The stranded node analyzer <NUM> may select another HVAC device and return to step <NUM>. If all HVAC devices have been examined, the stranded node analyzer <NUM> may generate a look up table indicating dependencies of the HVAC devices.

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 invention as long as all such modification fall within the scope of the protection defined in the appended claims.

Claim 1:
A controller (<NUM>) for an energy plant comprising a plurality of heat, ventilation and air conditioning, HVAC, devices, the controller (<NUM>) comprising:
- a processing circuit (<NUM>) comprising a processor (<NUM>) and memory (<NUM>) storing instructions executed by the processor (<NUM>), the processing circuit (<NUM>) configured to:
- determine schematic relationships of a plurality of the HVAC devices of the energy plant based on connections of the plurality of HVAC devices, each HVAC device configured to operate according to a corresponding operating parameter, by:
- removing a first HVAC device of the plurality of HVAC devices;
- detecting a stranded node after removing the first HVAC device; and
- determining one or more second HVAC devices coupled to the stranded node to be schematically dependent on the first HVAC device;
- determine a reduced subset of the plurality of HVAC devices based on the schematic relationships;
- predict thermodynamic states of the reduced subset;
- determine a set of operating parameters of the plurality of HVAC devices based on the predicted thermodynamic states; and
- operate the plurality of HVAC devices according to the set of operating parameters.