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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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 11-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (Liu, Y., Park, B., Sun, K., Dimitrovski, A., & Simunovic, S. (2022). Parallel-in-time power system simulation using a differential transformation based adaptive parareal method. IEEE Open Access Journal of Power and Energy, 10, 61-72.).
Regarding Claim 1, Liu teaches:
obtaining a model of an electrical power system; (Abstract, parallel-in-time simulation of large-scale power systems; Section 1, transient stability assessment, of a large-scale power grid under contingencies; Section 1, high-dimensional nonlinear power system model; Section 2, simulating a power system model)
executing a simulation of the model including by separately simulating, in parallel, behaviors of the electric power system during each of a consecutive series of time periods to provide individual simulation results for each time period, (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
where the consecutive series of time periods together represent a simulation time frame over which the behaviors of the electric power system are simulated; (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems)
combining the individual simulation results into a simulation output for the simulation time frame; and (Fig. 1; Section 1, Then, a fine solver is used to solve all coarse time steps in parallel, whose solutions are used to update the initial values of each coarse time step. Finally, the above procedure is performed iteratively until convergence.)
providing, for display, the simulation output. (Section II., Then, the Parareal method is applied to each window sequentially, and the trajectories of state variables in all windows are connected together to give the trajectory of desired simulation length.; Fig. 4; Section III.C., Algorithm 3 insert shows the proposed VOVS-DT strategy, where the outputs include not only the trajectories of state variables but also the variable time step lengths h(tn) and variable orders K(tn) during the simulation; Section III.C., the outputs include not only the trajectory of state variables but also the variable window lengths H and the number of iterations M in each window during the simulation; For the DT-based adaptive Parareal method, Fig. 4 shows the trajectories of rotor angles of all generators, voltages of all buses, and electrical power outputs of all generators.)
Regarding Claim 2, Liu teaches:
receiving user input defining simulation parameters that comprise one or more of: a length of the simulation time frame, a length of the time periods, a number of the time periods, a load curve data representing a baseline load curve of the electrical power system over a canonical time-period, and mutation data representing one or more alterations to the model and the baseline load curve to be applied during different time periods of the simulation. (Section III.B., Algorithm 1 DT Method … input: t0,tend,x0,h,K; Section III.C., Algorithm 3 VOVS-DT Strategy … input: t0,tend,x0,h0,K0; see also algorithm 4 inputs in that section.; Section IV., the parameters in the algorithms are selected as …)
Regarding Claim 3, Liu teaches:
assigning a respective time-period to each of a plurality of processors, where each processor is configured to separately execute simulation of behaviors of the electric power system during the respective time-period. (Section I., In the parallel computing category, existing methods mainly parallelize computations among multiple simulation runs for different contingencies. They still have limited ability of parallelization on a single simulation run, even with simplified models and many parallel processors in high-performance computers; Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
Regarding Claim 4, Liu teaches:
providing, to each processor, the model of the electric power system, (Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
load curve data representing a baseline load curve of the electrical power system, and (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; examiner notes that the coarse data falls within the scope of the claim language here)
mutation data representing one or more alterations, relevant to the respective time period, to at least one of the model or the load curve data. (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; examiner notes that the coarse solution update falls within the scope of the claim language here)
Regarding Claim 5, Liu teaches:
generating, for one or more time periods, a time-period specific load curve data representing a baseline load curve of the electrical power system that is adjusted to incorporate mutation data indicating one or more changes to the load curve relevant for the respective time period; and (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; examiner notes that the coarse solution update falls within the scope of the claim language here)
providing, to each processor, time-period specific load curve data associated with the respective time-period assigned to the processor. (Section I., In the parallel computing category, existing methods mainly parallelize computations among multiple simulation runs for different contingencies. They still have limited ability of parallelization on a single simulation run, even with simplified models and many parallel processors in high-performance computers; see Section II. as cited for the prior limitation)
Regarding Claim 6, Liu teaches:
generating, for one or more time periods, a time-period specific model representing the model of the electrical power system that is adjusted to incorporate mutation data indicating one or more changes to the electric power system relevant for the respective time-period; and (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; examiner notes that the coarse solution update falls within the scope of the claim language here)
providing, to each processor, the time-period specific model of the electric power system associated with the respective time-period assigned to the processor. (Section I., In the parallel computing category, existing methods mainly parallelize computations among multiple simulation runs for different contingencies. They still have limited ability of parallelization on a single simulation run, even with simplified models and many parallel processors in high-performance computers; see Section II. as cited for the prior limitation)
Regarding Claim 7, Liu teaches:
applying, for each time-period subsequent to a first time period, estimated initial conditions of the electric power system at a start time of the respective time period; and (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; examiner notes that the coarse data falls within the scope of the claim language here)
verifying, for at least one particular time-period subsequent to a first time period, accuracy of the estimated initial conditions for the particular time period including by determining whether the estimated initial conditions for the particular time period are within a threshold value of simulation output values from simulation of an immediately preceding time period. (Section II., Then, the block of initial coarse evaluation computes the initial coarse solutions xc,0n by performing the simulation over [t0 , tN ] in serial, using a coarse operator C with the coarse step length hc . Afterward, the iteration process is performed between the blocks of fine evaluation and the coarse solution update. The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf . The block of coarse solution update is to correct the coarse solution at each coarse interval. The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.; see the "pre-defined threshold" option of the reference as cited here.)
Regarding Claim 8, Liu teaches:
responsive to determining that the estimated initial conditions for the particular time period are not within the threshold value of the simulation output values from simulation of the immediately preceding time period: (Section II., The iteration process continues until the stopping criteria is met, e.g., the differences of coarse solutions between the mth iteration and the (m+1 )th iteration are smaller than a pre-defined threshold, or the maximum number of iterations is reached.)
adjusting the estimated initial conditions for the particular time-period based on the simulation output values to obtain adjusted initial conditions; and (Section II., the iteration process is performed between the blocks of fine evaluation and the coarse solution update.)
re-simulating behaviors of the electric power system for at least a portion of the particular time-period by applying the adjusted initial conditions. (Section II., the iteration process is performed between the blocks of fine evaluation and the coarse solution update … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf )
Regarding Claim 11, Liu teaches:
where the mutation data comprises one or more of: an addition of a solar power source to the model at a respective time-period, a change to an electric system component at a respective time-period, and an expansion of the electric power system at a respective time-period. (Section IV., The proposed DT-based adaptive Parareal method is first tested on the IEEE 39-bus system [26] with classical generator models, and the Polish 2383-bus system [31] with detailed models of generators, exciters, governors, and turbines … For the 2383-bus system, the simulated contingency is a permanent three-phase fault at bus 9 applied at t=1 second and cleared after 0.4 seconds by tripping the line between bus 6 and bus 9.)
Regarding Claim 12, Liu teaches:
a central computing system in communication with a plurality of processing cores; and one or more data stores coupled to the central computing system and the processing cores, the one or more data stores having instructions stored thereon which, when executed by the central computing system and the processing cores, causes the central computing system and the processing cores to perform operations comprising: (Section I., In the parallel computing category, existing methods mainly parallelize computations among multiple simulation runs for different contingencies. They still have limited ability of parallelization on a single simulation run, even with simplified models and many parallel processors in high-performance computers; Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
obtaining a model of an electrical power system; (Abstract, parallel-in-time simulation of large-scale power systems; Section 1, transient stability assessment, of a large-scale power grid under contingencies; Section 1, high-dimensional nonlinear power system model; Section 2, simulating a power system model)
executing a simulation of the model including by separately simulating, in parallel on different ones of the processing cores, behaviors of the electric power system during each of a consecutive series of time periods to provide individual simulation results for each time period, (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
where the consecutive series of time periods together represent a simulation time frame over which the behaviors of the electric power system are simulated; (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems)
combining the individual simulation results into a simulation output for the simulation time frame; and (Fig. 1; Section 1, Then, a fine solver is used to solve all coarse time steps in parallel, whose solutions are used to update the initial values of each coarse time step. Finally, the above procedure is performed iteratively until convergence.)
providing, for display, the simulation output. (Section II., Then, the Parareal method is applied to each window sequentially, and the trajectories of state variables in all windows are connected together to give the trajectory of desired simulation length.; Fig. 4; Section III.C., Algorithm 3 insert shows the proposed VOVS-DT strategy, where the outputs include not only the trajectories of state variables but also the variable time step lengths h(tn) and variable orders K(tn) during the simulation; Section III.C., the outputs include not only the trajectory of state variables but also the variable window lengths H and the number of iterations M in each window during the simulation; For the DT-based adaptive Parareal method, Fig. 4 shows the trajectories of rotor angles of all generators, voltages of all buses, and electrical power outputs of all generators.)
Regarding Claims 13-17 and 19:
Claims 13-17 and 19 are substantially similar to claims 2-6 and 9 respectively, and are rejected under the same grounds as those claims above.
Regarding Claim 20, Liu teaches:
obtaining a model of an electrical power system; (Abstract, parallel-in-time simulation of large-scale power systems; Section 1, transient stability assessment, of a large-scale power grid under contingencies; Section 1, high-dimensional nonlinear power system model; Section 2, simulating a power system model)
executing a first simulation of the model for a first time period to obtain first simulation results representing behaviors of the electric power system during the first time-period; (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
in parallel with the first simulation, executing a second simulation of the model for a second time period to obtain second simulation results representing behaviors of the electric power system during the second time period, the second time period being immediately consecutive to the first time period within a consecutive series of time periods that together represent a simulation time frame during which behaviors of the electric power system are simulated; (Fig. 1; Abstract, parallel-in-time simulation of large-scale power systems; Section 2, Fig. 1 illustrates the basic idea of the Parareal method for simulating a power system model … The block of fine evaluation is to provide a better solution xf,0n at each interval by simulating each coarse interval [tn , tn+1 ] in parallel using a fine operator F with the fine step length hf)
combining the first simulation results and the second simulation results into a simulation output for the simulation time frame; and (Fig. 1; Section 1, Then, a fine solver is used to solve all coarse time steps in parallel, whose solutions are used to update the initial values of each coarse time step. Finally, the above procedure is performed iteratively until convergence.)
providing, for display, the simulation output. (Section II., Then, the Parareal method is applied to each window sequentially, and the trajectories of state variables in all windows are connected together to give the trajectory of desired simulation length.; Fig. 4; Section III.C., Algorithm 3 insert shows the proposed VOVS-DT strategy, where the outputs include not only the trajectories of state variables but also the variable time step lengths h(tn) and variable orders K(tn) during the simulation; Section III.C., the outputs include not only the trajectory of state variables but also the variable window lengths H and the number of iterations M in each window during the simulation; For the DT-based adaptive Parareal method, Fig. 4 shows the trajectories of rotor angles of all generators, voltages of all buses, and electrical power outputs of all generators.)
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Liu (Liu, Y., Park, B., Sun, K., Dimitrovski, A., & Simunovic, S. (2022). Parallel-in-time power system simulation using a differential transformation based adaptive parareal method. IEEE Open Access Journal of Power and Energy, 10, 61-72.) in view of Rasekh (US 10984163 B1).
Regarding Claim 10:
Liu does not teach in particular, but Rasekh teaches:
where each time-period, except for a last one of the time periods, overlaps with a portion of an immediately subsequent time-period. (col 4 line 10-25, An overlap time period 306 that has a specific duration (e.g., equal to or larger than a time constant τ) is padded to the beginning of the window 304 ... The duration of the overlap time period 306 is selected to be equal to or larger than the time constant τ so that after the overlap time period 306 has elapsed, the simulation results of the window 304 do not depend on one or more waveforms associated with the preceding window 302.; col 2 line 40-50, in response to the one or more error parameters not being within a range, adjust the duration of the one or more overlap time periods until the one or more error parameters fall within the range; and in response to the one or more error parameters being within the range, generate a simulation output based at least in part on the simulation results)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the overlap time period of Rasekh to the power system simulation of Liu, specifically for its fine time blocks, in order to ensure the simulation results of the window are accurate (Rasekh, col 4 line 35-40).
Allowable Subject Matter
Claims 9 and 18 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 9 and 18 recite in part: “… comprises re-simulating only a period of time during which the simulation output values associated with the particular time period did not converge with the simulation output values associated with the immediately preceding time period.”
The prior art does iteratively re-simulate fine intervals until convergence, but the re-simulation is done for the entire fine interval that needs its accuracy improved - there is no teaching or suggestion anywhere in the art of only re-simulating the portion of the fine interval that covers the period of time where there is no convergence. Note that the full context and antecedent basis of claim 9, including its preceding claims, is required for this distinction, as otherwise the language may include details such as selective simulation of the full fine intervals that do not converge in the context of a larger coarse interval (as opposed to the claims which would require a portion of the fine interval be re-simulated and a portion not).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Duan (US 20170153682 A1) discusses parallel execution of power system simulations with complex analysis of divergence indicators between parallel entities.
Gurrala (Gurrala, G., Dimitrovski, A., Pannala, S., Simunovic, S., & Starke, M. (2015). Parareal in time for fast power system dynamic simulations. IEEE Transactions on Power Systems, 31(3), 1820-1830.) discusses parallel in time simulation of power systems using Parareal in detail and provides analysis of how it manages convergence.
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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189
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