SYSTEMS AND METHODS FOR TIME USE OPTIMIZATION

• US 20180150045A1
• Filed: 11/21/2017
• Published: 05/31/2018
• Est. Priority Date: 11/29/2016
• Status: Active Grant

• Current Assignee: Virtual Peaker, Inc.
• Original Assignee: Virtual Peaker, Inc.
• Inventors: Burke, William J.
• Granted Patent: US 10,642,239 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 13/021 in which a variable is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 19/0426 Programming the control sequence
• CPC Class Codes: G05B 19/048 Monitoring; Safety
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Systems and methods for time use optimization are provided. One embodiment of a method includes determining time of use pricing data associated with purchase of energy from an energy provider, partitioning a predetermined amount of time into a plurality of segments, where the plurality of segments corresponds with the higher cost tier and the lower cost tier, and creating an energy set point schedule for setting a set point of a controllable device, where the energy set point schedule sets the set point of the controllable device to a predetermined value for each of the plurality of segments. Some embodiments include determining energy utilized by the controllable device during at least a portion of the energy set point schedule and iteratively altering the energy set point schedule, based on a comparison of the energy utilized and a current status of the energy set point schedule.

Claims

1. A method for time of use optimization comprising:

determining time of use pricing data associated with purchase of energy from an energy provider, wherein the time of use pricing data includes a higher cost tier associated with a higher cost for the energy during a first predetermined time period and a lower cost tier associated with a lower cost for the energy during a second predetermined time period;

partitioning a predetermined amount of time into a plurality of segments, wherein the plurality of segments corresponds with the higher cost tier and the lower cost tier;

creating an energy set point schedule for setting a set point of a controllable device, wherein the energy set point schedule sets the set point of the controllable device to a predetermined value for each of the plurality of segments;

determining energy utilized by the controllable device during at least a portion of the energy set point schedule; and

iteratively altering the energy set point schedule, based on a comparison of the energy utilized and a current status of the energy set point schedule.

2. The method of claim 1, wherein the controllable device includes at least one of the following:

a water heater, a home ventilation and air condition (HVAC) system, or a battery.

3. The method of claim 1, wherein the energy set point schedule is altered based on the following:

T_i(k+1)=T_i(k)−

ρ

γ

E_i(k);

T_i-1(k+1)=T_i-1(k)+ε

γ

E_i(k) when P_i-1<

P_i

T_i(k+1)=T_i(k);

T_i-1(k+1)=T_i-1(k) when P_i-1>

=P_i

wherein k is a current iteration, T_i is a scheduled state variable at a time segment i, E_i is a change in energy over a time segment i, ρ

>

0 and ε

>

0 are learning constants, and γ

is a conversion from energy to state variable.

4. The method of claim 1, further comprising altering the energy set point schedule to maintain a desired energy output while minimizing cost incurred for the energy.

5. The method of claim 1, wherein the energy set point schedule includes segments for each day of a week.

6. The method of claim 1, further comprising receiving a goal for the energy set point schedule, wherein the goal is utilized to determine a characteristic of the energy set point schedule.

7. A system for time of use optimization comprising:

a controllable device that includes an energy storage component for storing energy and an energy distribution component for distributing the energy to an environment, wherein the energy is purchased from an energy provider; and

a computing device that includes a processor and a memory component, wherein the memory component stores logic that, when executed by the processor, causes the system to perform at least the following;

receive time of use pricing data associated with purchase of the energy from the energy provider, wherein the time of use pricing data includes a higher cost tier associated with a higher cost for the energy during a first predetermined time period and a lower cost tier associated with a lower cost for the energy during a second predetermined time period;

determine a desired energy output for the controllable device;

create an energy set point schedule that controls a setting of the controllable device, such that the energy set point schedule causes the computing device to adjust the setting of the controllable device at predetermined segments to maintain a desired energy set point for the controllable device, while factoring the time of use pricing data;

collect data regarding an actual demand for the energy, wherein the data regarding the actual demand includes an amount of energy actually used, a time of actual use, and a cost of the energy actually used, based on the time of use pricing data; and

iteratively alter the energy set point schedule based on the data regarding the actual demand for the energy.

8. The system of claim 7, wherein the controllable device includes at least one of the following:

a water heater, a home ventilation and air condition (HVAC) system, or a battery.

9. The system of claim 7, wherein the energy set point schedule is altered based on the following:

T_i(k+1)=T_i(k)−

ρ

γ

E_i(k);

T_i-1(k+1)=T_i-1(k)+ε

γ

E_i(k) when P_i-1<

P_i

T_i(k+1)=T_i(k);

T_i-1(k+1)=T_i-1(k) when P_i-1>

=P_i

wherein k is a current iteration, T_i is a scheduled state variable at a time segment i, E_i is a change in energy over a time segment i, ρ

>

0 and ε

>

0 are learning constants, and γ

is a conversion from energy to state variable.

10. The system of claim 7, wherein the logic further causes the system to alter the energy set point schedule to maintain the desired energy output while minimizing cost incurred for the energy.

11. The system of claim 7, wherein in response to a determination that energy used during the first predetermined time period is greater than about zero, the logic causes the system to alter the energy set point schedule at the first predetermined time period to a lower value.

12. The system of claim 7, wherein in response to a determination that energy used during the first predetermined time period is greater than about zero, the logic causes the system to adjust an energy set point of a segment that precedes the higher cost tier to a higher value.

13. The system of claim 7, wherein in response to a determination that energy used during the first predetermined time period is less than about zero, adjust an energy set point of a segment that precedes the higher cost tier to a lower value.

14. A non-transitory computer-readable medium that stores logic that, when executed by a computing device causes the computing device to perform at least the following:

determine time of use pricing data associated with purchase of energy from an energy provider, wherein the time of use pricing data includes a higher cost tier associated with a higher cost for the energy during a first predetermined time period and a lower cost tier associated with a lower cost for the energy during a second predetermined time period;

partition a predetermined amount of time into a plurality of segments, wherein the plurality of segments corresponds with the higher cost tier and the lower cost tier;

create an energy set point schedule for setting a set point of a controllable device, wherein the energy set point schedule sets the set point of the controllable device to a predetermined value for each of the plurality of segments;

after conclusion of the first predetermined time period, compute energy utilized by the controllable device during a segment associated with the higher cost tier; and

in response to a determination that energy used during the first predetermined time period is greater than about zero, adjust an energy set point of a segment that precedes the higher cost tier to a higher value.

15. The non-transitory computer-readable medium of claim 14, wherein the controllable device includes at least one of the following:

a water heater, a home ventilation and air condition (HVAC) system, or a battery.

16. The non-transitory computer-readable medium of claim 14, wherein the energy set point schedule is created based on the following:

T_i(k+1)=T_i(k)−

ρ

γ

E_i(k);

T_i-1(k+1)=T_i-1(k)+ε

γ

E_i(k) when P_i-1<

P_i

T_i(k+1)=T_i(k);

T_i-1(k+1)=T_i-1(k) when P_i-1>

=P_i

wherein k is a current iteration, T_i is a scheduled state variable at a time segment i, E_i is a change in energy over a time segment i, ρ

>

0 and ε

>

0 are learning constants, and γ

is a conversion from energy to state variable.

17. The non-transitory computer-readable medium of claim 14, wherein the logic causes the computing device to alter the energy set point schedule to maintain a desired energy output while minimizing cost incurred for the energy.

18. The non-transitory computer-readable medium of claim 14, wherein the energy set point schedule includes segments for each day of a week.

19. The non-transitory computer-readable medium of claim 14, wherein the logic further causes the computing device to group the plurality of segments according to the higher cost tier and the lower cost tier.

20. The non-transitory computer-readable medium of claim 14, wherein the energy set point schedule is iteratively adjusted, based on an actual energy usage.