Abstract: A building management system includes building equipment configured to consume electrical energy and generate thermal energy, thermal energy storage configured to store at least a portion of the thermal energy generated by the building equipment and to discharge the stored thermal energy, electrical energy storage configured to store electrical energy purchased from a utility and to discharge the stored electrical energy, and a controller. The controller is configured to determine, for each time step within a time horizon, an optimal amount of electrical energy stored or discharged by the electrical energy storage by optimizing a value function.

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 building energy system includes equipment operable to consume, store, or generate one or more resources, a utility connection configured to obtain, from a utility provider, a first resource subject to a load-following-block rate structure and provide the first resource to the equipment, and a controller. The controller provides an objective function indicating a total value of purchasing the first resource from the utility provider over a plurality of time steps. The objective function includes a first term representing a load-following-block of the first resource from the utility provider as being sourced from a first supplier at a fixed rate and a second term representing a remainder of the first resource from the utility provider as being sourced from a second supplier at a variable rate. The controller controls the equipment based on a result of an optimization of the objective function.

Abstract: A method for generating an optimal nominated capacity value for participation in a capacity market program (CMP) includes generating, by a processing circuit, an objective function comprising a nominated capacity term, wherein the nominated capacity term indicates a nominated capacity value, wherein the nominated capacity value is a curtailment value that a facility is on standby to reduce its load by in response to receiving a dispatch from a utility. The method includes optimizing, by the processing circuit, the objective function to determine the optimal nominated capacity value for a program operating period and transmitting, by the processing circuit, the optimal nominated capacity value to one or more systems associated with the CMP to participate in the CMP.

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: A system includes a processing circuit configured to provide an energy use setpoint based on energy data including an energy characteristic and subject to a constraint based on a variable condition of a building. The processing circuit is configured to estimate a value for the energy use setpoint that will result in the variable condition of the building satisfying the constraint and provide a control signal for equipment based on a difference between the energy use setpoint and a measured energy use.

Abstract: A building energy system includes equipment operable to consume, store, or generate one or more resources subject to a block-and-index rate structure. The building energy system includes a controller configured to obtain a cost function that represents a block of the resource(s) from the utility provider as being sourced from a first supplier at a fixed rate and a remainder of the resource(s) from the utility provider as being sourced from a second supplier at a variable rate. The controller is configured to optimize the cost function to generate values for one or more decision variables that indicate an amount of resource(s) to purchase, store, generate, or consume at each of a plurality of time steps, and control the equipment to achieve the values of the one or more decision variables at each of the time steps.

Abstract: A method for controlling an energy production and distribution system includes identifying sources that supply input resources, subplants that produce output resources using the input resources, and sinks that consume the output resources. The method includes obtaining a cost function including a cost of producing the output resources and generating a transit time constraint that requires the input resources be sent from the sources to the subplants at a first departure time that occurs before a first arrival time at which the input resources are predicted to be used by the subplants. The method includes solving an optimization problem to determine an amount of the output resources to produce at each of multiple time steps within a time period. Solving the optimization problem includes performing an optimization of the cost function subject to the transit time constraint.

Abstract: A system for allocating one or more resources including electrical energy across equipment that operate to satisfy a resource demand of a building. The system includes electrical energy storage including one or more batteries configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The system further includes a controller configured to determine an allocation of the one or more resources by performing an optimization of a value function. The value function includes a monetized cost of capacity loss for the electrical energy storage predicted to result from battery degradation due to a potential allocation of the one or more resources. The controller is further configured to use the allocation of the one or more resources to operate the electrical energy storage.

Abstract: A method for operating equipment according to sequence of operation using geometric models including obtaining a first geometric model for a first set of equipment and a second geometric model for a second set of equipment, the first set of equipment and the second set of equipment defined by the sequence of operation for the equipment, locating, on the first geometric model, a first nearest operating point based on a desired operating point, generating a first modified geometric model by removing one or more operating points that do not satisfy the first nearest operating point, generating a merged geometric model by merging the first modified geometric model with the second geometric model, locating a second nearest operating point based on a modified desired operating point, and operating the equipment in accordance with the first nearest operating point and the second nearest operating point.

Abstract: A controller for a building control system includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. Each of the zone groupings define zone groups and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from zone groupings based on the data associated with zones and using the particular zone grouping to generate control signals to operate equipment of the building control system to provide heating or cooling to the zones.

Abstract: An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.

Abstract: A method for controlling HVAC equipment for a building includes generating, based on historical building data, a discomfort tolerance defining an acceptable amount of occupant discomfort, determining a first value of an environmental condition at which the occupant discomfort is predicted to exceed the discomfort tolerance in a first direction, determining a second value of the environmental condition at which the occupant discomfort is predicted to exceed the discomfort tolerance in a second direction opposite the first direction, and controlling the HVAC equipment to maintain the environmental condition between the first value and the second value.

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 (energy) 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: A model predictive maintenance system for building equipment including an equipment controller to operate the building equipment to affect a variable state or condition in a building. The system includes an operational cost predictor to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor to predict a cost of performing maintenance on the building equipment, and a cost incentive manager to determine whether any cost incentives are available and, in response to a determination that cost incentives are available, identify the cost incentives. The system includes an objective function optimizer to optimize an objective function to predict a total cost associated with the building equipment over the duration of the optimization period. The objective function includes the predicted cost of operating the building equipment, the predicted cost of performing maintenance on the building equipment, and, if available, the cost incentives.

Abstract: A method for operating a subplant included in a central plant includes obtaining instruction-based equipment models associated with devices included in the subplant and comprises operating points that define an operation of the devices, generating, for each instruction-based equipment models, a geometric equipment model using the operating points from a particular instruction-based equipment model, the geometric equipment model defining at least one operating domain associated with the particular device, merging geometric equipment models to form a geometric subplant model, the geometric subplant model defining an operation of the subplant comprising devices associated with the geometric equipment models, receiving a desired operating point comprising a load value, determining, relative to the desired operating point, a nearest operating point on the geometric subplant model, setting the nearest operating point on the geometric subplant model as an actual operating point, and operating the subplant at the act

Abstract: A cascaded control system is configured to control power consumption of a building during a demand limiting period. The cascaded control system includes an energy use setpoint generator and a feedback controller. The energy use setpoint generator is configured to use energy pricing data and measurements of a variable condition within the building to generate an energy use setpoint during the demand limiting period. The feedback controller is configured to use a difference between the energy use setpoint and a measured energy use to generate a control signal for building equipment that operate to affect the variable condition within the building during the demand limiting period.

Abstract: A controller for a building system receives training data including input data and output data. The output data indicate a state of the building system affected by the input data. The controller pre-processes the training data using a first set of pre-processing options to generate a first set of training data and pre-processes the training data using a second set of pre-processing options to generate a second set of training data. The controller performs a multi-stage optimization process to identify multiple different sets of model parameters of a dynamic model for the building system. The multi-stage optimization process includes a first stage in which the controller uses the first set of training data to identify a first set of model parameters and a second stage in which the controller uses the second set of training data to identify a second set of model parameters. The controller uses the dynamic model to operate the building system.

Abstract: An energy optimization system for participating in a capacity market program (CMP) includes one or more memory devices storing instructions, that, when executed on one or more processors, cause the one or more processors to receive a nominated capacity value, generate an objective function and one or more CMP constraints, wherein the one or more CMP constraints cause an optimization of the objective function with the CMP constraints to generate a resource allocation that reduces the load of the facility by the nominated capacity value in response to receiving the dispatch from the utility, receive the dispatch from the utility, optimize the objective function based on the nominated capacity value, the dispatch, and the one or more CMP constraints to determine the resource allocation, and control one or more pieces of building equipment based on the determined resource allocation.

Abstract: A central plant includes storage devices configured to store resources purchased from a utility or generated by the central plant and to discharge the one or more resources. The central plant includes a controller configured to obtain a cost function comprising a peak load contribution (PLC) term representing a cost based on an amount of the one or more resources purchased during coincidental peak (CP) subperiods. The controller is configured to modify the cost function by applying a peak subperiods mask to the PLC term, wherein, for each subperiod, the peak subperiods mask modifies a portion of the PLC term corresponding to the subperiod based on a probability that the subperiod will be one of the CP subperiods. The controller is also configured to perform an optimization of the modified cost function.