Abstract:
A frequency response optimization includes a battery that stores and discharges electric power, a power inverter that uses battery power setpoints to control an amount of the electric power stored or discharged from the battery, and a frequency response controller. The frequency response controller receives a regulation signal from an incentive provider, predicts future values of the regulation signal, and uses the predicted values of the regulation signal to generate the battery power setpoints for the power inverter.
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:
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:
A control system with asynchronous wireless data transmission includes a wireless sensor and a controller. The wireless sensor includes a measurement device configured to collect a plurality of samples of a measured variable at a plurality of different sampling times, a transmission generator configured to generate a compressed data object containing the plurality of samples of the measured variable, and a wireless radio configured to transmit the compressed data object at a transmission time asynchronous with at least one of the sampling times. The controller includes an object decompressor configured to extract the plurality of samples of the measured variable from the compressed data object and a feedback controller configured to use one or more of the extracted samples of the measured variable to modulate a control signal for a plant that operates to affect the measured variable.
Abstract:
An integrated device in an HVAC system is configured to modify an environmental condition of a building. The integrated device includes a valve configured to regulate a flow of a fluid through a conduit and an actuator. The actuator includes a motor and a drive device. The drive device is driven by the motor and coupled to the valve for driving the valve between multiple positions. The integrated device further includes a processing circuit coupled to the motor. The processing circuit is configured to detect device identifying information for the valve or the actuator and to detect the integrated device location within the building.
Abstract:
An optimization controller for a battery includes a high level controller configured to receive a regulation signal from an incentive provider at 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:
A frequency response optimization includes a battery that stores and discharges electric power, a power inverter that uses battery power setpoints to control an amount of the electric power stored or discharged from the battery, and a frequency response controller. The frequency response controller receives a regulation signal from an incentive provider, predicts future values of the regulation signal, and uses the predicted values of the regulation signal to generate the battery power setpoints for the power inverter.
Abstract:
A central plant that generates and provides resources to a building. The central plant includes an electrical energy storage subplant configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The central plant includes a plurality of generator subplants that consume one or more input resources. The central plant includes a controller configured to determine, for each time step within a time horizon, an optimal allocation of the input resources and 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. The total monetary value includes revenue from participating in incentive-based demand response programs as well as costs associated with resource consumption, equipment degradation, and losses in battery capacity.
Abstract:
A building management system (BMS) includes a baseline model generator configured to receive an initial set of predictor variables for potential use in an energy usage model for a building, generate a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables, remove one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables, generate a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables, calculate a test statistic for the removed variable using a difference between the first set of coefficients and the second set of coefficients, and automatically select the removed predictor variable for use in the baseline energy usage model in response the test statistic exceeding a critical value.
Abstract:
A controller for a building management system includes a first data interface configured to receive data from the building management system and a processing circuit including a processor and a memory device storing a fault detection rule having an initial threshold value. The processing circuit is configured to detect a first fault in the building management system using the stored fault detection rule having the initial threshold value and to use the data from the building management system to determine whether an adjustment to the stored fault detection rule is needed. In response to a determination that an adjustment to the stored fault detection rule is needed, the processing circuit is configured to calculate a new threshold value for the stored fault detection rule and update the stored fault detection rule by replacing the initial threshold value with the new threshold value.