EP4729860A1

EP4729860A1 - Learning-based, grid-interactive, and energy-efficient heat pump system; extensible to device-agnostic coordination

Info

Publication number: EP4729860A1
Application number: EP25209314.1A
Authority: EP
Inventors: Bingqing Chen; Mohamad Nasab
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2024-10-18
Filing date: 2025-10-17
Publication date: 2026-04-22
Also published as: US20260110472A1

Abstract

A split-inverter heat pump system includes a continuously variable speed compressor motor operably connected to a refrigerant compressor of a condenser unit of the heat pump system, a compressor motor controller, a network device, and a thermostat. The compressor motor controller is configured to generate a compressor power signal corresponding to electrical power consumed by the compressor motor in operating the refrigerant compressor. The compressor motor controller is configured to control a rotational speed of the compressor motor according to an electrical power level within a range of electrical power levels based on a compressor control signal. The network device is operably connected to the compressor motor controller and is configured to receive the compressor power signal from the compressor motor controller. The network device is configured to transmit the compressor power signal to a remote computer over the Internet. The thermostat is configured to receive an ambient temperature signal.

Description

Field

This disclosure relates to the field of energy efficiency in buildings and residences, and, in particular, to systems and methods for efficiently operating a heat pump system.

Background

Heat pumps use electricity to transfer heat from a cool space to a warm space making the cool space cooler and warm space warmer. A heat pump may operate to keep a business or home at a comfortable warm temperature in colder months by transferring heat from the outside environment into the business or home. Moreover, a type of heat pump known as a split heat pump is operable to keep a business or home at a comfortable cool temperature in warmer months by transferring heat from inside the business or home to the outside environment. Heat pumps operate using electricity, instead of burning fossil fuels like a natural gas furnace. In some instances, heat pumps are supplied with electricity from clean energy sources, such as wind power and solar power.
There have been improvements in heat pump technology including heat pump inverter technology. Inverter technology operates a compressor motor of the heat pump at a selected speed, which optimizes the efficiency of the heat pump and the comfort of the occupants. The selected speed of the motor is within a range of many possible speeds (e.g., one hundred speeds from 1% to 100% of a maximum speed), as compared to other non-inverter technology systems in which the compressor motor is operable at only a limited fixed number of speeds (e.g., only two speeds in a two-stage system), such as approximately 65% or 100% of the maximum speed.
A heat pump typically operates in conjunction with a thermostat that measures an indoor temperature and receives at least one temperature setpoint from a user. The heat pump is activated by the thermostat, for heating or cooling, based on a comparison of the measured indoor temperature to the temperature setpoint. In considering a traditional thermostat connected to a single-stage heat pump system, the underlying operation of the heat pump is typically manufacturer-dependent and is opaque to users and electrical grid operators.
Connected thermostats (also known as smart thermostats) are Wi-Fi connected thermostats configured to transmit and to receive data using the Internet corresponding to a heat pump system. Connected thermostats enable remote control of the on and off cycle of the heat pump, for example. However, even with a connected thermostat, operation of a corresponding inverter-style heat pump is sub-optimal.
Based on the above, advancements in heat pump control are desirable in order to further increase the energy efficiency in which a heat pump system is operated, including inter-style heat pumps and split heat pumps.

Summary

According to an exemplary embodiment of the disclosure, a split-inverter heat pump system includes a continuously variable speed compressor motor, a compressor motor controller, a network device, and a thermostat. The continuously variable speed compressor motor is operably connected to a refrigerant compressor of a condenser unit of the heat pump system. The compressor motor controller is configured to generate a compressor power signal corresponding to electrical power consumed by the compressor motor in operating the refrigerant compressor. The compressor motor controller is configured to control a rotational speed of the compressor motor according to an electrical power level within a range of electrical power levels based on a compressor control signal. The network device is operably connected to the compressor motor controller and is configured to receive the compressor power signal from the compressor motor controller. The network device is configured to transmit the compressor power signal to a remote computer over the Internet. The thermostat is configured to receive (i) an ambient temperature signal from a temperature sensor located in a zone that is temperature controlled by the heat pump system, and (ii) a temperature setpoint. The thermostat configured to transmit the ambient temperature signal and the temperature setpoint to the remote computer over the Internet. The remote computer is configured to generate the compressor control signal based on the ambient temperature signal, the temperature setpoint, and the compressor power signal. The network device is configured to receive the generated compressor control signal from the remote computer and to supply the compressor control signal to the compressor motor controller. The compressor motor controller operates the compressor motor based on the supplied compressor control signal to control the temperature of the zone at the temperature setpoint.
According to another exemplary embodiment of the disclosure, a computer-implemented method of controlling power consumption and generation includes receiving, with a thermostat, an ambient temperature signal corresponding to an ambient temperature of a zone that is temperature controlled by a split inverter heat pump system, and receiving, with a network device, a compressor power signal corresponding to electrical power consumed by a continuously variable speed compressor motor. The compressor motor is operably connected to a refrigerant compressor of a condenser unit of the heat pump system. The method further includes building, using a remote computer operably connected to the thermostat and the network device through the Internet, a thermodynamic model of the zone based on at least the ambient temperature signal and the compressor power signal. The thermodynamic model is configured to output a temperature of the zone over time. The method includes receiving, with the remote computer, thermal comfort rules specifying a predetermined setpoint temperature range of the zone, and generating, using the remote computer, a compressor control signal using the thermodynamic model and the thermal comfort rules. The compressor control signal specifying an electrical power level at which the compressor motor is operated, and the compressor control signal configured to operate the compressor motor in a manner that causes the heat pump system to maintain a temperature of the zone within the predetermined setpoint temperature range and to optimize the electrical power consumed by the compressor motor to operate the compressor. The method also includes supplying the compressor control signal to the compressor motor and operating the heat pump system to control the temperature of the zone within the predetermined setpoint temperature range.

Brief Description of the Figures

The above-described features and advantages, as well as others, should become more readily apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying figures in which:

FIG. 1 is a block diagram of an environment of a heat pump system;
FIG. 2 is a block diagram of a heat pump of the heat pump system of FIG. 1;
FIG. 3 is a flowchart illustrating an exemplary method of operating the heat pump system; and
FIG. 4 is a block diagram summarizing a coordination procedure between devices of the heat pump system and the environment.

Detailed Description

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiments illustrated in the drawings and described in the following written specification. It is understood that no limitation to the scope of the disclosure is thereby intended. It is further understood that this disclosure includes any alterations and modifications to the illustrated embodiments and includes further applications of the principles of the disclosure as would normally occur to one skilled in the art to which this disclosure pertains.
Aspects of the disclosure are disclosed in the accompanying description. Alternate embodiments of the disclosure and their equivalents may be devised without parting from the spirit or scope of the disclosure. It should be noted that any discussion herein regarding "one embodiment", "an embodiment", "an exemplary embodiment", and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, and that such particular feature, structure, or characteristic may not necessarily be included in every embodiment. In addition, references to the foregoing do not necessarily comprise a reference to the same embodiment. Finally, irrespective of whether it is explicitly described, one of ordinary skill in the art would readily appreciate that each of the particular features, structures, or characteristics of the given embodiments may be utilized in connection or combination with those of any other embodiment discussed herein.
For the purposes of the disclosure, the phrase "A and/or B" means (A), (B), or (A and B). For the purposes of the disclosure, the phrase "A, B, and/or C" means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C).
The terms "comprising," "including," "having," and the like, as used with respect to embodiments of the disclosure, are synonymous.
As shown in FIG. 1, a heat pump system 100 includes a heat pump 104, a thermostat 108, an outdoor temperature sensor 112, an input device 120, and an occupancy sensor 124. The heat pump system 100 is connected to a mains power supply 128 along with a supply-side device 132 and a demand-side device 136. The heat pump system 100 is further connected to a remote computer 140, such as a cloud computing network, through the Internet 144. The supply-side device 132 stores and/or generates electricity, and the demand-side device 136 consumes electricity from the mains power connection 128 and/or the supply-side device 132.
As disclosed herein, the thermostat 108 and the remote computer 140 are operably connected to the Internet 144 and are configured to operate the heat pump 104 (shown in greater detail in FIG. 2) to heat and to cool a zone (at least a portion of a home or business) in a manner based on operating states and operating factors of the heat pump 104 that are unobservable to typical thermostats. Thus, the heat pump system 100 disclosed herein offers greater observability and controllability as compared to known heat pumps, resulting in a more energy-efficient operation of the heat pump system 100, as well as a more comfortable zone that is reliably and accurately maintained at a desired temperature or within a desired temperature range. Each element of the heat pump system 100 is described herein.
With reference to FIG. 1, the thermostat 108 includes a temperature sensor 148, a memory 152, and a network device 154, each operably connected to a controller 156. The temperature sensor 148 is configured to generate an ambient temperature signal corresponding to an ambient temperature of a zone of which the heat pump 104 is configured to heat and/or cool. In the illustrated embodiment, only one temperature sensor 148 is illustrated. In other embodiments, the thermostat 108 is configured to operate with a plurality of temperature sensors 148 located in a plurality of zones, for example.
The network device 154 of the thermostat 108 operably connects the thermostat 108 to the Internet 144. The network device 154 is configured to receive data from the controller 156 and to transmit the data to the remote computer 140 over the Internet 144. The network device 154 is also configured to receive data from the remote computer 140 over the Internet 144, and to provide the data from the remote computer 140 to the controller 156 of the thermostat 108. In one embodiment, the network device 154 is a Wi-Fi device and in another embodiment, the network device 154 operates using cellular data or mobile data.
The memory 152 of the thermostat 108 is an electronic data storage unit, which is also referred to herein as a non-transitory computer readable storage medium. The memory 152 is configured to store program instruction data as well as sensor data, such as temperature data from the temperature sensor(s) 148 and occupancy data from the occupancy sensor(s) 124. Moreover, the memory 152 is configured to store any other electronic data associated with the heat pump system 100. The program instruction data includes computer executable instructions for operating the heat pump system 100.
The controller 156 of the thermostat 108 is configured to execute the program instruction data for controlling the heat pump system 100. The controller 156 is provided as a microprocessor, a processor, or any other type of structural electronic control chip.
In a specific embodiment, the thermostat 108 is a 24V thermostat configured to operate with signals C, O/B, R, Y, G, W1, and W2, for example.
As shown in FIG. 1, the outdoor temperature sensor 112 is configured to generate an outdoor temperature signal corresponding to an outdoor air temperature. The outdoor temperature sensor 112 is electrically connected to the heat pump 104, to provide the outdoor temperature signal to a controller 238 of the heat pump 104, for processing.
The input device 120 is configured to receive user inputs from an occupant of the zone or a user of the heat pump system 100. The received inputs correspond to a desired temperature of the zone, which is also referred to as a temperature setpoint of the heat pump system 100. Electronic data corresponding to the received inputs are provided to the controller 156 of the thermostat 108. The desired temperature may also be input as a desired temperature range.
The occupancy sensor 124 is operably connected to the thermostat 108 and is configured to generate an occupancy signal indicating that people are present in the zone or that people are not present in the zone. The occupancy sensor 124, in one embodiment, is a passive infrared sensor or an ultrasonic sensor, and in other embodiments is provided as any suitable occupancy detecting device. In one embodiment, a plurality of the occupancy sensors 124 are provided with at least one occupancy sensor located in each zone that is temperature controlled by the heat pump system 100.
In FIG. 1, the remote computer 140 is a computer or group of computers operably connected to the Internet 144. For example, the remote computer 140 is an on-demand cloud computing platform having storage, servers, and corresponding databases. The simplified remote computer 140 of FIG. 1 is illustrated as having a non-transitory electronic computer readable storage medium shown as the memory 150. The memory 150 is configured to store a thermodynamic model 158 used to operate the heat pump system 100, among other data. The remote computer 140 is configured to process data and to generate the thermodynamic model 158, as described below. Thus, the heat pump 104 is operated from the remote computer 140.
The mains power connection 128 is an electrical connection to an electrical grid source of electricity, as generated by a utility provider. Electrical power is drawn from the electrical grid through the mains power connection 128, and electrical power is supplied to the electrical grid through the mains power connection 128.
The supply-side device 132 is electrically connected to the heat pump system 100, the demand-side device 136, and the mains power connection 128. The supply-side device 132 is representative of electrical devices that store or generate electrical energy. Exemplary supply-side devices 132 include wind generators, photovoltaic panels (e.g., solar panels), and battery-based electricity storage systems. The supply-side device 132 is configured to supply electrical energy to the heat pump system 100, the demand-side device 136, and the electrical grid.
The supply-side device 132 is configured to provide an energy availability signal to the remote computer 140, having data indicating an availability of electrical energy from the supply-side device 132 that could be used to power the heat pump system 100 for heating or cooling the zone.
As shown in FIG. 1, the demand-side device 136 is electrically connected to the heat pump system 100, the supply-side device 132, and the mains power connection 138. The demand-side device 136 is a consumer of electrical power, such as a home appliance (washing machine, clothes dryer, dishwasher, and water heater) or an electrical vehicle charging system that is configured to charge the batteries of an electric vehicle.
The demand-side device 136 is configured to provide an energy usage signal to the remote computer 140, having data indicating an energy usage over time of the demand-side device 136.
With reference to FIG. 2, the heat pump 104 of the heat pump system 100 is shown in greater detail. The heat pump 104 includes an air handler unit 204 that is located indoors and a condenser unit 208 that is located outdoors. The heat pump 104, in one embodiment, is an inverter ducted split heat pump (i.e., a split-inverter heat pump) that is operable as a heating device for heating at least one zone and as a cooling device for cooling the at least one zone.
The air handler unit 204 includes an evaporator 212 operably connected to a first expansion valve 216. The evaporator 212 is also referred to as an evaporator coil. The air handler unit 204 also includes a temperature sensor 220 and a pressure sensor 224 operably connected to the evaporator 212. The air handler unit 204 further includes a blower motor 228 operably connected to a fan 232 and a blower motor controller 236. An airflow sensor 240 is operably connected to the fan 232. A data connection is present between the air handler 204 and the condenser unit 208 for the bidirectional transmission of data and signals.
The temperature sensor 220 of the air handler unit 204 is configured to generate an evaporator temperature signal corresponding to a temperature of the evaporator 212. In one embodiment, the evaporator temperature signal is representative of a temperature of a refrigerant in the evaporator 212. The evaporator temperature signal is supplied to a network device 116 and the controller 238 of the heat pump 104, which is included in the condenser unit 208 in one embodiment.
As shown in FIG. 2, the pressure sensor 224 of the air handler unit 204 is configured to generate an evaporator pressure signal corresponding to a pressure of the evaporator 212. In one embodiment, the evaporator pressure signal is representative of a pressure of the refrigerant in the evaporator 212. The evaporator pressure signal is supplied to the network device 116 and the controller 238 of the heat pump 104.
The blower motor 228 is a continuously variable speed motor that is operably connected to the fan 232 to generate an airflow across the evaporator 212. In one embodiment, the blower motor 228 is a variable frequency drive (VFD) motor having a rotational speed and power consumption that is controllable at a very fine granularity (in 1.0 % increments of a maximum speed and/or maximum power), thereby making the speed and power consumption continuously variable. In other embodiments, the blower motor 228 is any type of electric motor having a continuously variable speed within a range of speeds and a continuously variable power consumption within a range of power consumption. The speed of rotation of an output shaft of the blower motor 228 directly corresponds to a speed of rotation of the fan 232, which is fixedly connected to the output shaft.
The blower motor controller 236 is configured to control the rotational speed of the blower motor 228 and the corresponding fan 232. Specifically, the blower motor controller 236 controls the blower motor 228 at a predetermined blower set speed within the continuous range of blower speeds. An exemplary range of blower speeds is from zero rotations per minute (0 rpm) to five hundred rotations per minute (500 rpm) or more depending on the embodiment. The rotational speed of the blower motor 228 and the fan 232 is referred to as the blower set speed.
The controller 156 of the thermostat 108 and/or the controller 238 of the heat pump 104 is configured to set the blower set speed by sending a corresponding electrical signal to the blower motor controller 236. The blower motor controller 236 operates the blower motor 228 at the blower set speed (based on the blower control signal).
With reference to FIG. 2, the blower motor controller 236 is configured to generate signals indicating the operating state of the blower motor 228. For example, the blower motor controller 236 generates and outputs a blower speed signal corresponding to the rotational speed of the blower motor 228 and the fan 232. The blower motor controller 236 is also configured to generate a blower power signal corresponding to an amount of electrical power consumed by the blower motor 228 to rotate the fan 232. The blower speed signal and the blower power signal are provided to the network device 116 and the controller 238 of the heat pump 104. The blower motor controller 236 controls a speed and a power consumption of the blower motor 228 based on a received blower control signal.
The airflow sensor 240 is configured to generate an airflow signal corresponding to the airflow generated by the fan 232. The airflow signal is provided to the network device 116 and the controller 238 of the heat pump 104.
The first thermal expansion valve 216 is operably connected to the evaporator 212, and is configured to control a flow of the refrigerant based on a first predetermined valve setting within a first range of valve settings. An electronic unit of the first thermal expansion valve 216 is configured to generate a first expansion valve signal corresponding to the first predetermined valve setting. The first expansion valve signal is provided to the network device 116 and the controller 238 of the heat pump 104.
With continued reference to FIG. 2, the condenser unit 208 includes the controller 238 and the network device 116 of the heat pump 104. The condenser unit 208 also includes a condenser 244 operably connected to a second expansion valve 246 and a reversing valve 248. The condenser 244 is also referred to as a condenser coil. The condenser unit 208 further includes a temperature sensor 252 and a pressure sensor 256 operably connected to the condenser 244. The condenser unit 208 includes a compressor motor 260 operably connected to a compressor 264, and a fan motor 262 operably connected to a fan 266. The compressor motor 264 and the fan motor 262 are operably connected to a compressor motor controller 268.
The network device 116 of the condenser unit 208 is operably connected to the thermostat 108, the supply-side device 132, the demand-side device 136, the remote computer 140, and the network device 154 of the thermostat 108, and the Internet 144. The network device 116 is configured to receive data from the controller 156 of the thermostat 108 and the controller 238 of the heat pump 104 and to transmit the data to the remote computer 140 over the Internet 144. The network device 116 is also configured to receive data from the remote computer 140 over the Internet 144. Moreover, the network device 116 transmits and provides data received from the remote computer 140 to the controller 156 of the thermostat 108 and the controller 238 of the heat pump 108. In one embodiment, the network device 116 is a Wi-Fi device and in another embodiment, the network device 116 operates using cellular data or mobile data. For example, the network device 116 is provided as a 4G gateway in one embodiment. The network device 116, in one embodiment, uses a propriety communication protocol to communicate with the sensors and controllers of the air handler 204 and the condenser unit 208. The network device 116, in one embodiment, is located in an outdoor environment and is mounted on the condenser unit 208.
The network device 116 is the component that offers the improved operability of the heat pump system 100 by collecting the signals and data from the heat pump 104 and transmitting the collected signals and data to the remote computer 140 for processing. As described, the remote computer 140 generates improved / optimized control signals, which are received by the network device 116 and then applied to the heat pump 104.
The memory 242 of the condenser unit 208 is an electronic data storage unit, which is also referred to herein as a non-transitory computer readable storage medium. The memory 242 is configured to store program instruction data as well as sensor data. Moreover, the memory 242 is configured to store any other electronic data associated with the heat pump system 100. The program instruction data includes computer executable instructions for operating the heat pump system 100.
The temperature sensor 252 of the condenser unit 208 is configured to generate a condenser temperature signal corresponding to a temperature of the condenser 244. In one embodiment, the condenser temperature signal is representative of a temperature of the refrigerant in the condenser 244. The condenser temperature signal is supplied to the controller 238 and the network device 116.
As shown in FIG. 2, the pressure sensor 256 is configured to generate a condenser pressure signal corresponding to a pressure of the condenser 244. In one embodiment, the condenser pressure signal is representative of a pressure of the refrigerant in the condenser 244. The condenser pressure signal is supplied to the controller 238 and the network device 116.
The compressor 264 is a refrigerant compressor that is configured to compress the refrigerant received from the evaporator 212 and to send the compressed refrigerant into the condenser 244. The compressor 264 compresses the refrigerant in response to rotation of a compressor input.
The compressor motor 260 is a continuously variable speed motor that is operably connected to the compressor input of the compressor 264 to operate the compressor 264 and compress the refrigerant. In one embodiment, the compressor motor 260 is a variable frequency drive (VFD) motor having a rotational speed and power consumption that is controllable at a very fine granularity (in 1.0 % increments of a maximum speed and/or maximum power), thereby making the speed and power consumption continuously variable. In other embodiments, the compressor motor 260 is any type of electric motor having a continuously variable speed within a range of speeds and a continuously variable power consumption within a range of power consumption. The speed of rotation of the output shaft of the compressor motor 260 directly corresponds to a speed of the compressor 264.
The fan motor 262 is a continuously variable speed motor that is operably connected to the fan 266 to rotate the fan 266 to generate an airflow through the condenser 244. In one embodiment, the fan motor 262 is a variable frequency drive (VFD) motor having a rotational speed and power consumption that is controllable at a very fine granularity (in 1.0 % increments of a maximum speed and/or maximum power), thereby making the speed and power consumption continuously variable. In other embodiments, the fan motor 262 is any type of electric motor having a continuously variable speed within a range of speeds and a continuously variable power consumption within a range of power consumption. The speed of rotation of the output shaft of the fan motor 262 directly corresponds to a speed of the fan 266.
The compressor motor controller 268 is configured to control the rotational speed of the compressor motor 260 and the compressor 264. Moreover, the compressor motor controller 268 is configured to control the rotational speed of the fan motor 262 and the fan 266. Specifically, the compressor motor controller 268 controls the compressor motor 260 at a predetermined compressor set speed within the continuous range of compressor speeds. An exemplary range of compressor speeds is from zero rotations per minute (0 rpm) to five hundred rotations per minute (500 rpm) or more depending on the embodiment. The rotational speed of the compressor motor 260 and the compressor 264 is referred to as the compressor set speed.
The compressor motor controller 268 controls the fan motor 262 at a predetermined fan set speed within a continuous range of fan speeds. An exemplary range of fan speeds is from zero rotations per minute (0 rpm) to five hundred rotations per minute (500 rpm) or more depending on the embodiment. The rotational speed of the fan motor 262 and the fan 266 is referred to as the fan set speed.
The compressor motor controller 268 is further configured to generate signals indicating the operating state of the compressor motor 260 and the operating state of the fan motor 262. For example, the compressor motor controller 268 generates and outputs a compressor speed signal corresponding to the rotational speed of the compressor motor 260 and the compressor 264. The compressor motor controller 268 is also configured to generate a compressor power signal corresponding to an amount of electrical power consumed by the compressor motor 260 as is required to operate the compressor 264. The compressor speed signal and the compressor power signal are provided to the network device 116 and the controller 238. The compressor motor controller 268 controls a speed and a power consumption of the compressor motor 260 based on a received compressor control signal.
The compressor motor controller 268 also generates and outputs a fan speed signal corresponding to the rotational speed of the fan motor 262 and the fan 266. The compressor motor controller 268 is configured to generate a fan power signal corresponding to an amount of electrical power consumed by the fan motor 262 as is required to operate (i.e., rotate) the fan 266. The fan speed signal and the fan power signal are provided to the network device 116 and the controller 238. The compressor motor controller 268 controls a speed and a power consumption of the fan motor 262 based on a received fan control signal.
In the illustrated embodiment, the compressor motor controller 268 controls both the fan motor 262 and the compressor motor 260. In another embodiment, the compressor motor 260 is connected to a separate motor controller that controls only the compressor motor 260, and the fan motor 262 is connected to another separate motor controller that controls only the fan motor 262.
The reversing valve 248 of the condenser unit 208 is configured to change a direction of the flow of refrigerant in the heat pump 104. In a first configuration the reversing valve 248 configures the flow of refrigerant for heating the zone, and in a second configuration the reversing valve 248 configures the flow of refrigerant for cooling the zone. The controller 238 directly controls the configuration of the reversing valve 248 by sending a corresponding reversing valve signal directly to the reversing valve 248. In one embodiment, the reversing valve 248 is electronically configurable in a first state for heating operations and a second state for cooling operations.
The second thermal expansion valve 246 is operably connected to the condenser 244, and is configured to control a flow of the refrigerant based on a second predetermined valve setting within a second range of valve settings. An electronic unit of the second thermal expansion valve 246 is configured to generate a second expansion valve signal corresponding to the second predetermined valve setting. The second expansion valve signal is provided to the network device 116 and the controller 238.
The controller 238 of the heat pump 104 is configured to execute program instruction data for controlling the heat pump system 100. The controller 238 is provided as a microprocessor, a processor, or any other type of structural electronic control chip. For example, the controller 238 is configured to set the compressor set speed of the motor 260 and the compressor 264 by sending a corresponding electrical signal (the compressor control signal) to the compressor motor controller 268. The controller 238 is further configured to set the fan set speed of the motor 262 and the fan 266 by sending a corresponding electrical signal (the fan control signal) to the compressor motor controller 268. Additionally, the controller 238 is configured to set the blower set speed of the motor 228 and fan 232 by sending a corresponding electrical signal (the blower control signal) to the blower motor controller 236.
In operation and with reference to the flowchart of FIG. 3, the heat pump system 100 performs a computer-implemented method 300 of controlling power consumption and generation. The method 300 of operating the heat pump system 100 utilizes temperature, pressure, and power consumption signals in conjunction with rules from the operator (i.e., user) and additional rules from the power utility to determine the operating state of the heat pump 104. Such an approach cannot be performed by known connected thermostats, which have access to only one or two heating wires, one or two cooling wires, and an indoor fan wire according to the standardized color-coded wires common to HVAC equipment across manufacturers. The method 300 is described in detail below.
The method 300 starts at block 304, which describes receiving sensor signals and power signals. The controller 156 of the thermostat 108 receives the ambient temperature signal from the temperature sensor 148 corresponding to the ambient temperature of the zone that is temperature controlled by the heat pump system 100. The thermostat 108 also receives the occupancy signal from the occupancy sensors 124 indicating in which zone people are present.
The controller 156 of the thermostat 108 further receives a setpoint temperature or a setpoint temperature range corresponding to a desired temperature of the zone(s). The set point temperature is input to the thermostat 108 using the input device 120 or is received by the thermostat 108 from the remote computer 140 over the Internet 144. In an example, the setpoint temperature is 70° F or the setpoint temperature range is from 68° F to 72° F.
The signals and data received by the controller 156 of the thermostat 108 are stored, at least temporally, in the memory 152.
At block 304, the controller 238 of the heat pump 104 receives the outdoor temperature signal from the outdoor temperature sensor 112 corresponding to the air temperature outside in the area of the condenser unit 208.
The controller 238 of the heat pump 104, at block 304, also receives a plurality of signals that are typically unavailable to HVAC systems including the compressor power signal corresponding to the amount of electrical power consumed by the compressor motor 260 in operating the compressor 264, the blower power signal corresponding to the amount of electrical power consumed by the blower motor 228 in operating the fan 232, and the fan power signal corresponding to the amount of electrical power consumed by the fan motor 262 in operating the fan 266.
The controller 238 of the heat pump 104 further receives the pressure signals from the pressure sensors 224, 256 and the temperature signals from the temperature sensors 220, 252. Additionally, the controller 238 receives the airflow signal from the airflow sensor 240, the first and second expansion valve signals from the expansion valves 216, 246, and the reversing valve signal from the reversing valve 248. Any or all of these signals may be used, along with other data, in building the thermodynamic model 158, as described below.
Many of the above-mentioned signals are not provided to known thermostats and are unobservable in known heat pumps. Thus, block 304 of the method 300 represents a collection of signals and data from the heat pump system 100 that increases the observability into the operation of the system 100. The received signals and data are saved to the corresponding memory 152, 242 and are available for processing by the corresponding controller 156, 238. The network device 116 and the controller 238 enable the extraction and transmission of these signals out of the heat pump 104 for processing by the remote computer 140.
Block 304 of the method 300 further includes transmitting the received signals and data from the corresponding memory 152, 242 to the remote computer 140 using the corresponding network device 116, 154 over the Internet 144. In one embodiment, the signals and data from the memory 152 of the thermostat 108 are transmitted to the remote computer 140 using the network device 154 over Wi-Fi, and the signals and data from the memory 242 of the heat pump 104 are transmitted to the remote computer 140 using the network device 116 over a cellular data connection. Data is sent from the thermostat 108 and the heat pump 104 to the remote computer 140 at five to fifteen minute intervals, for example, so that optimization and rebuilding can occur regularly and periodically.
The method 300 at block 304 also includes receiving weather forecast data using the remote computer 140 as is available from weather data servers connected to the Internet 144. The weather forecast data includes at least a prediction of an outdoor temperature in the area in which the heat pump system 100 is located.
Next, at block 308, the method 300 includes building the thermodynamic model 158 of the zone using the remote computer 140, and storing the thermodynamic model 158 in the memory 150. The thermodynamic model 158 is configured to model a temperature of the zone or zones over time. Thus, in embodiment, each zone has a corresponding thermodynamic model 158. In one embodiment, the thermodynamic model 158 is built using historical data of the zone and the heat pump system 100. For example, the data used to build the thermodynamic model 158 include, but are not limited to, the ambient temperature signal, the power level of the compressor motor 260 (the compressor power signal), the speed of the compressor motor 260 (the compressor speed signal), the power level of the blower motor 228 (the blower power signal), the speed of the blower motor 228 (the blower speed signal), the power level of the fan motor 262 (the fan power signal), the speed of the fan motor 262 (the fan speed signal), the pressures from the sensors 224, 256, the temperature from the sensors 220, 252, the airflow signal, the reversing valve signal, the expansion valve signals, the weather forecast, and thermal gains determined from the occupancy signal. The thermodynamic model 158 in one embodiment is a machine learning algorithm.
The thermodynamic model 158 can be built ready for use with as little as one day of historical data and signals from the heat pump system 100. Thereafter, the thermodynamic model 158 is updated, adapted, and/or rebuilt as the heat pump system 100 generates additional data and signals. The thermodynamic model 158, in one embodiment, is built and improved over a predetermined time period of one year in order to account for seasonal differences in the outdoor air temperature and the resultant requirements for maintaining the zone at the setpoint temperature. Specifically, according to this approach, the thermodynamic model 158 is built using outdoor air temperature data, from the sensor 112, that is collected over a predetermined time period of one year. The corresponding thermodynamic model 158 accounts for seasonal variability in the requirements for operating the compressor motor 260, the blower motor 228, the fan motor 262, the expansion valve 216, and the reversing valve 248.
In an embodiment, the thermodynamic model 158 is continually updated (i.e. rebuilt) based on additional data generated by the heat pump system 100. Accordingly, an initial set of thermodynamic model 158 parameters are determined using a small set of operational data (e.g., collected over one to two days). As more data come in, the thermodynamic model 158 is continuously updated based on new observations and the power consumption of the motors 228, 260, 262 among other signals and data. Furthermore, as the heat pump 104 operates over seasons, the remote computer 140 retains data of previous seasons in the memory 150. For instance, as the season transitions from spring to summer, the remote computer 140 adapts the thermodynamic model 158 for warmer weather, and retains in the memory 150 the thermodynamic model 158 of spring operation.
The heat pump system 100 is described as being learning-based because the thermodynamic model 158 is built based on the specific thermodynamic load of the zone. The thermodynamic model 158 "learns" from the data that is provided from at least the temperature sensor 148 and the motor controllers 236, 268.
The thermodynamic model 158 of the method 300 cannot be built by thermostats that are connected to only the standardized color-coded wires common to HVAC equipment across manufacturers, because the standardized color-coded wires do not make available the blower power signal, the compressor power signal, the fan power signal, and many of the other signals that are used by the remote computer 140 to build the thermodynamic model 158.
Next at block 312 of the method 300, the thermostat 108 and/or the remote computer 140 receives rules for operating the heat pump 104, and then a distinct optimization step/objective is performed by the remote computer 140 using the output of the thermodynamic model 158. In the method 300, the thermodynamic model 158 and the optimization of the heat pump system 100 are two distinct components of the algorithm.
In block 312, first the thermodynamic model 158 outputs how the indoor air temperature will evolve over time given present / current model inputs (as opposed to the historical data used to build the model 158), such as occupancy, outdoor weather, and heat pump operation. Specifically, the model inputs to the thermodynamic model 158 include the presently generated: ambient temperature signal, the compressor power signal, the compressor speed signal, the blower power signal, the blower speed signal, the fan power signal, the fan speed signal. Additional presently generated model inputs include: the pressures from the sensors 224, 256, the temperature from the sensors 220, 252, the airflow signal, the reversing valve signal, the expansion valve signals, the weather forecast, and thermal gains determined from the occupancy signal. Any other present or current signal of type used to build the thermodynamic model 158 may additionally or alternatively be applied to the thermodynamic model 158 as an input to the model.
In response, to the model inputs, the thermodynamic model 158 generates an output that is a predicted temperature of the zone over time. For example, an output of the thermodynamic model 158 includes, but is not limited to, a prediction of a temperature in the zone over the next twelve hours (or any other predetermined time period from two hours to two days). In a very simplified example, when the model input includes the ambient temperature of the zone and the compressor power signal indicates that the compressor motor 260 is operating at 70% power, the output of the thermodynamic model 158 indicates that the zone will be at 70° F for one hour, then the temperature will fall to 69° F for one hour, then will fall to 68° F the following hour, as may occur during the evening when the outdoor temperature decreases. Typically, the accuracy of the thermodynamic model 158 is increased by incorporating at least several of the above listed inputs.
Second, the distinct optimization step of block 312 uses the temperature output from the thermodynamic model 158 and a set of objectives (i.e., the rules) to determine the optimal heat pump operation over a planning horizon. The optimization objectives can include thermal comfort, energy cost, energy efficiency, and grid-level objectives. The heat pump system 100 is configured to optimize and balance multiple objectives simultaneously. The optimization results in the generation of at least one of the compressor control signal and the blower control signal, which is used to operate the heat pump system 100.
According to the optimization objective, the remote computer 140 uses the temperature of the zone as predicted by the thermodynamic model 158 to determine and/or to predict the amount of electricity needed to maintain the zone at the temperature setpoint and/or to change the zone to a different temperature set point. The remote computer 140 also uses the thermodynamic model 158 to control the operation of the heat pump system 100 in a manner that optimizes the electrical power consumed by components of the heat pump 104, such by controlling the power and speed of the blower motor 228, the condenser motor 262, and the fan motor 262 using the compressor control signal, the blower control signal, and the fan control signal.
The rules, in one embodiment, are provided by a user of the heat pump system 100 and/or an electric utility company that supplies electric power to the mains power connection 128. In one example, the rules are thermal comfort rules and specify a predetermined setpoint temperature range of the zone. The thermal comfort rules may therefore specify a setpoint temperature from 73° F to 75° F for cooling the zone. In another embodiment, the rules are electrical provider rules that specify a cost of electrical power from the electricity provider at each time of the day. Thus, for example, the electrical provider rules may specify that from 6:00 am to 9:00 pm electricity is sold at $0.18 per kilowatt hour (kWh) and from 9:00 pm to 6:00 am electricity is sold at $0.12 per kilowatt hour (kWh). According to a different electrical provider rule, during an exemplary curtailment demand response event, the heat pump system 100 is operated at no more than 70% of a maximum power consumption. The electrical provider rules are also referred to as time-of-use tariffs. Any other rule concerning the power consumption of the heat pump system 100 and/or the setpoint temperature of the thermostat 108 may be provided to the thermostat 108 as a rule that is received at block 312 of the method 300.
The thermal comfort rules and the electrical provider rules are variable or changeable depending on conditions and are not fixed or static. The rules are also referred to herein as objectives or preferences.
Continuing the above mentioned simplified example, an optimization approach may change the power level at which the compressor motor is operated in order to maintain the zone temperature at 70° F for several hours. Additionally or alternatively, the optimization approach may change the power level at which the compressor motor is operated in order to use less power at 6:00 am when the cost of electricity increases, for example.
In another example, during a curtailment demand response event, the compressor motor 260 is operated at no more than 70% of a maximum power consumption. However, operating the compressor motor 260 at less than or equal to 70% of the maximum power consumption for an extended period of time (i.e., four hours) typically leads to the temperature of the zone exiting the temperature boundaries of the thermal comfort rules. Given this prediction from the thermodynamic model 158, the operation of the heat pump system 100 is optimized by preemptively heating / cooling the zone before the curtailment event.
According to another optimization example, upon startup of the heat pump system 100 initially the inverter-based compressor motor 260 and inverter-based blower motor may each be operated at 80% power consumption (80% speed), but then the percentage of power consumption is incrementally reduced / optimized as the zone nears the temperature setpoint. When the zone is at the temperature setpoint, for example, the compressor motor 260 may be operated at 15% power consumption and the blower motor 228 may be operated at 27% power consumption, as controlled by the compressor control signal and the blower control signal, to maintain the zone at the setpoint temperature.
Next, at block 316 of the method 300 the remote computer 140 generates control signals for operating the heat pump 104 according to the selected optimization described above. The following control signals are generated from the thermodynamic model 158 and the rules including control signals for operating the compressor motor 260 (the compressor control signal), the blower motor 228 (the blower control signal), the fan motor 262 (the fan control signal), the expansion valve 216, 246 (the expansion valve control signals), the reversing valve 248 (the reversing valve control signal), among any other available control signal. The control signals specify a selected operating point of the corresponding component within a range of operating points of each of the compressor motor 260, the blower motor 228, the fan motor 262, and the expansion valves 216, 246. In connection with the reversing valve 248, the control signal specifies the state of the reversing valve 248. The control signals are supplied to the corresponding component in order to control operation of that component. The control signals operate the corresponding components according to the optimization approach as determined from the rules and the output of the thermodynamic model 158.
In an example, the compressor control signal specifies the electrical power level at which the compressor motor 260 is operated in order to comply with the thermal comfort rules and the electrical provider rules while also minimizing and/or optimizing the electrical power consumed by the compressor motor 260 to operate the compressor 264. Due to the inverter-based system, the compressor control signal operates the compressor motor 260 at a selected level of power consumption within a range of 0% to 100% of a maximum power consumption, by 1% (or smaller) increments.
The blower control signal specifies the electrical power level at which the blower motor 228 is operated in order to comply with the thermal comfort rules and the electrical provider rules while also minimizing and/or optimizing the electrical power consumed by the blower motor 228. Due to the inverter-based system, the blower control signal operates the blower motor 228 at a selected level of power consumption within a range of 0% to 100% of a maximum power consumption, by 1% (or smaller) increments.
The fan control signal specifies the electrical power level at which the fan motor 262 is operated in order to comply with the thermal comfort rules and the electrical provider rules while also minimizing and/or optimizing the electrical power consumed by the fan motor 262. Due to the inverter-based system, the fan control signal operates the fan motor 262 at a selected level of power consumption within a range of 0% to 100% of a maximum power consumption, by 1% (or smaller) increments.
As an example, the thermodynamic model 158 is used with thermal comfort rules to generate the compressor control signal, the blower control signal, and the fan control signal. The compressor control signal, the blower control signal, and the fan control signal are continually updated by the remote computer 140 to control the compressor motor 260 in a manner that temperature controls the zone while minimizing or optimizing the electrical power consumed by the compressor motor 260. Moreover, in at least some embodiments, the expansion valve control signals are continually updated by the remote computer 140 to control the expansion valves 216, 246 in a manner that temperature controls the zone while minimizing or optimizing the electrical power consumed by the heat pump system 100.
Again, as noted, the typical thermostat and heat pump does not and cannot generate the compressor control signal, the blower control signal, and the fan control signal because the typical thermostat cannot observe at least the compressor motor from the standardized wires included in known HVAC systems.
In considering a mathematical example of this approach it is desired to optimize operation of the heat pump 104 or energy management use cases, such as reducing energy consumption and cost, providing grid services, and improving thermal comfort. The example is abstracted into Equations (1). $\begin{array}{l} \min_{u_{1 : T}} & \sum_{t} J (x_{t}, u_{t}) \\ s . t . & x_{t + 1} = f (x_{t}, u_{t}, d_{t}) \\ {\underline{x}}_{t} \leq x \leq {\bar{x}}_{t} \\ {\underline{u}}_{t} \leq u \leq {\bar{u}}_{t} \end{array}$
In Equations (1), x_t is the ambient temperature of the zone as determined by the temperature sensor 148 time t. The variable u_t is a control action, the function J(x_t, u_t ) is an objective function, and the function f(x_t, u_t, d_t ) characterizes how the thermodynamics evolve over time. The variables x and x denote a lower and an upper bound of the variable x. In the case of a household with multiple zones, the remote computer 140 takes a weighted average of the zone temperatures, where the weights are determined by the area and the importance of each zone. The control action is selected based on the specific system and the desired objective(s). For instance, considering the heat pump system 100 that includes the motors 228, 260, 262 having variable frequency drive, the control action is defined as a percentage of rated power or power consumption. The variable u refers to power consumption. The limits x and x are imposed by the thermal comfort rules (e.g., 73° F ≤ x_t ≤ 75° F) as the desired setpoint temperature. The limits may also be imposed as a rule concerning the physical limits of the equipment and/or the electrical provider rules (e.g., power consumption should not exceed 70% of rated power during a general curtailment demand response event, u_t ≤ 70% × P̃, where P̃ is a maximum power consumption of the heat pump system 100).
J(x_t, u_t ) is the objective function. The remote computer 140 is configured to consider multiple, potentially competing objective functions, e.g., comfort and energy consumption. In an example, the comfort objective is a deviation from the setpoint temperature, i.e. (x_t - x_sp,t )² . Then, reducing total energy consumption while maintaining comfort is characterized as J(x_t, u_t ) = (x_t - x_sp,t )² + λu_t , where λ is a hyper-parameter that characterizes how to balance the two objectives. Alternatively, if the goal is to reduce the energy consumption of the heat pump system 100, the objective is written as J(x_t, u_t ) = (x_t - x_sp,t )² + λc_tu_t, where c_t is the time-of-use tariff at time t. By utilizing the appropriate object function, the method 300 accommodates a wide variety and combinations of energy management objectives.
To facilitate the later discussion on extension to any number of heterogeneous devices, a more concise notation introduced. Vectors are denoted with boldface, lower-case letters, e.g., u = u _1:T, and the constraints in Equations 1 as a set = {u_t:T |x _t+1 = f(x_t, u_t, d_t ); x _t ≤ x ≤ x _t ; u _t ≤ u ≤ u _t }. $I_{C}$ represents an indicator function w.r.t. the constraint set , such that $I_{C} (x) = 0$ if x ∈ , else $I_{C} (x) = \infty$ . Intuitively, the indicator function poses an infinitely large penalty on the objection function for violation of the constraints. Thus, the optimization problem posed in Equations 1 is equivalent to min _u f(u), where $f (u) = I_{C} (u) + \sum_{t} J (x_{t}, u_{t})$ . The subscript i, refers to the i^th device. Based on the above, energy optimization for the i^th device is captured by: $\min_{u_{i}} f_{i} (u_{i})$
With the above concepts, the predictive thermodynamic model 158 is learned according to the following approach. To optimize operation of the heat pump system 100, the thermodynamic model 158 represents how the thermodynamics of the zone evolve given a certain action, as characterized by f(x_t, u_t, d_t ). While there are other methods to model thermodynamics, in this example the selected consideration is scalability and the resultant solution is applicable the heat pump system 100 with little to no human inputs. The method 300 is a data-driven approach, where the model orders and model parameters of the thermodynamic model 158 are determined from data. Another consideration in this example is the computation cost. The thermodynamic model 158 form is such that both learning and optimization are computationally inexpensive. Another consideration is interpretability. The thermodynamic model 158 is interpretable, such that engineers can examine the thermodynamic model 158 if needed. Given these considerations, an autoregressive thermodynamic model 158 is built according to Equation 3 below. ${\hat{x}}_{t + 1} = f (x_{t}, u_{t}, d_{t}) = \sum_{i = 0}^{p - 1} a_{t} T_{t - i} + \sum_{i = t}^{q - 1} b_{u, i} u_{t - i} + b_{d} d_{t}$
To be amenable to automation, model orders p and q with Akaike Information Criterion (AIC) are selected. Performance of the thermodynamic model 158 is validated with multistep prediction error. To find the thermodynamic model 158 parameters θ = {a, b_u, b_t }, the prediction error = (x_i - x̂_i )² is minimized.
At block 320 of the method 300, the heat pump 104 is operated using the control signals generated by remote computer 140 to condition the air in the zone based on the temperature setpoint. The remote computer 140 transmits the control signals (for example, the compressor control signal, the blower control signal, and the fan control signal) to the controller 238. The heat pump system 100 improves operation of the heat pump 104 by maintaining the zone at the setpoint temperature or within the setpoint temperature range, while minimizing and/or optimizing the electricity consumed by the heat pump system 100 and adhering to thermal comfort rules from the user and/or electrical provider rules from the electricity provider. The control signals control the power consumption of the compressor motor 260 and the blower motor 228 to within at least 1% of an optimal power level (from 0% to 100% of a maximum power consumption).
In another embodiment according to the method 300, the heat pump system 100 is configured to operate with the demand-side device 136 and the supply-side device 132 for optimizing overall energy consumption. According to this approach, at block 304 the remote computer 140 receives a power demand signal corresponding to electrical power consumed by the demand-side device 136 through the Internet 144. The demand-side device 136 is electrically connected to the electrical network to which the heat pump system 100 is electrically connected, as may be provided by the mains power connection 128. The remote computer 140 also receives a power supply signal corresponding to an availability of electrical power from the supply-side device 132 electrically connected to the same electrical network through the Internet 144. Thus, at block 304, the thermostat 108 is provided data of the power usage and power availability of other devices and appliances on the same electrical network.
At block 308, the thermostat 108 builds the thermodynamic model 158 of the zone based on the inputs described above as well as the power demand signal and the power supply signal. According to this approach, the thermodynamic model 158 is further configured to model the temperature of zone based on the operation of the demand-side device 136 and the power available from the supply-side device 132.
Next, at blocks 312 and 316 the remote computer 140 receives the thermal comfort rules and the electrical provider rules. At block 316, the remote computer 140 generates the control signals including the compressor control signal. Based on the additional data provided in building the thermodynamic model 158, the compressor control signal may be configured to optimize (block 312) the electrical power consumed by the demand-side device 136. For example, the remote computer 140 operates as a power controller for the heat pump 104 and the demand-side device 136 that enables operation of the demand-side device 136 during periods of less expensive electrical energy from the energy provider and that operates the heat pump 104 to build up a temperature buffer during the period of less expensive energy. This minimizes the operation of both the demand-side device 136 and the heat pump 104 during more expensive energy periods.
Moreover, based on the additional data provided in building the thermodynamic model 158, the compressor control signal is further configured to optimize electrical power drawn from the supply-side device 132 for operating the demand-side device 136 and the heat pump system 100. According to this approach, in an example, the remote computer 140 operates as a power controller that configures the demand-side device 136 and the heat pump 104 to consume electrical energy from the supply-side device 132 during periods of more expensive electrical energy from the energy provider. The remote computer 140 further configures the demand-side device 136 and the heat pump 104 to consume electrical energy from the energy provider through the mains power connection 128 during less expensive periods. Moreover, based at least on the electrical provider rules, the remote computer 140 optimizes the supply-side device 132 to provide electrical energy to the electrical grid through the mains power connection 128 or another suitable electrical connection. This approach configures the heat pump 104 and the demand-side device 136 to operate using the most cost effective energy source.
The heat pump system 100 is grid-interactive because, operation of the heat pump system 100 may result in drawing electricity from the electrical grid through the mains power connection 128 or supplying electricity to the electrical grid through the mains power connection 128. The interaction with the electricity grid is at least based on the electrical provider rules, so that the interaction is optimized for cost.
The above approach is extendible to coordination of any number of heterogeneous devices (i.e., the demand-side devices 136, the supply-side devices 132, and any number of the heat pumps 104). Demand flexibility refers to the flexibility of the demand-side devices 136 to reduce, shift, or modulate their loads in response to price or control signals.
The heat pump system 100 is described as being device-agnostic and provides a device-agnostic coordination layer because the remote computer 140 is operable with any type of electrical energy producing device and any type of electrical energy consuming device. The remote computer 140 using the thermodynamic model 158, the thermal comfort rules, and the electrical provider rules provides an operating approach for any device 132, 136 that consumes or provides electrical energy, and is therefore agnostic to the specific function or operation of the device 132, 136.
In considering a mathematical example of this approach, the operating approach should optimize a grid-level objective g(·) as a function of aggregate energy consumption, while balancing the objectives at individual devices 104, 132, 136 (Equation 2), thereby arriving at: $\min_{u_{i}} \sum_{i} f_{i} (u_{i}) + g (\sum_{i} u_{i})$
To handle a large number of heterogeneous devices 104, 132, 136, the approach adopts a distributed control framework and decomposes the problem, such that each device 104, 132, 136 is responsible for its own control and coordination with devices 104, 132, 136 to find a grid-level solution. Specifically, the approach uses the alternating direction method of multipliers (ADMM), a well-established distributed convex optimization algorithm. By introducing a copy of the decision variable u_i as v_i, the problem is rewritten in a decomposable and ADMM-compatible form. $\begin{array}{l} \min_{u_{i}} & \sum_{i} f_{i} (u_{i}) + g (\sum_{i} v_{i}) \\ s . t . & u_{i} - v_{i} = 0, i = 1, \dots, N \end{array}$
This problem formulation is a canonical problem called the sharing problem, and may be solved by the following algorithm: $\begin{array}{l} u_{i}^{(k + 1)} = & \underset{u_{i}}{argmin} f_{i} (u_{i}) + \frac{ρ}{2} {‖u_{i} - u_{i}^{(k)} + {\bar{u}}^{(k)} - {\bar{v}}^{(k)} + {\bar{w}}^{(k)}‖}_{2}^{2} \\ {\bar{v}}^{(k + 1)} = & \underset{\bar{v}}{argmin} g (N \bar{v}) + \frac{Nρ}{2} {‖{\bar{u}}^{(k + 1)} - \bar{v} + {\bar{w}}^{(k)}‖}_{2}^{2} \\ {\bar{w}}^{(k + 1)} = & {\bar{w}}^{(k)} + {\bar{u}}^{(k + 1)} - {\bar{v}}^{(k + 1)} \end{array}$
For intuition, the sharing problem is interpreted as the agents coordinating their decisions so as to strike a balance between the local and the global objectives. Hence, u_i and v_i are each agent's solutions to its local problem and the global objective, respectively. The dual variable w_i , as calculated w-update step, is the cumulative disagreement between u_i and v_i . Thus, w_i , which we also call the incentive variable, communicates how to adjust each agent's solutions such that they would agree, i.e., u_i = v_i . Thus, in the u-update step, each agent solves its local problem, while mindful of its solution to the global problem. Similarly, in the v-update step, the agents jointly optimize the global objective, while ensuring their decisions are close to those of their local problems.
Figure 4 summarizes the coordination procedure between the aggregator and individual devices 104, 132, 136. In the above example, the remote computer 140 is the load aggregator when operating as the power controller. Firstly, each TCL updates its action, u_i, locally. Then, the aggregator collects the actions from all agents to find the mean, u, and sequentially updates v, and w. Finally, the load aggregator broadcasts u, v, and w to all devices 104, 132, 136 such that they can update u_i locally. This procedure repeats until convergence.
The above approach uses ADMM, but it is noted that alternative distributed optimization algorithms exist in the literature, such as consensus-based approaches that are especially relevant for distributed energy management.
Below the advantages of the heat pump system 100 are re-iterated. Firstly, the grid-level problem is naturally decomposed into sub-problems. Thus, each device 104, 132, 136 can ensure its local objective and constraints are satisfied, without sharing them with the load aggregator, thereby preserving privacy. Secondly, the u-update step at each TCL is computed in parallel. Thus, the approach is highly scalable to large population. In fact, the computation at the aggregator is constant w.r.t. to the number of devices 104, 132, 136. Finally, ADMM is guaranteed to converge to the population-level optimum given a convex problem. A sufficient condition for convexity is convex f_i (·) and g(·), and being a convex set. A variety of grid objectives can be formulated as convex problems, and is convex for the heat pump 104 due to that thermodynamic model 158 being affine (Equation 3).
Given the dire consequences of climate change, there is growing incentive to curb carbon emissions by reducing fossil fuel consumption, improving energy efficiency, and increasing renewable energy penetration, among other approaches. Heat pumps already make great strides towards climate change mitigation by replacing fossil fuel-powered combustive heating. Heating accounts for more than 40% energy consumption in US homes and 20% fossil fuel usage. Heat pumps can eliminate using fossil fuels for heating when paired with renewable energy.
The climate benefits of heat pumps can be further accentuated through improved energy efficiency. It is estimated the heat pump system 100 reduces the energy consumption of the heat pump 104 by up to 30% as compared to the scheduled-based operation.
Moreover, heat pumps facilitate renewable energy integration. Variable renewable energy resources (such as solar and wind) introduce unintended challenges for grid operators. The intermittent and variable nature of renewable generation makes it difficult to balance supply and demand of energy in the power grid. Heat pumps, along with other demand-side resources, can coordinate with each other to provide demand flexibility. To address the challenge of matching the supply and demand of energy, a promising solution is to tap into the inherent flexibility of demand-side resources to reduce, shift, or modulate their loads in response to price or control signals. Such demand flexibility can be utilized to provide grid services, improve grid resiliency, and reduce operating costs.
While the disclosure has been illustrated and described in detail in the drawings and foregoing description, the same should be considered as illustrative and not restrictive in character. It is understood that only the preferred embodiments have been presented and that all changes, modifications and further applications that come within the spirit of the disclosure are desired to be protected.

Claims

A split-inverter heat pump system, comprising:
a continuously variable speed compressor motor operably connected to a refrigerant compressor of a condenser unit of the heat pump system;

a compressor motor controller configured to generate a compressor power signal corresponding to electrical power consumed by the compressor motor in operating the refrigerant compressor, the compressor motor controller configured to control a rotational speed of the compressor motor according to an electrical power level within a range of electrical power levels based on a compressor control signal;

a network device operably connected to the compressor motor controller and configured to receive the compressor power signal from the compressor motor controller, the network device configured to transmit the compressor power signal to a remote computer over the Internet;

a thermostat configured to receive (i) an ambient temperature signal from a temperature sensor located in a zone that is temperature controlled by the heat pump system, and (ii) a temperature setpoint, the thermostat configured to transmit the ambient temperature signal and the temperature setpoint to the remote computer over the Internet,

wherein the remote computer is configured to generate the compressor control signal based on the ambient temperature signal, the temperature setpoint, and the compressor power signal, and

wherein the network device is configured to receive the generated compressor control signal from the remote computer and to supply the compressor control signal to the compressor motor controller, and

wherein the compressor motor controller operates the compressor motor based on the supplied compressor control signal to control the temperature of the zone at the temperature setpoint.
The split-inverter heat pump system of claim 1, wherein the remote computer is configured to build a thermodynamic model of the zone based on the ambient temperature signal and the compressor power signal, the thermodynamic model configured to output a temperature of the zone over time.
The split-inverter heat pump system of claim 2, wherein:
the remote computer is configured to receive thermal comfort rules specifying a predetermined setpoint temperature range of the zone; and

the remote computer is configured to generate the compressor control signal using the thermodynamic model and the thermal comfort rules, the compressor control signal configured operate the compressor motor in a manner that causes the heat pump system to maintain the temperature of the zone within the predetermined setpoint temperature range,

wherein the temperature setpoint is within the predetermined setpoint temperature range.
The split-inverter heat pump system of claim 1, further comprising:
a continuously variable speed blower motor operably connected to a fan of an evaporator of the heat pump system; and

a blower motor controller configured to generate a blower power signal corresponding to electrical power consumed by the blower motor in operating the fan, the blower motor controller configured to control a rotational speed of the blower motor according to an electrical power level within a range of electrical power levels based on a blower control signal,

wherein the network device is operably connected to the blower motor controller and is configured to receive the blower power signal from the blower motor controller, the network device configured to transmit the blower power signal to the remote computer over the Internet;

wherein the remote computer is configured to generate the blower control signal based on the ambient temperature signal, the temperature setpoint, the compressor power signal, and the blower power signal,

wherein the network device is configured to receive the generated blower control signal from the remote computer and to supply the blower control signal to the blower motor controller, and

wherein the blower motor controller operates the blower motor based on the supplied blower control signal to control the temperature of the zone at the temperature setpoint.
The split-inverter heat pump system of claim 4, further comprising:
an airflow sensor configured to generate an airflow signal corresponding to airflow generated by the fan,

wherein the network device is configured to receive the airflow signal,

wherein the network device is configured to transmit the airflow signal to the remote computer over the Internet, and

wherein the remote computer is configured to generate the blower control signal based on the ambient temperature signal, the temperature setpoint, the compressor power signal, the blower power signal, and the airflow signal.
The split-inverter heat pump system of claim 1, further comprising:
an evaporator temperature sensor configured to generate an evaporator temperature signal corresponding to a temperature of an evaporator of the heat pump system; and

a condenser temperature sensor configured to generate a condenser temperature signal corresponding to a temperature of the condenser of the heat pump system,

wherein the network device is configured to receive the evaporator temperature signal and the condenser temperature signal,

wherein the network device is configured to transmit the evaporator temperature signal and the condenser temperature signal to the remote computer over the Internet, and

wherein the remote computer is configured to generate the compressor control signal based on the ambient temperature signal, the temperature setpoint, the compressor power signal, the evaporator temperature signal, and the condenser temperature signal.
The split-inverter heat pump system of claim 6, further comprising:
a first thermal expansion valve operably connected to the evaporator and configured to control a flow of refrigerant based on a first predetermined valve setting within a first range of valve settings, the first thermal expansion valve configured to generate a first expansion valve signal corresponding to the first predetermined valve setting; and

a second thermal expansion valve operably connected to the condenser and configured to control the flow of refrigerant based on a second predetermined valve setting within a second range of valve settings, the second thermal expansion valve configured to generate a second expansion valve signal corresponding to the second predetermined valve setting,

wherein the network device is configured to receive the first expansion valve signal and the second expansion valve signal,

wherein the network device is configured to transmit the first expansion valve signal and the second expansion valve signal to the remote computer over the Internet, and

wherein the remote computer is configured to generate the compressor control signal based on the ambient temperature signal, the temperature setpoint, the compressor power signal, the evaporator temperature signal, the condenser temperature signal, the first expansion valve signal, and the second expansion valve signal.
The split-inverter heat pump system of claim 7, further comprising:
a condenser pressure sensor configured to generate a condenser pressure signal corresponding a pressure of the refrigerant in the condenser; and

an evaporator pressure sensor configured to generate an evaporator pressure signal corresponding to a pressure of the refrigerant in the evaporator,

wherein the network device is configured to receive the condenser pressure signal and the evaporator pressure signal,

wherein the network device is configured to transmit the condenser pressure signal and the evaporator pressure signal to the remote computer over the Internet, and

wherein the remote computer is configured to generate the compressor control signal based on the ambient temperature signal, the temperature setpoint, the compressor power signal, the evaporator temperature signal, the condenser temperature signal, the first expansion valve signal, the second expansion valve signal, the condenser pressure signal, and the evaporator pressure signal.
The split-inverter heat pump system of claim 1, wherein:
the network device is operably connected to the remote computer through a cellular data connection,

the network device is located in an outdoor environment and is mounted on the condenser unit, and

the thermostat is operably connected to the remote computer through a Wi-Fi data connection provided by another network device of the thermostat.
A computer-implemented method of controlling power consumption and generation, comprising:
receiving, with a thermostat, an ambient temperature signal corresponding to an ambient temperature of a zone that is temperature controlled by a split inverter heat pump system;

receiving, with a network device, a compressor power signal corresponding to electrical power consumed by a continuously variable speed compressor motor, the compressor motor operably connected to a refrigerant compressor of a condenser unit of the heat pump system;

building, using a remote computer operably connected to the thermostat and the network device through the Internet, a thermodynamic model of the zone based on at least the ambient temperature signal and the compressor power signal, the thermodynamic model configured to output a temperature of the zone over time;

receiving, with the remote computer, thermal comfort rules specifying a predetermined setpoint temperature range of the zone;

generating, using the remote computer, a compressor control signal using the thermodynamic model and the thermal comfort rules, the compressor control signal specifying an electrical power level at which the compressor motor is operated, and the compressor control signal configured to operate the compressor motor in a manner that causes the heat pump system to maintain a temperature of the zone within the predetermined setpoint temperature range and to optimize the electrical power consumed by the compressor motor to operate the compressor; and

supplying the compressor control signal to the compressor motor and operating the heat pump system to control the temperature of the zone within the predetermined setpoint temperature range.
The computer-implemented method of claim 10, wherein the remote computer generates parameters of the thermodynamic model by minimizing a prediction error.
The computer-implemented method of claim 10, further comprising:
receiving, with the remote computer, electrical provider rules that specify a cost of electrical power from an electricity provider at each time of the day; and

generating, using the remote computer, the compressor control signal using the thermodynamic model, the thermal comfort rules, and the electrical provider rules,

wherein the compressor control signal is further configured to optimize a cost of operating the heat pump system using the output of the thermodynamic model and the electrical provider rules.
The computer-implemented method of claim 10, wherein:
the remote computer builds the thermodynamic model based on outdoor air temperature data collected over a predetermined time period,

the predetermined time period is one year, and

the thermodynamic model accounts for seasonal differences in the outdoor air temperature data in modeling the temperature of the zone.
The computer-implemented method of claim 10, further comprising:
receiving, with the network device, a blower power signal corresponding to electrical power consumed by a continuously variable speed blower motor operably connected to a fan of an evaporator of the heat pump system;

building, using the remote computer, the thermodynamic model of the zone based on the ambient temperature, the compressor power signal, and the blower power signal;

generating, using the remote computer, a blower control signal using the thermodynamic model and the thermal comfort rules, the blower control signal specifying an electrical power level at which the blower motor is operated, and the blower control signal configured to operate the blower motor in a manner that causes the heat pump system to maintain the temperature of the zone within the predetermined setpoint temperature range and to optimize the electrical power consumed by the blower motor; and

supplying the blower control signal to the blower motor and operating the heat pump system to control the temperature of the zone within the predetermined setpoint temperature range.
The computer-implemented method of claim 14, further comprising:
receiving, with the remote computer, electrical provider rules that specify a cost of electrical power from an electricity provider at each time of the day; and

generating, using the remote computer, the blower control signal using the thermodynamic model, the thermal comfort rules, and the electrical provider rules,

wherein the blower control signal is further configured to optimize a cost of operating the heat pump system using the output of the thermodynamic model and the electrical provider rules.
The computer-implemented method of claim 10, further comprising:
receiving, with the remote computer, a power demand signal corresponding to electrical power consumed by a demand-side device electrically connected to an electrical network to which the heat pump system is electrically connected; and

building, using the remote computer, the thermodynamic model of the zone based on the ambient temperature, the compressor power signal, and the power demand signal,

wherein the compressor control signal is further configured to optimize a timing, a cost, and/or a quantity of electrical power consumed by the demand-side device using at least the output of the thermodynamic model.
The computer-implemented method of claim 16, further comprising:
receiving, with the remote computer, a power supply signal corresponding to an availability of electrical power from a supply-side device electrically connected to the electrical network; and

building, using the remote computer, the thermodynamic model of the zone based on the ambient temperature, the compressor power signal, the power demand signal, and the power supply signal,

wherein the compressor control signal is further configured to optimize electrical power drawn from the supply-side device for operating the heat pump system using at least the output of the thermodynamic model.
The computer-implemented method of claim 17, wherein:
the demand-side device includes at least one of a washing machine, a clothes dryer, a dishwasher, and an electric vehicle, and

the supply-side device includes at least one of a battery-based electricity storage system and a photovoltaic system.