DETAILED ACTION
Claims 1 and 3-21 are presented for examination. Claims 1, 6, 13, 17 and 19 are amended. Claim 2 is cancelled. Claim 21 is new. This office action is response to the submission on 2/27/2026.
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 .
Response to Arguments
With respect to 35 U.S.C. §112(b) Rejections:
Applicant’s arguments, see page 9 of applicant response filed 2/27/2026, with respect to claim 13 have been fully considered and are persuasive. The 35 U.S.C. §112(b) Rejection of claim 13 has been withdrawn.
With respect to 35 U.S.C. §103 Rejections:
Applicant’s arguments, see pages 9-10 of applicant response filed 2/27/2026, with respect to the rejections of claims 1, 9 and 17 under 35 U.S.C. §103 have been fully considered and are persuasive. Applicant argues that examiner did not establish Wanik (Non-Patent Literature: Intelligent Voltage Limit Violation Prediction for Active Distribution Network) as prior art. Examiner agrees. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Yeh (US20210057916A1) with respect to independent claims 1, 9 and 17 and Do Rosario et al. (US20160233682A1) with respect to dependent claims 4, 13 and 18.
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.
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, 5-8, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Henselmeyer (US20200395755A1) in view of Chung et al. (KR102281229B1) (citations to examiner provided translation), further in view of Yeh (US20210057916A1).
Claim 1:
Henselmeyer teaches “A method, comprising: measuring, at one or more of a plurality of phasor measurement units (PMUs) connected between a power grid and associated loads, a present power produced by photovoltaic systems deployed downstream from the one or more of the plurality of PMUs; measuring, at the one or more of the plurality of PMUs, a present demand for power from the associated loads;” (Henselmeyer teaches measurement locations 9 and 10, which would monitor the power demand and power generated, in a power distribution grid including electrical loads 5 and that the branches include photovoltaic generators 12 in Henselmeyer [0036] "Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a simple example of a power distribution grid 1. The power distribution grid 1 has a medium-voltage level 2 connected via a substation to a measurement location 8 and via a transformer 11 to a low-voltage level 3 of the distribution grid. The distribution grid 3 has two sections 6, 7 or branches. Each branch has multiple generators 12 of electric power, which are each depicted as a circuit. These can be wind turbines or photovoltaic installations, for example. Electrical loads 5 are depicted as arrows, the electrical loads 5 being able to be single-family homes or the like, for example. In section 7, there is provision for a measurement location 9 in the connecting line to the distribution grid 3. In the section 6, there is provision for a measurement location 10 in the connecting line to the distribution grid 3."; Henselmeyer teaches that measuring devices may be phasor measurement units in Henselmeyer [0012] "Measuring devices can be for example voltmeters, ammeters, phasor measurement units (PMUs), remote terminal units or smart meters, intelligent electrical devices (IEDs) for monitoring switches and other equipment, control devices e.g. for smart substations or protection equipment."
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Henselmeyer does not appear to explicitly teach “generating a power production prediction by the photovoltaic systems for an upcoming time period;”, or “generating a demand prediction for power from the associated loads for the upcoming time period;” However, Chung does teach these claim limitations.
Chung teaches “generating a power production prediction by the photovoltaic systems for an upcoming time period;” (Chung teaches predicting the next day's renewable energy generation amount generated by a solar power generator 300 i.e. a power production prediction by photovoltaic systems for an upcoming time period in Chung [0050-0051] "The prediction unit (110) can predict the amount of solar power generated by a solar power generator (300) as a renewable energy generator connected to a distribution system and the amount of load consumed by a load (400). The prediction unit (110) can predict the next day's renewable energy generation amount using a first deep neural network model that has been pre-trained to predict the renewable energy generation amount using past solar irradiance data. Here, the first deep neural network model (first prediction model) may include a neural network model pre-trained using training data including dry bulb temperature, dew point, humidity, and solar irradiance data."),
“generating a demand prediction for power from the associated loads for the upcoming time period;” (Chung teaches predicting the next day's load i.e. a demand prediction for power in Chung [0052] "Additionally, the prediction unit (110) can predict the next day's load by using a second deep neural network model that has been pre-trained to predict the load by using past load data. Here, the second deep neural network model (second prediction model) may include a pre-trained neural network model using training data including population, maximum temperature, and load data."), and
Henselmeyer and Chung are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer and Chung before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer to prediction of future power generation and load in order to determine whether to adjust a transformer tap position of Chung because adding the Device and method for controlling voltage in power distribution system of Chung would solve the problem of line overvoltage, increase the acceptance rate of renewable energy, and suppress unnecessary tap operations as described in Chung [0017-0020] “According to this embodiment, the problem of line overvoltage in a large-capacity distributed power supply interconnection environment can be solved. Additionally, by maintaining the voltage of the distribution line at a low level at all times, additional voltage fluctuation margin can be secured, which can increase the acceptance rate of renewable energy. In addition, by using the voltage control plan of the tap controller based on the artificial intelligence deep neural network, not only can the voltage problem that cannot be solved during the start/delay time of the tap operation be solved, but also unnecessary tap operations can be suppressed below a certain level to ensure the life of the tap controller, and frequent tap operations can be suppressed to maintain good voltage quality of the distribution line. In addition, by maintaining the voltage of the distribution lines generally low within a range that does not generate low voltage in the distribution system at all times, the load reduction effect can be achieved and the cost of purchasing electricity can be expected to be reduced.”
Neither Henselmeyer or Chung appear to explicitly teach “and taking a mitigation action based on the power production prediction and the demand prediction indicating a predicted voltage constraint violation in the upcoming time period that includes injecting reactive power into the power grid from one or more PV systems during the upcoming time period via inverters associated with the one or more PV systems.” However, Yeh does teach this claim limitation (Yeh teaches a model predictive controller that has an objective function of minimizing power loss by injecting or absorbing reactive power in Yeh [0046]
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Yeh teaches that the model predictive controller may direct inverters to adjust reactive power i.e. it takes a mitigating action in order to guarantee the voltage constraint in Yeh [0052-0053] "If power equipment at a node is turned on, then the power load at that node increases abnormally, and the voltage along the entire distribution grid will suddenly drop. This consequently impacts the power loads of all the nodes on the grid. In such a case, the inverter may increase reactive power output to compensate such an abrupt change. Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint.).
Henselmeyer, Chung, and Yeh are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer, Chung, and Yeh before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer modified to include the Device and method for controlling voltage in power distribution system of Chung, to include the injection of reactive power based on a prediction of a voltage violation of Yeh because adding the Controllers for photovoltaic-enabled distribution grid of Yeh would minimize power loss and guarantee the voltage constraint and enable incorporation of feedback, causing the control scheme to be less sensitive to model errors and disturbances as described in Yeh [0053] "Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint. MPC is an optimization-based discrete approach to regulate a system subject to input/state/output constraints. It solves an optimization problem online and yields a sequence of control actions for the entire prediction horizon, H. However, only the first control action is implemented, and at the next time step the entire optimization problem will be resolved with the latest measurements. It also forces control action to reach the optimal steady-state input qj g* after time length H′. This receding horizon control scheme not only optimizes the open-loop performances over the entire horizon but also incorporate the feedback from the data measurement to update the optimization problem at each time step. Thus, the MPC is less sensitive to model errors and disturbances. However, the online computation demand of the MPC may be heavy if the model is high dimensional, with a long prediction horizon, or too many constraints are incorporated in the formula. In addition, the computational time may also depend on the structure of the problem, such as sparsity and the solver."
Claim 3:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 1, wherein an artificial neural network generates the power production prediction and the demand prediction” (Chung teaches predicting the next day's renewable energy generation amount generated by a solar power generator 300 and the predicted load using a neural network in Chung [0050-0052] "The prediction unit (110) can predict the amount of solar power generated by a solar power generator (300) as a renewable energy generator connected to a distribution system and the amount of load consumed by a load (400). The prediction unit (110) can predict the next day's renewable energy generation amount using a first deep neural network model that has been pre-trained to predict the renewable energy generation amount using past solar irradiance data. Here, the first deep neural network model (first prediction model) may include a neural network model pre-trained using training data including dry bulb temperature, dew point, humidity, and solar irradiance data. Additionally, the prediction unit (110) can predict the next day's load by using a second deep neural network model that has been pre-trained to predict the load by using past load data. Here, the second deep neural network model (second prediction model) may include a pre-trained neural network model using training data including population, maximum temperature, and load data."), and
“and determines whether the power production prediction and the demand prediction indicate the predicted voltage constraint violation in the upcoming time period.” (Chung teaches adjusting a tap position when a predicted voltage exceeds a first reference value or is lower than a second reference value i.e. it predicts a voltage constraint violation based on the renewable energy generation amount and predicted load in Chung [0070] "Specifically, the establishment unit (120) can perform a tidal current calculation using the predicted renewable energy generation amount and the predicted load amount to generate a predicted voltage distribution curve of the distribution system, and based on this, can establish a voltage control plan that schedules the tap operation of the tap control device to be controlled before the voltage maintenance regulation is violated.Here, the establishment unit (120) can establish a voltage control plan that schedules the tap to be lowered before the occurrence of the overvoltage problem by considering the number of tap-lowering operations when an overvoltage problem occurs in which the voltage of the distribution system exceeds a first reference value (e.g., voltage upper limit value, 1.02 pu).In addition, the establishment unit (120) can establish a voltage control plan that schedules the tap to be raised before the occurrence of the low voltage problem by considering the number of tap raising operations when a low voltage problem occurs in which the voltage of the distribution system is lower than the second reference value (e.g., voltage lower limit value, 0.99pu).").
Claim 5:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 1, wherein each PMU of the plurality of PMUs is installed at a corresponding junction between medium voltage and low voltage in the power grid.” (Henselmeyer teaches a transformer 11 which transforms medium voltage to low-voltage in Henselmeyer [0036] "Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a simple example of a power distribution grid 1. The power distribution grid 1 has a medium-voltage level 2 connected via a substation to a measurement location 8 and via a transformer 11 to a low-voltage level 3 of the distribution grid. The distribution grid 3 has two sections 6, 7 or branches. Each branch has multiple generators 12 of electric power, which are each depicted as a circuit. These can be wind turbines or photovoltaic installations, for example. Electrical loads 5 are depicted as arrows, the electrical loads 5 being able to be single-family homes or the like, for example. In section 7, there is provision for a measurement location 9 in the connecting line to the distribution grid 3. In the section 6, there is provision for a measurement location 10 in the connecting line to the distribution grid 3."; Henselmeyer Fig. 1 [As shown above in claim 1] teaches the measurement locations 8, 9, and 10 being on the primary and secondary side of transformer 11.).
Claim 6:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 1, wherein taking the mitigation action further includes: adjusting a tap position on a substation transformer serving the power grid from a generator source to the associated loads based on the power production prediction and the demand prediction indicating a predicted voltage constraint violation in the upcoming time period,” (Chung teaches adjusting a tap position when a predicted voltage, which is based on the predicted renewable energy generation amount and the predicted load amount, is greater than a first reference value or lower than a second reference value i.e. based on the power and demand prediction indicating a predicted voltage constraint violation in the upcoming time period in Chung [0070] "Specifically, the establishment unit (120) can perform a tidal current calculation using the predicted renewable energy generation amount and the predicted load amount to generate a predicted voltage distribution curve of the distribution system, and based on this, can establish a voltage control plan that schedules the tap operation of the tap control device to be controlled before the voltage maintenance regulation is violated.Here, the establishment unit (120) can establish a voltage control plan that schedules the tap to be lowered before the occurrence of the overvoltage problem by considering the number of tap-lowering operations when an overvoltage problem occurs in which the voltage of the distribution system exceeds a first reference value (e.g., voltage upper limit value, 1.02 pu).In addition, the establishment unit (120) can establish a voltage control plan that schedules the tap to be raised before the occurrence of the low voltage problem by considering the number of tap raising operations when a low voltage problem occurs in which the voltage of the distribution system is lower than the second reference value (e.g., voltage lower limit value, 0.99pu)."), and
“wherein the substation transformer is installed at a junction between high voltage and medium voltage in the power grid.” (Chung teaches the OLTC for a transformer which is connected to high-voltage distribution lines in Chung [0041] "When distributed power sources are connected to high-voltage distribution lines, the load current of the distribution lines changes due to the fluctuating output, which can cause voltage problems at the user (low-voltage distribution system). Accordingly, the tap position of the on-line tap controller (OLTC) must be changed, but in reality, it is difficult to control the tap of the tap controller according to the operating status of the distributed power source. Therefore, as a solution to the voltage problem caused by distributed power sources, there are a program method that adjusts the tap of the predicted voltage over time, a constant transmission voltage method that transmits a constant transmission voltage value regardless of the distributed power generation and load, an LDC (line drop compensation) method that automatically adjusts the transmission voltage by compensating for the voltage drop of the line according to the size of the changing load current by voltage adjustment elements such as a predetermined equivalent impedance and load center point voltage, and a program method that adjusts the voltage set over time."
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Claim 7:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 1, wherein the predicted voltage constraint violation is indicated when a voltage drop or rise greater than 5 percent of a nominal voltage limit is predicted to occur in the upcoming time period.” (Henselmeyer teaches that in the event of a predicted voltage greater or less than 10% the prescribed voltage (Examiner notes that +/- 10% was merely an example, and that a band of 5% may be used instead if desired), it will enact a countermeasure in Henselmeyer [0025] "In a further preferred embodiment of the method according to the invention, the prediction of a future grid state is taken as a basis for using an error correction device to select a countermeasure in order to avoid limit value infringements. By way of example, a prescribed voltage band of +/−10% around the envisaged rated voltage can be provided with an upper and a lower limit value. If the predicted voltage is above or below these limit values or there is even the threat of a power failure, then a countermeasure accordingly needs to be taken at present. A countermeasure within the context of the invention is for example a reduction in the consumption of an individual load or of a load group or a reduction in the feed power from a power generator or a group of power generators or a change in a timetable for a load or generator of electric power. By way of example, appropriate control commands for specified values can be sent to responsive equipment in the power grid.").
Claim 8:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 1, wherein the upcoming time period has a duration of one hour,” (Henselmeyer teaches the prediction preferably having a comparable time resolution as historical data, the time period being one hour in Henselmeyer [0040] "a) historical measurement data pertaining to the real power, which preferably have a comparable time resolution to the prediction to be provided as the end result; e.g. 15 minutes or one hour. In this context, “historical” denotes longer measurement periods for capturing the measurement data provided with timestamps, e.g. over days, weeks or months, an identification of the type of day (weekday, workday, weekend, public holiday, etc.) also being advantageous, in particular. Furthermore, the historical data pertaining to the real power relate to grids without correction measures such as e.g. downward regulation. Should the historical data pertaining to the real power already relate to “corrected” grids, it must be known what has been corrected in order to perform appropriate conversion (backward correction) of the historical data."), and
“wherein the present power produced and the present demand are measured during an hour prior to the upcoming time period.” (Henselmeyer teaches the prediction preferably having a comparable time resolution as historical data, the time period being one hour in Henselmeyer [0040] "a) historical measurement data pertaining to the real power, which preferably have a comparable time resolution to the prediction to be provided as the end result; e.g. 15 minutes or one hour. ").
Claim 9:
Henselmeyer teaches “A method, comprising: measuring, at one or more of a plurality of phasor measurement units (PMUs) connected between a power grid and associated loads, a present power produced by photovoltaic systems deployed downstream from the one or more of the plurality of PMUs; measuring, at the one or more of the plurality of PMUs, a present demand for power from the associated loads;” (Henselmeyer teaches measurement locations 9 and 10, which would monitor the power demand and power generated, in a power distribution grid including electrical loads 5 and that the branches include photovoltaic generators 12 in Henselmeyer [0036] "Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a simple example of a power distribution grid 1. The power distribution grid 1 has a medium-voltage level 2 connected via a substation to a measurement location 8 and via a transformer 11 to a low-voltage level 3 of the distribution grid. The distribution grid 3 has two sections 6, 7 or branches. Each branch has multiple generators 12 of electric power, which are each depicted as a circuit. These can be wind turbines or photovoltaic installations, for example. Electrical loads 5 are depicted as arrows, the electrical loads 5 being able to be single-family homes or the like, for example. In section 7, there is provision for a measurement location 9 in the connecting line to the distribution grid 3. In the section 6, there is provision for a measurement location 10 in the connecting line to the distribution grid 3."; Henselmeyer teaches that measuring devices may be phasor measurement units in Henselmeyer [0012] "Measuring devices can be for example voltmeters, ammeters, phasor measurement units (PMUs), remote terminal units or smart meters, intelligent electrical devices (IEDs) for monitoring switches and other equipment, control devices e.g. for smart substations or protection equipment.").
Henselmeyer does not appear to explicitly teach “generating a power production prediction by the photovoltaic systems for an upcoming time period;”, or “generating a demand prediction for power from the associated loads for the upcoming time period;”. However, Chung does teach these claim limitations.
Chung teaches “generating a power production prediction by the photovoltaic systems for an upcoming time period;” (Chung teaches predicting the next day's renewable energy generation amount generated by a solar power generator 300 i.e. a power production prediction by photovoltaic systems for an upcoming time period in Chung [0050-0051] "The prediction unit (110) can predict the amount of solar power generated by a solar power generator (300) as a renewable energy generator connected to a distribution system and the amount of load consumed by a load (400). The prediction unit (110) can predict the next day's renewable energy generation amount using a first deep neural network model that has been pre-trained to predict the renewable energy generation amount using past solar irradiance data. Here, the first deep neural network model (first prediction model) may include a neural network model pre-trained using training data including dry bulb temperature, dew point, humidity, and solar irradiance data."), and
“generating a demand prediction for power from the associated loads for the upcoming time period;” (Chung teaches predicting the next day's load i.e. a demand prediction for power in Chung [0052] "Additionally, the prediction unit (110) can predict the next day's load by using a second deep neural network model that has been pre-trained to predict the load by using past load data. Here, the second deep neural network model (second prediction model) may include a pre-trained neural network model using training data including population, maximum temperature, and load data.").
Henselmeyer and Chung are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer and Chung before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer to prediction of future power generation and load in order to determine whether to adjust a transformer tap position of Chung because adding the Device and method for controlling voltage in power distribution system of Chung would solve the problem of line overvoltage, increase the acceptance rate of renewable energy, and suppress unnecessary tap operations as described in Chung [0017-0020] “According to this embodiment, the problem of line overvoltage in a large-capacity distributed power supply interconnection environment can be solved. Additionally, by maintaining the voltage of the distribution line at a low level at all times, additional voltage fluctuation margin can be secured, which can increase the acceptance rate of renewable energy. In addition, by using the voltage control plan of the tap controller based on the artificial intelligence deep neural network, not only can the voltage problem that cannot be solved during the start/delay time of the tap operation be solved, but also unnecessary tap operations can be suppressed below a certain level to ensure the life of the tap controller, and frequent tap operations can be suppressed to maintain good voltage quality of the distribution line. In addition, by maintaining the voltage of the distribution lines generally low within a range that does not generate low voltage in the distribution system at all times, the load reduction effect can be achieved and the cost of purchasing electricity can be expected to be reduced.”
Henselmeyer in view of Chung does not appear to explicitly teach “and injecting reactive power into the power grid from one or more PV systems during the upcoming time period via inverters associated with the one or more PV systems based on the power production prediction and the demand prediction indicating a predicted voltage constraint violation in the upcoming time period.” However, Yeh does teach this claim limitation (Yeh teaches a model predictive controller that has an objective function of minimizing power loss by injecting or absorbing reactive power in Yeh [0046]
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Yeh teaches that the model predictive controller may direct inverters to adjust reactive power i.e. it takes a mitigating action in order to guarantee the voltage constraint in Yeh [0052-0053] "If power equipment at a node is turned on, then the power load at that node increases abnormally, and the voltage along the entire distribution grid will suddenly drop. This consequently impacts the power loads of all the nodes on the grid. In such a case, the inverter may increase reactive power output to compensate such an abrupt change. Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint.).
Henselmeyer, Chung, and Yeh are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer, Chung, and Yeh before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer modified to include the Device and method for controlling voltage in power distribution system of Chung, to include the injection of reactive power based on a prediction of a voltage violation of Yeh because adding the Controllers for photovoltaic-enabled distribution grid of Yeh would minimize power loss and guarantee the voltage constraint and enable incorporation of feedback, causing the control scheme to be less sensitive to model errors and disturbances as described in Yeh [0053] "Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint. MPC is an optimization-based discrete approach to regulate a system subject to input/state/output constraints. It solves an optimization problem online and yields a sequence of control actions for the entire prediction horizon, H. However, only the first control action is implemented, and at the next time step the entire optimization problem will be resolved with the latest measurements. It also forces control action to reach the optimal steady-state input qj g* after time length H′. This receding horizon control scheme not only optimizes the open-loop performances over the entire horizon but also incorporate the feedback from the data measurement to update the optimization problem at each time step. Thus, the MPC is less sensitive to model errors and disturbances. However, the online computation demand of the MPC may be heavy if the model is high dimensional, with a long prediction horizon, or too many constraints are incorporated in the formula. In addition, the computational time may also depend on the structure of the problem, such as sparsity and the solver."
Claim 10:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 9, further comprising, in response to detecting the predicted voltage constraint violation in the upcoming time period: adjusting a tap position on a substation transformer serving the power grid from a generator source to the associated loads.” (Chung teaches adjusting a tap position when a predicted voltage exceeds a first reference value or is lower than a second reference value i.e. it predicts a voltage constraint violation based on the renewable energy generation amount and predicted load in Chung [0070] "Specifically, the establishment unit (120) can perform a tidal current calculation using the predicted renewable energy generation amount and the predicted load amount to generate a predicted voltage distribution curve of the distribution system, and based on this, can establish a voltage control plan that schedules the tap operation of the tap control device to be controlled before the voltage maintenance regulation is violated.Here, the establishment unit (120) can establish a voltage control plan that schedules the tap to be lowered before the occurrence of the overvoltage problem by considering the number of tap-lowering operations when an overvoltage problem occurs in which the voltage of the distribution system exceeds a first reference value (e.g., voltage upper limit value, 1.02 pu).In addition, the establishment unit (120) can establish a voltage control plan that schedules the tap to be raised before the occurrence of the low voltage problem by considering the number of tap raising operations when a low voltage problem occurs in which the voltage of the distribution system is lower than the second reference value (e.g., voltage lower limit value, 0.99pu).").
Claim 11:
The limitations of claim 11 are substantially the same as claim 6 and it is rejected for the same reasons.
Claim 12:
The limitations of claim 12 are substantially the same as claim 3 and it is rejected for the same reasons.
Claim 14:
The limitations of claim 14 are substantially the same as claim 5 and it is rejected for the same reasons.
Claim 15:
The limitations of claim 15 are substantially the same as claim 7 and it is rejected for the same reasons.
Claim 16:
The limitations of claim 16 are substantially the same as claim 8 and it is rejected for the same reasons.
Claim 17:
Henselmeyer teaches “A system, comprising: a processor; and a memory storing instructions that, when executed by the processor, perform operations” (Henselmeyer teaches the state estimation device includes at least one processor for performing the method disclosed i.e. it has memory storing instructions in Henselmeyer [0013] "The state estimation device has at least one processor in order to allow complex calculations for performing the naive Bayes method. A computer center or a physically distributed server and database architecture such as a cloud can also be used."; Henselmeyer teaches that a central computer has data memories in Henselmeyer [0011] "A central computer arrangement has processors, data memories and screens, for example. The term “central” means that all essential measurement data from the power grid and all essential control commands for the power grid are processed centrally."), and
“operations including: measuring, at one or more of a plurality of phasor measurement units (PMUs) connected between a power grid and associated loads, a present power produced by photovoltaic systems deployed downstream from the one or more of the plurality of PMUs; measuring, at the one or more of the plurality of PMUs, a present demand for power from the associated loads;” (Henselmeyer teaches measurement locations 9 and 10, which would monitor the power demand and power generated, in a power distribution grid including electrical loads 5 and that the branches include photovoltaic generators 12 in Henselmeyer [0036] "Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a simple example of a power distribution grid 1. The power distribution grid 1 has a medium-voltage level 2 connected via a substation to a measurement location 8 and via a transformer 11 to a low-voltage level 3 of the distribution grid. The distribution grid 3 has two sections 6, 7 or branches. Each branch has multiple generators 12 of electric power, which are each depicted as a circuit. These can be wind turbines or photovoltaic installations, for example. Electrical loads 5 are depicted as arrows, the electrical loads 5 being able to be single-family homes or the like, for example. In section 7, there is provision for a measurement location 9 in the connecting line to the distribution grid 3. In the section 6, there is provision for a measurement location 10 in the connecting line to the distribution grid 3."; Henselmeyer teaches that measuring devices may be phasor measurement units in Henselmeyer [0012] "Measuring devices can be for example voltmeters, ammeters, phasor measurement units (PMUs), remote terminal units or smart meters, intelligent electrical devices (IEDs) for monitoring switches and other equipment, control devices e.g. for smart substations or protection equipment."
Henselmeyer does not appear to explicitly teach “generating a power production prediction by the photovoltaic systems for an upcoming time period;”, or “generating a demand prediction for power from the associated loads for the upcoming time period;” However, Chung does teach these claim limitations.
Chung teaches “generating a power production prediction by the photovoltaic systems for an upcoming time period;” (Chung teaches predicting the next day's renewable energy generation amount generated by a solar power generator 300 i.e. a power production prediction by photovoltaic systems for an upcoming time period in Chung [0050-0051] "The prediction unit (110) can predict the amount of solar power generated by a solar power generator (300) as a renewable energy generator connected to a distribution system and the amount of load consumed by a load (400). The prediction unit (110) can predict the next day's renewable energy generation amount using a first deep neural network model that has been pre-trained to predict the renewable energy generation amount using past solar irradiance data. Here, the first deep neural network model (first prediction model) may include a neural network model pre-trained using training data including dry bulb temperature, dew point, humidity, and solar irradiance data."),
“generating a demand prediction for power from the associated loads for the upcoming time period;” (Chung teaches predicting the next day's load i.e. a demand prediction for power in Chung [0052] "Additionally, the prediction unit (110) can predict the next day's load by using a second deep neural network model that has been pre-trained to predict the load by using past load data. Here, the second deep neural network model (second prediction model) may include a pre-trained neural network model using training data including population, maximum temperature, and load data."), and
Henselmeyer and Chung are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer and Chung before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer to prediction of future power generation and load in order to determine whether to adjust a transformer tap position of Chung because adding the Device and method for controlling voltage in power distribution system of Chung would solve the problem of line overvoltage, increase the acceptance rate of renewable energy, and suppress unnecessary tap operations as described in Chung [0017-0020] “According to this embodiment, the problem of line overvoltage in a large-capacity distributed power supply interconnection environment can be solved. Additionally, by maintaining the voltage of the distribution line at a low level at all times, additional voltage fluctuation margin can be secured, which can increase the acceptance rate of renewable energy. In addition, by using the voltage control plan of the tap controller based on the artificial intelligence deep neural network, not only can the voltage problem that cannot be solved during the start/delay time of the tap operation be solved, but also unnecessary tap operations can be suppressed below a certain level to ensure the life of the tap controller, and frequent tap operations can be suppressed to maintain good voltage quality of the distribution line. In addition, by maintaining the voltage of the distribution lines generally low within a range that does not generate low voltage in the distribution system at all times, the load reduction effect can be achieved and the cost of purchasing electricity can be expected to be reduced.”
Neither Henselmeyer or Chung appear to explicitly teach “and taking a mitigation action based on the power production prediction and the demand prediction indicating a predicted voltage constraint violation in the upcoming time period of that includes: injecting reactive power into the power grid from one or more PV systems during the upcoming time period via inverters associated with the one or more PV systems.” However, Yeh does teach this claim limitation (Yeh teaches a model predictive controller that has an objective function of minimizing power loss by injecting or absorbing reactive power in Yeh [0046]
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Yeh teaches that the model predictive controller may direct inverters to adjust reactive power i.e. it takes a mitigating action in order to guarantee the voltage constraint in Yeh [0052-0053] "If power equipment at a node is turned on, then the power load at that node increases abnormally, and the voltage along the entire distribution grid will suddenly drop. This consequently impacts the power loads of all the nodes on the grid. In such a case, the inverter may increase reactive power output to compensate such an abrupt change. Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint.).
Henselmeyer, Chung, and Yeh are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer, Chung, and Yeh before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer modified to include the Device and method for controlling voltage in power distribution system of Chung, to include the injection of reactive power based on a prediction of a voltage violation of Yeh because adding the Controllers for photovoltaic-enabled distribution grid of Yeh would minimize power loss and guarantee the voltage constraint and enable incorporation of feedback, causing the control scheme to be less sensitive to model errors and disturbances as described in Yeh [0053] "Given the model prediction and real-time measurement, the model predictive controller may direct inverters to generate enough reactive power qj g to minimize the power loss and guarantee the voltage constraint. MPC is an optimization-based discrete approach to regulate a system subject to input/state/output constraints. It solves an optimization problem online and yields a sequence of control actions for the entire prediction horizon, H. However, only the first control action is implemented, and at the next time step the entire optimization problem will be resolved with the latest measurements. It also forces control action to reach the optimal steady-state input qj g* after time length H′. This receding horizon control scheme not only optimizes the open-loop performances over the entire horizon but also incorporate the feedback from the data measurement to update the optimization problem at each time step. Thus, the MPC is less sensitive to model errors and disturbances. However, the online computation demand of the MPC may be heavy if the model is high dimensional, with a long prediction horizon, or too many constraints are incorporated in the formula. In addition, the computational time may also depend on the structure of the problem, such as sparsity and the solver."
Claim 19:
Henselmeyer in view of Chung, further in view of Yeh teaches “The system of claim 21, wherein each PMU of the plurality of PMUs is installed at a corresponding junction between medium voltage and low voltage in the power grid” (Henselmeyer teaches a transformer 11 which transforms medium voltage to low-voltage in Henselmeyer [0036] "Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a simple example of a power distribution grid 1. The power distribution grid 1 has a medium-voltage level 2 connected via a substation to a measurement location 8 and via a transformer 11 to a low-voltage level 3 of the distribution grid. The distribution grid 3 has two sections 6, 7 or branches. Each branch has multiple generators 12 of electric power, which are each depicted as a circuit. These can be wind turbines or photovoltaic installations, for example. Electrical loads 5 are depicted as arrows, the electrical loads 5 being able to be single-family homes or the like, for example. In section 7, there is provision for a measurement location 9 in the connecting line to the distribution grid 3. In the section 6, there is provision for a measurement location 10 in the connecting line to the distribution grid 3."; Henselmeyer Fig. 1 [As shown above in claim 1] teaches the measurement locations 8, 9, and 10 being on the primary and secondary side of transformer 11.), and
“and wherein the substation transformer is installed at a junction between high voltage and medium voltage in the power grid.” (Chung teaches the OLTC for a transformer which is connected to high-voltage distribution lines in Chung [0041] "When distributed power sources are connected to high-voltage distribution lines, the load current of the distribution lines changes due to the fluctuating output, which can cause voltage problems at the user (low-voltage distribution system). Accordingly, the tap position of the on-line tap controller (OLTC) must be changed, but in reality, it is difficult to control the tap of the tap controller according to the operating status of the distributed power source. Therefore, as a solution to the voltage problem caused by distributed power sources, there are a program method that adjusts the tap of the predicted voltage over time, a constant transmission voltage method that transmits a constant transmission voltage value regardless of the distributed power generation and load, an LDC (line drop compensation) method that automatically adjusts the transmission voltage by compensating for the voltage drop of the line according to the size of the changing load current by voltage adjustment elements such as a predetermined equivalent impedance and load center point voltage, and a program method that adjusts the voltage set over time.").
Claim 20:
The limitations of claim 20 are substantially the same as claim 7 and it is rejected for the same reasons.
Claim 21:
Henselmeyer in view of Chung, further in view of Yeh teaches “The system of claim 17, wherein taking the mitigation action further includes adjusting a tap position on a substation transformer serving the power grid from a generator source to the associated loads.” (Chung teaches adjusting a tap position when a predicted voltage, which is based on the predicted renewable energy generation amount and the predicted load amount, is greater than a first reference value or lower than a second reference value i.e. based on the power and demand prediction indicating a predicted voltage constraint violation in the upcoming time period in Chung [0070] "Specifically, the establishment unit (120) can perform a tidal current calculation using the predicted renewable energy generation amount and the predicted load amount to generate a predicted voltage distribution curve of the distribution system, and based on this, can establish a voltage control plan that schedules the tap operation of the tap control device to be controlled before the voltage maintenance regulation is violated.Here, the establishment unit (120) can establish a voltage control plan that schedules the tap to be lowered before the occurrence of the overvoltage problem by considering the number of tap-lowering operations when an overvoltage problem occurs in which the voltage of the distribution system exceeds a first reference value (e.g., voltage upper limit value, 1.02 pu).In addition, the establishment unit (120) can establish a voltage control plan that schedules the tap to be raised before the occurrence of the low voltage problem by considering the number of tap raising operations when a low voltage problem occurs in which the voltage of the distribution system is lower than the second reference value (e.g., voltage lower limit value, 0.99pu).").
Claims 4, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Henselmeyer (US20200395755A1) in view of Chung et al. (KR102281229B1) (citations to examiner provided translation), further in view of Yeh (US20210057916A1), further in view of Do Rosario et al. (US20160233682A1)
Claim 4:
Henselmeyer in view of Chung, further in view of Yeh teaches “The method of claim 3,” as described above. None of Henselmeyer, Chung, or Yeh appear to explicitly teach “wherein the artificial neural network is trained using the Levenberg-Marquardt backpropagation algorithm using historical data for power demand and PV generation.” However, Do Rosario does teach this claim limitation (Do Rosario teaches a neural network may be trained using the Levenberg-Marquardt method in Do Rosario [0077] "Cleanup of the raw data further reduced the data set by removing data points in which GHI, DHI, and DNI levels were very low. This filtering reduces the amount of time and memory needed for analysis. The cleaned data set can be further processed using non-parametric model generating tools such as a fuzzy inference system generator and/or a back-propagation neural network training tool. Fuzzy c-means clustering can be used to cluster values for each variable which produces fuzzy membership functions for each of the variables in the input matrix and output matrix. It can then determine rules to map each of the fuzzy inputs to the outputs to best match the training data set. The neural network training tool can use a back propagation method (e.g., a Levenberg-Marquardt method) to train the network to minimize its mean squared error performance. Differences in the observed and predicted data points generally correspond to the presence of clouds or other anomalies that could not be predicted an hour in advance using the variables input to the function."; Do Rosario teaches that nthe trained neural network can be used to predict power production by PV systems in Do Rosario [0096] "In addition, the resource allocation 327 can monitor sensor indications and predict changes in power production by the sustainable energy resources 103 such as a PV system. As previously discussed, neural networks and/or other expert systems can be used to predict power production by PV systems based upon monitored conditions.").
Henselmeyer, Chung, Yeh, and Do Rosario are analogous art because they are from the same field of endeavor of predicting future power grid conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Henselmeyer, Chung, Yeh, and Do Rosario before him/her, to modify the teachings of a Method and arrangement for estimating a grid state of a power distribution grid of Henselmeyer modified to include the Device and method for controlling voltage in power distribution system of Chung, further modified to include the injection of reactive power based on a prediction of a voltage violation of Yeh to include the training of a neural network using Levenberg-Marquardt back propagation of Do Rosario because adding the Power Quality of Service Optimization for Microgrids of Do Rosario would minimize its mean squared error performance as described in Do Rosario [0077] " Cleanup of the raw data further reduced the data set by removing data points in which GHI, DHI, and DNI levels were very low. This filtering reduces the amount of time and memory needed for analysis. The cleaned data set can be further processed using non-parametric model generating tools such as a fuzzy inference system generator and/or a back-propagation neural network training tool. Fuzzy c-means clustering can be used to cluster values for each variable which produces fuzzy membership functions for each of the variables in the input matrix and output matrix. It can then determine rules to map each of the fuzzy inputs to the outputs to best match the training data set. The neural network training tool can use a back propagation method (e.g., a Levenberg-Marquardt method) to train the network to minimize its mean squared error performance. Differences in the observed and predicted data points generally correspond to the presence of clouds or other anomalies that could not be predicted an hour in advance using the variables input to the function."
Claim 13:
The limitations of claim 13 are substantially the same as claim 5 and it is rejected for the same reasons.
Claim 18:
Henselmeyer in view of Chung, further in view of Yeh, further in view of Do Rosario teaches “The system of claim 17, wherein an artificial neural network generates the power production prediction and the demand prediction” (Chung teaches predicting the next day's renewable energy generation amount generated by a solar power generator 300 and the predicted load using a neural network in Chung [0050-0052] "The prediction unit (110) can predict the amount of solar power generated by a solar power generator (300) as a renewable energy generator connected to a distribution system and the amount of load consumed by a load (400). The prediction unit (110) can predict the next day's renewable energy generation amount using a first deep neural network model that has been pre-trained to predict the renewable energy generation amount using past solar irradiance data. Here, the first deep neural network model (first prediction model) may include a neural network model pre-trained using training data including dry bulb temperature, dew point, humidity, and solar irradiance data. Additionally, the prediction unit (110) can predict the next day's load by using a second deep neural network model that has been pre-trained to predict the load by using past load data. Here, the second deep neural network model (second prediction model) may include a pre-trained neural network model using training data including population, maximum temperature, and load data."),
“and determines whether the power production prediction and the demand prediction indicate the predicted voltage constraint violation in the upcoming time period.” (Chung teaches adjusting a tap position when a predicted voltage exceeds a first reference value or is lower than a second reference value i.e. it predicts a voltage constraint violation based on the renewable energy generation amount and predicted load in Chung [0070] "Specifically, the establishment unit (120) can perform a tidal current calculation using the predicted renewable energy generation amount and the predicted load amount to generate a predicted voltage distribution curve of the distribution system, and based on this, can establish a voltage control plan that schedules the tap operation of the tap control device to be controlled before the voltage maintenance regulation is violated.Here, the establishment unit (120) can establish a voltage control plan that schedules the tap to be lowered before the occurrence of the overvoltage problem by considering the number of tap-lowering operations when an overvoltage problem occurs in which the voltage of the distribution system exceeds a first reference value (e.g., voltage upper limit value, 1.02 pu).In addition, the establishment unit (120) can establish a voltage control plan that schedules the tap to be raised before the occurrence of the low voltage problem by considering the number of tap raising operations when a low voltage problem occurs in which the voltage of the distribution system is lower than the second reference value (e.g., voltage lower limit value, 0.99pu)."), and
“wherein the artificial neural network is trained using the Levenberg-Marquardt backpropagation algorithm using historical data for power demand and PV generation.” (Do Rosario teaches a neural network may be trained using the Levenberg-Marquardt method in Do Rosario [0077] "Cleanup of the raw data further reduced the data set by removing data points in which GHI, DHI, and DNI levels were very low. This filtering reduces the amount of time and memory needed for analysis. The cleaned data set can be further processed using non-parametric model generating tools such as a fuzzy inference system generator and/or a back-propagation neural network training tool. Fuzzy c-means clustering can be used to cluster values for each variable which produces fuzzy membership functions for each of the variables in the input matrix and output matrix. It can then determine rules to map each of the fuzzy inputs to the outputs to best match the training data set. The neural network training tool can use a back propagation method (e.g., a Levenberg-Marquardt method) to train the network to minimize its mean squared error performance. Differences in the observed and predicted data points generally correspond to the presence of clouds or other anomalies that could not be predicted an hour in advance using the variables input to the function."; Do Rosario teaches the trained neural network can be used to predict power production by PV systems in Do Rosario [0096] "In addition, the resource allocation 327 can monitor sensor indications and predict changes in power production by the sustainable energy resources 103 such as a PV system. As previously discussed, neural networks and/or other expert systems can be used to predict power production by PV systems based upon monitored conditions.").
Conclusion
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/Z.A.C./Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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