Abstract:
A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.
Abstract:
An electrical energy storage system includes a battery configured to store and discharge electric power to an energy grid, a power inverter configured to use battery power setpoints to control an amount of the electric power stored or discharged from the battery, and a controller. The controller is configured to generate optimal values for the battery power setpoints as a function of both an estimated amount of battery degradation and an estimated amount of frequency response revenue that will result from the battery power setpoints.
Abstract:
A central plant includes an electrical energy storage subplant configured to store electrical energy, a plurality of generator subplants configured to consume one or more input resources, including discharged electrical energy, and a controller. The controller is configured to determine, for each time step within a time horizon, an optimal allocation of the input resources. The controller is configured to determine optimal allocation of the output resources for each of the subplants in order to optimize a total monetary value of operating the central plant over the time horizon.
Abstract:
A control system for cost optimal operation of an energy facility including equipment covered by a maintenance contract includes equipment configured to operate during an optimization period and a controller. The controller modifies a cost function to include a maintenance cost term that defines a maintenance cost as a function of a rate variable and an equipment usage variable. The controller simulates a cost of operating the energy facility over the optimization period at each of a plurality of different values of the rate variable, selects a value of the rate variable that results in a lowest cost of operating the energy facility over the optimization period, performs an online optimization of the cost function with the rate variable set to the selected value to generate one or more setpoints for the equipment, and operates the equipment during the optimization period in accordance with the generated setpoints.
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 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:
An energy storage system includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge the stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including multiple demand charges. Each of the demand charges corresponds to a demand charge period and defines a cost based on a maximum amount of the electrical energy purchased from the utility during any time step within the corresponding demand charge period. The controller is configured to modify the cost function by applying a demand charge mask to each of the multiple demand charges. The demand charge masks cause the controller to disregard the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge.
Abstract:
A frequency regulation and ramp rate control system includes a battery configured to store and discharge electric power, a battery power inverter configured to control an amount of the electric power in the battery, a photovoltaic power inverter configured to control an electric power output of a photovoltaic field, and a controller. The controller generates a battery power setpoint for the battery power inverter and a photovoltaic power setpoint for the photovoltaic power inverter. The generated setpoints cause the battery power inverter and the photovoltaic power inverter to simultaneously perform both frequency regulation and ramp rate control.
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 is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system. The controller is configured to linearize the demand charge term by imposing demand charge constraints and to mask each of the demand charge constraints that applies to an inactive pricing period. The controller is configured to determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints and to provide the optimal values of the one or more setpoints to equipment of the building system that operate to affect the maximum power usage.