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: 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.

Abstract: A controller for operating building equipment of a building. The controller includes one or more processors. The controller includes one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include comparing one or more model parameters of predictive models describing zones of the building to determine one or more zone groups for the building. The operations include generating one or more zone group models corresponding to the one or more zone groups. The operations include operating the building equipment using the one or more zone group models to affect a variable state or condition of the building.

Abstract: A controller for performing a peer analysis of a predictive model for a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include comparing model parameters of a set of predictive models corresponding to a set of building zones, the model parameters indicating system dynamics of the building zones, to determine whether any of the building zones are exhibiting abnormal system dynamics. The operations include initiating a corrective action in response to determining that at least one of the building zones is exhibiting abnormal system dynamics.

Abstract: An environmental control system for a building space including heating, ventilation, or air conditioning (HVAC) equipment that operates to control a temperature of the building space. The system includes lighting equipment that operates to control a luminosity and affect a heat load disturbance for the building space. The system includes an environmental controller including a processing circuit configured to predict the heat load disturbance based on potential operating states of the lighting equipment over a time period. The heat load disturbance affects the temperature of the building space. The processing circuit is configured to generate control decisions for the HVAC and lighting equipment based on the predicted heat load disturbance and subject to constraints on the temperature and luminosity of the building space. The processing circuit is configured to operate the HVAC and lighting equipment based on the control decisions.

Abstract: An energy storage system for a building includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including a peak load contribution (PLC) term. The PLC term represents a cost based on electrical energy purchased from the utility during coincidental peak hours in an optimization period. The controller is configured to modify the cost function by applying a peak hours mask to the PLC term. The peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the energy storage controller to disregard the electrical energy purchased from the utility during any hours not identified as projected peak hours when calculating a value for the PLC term.

Abstract: An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

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: A smart edge controller for building equipment that operates to affect a variable state or condition within a building. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including obtaining sensor data indicating environmental conditions of the building and include determining an amount of available processing resources at the smart edge controller or at the building equipment. The operations include automatically scaling a level of complexity of an optimization of a cost function based on the available processing resources and include performing the optimization of the cost function at the automatically scaled level of complexity to generate a first setpoint trajectory. The first setpoint trajectory includes operating setpoints for the building equipment at time steps within an optimization period.

Abstract: A controller for a building including one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include parsing a computer-aided design (CAD) file or a building information model (BIM) file for the building to identify building equipment that operates to affect a variable state or condition of a zone of the building. The operations include generating one or more zone models describing one or more control relationships between the building equipment and the variable state or condition of the zone based on the CAD file or the BIM file. The operations include using the one or more zone models to perform a model-based operation for the building equipment.

Abstract: A controller for building equipment that operate to provide heating or cooling for a building or campus. The controller includes a processing circuit configured to perform an optimization of an objective function subject to an override constraint to determine amounts of one or more resources to be produced by the building equipment and control the building equipment to produce the amounts of the one or more resources determined by performing the optimization subject to the override constraint. The override constraint overrides an output of the optimization by specifying an override amount of a first resource of the one or more resources to be produced by a first subset of the building equipment.

Abstract: A frequency response optimization system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery at each of a plurality of time steps during a frequency response period, and a frequency response controller. The frequency response controller is configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, use the statistics of the regulation signal to generate an optimal frequency response midpoint that achieves a desired change in a state-of-charge (SOC) of the battery while participating in a frequency response program, and use the midpoints to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.

Abstract: A building energy system includes a controller configured to obtain representative loads and rates for a plurality of scenarios and generate a cost function comprising a risk attribute and multiple demand charges. Each of the demand charges corresponds to a demand charge period and defines a cost based on a maximum amount of at least one of the energy resources purchased within the corresponding demand charge period. The controller is configured to determine, for each of the multiple demand charges, a peak demand target for the corresponding demand charge period by performing a first optimization of the risk attribute over the plurality of the scenarios, allocate an amount of the one or more energy resources to be consumed, produced, stored, or discharged by the building equipment by performing a second optimization subject to one or more constraints based on the peak demand target for each of the multiple demand charges.

Abstract: A controller for operating building equipment of a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including obtaining a first setpoint trajectory from a cloud computation system. The first setpoint trajectory includes setpoints for the building equipment or for a space of the building. The setpoints correspond to time steps of an optimization period. The operations include determining whether a connection between the controller and the cloud computation system is active or inactive at a time step of the optimization period and determining an active setpoint for the time step of the optimization period using either the first or second setpoint trajectory based on whether the connection between the controller and the cloud computation system is active or inactive at the time step. The operations include operating the building equipment based on the active setpoint.

Abstract: An optimization controller for a battery includes a high level controller configured to receive a regulation signal from an incentive provider cat a data fusion module, determine statistics of the regulation signal, and use the statistics of the regulation signal to generate a frequency response midpoint. The optimization controller further includes a low level controller configured to use the frequency response midpoint to determine optimal battery power setpoints and use the optimal battery power setpoints to control an amount of electric power stored or discharged from the battery during a frequency response period.

Abstract: An air conditioner controller for an air conditioner is configured to obtain a first value of a performance value from a predetermined component of the air conditioner, and estimate a total cost. The controller is configured to estimate the total cost including an operation cost and a renewal cost of the air conditioner based on the first value of the performance variable obtained from the predetermined component of the air conditioner and a second value of the performance variable estimated by an operational model of the air conditioner. The total cost is a total cost of operating and renewing the air conditioner for a time period after a time at which the first value of the performance variable is obtained. The controller is configured to output the total cost via a user interface.