DETAILED ACTION
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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 5/8/26, Applicant, on 8/7/26, amended claims. Claims 1-3, 5-11, 13-18, and 20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
The 101 rejections are withdrawn, as the claim now includes issuing a command to control unit to return the selected condition to a previous value and generating control signals for control of battery management system (BMS) or microgrid controller. The claim is now viewed as not directed to an abstract idea, and to a practical application at step 2a, prong two.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-11, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Asghari (US 2013/0024042) in view of Fife (US 2020/0006946).
Concerning claim 1, Asghari discloses:
A method of monitoring and/or operating a power system asset (Asghari – see par 27 - Primary Management (PM) 20: In this step, primary decisions about power flow between generation resources and the energy storage unit will be made based the information from different generation assets, battery state of charge (SOC) and availability of the grid; see par 56 - In one embodiment for the grid-tied microgrids which include wind turbine and PV solar panels as generation assets and Li-Ion battery as storage device, the system is optimally designed using HOMER.RTM. with real wind speed, solar radiation, grid electricity price, and load demand data.), the method comprising the following steps performed by a computing system (Asghari – see par 56 - dynamic model of each component for the designed system is developed in MATLAB/Simulink.RTM (i.e. software for a computer).
see also Fife – see par 50 - the controller 110 includes one or more processors and suitable storage media, which stores programming in the form of executable instructions which are executed by the processors to implement the control processes):
receiving, from at least one data acquisition unit, measurements indicative of a state of the power system asset (Asghari see par 52 - One embodiment provides a closed-loop operation. In this embodiment, the power management system is equipped with direct measurement and/or estimation tools for energy storage performance and other parameters in the network. Output commands from the management system are updated in discrete time intervals based on the feedback signals from sensor/estimator modules. The State of charge (SOC) (depth of discharge (DOD)) of a storage device measurement is continuously monitored SOC (DOD) to ensure it is within the recommended range. In case of any breach of SOC limits, the power management system halts the energy storage operation until necessary actions are taken by the user. The system continuously monitors temperature of a storage device to ensure it is within the recommended range;
see also Fife par 59 - FIG. 1, the building electrical system 102 may provide information to the controller 110, such as in a form of providing process variables. The process variables may provide information, or feedback, as to a status of the building electrical system 102 and/or one or more components (e.g., loads, generators, ESSs) therein. For example, the process variable may provide one or more measurements of a state of the electrical system);
processing the received measurements to determine a series of sets of first model parameter values of a power system asset model, each of the sets of first model parameter values being determined for a different time or time interval (Applicant’s [0153] states “The first determination module 32 may be operative to process measurements (e.g., a voltage and a current) measured time-sequentially and/or received at the interface 31 at time-sequential sampling times into one set of first model parameter values.”)
Asghari see par 52 - Output commands from the management system are updated in discrete time intervals based on the feedback signals from sensor/estimator modules. see par 53 - if the estimated operational conditions of the energy storage are out of recommended range, the management system will not operate the storage unit. The system also estimates discharge power of the storage unit in the next control time-step based on forecast of generation and demand levels in the network. );
determining, using the series of sets of first model parameter values, a set of second model parameter values of a parameter evolution model different from the power system asset model, the parameter evolution model describing an evolution of one, several, or all first model parameter values of the power system asset model (Applicant’s [0181], FIG. 5 as published states “ For illustration, if the parameter evolution model is based on linear regression and the time evolution for all n first model parameter values is quantified by the parameter evolution model, m=n. If the parameter evolution model is based on linear regression and the time evolution for only one or only a sub-set of the n first model parameter values is quantified by the parameter evolution model, m<n.”;
[0158] as published “The set of second model parameter values may be determined by at least one of machine learning, fitting, or regression analysis, in particular linear regression, using several or all of the determined sets of first of model parameter values as inputs.”
(Asghari – see par 31 - The actual charge life of the cell is a function of the DoD. Therefore, the effective ampere-hour discharge in a given discharge event may be more or less than the actual discharge based on the actual DoD. In order to determine this functional relationship, the following function has been used to perform the best fit to the cell cycle life data vs. DoD. Different methods can be applied to perform the curve fitting. Two different methods have been used in this study including particle swarm optimization (PSO) and non-linear least square (NLLS) method from MATLAB curve fitting toolbox. PSO, as a heuristic optimization technique, is able to achieve optimal solution in a small fraction of a second. Also, NLLS is a popular regression method. see par 47 - Different methods can be used to perform curve fitting on battery characteristics which are provided by the battery manufacturer or obtained from experiments. Curve fitting is necessary in order to define analytical functions describing the behavior of a battery to be used during the battery life estimation process).
generating and providing an output in dependence on at least one of the sets of first model parameter values of the power system asset model and the set of second model parameter values of the parameter evolution model (Examiner notes the output is based on EITHER 1st parameters or 2nd parameters – Applicant’s 0161 as published states “] The output may include any one or any combination of the following: [0162] information on current and/or future States of the power system asset [0163] one or several key performance indicators (KPIs) determined for current and/or future [0164] States of the power system asset [0165] information on a previous, current and/or future performance or remaining useful life (RUL) of the power system asset [0166] an alarm, warning, or other indicator generated based on previous and/or current and/or future States of the power system asset [0167] a control signal generated based on previous and/or current and/or future States of the power system asset.”
Asghari – see par 47 - Different methods can be used to perform curve fitting on battery characteristics which are provided by the battery manufacturer or obtained from experiments. Curve fitting is necessary in order to define analytical functions describing the behavior of a battery to be used during the battery life estimation process.; claim 18 “estimating the battery life (i.e. degradation)”; see par 52 - One embodiment provides a closed-loop operation. see par 54- Charging conditions of the storage unit can be controlled to optimize battery life. The system ensures the battery is charged at a slow rate to enhance the battery lifetime. The system regulates temperature of the storage unit. The system regulates discharging condition of the storage unit. The system accumulates all information related to the discharge history of a storage unit over time and makes future discharge decision based on accumulated information. The system evaluates each likely discharge event in the future individually and makes decision based on the impact of individual discharge events on the storage life. The system regulates discharge events of a storage unit to achieve a certain objective function in the network. The system only discharges the battery if the accumulated impact of the new discharge event and all previous discharge events result in a storage life of greater than or equal to a pre-specified number of years;
see also Fife – See par 294 – battery degradation is written in the form of a time or SoC derivative that can be integrated numerically as part of the cost function control simulation to yield battery degradation during the future time domain. In one embodiment, this degradation derivative can be comprised of two components: a wear component (or throughput component) and an aging component. The components can be numerically integrated vs. time using an estimate of the battery SoC at each time step in one embodiment. see par 321 - Similar to the degradation model described above, the efficiency and maximum charge and discharge rates may be parameterized with constants that achieve a substantial “fit” between the model and the expected battery performance. Once these battery performance models are defined and parameters are provided, they may be used in the cost function control simulation to better predict the outcome of application of various control parameter sets).
Asghari discloses using estimates for a next control-time step based on forecast of generation and demand levels in the network, considering depth of discharge, discharge current, battery voltage, estimated life of storage unit, and using particle swarm optimization (PSO) (See par 53).
Fife discloses:
wherein determining the set of second model parameter values comprises issuing a command to at least one control unit to adjust a selected one of an ambient condition and an operating condition of the power system asset, determining at least two of the sets of first model parameter values while the selected condition is adjusted, and thereafter issuing a further command to the at least one control unit to return the selected condition to a previous value thereof (Fife – see par 127 - n certain embodiments, a control parameter set X can be defined (in conjunction with a control law) that is to be applied to the electrical system, how they should behave, and at what times in the future time domain they should be applied. In some embodiments, the cost function can be evaluated by performing a simulation of electrical system operation with a provided set X of control parameters. The control laws specify how to use X and the process variables to determine the control variables. The cost function can then be prepared or otherwise developed to consider the control parameter set X. see par 128 - . This example method continuously can “tune” possible control sets until an optimal set is found. As shorthand notation, we may refer to these certain example embodiments of an economically optimizing electrical system controller (EOESC). see par 133 - An EOESC may yield not only a control to be applied at the present time, but also the planned sequence of future controls. This means one execution of an EOESC can generate a lasting set of controls that can be used into the future rather than a single control to be applied at the present. This can be useful in case a) the optimization algorithm takes a significant amount of time to execute, or b) there is a communication interruption between the processor calculating the control parameter values and the processor interpreting the control parameters and sending control variables to the electrical system.).
Asghari and Fife disclose:
wherein generating and providing the output comprises generating, based on a forecast of a future evolution of the first model parameter values generated using the parameter evolution model, control signals to a battery management system (BMS) or microgrid controller that control at least one of a charge rate, discharge rate, power system asset ambient temperature, depth of discharge, and a protection system (Asghari – see par 23, FIG. 1A - FIG. 1A shows an exemplary system with power management units. In FIG. 1A, a management frame work 10 controls a primary management (PM) system 20 and a battery life estimater (BLE) 30. Batteries are usually equipped with battery management system (BMS) which is defined as any electronic device that manages a rechargeable battery (cell or battery pack), by monitoring its state, calculating secondary data, reporting that data, protecting it, controlling its environment, and/or balancing it. However, there is no real-time supervisory power management system on the top level of microgrid to regulate battery discharge in order to maximize its lifetime. see par 46 - The real-time power management framework enables cost-of-energy based discharge pattern for storage device to maximize its lifetime. A top-level supervisory real-time power management framework has been developed in order to guarantee the maximum possible battery lifetime based on the final price of energy, $/kWh. see par 53 - if the estimated discharge power is out of recommended range, the management system will not operate the storage unit. Depth of discharge and discharge current of the storage unit in the next control time-step is estimated based on forecast of generation and demand levels in the network, current value of depth of discharge and battery voltage. If the estimated depth of discharge and/or discharge current is out of recommended range, the management system will not operate the storage unit. The system estimates the life of the storage unit at each control time-step and halts the energy storage operation if it is close to end of its life. In one embodiment, particle swarm optimization (PSO) can be used to find the most accurate life estimation. see par 56 - In one embodiment for the grid-tied microgrids which include wind turbine and PV solar panels as generation assets and Li-Ion battery as storage device, the system is optimally designed using HOMER.RTM. with real wind speed, solar radiation, grid electricity price, and load demand data. A dynamic model of each component for the designed system is developed in MATLAB/Simulink.RTM.. Various power management strategies are then implemented on the dynamic model of the microgrid with actual data for a year.
see also Fife – par 48 - An ESS of an electrical system may include one or more storage devices and any number of power conversion devices; The energy storage devices may be different in different implementations of the ESS. A battery is a familiar example; par 49, FIG. 1 - The building electrical system 102 includes one or more loads 122, one or more generators 124, and an energy storage system (ESS) 126. see par 59 - The controller 110 receives the process variables for determining values for control variables to be communicated to the building electrical system 102 to effectuate a change to the building electrical system 102 toward meeting a controller objective for the building electrical system 102. For example, the controller 110 may provide a control variable to adjust the load 122, to increase or decrease generation by the generator 124, and to utilize (e.g., charge or discharge) the ESS 126. For example, the controller 110 may provide a control variable to adjust the load 122, to increase or decrease generation by the generator 124, and to utilize (e.g., charge or discharge) the ESS 126. The controller 110 may also receive a configuration (e.g., a set of configuration elements), which may specify one or more constraints of the electrical system 102. The controller 110 may also receive external inputs (e.g., weather reports, changing tariffs, fuel costs, event data), which may inform the determination of the values of the control variables. A set of external inputs may be received by the controller 110; see par 81 – examples of configuration element include ESS degradation properties as a function of “discharge or charge rate”; see par 128 - Since X.sub.opt represents the control parameters, this example process fully specifies the control that will provide minimum cost (e.g., optimal) operation during the future time domain; see par 133 - sending control variables to the electrical system.).
Asghari and Fife are analogous art as they are directed to performing measurements and modeling/estimations on equipment that generate electricity/voltage (Asghari Abstract, par 56; Fife Abstract, par 39). Asghari discloses using estimates for a next control-time step based on forecast of generation and demand levels in the network, considering depth of discharge, discharge current, battery voltage, estimated life of storage unit, and using particle swarm optimization (PSO) (See par 53).
Fife improves upon Asghari by disclosing simulating to provide control parameters and process variables for use in different time domains, and sometimes the controls can be lasting for the future (See par 127-128, 33). One of ordinary skill in the art would be motivated to further include simulating and optimizing control parameters and using settings from different time domains in a future time domain to efficiently improve upon the estimates for a next control-time step from a forecast and optimization in Asghari.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
estimated operational conditions for managing a microgrid as disclosed in Asghari, and further using simulations to provide control parameters and process variables for different time domains as disclosed in Fife, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 2, Asghari and Fife disclose:
The method of claim 1, wherein the parameter evolution model describes the evolution of one, several, or all first model parameter values of the power system asset model as a function of operating conditions and/or ambient conditions
(Applicant’s [0222] as published states “At step 131, information on one or several operating condition(s) and/or ambient condition(s) is received. The information may be received via a user interface. The information on the operating condition(s) may specify a charging or discharging current, a depth of discharge, or other information relating to operation of the BESS. The information on ambient condition(s) may specify an ambient temperature in an enclosure in which the BESS is housed.”
Asghari – see par 31 - Different methods can be applied to perform the curve fitting. Two different methods have been used in this study including particle swarm optimization (PSO) and non-linear least square (NLLS) method from MATLAB curve fitting toolbox. PSO, as a heuristic optimization technique, is able to achieve optimal solution in a small fraction of a second. Also, NLLS is a popular regression method. see par 38 - One embodiment uses article swarm optimization (PSO) as a heuristic optimization technique to achieve optimal solution in a small fraction of a second and is a curve fitting tool compatible with exponential nonlinear battery characteristics in terms of cycle lives versus depth of discharges. see par 52 - The system continuously monitors temperature of a storage device to ensure it is within the recommended range. In case of any breach of temperature limits, the power management system halts the energy storage operation until necessary actions are taken by the user; see par 56 - A dynamic model of each component for the designed system is developed in MATLAB/Simulink.RTM.. Various power management strategies are then implemented on the dynamic model of the microgrid with actual data for a year.
see also Fife – see par 169 - A method of predicting load, according to one embodiment of the present disclosure, may perform a load prediction considering historic periodic trends or shapes such as a daily trend or shape. The load prediction can execute every time an EO executes an EO process, or it can execute more or less frequently. The load prediction may be executed by performing a regression of a parameterized historic load shape against historic load data (typically less than or equal to 24 hours) in one embodiment. Regression algorithms such as least squares may be used. A compilation of historic trends may be recorded as a historic average (or typical) profile or an average load shape. see par 318 - FIG. 20 is a pair of graphs 2010, 2020 that illustrate a battery's lifetime. Graph 2010 shows the manufacturer's data 2012 for battery cycle life (number of cycles) versus depth of discharge under continuous cycling conditions. Graph 2020 shows the manufacturer's data 2022 for battery lifetime (in years) versus depth of discharge assuming one cycle per day).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 3, Asghari discloses in claim 18 that “estimating battery life” refers to “degradation” and that “Curve fitting can be used to define analytical functions describing the behavior of a battery to be used during the battery life estimation process. One embodiment uses article swarm optimization (PSO) as a heuristic optimization technique to achieve optimal solution in a small fraction of a second and is a curve fitting tool” (See par 38).
Fife discloses:
The method of claim 1, wherein generating the output comprises performing degradation diagnostics using the parameter evolution model, wherein the output is generated as a function of a result of the degradation diagnostics (Applicant’s [0197] as published states “ Suggested control actions that enhance the RUL or that slow down a degradation of other KPIs may be automatically determined (e.g., by identifying operational and/or ambient conditions that are optimum with regard to mitigating future performance degradation while meeting desired performance of the power system asset).”
Fife – See par 294 – battery degradation is written in the form of a time or SoC derivative that can be integrated numerically as part of the cost function control simulation to yield battery degradation during the future time domain. In one embodiment, this degradation derivative can be comprised of two components: a wear component (or throughput component) and an aging component. The components can be numerically integrated vs. time using an estimate of the battery SoC at each time step in one embodiment. see par 321 - Similar to the degradation model described above, the efficiency and maximum charge and discharge rates may be parameterized with constants that achieve a substantial “fit” between the model and the expected battery performance. Once these battery performance models are defined and parameters are provided, they may be used in the cost function control simulation to better predict the outcome of application of various control parameter sets).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 5, Asghari and Fife disclose:
The method of claim 1, wherein the forecast is used to generate an output that comprises at least one of the following:
a remaining useful life (RUL) based on the forecast future evolution (Asghari – see par 38-39 - Curve fitting can be used to define analytical functions describing the behavior of a battery to be used during the battery life estimation process. One embodiment uses article swarm optimization (PSO) as a heuristic optimization technique to achieve optimal solution in a small fraction of a second and is a curve fitting tool; FIG. 3 shows an exemplary battery life estimator 310.);
a future power system asset state; or
a future performance of the power system asset (Asghari see par 52 - In this embodiment, the power management system is equipped with direct measurement and/or estimation tools for energy storage performance and other parameters in the network; see par 53 - The system continuously monitors SOH of a storage device to ensure it is within the recommended range. In case of any breach of SOH limits, the power management system halts the energy storage operation until necessary actions are taken by the user. The system can be based on direct measurement as well as estimation of some parameters related to the energy storage unit.
see also Fife – see par 291 - A battery's condition, lifetime, and/or state of health (SoH) may be modeled and/or determined by its degradation rate (or rate of reduction of capacity and its capacity at end of life). see par 293 - the battery degradation and its associated cost is included as a cost element in the cost function. By including battery degradation cost in the cost function, as the EO executes to find the minimum cost, the EO can effectively consider the contribution of battery degradation cost for each possible control parameter set X. In other words, the EO can take into account a battery degradation cost when determining (e.g., from a continuum of infinite control possibilities) an optimal control parameter set X.sub.opt. To accomplish this, a parameterized model of battery performance, especially its degradation rate, can be developed and used in the cost function during the simulation of potential control solutions (e.g., sets of control parameters X). The battery parameters (or constants) for any battery type can be determined that provide a closest fit (or sufficiently close fit within a prescribed tolerance) between the model and the actual battery performance or degradation; see par 316 - Once the battery capacity lost is determined over the future time domain by numerically integrating the above equation, the cost of operating the battery (e.g., a battery degradation cost) during that future time domain can be calculated where BatteryCost.sub.total is a total battery cost (for example an initial or net present cost) and C.sub.f,EoL is the fractional battery capacity remaining at end of life).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 6, Asghari and Fife disclose:
The method of claim 1, wherein the parameter evolution model describes the evolution of one, several, or all first model parameter values of the power system asset model as a function of ambient conditions and/or operating conditions, and the method comprises using the parameter evolution model to generate several forecasts for a future evolution of the one, several, or all first model parameter values of the power system asset model, the several forecasts being determined for different future ambient conditions and/or operating conditions (Asghari – see par 53 - . If the estimated depth of discharge and/or discharge current is out of recommended range, the management system will not operate the storage unit. The system estimates the life of the storage unit at each control time-step and halts the energy storage operation if it is close to end of its life. In one embodiment, particle swarm optimization (PSO) can be used to find the most accurate life estimation. Non-heuristic methods can be used to find the most accurate life estimation. The system can be based on direct measurement as well as estimation of some parameters related to the energy storage unit. The management system regulates the future operational condition of the storage unit based on measured/estimated parameters.
Fife – see par 293 - the battery degradation and its associated cost is included as a cost element in the cost function. By including battery degradation cost in the cost function, as the EO executes to find the minimum cost, the EO can effectively consider the contribution of battery degradation cost for each possible control parameter set X. In other words, the EO can take into account a battery degradation cost when determining (e.g., from a continuum of infinite control possibilities) an optimal control parameter set X.sub.opt. To accomplish this, a parameterized model of battery performance, especially its degradation rate, can be developed and used in the cost function during the simulation of potential control solutions (e.g., sets of control parameters X). The battery parameters (or constants) for any battery type can be determined that provide a closest fit (or sufficiently close fit within a prescribed tolerance) between the model and the actual battery performance or degradation).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 7, Asghari and Fife disclose:
The method of claim 6, further comprising receiving user input specifying at least a sub-set of the future ambient conditions and/or operating conditions, and/or automatically generating at least a sub-set of the future ambient conditions and/or operating conditions (Asghari – see par 51 - The system also limits the maximum operation time of a storage unit within each time interval (e.g. hour, day, month, . . . ). Lifetime of a storage unit is increased by indirectly by imposing constraints on its operational power. The system limits the minimum discharge power of a storage unit thus reduces its operational time
see also Fife -see par 323- The determined control plan may include a plurality of sets of parameters each to be applied for a different time segment within an upcoming time domain. The EO 2100 may determine the control plan based on a set of configuration elements specifying one or more constraints of the electrical system 2118 and defining one or more cost elements associated with operation of the electrical system. The EO 2100 may also determine the control plan based on a set of process variables that provide one or more measurements of a state of the electrical system 2118).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 8, Asghari discloses that in FIG. 7 (see page 4 – a measurement of temperature relative to time over each minute) that cooling the solar cells leads to decreasing a stabilized temperature and transient time is decreased (See page 5, Col. 2, last section) and discloses that long-term temperature operation of solar cells leads to aging and deterioration of solar cells and that with proper cooling/heating, solar cells do not overheat, and their lifetime is extended and the efficiency drop is decelerated (See page 6, col. 1, 1st paragraph).
Fife discloses:
The method of claim 1, comprising:
using the power system asset model to perform diagnostics for events that happen on a first time scale (Asghari – see par 53 - The system also estimates discharge power of the storage unit in the next control time-step based on forecast of generation and demand levels in the network. If the estimated discharge power is out of recommended range, the management system will not operate the storage unit. Depth of discharge and discharge current of the storage unit in the next control time-step is estimated based on forecast of generation and demand levels in the network, current value of depth of discharge and battery voltage.
see also Fife – see par 321 - Similar to the degradation model described above, the efficiency and maximum charge and discharge rates may be parameterized with constants that achieve a substantial “fit” between the model and the expected battery performance. Once these battery performance models are defined and parameters are provided, they may be used in the cost function control simulation to better predict the outcome of application of various control parameter sets. see par 441 - In the example of FIG. 30, a random variable is used to account for the uncertainty in a forecasted load 3002. Thus, the graphical representation 3000 includes a plot of forecasted load 3002 over the entire upcoming time domain. Possible values for control variables or decision variables for controlling an electrical system may be fluctuated to minimize an expected value of the cost function.; see par 450, FIG. 31 - generating 3110 probability distribution functions corresponding to probability density of a given random variable value occurring at different points in time of a future period of time includes accounting for predicted fluctuations in one or more configuration elements of the electrical system. By way of non-limiting example, the one or more configuration elements of the electrical system may include one or more of an energy storage system (ESS) configuration, … an ESS degradation), and
using the parameter evolution model to identify trends that happen on a second time scale, the second time scale exceeding the first time scale (Applicant’s [0255] as published states “Steps 161-167 may be considered a procedure for performing diagnostics of slowly evolving changes, such as degradation.” [0256] as published states “The slowly evolving changes/events in the system may be indicative of degradation.” Fife – see par 450, FIG. 31 - generating 3110 probability distribution functions corresponding to probability density of a given random variable value occurring at different points in time of a future period of time includes accounting for predicted fluctuations in one or more configuration elements of the electrical system. By way of non-limiting example, the one or more configuration elements of the electrical system may include one or more of an energy storage system (ESS) configuration, … an ESS degradation; See par 451 - The method 3100 also includes determining 3120 a set of control values for a set of control variables that correspond to a minimum expected value of the economic cost of operating the electrical system over the future period of time. Accordingly, decision values of the decision variables may be varied to determine the minimum expected value of the cost of operating the electrical system in order to determine an optimal set of control values for the control variables. The method 3100 further includes controlling 3130 the electrical system based on the determined set of control values.).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above. In addition, Fife improves upon Asghari by analyzing fluctuations at different points of time related to configuration and degradation.
Concerning claim 9, Asghari discloses that its closed-loop operation includes updates from sensor/estimator modules to inform the output commands (See par 52). Fife discloses:
The method of claim 8, wherein at least one of the sets of first model parameter values of the power system asset model is used in combination with measurements to compute a current power system asset state, to compute residuals between measurements and observables estimated from the current power system asset state, and to perform diagnostics for events that happen on the first time scale based on the residuals (Fife – see par 169 - A method of predicting load, according to one embodiment of the present disclosure, may perform a load prediction considering historic periodic trends or shapes such as a daily trend or shape. The load prediction can execute every time an EO executes an EO process, or it can execute more or less frequently. The load prediction may be executed by performing a regression of a parameterized historic load shape against historic load data (typically less than or equal to 24 hours) in one embodiment. Regression algorithms such as least squares may be used. A compilation of historic trends may be recorded as a historic average (or typical) profile or an average load shape. See par 396 - the uncertainty data 2619 may also be updated with the current data 2618. Similarly as discussed above with respect to the model data 2616, the uncertainty data 2619 may be updated using weighted averages. For example, a long-term probability distribution may be stored along with only a few most recent current data 2618 profiles for a period of time. The error or uncertainty metrics of the long-term probability distribution may be updated with error or uncertainty metrics of the few most recent current data 2618 profiles by computing a weighted average between the error or uncertainty metrics of the long-term probability distribution and the error or uncertainty metrics of the few most recent current data 2618 profiles. see par 427 - In some embodiments, fitting 2920 a PDF to the observed input data includes fitting the PDF to error data generated by comparing currently observed data (e.g., the current data 2618) measured from the load 522A to predicted or forecasted data (e.g., forecasted data generated based on the model data 2616 of FIG. 26)).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 and 8 above. In addition, Fife improves upon Asghari’s updating estimates with measurement, by analyzing fluctuations at different points of time related to configuration and degradation.
Concerning claim 10, Asghari and Fife disclose:
The method of claim 1, wherein the output comprises one or several of:
information output via a human machine interface (HMI) (Fife – see par 155 - The optimal control parameter set X.sub.opt is then output 616. In some embodiments, the output 616 of the optimal control parameter set X.sub.opt may be stored locally, such as to memory, storage, circuitry, and/or a processor disposed local to the EO process 601a. In some embodiments, the outputting 616 may include transmission of the optimal control parameter set X.sub.opt over a communication network to a remote computing device, such as the HSC 540 of FIG. 5. see par 328 - The input/output interface 2106 may facilitate interfacing with one or more input devices and/or one or more output devices. The input device(s) may include a keyboard, mouse, touch screen, light pen, tablet, microphone, sensor, or other hardware with accompanying firmware and/or software. The output device(s) may include a monitor or other display,); or
control signals that are output to a controller, in particular a battery management system (BMS) or microgrid controller (Asghari –see par 15-16 - The system provides top-level supervisory control which can be used in different applications such as distributed energy storage systems (DESS) and commercial and industrial (C&I) energy management systems. also applicable for a real-time management framework for a grid-tied microgrid based on storage life and cost estimation. see par 23 - Batteries are usually equipped with battery management system (BMS) which is defined as any electronic device that manages a rechargeable battery (cell or battery pack), by monitoring its state, calculating secondary data, reporting that data, protecting it, controlling its environment, and/or balancing it. see par 55 -. In case of any shortage in the local generation (negative mismatch power), the primary power management system triggers the secondary power management unit to remedy the shortage either by importing power from the grid or discharging the battery (or both).
Fife – See par 48 - An ESS of an electrical system may include one or more storage devices and any number of power conversion devices; The energy storage devices may be different in different implementations of the ESS. A battery is a familiar example; par 49, FIG. 1 - The building electrical system 102 includes one or more loads 122, one or more generators 124, and an energy storage system (ESS) 126. see par 140 - The processors may be computers, microcontrollers, CPUs, logic devices, or any other digital or analog device that can operate on pre-programmed instructions. If more than one processor is used, they can be connected electrically, wirelessly, or optically to pass signals between one another. In addition, the control variables can be communicated to the electrical system components electrically, wirelessly, or optically or by any other means).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above. In addition, Fife improves upon Asghari by disclosing having an output device of a display.
Concerning claim 11, Asghari and Fife disclose:
The method of claim 1, wherein the output comprises power system asset health information (Asghari – see par 53 - The system also provides State of health measurement (SOH. The system continuously monitors SOH of a storage device to ensure it is within the recommended range.
see also Fife – see par 291 - A battery's condition, lifetime, and/or state of health (SoH) may be modeled and/or determined by its degradation rate (or rate of reduction of capacity and its capacity at end of life); see also FIG. 21, 2106 interface; 2116 client computing device for receiving information).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning claim 15, Asghari and Fife disclose:
The method of claim 1, wherein
determining a set of first model parameter values comprises solving a discrete-time linear differential equation; and/or
determining the set of second model parameter values comprises at least one of machine learning, fitting, regression analysis, in particular linear regression (Asghari see par 31 - Different methods can be applied to perform the curve fitting. Two different methods have been used in this study including particle swarm optimization (PSO) and non-linear least square (NLLS) method from MATLAB curve fitting toolbox. PSO, as a heuristic optimization technique, is able to achieve optimal solution in a small fraction of a second. Also, NLLS is a popular regression method).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Concerning independent claim 16, Asghari discloses using Matlab software for its model (See par 56).
Fife discloses:
A computing system (Fife – see par 50 - The controller 110 may include electronic hardware and software in one embodiment. In one example arrangement, the controller 110 includes one or more processors and suitable storage media, which stores programming in the form of executable instructions which are executed by the processors to implement the control processes. See FIG. 21-22, e.g. processor 2102 ), comprising:
an interface operative to receive measurements indicative of a state of a power system asset (Asghari see par 52 – [same as cl. 1
Fife – see par 50 - The controller 110 may include electronic hardware and software in one embodiment. In one example arrangement, the controller 110 includes one or more processors and suitable storage media, which stores programming in the form of executable instructions which are executed by the processors to implement the control processes. See FIG. 21-22, e.g. processor 2102]); and
at least one integrated circuit coupled to the interface and operative to (Fife – see par 326 – FIG. 21, program modules 2120; The modules, components, and/or facilities disclosed herein may be implemented and/or embodied as a driver, a library, an interface…. portions of modules include… circuits, interface components)
process the received measurements to determine a series of sets of first model parameter values for a power system asset model, each of the sets of first model parameter values being determined for a different time or time interval (Asghari see par 52 [same as cl. 1]),
The remaining limitations are the same as claim 1 above. It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above.
Claims 13-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Asghari (US 2013/0024042), in view of Fife (US 2020/0006946), as applied to claims 1-3, 5-11, and 15-16 above, and further in view of Belabbas, et. al., “Power management and control strategies for off-grid hybrid power systems with renewable energies and storage,” 2019, Energy Systems, Vol. 10, No. 2, pages 355-384.
Concerning claim 13, Asghari and Fife disclose:
The method of claim 1, wherein the power system asset is or comprises a rechargeable energy storage system (ESS), in particular an electro-chemical ESS… (Asghari – see par 15 - The system is also valid for a wide range of batteries. The system provides top-level supervisory control which can be used in different applications such as distributed energy storage systems (DESS) and commercial and industrial (C&I) energy management systems. Fife – See par 48 - An ESS of an electrical system may include one or more storage devices and any number of power conversion devices; The energy storage devices may be different in different implementations of the ESS. A battery is a familiar example of a chemical energy storage device; see par 318 - Graph 2010 shows the manufacturer's data 2012 for battery cycle life (number of cycles) versus depth of discharge under continuous cycling conditions. Graph 2020 shows the manufacturer's data 2022 for battery lifetime (in years) versus depth of discharge assuming one cycle per day.)
Belabbas discloses BESS:
The method of claim 1, wherein the power system asset is or comprises a rechargeable energy storage system (ESS), in particular an electro-chemical ESS, “in particular a rechargeable battery energy storage system (BESS)” (Belabbas – see page 355, Abstract - paper presents a simulation study of standalone hybrid Distributed Generation Systems (DGS) with Battery Energy Storage System (BESS); see page 357, 3rd to last paragraph - lithium-ion (Li-ion) batteries are commonly employed to stock the surplus of energy derived from RPS and release it at a later stage. It has higher power density and voltage range as compared to other BESS).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above. In addition, Asghari, Fife, and Belabbas are analogous art as they are directed to performing measurements and modeling/estimations on equipment that generate electricity/voltage (Asghari Abstract, par 56; Fife Abstract, par 39; Belabbas Abstract). Asghari discloses energy storage for batteries (See par 15). Fife improves upon Asghari by disclosing a battery or “ESS” for storage. Belabbas improves upon Asghari and Fife by disclosing having a BESS. One of ordinary skill in the art would be motivated to further include a BESS to efficiently improve upon the battery and energy storage systems in Asghari and the ESS for storage in Fife.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
estimated operational conditions for managing a microgrid as disclosed in Asghari, and further using simulations to provide control parameters and process variables for different time domains as disclosed in Fife, and further using a BESS as disclosed in Belabbas, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 14, Asghari, Fife, and Belabbas disclose:
The method of claim 13, wherein the ESS comprises a plurality of cells or a plurality of cell strings, and wherein the sets of first model parameter values and the set of second model parameter values of a parameter evolution model are determined separately for each of the plurality of cells or cell strings (Asghari – see par 30 – each cell has a finite useful life; as function of DoD (Depth of discharge);
see also Fife – see par 83 ESS Configuration includes, if a battery, number of cells in series and parallel;
see also Belabbas page 360, 1st paragraph - Where I0 is the diode saturation current, a is the diode ideality factor, Vt is the thermal voltage, Ns represents the number of cells connected in series, K denotes the Boltzmann’s constant, T is the actual temperature and q is the charge of the electron; see equation 5 using Ns to calculate thermal voltage for each “set of cells”; current Io then calculated in equation 7 based on the Vt).
It would be obvious to combine Asghari and Fife and Belabbas for the same reasons as claim 1 and 13 above.
Concerning claim 17, Asghari, Fife, and Belabbas disclose:
A system (Fife – see par 50 - The controller 110 may include electronic hardware and software in one embodiment. In one example arrangement, the controller 110 includes one or more processors and suitable storage media, which stores programming in the form of executable instructions which are executed by the processors to implement the control processes. See FIG. 21-22, e.g. processor 2102), comprising:
a battery energy storage system (BESS) (Belabbas – see page 355, Abstract - paper presents a simulation study of standalone hybrid Distributed Generation Systems (DGS) with Battery Energy Storage System (BESS));
a data acquisition unit operative to collect measurements indicative of a state of the BESS (Asghari see par 52 [same as cl. 1];
see also Fife par 59 - FIG. 1, the building electrical system 102 may provide information to the controller 110, such as in a form of providing process variables. The process variables may provide information, or feedback, as to a status of the building electrical system 102 and/or one or more components (e.g., loads, generators, ESSs) therein. For example, the process variable may provide one or more measurements of a state of the electrical system); and
the computing system of claim 16 coupled to the data acquisition unit (Fife – see par 50 - The controller 110 may include electronic hardware and software in one embodiment. In one example arrangement, the controller 110 includes one or more processors and suitable storage media, which stores programming in the form of executable instructions which are executed by the processors to implement the control processes. See FIG. 21-22, e.g. processor 2102).
It would be obvious to combine Asghari and Fife and Belabbas for the same reasons as claim 1, 13, and 16 above.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Asghari (US 2013/0024042), in view of Fife (US 2020/0006946), as applied to claims 1-3, 5-11, and 15-16 above, and further in view of Zeng (CN 105743126).
Concerning claim 18, Asghari discloses “ In case of any breach of SOC limits, the power management system halts the energy storage operation until necessary actions are taken by the user. The system continuously monitors temperature of a storage device to ensure it is within the recommended range. In case of any breach of temperature limits, the power management system halts the energy storage operation until necessary actions are taken by the user.” (See par 52). Fife discloses having costs of benefits associated with a change energy between beginning and end of the future time domain (See par 142) and having an output display in FIG. 21-22 (see e.g. 2106 in par 328).
Zeng discloses:
The method of claim 10, wherein the information output via the human machine interface comprises an alarm or warning (Zeng – see page 5, 3rd paragraph – microgrid; power system fault analysis includes management of battery energy storage system and load; see page 7, 3rd paragraph – FIG. 3, human-computer interaction module; accident alarm unit; transmits collected to a computer with alarm when system is about to or has failed; see page 8, 2nd to last paragraph – fault alarm and fault diagnosis unit based on expert system in grid energy management system).
It would be obvious to combine Asghari and Fife for the same reasons as claim 1 above. Asghari, Fife, and Zeng are analogous art as they are directed to performing measurements and modeling/estimations on cells that generate electricity/voltage (Asghari Abstract, par 56; Fife Abstract, par 39; Zeng Abstract – with photovoltaic distributed power). Asghari discloses that limits/constraints can be breached, and a user must act accordingly (See par 52-53). Fife discloses having costs of benefits associated with a change energy between beginning and end of the future time domain (See par 142) and having an output display in FIG. 21-22 (see e.g. 2106 in par 328).
Zeng improves upon Asghari and Fife by disclosing having alarms. One of ordinary skill in the art would be motivated to further include alarms to efficiently improve upon the existence of SOC/temperature limits being breached in Asghari and the showing of cost of benefits with different changes in Fife.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
estimated operational conditions for managing a microgrid as disclosed in Asghari, and further using simulations to provide control parameters and process variables for different time domains as disclosed in Fife, and further having alarms as disclosed in Zeng, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Asghari (US 2013/0024042), in view of Fife (US 2020/0006946), as applied to claims 1-3, 5-11, and 15-16 above, and further in view of Luzi et al., “A PSO algorithm for transient dynamic modeling of lithium cells through a nonlinear RC filter,” 2016 IEEE Congress on Evolutionary Computation (CEC), pages 279-286.
Concerning claim 20, Asghari discloses looking at future discharge decisions based on accumulated information over time and controlling relative to next time-step (See par 53-54). Fife discloses control variable to utilize discharge of ESS 126 (see par 159) and having battery condition, lifetime, and/or SoH (State of Health) evaluated based on series “resistance” of the battery (See par 291-292).
Luzi discloses:
The method of claim 15, wherein the set of first model parameter values comprises parameter values for:
a self-discharging resistance (Luzi – page 279, col. 1, last paragraph - In both the energetic sustainability and the sustainable mobility a key device is the Battery Management System (BMS). It includes the electronics and the algorithms aiming to monitor, protect and manage the battery pack. The principal
tasks of an effective BMS are: to preserve the batteries health preventing damages due to over-charging or over-discharging; to estimate the State of Charge (SoC) and the State of Health (SoH); to execute the cell balancing; page 281, col. 1, last paragraph - With the purpose of modeling the nonlinearities of the cell through a connection of nonlinear components [10], the dynamic contribution has been modeled by means of a single nonlinear RC filter. The proposed model is shown in Fig.3. It is composed of the parallel connection of a standard linear resistor and a nonlinear voltage driven capacitor; see page 281, col. 2, last paragraph - the governing equation for the nonlinear RC dipole shown in Fig.3 can be derived. Let Ic, Ir, Iin and Vdyn be the current flowing in the capacitor, the current flowing in the resistor, the input current of the cell and the voltage measured on the parallel between Rdyn and Cdyn respectively, then);
a first RC element to model hysteresis for charging and discharging (Luzi page 285, Col. 1, 3rd paragraph - the hysteresis is modeled switching between the charging and the discharging OCV-SoC curves according with the sign of the input current. This approach aims at better investigating how the hysteresis affects the accuracy of the models);
a second RC element to model long-time transients; and
a third RC element to model short-time transients (Luzi discloses both of last two limitations – See page 281, Section III – “Proposed Transient Model”, the dynamic behavior is commonly modeled by means of a series of RC parallel groups; See page 282, Section IV – “Nonlinear RC Filter Parameters Estimation”
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Asghari, Fife, and Luzi are analogous art as they are directed to performing measurements and modeling/estimations on cells that generate electricity/voltage (Asghari Abstract, par 56; Fife Abstract, par 39; Luzi Abstract – with ESS and Battery Management System). Asghari discloses looking at future discharge decisions based on accumulated information over time and controlling relative to next time-step (See par 53-54). Fife discloses control variable to utilize discharge of ESS 126 (see par 159) and having battery condition, lifetime, and/or SoH (State of Health) evaluated based on series “resistance” of the battery (See par 291-292). Luzi improves upon Asghari and Fife by disclosing having discharge, hysteresis for charging and discharging, and “transients” modeled with RC elements. One of ordinary skill in the art would be motivated to further include discharge, hysteresis for charging and discharging, and “transients” modeled with RC element to efficiently improve upon the modelling and controlling for the future based on accumulated information on discharging in Asghari and discharge and resistance in Fife.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
estimated operational conditions for managing a microgrid as disclosed in Asghari, and further simulations to provide control parameters and process variables for different time domains as disclosed in Fife, and further discharge, hysteresis for charging and discharging, and “transients” modeled with RC elements as disclosed in Luzi, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Response to Arguments
Applicant's arguments filed 8/7/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
Applicant’s arguments are moot in view of the new rejections necessitated by the amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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