Abstract:
An energy plant includes a plurality of subplants, a high level optimizer, a low level optimizer, and a controller. The plurality of subplants include a cogeneration subplant configured to generate steam and electricity and a chiller subplant electrically coupled to the cogeneration subplant and configured to consume the electricity generated by the cogeneration subplant. The high level optimizer is configured to determine recommended subplant loads for each of the plurality of subplants. The recommended subplant loads include a rate of steam production and a rate of electricity production of the cogeneration subplant and a rate of electricity consumption of the chiller subplant. The low level optimizer is configured to determine recommended equipment setpoints for equipment of the plurality of subplants based on the recommended subplant loads. The controller is configured to operate the equipment of the plurality of subplants based on the recommended equipment setpoints.
Abstract:
A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level model predictive controller (MPC), and a plurality of low-level airside MPCs. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to the airside subsystem. The high-level MPC is configured to perform a high-level optimization to generate an optimal airside subsystem load profile for each airside subsystem. The optimal airside subsystem load profiles optimize energy cost. Each of the low-level airside MPCs corresponds to one of the airside subsystems and is configured to perform a low-level optimization to generate optimal airside temperature setpoints for the corresponding airside subsystem using the optimal airside subsystem load profile for the corresponding airside subsystem. Each of the low-level airside MPCs is configured to use the optimal airside temperature setpoints for the corresponding airside subsystem to operate the airside HVAC equipment of the corresponding airside subsystem.
Abstract:
A building control system includes a wireless measurement device and a controller. The wireless measurement device measures a plurality of values of an environmental variable and uses the plurality of measured values to predict one or more future values of the environmental variable. The wireless device periodically transmits, at a transmission interval, a message that includes a current value of the environmental variable and the one or more predicted values of the environmental variable. The controller receives the message from the wireless device and parses the message to extract the current value and the one or more predicted future values of the environmental variable. The controller periodically and sequentially applies, at a controller update interval shorter than the transmission interval, each of the extracted values as an input to a control algorithm that operates to control the environmental variable.
Abstract:
A system for visualizing equipment utilization in a central plant includes a subplant monitor, a subplant utilization database, and a dispatch graphical user interface (GUI) generator. The subplant monitor receives subplant utilization data including an indication of a thermal energy load served by each of a plurality of subplants of the central plant. The subplant utilization database stores the subplant utilization data for each of a plurality of time steps. The dispatch GUI generator generates a dispatch GUI using the subplant utilization data. The dispatch GUI includes an indication of the thermal energy load served by each of the plurality of subplants for each of the plurality of time steps. The dispatch GUI may include a set of stacked bars for each of the time steps. Each of the stacked bars may represent the thermal energy load served by one of the subplants during a corresponding time step.
Abstract:
An energy plant includes a plurality of subplants, a high level optimizer, a low level optimizer, and a controller. The plurality of subplants include a cogeneration subplant configured to generate steam and electricity and a chiller subplant electrically coupled to the cogeneration subplant and configured to consume the electricity generated by the cogeneration subplant. The high level optimizer is configured to determine recommended subplant loads for each of the plurality of subplants. The recommended subplant loads include a rate of steam production and a rate of electricity production of the cogeneration subplant and a rate of electricity consumption of the chiller subplant. The low level optimizer is configured to determine recommended equipment setpoints for equipment of the plurality of subplants based on the recommended subplant loads. The controller is configured to operate the equipment of the plurality of subplants based on the recommended equipment setpoints.
Abstract:
A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.
Abstract:
A model predictive maintenance system for building equipment including one or more processing circuits including processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining an objective function that defines a cost of operating the building equipment and performing maintenance on the building equipment as a function of operating decisions and maintenance decisions for the building equipment for time steps within a time period. The operations include performing an optimization of the objective function to generate a maintenance and replacement strategy for the building equipment over a duration of an optimization period. The operations include estimating a savings loss predicted to result from a deviation from the maintenance and replacement strategy. The operations include adjusting an amount of savings expected to be achieved by energy conservation measures for the building equipment based on the savings loss.
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:
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 heating, ventilation, or air conditioning (HVAC) system for a building includes one or more processing circuits having one or more processors and one or more non-transitory computer-readable media containing program instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations including providing an optimization function for operating HVAC equipment over a future time period including a plurality of time steps and using the optimization function to generate a time series of temperature setpoints for the plurality of time steps in the future time period. The time series of temperature setpoints achieve a target value of the optimization function over the future time period. The operations include operating the HVAC equipment to drive indoor air temperature toward a first temperature setpoint of the time series of temperature setpoints for a first time step of the plurality of time steps.