Systems and methods for energy cost optimization in a building system
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G05B-015/02
G05B-019/042
G05B-023/02
출원번호
US-0068470
(2016-03-11)
등록번호
US-10007259
(2018-06-26)
발명자
/ 주소
Turney, Robert D.
Wenzel, Michael J.
출원인 / 주소
Johnson Controls Technology Company
대리인 / 주소
Foley & Lardner LLP
인용정보
피인용 횟수 :
0인용 특허 :
101
초록▼
A controller is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a
A controller is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system. The controller is configured to linearize the demand charge term by imposing demand charge constraints and to mask each of the demand charge constraints that applies to an inactive pricing period. The controller is configured to determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints and to provide the optimal values of the one or more setpoints to equipment of the building system that operate to affect the maximum power usage.
대표청구항▼
1. A computer-implemented method for minimizing energy cost in a building system, the method comprising: using an energy cost function to predict, by a controller for the building system, a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the
1. A computer-implemented method for minimizing energy cost in a building system, the method comprising: using an energy cost function to predict, by a controller for the building system, a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller, the energy cost function comprising a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system;linearizing, by the controller, the demand charge term by imposing demand charge constraints, wherein each of the demand charge constraints applies to a time-step and a pricing period;masking, by the controller, each of the demand charge constraints that applies to an inactive pricing period, wherein a demand charge constraint applies to an inactive pricing period if the time-step to which the demand charge constraint applies does not occur during the pricing period to which the demand charge constraint applies;determining, by the controller, optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints;providing the optimal values of the one or more setpoints to equipment of the building system; andoperating the equipment of the building system using the optimal values of the one or more setpoints so as to affect the maximum power usage. 2. The method of claim 1, wherein using the energy cost function to predict the total cost of energy comprises using time-varying pricing information to determine a cost of energy purchased from the energy provider during each of a plurality of time steps in a prediction period. 3. The method of claim 2, wherein the time-varying pricing information comprises a plurality of pricing structures including at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered. 4. The method of claim 1, wherein the energy cost function comprises a demand charge for each of a plurality of pricing periods. 5. The method of claim 1, further comprising receiving temperature constraints defining an acceptable temperature range for the building system; wherein performing the optimization procedure comprises minimizing the total cost of energy subject to the temperature constraints. 6. The method of claim 1, further comprising using a dynamic model describing heat transfer characteristics of the building system to predict a temperature of the building system as a function of the one or more setpoints provided by the controller. 7. The method of claim 6, further comprising: receiving a feedback signal indicating a measured temperature of the building system and a measured power usage of the building system; andusing the dynamic model and the feedback signal to formulate equality constraints;wherein performing the optimization procedure comprises minimizing the total cost of energy subject to the equality constraints. 8. The method of claim 1, wherein linearizing the demand charge term by imposing demand charge constraints comprises: replacing a nonlinear expression of the demand charge term with a linear expression that defines demand charge as a linear function of a demand charge variable; andimposing a demand charge constraint for each time-step that constrains the demand charge variable to a value greater than or equal to power consumption of the building system during the time-step. 9. The method of claim 1, wherein masking each of the demand charge constraints that applies to an inactive pricing period comprises, for each of the demand charge constraints: identifying the pricing period to which the demand charge constraint applies;identifying the time-step to which the demand charge constraint applies;determining whether the identified time-step occurs during the identified pricing period; andselectively masking the demand charge constraint in response to a determination that the identified time-step does not occur during the identified pricing period. 10. The method of claim 1, wherein performing the optimization procedure comprises minimizing the total cost of energy subject to any demand charge constraints that have not been masked as a result of the masking. 11. A system for minimizing energy cost in a building system, the system comprising a controller configured to: use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller, the energy cost function comprising a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system;linearize the demand charge term by imposing demand charge constraints, wherein each of the demand charge constraints applies to a time-step and a pricing period;mask each of the demand charge constraints that applies to an inactive pricing period, wherein a demand charge constraint applies to an inactive pricing period if the time-step to which the demand charge constraint applies does not occur during the pricing period to which the demand charge constraint applies;determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints; andprovide the optimal values of the one or more setpoints to equipment of the building system; andoperate the equipment of the building system using the optimal values of the one or more setpoints so as to affect the maximum power usage. 12. The system of claim 11, wherein the controller is configured to predict the total cost of energy by using time-varying pricing information to determine a cost of energy purchased from the energy provider during each of a plurality of time steps in a prediction period. 13. The system of claim 12, wherein the time-varying pricing information comprises a plurality of pricing structures including at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered. 14. The system of claim 11, wherein the energy cost function comprises a demand charge for each of a plurality of pricing periods. 15. The system of claim 11, wherein the controller is configured to: receive temperature constraints defining an acceptable temperature range for the building system; andperform the optimization procedure by minimizing the total cost of energy subject to the temperature constraints. 16. The system of claim 11, wherein the controller is configured to use a dynamic model describing heat transfer characteristics of the building system to predict a temperature of the building system as a function of the one or more setpoints provided by the controller. 17. The system of claim 16, wherein the controller is configured to: receive a feedback signal indicating a measured temperature of the building system and a measured power usage of the building system;use the dynamic model and the feedback signal to formulate equality constraints; andperform the optimization procedure by minimizing the total cost of energy subject to the equality constraints. 18. The system of claim 11, wherein the controller is configured to: linearize the demand charge term by replacing a nonlinear expression of the demand charge term with a linear expression that defines demand charge as a linear function of a demand charge variable; andimpose a demand charge constraint for each time-step that constrains the demand charge variable to a value greater than or equal to power consumption of the building system during the time-step. 19. The system of claim 11, wherein the controller is configured to mask each of the demand charge constraints that applies to an inactive pricing period by: identifying the pricing period to which the demand charge constraint applies;identifying the time-step to which the demand charge constraint applies;determining whether the identified time-step occurs during the identified pricing period; andselectively masking the demand charge constraint in response to a determination that the identified time-step does not occur during the identified pricing period. 20. The system of claim 11, wherein the controller is configured to perform the optimization procedure by minimizing the total cost of energy subject to any demand charge constraints that have not been masked.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (101)
Fan,Junqiang; Backstrom,Johan U., Apparatus and method for coordinating controllers to control a paper machine or other machine.
Gloudeman Jeffrey J. ; Gottschalk Donald A. ; Kraemer C. Richard ; Rasmussen David E., Common object architecture supporting application-centric building automation systems.
Morshedi Abdol M. (Houston TX) Cutler Charles R. (Houston TX) Fitzpatrick Thomas J. (Katy TX) Skrovanek Thomas A. (Houston TX), Dynamic process control.
Liebl Ronald J. (Mukwonago WI) Bronikowski Alan J. (South Milwaukee WI) Holdorf Thomas C. (Mukwonago WI) Strojny Lawrence J. (Oostburg WI) Tellier Mark W. (Milwaukee WI), Energy control system.
Mehta,Ashish; Wojsznis,Peter; Wojsznis,Wilhelm K.; Blevins,Terrence L.; Thiele,Dirk; Ottenbacher,Ron; Nixon,Mark, Integrated model predictive control and optimization within a process control system.
Vouzis, Panagiotis; Bleris, Leonidas; Arnold, Mark G.; Kothare, Mayuresh V., Iterative matrix processor based implementation of real-time model predictive control.
Boe, Eugene; Piche, Stephen; Martin, Gregory D., Method and apparatus for attenuating error in dynamic and steady-state processes for prediction, control, and optimization.
Davis Glenn A. ; Poche David M., Method and apparatus using distributed intelligence for applying real time pricing and time of use rates in wide area network including a headend and subscriber.
Pekar, Jaroslav; Strewart, Greg; Kihas, Dejan; Borrelli, Francesco, Method and system for combining feedback and feedforward in model predictive control.
Lu Zhuxin J. (Glendale AZ) MacArthur J. Ward (Scottsdale AZ) Horn Brian C. (Phoenix AZ), Method of multivariable predictive control utilizing range control.
Pekar, Jaroslav; Borralli, Francesco; Stewart, Gregory, Methods and systems for the design and implementation of optimal multivariable model predictive controllers for fast-sampling constrained dynamic systems.
Stewart,Gregory E.; Kolavennu,Soumitri N.; Borrelli,Francesco; Hampson,Gregory J.; Shahed,Syed M.; Samad,Tariq; Rhodes,Michael L., Multivariable control for an engine.
Sloop, Christopher Dale; Oberholzer, David; Marshall, Robert S.; Kim, Jungho; Siemann, Michael, Optimizing and controlling the energy consumption of a building.
Bohrer, Philip J.; Merten, Gregory J.; Schnell, Robert J.; Atlass, Michael B., Profile based method for deriving a temperature setpoint using a `delta` based on cross-indexing a received price-point level signal.
MacArthur J. Ward (Minneapolis MN) Wahlstedt David A. (Minneapolis MN) Woessner Michael A. (Minneapolis MN) Foslien Wendy K. (Minneapolis MN), Receding horizon based adaptive control having means for minimizing operating costs.
Lu, Joseph; Morningred, John Duane, System and method for continuous supply chain control and optimization using stochastic calculus of variations approach.
Plumer, Edward Stanley; Sayyar-Rodsari, Bijan; Schweiger, Carl Anthony; Ferguson, II, Ralph Bruce; Johnson, William Douglas; Axelrud, Celso, System and method for enterprise modeling, optimization and control.
Das,Indraneel; Rey,Gonzalo, System and method for exploiting a good starting guess for binding constraints in quadratic programming with an infeasible and inconsistent starting guess for the solution.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.