Demand response technology utilizing a simulation engine to perform thermostat-based demand response simulations

• US 10,241,528 B1
• Filed: 12/01/2016
• Issued: 03/26/2019
• Est. Priority Date: 12/01/2015
• Status: Active Grant

• Current Assignee: Energyhub Incorporated (Alarm.com Holdings, Inc.)
• Original Assignee: Energyhub Incorporated (Alarm.com Holdings, Inc.)
• Inventors: Frader-Thompson, Seth, Hertz-Shargel, Benjamin, DeBenedittis, Michael
• Primary Examiner(s): Suryawanshi, Suresh
• Application Number:
  US15/366,212
• Time in Patent Office: 845 Days
• Field of Search: 700276
• CPC Class Codes: F24F 11/80 for controlling the temperature of the supplied air
• CPC Class Codes: G05B 15/00 Systems controlled by a computer G05B13/00, G05B19/00 take precedence; automatic controllers with particular characteristics G05B11/00
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G05D 23/1923 using thermal energy, the cost of which varies in function of time
• CPC Class Codes: G06F 2119/08 Thermal analysis or thermal optimisation
• CPC Class Codes: G06F 30/13 Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2310/14 The load or loads being home appliances
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 7/00 Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/244 the home appliances being or involving heating ventilating and air conditioning [HVAC] units

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a thermostat-based demand response event. In one aspect, a method includes accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature and building a model for each of the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature. The method also includes using a simulation engine to achieve a target load shed and load reduction shape for a thermostat-based demand response event, and performing the thermostat-based demand response event based on results of the simulation engine.

Claims

1. A method of performing a thermostat-based demand response event, the method comprising:

accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature;

generating models for the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature;

controlling a simulation engine to perform thermostat-based demand response simulations using the models to identify a simulation that achieves a target load shed and load reduction shape for load to be consumed by the sites during a thermostat-based demand response event performed across the sites; and

performing the thermostat-based demand response event based on results of the simulation engine.

2. The method of claim 1, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to set a specific amount of load shed.

3. The method of claim 1, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to set a specific shape for the load shed.

4. The method of claim 1, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to provide a target amount of load reduction for a specific load control window.

5. The method of claim 1, wherein performing the thermostat-based demand response event comprises providing consistent occupant comfort and a specific level of load shed.

6. The method of claim 1, wherein performing the thermostat-based demand response event comprises providing a predictable and flat-shaped load reduction over a duration of the demand response event.

7. The method of claim 1, wherein performing the thermostat-based demand response event comprises performing the thermostat-based demand response event in response to a schedule set by a grid operator.

18. The method of claim 3, wherein the simulation engine sets the load reduction shape as the specific shape for the load shed further comprising defining the specific shape that indicates a constant difference between a baseline load curve and a firm load dispatch curve.

19. The method of claim 18, wherein the baseline load curve comprises a specific curve with a shape indicating an amount of load consumed by the sites over a predetermined period.

20. The method of claim 18, wherein the firm load dispatch curve comprises a simulated load curve that is modified through an optimization of a physical load curve, wherein the physical load curve represents a sum of loads from the sites and the firm load dispatch curve represents the modified version of the physical load curve through simulations of one or more optimized demand response events.

8. A system comprising:

one or more processors and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising;

accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature;

generating models for the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature;

controlling a simulation engine to perform thermostat-based demand response simulations using the models to identify a simulation that achieves a target load shed and load reduction shape for load to be consumed by the sites during a thermostat-based demand response event performed across the sites; and

performing the thermostat-based demand response event based on results of the simulation engine.

9. The system of claim 8, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to set a specific amount of load shed.

10. The system of claim 8, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to provide a target amount of load reduction for a specific load control window.

11. The system of claim 8, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to provide a target amount of load reduction for a specific load control window.

12. The system of claim 8, wherein performing the thermostat-based demand response event comprises providing consistent occupant comfort and a specific level of load shed.

13. The system of claim 8, wherein performing the thermostat-based demand response event comprises providing a predictable and flat-shaped load reduction over a duration of the demand response event.

14. The system of claim 8, wherein performing the thermostat-based demand response event comprises performing the thermostat-based demand response event in response to a schedule set by a grid operator.

15. A non-transitory computer-readable medium storing instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature;

generating models for the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature;

controlling a simulation engine to perform thermostat-based demand response simulations using the models to identify a simulation that achieves a target load shed and load reduction shape for load to be consumed by the sites during a thermostat-based demand response event performed across the sites; and

performing the thermostat-based demand response event based on results of the simulation engine.

16. The non-transitory computer-readable medium of claim 15, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to set a specific amount of load shed.

17. The non-transitory computer-readable medium of claim 15, wherein using the simulation engine to achieve the target load shed and the load reduction shape comprises using the simulation engine to set a specific shape for the load shed.