Power system load flexibility forecasting

Patent metadata

Field	Value
Publication number	US-11374409-B2
Application number	US-201715477696-A
Country	US
Kind code	B2
Filing date	Apr 3, 2017
Priority date	Apr 3, 2017
Publication date	Jun 28, 2022
Grant date	Jun 28, 2022

Abstract

The example embodiments are directed to a system and method for forecasting load flexibility of a power grid. In one example, the method includes receiving temperature values associated with temperature set points of a plurality of loads that are included on a power grid, forecasting a flexibility of the plurality of loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the plurality of loads, and outputting information about the forecasted flexibility for display to a display device. The MINLP optimization performs the forecasting of the load flexibility on a fine-grained basis in comparison to conventional methods and is still fast enough that it can be computed in real-time.

First claim

What is claimed is:

1. A computer-implemented method for forecasting load flexibility based on nominal power demand comprising:
receiving temperature values associated with temperature set points of a plurality of loads serviced by a power grid;
forecasting a flexibility of the loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the loads, wherein the polynomial-time MINLP optimization is independently performed for each load based on a hybrid automata model of the loads and a respective quality of service constraint of a thermostatic controller for a respective load, wherein the polynomial-time MINLP optimization is solved using a branch-and-bound algorithm having a plurality of nodes corresponding to a sequence of switches that model on and off switching behavior over time for the loads, wherein a cost function analysis is performed before and after each switch in the sequence; and
determining operational control updates for the loads based on the forecasted flexibility and transmitting the operational control updates to the loads, wherein an amount of power provided by the power grid for operating the loads is based on the operational control updates.

2. The computer-implemented method of claim 1 , wherein the forecasted flexibility for each load comprises a minimum and a maximum deviation from a nominal power demand of the load while maintaining the quality of service constraint for the load.

3. The computer-implemented method of claim 1 , wherein the loads comprise one or more thermostatically controlled loads (TCL).

4. The computer-implemented method of claim 3 , wherein the received temperature values for the one or more TCLs comprises at least one of a zone temperature measurement or a zone set point temperature for each TCL.

5. The computer-implemented method of claim 1 , wherein the forecasting further comprises determining an amount of available power at a future point in time for the power grid based on the forecasted flexibility of the loads.

6. The computer-implemented method of claim 1 , further comprising performing market bidding for electricity based on the forecasted flexibility of the loads.

7. The computer implemented method of claim 1 , wherein the forecasting of the flexibility of the loads using the MINLP optimization based on the received temperature values for the loads is further based on a supply air flow received from one or more of the loads.

8. The computer implemented method of claim 1 , wherein one or more of the loads includes a heating, ventilation and air-conditioning (HVAC) system.

9. A computer system for forecasting load flexibility based on nominal power demand comprising:
a network interface configured to receive temperature values associated with temperature set points of a plurality of loads serviced by a power grid;
a processor configured to (i) forecast a flexibility of the loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the loads, wherein the polynomial-time MINLP optimization is independently performed for each load based on a hybrid automata model of the loads and a respective quality of service constraint of a thermostatic controller for the respective load, wherein the polynomial-time MINLP optimization is solved using a branch-and-bound algorithm having a plurality of nodes corresponding to a sequence of switches that model on and off switching behavior over time for the loads, wherein a cost function analysis is performed before and after each switch in the sequence, and (ii)
determine operational control updates for the loads based on the forecasted flexibility and transmitting the operational control updates to the loads, wherein an amount of power provided by the power grid for operation of the loads is based on the operational control updates.

10. The computer system of claim 9 , wherein the flexibility forecasted by the processor for each load comprises a minimum and a maximum deviation from a nominal power demand of the load while maintaining the quality of service constraint for the load.

11. The computer system of claim 9 , wherein the loads comprise one or more of thermostatically controlled loads (TCL).

12. The computer system of claim 11 , wherein the received temperature values for the one or more TCLs comprises at least one of a zone temperature measurement or a zone set point temperature for each TCL.

13. The computer system of claim 9 , wherein the processor is further configured to determine an amount of available power at a future point in time for the power grid based on the forecasted flexibility of the loads.

14. The computer system of claim 9 , wherein the processor is further configured to perform market bidding for electricity based on the forecasted flexibility of the loads.

15. The computer system of claim 9 , wherein the processor configured to forecast the flexibility of the loads using the polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the loads is further configured to forecast the flexibility of the loads using MINLP based on a supply air flow received from one or more of the loads.

16. A non-transitory computer readable medium having stored therein instructions that when executed cause a computer to perform a method for forecasting load flexibility based on nominal power demand, the method comprising:
receiving temperature values associated with temperature set points of a plurality of loads serviced by a power grid;
forecasting a flexibility of the loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the loads, wherein the polynomial-time MINLP optimization is independently performed for each load based on a hybrid automata model of the loads and a respective quality of service constraint of a thermostatic controller for a respective load, wherein the polynomial-time MINLP optimization is solved using a branch-and-bound algorithm having a plurality of nodes corresponding to a sequence of switches that model on and off switching behavior over time for the loads, wherein a cost function analysis is performed before and after each switch in the sequence; and
determining operational control updates for the loads based on the forecasted flexibility and transmitting the operational control updates to the loads, wherein an amount of power provided by the power grid for operating the loads is based on the operational control updates.

17. The non-transitory computer readable medium of claim 16 , wherein the forecasted flexibility for each load comprises a minimum and a maximum deviation from a nominal power demand of the load while maintaining the quality of service constraint for the load.

18. The non-transitory computer readable medium of claim 16 , wherein the instructions that when executed cause the computer to perform the method for forecasting the flexibility of the loads using the polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the loads is further configured to forecast the flexibility of the loads using MINLP based on a supply air flow received from one or more of the loads.

19. The non-transitory computer readable medium of claim 16 , wherein the instructions that when executed cause the computer to perform the method for forecasting further comprises determining an amount of ava

Assignees

• Savant Tech Llc

Inventors

• Genc Sahika
• Aravind Deepak
• Pan Yan
• Acharya Naresh
• Baone Chaitanya Ashok

Classifications

• H02J2105/12

  supplying households or buildings · CPC title

• H02J3/14 Primary

  by switching loads on to, or off from, the networks, e.g. progressively balanced loading · CPC title

• H02J3/003 Primary

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• Y02B70/3225

  Demand response systems, e.g. load shedding, peak shaving · CPC title

• H02J3/381

  Dispersed generators · CPC title