Systems and methods for optimizing building-to-grid integration

Patent metadata

Field	Value
Publication number	US-11177656-B2
Application number	US-201816003570-A
Country	US
Kind code	B2
Filing date	Jun 8, 2018
Priority date	Jun 8, 2017
Publication date	Nov 16, 2021
Grant date	Nov 16, 2021

Abstract

A computing device can generate predictions for future consumptions for one or more buildings based on a variety of factors. The factors can include a local climate corresponding to each building, a mass and heat transfer for each building, a daily operation for each building, and an occupancy behavior for each building. A power flow can be determined for one or more power generators. The power flow can be determined based on the predictions of future consumption. A control input vector can be determined for the one or more buildings.

First claim

Therefore, at least the following is claimed:

1. A system comprising:
at least one data store; and
at least one computing device in communication with the at least one data store, the at least one computing device comprising at least one processor circuit being configured to at least:
generate a plurality of predictions of future building energy consumption individually corresponding to at least one building of a plurality of buildings, the plurality of predictions of future building energy consumption based at least in part on at least one of a respective local climate about the at least one building, a respective mass and heat transfer property of the at least one building, and a respective occupancy behavior of the at least one building;
determine a power flow for at least one power generator in a power network supplying the plurality of buildings, the power flow in the power network determined based at least in part on the plurality of predictions of future building energy consumption;
determine at least one building control input vector for the plurality of buildings based at least in part on the power flow, where building systems of the at least one building are operated or controlled based at least in part upon the at least one building control input vector; and
maintain a preferred zone temperature within a first building of the plurality of buildings without exceeding respective zone temperature bounds, where dynamics of the preferred zone temperature is represented by

T
.

zone

⁡

(
t
)

=

T
wall

⁡

(
t
)

-

T
zone

⁡

(
t
)

C
zone

⁢

R
1

+

T
amb

⁡

(
t
)

-

T
zone

⁡

(
t
)

C
zone

⁢

R
win

+

Q
.

int

⁡

(
t
)

+

Q
.

HVAC

⁡

(
t
)

C
zone

.

where T zone (t) and T wall (t) are respectively zone and wall temperatures of the first building, T amb (t) is an ambient temperature outside the first building, {dot over (Q)} int (t) is a total internal heat gain from heat sources in the first building, {dot over (Q)} HVAC (t) is a cooling load of the building systems of the first building, C zone is a thermal capacity of the first building, and R win and R 1 are thermal resistance parameters of façade elements and walls of the first building.

2. The system of claim 1 , wherein the plurality of predictions of future building energy consumption are based at least in part on a control input vector corresponding to the plurality of buildings.

3. The system of claim 1 , wherein the at least one computing device is further configured to at least minimize a cost function associated with a cost of operation of the at least one power generator, subject to constraints associated with the power network and the plurality of buildings, and a cost function associated with the cost of the operation of the at least one building, subject to the constraints.

4. The system of claim 1 , wherein the at least one computing device is further configured to at least generate a plurality of local control signals for the plurality of buildings and the at least one power generator.

5. The system of claim 1 , wherein the at least one power generator comprises a plurality of power generators coupled to a plurality of buses in the power network.

6. The system of claim 1 , wherein the plurality of predictions correspond to a prediction horizon.

7. The system of claim 1 , wherein the power flow is determined based at least in part on an inertia coefficient and a damping coefficient corresponding to the at least one power generator.

8. A method comprising:
generating, by at least one computing device, a plurality of predictions of future building energy consumption individually corresponding to at least one building of a plurality of buildings, the plurality of predictions of future building energy consumption based at least in part on at least one of a respective local climate about the at least one building, a respective mass and heat transfer property of the at least one building, and a respective occupancy behavior of the at least one building;
determining, by the at least one computing device, an optimal power flow for at least one power generator in a power network supplying the plurality of buildings, the optimal power flow determined based at least in part on the plurality of predictions of future building energy consumption;
determining, by the at least one computing device, at least one building control input vector for the plurality of buildings based at least in part on the opt

Assignees

• Dong Bing
• Taha Ahmad
• Gatsis Nikolaos
• Li Zhaoxuan
• Pipri Ankur
• Univ Texas

Inventors

• Dong Bing
• Taha Ahmad
• Gatsis Nikolaos
• Li Zhaoxuan
• Pipri Ankur

Classifications

• H02J2105/55

  according to an economic condition, e.g. tariff-based load management · CPC title

• H02J2105/12

  supplying households or buildings · CPC title

• H02J3/381 Primary

  Dispersed generators · CPC title

• H02J3/003 Primary

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

• Y04S50/10

  Energy trading, including energy flowing from end-user application to grid · CPC title