초록 ▼

Embodiments of the present invention assist customers in managing the four types of energy assets, that is, generation, storage, usage, and controllable load assets. Embodiments of the present invention for the first time develop and predict a customer baseline (“CBL”) usage of electricity, using a

Embodiments of the present invention assist customers in managing the four types of energy assets, that is, generation, storage, usage, and controllable load assets. Embodiments of the present invention for the first time develop and predict a customer baseline (“CBL”) usage of electricity, using a predictive model based on simulation of energy assets, based on business as usual (“BAU”) of the customer's facility. The customer is provided with options for operating schedules based on algorithms, which allow the customer to maximize the economic return on its generation assets, its storage assets, and its load control assets. Embodiments of the invention enable the grid to verify that the customer has taken action to control load in response to price. This embodiment of the invention calculates the amount of energy that the customer would have consumed, absent any reduction of use made in response to price. Specifically, the embodiment models the usage of all the customer's electricity consuming devices, based on the customer's usual conditions. This model of the expected consumption can then be compared to actual actions taken by the customer, and the resulting consumption levels, to verify that the customer has reduced consumption and is entitled to payment for the energy that was not consumed.

대표청구항 ▼

1. A method for optimizing the use of electric energy by a facility comprising a plurality of energy assets, the method comprising: modeling, using a computer, the usage of electric energy by the facility, by creating a dynamic simulation model of the plurality of energy assets of the facility; wher

1. A method for optimizing the use of electric energy by a facility comprising a plurality of energy assets, the method comprising: modeling, using a computer, the usage of electric energy by the facility, by creating a dynamic simulation model of the plurality of energy assets of the facility; wherein the dynamic simulation model of the facility comprises a computation of thermodynamics properties of the facility, andwherein the dynamic simulation model is adaptive to physical changes in the facility based on a parametric estimation;calculating, sing the computer, a customer baseline (“CBL”) based on the dynamic simulation model, wherein the CBL is predictive of energy consumption of the facility as a function of time;storing the dynamic simulation model in the computer;determining, using the computer, a plan for controlling the operation of at least one of the plurality of energy assets to reduce an energy provider's overall charge to the facility for electric energy, or to provide a revenue source to the facility, the determining being done at least on the basis of the dynamic simulation model, the CBL, and variations in the forecast wholesale electricity price of electric energy in the wholesale market during a defined time period;generating, using the computer, control signals based on the plan from data derived from the computer; andcontrolling, using the generated control signals, at least one of the plurality of energy assets so that operation of the facility conforms to the plan. 2. The method of claim 1, wherein the plurality of energy assets comprise at least one of: generation, storage, usage, and controllable load assets. 3. The method of claim 1, wherein the determining the plan for controlling the operation of at least one of the plurality of energy assets is also based on weather prediction data provided to the computer and price prediction data provided to the computer. 4. The method of claim 1, wherein said at least one of the plurality of energy assets is a controllable air conditioning unit. 5. The method of claim 1, wherein the facility further comprises an energy storage device and a solar generator connected to the energy storage device, the method further comprising: controlling a use of the solar generation by a control signal automatically generated from electric data derived from the computer, to charge the energy storage device with solar generation during a time when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by a control signal automatically generated from electronic data derived from the computer, to discharge the energy storage device to a utility power grid when the forecast wholesale electricity price of energy charged to the facility by the energy provider is above a second predetermined level. 6. The method of claim 1, wherein the facility further comprises an energy storage device connected to a utility power grid, the method further comprising: controlling the energy storage device by control signals automatically generated from electronic data derived from the computer to charge the energy storage device when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by control signals automatically generated from electronic data derived from the computer to discharge the energy storage device to the utility power grid when the forecast wholesale electricity price of energy charged is above a second predetermined level. 7. The method of claim 1, wherein the dynamic simulation model takes as input at least one of a weather forecast and a prediction of the occupancy of the facility. 8. The method of claim 1, wherein the dynamic simulation model predicts the dynamic load consumption behavior of the facility and the plurality of energy assets. 9. The method of claim 1, wherein the dynamic simulation model is created using an iterative process, and wherein the iterative process comprises tuning the dynamic simulation model based on data representative of physical properties of the facility. 10. A computer system for optimizing use of electric energy by a facility comprising a plurality of energy assets, the computer system comprising: at least one memory to store processor-executable instructions; andat least one processor communicatively coupled to the at least one memory, wherein, upon execution of the processor-executable instructions, the at least one processor executes a method comprising: modeling, using a computer, the usage of electric energy by the facility, by creating a dynamic simulation model of the plurality of energy assets of the facility; wherein the dynamic simulation model of the facility comprises a computation of thermodynamics properties of the facility, andwherein the dynamic simulation model is adaptive to physical changes in the facility based on a parametric estimation;calculating, sing the computer, a customer baseline (“CBL”) based on the dynamic simulation model, wherein the CBL is predictive of energy consumption of the facility as a function of time;storing the dynamic simulation model in the computer;determining, using the computer, a plan for controlling the operation of at least one of the plurality of energy assets to reduce the electricity provider's overall charge to the facility for electric energy, or to provide a revenue source to the facility, the determining being done at least on the basis of the dynamic simulation model, the CBL, and variations in the forecast wholesale electricity price of electric energy in the wholesale market during a defined time period;generating, using the computer, control signals based on the plan from data derived from the computer; andcontrolling, using the generated control signals, at least one of the plurality of energy assets so that operation of the facility conforms to the plan. 11. The computer system of claim 10, wherein the plurality of energy assets comprise at least one of: generation, storage, usage, and controllable load assets. 12. The computer system of claim 10, wherein the determining a plan for controlling the operation of at least one of the plurality of energy assets is also based on weather prediction data provided to the computer and price prediction data provided to the computer. 13. The computer system of claim 10, wherein said at least one of the plurality of energy assets is a controllable air conditioning unit. 14. The computer system of claim 10, wherein the facility further comprises an energy storage device and a solar generator connected to the energy storage device, the method further comprising: controlling a use of the solar generation by a control signal automatically generated from electric data derived from the computer, to charge the energy storage device with solar generation during a time when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by a control signal automatically generated from electronic data derived from the computer, to discharge the energy storage device to a utility power grid when the forecast wholesale electricity price of energy charged to the facility by the energy provider is above a second predetermined level. 15. The computer system of claim 10, wherein the facility further comprises an energy storage device connected to a utility power grid, the method further comprising controlling the energy storage device by control signals automatically generated from electronic data derived from the computer to charge the energy storage device when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by control signals automatically generated from electronic data derived from the computer to discharge the energy storage device to the utility power grid when the forecast wholesale electricity price of energy charged is above a second predetermined level. 16. The computer system of claim 10, wherein the dynamic simulation model takes as input at least one of a weather forecast and a prediction of the occupancy of the facility. 17. The computer system of claim 10, wherein the dynamic simulation model predicts the dynamic load consumption behavior of the facility and the plurality of energy assets. 18. The computer system of claim 10, wherein the dynamic simulation model is created using an iterative process, and wherein the iterative process comprises tuning the dynamic simulation model based on data representative of physical properties of the facility. 19. A non-transitory machine readable memory medium having at least one program embodied thereon, the at least one program including processor-executable instructions which when executed cause a processor to execute a method comprising: modeling, using a computer, the usage of electric energy by the facility, by creating a dynamic simulation model of the plurality of energy assets of the facility; wherein the dynamic simulation model of the facility comprises a computation of thermodynamics properties of the facility, andwherein the dynamic simulation model is adaptive to physical changes in the facility based on a parametric estimation;calculating, using the computer, a customer baseline (“CBL”) based on the dynamic simulation model, wherein the CBL is predictive of energy consumption of the facility as a function of time;storing the dynamic simulation model in the computer;determining, using the computer, a plan for controlling the operation of at least one of the plurality of energy assets to reduce the electricity provider's overall charge to the facility for electric energy, or to provide a revenue source to the facility, the determining being done at least on the basis of the dynamic simulation model, the CBL, and variations in the forecast wholesale electricity price of electric energy in the wholesale market during a defined time period;generating, using the computer, control signals based on the plan from data derived from the computer; andcontrolling, using the generated control signals, at least one of the plurality of energy assets so that operation of the facility conforms to the plan. 20. The memory medium in claim 19, wherein the plurality of energy assets comprise at least one of: generation, storage, usage, and controllable load assets. 21. The memory medium in claim 19, wherein the step of determining a plan for controlling the operation of at least one of the plurality of energy assets is also based on weather prediction data provided to the computer and price prediction data provided to the computer. 22. The memory medium in claim 19, wherein said at least one of the plurality of energy assets is a controllable air conditioning unit. 23. The memory medium in claim 19, wherein the facility further comprises an energy storage device and a solar generator connected to the energy storage device, the method further comprising: controlling the use of the solar generation by a control signal automatically generated from electric data derived from the computer, to charge the energy storage device with solar generation during a time when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by a control signal automatically generated from electronic data derived from the computer, to discharge the energy storage device to a utility power grid when the forecast wholesale electricity price of energy charged to the facility by the energy provider is above a second predetermined level. 24. The memory medium in claim 19, wherein the facility further comprises an energy storage device connected to a utility power grid, the method further comprising controlling the energy storage device by control signals automatically generated from electronic data derived from the computer to charge the energy storage device when the forecast wholesale electricity price of energy is below a first predetermined level; andcontrolling the energy storage device by control signals automatically generated from electronic data derived from the computer to discharge the energy storage device to the utility power grid when the forecast wholesale electricity price of energy charged is above a second predetermined level. 25. The memory medium in claim 19, wherein the dynamic simulation model takes as input at least one of a weather forecast and a prediction of the occupancy of the facility. 26. The memory medium in claim 19, wherein the dynamic simulation model predicts the dynamic load consumption behavior of the facility and the plurality of energy assets. 27. The memory medium in claim 19, wherein the dynamic simulation model is created using an iterative process, and wherein the iterative process comprises tuning the dynamic simulation model based on data representative of physical properties of the facility.