Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 4-6 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Objections
Claim 5 rt has an empty box above the symbol for relative power. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al (US PUB. 20210281077, herein Cai) in view of Hughes et al (NPL Virtual Battery Models for Load Flexibility from Commercial Buildings, 2015 48th Hawaii International Conference on System Sciences).
Regarding claim 1, Cai teaches Method of operating an electrical power distribution system, said power distribution system comprising:
- at least one electrical power generator with uncontrollable variable supply (0007);
- at least one electrical power consuming resource with controllable demand and subject to at least one state constraint, said electrical power consuming resource with controllable demand being arranged to be controlled by an internal controller (0042 “A power deficit refers to the amount of available energy associated with the PV energy resource 106 being insufficient to satisfy the power demand. The power control system 104 (e.g., the optimizer module) determines that a first portion of the power demand is to be satisfied by utilizing the available energy associated with the PV energy resource 106. The power control system 104 determines an amount of energy to be provided by the engine 108 and/or an amount of energy to be provided by the battery 110 to satisfy the second portion of the power demand based on a predicted load profile for a subsequent time period occurring after the time period specified in the power demand, a predicted amount of energy that the PV energy resource 106 will be able to provide during the subsequent time period, an engine cost, and/or a battery cost”);
- a system controller adapted to send requests to said internal controlled to request variation in said controllable demand (0042);
- an electrical power distribution network arranged to transmit electrical power from said at least one electrical power generator to said at least one electrical power consuming resource with controllable demand (0034);
said method comprising steps of:
- determining a set of feasible power consumption requests that said at least one electrical power consuming resource with controllable demand can fulfil, [on the basis of said virtual battery model] in function of the current state of said electrical power consuming resource with controllable demand and external conditions (0009 “a system may include a plurality of power resources; and a power control device. The power control device may be configured to: receive a power demand; receive a first weather forecast for a first time period and a second weather forecast for second time period, wherein the second time period is after the first time period; determine a first supply of power available from a first power resource, of the plurality of power resources, over the first time period based on the first weather forecast and a second supply of power available from the first power resource over the second time period based on the second weather forecast; determine a power deficit for the first time period based on the power demand and the first supply of power; determine a cost associated with utilizing a third supply of power from a second power resource, of the plurality of power resources, over the first time period; determine a cost associated with utilizing the fourth supply of power from a third power resource, of the plurality of power resources, over the first time period; select one or more of the second power resource or the third power resource, as a selected power source, to satisfy the power deficit based on the second supply of power, the cost associated with utilizing the third supply of power, and the cost associated with utilizing the fourth supply of power; and transmit a request to the first power resource and the selected power resource to cause the first power resource and the selected power resource to supply power to satisfy the power demand.”);
and - sending requests from said system controller to said internal controller of said electrical power consuming resource with variable demand to increase or decrease its power consumption on the basis of power available on said electrical power distribution network and said set of feasible power consumption requests (0033, 0035, 0042),
wherein said [virtual battery model] comprises:
- a first block which receives a current state of said electrical power consuming resource with controllable demand, and current external conditions, said first bloc determining a state change of said electrical power consuming resource with controllable demand due to nominal behaviour of said internal controller, said first block being active only when said system controller does not request said electrical power consuming resource with controllable demand to consume more or less power (0082 “ the power control system 104 utilizes a machine learning model, such as a PV prediction model, to determine the amount of energy associated with the PV energy resource 106 available to be provided to a load during the subsequent time period. The PV prediction model receives, as inputs, real-time weather information (e.g., a weather forecast) and historical weather information to determine the amount of energy associated with the PV energy resource 106 available to be provided to a load during the subsequent time period, as described below”, 0083, 0033 “the amount of available energy associated with the PV energy resource 106 is equal to the power demand. The power control system 104 (e.g., the optimizer module) determines that the power demand can be satisfied by utilizing the available energy associated with the PV energy resource 106 based on the amount of available energy associated with the PV energy resource 106 being equal to the power demand.”);
- a second block which receives said current request, current external conditions and a prediction of said external conditions at a time in the future (0082, 0083 “a weather forecast is input into the PV prediction model. The weather forecast may indicate one or more weather conditions associated with a time period (e.g., an hour, a day, a week, and/or the like). The power control system 104 trains the PV prediction model based on the one or more weather conditions indicated in one or more weather forecasts. The power control system 104 trains the PV prediction model using historical data associated with an amount of available energy associated with the PV energy resource 106 according to the one or more weather conditions. Using the historical data and the one or more weather conditions as inputs to the PV prediction model, the power control system 104 determines an amount of available energy associated with the PV energy resource 106 for the subsequent time period”), said second block being active only when said system controller requests that said electrical power consuming resource with controllable demand consume more or less power, said second block outputting a state change of said electrical power consuming resource with controllable demand due to predicted changes in external conditions (0035 “n some implementations, the amount of available energy associated with the PV energy resource 106 is greater than the power demand. The power control system 104 (e.g., the optimizer module) determines that the power demand can be satisfied by a portion of the available energy associated with the PV energy resource 106 based on the amount of available energy associated with the PV energy resource 106 being greater than the power demand. The power control system 104 determines that the surplus portion of the available energy associated with the PV energy resource 106, is to be provided to, and stored by, the battery 110”);
- a third block which receives the current request and determines a state change of said electrical power consuming resource with controllable demand incurred by said current request (0055, 0144),
said state changes being combined such that said [virtual battery model] outputs a predicted state of said electrical power consuming resource with controllable demand at said time in the future on the basis of a current state of said electrical power consuming resource with controllable demand, of said current external conditions, of said prediction of said external conditions at said time in the future, and of said current request (0043 “the power control system 104 utilizes machine learning techniques to determine the predicted amount of energy that the PV energy resource 106 will be able to provide during the subsequent time period and the predicted load profile. For example, the power control system 104 may train and utilize a PV energy prediction model to predict an amount of energy that the PV energy resource 106 will be able to provide during the subsequent time period, as described elsewhere herein. The power control system 104 may train and utilize a load profile prediction model to predict a power demand for the subsequent time period, as described elsewhere herein.”).
The cited prior art do not teach - determining a virtual battery model of said at least one electrical power consuming resource with controllable demand, determining a set of feasible power consumption requests that said at least one electrical power consuming resource with controllable demand can fulfil, [on the basis of said virtual battery model, and virtual battery model outputs a predicted state of said electrical power.
Hughes teaches - determining a virtual battery model of said at least one electrical power consuming resource with controllable demand (abstract “aggregate flexibility of a collection of diverse residential air-conditioning loads has previously been shown to be well modeled as a virtual battery using first principles load models”),
determining a set of feasible power consumption requests that said at least one electrical power consuming resource with controllable demand can fulfil (taught by Cai), on the basis of said virtual battery model (page 2630 3.2, 3.3)
virtual battery model outputs a predicted state of said electrical power (page 2630 3.2, 3.3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Cai with the teachings of Hughes since Hughes teaches a means for accurately modelling many buildings and their equipment in order to help with frequency regulation (2627).
Regarding claim 2, the cited prior art teach method according to claim 1.
Cai teaches wherein said step of determining a set of feasible power consumption requests is carried out by a flexibility forecasting system on the basis of:
- the current state of said electrical power consuming resource with controllable demand;
- current external conditions (0082);
- predictions of external conditions at at least one time in the future (0083 “a weather forecast is input into the PV prediction model. The weather forecast may indicate one or more weather conditions associated with a time period (e.g., an hour, a day, a week, and/or the like). The power control system 104 trains the PV prediction model based on the one or more weather conditions indicated in one or more weather forecasts. The power control system 104 trains the PV prediction model using historical data associated with an amount of available energy associated with the PV energy resource 106 according to the one or more weather conditions. Using the historical data and the one or more weather conditions as inputs to the PV prediction model, the power control system 104 determines an amount of available energy associated with the PV energy resource 106 for the subsequent time period.”);
- a confidence level of the ability that said electrical power consuming resource with controllable demand can fulfil a request; - uncertainty in said set of feasible power consumption requests (0085 “power control system 104 (e.g., the optimizer module) determines the final power supply plan based on the initial power supply plan and/or the predicted power supply plan. For example, the power control system 104 may modify the initial power supply plan to optimize or minimize a cost (e.g., the engine cost and/or the battery cost) associated with providing sufficient energy to satisfy the second portion of the power demand and to optimize or minimize a cost associated with providing sufficient energy to satisfy a power demand indicated by the predicted load profile”, 0159-0161);
said flexibility forecasting system outputting a maximum and minimum power consumption request that can be fulfilled at said at least one time in the future (0161, 0080).
Regarding claim 7, the cited prior art teach Computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method steps of the method of claim 1 (see rejection of claim 1).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al (US PUB. 20210281077, herein Cai) in view of Hughes et al (NPL Virtual Battery Models for Load Flexibility from Commercial Buildings, 2015 48th Hawaii International Conference on System Sciences) in further view of Sun et al (US PUB. 20210304306, herein Sun).
Regarding claim 3, the cited prior art teach Method according to claim 2.
The cited prior art do not teach wherein said uncertainty in said set of feasible power consumption requests exploits a coherent risk measure such as the Conditional Value at Risk.
Sun teaches wherein said uncertainty in said set of feasible power consumption requests exploits a coherent risk measure such as the Conditional Value at Risk (0038).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Cai and Hughes with the teachings of Sun since Sun teaches a means for managing risk for power plants (0011).
Conclusion
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
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