Abstract: A control system for a central energy facility with distributed energy storage includes a high level coordinator, a low level airside controller, a central plant controller, and a battery controller. The high level coordinator is configured to perform a high level optimization to generate an airside load profile for an airside system, a subplant load profile for a central plant, and a battery power profile for a battery. The low level airside controller is configured to use the airside load profile to operate airside HVAC equipment of the airside subsystem. The central plant controller is configured to use the subplant load profile to operate central plant equipment of the central plant. The battery controller is configured to use the battery power profile to control an amount of electric energy stored in the battery or discharged from the battery at each of a plurality of time steps in an optimization period.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes an air handling unit (AHU) and a controller. The AHU is configured to provide mechanical cooling for a cooling load in the building when operating in a mechanical cooling state and provide free cooling for the cooling load in the building when operating in a free cooling state. The controller is configured to calculate a minimum free cooling time based on an estimated cost savings resulting from operating in the free cooling state relative to operating in the mechanical cooling state and transition the AHU from operating in the mechanical cooling state to operating in the free cooling state in response to predicting that outside air temperature will be less than a free cooling temperature threshold for at least the minimum free cooling time.

Abstract: A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable storing instructions for operating an energy plant comprised of heating, ventilation and air conditioning (HVAC) devices. In one aspect, the system generates gradient data indicating a gradient of operating performance of the energy plant with respect to values of a plurality of control variables of HVAC devices. The system determines, from the plurality of control variables, a reduced group of control variables of the HVAC devices based on the gradient data. The system determines a set of values of the reduced group of control variables. The system operates the energy plant according to the determined set of values of the reduced group of control variables.

Abstract: Disclosed herein are a system, method, and non-transitory computer readable medium for operating an energy plant. In one aspect, a system determines a ratio of flow rates between devices of the energy plant connected in parallel with each other in a branch. The system generates a candidate set of operating parameters of the devices according to the ratio of flow rates. The system predicts thermodynamic states of the devices operating according to the candidate set of operating parameters. The system determines whether the predicted thermodynamic states satisfy constraints of the devices. The system determines whether the predicted thermodynamic states satisfy a target thermal energy load of the branch based on the ratio of the flow rates. The system operates the energy plant according to the candidate set of operating parameters, in response to determining that the predicted thermodynamic states satisfy the constraints and the target thermal energy load.

Abstract: Disclosed herein are a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system obtains thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The system determines, 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 system determines operating parameters of the energy plant according to the determined operating state. The system operates the energy plant according to the determined operating parameters.

Abstract: Systems and methods for predicting a plurality of thermodynamic states of a plurality of heat, ventilation, and air conditioning (HVAC) devices of an energy plant are disclosed. The system includes a processor and a non-transitory computer readable medium storing instructions when executed causing the processor to: obtain plant netlist data describing the plurality of HVAC devices of the energy plant and connections of the plurality of HVAC devices to corresponding nodes; identify, from the first reduced subset, a second reduced subset of the plurality of thermodynamic states to be predicted by propagating a known value of the plurality of thermodynamic states using a linear solver; predict the second reduced subset of the plurality of thermodynamic states using a non-linear solver; and determine the plurality of thermodynamic states of the energy plant at the plurality of nodes based on the predicted second reduced subset of the plurality of thermodynamic states.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, a system determines schematic relationships of a plurality of heat, ventilation, and air conditioning (HVAC) devices of the energy plant based on connections of the plurality of HVAC devices. Each HVAC device is configured to operate according to a corresponding operating parameter. The system determines, from a plurality of HVAC devices, a reduced subset of the HVAC devices based on the schematic relationships. The system predicts thermodynamic states of the reduced subset. The system determines a set of operating parameters of the plurality of HVAC devices based on the thermodynamic states. The system operates the plurality of HVAC devices according to the set of operating parameters.

Abstract: Systems and methods for predicting a plurality of thermodynamic states of a plurality of heat, ventilation, and air conditioning (HVAC) devices of an energy plant are disclosed. The system includes a processor and a non-transitory computer readable medium storing instructions when executed causing the processor to: obtain plant netlist data describing the plurality of HVAC devices of the energy plant and connections of the plurality of HVAC devices to corresponding nodes; identify, from the plurality of thermodynamic states of the energy plant at a plurality of nodes, a reduced subset of the plurality of thermodynamic states to be predicted based on the connections of the plurality of HVAC devices; predict the reduced subset of the plurality of thermodynamic states using a non-linear solver; and determine the plurality of thermodynamic states of the energy plant based on the reduced subset of the predicted thermodynamic states.

Abstract: An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment.

Abstract: An energy optimization system for a building includes a processing circuit configured to generate a user interface including an indication of one or more economic load demand response (ELDR) operation parameters, one or more first participation hours, and a first load reduction amount for each of the one or more first participation hours. The processing circuit is configured to receive one or more overrides of the one or more first participation hours from the user interface, generate one or more second participation hours, a second load reduction amount for each of the one or more second participation hours, and one or more second equipment loads for the one or more pieces of building equipment based on the received one or more overrides, and operate the one or more pieces of building equipment to affect an environmental condition of the building based on the one or more second equipment loads.

Abstract: An energy optimization system for a building includes a processing circuit configured to provide a first bid including one or more first participation hours and a first load reduction amount for each of the one or more first participation hours to a computing system. The processing circuit is configured to operate one or more pieces of building equipment based on one or more first equipment loads and receive one or more awarded or rejected participation hours from the computing system responsive to the first bid. The processing circuit is configured to generate one or more second participation hours, a second load reduction amount for each of the one or more second participation hours, and one or more second equipment loads based on the one or more awarded or rejected participation hours and operate the one or more pieces of building equipment based on the one or more second equipment loads.

Abstract: Systems and methods for low level central plant optimization are provided. A controller for the central plant uses binary optimization to determine one or more feasible on/off configurations for equipment of the central plant that satisfy operating constraints and meet a thermal energy load setpoint. The controller determines optimum operating setpoints for each feasible on/off configuration and generates operating parameters including at least one of the feasible on/off configurations and the optimum operating setpoints. The operating parameters optimize an amount of energy consumed by the central plant equipment. The controller outputs the generated operating parameters via a communications interface for use in controlling the central plant equipment.

Abstract: A building management includes building equipment operable to affect a variable state or condition of a building and a controller. The controller is configured to identify one or more facility improvement measures (FIMS), each of the FIMS representing a potential upgrade or addition to the building equipment. The controller is further configured to perform a design of experiments analysis to determine a plurality of combinations of the FIMS, each combination including one or more of the FIMS and a level for each FIM in the combination. The controller is configured to generate an objective function based on the combinations of the FIMS. The objective function indicates an economic value as a function of the FIMS and optimize the objective function to determine an optimal combination of the FIMS.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes an air handling unit (AHU) and a controller. The AHU is configured to provide mechanical cooling for a cooling load in the building when operating in a mechanical cooling state and provide free cooling for the cooling load in the building when operating in a free cooling state. The controller is configured to calculate a minimum free cooling time based on an estimated cost savings resulting from operating in the free cooling state relative to operating in the mechanical cooling state and transition the AHU from operating in the mechanical cooling state to operating in the free cooling state in response to predicting that outside air temperature will be less than a free cooling temperature threshold for at least the minimum free cooling time.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes, a chiller, a heat exchanger separate from the chiller, and a controller. The chiller is configured to provide mechanical cooling for a cooling load in the building when the HVAC system operates in a mechanical cooling state. The heat exchanger is configured to provide free cooling for the cooling load in the building when the HVAC system operates in a free cooling state. The controller is configured to predict outside air temperature and transition the HVAC system from operating in the mechanical cooling state to operating in the free cooling state in response to a determination that the predicted outside air temperature will be less than the free cooling temperature threshold for at least the minimum free cooling time.

Abstract: A state-based control system for an air handling unit (AHU) includes a finite state machine configured to transition between a high cooling load state and a low cooling load state. In the high cooling load state, the system maintains the temperature of a supply airstream provided by the AHU at a fixed setpoint and controls the temperature of a building zone by modulating the speed of a supply air fan. In the low cooling load state, the system operates the supply air fan at a fixed speed and controls the zone temperature by modulating an amount of cooling applied to the supply airstream by one or more cooling stages. A feed-forward module manages disturbances caused by adding or shedding cooling stages by applying a feed-forward gain to the supply air fan setpoint.

Abstract: A system for monitoring and controlling a central plant includes a high level optimizer, a subplant monitor, a user interface, and a dispatch graphical user interface (GUI) generator. The central plant includes a plurality of subplants configured to serve a thermal energy load. The high level optimizer is configured to determine recommended subplant loads for each of the plurality of subplants. The subplant monitor is configured to monitor the central plant and identify actual subplant loads for each of the plurality of subplants. The user interface is configured to receive manual subplant loads specified by a user. The dispatch GUI generator is configured to generate a dispatch GUI and present the dispatch GUI via the user interface. The dispatch GUI includes the recommended subplant loads, the actual subplant loads, and the manual subplant loads.

Abstract: Systems and methods for controlling a central plant for a building are provided. The central plant has a plant load. An optimal combination of plant equipment for meeting the plant load is estimated. Estimating the optimal combination of plant equipment includes using binary optimization to determine at least two potential combinations of plant equipment. Estimating the optimal combination of plant equipment also includes using nonlinear optimization to determine a potential power consumption minimum for each of the at least two potential combinations. The central plant is controlled according to the estimated optimal combination of plant equipment.