[미국특허]
Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-019/00
G06Q-040/04
G06Q-030/02
G06Q-050/06
G06Q-030/06
G06Q-010/06
G05B-015/02
G05B-013/02
H02J-003/00
출원번호
US-0888323
(2013-05-06)
등록번호
US-9098876
(2015-08-04)
발명자
/ 주소
Steven, Alain P.
Hameyie, Eunice B.
Wen, Jin
Wu, Teresa
Sunder, Ajay
출원인 / 주소
VIRIDITY ENERGY, INC.
대리인 / 주소
McCarter & English, LLP
인용정보
피인용 횟수 :
15인용 특허 :
61
초록▼
The apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier. The apparatuses and methods herein can be used to generate suggested operating schedules for the energy assets that including a controllable energy asset, usin
The apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier. The apparatuses and methods herein can be used to generate suggested operating schedules for the energy assets that including a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based on a parametric estimation using at least one model parameter. The model parameter is at least one of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.
대표청구항▼
1. An apparatus for determining a suggested operating schedule for at least one energy asset operated by an energy customer, the apparatus comprising: at least one communication interface;at least one memory to store processing unit-executable instructions and an objective function for the at least
1. An apparatus for determining a suggested operating schedule for at least one energy asset operated by an energy customer, the apparatus comprising: at least one communication interface;at least one memory to store processing unit-executable instructions and an objective function for the at least one energy asset, wherein the at least one energy asset comprises at least one controllable energy asset, wherein the objective function facilitates a determination of the suggested operating schedule for the at least one energy asset based at least in part on data representative of model parameters, and wherein the model parameters are: (a) an operation characteristic of the at least one controllable energy asset, (b) a thermodynamic property of the at least one energy asset, (c) a projected environmental condition during time period T; andat least one processing unit communicatively coupled to the at least one memory, wherein, upon execution of the processing unit-executable instructions, the at least one processing unit: A) prior to time period T, determines the suggested operating schedule based on an optimization of the objective function over time period T,wherein the objective function is determined based on a dynamic simulation model of the energy profile of the at least one energy asset, a customer baseline (CBL) energy profile for the at least one energy asset, and a forecast wholesale electricity price, over time period T, associated with a wholesale electricity market,wherein the dynamic simulation model is adaptive to physical changes in the at least one energy asset based on a parametric estimation using at least one of the model parameters, andwherein the dynamic simulation model is trained using the data; andB) controls the at least one communication interface to transmit to the energy customer the suggested operating schedule determined in A), and/or controls the at least one memory so as to store the determined suggested operating schedule,wherein the operation of the at least one energy asset according to the suggested operating schedule, over a time period T, facilitates generation of energy-related revenue based at least in part on the wholesale electricity market. 2. The apparatus of claim 1, wherein the CBL energy profile is computed based on applying the dynamic simulation model to the data representative of the operation characteristic of the at least one controllable energy asset, the thermodynamic property of the building asset, and an environmental condition, all during a time period TA prior to time period T. 3. The apparatus of claim 1, wherein the data representative of the projected environmental condition is data representative of at least one of an ambient temperature of the environment in which the building asset is located; a humidity of the environment in which the building asset is located; an amount of solar irradiance of the environment in which the building asset is located, an amount of cloud cover of the environment in which the building asset is located, an outside air temperature, an outside air humidity, an outside air enthalpy, an outside air wet bulb temperature, a dewpoint temperature, and a heat index. 4. The apparatus of claim 1, wherein the at least one energy asset comprises at least one building asset. 5. The apparatus of claim 4, wherein the data representative of the thermodynamic property of the building asset is data representative of at least one of an occupancy schedule of the building asset, a relative humidity of the building asset, a temperature of the building asset, and a lighting level of the building asset. 6. The apparatus of claim 1, wherein the operation characteristic of the at least one controllable energy asset is a load use schedule. 7. The apparatus of claim 6, wherein the load use schedule imposes a maximum allowable load drawn by the at least one controllable energy asset over a time interval that is less than time period T. 8. The apparatus of claim 7, wherein the load use schedule impose a different value of maximum allowable load at different time intervals during time period T. 9. The apparatus of claim 1, wherein the operation characteristic of the at least one controllable energy asset is an energy consumption profile as a function of time of the at least one controllable energy asset. 10. The apparatus of claim 1, wherein the operation characteristic of the at least one controllable energy asset is a set point. 11. The apparatus of claim 1, wherein the thermodynamic property of the at least one energy asset is a zone temperature. 12. The apparatus of claim 1, wherein, upon execution of the processor-executable instructions, the at least one processing unit determines the suggested operating schedule for the at least one energy asset using the objective function in A) by minimizing a net energy-related cost over the time period T, wherein the net-energy related cost is based at least in part on: an electricity consumption by the at least one controllable energy asset; andthe CBL energy profile; andwherein the energy-related revenue available to the energy customer is based at least in part on the minimized net energy-related cost. 13. The apparatus of claim 12, wherein the net energy-related cost is specified as a difference between an electricity supply cost and a demand response revenue over the time period T. 14. The apparatus of claim 1, wherein the at least one processing unit determines the suggested operating schedule for the at least one energy asset as at least one bias signal, as an interruptible load function, or as at least one use modulation signal. 15. The apparatus of claim 14, wherein the at least one processing unit determines the suggested operating schedule for the at least one energy asset in (A) as at least one bias signal, and controls the at least one communication interface in (B) to transmit to the energy customer the at least one bias signal at different times during time period T. 16. The apparatus of claim 14, wherein the at least one processing unit controls the at least one communication interface to transmit to the energy customer the suggested operating schedule as at least one use modulation signal, and wherein the operation of the at least one energy asset according to the at least one use modulation signal causes a modulation with time of the load use of the controllable energy asset. 17. The apparatus of claim 1, wherein the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of the model parameters. 18. The apparatus of claim 17, wherein the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of a zone temperature of the at least one energy asset, a load schedule of the at least one energy asset, the projected environmental condition, and a control setpoint of the at least one controllable energy asset. 19. The apparatus of claim 18, wherein the zone temperature of the at least one energy asset is a semi-linear regression over at least one of the projected environmental condition, the load schedule of the at least one energy asset, and the control setpoint of the at least one controllable energy asset. 20. The apparatus of claim 1, wherein: the at least one energy asset is at least one building asset; andthe at least one controllable energy asset comprises at least one heating, ventilation and air conditioning (HV AC) system to control a variable internal temperature of the at least one building asset.
Brickfield, Peter J.; Mahling, Dirk; Noyes, Mark; Weaver, David, Automatic energy management and energy consumption reduction, especially in commercial and multi-building systems.
El Gasseir, Mohamed M.; Epp, H. D. Kenneth, Electricity market-oriented dc-segmentation design and optimal scheduling for electrical power transmission.
Bartels, Carlton; White, Adam, Electronic trading system for simulating the trading of carbon dioxide equivalent emission reductions and methods of use.
Brown ; Jr. Robert J. (6688 Serena La. Boca Raton FL 33433) Romanowiz James D. (2919 Banyan Rd. Boca Raton FL 33432) Staples Charles W. (270 NW. 36th St. Boca Raton FL 33431), Energy management and home automation system.
Besore, John K.; Drake, Jeff Donald; Finch, Michael F.; Franks, Darin; Roetker, John Joseph; Root, Steven Keith; Venkatakrishnan, Natarajan; Watson, Eric K., Energy management of household appliances.
Venkatakrishnan, Natarajan; Finch, Michael Francis; Bultman, Robert Marten; Worthington, Timothy Dale; Bingham, David C.; Drake, Jeff Donald; Watts, William Anthony; Nolan, Kevin Farrelly; Emery, Cathy Diane; Kobraei, Henry, Low cost and flexible energy management system.
Venkatakrishnan, Natarajan; Finch, Michael Francis; Bultman, Robert Marten; Worthington, Timothy Dale; Bingham, David C.; Drake, Jeff Donald; Watts, William Anthony; Nolan, Kevin Farrelly; Emery, Cathy Diane; Kobraei, Henry, Low cost and flexible energy management system with a scheduling capability.
Mcconnell, Robert S.; Hepperla, Paul; Johnson, Daniel T., Method and system for tracking and managing destruction, reconstitution, or reclamation of regulated substances.
Massey, Jerry Steven; Schmid, James Joseph; Meyerhofer, Mark Joseph; Wilson, Bobby Antione; Sierra, Jaime Alberto, Method, system and computer program product for scheduling demand events.
Balan,Chellappa; Bose,Sumit; Ye,Zhihong; Bebic,Jovan; de Bedout,Juan; Liu,Yan; Garces,Luis, Multi-tier benefit optimization for operating the power systems including renewable and traditional generation, energy storage, and controllable loads.
Wu Zhijian James ; Chen Gang ; Lee Anson ; Franks Kerry D. ; McDonald Timothy L. ; Tamm James R., Prediction of internal temperature of a battery using a non-linear dynamic model.
Cmar Gregory (379 Namant Rd. Namant MA 01908), Process for identifying patterns of electric energy effects of proposed changes, and implementing such changes in the fa.
Dawson, Christopher J.; Diluoffo, Vincenzo V.; Hamilton, II, Rick A.; Kendzierski, Michael D., System and method for dynamically managing blowers and vents.
Ehlers, Gregory A.; Turner, James H.; Beaudet, Joseph; Strich, Ronald; Loughmiller, George, System and method of controlling delivery and/or usage of a commodity.
Dawson, Christopher J.; Diluoffo, Vincenzo V.; Hamilton, II, Rick A.; Kendzierski, Michael D., System and method to control data center air handling systems.
Fehr,Stephen L.; Hutchinson,Linda A., Systems and methods for calculating and predicting near term production cost, incremental heat rate, capacity and emissions of electric generation power plants based on current operating and, optionally, atmospheric conditions.
Meghani, Ravi; Srivastava, Rohit; Mandal, Subhasis; Ghosh, Sudipta; Srivastava, Anurag, Architecture for energy management of multi customer multi time zone distributed facilities.
Horesh, Raya; Lee, Young M.; Liberti, Leo S., HVAC system control integrated with demand response, on-site energy storage system and on-site energy generation system.
Wang, Chengmin; Sun, Weiqing; Yi, Tao; Li, Hongzhong; Liu, Yong; Duan, Jianmin; Xiao, Dingyao, Method for optimizing the flexible constraints of an electric power system.
Hoff, Thomas E., System and method for providing constraint-based heating, ventilation and air-conditioning (HVAC) system optimization with the aid of a digital computer.
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