US20180240200A1 - Method and device for modeling a long-time-scale photovoltaic output time sequence - Google Patents
Method and device for modeling a long-time-scale photovoltaic output time sequence Download PDFInfo
- Publication number
- US20180240200A1 US20180240200A1 US15/751,471 US201615751471A US2018240200A1 US 20180240200 A1 US20180240200 A1 US 20180240200A1 US 201615751471 A US201615751471 A US 201615751471A US 2018240200 A1 US2018240200 A1 US 2018240200A1
- Authority
- US
- United States
- Prior art keywords
- weather
- time sequence
- output
- transfer
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012546 transfer Methods 0.000 claims abstract description 72
- 238000005311 autocorrelation function Methods 0.000 claims description 25
- 230000014509 gene expression Effects 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 abstract description 8
- 238000004519 manufacturing process Methods 0.000 abstract description 5
- 230000002349 favourable effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 10
- 238000010248 power generation Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24S—SOLAR HEAT COLLECTORS; SOLAR HEAT SYSTEMS
- F24S2201/00—Prediction; Simulation
Definitions
- the disclosure relates to a modeling technology, and particularly to a method and device for modeling a long-time-scale photovoltaic output time sequence.
- Photovoltaic power generation is a renewable energy technology with greatest potential and highest application value after wind power generation, and photovoltaic power generation is rapidly developed in China under the support of a series of supporting policies.
- Photovoltaic power generation is a renewable energy technology with greatest potential and highest application value after wind power generation, and photovoltaic power generation is rapidly developed in China under the support of a series of supporting policies.
- deeply understanding a characteristic and rule of photovoltaic output may accurately master influence of photovoltaic grid connection on the power system and enable the power system to more effectively solve a problem about photovoltaic access.
- An existing weather simulation technology may only implement annual/monthly photovoltaic power prediction, may not implement long-time-scale power prediction, and may not directly obtain a time sequence useful for analogue simulation of time sequence production of a power system. Therefore, it is necessary to model a photovoltaic output time sequence to accurately master an output change rule of photovoltaic power generation and provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
- an embodiment of the disclosure provides a long-time-scale photovoltaic output time sequence modeling method.
- a characteristic of a photovoltaic output time sequence is analyzed, and a Markov chain is adopted to simulate transfer processes of each weather type and acquire probabilities of transfer to generate a simulated photovoltaic sequence, thereby proposing a new method to build a future photovoltaic output scenario.
- the embodiment of the disclosure is implemented by adopting the following technical solution.
- the embodiment of the disclosure provides a method for modeling a long-time-scale photovoltaic output time sequence, which includes that:
- weather types of days corresponding to the photovoltaic output is acquired, the weather types including at least one of clear weather, cloudy weather, overcast weather or changing weather;
- the operation that the probabilities of transfer between each type of weather are calculated respectively includes that: a Markov chain is adopted to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
- P k being the probability of transfer of the clear weather to another weather type
- k representing a weather type
- N k being a number of times of transfer
- N 1 being a number of times of occurrence of the clear weather.
- the following step is further included: the probabilities of transfer between the other weather types are sequentially obtained by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- the operation that the simulated time sequence of the photovoltaic output within the preset time scale is generated includes that: the weather types and corresponding relative outputs within the preset time scale are sequentially and randomly extracted according to the probabilities of transfer between each weather type, and products of the relative outputs and a predetermined threshold value are calculated to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart for reflecting changes of a Probability Density Function (PDF), an Autocorrelation Function (ACF) and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- PDF Probability Density Function
- ACF Autocorrelation Function
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min ⁇ t ⁇ 60 min;
- the maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- the operation that the validity of the simulated time sequence is verified includes that:
- RMSE Root-Mean-Square Error
- ⁇ i ⁇ [C f , C d , C r ], ⁇ i is a unit vector, and represents a function value of each characteristic of the simulated time sequence, y i represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ⁇ with a value range of 0.1 ⁇ 0.2.
- An embodiment of the disclosure provides a device for modeling a long-time-scale photovoltaic output time sequence, wherein the device includes: a data acquisition unit, configured to acquire historical data of a photovoltaic power station, and select a photovoltaic output with a time length of one year and a time resolution of 15 mins;
- an acquisition unit configured to acquire weather types of days corresponding to the photovoltaic output from a weather station, the weather types including at least one of clear weather, cloudy weather, overcast weather or changing weather;
- a processing unit configured to calculate probabilities of transfer between each type of weather respectively
- a generation unit configured to generate a simulated time sequence of the photovoltaic output within a preset time scale
- an evaluation unit configured to verify validity of the simulated time sequence.
- the processing unit is further configured to: adopt a Markov chain to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
- P k being the probability of transfer of the clear weather to another weather type
- k representing a weather type
- N k being a number of times of transfer
- N 1 being a number of times of occurrence of the clear weather.
- the device further includes: a probability acquisition unit, configured to sequentially obtain the probabilities of transfer between the other weather types by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- the generation unit is further configured to: sequentially and randomly extract the weather types and corresponding relative outputs within the preset time scale according to the probabilities of transfer between each weather type, and calculate products of the relative output and a predetermined threshold value to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart for reflecting changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min ⁇ t ⁇ 60 min;
- the maximum PDF is a difference value between maximum output and minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- the evaluation unit is further configured to:
- ⁇ i ⁇ [C f , C d , C r ]
- ⁇ i is a unit vector and represents a function value of each characteristic of the simulated time sequence
- y i represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence
- n is a length of a function value set of each characteristic of the time sequence
- RMSE is smaller than ⁇ with a value range of 0.1 ⁇ 0.2.
- the Markov chain is adopted to simulate the transfer processes of each type of weather and calculate the probabilities of transfer between each weather type; and uncertain characteristics such as randomness and fluctuation of photovoltaics are simulated, and compared with other methods, a building structure is more consistent with characteristics of the photovoltaic output, and truthfully and accurately represent a future photovoltaic output condition.
- Annual and monthly photovoltaic output simulation time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be generated according to a requirement to provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
- FIG. 1 is a flowchart of a long-time-scale photovoltaic output time sequence modeling method according to an embodiment of the disclosure.
- FIG. 2 - FIG 5 are schematic diagrams of parameter comparison between a historical time sequence and a simulated time sequence according to an embodiment of the disclosure, wherein
- FIG. 2 is a schematic diagram of a probability density
- FIG. 3 is a schematic diagram of a 15 min probability density
- FIG. 4 is a schematic diagram of a 60 min probability density
- FIG. 5 is a schematic diagram of autocorrelation coefficient comparison.
- FIG. 1 shows a long-time-scale photovoltaic output time sequence modeling method according to an embodiment of the disclosure. The method includes the following steps.
- Step 101 historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected.
- Step 102 weather types of days corresponding to the photovoltaic output are acquired from a weather station, the weather types including clear weather, cloudy weather, overcast weather and changing weather.
- Step 103 probabilities of transfer between each type of weather are calculated respectively, a Markov chain being adopted to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
- P k being the probability of transfer of the clear weather to another weather type
- k representing a weather type
- N k being a number of times of transfer
- N 1 being a number of times of occurrence of the clear weather.
- the probabilities of transfer between the other weather types are sequentially obtained by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- subscript 1 being adopted for the cloudy weather type
- subscript 2 being adopted for the clear weather type
- subscript 3 being adopted for the overcast weather type
- subscript 4 being adopted for the changing weather type
- P (1-1) , P (1-3) , P (1-3) , and P (1-4) representing the probabilities of transfer of the cloudy weather type to the other weather types respectively
- N (1-1) , N (1-2) , N (1-3) and N (1-4) representing numbers of times of transfer of the cloudy weather to the other weather types respectively
- N (1) representing a number of times of occurrence of the cloudy weather type.
- the probabilities of transfer of the overcast weather and the changing weather may be calculated.
- Step 104 a simulated time sequence of the photovoltaic output within a preset time scale is generated.
- the weather types and corresponding relative outputs within the preset time scale are sequentially and randomly extracted according to the probabilities of transfer between each weather type, and products of the relative outputs and a predetermined threshold value are calculated to generate the simulated time sequence of the photovoltaic output.
- the predetermined threshold value is a standard value customized according to historical photovoltaic data and a historical time sequence.
- the simulated time sequence is a curve chart and is configured to reflect changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales.
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min ⁇ t ⁇ 60 min.
- the maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- Step 105 validity of the simulated time sequence is verified, as shown in each schematic diagram of FIG. 2 to FIG. 5 .
- a specific processing process of the step includes the following steps.
- Step 1051 the PDF C f , short-duration fluctuation characteristic C d and ACF C r of the simulated time sequence are defined respectively.
- Step 1052 an RMSE of each characteristic is adopted to quantitatively evaluate the validity of the time sequence, an expression being:
- ⁇ i [C f , C d , C r ], ⁇
- y i represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of the historical time sequence
- n is a length of a function value set of each characteristic of the time sequence
- RMSE is smaller than ⁇ with a value range of 0.1 ⁇ 0.2.
- FIG. 2 is a schematic diagram of a probability density. As shown in FIG. 2 , when ⁇ i ⁇ C f , the function value of the PDF of the simulated time sequence is represented, and at this moment, y i represents the function value of the PDF, corresponding to the PDF of the simulated time sequence, of the historical time sequence.
- FIG. 3 is a schematic diagram of a 15 min probability density
- FIG. 4 is a schematic diagram of a 60 min probability density. As shown in FIG. 3 and FIG.
- FIG. 5 is a schematic diagram of autocorrelation coefficient comparison. As shown in FIG. 5 , when ⁇ i ⁇ C r , the function value of the ACF of the simulated time sequence is represented, and at this moment, y i represents the function value of the ACF, corresponding to the ACF of the simulated time sequence, of the historical time sequence.
- An embodiment of the disclosure provides a long-time-scale photovoltaic output time sequence modeling device, which includes:
- a data acquisition unit configured to acquire historical data of a photovoltaic power station, and select a photovoltaic output with a time length of one year and a time resolution of 15 mins;
- an acquisition unit configured to acquire weather types of days corresponding to the photovoltaic output from a weather station, the weather types including clear weather, cloudy weather, overcast weather and changing weather;
- a processing unit configured to calculate probabilities of transfer between each type of weather respectively
- a generation unit configured to generate a simulated time sequence of the photovoltaic output within a preset time scale
- an evaluation unit configured to verify validity of the simulated time sequence.
- the processing unit is further configured to: adopt a Markov chain to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
- P k being the probability of transfer of the clear weather to another weather type
- k representing a weather type
- N k being a number of times of transfer
- N 1 being a number of times of occurrence of the clear weather.
- the device further includes: a probability acquisition unit, configured to sequentially obtain the probabilities of transfer between the other weather types by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- the generation unit is further configured to: sequentially and randomly extract the weather types and corresponding relative outputs within the preset time scale according to the probabilities of transfer between each weather type, and calculate products of the relative output and a predetermined threshold value to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart, and is configured to reflect changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min ⁇ t ⁇ 60 min;
- the maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- the evaluation unit is further configured to:
- ⁇ i ⁇ [C f , C d , C r ], ⁇ i is a unit vector, and represents a function value of each characteristic of the simulated time sequence, y i represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ⁇ with a value range of 0.1 ⁇ 0.2.
- the Markov chain is adopted to simulate the transfer processes of each type of weather and calculate the probabilities of transfer between each weather type; and uncertain characteristics such as randomness and fluctuation of photovoltaics are simulated, and compared with other methods, a building structure is more consistent with characteristics of the photovoltaic output, and truthfully and accurately represent a future photovoltaic output condition.
- Annual and monthly photovoltaic output simulation time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be generated according to a requirement to provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Photovoltaic Devices (AREA)
Abstract
Description
- The disclosure relates to a modeling technology, and particularly to a method and device for modeling a long-time-scale photovoltaic output time sequence.
- Photovoltaic power generation is a renewable energy technology with greatest potential and highest application value after wind power generation, and photovoltaic power generation is rapidly developed in China under the support of a series of supporting policies. Along with increase of a proportion of photovoltaic power generation in power of the whole power system, deeply understanding a characteristic and rule of photovoltaic output may accurately master influence of photovoltaic grid connection on the power system and enable the power system to more effectively solve a problem about photovoltaic access.
- An existing weather simulation technology may only implement annual/monthly photovoltaic power prediction, may not implement long-time-scale power prediction, and may not directly obtain a time sequence useful for analogue simulation of time sequence production of a power system. Therefore, it is necessary to model a photovoltaic output time sequence to accurately master an output change rule of photovoltaic power generation and provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
- In order to achieve the purpose, an embodiment of the disclosure provides a long-time-scale photovoltaic output time sequence modeling method. A characteristic of a photovoltaic output time sequence is analyzed, and a Markov chain is adopted to simulate transfer processes of each weather type and acquire probabilities of transfer to generate a simulated photovoltaic sequence, thereby proposing a new method to build a future photovoltaic output scenario.
- The embodiment of the disclosure is implemented by adopting the following technical solution.
- The embodiment of the disclosure provides a method for modeling a long-time-scale photovoltaic output time sequence, which includes that:
- historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected;
- weather types of days corresponding to the photovoltaic output is acquired, the weather types including at least one of clear weather, cloudy weather, overcast weather or changing weather;
- probabilities of transfer between each type of weather are calculated respectively;
- a simulated time sequence of the photovoltaic output within a preset time scale is generated; and
- validity of the simulated time sequence is verified.
- In an implementation mode of the embodiment of the disclosure, the operation that the probabilities of transfer between each type of weather are calculated respectively includes that: a Markov chain is adopted to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
-
- in formula (1), Pk being the probability of transfer of the clear weather to another weather type, k representing a weather type, N k being a number of times of transfer and N1 being a number of times of occurrence of the clear weather.
- In an implementation mode of the embodiment of the disclosure, the following step is further included: the probabilities of transfer between the other weather types are sequentially obtained by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- In an implementation mode of the embodiment of the disclosure, the operation that the simulated time sequence of the photovoltaic output within the preset time scale is generated includes that: the weather types and corresponding relative outputs within the preset time scale are sequentially and randomly extracted according to the probabilities of transfer between each weather type, and products of the relative outputs and a predetermined threshold value are calculated to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart for reflecting changes of a Probability Density Function (PDF), an Autocorrelation Function (ACF) and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min≤t≤60 min;
- the maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- In an implementation mode of the embodiment of the disclosure, the operation that the validity of the simulated time sequence is verified includes that:
- the PDF Cf, short-duration fluctuation characteristic Cd and ACF Cr of the simulated time sequence are defined respectively; and
- a Root-Mean-Square Error (RMSE) of each characteristic is adopted to quantitatively evaluate the validity of the time sequence, an expression being:
-
- where ŷi ∈[Cf, Cd, Cr], ŷi is a unit vector, and represents a function value of each characteristic of the simulated time sequence, yi represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ε with a value range of 0.1˜0.2.
- An embodiment of the disclosure provides a device for modeling a long-time-scale photovoltaic output time sequence, wherein the device includes: a data acquisition unit, configured to acquire historical data of a photovoltaic power station, and select a photovoltaic output with a time length of one year and a time resolution of 15 mins;
- an acquisition unit, configured to acquire weather types of days corresponding to the photovoltaic output from a weather station, the weather types including at least one of clear weather, cloudy weather, overcast weather or changing weather;
- a processing unit, configured to calculate probabilities of transfer between each type of weather respectively;
- a generation unit, configured to generate a simulated time sequence of the photovoltaic output within a preset time scale; and
- an evaluation unit, configured to verify validity of the simulated time sequence.
- In an implementation mode of the embodiment of the disclosure, the processing unit is further configured to: adopt a Markov chain to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
-
- in formula (1), Pk being the probability of transfer of the clear weather to another weather type, k representing a weather type, Nk being a number of times of transfer and N1 being a number of times of occurrence of the clear weather.
- In an implementation mode of the embodiment of the disclosure, the device further includes: a probability acquisition unit, configured to sequentially obtain the probabilities of transfer between the other weather types by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- In an implementation mode of the embodiment of the disclosure, the generation unit is further configured to: sequentially and randomly extract the weather types and corresponding relative outputs within the preset time scale according to the probabilities of transfer between each weather type, and calculate products of the relative output and a predetermined threshold value to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart for reflecting changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min≤t≤60 min;
- the maximum PDF is a difference value between maximum output and minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- In an implementation mode of the embodiment of the disclosure, the evaluation unit is further configured to:
- define the PDF Cf, short-duration fluctuation characteristic Cd and ACF Cr of the simulated time sequence respectively; and
- adopt an RMSE of each characteristic to quantitatively evaluate the validity of the time sequence, an expression being:
-
- where ŷi ∈[Cf, Cd, Cr], ŷi is a unit vector and represents a function value of each characteristic of the simulated time sequence, yi represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ε with a value range of 0.1˜0.2.
- Compared with a conventional art, adopting the embodiments of the disclosure may achieve the following beneficial effects: the Markov chain is adopted to simulate the transfer processes of each type of weather and calculate the probabilities of transfer between each weather type; and uncertain characteristics such as randomness and fluctuation of photovoltaics are simulated, and compared with other methods, a building structure is more consistent with characteristics of the photovoltaic output, and truthfully and accurately represent a future photovoltaic output condition. Annual and monthly photovoltaic output simulation time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be generated according to a requirement to provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
-
FIG. 1 is a flowchart of a long-time-scale photovoltaic output time sequence modeling method according to an embodiment of the disclosure. -
FIG. 2 -FIG 5 are schematic diagrams of parameter comparison between a historical time sequence and a simulated time sequence according to an embodiment of the disclosure, wherein -
FIG. 2 is a schematic diagram of a probability density; -
FIG. 3 is a schematic diagram of a 15 min probability density; -
FIG. 4 is a schematic diagram of a 60 min probability density; and -
FIG. 5 is a schematic diagram of autocorrelation coefficient comparison. - Specific implementation modes of the disclosure will be further described below in combination with the drawings in detail.
-
FIG. 1 shows a long-time-scale photovoltaic output time sequence modeling method according to an embodiment of the disclosure. The method includes the following steps. - In
Step 101, historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected. - In
Step 102, weather types of days corresponding to the photovoltaic output are acquired from a weather station, the weather types including clear weather, cloudy weather, overcast weather and changing weather. - In
Step 103, probabilities of transfer between each type of weather are calculated respectively, a Markov chain being adopted to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being: -
- in formula (1), Pk being the probability of transfer of the clear weather to another weather type, k representing a weather type, Nk being a number of times of transfer and N1 being a number of times of occurrence of the clear weather.
- The probabilities of transfer between the other weather types are sequentially obtained by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- For example, expressions for calculating the probabilities of transfer of the cloudy weather to the other weather types are:
-
- in the formulae,
subscript 1 being adopted for the cloudy weather type, subscript 2 being adopted for the clear weather type, subscript 3 being adopted for the overcast weather type, subscript 4 being adopted for the changing weather type, P(1-1), P(1-3), P(1-3), and P(1-4) representing the probabilities of transfer of the cloudy weather type to the other weather types respectively, N(1-1), N(1-2), N(1-3) and N(1-4) representing numbers of times of transfer of the cloudy weather to the other weather types respectively, and N(1) representing a number of times of occurrence of the cloudy weather type. Similarly, the probabilities of transfer of the overcast weather and the changing weather may be calculated. - In
Step 104, a simulated time sequence of the photovoltaic output within a preset time scale is generated. - The weather types and corresponding relative outputs within the preset time scale are sequentially and randomly extracted according to the probabilities of transfer between each weather type, and products of the relative outputs and a predetermined threshold value are calculated to generate the simulated time sequence of the photovoltaic output. The predetermined threshold value is a standard value customized according to historical photovoltaic data and a historical time sequence. The simulated time sequence is a curve chart and is configured to reflect changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales.
- The short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min≤t≤60 min.
- The maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- In
Step 105, validity of the simulated time sequence is verified, as shown in each schematic diagram ofFIG. 2 toFIG. 5 . - Here, a specific processing process of the step includes the following steps.
- In Step 1051, the PDF Cf, short-duration fluctuation characteristic Cd and ACF Cr of the simulated time sequence are defined respectively.
- In Step 1052, an RMSE of each characteristic is adopted to quantitatively evaluate the validity of the time sequence, an expression being:
-
- where ŷi ∈[Cf, Cd, Cr], ŷ, is a unit vector, and represents a function value of each characteristic of the simulated time sequence, yi represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of the historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ε with a value range of 0.1˜0.2.
-
FIG. 2 is a schematic diagram of a probability density. As shown inFIG. 2 , when ŷi ∈Cf, the function value of the PDF of the simulated time sequence is represented, and at this moment, yi represents the function value of the PDF, corresponding to the PDF of the simulated time sequence, of the historical time sequence.FIG. 3 is a schematic diagram of a 15 min probability density, andFIG. 4 is a schematic diagram of a 60 min probability density. As shown inFIG. 3 andFIG. 4 , when ŷi ∈Cd, the function value of the short-duration fluctuation characteristic of the simulated time sequence is represented, and at this moment, yi represents the function value of the short-duration fluctuation characteristic, corresponding to the short-duration fluctuation characteristic of the simulated time sequence, of the historical time sequence.FIG. 5 is a schematic diagram of autocorrelation coefficient comparison. As shown inFIG. 5 , when ŷi ∈Cr, the function value of the ACF of the simulated time sequence is represented, and at this moment, yi represents the function value of the ACF, corresponding to the ACF of the simulated time sequence, of the historical time sequence. - An embodiment of the disclosure provides a long-time-scale photovoltaic output time sequence modeling device, which includes:
- a data acquisition unit, configured to acquire historical data of a photovoltaic power station, and select a photovoltaic output with a time length of one year and a time resolution of 15 mins;
- an acquisition unit, configured to acquire weather types of days corresponding to the photovoltaic output from a weather station, the weather types including clear weather, cloudy weather, overcast weather and changing weather;
- a processing unit, configured to calculate probabilities of transfer between each type of weather respectively;
- a generation unit, configured to generate a simulated time sequence of the photovoltaic output within a preset time scale; and
- an evaluation unit, configured to verify validity of the simulated time sequence.
- In an implementation mode of the embodiment of the disclosure, the processing unit is further configured to: adopt a Markov chain to simulate transfer processes of each type of weather and acquire the probabilities of transfer between each weather type, an expression being:
-
- in formula (1), Pk being the probability of transfer of the clear weather to another weather type, k representing a weather type, Nk being a number of times of transfer and N1 being a number of times of occurrence of the clear weather.
- In an implementation mode of the embodiment of the disclosure, the device further includes: a probability acquisition unit, configured to sequentially obtain the probabilities of transfer between the other weather types by virtue of a method for calculating the probabilities of transfer of the clear weather to the other weather types.
- In an implementation mode of the embodiment of the disclosure, the generation unit is further configured to: sequentially and randomly extract the weather types and corresponding relative outputs within the preset time scale according to the probabilities of transfer between each weather type, and calculate products of the relative output and a predetermined threshold value to generate the simulated time sequence of the photovoltaic output, wherein the simulated time sequence is a curve chart, and is configured to reflect changes of a PDF, ACF and short-duration fluctuation characteristic of photovoltaic output of multiple time scales;
- the short-duration fluctuation characteristic is a maximum PDF of the photovoltaic output within a time scale t, 15 min≤t≤60 min;
- the maximum PDF is a difference value between a maximum output and a minimum output within the time scale t; and the difference value is positive if the maximum output appears after the minimum output, and the difference value is negative if it appears before the minimum output.
- In an implementation mode of the embodiment of the disclosure, the evaluation unit is further configured to:
- define the PDF Cf, short-duration fluctuation characteristic Cd and ACF Cr of the simulated time sequence respectively; and
- adopt an RMSE of each characteristic to quantitatively evaluate the validity of the time sequence, an expression being:
-
- where ŷi ∈[Cf, Cd, Cr], ŷi is a unit vector, and represents a function value of each characteristic of the simulated time sequence, yi represents a function value of each characteristic, corresponding to each characteristic of the simulated time sequence, of a historical time sequence, n is a length of a function value set of each characteristic of the time sequence, RMSE is smaller than ε with a value range of 0.1˜0.2.
- It should finally be noted that: the above embodiments are adopted to not limit but only describe the technical solutions of the disclosure, and although the disclosure has been described with reference to the above embodiments in detail, those skilled in the art should understand that: modifications or equivalent replacements may still be made to the specific implementation modes of the disclosure, and any modifications or equivalent replacements made without departing from the spirit and scope of the disclosure shall fall within the scope of the claims of the disclosure.
- By adopting the embodiments of the disclosure, the Markov chain is adopted to simulate the transfer processes of each type of weather and calculate the probabilities of transfer between each weather type; and uncertain characteristics such as randomness and fluctuation of photovoltaics are simulated, and compared with other methods, a building structure is more consistent with characteristics of the photovoltaic output, and truthfully and accurately represent a future photovoltaic output condition. Annual and monthly photovoltaic output simulation time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be generated according to a requirement to provide indispensable basic data for analogue simulation of time sequence production including massive new energy, annual new energy resource consumption capability analysis and annual planning.
Claims (17)
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510639474.5 | 2015-09-30 | ||
CN201510639474.5A CN106557828A (en) | 2015-09-30 | 2015-09-30 | A kind of long time scale photovoltaic is exerted oneself time series modeling method and apparatus |
PCT/CN2016/087809 WO2017054537A1 (en) | 2015-09-30 | 2016-06-30 | Long-time scale photovoltaic output time sequence modelling method and apparatus |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2016/087809 A-371-Of-International WO2017054537A1 (en) | 2015-09-30 | 2016-06-30 | Long-time scale photovoltaic output time sequence modelling method and apparatus |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/963,073 Continuation US10290066B2 (en) | 2015-09-30 | 2018-04-25 | Method and device for modeling a long-time-scale photovoltaic output time sequence |
Publications (1)
Publication Number | Publication Date |
---|---|
US20180240200A1 true US20180240200A1 (en) | 2018-08-23 |
Family
ID=58417673
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/751,471 Abandoned US20180240200A1 (en) | 2015-09-30 | 2016-06-30 | Method and device for modeling a long-time-scale photovoltaic output time sequence |
US15/963,073 Active US10290066B2 (en) | 2015-09-30 | 2018-04-25 | Method and device for modeling a long-time-scale photovoltaic output time sequence |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/963,073 Active US10290066B2 (en) | 2015-09-30 | 2018-04-25 | Method and device for modeling a long-time-scale photovoltaic output time sequence |
Country Status (3)
Country | Link |
---|---|
US (2) | US20180240200A1 (en) |
CN (1) | CN106557828A (en) |
WO (1) | WO2017054537A1 (en) |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109711870A (en) * | 2018-12-13 | 2019-05-03 | 江苏中科瀚星数据科技有限公司 | A kind of prediction of residential block electric car charging load and configuration method |
CN110334870A (en) * | 2019-07-09 | 2019-10-15 | 福州大学 | Photovoltaic plant short term power prediction technique based on gating cycle unit networks |
US10492381B2 (en) * | 2014-03-26 | 2019-12-03 | Sun'r | Electricity generation method adapted to crops |
CN111602156A (en) * | 2019-01-18 | 2020-08-28 | 株式会社秀住房 | Method for precisely predicting power generation amount calculated from expected value, system for precisely predicting power generation amount calculated from expected value, and program for precisely predicting power generation amount calculated from expected value |
CN112149905A (en) * | 2020-09-25 | 2020-12-29 | 福州大学 | Photovoltaic power station short-term power prediction method based on wavelet transformation and wavelet neural network |
CN112907108A (en) * | 2021-03-15 | 2021-06-04 | 福州大学 | Multi-stage installed capacity planning method for offshore wind power plant |
CN113570267A (en) * | 2021-08-02 | 2021-10-29 | 福州万山电力咨询有限公司 | Method and terminal for determining spontaneous self-use proportion of distributed photovoltaic power generation |
CN113779861A (en) * | 2021-07-23 | 2021-12-10 | 国网河北省电力有限公司电力科学研究院 | Photovoltaic power prediction method and terminal equipment |
TWI783605B (en) * | 2021-08-02 | 2022-11-11 | 崑山科技大學 | Solar power generation prediction method |
Families Citing this family (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11068563B2 (en) * | 2011-07-25 | 2021-07-20 | Clean Power Research, L.L.C. | System and method for normalized ratio-based forecasting of photovoltaic power generation system degradation with the aid of a digital computer |
CN107093007B (en) * | 2017-03-31 | 2020-12-22 | 华南理工大学 | Power distribution network reliability assessment method considering light storage continuous loading capacity |
CN108601035B (en) * | 2018-04-26 | 2022-04-22 | 南京邮电大学 | WMSNs node scheduling method based on solar energy collection model |
CN109460893B (en) * | 2018-09-26 | 2024-08-09 | 中国电力科学研究院有限公司 | Photovoltaic power station weather type correlation index calculation method and system |
CN109524983B (en) * | 2018-10-25 | 2022-04-01 | 国家电网有限公司 | Photovoltaic output modeling method based on typical state |
CN109492315B (en) * | 2018-11-19 | 2022-10-25 | 西安交通大学 | Spatio-temporal correlation wind-solar sequence simulation method based on Copula function |
CN109524993B (en) * | 2018-12-19 | 2020-06-02 | 中国农业大学 | Wind power photovoltaic typical cycle output scene generation method for medium-long term optimization scheduling |
CN109449993B (en) * | 2018-12-26 | 2021-10-22 | 西安交通大学 | Multi-time scale production simulation method for multi-energy complementary power system |
WO2020148940A1 (en) * | 2019-01-18 | 2020-07-23 | 株式会社ヒデ・ハウジング | Solar irradiance occurrence probability distribution analysis method, solar irradiance occurrence probability distribution analysis system, solar irradiance occurrence probability distribution analysis program, solar irradiance normalization statistical analysis system, solar irradiance normalization statistical analysis method, and solar irradiance normalization statistical analysis program |
CN110378504B (en) * | 2019-04-12 | 2023-04-07 | 东南大学 | Photovoltaic power generation climbing event probability prediction method based on high-dimensional Copula technology |
CN110544016B (en) * | 2019-08-09 | 2022-02-15 | 国网江苏省电力有限公司电力科学研究院 | Method and equipment for evaluating influence degree of meteorological factors on fault probability of power equipment |
CN110610262B (en) * | 2019-08-27 | 2023-06-27 | 天津大学 | Long-time scale photovoltaic time sequence output generation method considering weather elements |
CN110717277A (en) * | 2019-10-14 | 2020-01-21 | 河北工业大学 | Time sequence wind speed simulation method |
CN111861259B (en) * | 2020-07-30 | 2022-07-15 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Load modeling method, system and storage medium considering time sequence |
CN112116127B (en) * | 2020-08-20 | 2024-04-02 | 中国农业大学 | Photovoltaic power prediction method based on association of meteorological process and power fluctuation |
CN112671035A (en) * | 2020-12-21 | 2021-04-16 | 北京华能新锐控制技术有限公司 | Virtual power plant energy storage capacity configuration method based on wind power prediction |
CN112861363B (en) * | 2021-02-23 | 2022-09-27 | 国网冀北电力有限公司智能配电网中心 | Photovoltaic power generation state estimation method and device and terminal equipment |
CN115189421B (en) * | 2022-08-01 | 2024-08-13 | 长沙理工大学 | Sunny day judging method based on photovoltaic power station output characteristic analysis |
CN117411436B (en) * | 2023-12-15 | 2024-03-15 | 国网浙江省电力有限公司金华供电公司 | Photovoltaic module state detection method, system and storage medium |
Family Cites Families (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8200370B2 (en) * | 2008-12-04 | 2012-06-12 | American Power Conversion Corporation | Energy reduction |
US20110066401A1 (en) * | 2009-09-11 | 2011-03-17 | Wattminder, Inc. | System for and method of monitoring and diagnosing the performance of photovoltaic or other renewable power plants |
WO2012127585A1 (en) * | 2011-03-18 | 2012-09-27 | 富士通株式会社 | Operation schedule creating method, operation schedule creating apparatus, and operation schedule creating program |
WO2013136419A1 (en) * | 2012-03-12 | 2013-09-19 | 富士通株式会社 | Method for creating operation plan, operation plan creation program and operation plan creation device |
CN102880989B (en) * | 2012-09-05 | 2016-04-20 | 中国电力科学研究院 | A kind of wind power output time series modeling method |
CN103810534B (en) * | 2013-12-11 | 2015-03-25 | 广西电网公司电力科学研究所 | Photovoltaic power output prediction method based on genetic neural network |
CN104182889B (en) * | 2014-08-18 | 2017-11-21 | 国家电网公司 | A kind of history wind power output data processing and fluctuation discrimination method |
CN104218574A (en) * | 2014-08-29 | 2014-12-17 | 国家电网公司 | Modeling method of photovoltaic output random model reflecting solar radiation intensity variation characteristics |
CN104182914B (en) * | 2014-09-05 | 2017-06-23 | 国家电网公司 | A kind of wind power output time series modeling method based on wave characteristic |
CN104616078B (en) * | 2015-02-03 | 2017-12-22 | 河海大学 | Photovoltaic system electricity generation power Forecasting Methodology based on Spiking neutral nets |
-
2015
- 2015-09-30 CN CN201510639474.5A patent/CN106557828A/en active Pending
-
2016
- 2016-06-30 US US15/751,471 patent/US20180240200A1/en not_active Abandoned
- 2016-06-30 WO PCT/CN2016/087809 patent/WO2017054537A1/en active Application Filing
-
2018
- 2018-04-25 US US15/963,073 patent/US10290066B2/en active Active
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10492381B2 (en) * | 2014-03-26 | 2019-12-03 | Sun'r | Electricity generation method adapted to crops |
CN109711870A (en) * | 2018-12-13 | 2019-05-03 | 江苏中科瀚星数据科技有限公司 | A kind of prediction of residential block electric car charging load and configuration method |
CN111602156A (en) * | 2019-01-18 | 2020-08-28 | 株式会社秀住房 | Method for precisely predicting power generation amount calculated from expected value, system for precisely predicting power generation amount calculated from expected value, and program for precisely predicting power generation amount calculated from expected value |
US10998725B2 (en) | 2019-01-18 | 2021-05-04 | Hide Housing Corporation | Electric power generation prediction method based on expected value calculation, electric power generation prediction system based on expected value calculation, and electric power generation prediction program product based on expected value calculation |
CN110334870A (en) * | 2019-07-09 | 2019-10-15 | 福州大学 | Photovoltaic plant short term power prediction technique based on gating cycle unit networks |
CN112149905A (en) * | 2020-09-25 | 2020-12-29 | 福州大学 | Photovoltaic power station short-term power prediction method based on wavelet transformation and wavelet neural network |
CN112907108A (en) * | 2021-03-15 | 2021-06-04 | 福州大学 | Multi-stage installed capacity planning method for offshore wind power plant |
CN113779861A (en) * | 2021-07-23 | 2021-12-10 | 国网河北省电力有限公司电力科学研究院 | Photovoltaic power prediction method and terminal equipment |
CN113570267A (en) * | 2021-08-02 | 2021-10-29 | 福州万山电力咨询有限公司 | Method and terminal for determining spontaneous self-use proportion of distributed photovoltaic power generation |
TWI783605B (en) * | 2021-08-02 | 2022-11-11 | 崑山科技大學 | Solar power generation prediction method |
Also Published As
Publication number | Publication date |
---|---|
WO2017054537A1 (en) | 2017-04-06 |
CN106557828A (en) | 2017-04-05 |
US10290066B2 (en) | 2019-05-14 |
US20180240048A1 (en) | 2018-08-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10290066B2 (en) | Method and device for modeling a long-time-scale photovoltaic output time sequence | |
Jiang et al. | Very short-term wind speed forecasting with Bayesian structural break model | |
Aien et al. | Probabilistic power flow of correlated hybrid wind‐photovoltaic power systems | |
JP6384065B2 (en) | Information processing apparatus, learning method, and program | |
US20150160373A1 (en) | Computer-implemented data analysis methods and systems for wind energy assessments | |
Yeh et al. | Simplex simplified swarm optimisation for the efficient optimisation of parameter identification for solar cell models | |
CN102945223A (en) | Method for constructing joint probability distribution function of output of a plurality of wind power plants | |
CN103020423A (en) | Copula-function-based method for acquiring relevant characteristic of wind power plant capacity | |
CN104091077B (en) | A kind of wind energy turbine set photovoltaic plant combines analogy method of exerting oneself | |
CN104331572A (en) | Wind power plant reliability modeling method considering correlation between air speed and fault of wind turbine generator | |
CN107704992A (en) | The method and device of transmission line lightning stroke risk assessment | |
CN105809264B (en) | Power load prediction method and device | |
CN103699800A (en) | Ultrashort-period wind speed prediction method based on frequency-domain multi-scale wind speed signal predictability | |
CN112100911A (en) | Solar radiation prediction method based on deep BISLTM | |
Scholz et al. | A cyclic time-dependent Markov process to model daily patterns in wind turbine power production | |
US9328719B2 (en) | Method of calculating available output power of wind farm | |
Kaplan et al. | A novel method based on Weibull distribution for short-term wind speed prediction | |
D′ Amico et al. | Reliability Measures of Second‐Order Semi‐Markov Chain Applied to Wind Energy Production | |
CN114819374A (en) | Regional new energy ultra-short term power prediction method and system | |
Yan et al. | A robust probabilistic wind power forecasting method considering wind scenarios | |
Lin et al. | Adaptive slime mould algorithm for optimal design of photovoltaic models | |
CN103927597A (en) | Ultra-short-term wind power prediction method based on autoregression moving average model | |
KR20220088946A (en) | Radial prediction processing method, stack generalization model training method and apparatus | |
Laib et al. | Spatial modelling of extreme wind speed distributions in Switzerland | |
CN112581311B (en) | Method and system for predicting long-term output fluctuation characteristics of aggregated multiple wind power plants |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: CHINA ELECTRIC POWER RESEARCH INSTITUTE COMPANY LI Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, WEISHENG;LIU, CHUN;LI, CHI;AND OTHERS;SIGNING DATES FROM 20180126 TO 20180130;REEL/FRAME:047883/0779 Owner name: CLP PURI ZHANGBEI WIND POWER RESEARCH & TESTING CO Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, WEISHENG;LIU, CHUN;LI, CHI;AND OTHERS;SIGNING DATES FROM 20180126 TO 20180130;REEL/FRAME:047883/0779 Owner name: STATE GRID CORPORATION OF CHINA, CHINA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, WEISHENG;LIU, CHUN;LI, CHI;AND OTHERS;SIGNING DATES FROM 20180126 TO 20180130;REEL/FRAME:047883/0779 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |