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:
Systems and methods for controlling a central plant for a building are provided. The central plant has a plant load. An optimal combination of plant equipment for meeting the plant load is estimated. Estimating the optimal combination of plant equipment includes using binary optimization to determine at least two potential combinations of plant equipment. Estimating the optimal combination of plant equipment also includes using nonlinear optimization to determine a potential power consumption minimum for each of the at least two potential combinations. The central plant is controlled according to the estimated optimal combination of plant equipment.
Abstract:
A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.
Abstract:
A system includes processors configured to perform operations including obtaining a base design resource production of a building asset and a base resource production data set comprising a base resource production of the building asset at a plurality of operating points, calculating a scaled resource production data set comprising a scaled resource production of the building asset at the plurality of operating points by scaling the base resource production data set based on a new design resource production of the building asset relative to the base design resource production of the building asset, generating a resource consumption data set comprising a resource consumption of the building asset at the plurality of operating points based on the scaled resource production data set, and initiating an automated action based on the scaled resource production data set and the resource consumption data set.
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 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:
Systems and methods for predicting a plurality of thermodynamic states of a plurality of heat, ventilation, and air conditioning (HVAC) devices of an energy plant are disclosed. The system includes a processor and a non-transitory computer readable medium storing instructions when executed causing the processor to: obtain plant netlist data describing the plurality of HVAC devices of the energy plant and connections of the plurality of HVAC devices to corresponding nodes; identify, from the first reduced subset, a second reduced subset of the plurality of thermodynamic states to be predicted by propagating a known value of the plurality of thermodynamic states using a linear solver; predict the second reduced subset of the plurality of thermodynamic states using a non-linear solver; and determine the plurality of thermodynamic states of the energy plant at the plurality of nodes based on the predicted second reduced subset of the plurality of thermodynamic states.
Abstract:
A heating, ventilation, or air conditioning (HVAC) system for a building includes an air handling unit (AHU) and a controller. The AHU is configured to provide mechanical cooling for a cooling load in the building when operating in a mechanical cooling state and provide free cooling for the cooling load in the building when operating in a free cooling state. The controller is configured to calculate a minimum free cooling time based on an estimated cost savings resulting from operating in the free cooling state relative to operating in the mechanical cooling state and transition the AHU from operating in the mechanical cooling state to operating in the free cooling state in response to predicting that outside air temperature will be less than a free cooling temperature threshold for at least the minimum free cooling time.
Abstract:
A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.
Abstract:
Systems and methods for predicting a plurality of thermodynamic states of a plurality of heat, ventilation, and air conditioning (HVAC) devices of an energy plant are disclosed. The system includes a processor and a non-transitory computer readable medium storing instructions when executed causing the processor to: obtain plant netlist data describing the plurality of HVAC devices of the energy plant and connections of the plurality of HVAC devices to corresponding nodes; identify, from the first reduced subset, a second reduced subset of the plurality of thermodynamic states to be predicted by propagating a known value of the plurality of thermodynamic states using a linear solver; predict the second reduced subset of the plurality of thermodynamic states using a non-linear solver; and determine the plurality of thermodynamic states of the energy plant at the plurality of nodes based on the predicted second reduced subset of the plurality of thermodynamic states.