Method and apparatus for analyzing electricity consumption characteristic data related to wind-solar-energy storage, system, device, and medium

Abstract

Disclosed in the present invention are a method and apparatus for analyzing electricity consumption characteristic data related to wind-solar-energy storage, a system, a device, and a medium. The method comprises: acquiring raw time-series information related to wind-solar-energy storage, the raw time-series information comprising raw time-series data of wind power output, raw time-series data of photovoltaic output, and raw time-series data of load; using the raw time-series information to calculate a characteristic data value of wind-solar-energy storage by using time as the periodic dimension; and after an association curve corresponding to the characteristic data value is determined, using the association curve and the characteristic data value to calculate boundary data of wind-solar-energy storage, and using the boundary data to analyze electricity consumption characteristic data. In the present invention, boundary data can be determined by means of different pieces of raw time-series information and electricity consumption characteristic data can be analyzed, thereby adapting to the actual situations of energy systems, and reducing analysis deviation, and thus improving analysis accuracy.

Description

Power consumption feature data analysis method, device, system and equipment related to wind-solar energy storage and medium TECHNICAL FIELD

[0001] The present application relates to the technical field of power data analysis, and in particular to a power consumption feature data analysis method, device, system, equipment and medium related to wind-solar energy storage. BACKGROUND

[0002] With the increasing energy demand of global economic development, and the gradual depletion of various traditional fossil energy, the call for large-scale development of renewable energy such as wind and solar energy is becoming more and more urgent. However, due to the intermittent and random nature of wind and solar resources, wind and solar output has strong volatility. In order to promote the combined use of various energies and achieve supply and demand balance, it is necessary to accurately analyze the power consumption feature data of different energies.

[0003] In order to determine the characteristics of each energy, the commonly used analysis method is to determine the power generation parameters of different renewable energies such as power generation power and energy storage capacity, and then simulate the power consumption scene through simulation algorithm, and then analyze the power consumption feature data according to the real-time collected data, including power consumption load, power consumption power, total power consumption and power shortage.

[0004] However, the commonly used method has the following technical problems: each renewable energy is coordinated to operate, and only the power generation parameters of a single energy are used for power consumption feature analysis, the analysis accuracy is low, and the actual deviation is large, which is difficult to meet the analysis needs of users. SUMMARY

[0005] The present application provides a power consumption feature data analysis method, device, system, equipment and medium related to wind-solar energy storage, which can solve one or more of the above technical problems.

[0006] The first aspect of the embodiment of the present application provides a power consumption feature data analysis method related to wind-solar energy storage, which comprises:

[0007] Obtaining original time sequence information related to wind-solar energy storage, the original time sequence information comprising: original time sequence data of wind power output, original time sequence data of photovoltaic output and original time sequence data of load;

[0008] Using the original time sequence information to calculate the feature data value of wind-solar energy storage in time cycle dimension;

[0009] After determining the associated curve corresponding to the feature data value, using the associated curve and the feature data value to calculate the boundary data of wind-solar energy storage, and using the boundary data to analyze the power consumption feature data.

[0010] In a possible implementation manner of the first aspect, the calculating, by using the original time sequence information in a time period dimension, of the feature data value of the wind-solar-storage energy includes:

[0011] extracting a time sequence full cycle maximum value from the original time sequence information, and calculating a time sequence data standard value by using the time sequence full cycle maximum value;

[0012] calculating a standard extreme value by using the time sequence data standard value in a time period dimension, and the time in the period dimension is respectively year, month and day;

[0013] calculating the feature data value of the wind-solar-storage energy by using the standard extreme value.

[0014] In a possible implementation manner of the first aspect, the time sequence full cycle maximum value is as follows:

[0015] wherein, is a full cycle maximum value of the i-th type of input time sequence data, i is a type of input time sequence data, and j is a subscript of the time sequence data;

[0016] The time sequence data standard value is as follows:

[0017] wherein, is a standard value of the corresponding time sequence data input type at the j-th time point, P j is a nominal value of the input data at the j-th time point. is a maximum value of the nominal value of the corresponding time sequence data input type.

[0018] In a possible implementation manner of the first aspect, the feature data value includes a curve average load level value, a daily peak-valley difference rate, a maximum peak-valley difference rate, a load imbalance coefficient, a curve average output level value, an output rate, and a credible guaranteed output rate.

[0019] In a possible implementation manner of the first aspect, the correlation curve includes a split load correlation curve, a split node correlation curve and a system correlation curve.

[0020] The boundary data includes an average output value, a boundary electric quantity, a boundary time, and a time point load demand.

[0021] The power consumption feature data includes a maximum peak-valley difference, a new energy penetration rate, a new energy power generation, an average load rate, a total power shortage and a maximum power gap.

[0022] In a possible implementation manner of the first aspect, after the step of analyzing the power consumption feature data by using the boundary data, the method further includes:

[0023] After the boundary curve is constructed by using the boundary data, the power consumption feature data and the boundary curve are displayed.

[0024] The combination information of the user is acquired, the demand data is filtered according to the combination information, and the analysis report is generated by using the demand data, wherein the combination information is the data selected by the user after viewing the power consumption feature data and the boundary curve.

[0025] The second aspect of the embodiment of the application provides a power consumption feature data analysis device related to wind-solar energy storage, and the device comprises:

[0026] An acquisition module is configured to acquire original time sequence information related to wind-solar energy storage, wherein the original time sequence information comprises original time sequence data of wind power output, original time sequence data of photovoltaic output and original time sequence data of load.

[0027] A feature data calculation module is configured to calculate feature data values of wind-solar energy storage by using the original time sequence information in a time period dimension.

[0028] An analysis module is configured to calculate boundary data of wind-solar energy storage by using the associated curve corresponding to the feature data values and the feature data values after determining the associated curve, and analyze power consumption feature data by using the boundary data.

[0029] In a possible implementation manner of the second aspect, the calculation of the feature data values of wind-solar energy storage by using the original time sequence information in the time period dimension comprises:

[0030] After the time sequence full-period maximum value is extracted from the original time sequence information, the time sequence data standard value is calculated by using the time sequence full-period maximum value.

[0031] The time sequence data standard value is calculated to obtain an extreme value in the time period dimension, and the time in the period dimension is respectively year, month and day.

[0032] The feature data values of wind-solar energy storage are calculated by using the extreme value.

[0033] In a possible implementation manner of the second aspect, the time sequence full-period maximum value is as follows:

[0034] wherein, is a full-period maximum value of the i-th type of input time sequence data, i is the type of input time sequence data, and j is the subscript of the time sequence data.

[0035] The time sequence data standard value is as follows:

[0036] wherein, P is a standard value of the corresponding time-series data input type at the jth point in time j P is a nominal value of the input data at the jth point in time. P is a maximum value of the nominal value of the corresponding time-series data input type.

[0037] In a possible implementation of the second aspect, the feature data value includes a curve average load level value, a daily peak-valley difference rate, a maximum peak-valley difference rate, a load imbalance coefficient, a curve average output level value, an output rate, and a reliable guaranteed output rate.

[0038] In a possible implementation of the second aspect, the correlation curve includes a split-load correlation curve, a split-node correlation curve, and a system correlation curve.

[0039] The boundary data includes an average output value, a boundary electric quantity, a boundary time, and a load demand at a time point.

[0040] The power consumption feature data includes a maximum peak-valley difference, a new energy penetration rate, a new energy power generation amount, an average load rate, a total power shortage amount, and a maximum power gap.

[0041] In a possible implementation of the second aspect, the device further includes:

[0042] The display module is configured to display the power consumption feature data and the boundary curve after the boundary curve is constructed using the boundary data.

[0043] The report generation module is configured to obtain combination information of a user, filter demand data according to the combination information, and generate an analysis report using the demand data, where the combination information is data selected by the user after viewing the power consumption feature data and the boundary curve.

[0044] A third aspect of the embodiment of the present application provides a wind-solar-storage integrated scheduling operation system, which is applicable to the power consumption feature data analysis method for wind-solar energy storage as described above, and includes a feature data analysis module, a production scheduling operation module, and a result evaluation analysis module.

[0045] The feature data analysis module is configured to calculate and analyze indexes of wind and solar output distribution and power supply time period output rate to obtain feature data.

[0046] The production scheduling operation module is configured to correlate or import a power grid model, deduce boundary data of load, wind turbine, photovoltaic turbine, and external power in the power grid model, and perform data display preview and algorithm calculation.

[0047] The result evaluation analysis module is configured to generate an evaluation analysis report using the calculated data.

[0048] Compared with the prior art, the method, device, system, equipment and medium provided by the embodiment of the application have the beneficial effect that the original time sequence information about wind-solar-storage energy can be obtained, the feature data value of wind-solar-storage energy is calculated by using the original time sequence information in the time cycle dimension, the boundary data of wind-solar-storage energy is calculated by using the correlation curve corresponding to the feature data value and the correlation curve after the correlation curve is determined, and the power consumption feature data is analyzed by using the boundary data. The boundary data is determined by using different original time sequence information, and the power consumption feature data is analyzed, so as to fit the actual situation of each energy system, reduce the analysis deviation, and improve the analysis accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0049] FIG. 1 is a flowchart of a method for analyzing power consumption feature data about wind-solar-storage energy according to an embodiment of the application.

[0050] FIG. 2 is a structural diagram of a device for analyzing power consumption feature data about wind-solar-storage energy according to an embodiment of the application.

[0051] FIG. 3 is a structural diagram of a wind-solar-storage integrated dispatching and operation system according to an embodiment of the application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0053] To solve the above problems, the method for analyzing power consumption feature data about wind-solar-storage energy according to the embodiments of the application will be described and explained in detail below through the following specific embodiments.

[0054] Referring to FIG. 1, a flowchart of a method for analyzing power consumption feature data about wind-solar-storage energy according to an embodiment of the application is shown.

[0055] The method for analyzing power consumption feature data about wind-solar-storage energy is applicable to a wind-solar-storage integrated dispatching and operation system of a power grid, which can be mounted in a distribution network for data analysis.

[0056] For example, the method for analyzing power consumption feature data about wind-solar-storage energy can include the following steps.

[0057] S11, obtaining original time series information about wind-solar-storage, the original time series information including original time series data of wind power output, original time series data of photovoltaic output and original time series data of load.

[0058] In an embodiment, the original time series information is obtained in the form of an offline file or a data interface, and the original time series information can include original time series data of three types of wind power output, photovoltaic output and load. The basic form of the original time series data should include a time stamp and corresponding named value data with dimensions.

[0059] Specifically, in terms of time sampling granularity, one piece of data can be obtained every 15 minutes, every hour, or other larger or smaller granularities. The length of the data cycle is determined by the length of the original data sequence.

[0060] S12, calculating feature data values of wind-solar-storage by using the original time series information in the time cycle dimension.

[0061] In an embodiment, since the original time series information is a piece of data collected at a time, the feature data values can be calculated by using the original time series information in the time dimension.

[0062] In an embodiment, the feature data values include a curve average load level value, a daily peak-valley difference rate, a maximum peak-valley difference rate, a load imbalance coefficient, a curve average output level value, an output rate, a reliable guaranteed output rate, etc.

[0063] In an optional embodiment, the calculation of the feature data values of wind-solar-storage by using the original time series information in the time cycle dimension can include the following sub-steps:

[0064] S121, extracting a time series full cycle maximum value from the original time series information, and calculating a time series data unit value by using the time series full cycle maximum value.

[0065] S122, calculating a unit extreme value by using the time series data unit value in the time cycle dimension, and the time of the cycle dimension being year, month and day respectively.

[0066] S123, calculating the feature data values of wind-solar-storage by using the unit extreme value.

[0067] In an embodiment, the time series full cycle maximum value is as follows:

[0068] wherein, is a full cycle maximum value of the ith type of input time series data, i is the type of the input time series data, and j is the subscript of the time series data;

[0069] The time series data unit value is as follows:

[0070] wherein, is the unit value of the corresponding time series data input type at the j time point, P j is the nominal value of the input data at the j time point. is the maximum value of the nominal value of the corresponding time series data input type.

[0071] The unit extreme value can be divided into three different time granularities of years, months, and days. In an embodiment, the unit extreme value includes a period minimum value and a period maximum value.

[0072] wherein, the period minimum value is as follows:

[0073] The period maximum value is as follows:

[0074] According to the time stamp of the input original time series data, the values of the three periods of years, months, and days are taken. Assuming that the data is complete and intact, under the time sampling granularity of one data per hour, the time period length is:

[0075] Year: 8760;

[0076] Month: 720 (744);

[0077] Day: 24;

[0078] After obtaining the unit value and the period extreme value of the original time series data, the feature indicators can be calculated according to the three period dimensions of years, months, and days, so as to obtain the feature data value. For the feature indicators, the calculation can be divided into supply features and demand features.

[0079] wherein, for the demand features, the calculation is mainly carried out for the unit value of the load time series data, and specifically includes: curve maximum load level value, curve minimum load level, and curve average load level.

[0080] wherein, the curve maximum load level value can be specifically taken as the period maximum value under the corresponding year, month, and day period, and specifically can refer to the calculation formula of the period maximum value.

[0081] The curve minimum load level can be taken as the period minimum value under the corresponding year, month, and day period, and specifically can refer to the calculation formula of the period minimum value.

[0082] The curve average load level is as follows:

[0083] For the demand features, the feature data value also includes a daily peak-valley difference rate, which is as follows:

[0084] wherein, is the daily maximum peak-valley difference rate, are the maximum and minimum values calculated under the daily cycle, respectively.

[0085] For the demand feature, the feature data value further includes: the maximum peak-valley difference rate.

[0086] For the monthly and annual, there is a corresponding maximum daily peak-valley difference rate in the month or the year. The maximum peak-valley difference rate is as follows:

[0087] wherein, is the maximum daily peak-valley difference rate in the cycle, and d represents the subscript of the natural day in the cycle.

[0088] For the demand feature, the feature data value further includes: the load imbalance coefficient.

[0089] For the monthly and annual, there is a corresponding load imbalance coefficient in the month or the year as a load change feature. Taking the monthly load imbalance coefficient as an example, the calculation method is as follows:

[0090] wherein, is the maximum load value of the dth day in the month, and d is the total number of days in the statistical cycle, indicates the maximum load value in the month.

[0091] In an embodiment, the supply feature mainly carries out calculation on the unit value of the output time series data of wind power and photovoltaic.

[0092] Specifically, for the supply feature, the feature data value includes: the curve maximum output level, which can take the cycle maximum value under the corresponding annual, monthly, and daily cycle. Specifically, the calculation formula of the cycle maximum value can be referred to above.

[0093] For the supply feature, the feature data value further includes: the curve minimum output level. The curve minimum output level can take the cycle minimum value under the corresponding annual, monthly, and daily cycle. Specifically, the calculation formula of the cycle minimum value can be referred to above.

[0094] For the supply feature, the feature data value further includes: the curve average output level, which is as follows:

[0095] For the supply feature, the feature data value further includes: the output rate, which is as follows:

[0096] wherein: η jThe new energy output rate at the corresponding time point, The unit value at the corresponding time point.

[0097] For the supply feature, the feature data value further includes: a credible guaranteed output rate, which is the new energy output that is not less than the new energy output level at the given guarantee probability a% level within a period.

[0098] In an embodiment, the calculation sequence of the credible guaranteed output rate is as follows:

[0099] First, the new energy output unit values within the period are sorted in descending order.

[0100] Second, the sorted data is then renumbered from 1 to n.

[0101] Third, the new energy output unit value at the n*a% position is the credible guaranteed output rate at the guarantee probability.

[0102] Fourth, in order to make the index more rigorous, when n*a% is not an integer, the next integer is obtained by rounding up.

[0103] S13, after determining the associated curve corresponding to the feature data value, the boundary data of wind-solar-storage energy is calculated by using the associated curve and the feature data value, and the boundary data is used to analyze the electricity consumption feature data.

[0104] In an embodiment, the unit curve in the feature data value can be associated by the associated curve, so that the boundary deduction can be performed by using the curve to determine the boundary data of wind-solar-storage energy, and then the boundary data is used to analyze the electricity consumption feature data.

[0105] In an embodiment, the associated curve includes: a load-associated curve, a node-associated curve, and a system-associated curve.

[0106] The selected curve association mode includes: load association, node association, and system association, and the association object is the extracted system model and load data. The load association is the corresponding load name; the node association is the node name corresponding to the node set; and the system association is a data of the whole network.

[0107] In an embodiment, the selected associated unit value curve can select the unit value curve associated with each object, the boundary deduction of the load is associated with the "feature data analysis-load" data, and the maximum load value corresponding to the typical curve is input. The calculation formula of the final actual data is:

[0108] wherein, The time series index value.

[0109] In an embodiment, the curve granularity can also be selected. For the case of annual granularity, 8760-point or 288-point curve granularity can be selected, and only 96-point curve granularity is supported for minute-level granularity.

[0110] For calculating the boundary data, when all the associated objects have associated curves and maximum load reference values, the corresponding boundary curves can be generated according to the associated curves and the maximum load reference values, so as to determine the corresponding boundary data according to the boundary curves. Specifically, the maximum, minimum, average output and power, utilization hours and other KPI indicators of each curve can be counted to obtain the boundary data.

[0111] In an embodiment, the boundary data includes: an average output value, boundary power, boundary time, and a time point load demand.

[0112] The average output value is as follows:

[0113] In the above formula, x i represents the output at each time point.

[0114] The boundary power is calculated as follows:

[0115] When the curve granularity is 8760 points, the boundary power is as follows:

[0116] When the curve granularity is 288 points, the boundary power is as follows:

[0117] In the above formula, M = 12 represents 12 months in a year, and D i represents the number of days in the i-th month.

[0118] When the curve granularity is 96 points, the boundary power is as follows:

[0119] The boundary time is calculated as follows:

[0120] When the curve granularity is 8760 points, the boundary time is as follows:

[0121] When the curve granularity is 288 points, the boundary time is as follows:

[0122] When the curve granularity is 96 points, the boundary time is as follows:

[0123] The time point load demand is calculated as follows:

[0124] When the curve granularity is 8760, the point load demand is as follows:

[0125] In the above formula, P * is the unit value of the correlation curve.

[0126] When the curve granularity is 288, the point load demand is as follows:

[0127] In the above formula, P l ' oad represents the load demand.

[0128] When the curve granularity is 96, the point load demand is as follows:

[0129] After calculating the boundary data, the boundary curve can be constructed using the boundary data, and the running state data of each type of device at 8760 time points, including system demand, tie line, starting capacity, etc. The data curve of the corresponding index is displayed, and then the characteristic data of each power consumption is determined in the curve.

[0130] In an embodiment, the power consumption characteristic data includes: a system load curve, a tie line transmission power, a tie line power ratio, a power generation ratio, a storage power generation ratio, and a new energy curtailment rate.

[0131] The system load curve P load,system is calculated as follows:

[0132] P load,system =∑P load,i ;

[0133] In the above formula, P load,i represents each sub-object load curve, and the above formula represents the sum of each sub-object point load value at each time point to obtain the system load at each time point.

[0134] The tie line transmission power P line is calculated as follows:

[0135] P line =∑P line,i ;

[0136] In the above formula, P line,i represents the transmission power of each external tie line at the time point.

[0137] The tie line power ratio γ is calculated as follows:

[0138] The power generation proportion σ of the power supply is calculated as follows:

[0139] In the above formula, P VSS,i represents the output of the selected power supply at the selected time in the selected region. VSS,total represents the output of all power supplies at the selected time in the selected region.

[0140] The energy storage power generation proportion θ is calculated as follows:

[0141] In the above formula, P bess,i represents the output of the energy storage at the selected time in the selected region.

[0142] The new energy curtailment rate υ is calculated as follows:

[0143] In the above formula, P wind,i , P pv,i respectively represent the wind power curtailment and the photovoltaic curtailment at each time, P′ wind , P′ pv respectively represent the wind power prediction output curve and the photovoltaic prediction output curve at each time.

[0144] In an embodiment, in order to facilitate the user to view various data, as an example, the method can further include:

[0145] S14, after constructing the boundary curve using the boundary data, the power consumption characteristic data and the boundary curve are displayed.

[0146] S15, obtaining combination information of the user, filtering demand data according to the combination information and generating an analysis report using the demand data, wherein the combination information is data selected by the user after viewing the power consumption characteristic data and the boundary curve.

[0147] In an embodiment, the combination information can be data selected by the user after viewing the power consumption characteristic data and the boundary curve, for example, related data about power results, related data about power grid reliability, related data about energy production costs, etc.

[0148] After obtaining the above information, the analysis report can be generated using the above information.

[0149] The user can set factor conditions, combine extracted running modes, obtain combination information, and generate an evaluation analysis report according to the combination information. Through the analysis report, the typical scene running mode can be analyzed and evaluated in multiple dimensions and multiple levels, providing decision support for the construction of a new low-carbon power system.

[0150] For example, the user selects a production scheduling operation scheme and determines the planning evaluation analysis time. Then, the system load, tie-line power ratio, power generation ratio, etc. are displayed, and then the user can input the data to be extracted from the power results, reliability, and other three extraction dimensions to obtain combined information.

[0151] For the power results dimension, the data can include: system load, tie-line power ratio, power generation ratio, energy storage charging and discharging ratio, new energy curtailment rate, etc.

[0152] The system load can refer to the range in which the named value of the system load in the boundary condition of the boundary curve is located, and the condition specifies the maximum and minimum values.

[0153] The tie-line power ratio can refer to the proportion of the power received (or output) by the tie-line to the total power generation in the boundary condition of the boundary curve. The tie-line power ratio is specifically shown in the following formula:

[0154] wherein R c is the tie-line power ratio for a certain time period (moment, day), P ij is the power value corresponding to the j moment point of a certain tie-line. is the total output value of all power generation types at j moment.

[0155] The power generation ratio can refer to the proportion of the power of units such as wind, light, water, fire, and nuclear power (excluding tie-lines, energy storage devices, etc.) to the total power generation in the boundary condition of the boundary curve. The power generation ratio is specifically shown in the following formula:

[0156] wherein R c is the power generation ratio for a certain time period (moment, day), P ij is the power value corresponding to the j moment point of a certain power generation type. is the total output value of all power generation types at j moment.

[0157] The energy storage charging and discharging ratio is specifically shown in the following formula:

[0158] wherein R b is the energy storage charging and discharging ratio for a certain time period (moment, day), P j is the power value corresponding to the j moment point of the energy storage device. is the total output value of all power generation types at j moment.

[0159] The new energy curtailment rate is specifically shown in the following formula:

[0160] wherein R aLet P′ be the rate of curtailment of renewable energy during a certain time period (moment, day). j Let P be the predicted output value of the new energy source at time j. j The actual output of the new energy source at time j.

[0161] For the reliability dimension, the data can include: equipment load rate, system load shedding rate, and load shedding cost.

[0162] The equipment load factor refers to the ratio between the actual power flow of the line equipment and its maximum limit. The specific formula for the equipment load factor is as follows:

[0163] Among them, R l P represents the equipment load rate. ij P represents the active power of the power flow at time j for the i-th line. max This represents its maximum limit.

[0164] The system load shedding rate is the ratio of the difference between the system load forecast and the output forecast to the system load, and its value ranges from 0% to 100%.

[0165] The load shedding cost is the loss cost per unit of electrical load, which is manually assigned here.

[0166] For other dimensions, the data can include: energy production costs, carbon emissions, and carbon emission intensity.

[0167] Energy production costs can be defined by various power generation types (wind, solar, hydro, thermal, storage, nuclear), and the unit cost of producing each unit of electricity or power is given manually.

[0168] Carbon emissions can be defined as the amount of carbon dioxide emitted by the total load / output of the entire system, as shown in the following formula:

[0169] c=ΣP i ×C i ;

[0170] Where C represents the amount of carbon dioxide emitted by the entire system, and P i C represents the electrical quantity of the i-th type of load or output. i This represents the unit carbon emission coefficient corresponding to the i-th type of load / output.

[0171] Carbon emission intensity can be defined as the amount of carbon emissions from the entire system per unit time. The specific formula for carbon emission intensity is as follows:

[0172] Among them, C pFor carbon intensity, C is the total carbon emission in the whole period, and T is the time series constant, such as 24 for 1h interval, 96 for 15min interval, and so on.

[0173] After determining the above various combination information, the above information can be divided into two forms of combination within conditions and combination between different conditions, wherein the relationship between different conditions is parallel and all are "and" relationship, and within conditions, "and" and "or" keywords can be used for connection calculation. For example, the joint condition of system load and tie-line power ratio is as follows:

[0174] First, system load:

[0175] 100≤P≤500 or 660≤P≤1200;

[0176] Second, tie-line power ratio:

[0177] 5%≤R≤20%;

[0178] The meaning of the above condition combination is that when the system load is between 100MW and 500MW or between 660MW and 1200MW and the tie-line power ratio is between 5% and 20%, the requirement of the condition combination can be met, which is expressed by the condition expression as follows:

[0179] (100≤P≤500 or 660≤P≤1200)and(5%≤R≤20%)

[0180] Other condition splicing and combination cases are similar.

[0181] Then the data can be extracted according to the condition expression corresponding to the above combination information, and an analysis report can be generated.

[0182] Explanation of data extraction method.

[0183] When extracting boundary data or running scenarios according to condition combinations, the following two adaptive methods can be adopted, which are full-day condition adaptation and time-point condition adaptation, specifically:

[0184] First, full-day condition adaptation: specifically, by combining the constraints of conditions, an inequality screening condition for screening full-day indicators is constructed, and various conditions are calculated in turn to form a screening indicator summary table. By applying the condition inequality method, the final boundary data and typical days for analysis are selected. Taking the system load and tie-line constraint conditions in the foregoing as an example, the screening process is as follows:

[0185] The inequality check is performed on the load value of each time point of each day, and the day that meets the combination requirements at all times is selected as the candidate day.

[0186] In the date range determined by the previous candidate day, the corresponding daily tie-line power ratio value is also calculated, and the operation day within the set range is selected as the final result.

[0187] Second, the time point condition adaptation: specifically, the index value of each time point in the independent calculation period is calculated, and the inequality condition is used for screening, and the specific time value that meets the screening condition is selected.

[0188] In an embodiment, the analysis report mainly includes the following contents:

[0189] First, the load characteristic analysis, which includes the maximum peak-valley difference, total power consumption and average load, etc. The calculation method is as follows:

[0190] Maximum peak-valley difference As shown in the following formula:

[0191] Wherein, The maximum load and the minimum load are represented by Pmax and Pmin.

[0192] Total power consumption P total As shown in the following formula:

[0193] P total =∑P load,t ;

[0194] Wherein, P load,t represents the system load at each time.

[0195] Average load, as shown in the following formula

[0196] Wherein, N represents the number of time periods of the case

[0197] Second, the new energy characteristic, which includes the new energy penetration rate, new energy power generation and new energy curtailment rate, etc. The calculation method is as follows:

[0198] New energy penetration rate ζ, as shown in the following formula:

[0199] Wherein, P total represents the total of all types of power sources.

[0200] New energy power generation P re , as shown in the following formula:

[0201] P re = Pwind +P pv ;

[0202] Third, element load analysis, which includes line or main transformer load rate ranking and line or main transformer load rate timing analysis.

[0203] Average load rate As shown in the following formula:

[0204] Wherein, Q represents the sum of absolute values of the flow values of a line in the evaluation analysis period, a represents the line capacity, and N represents the total number of time periods in the evaluation analysis period.

[0205] The line or main transformer load rate timing analysis defaults to display the timing load rate analysis of the line with the highest average load rate ranking.

[0206] Fourth, power gap analysis, which includes total power shortage, maximum power gap and total load shedding duration analysis, and is calculated as follows:

[0207] Total power shortage As shown in the following formula:

[0208] Wherein, Indicates the system power shortage curve.

[0209] Maximum power gap As shown in the following formula:

[0210] Wherein, Indicates the system power shortage at each time.

[0211] Fifth, total load shedding duration, which is mainly the count of the number of time periods in which the system power shortage curve is non-zero.

[0212] In the embodiment, the application provides a power consumption feature data analysis method for wind-solar energy storage, which has the beneficial effects that the original timing information of wind-solar energy storage can be obtained, the feature data value of wind-solar energy storage is calculated by using the original timing information in the time cycle dimension, the boundary data of wind-solar energy storage is calculated by using the correlation curve corresponding to the feature data value and the feature data value, and the power consumption feature data is analyzed by using the boundary data. The boundary data is determined by using different original timing information, and the power consumption feature data is analyzed, which can be fitted to the actual situation of each energy system, thereby reducing the analysis deviation and improving the analysis accuracy.

[0213] The embodiment of the present application also provides a power consumption characteristic data analysis device for wind-solar energy storage, referring to Figure 2, which shows a structural schematic diagram of a power consumption characteristic data analysis device for wind-solar energy storage according to an embodiment of the present application.

[0214] For example, the power consumption characteristic data analysis device for wind-solar energy storage can include:

[0215] The acquisition module 201 is configured to acquire original time sequence information of wind-solar energy storage, wherein the original time sequence information includes original time sequence data of wind power output, original time sequence data of photovoltaic output and original time sequence data of load.

[0216] The calculation characteristic data module 202 is configured to calculate characteristic data values of wind-solar energy storage by using the original time sequence information in a time period dimension.

[0217] The analysis module 203 is configured to calculate boundary data of wind-solar energy storage by using the correlation curve corresponding to the characteristic data values and the characteristic data values after determining the correlation curve, and analyze power consumption characteristic data by using the boundary data.

[0218] Optionally, the calculation of the characteristic data values of wind-solar energy storage by using the original time sequence information in a time period dimension includes:

[0219] After extracting a time sequence full cycle maximum value from the original time sequence information, the time sequence data standard value is calculated by using the time sequence full cycle maximum value;

[0220] The time sequence data standard value is calculated to obtain an extreme value in a time period dimension, and the time in the time period dimension is respectively year, month and day.

[0221] The characteristic data values of wind-solar energy storage are calculated by using the extreme value.

[0222] Optionally, the time sequence full cycle maximum value is as follows:

[0223] Wherein, is a full cycle maximum value of the i-th type of input time sequence data, i is the type of input time sequence data, and j is the subscript of the time sequence data.

[0224] The time sequence data standard value is as follows:

[0225] Wherein, is a standard value of the corresponding time sequence data input type at the j-th point, P j is a nominal value of the input data at the j-th point. is a maximum value of the nominal value of the corresponding time sequence data input type.

[0226] Optionally, the characteristic data value comprises: a curve average load level value, a daily peak-valley difference rate, a maximum peak-valley difference rate, a load imbalance coefficient, a curve average output level value, an output rate, and a reliable guaranteed output rate.

[0227] Optionally, the correlation curve comprises: a sub-load correlation curve, a sub-node correlation curve, and a system correlation curve.

[0228] The boundary data comprises: an average output value, a boundary electric quantity, a boundary time, and a time point load demand.

[0229] The power consumption characteristic data comprises: a maximum peak-valley difference, a new energy penetration rate, a new energy power generation amount, an average load rate, a total power shortage amount, and a maximum power gap.

[0230] Optionally, the device further comprises:

[0231] The display module is configured to display the power consumption characteristic data and the boundary curve after constructing the boundary curve based on the boundary data.

[0232] The report generation module is configured to obtain combination information of a user, filter demand data according to the combination information, and generate an analysis report based on the demand data, wherein the combination information is data selected by the user after viewing the power consumption characteristic data and the boundary curve.

[0233] The embodiment of the present application further provides a wind-solar-storage integrated dispatching operation system, and refer to FIG. 3, which shows a structural schematic diagram of a wind-solar-storage integrated dispatching operation system provided by an embodiment of the present application.

[0234] For example, the wind-solar-storage integrated dispatching operation system can comprise:

[0235] The characteristic data analysis module is configured to calculate and analyze indexes of wind and light output distribution and power supply time period output rate to obtain characteristic data.

[0236] The production dispatching operation module is configured to associate or import a power grid model, deduce boundary data of load, wind turbine, photovoltaic turbine and external power in the power grid model, data display preview and algorithm calculation.

[0237] The result evaluation analysis module is configured to generate an evaluation analysis report based on the calculated data.

[0238] Specifically, the feature data analysis module can analyze and visually display typical features from the perspectives of supply and demand. For supply features, three interfaces of built-in trusted guarantee output, feature analysis, and statistical quantity are mainly displayed for the calculation and analysis of comprehensive indexes such as wind and light output distribution and power supply period output rate. For demand features, load change characteristics and annual peak-valley distribution characteristics are analyzed from the perspectives of multiple measurement points of the whole network load, marketing load, and net load.

[0239] The production scheduling operation module can include system model, boundary deduction, setting preview, algorithm execution, result analysis, and other parts, mainly supporting future production scheduling operation planning. In the production scheduling operation case management page, the production scheduling operation case can be created, including scheme name, simulation granularity, simulation start and end time, and simultaneously associating or directly importing the corresponding power grid model file. In addition, the case can be renamed, saved as another file, deleted, and the like.

[0240] The system model part can support displaying the power grid model associated or imported when creating the case, including unit, line, node, load, and other information. Directly adding, importing, and exporting operations can be supported in this page.

[0241] The unit page displays coal-fired, gas-fired, biomass and other, conventional hydropower, pumped storage, wind power, photovoltaic, and energy storage. The list form displays the information of each type of unit, including dispatching name, dispatching type, power generation type, node, capacity, voltage level, belonging station, production time, climbing rate, start-stop cost, energy storage conversion efficiency, charging and discharging cost, maximum energy storage, and other fields. For each unit, whether to take effect, edit, delete, and other operations can be supported, and by clicking edit, the corresponding fields can be popped up for modification.

[0242] The line page mainly displays the corresponding intra-zone line and inter-zone line. The intra-zone line mainly edits the line name, line type, transmission limit, first-end node, and end node, and the inter-zone line mainly edits the line name, line type, transmission limit, and node. For each line, editing and deleting operations can be supported, and by clicking edit, the corresponding fields can be popped up for modification.

[0243] The node page mainly displays the corresponding node name, partition name, and voltage level, and supports editing each field and deleting the node.

[0244] The load page displays the corresponding load name, reference load value, load loss value, node where the load is located, and belonging energy-using enterprise. Editing each field and deleting the load can be supported.

[0245] The boundary inference part can be used for inferring boundary data of loads, wind turbines, photovoltaic turbines and external power in the system model.

[0246] The setting preview part can be divided into six sub-pages, i.e., unit maintenance plan, unit start-stop plan, backup setting, unit output constraint, precision parameter, mutual aid ratio and constraint, which will be introduced as follows.

[0247] Unit maintenance plan: the maintenance state of a unit in each day is defined, i.e., maintenance or no maintenance. If the unit is defined as "maintenance" on MM / DD day, the unit will be forced to stop at 24:00 on this day, otherwise, the unit is free to optimize. The user can click "select interval range" to open the weather gauge range selection function, and define the maintenance state of the unit object for multiple days at a time, or define the data of a single day.

[0248] Unit start-stop plan: the effect is similar to "unit maintenance plan". When "stop" is selected, the unit will be forced to stop by default, and "start" is free to optimize.

[0249] Backup setting: the user can customize "system positive backup" and "system negative backup". The open button represents the start of backup setting constraint, and then the backup ratio is filled in the input box to complete the constraint setting. For constraint setting, if the positive backup is set to a%, then the sum of the installed capacity of the "start" unit + wind power output + photovoltaic output - external transmission power + purchased power >= load demand * (1 + a%); if the negative backup is set to a%, then the minimum output of the "start" unit - external transmission power + purchased power <= load demand * (1 - a%).

[0250] Unit output constraint: the unit output constraint can set "specified output" operation and "upper and lower limits" of unit output constraint for the unit. As the name implies, if the specified output is set, the unit will completely output the result according to the planned curve, and if the upper and lower limit constraint is set, the unit will be optimized within the range.

[0251] Precision parameter: set the algorithm calculation time and precision target, i.e., the deviation ratio of the current optimal value and the target optimal value.

[0252] Mutual aid ratio and constraint: the mutual aid ratio refers to if region A has a power surplus Q at time T, and the "mutual aid ratio" coefficient is set to a, then it can transmit Q*a to other regions at most, i.e., ∑F_A≤Q*a, where F_A refers to the external transmission mutual aid amount of A. In addition, when the default scheme is created, the upper and lower limit constraints of the transmission channel are set to: upper limit = line transmission limit, lower limit = -1*line transmission limit; the user can modify the transmission channel capacity, which is not allowed to exceed the original transmission limit.

[0253] The algorithm execution part can perform algorithm calculation and algorithm source file export (e-file format), and the algorithm log information will be printed in the log box below. The following is a brief description of some useful key information.

[0254] (1) Optimal time: the actual time spent by the algorithm to solve.

[0255] (2) Convergence GAP: the deviation ratio of the obtained solution from the target optimal solution (0.001 represents one thousandth)

[0256] The result analysis part can have five sub-pages: operation overview, component operation, energy flow transmission, power and energy balance, and comprehensive balance analysis.

[0257] Among them, the operation overview sub-page can view the overall operation information, such as 8760 period power shortage, consumption, and other data.

[0258] The component operation sub-page can display the instantaneous output and instantaneous statistical energy (energy obtained by integrating output) data of each type of power unit.

[0259] The energy flow transmission sub-page can display the instantaneous output data of each line section in the area and outside the area.

[0260] The power and energy balance sub-page can view the aggregated value data of each type of power source and the start-up position map data at the moment.

[0261] The comprehensive balance analysis sub-page can trace back the operation state data of each type of equipment at 8760 times, including system demand, tie line, and start-up capacity. After checking the multiple selection box on the left, the data curve of the corresponding index can be displayed visually.

[0262] The result evaluation analysis module mainly sets the factor conditions, extracts the operation mode condition combination, and generates an evaluation analysis report for the completed optimization calculation scheme. It performs multi-dimensional and multi-level analysis and evaluation of typical scenario operation modes, and provides decision support for the construction of a new low-carbon power system.

[0263] The evaluation analysis scheme page of the result evaluation analysis module supports custom creation of evaluation analysis cases, can display multiple production scheduling operation schemes for users to choose from, and also allows users to select planning evaluation analysis time and input the name of the evaluation analysis scheme. After the user confirms, the creation of the evaluation analysis scheme is completed.

[0264] Meanwhile, the result evaluation analysis module can be provided with a running mode extraction condition page, from which power results, reliability, and other three extraction dimensions display system load, tie-line power proportion, power supply power proportion, and other 11 running mode influence factors. The method extraction combination condition is at the bottom of the page. Conditions can be set for the influence factors for combination. The influence factors are displayed by "conditional" or "unconditional" labels to show whether the conditions have been set. The following illustrates system load and tie-line power proportion.

[0265] For example, for system load setting, the user can add, delete, check, and modify the conditions of system load.

[0266] Tie-line power proportion refers to the proportion of the power transmitted by the tie-line in the overall power generation during the operation of the power system.

[0267] The user can also add, delete, check, and modify the tie-line power proportion.

[0268] The classification and combination of each combination condition can refer to the above-mentioned embodiments. The condition classification can be divided into three categories and 11 subcategories. The specific names and definitions are as follows: power results, reliability, and others.

[0269] For each condition combination, it can be divided into internal condition combination and combination between different conditions. The relationship between different conditions is "and", while the internal condition can be connected and calculated by using "and" and "or" keywords.

[0270] For example, the joint condition of load and tie-line power proportion is as follows:

[0271] System load:

[0272] 100≤P≤500 or 660≤P≤1200;

[0273] Tie-line power proportion:

[0274] 5%≤R≤20%;

[0275] The meaning of the above condition combination is: when the system load is between 100MW and 500MW or between 660MW and 1200MW and the tie-line power proportion is between 5% and 20%, the condition combination requirement can be met. The condition expression is expressed as follows:

[0276] (100≤P≤500 or 660≤P≤1200)and(5%≤R≤20%)

[0277] The result evaluation analysis module can also generate a report, the page of which displays the status of the corresponding report production. If it fails, it supports re-execution of calculation. If it succeeds, it supports report viewing.

[0278] The analysis report mainly includes the following contents:

[0279] First, load characteristic analysis. Second, element load analysis. Third, power gap analysis.

[0280] The load characteristic analysis includes the maximum peak-valley difference, total power consumption, average load, and other indicators, total power consumption, average load, and new energy characteristics. The new energy characteristic analysis includes new energy penetration rate, new energy power generation, and new energy curtailment rate.

[0281] The element load analysis includes line or main transformer load rate ranking and line or main transformer load rate time series analysis.

[0282] The line or main transformer load rate time series analysis defaults to display the time series load rate analysis of the line with the highest average load rate ranking.

[0283] The power gap analysis can include total power shortage, maximum power gap, and total load shedding duration analysis.

[0284] In the evaluation analysis report page of the result evaluation analysis module, the optimization calculation scheme name of the evaluation analysis report and the time scale can be displayed.

[0285] Time range: the user can select the time for query, but the selected time range must be within the evaluation analysis time range;

[0286] Directory: the evaluation analysis report is divided into 9 modules, including "unit start-up position analysis", "load characteristics", "new energy characteristic analysis", "element load analysis", and "power gap analysis". The user can select the directory title to jump to the position for viewing.

[0287] Those skilled in the art can clearly understand that, for the convenience of description and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0288] Further, the embodiment of the present application also provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the power consumption characteristic data analysis method for wind-solar-storage energy as described in the foregoing embodiments.

[0289] Further, the embodiment of the present application also provides a computer readable storage medium, which stores a computer executable program, and the computer executable program is used to make a computer execute the power consumption characteristic data analysis method for wind-solar-storage energy as described in the foregoing embodiments.

[0290] It should be noted that in the description of the embodiments of the present application, the terms "upper", "lower", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the embodiments of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. When an element such as a layer, a region, or a substrate is referred to as being "on" or "above" another element, it can be directly on the other element, or there can be an intermediate element present. In contrast, when an element is referred to as being "directly on" or "directly above" another element, there is no intermediate element present. It should also be understood that when an element is referred to as being "below" or "under" another element, it can be directly below or under the other element, or there can be an intermediate element present. In contrast, when an element is referred to as being "directly below" or "directly under" another element, there is no intermediate element present. Unless specifically defined and limited in this specification, the terms "mount", "connected", "connection" should be interpreted broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0291] Those skilled in the art will appreciate that embodiments of the application can also provide computer program products. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) having computer-usable program code embodied therein.

[0292] The application is described with reference to the flowcharts and / or block diagrams according to the methods, devices (systems), apparatuses and computer program products of the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce the apparatus for implementing the functions specified in one or more flows in the flowcharts and / or one or more blocks in the block diagrams.

[0293] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart or flowsheets and / or block or blocks of the block diagrams.

[0294] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheets and / or block or blocks of the block diagrams.

[0295] The above description is only preferred embodiments of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for analyzing power consumption feature data on wind-solar energy storage, characterized in that, The method comprises: obtaining original time sequence information about wind-solar-storage, the original time sequence information comprising original time sequence data of wind power output, original time sequence data of photovoltaic output and original time sequence data of load; calculating feature data values of wind-solar-storage by using the original time sequence information in time cycle dimension; after determining an associated curve corresponding to the feature data values, calculating boundary data of wind-solar-storage by using the associated curve and the feature data values, and analyzing electricity consumption feature data by using the boundary data.

2. The method for analyzing power consumption feature data on wind-solar-storage energy according to claim 1, characterized in that, The calculating feature data values of wind-solar-storage by using the original time sequence information in time cycle dimension comprises: extracting time sequence full cycle maximum values from the original time sequence information, and calculating time sequence data standard values by using the time sequence full cycle maximum values; calculating standard extreme values by using the time sequence data standard values in time cycle dimension, the time in the cycle dimension being year, month and day respectively; calculating feature data values of wind-solar-storage by using the standard extreme values.

3. The method for analyzing power consumption feature data on wind-solar-storage energy according to claim 2, characterized in that, The maximum value of the timing cycle is given by the following equation: wherein, is a full cycle maximum value of the i-th type of input time sequence data, i is the type of input time sequence data, and j is the subscript of time sequence data. The timing data scale value is given by the following equation: wherein P is the value of the corresponding time series data input type at time j. j P is the value of the corresponding time series data input type at time j. is the maximum value of the corresponding time sequence data input type.

4. The method for analyzing power consumption feature data on wind-solar-storage energy according to claim 2, characterized in that, The feature data values comprise curve average load level value, daily peak-valley difference rate, maximum peak-valley difference rate, load imbalance coefficient, curve average output level value, output rate and reliable guaranteed output rate.

5. The method for analyzing power consumption feature data on wind-solar-storage energy according to claim 1, characterized in that, The associated curve comprises load-associated curve, node-associated curve and system-associated curve. The boundary data comprise average output value, boundary electric quantity, boundary time and time point load demand. The electricity consumption feature data comprise maximum peak-valley difference, new energy penetration rate, new energy power generation, average load rate, total power shortage and maximum power gap.

6. The method for analyzing power consumption feature data on wind-solar-storage energy according to any one of claims 1-5, characterized in that, After the step of analyzing electricity consumption feature data by using the boundary data, the method further comprises: after constructing a boundary curve by using the boundary data, displaying the electricity consumption feature data and the boundary curve; obtaining combination information of a user, screening demand data according to the combination information and generating an analysis report by using the demand data, wherein the combination information is data selected by the user after viewing the electricity consumption feature data and the boundary curve.

7. A device for analyzing power consumption feature data regarding wind-solar energy storage, characterized in that, The device comprises: an obtaining module configured to obtain original time sequence information about wind-solar-storage, the original time sequence information comprising original time sequence data of wind power output, original time sequence data of photovoltaic output and original time sequence data of load; a calculating feature data module configured to calculate feature data values of wind-solar-storage by using the original time sequence information in time cycle dimension; an analyzing module configured to, after determining an associated curve corresponding to the feature data values, calculate boundary data of wind-solar-storage by using the associated curve and the feature data values, and analyze electricity consumption feature data by using the boundary data.

8. A wind-solar-storage integrated dispatching operation system, characterized in that, The system is suitable for the electricity consumption feature data analysis method about wind-solar-storage according to any one of claims 1-6, and comprises a feature data analysis module, a production dispatching operation module and a result evaluation analysis module. The characteristic data analysis module is configured to calculate and analyze indexes of wind, light output distribution, and power supply time period output rate to obtain characteristic data. The production scheduling operation module is configured to associate or import a power grid model, deduce boundary data of loads, wind turbine generators, photovoltaic generators, and external power in the power grid model, and perform data display preview and algorithm calculation. The result evaluation analysis module is configured to generate an evaluation analysis report based on the calculated data.

9. An electronic device comprising: The memory, the processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for analyzing the electricity consumption characteristic data of wind-solar-storage energy when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer executable program, and the computer executable program is used to make the computer execute the method for analyzing the electricity consumption characteristic data of wind-solar-storage energy.

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