Method and device for measuring and calculating new energy consumption rate of power grid by considering seasonal difference

Abstract

The invention provides a power grid new energy consumption rate measuring and calculating method and device considering seasonal difference, and the method comprises the steps: determining typical weekly scenes of a plurality of heating periods and non-heating periods through a clustering method according to new energy load characteristic data, and determining the occurrence probability of each typical weekly scene and the prediction output of new energy at a plurality of moments in each typical weekly scene; solving through a time sequence operation optimization model to obtain new energy actual output at each moment in each typical week scene; and taking the occurrence probability of each typical week scene as a weight to respectively carry out weighted average on the new energy actual output and the new energy predicted output at multiple moments in the corresponding typical week scene, and dividing results obtained by weighted average to obtain a new energy consumption rate in a heating period and a new energy consumption rate in a non-heating period. The heating period and the non-heating period are distinguished, and the seasonal difference of new energy is reflected. And on the basis of the probability of each typical week scene, traversing all scenes to calculate weighted predicted generating capacity and actual generating capacity, so that the new energy consumption rate can be accurately calculated.

Description

Technical Field

[0001] This invention relates to the field of new energy, and in particular to a method and apparatus for calculating the grid new energy absorption rate that takes into account seasonal differences. Background Technology

[0002] In provinces rich in wind and solar energy resources in my country, the installed capacity of new energy sources is growing rapidly. New energy planning departments typically use time-series operation simulation methods to analyze system balance and new energy consumption. The power system time-series operation simulation method is a common means of analyzing system operation and calculating new energy consumption. It usually clusters new energy and load characteristic data to typical weeks or typical days, and then constructs a mathematical optimization model for calculation.

[0003] However, significant seasonal differences exist in wind and solar resource conditions, thermal power regulation capacity, and load energy consumption characteristics over long periods. This poses challenges to calculating power system balance and absorption rates over long time. For example, the regulation capacity of thermal power units in provinces with winter heating varies considerably between the heating and non-heating seasons. Currently, power system time-series operation simulation methods are universal across various clustering scenarios, failing to reflect the seasonal differences in wind and solar resource conditions, thermal power regulation capacity, and source-load characteristics, resulting in low calculation accuracy. Summary of the Invention

[0004] This invention provides a method and apparatus for calculating the grid renewable energy absorption rate that takes into account seasonal differences, in order to solve the shortcomings of existing technologies that are difficult to reflect seasonal differences, and to incorporate seasonal differences into time-series operation simulation and absorption rate calculation, thereby improving the accuracy of the calculation.

[0005] This invention provides a method for calculating the grid renewable energy absorption rate considering seasonal differences, comprising the following steps.

[0006] Based on the characteristics of renewable energy loads, clustering methods were used to determine typical weekly scenarios for multiple heating seasons and multiple typical weekly scenarios for multiple non-heating seasons. The probability of occurrence of each typical weekly scenario and the predicted renewable energy output at multiple times in each typical weekly scenario were also determined. The actual output of new energy at each moment in each typical weekly scenario is obtained by solving the time-series optimization model; Using the probability of occurrence of each typical weekly scenario during the heating season as weights, the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario are weighted averaged to obtain the weighted average of the actual output and the weighted average of the predicted output during the heating season. The new energy consumption rate during the heating season is obtained by dividing the weighted average of the actual output and the weighted average of the predicted output during the heating season. The probability of occurrence of each typical weekly scenario during the non-heating season is used as a weight to calculate the weighted average of the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario. The weighted average of the actual output and the weighted average of the predicted output during the non-heating season are then calculated. The non-heating season new energy consumption rate is obtained by dividing the weighted average of the actual output and the weighted average of the predicted output during the non-heating season.

[0007] According to the method for calculating the grid renewable energy absorption rate considering seasonal differences provided by the present invention, before obtaining the actual renewable energy output at each moment in each typical weekly scenario through solving the time-series operation optimization model, the method further includes: Determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function for multiple thermal power units, and add up the cost functions to determine the total system operation cost function; The objective function is obtained by setting the value of the total system operating cost function to a first preset value; The constraints are obtained by constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons respectively. Construct a time-series optimization model based on the objective function and constraints.

[0008] According to the present invention, a method for calculating the grid renewable energy absorption rate considering seasonal differences is provided, which determines the thermal power cost function, hydropower operation cost function, and renewable energy equivalent cost function for multiple thermal power units, including: The thermal power cost function of multiple thermal power units is determined based on the coal consumption cost and start-up and shutdown cost of multiple thermal power units in multiple scheduling periods; The hydropower operating cost function is determined based on the hydropower consumption coefficient and hydropower output. The equivalent cost function of new energy is determined based on the predicted and actual output of new energy at multiple times in each typical weekly scenario.

[0009] According to the present invention, a method for calculating the renewable energy absorption rate of a power grid considering seasonal differences is provided. The renewable energy absorption rate during the heating season includes: the wind power absorption rate during the heating season; the actual renewable energy output includes: the actual wind power output; the probability of occurrence of each typical weekly scenario during the heating season is used as a weight to calculate the weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times within the corresponding typical weekly scenario, and the weighted average is divided to obtain the renewable energy absorption rate during the heating season, which is achieved through the following formula: in, Wind power absorption rate during the heating season; , as well as These represent the probability of occurrence for typical weekly scenarios during the heating season. This represents the total number of time periods included in a typical weekly scenario. To provide the actual wind power output at each moment in each typical scenario; It contributes to wind power forecasting at various times in various typical scenarios; The renewable energy absorption rate during the non-heating season includes: the wind power absorption rate during the non-heating season; the actual and predicted renewable energy outputs at multiple times within each typical weekly scenario during the non-heating season are weighted and averaged using the probability of occurrence of each typical weekly scenario, and the weighted averages are divided to obtain the renewable energy absorption rate during the non-heating season, which is achieved through the following formula: ; in, Wind power absorption rate during the non-heating season; , as well as These represent the probability of occurrence for each typical weekly scenario during the non-heating season.

[0010] According to the method for calculating the grid renewable energy absorption rate considering seasonal differences provided by the present invention, it further includes: The occurrence probability of each typical weekly scenario is used as a weight to calculate the weighted average of the actual output and the predicted output of new energy at multiple times in the corresponding typical weekly scenario, so as to obtain the annual weighted average of the actual output and the annual weighted average of the predicted output. The annual new energy consumption rate is obtained by dividing the annual weighted average of the actual output and the annual weighted average of the predicted output.

[0011] This invention also provides a device for calculating the grid renewable energy absorption rate considering seasonal differences, comprising the following modules: The typical weekly scenario determination module is used to determine typical weekly scenarios for multiple heating seasons and multiple typical weekly scenarios for multiple non-heating seasons based on new energy load characteristic data through clustering methods, and to determine the occurrence probability of each typical weekly scenario and the predicted output of new energy at multiple times in each typical weekly scenario. The actual output module of new energy is used to obtain the actual output of new energy at each moment in each typical weekly scenario by solving the time-series operation optimization model; The module for calculating the renewable energy absorption rate during the heating season uses the probability of occurrence of each typical weekly scenario during the heating season as weights to calculate the weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario. The module then divides the weighted average of the actual renewable energy output and the predicted renewable energy output during the heating season to obtain the renewable energy absorption rate during the heating season. The module for calculating the renewable energy absorption rate during the non-heating season is used to take the probability of occurrence of each typical weekly scenario during the non-heating season as a weight and then perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario to obtain the weighted average of the actual output and the weighted average of the predicted output during the non-heating season. The module then divides the weighted average of the actual output and the weighted average of the predicted output during the non-heating season to obtain the renewable energy absorption rate during the non-heating season.

[0012] According to the present invention, a power grid renewable energy absorption rate calculation device considering seasonal differences further includes: The objective function setting submodule is used to determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function of multiple thermal power units. The total system operating cost function is determined by adding the cost functions. The objective function is obtained by setting the value of the total system operating cost function to the first preset value. The constraint setting submodule is used to obtain constraint conditions by separately constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons. The model building submodule is used to construct a time-series optimization model based on the objective function and constraints. The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the grid renewable energy absorption rate calculation method considering seasonal differences as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the grid renewable energy absorption rate calculation method considering seasonal differences as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the grid renewable energy absorption rate calculation method considering seasonal differences as described above.

[0015] The present invention provides a method and apparatus for calculating the grid renewable energy absorption rate considering seasonal differences. This method determines multiple typical weekly scenarios for the heating season and multiple typical weekly scenarios for the non-heating season based on renewable energy load characteristic data using a clustering method, and determines the occurrence probability of each typical weekly scenario and the predicted renewable energy output at multiple times within each typical weekly scenario. The actual renewable energy output at each time point within each typical weekly scenario is obtained by solving a time-series optimization model. Finally, the occurrence probability of each typical weekly scenario during the heating season is used as a weight to perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times within the corresponding typical weekly scenario. The weighted average of actual power output and the weighted average of predicted power output during the heating season are obtained, and the heating season renewable energy absorption rate is obtained by dividing the two weighted averages. For the non-heating season, the probability of occurrence of each typical weekly scenario is used as a weight to calculate the weighted average of actual power output and predicted power output at multiple times within the corresponding typical weekly scenario, resulting in the non-heating season weighted average of actual power output and non-heating season predicted power output. The non-heating season renewable energy absorption rate is obtained by dividing the non-heating season weighted average of actual power output and non-heating season predicted power output. Compared with existing power system time-series operation simulation methods that are universal across clustering scenarios and fail to reflect seasonal differences in wind and solar resource conditions, thermal power regulation capacity, and source-load characteristics, this invention performs scenario clustering analysis separately for the heating and non-heating seasons, reflecting the seasonal differences in renewable energy. Based on the probability of each typical weekly scenario, the weighted predicted power generation and actual power generation are calculated by traversing all scenarios. The absorption rate of new energy sources for each period and type can be accurately calculated by dividing the power generation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the method for calculating the grid renewable energy absorption rate that takes into account seasonal differences, provided by the present invention.

[0018] Figure 2 This is a schematic diagram of the power grid renewable energy absorption rate calculation device that takes into account seasonal differences, provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined with Figures 1-3 This invention is described.

[0022] Figure 1 This is a flowchart illustrating the method for calculating the grid renewable energy absorption rate considering seasonal differences, provided by the present invention. Figure 1 As shown, the method includes the following: Step 101: Based on the renewable energy load characteristic data, determine multiple typical weekly scenarios during the heating season and multiple typical weekly scenarios during the non-heating season using clustering methods, and determine the occurrence probability of each typical weekly scenario and the predicted renewable energy output at multiple times in each typical weekly scenario.

[0023] In step 101 above, the input data for new energy sources, load characteristics, heating season duration, and non-heating season duration are predetermined. For example, the heating season duration is from November 1st to March 31st of the following year, a total of 22 weeks; the non-heating season duration is from April 1st to October 31st, a total of 30 weeks. The wind power output curve, photovoltaic power output curve, and load curve for the heating season are all 22-row, 168-column matrices, representing 22 weeks of heating season, with 168 hours per week. The wind power output curve, photovoltaic power output curve, and load curve for the non-heating season are all 30-row, 168-column matrices, representing 30 weeks of non-heating season, with 168 hours per week.

[0024] The wind power output curves, photovoltaic power output curves, and load curves for the determined 22 weeks of the heating season are used to perform clustering, specifically K-means clustering, to obtain three typical weekly scenarios and the probability of occurrence for each scenario. Each typical weekly scenario includes the wind power output curves, photovoltaic power output curves, and load curves for 168 time periods within a week, which serve as the predicted values ​​for typical scenarios during the heating season.

[0025] The wind power output, solar power output, and load curves for the determined 30-week non-heating season are used to perform clustering, specifically K-means clustering, to obtain three typical weekly scenarios and the probability of each scenario occurring. Each typical weekly scenario includes wind power output, solar power output, and load curves for 168 time periods within a week, serving as the predicted values ​​for typical scenarios during the non-heating season.

[0026] Prior to step 102, this embodiment of the invention further includes steps A1 to A4: Step A1: Determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function for multiple thermal power units, and add up the cost functions to determine the total system operation cost function.

[0027] Step A2: Obtain the objective function by setting the value of the total system operating cost function to the first preset value.

[0028] Step A3: Obtain the constraints by constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons respectively.

[0029] Step A4: Construct a time-series optimization model based on the objective function and constraints.

[0030] In steps A1 to A4 above, the first preset value is set according to actual needs. For example, the first preset value may be the minimum value of the total operating cost of the system.

[0031] The calculation of the renewable energy absorption rate is based on a time-series operation optimization model. According to actual production conditions, the objective function of the time-series operation optimization model is set to minimize the total system operating cost. The total system operating cost includes the operating cost of thermal power, the start-up and shutdown cost of thermal power, the operating cost of hydropower, the equivalent cost of renewable energy, and the operating loss cost of energy storage.

[0032] The total operating cost function of the system is calculated using the following formula (1): (1) in, The equivalent total operating cost of the system, This represents the equivalent number of thermal power units. Number of scheduling periods For the equivalent power output of a thermal power unit; To determine the coal consumption cost of thermal power units, measures are taken regarding... The quadratic function representation; and The cost of unit startup and shutdown is related to the number of times the unit is started and shut down. For hydropower operating costs; This is equivalent to a wind curtailment penalty; This is equivalent to a penalty of abandoning light. This refers to the operating costs of energy storage.

[0033] Optionally, determining the thermal power cost function, hydropower operating cost function, and new energy equivalent cost function for multiple thermal power units in step A1 includes steps A11 to A13, including: Step A11: Determine the thermal power cost function for multiple thermal power units based on the coal consumption cost and start-up and shutdown costs of multiple thermal power units during multiple scheduling periods.

[0034] Step A12: Determine the hydropower operation cost function based on the hydropower consumption coefficient and hydropower output.

[0035] Step A13: Determine the equivalent cost function of new energy based on the predicted and actual output of new energy at multiple times in each typical weekly scenario.

[0036] In steps A11 to A13 above, The operating cost of hydropower can be expressed as a linear function: (2) in, This is the hydropower consumption coefficient. It provides power for hydroelectric power.

[0037] The equivalent cost of new energy sources is expressed using the equivalent curtailment penalty: (3) (4) in, As punishment for abandoning the wind; As punishment for abandoning light; Contribute to wind power forecasting; Contribute to photovoltaic forecasting; To actually contribute to wind power; To contribute to the actual development of photovoltaics; and The main variable to be solved is...

[0038] The operating cost of energy storage is expressed as: (5) in, Power for energy storage charging; This refers to the energy storage discharge power; This refers to the unit cost of energy storage.

[0039] In step A3 above, the constraints of the time-series simulation involve multiple aspects of the power system. Among them, the system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints are not significantly different between the heating season and the non-heating season.

[0040] Hydropower and new energy power range constraints: (6) (7) (8) in, This is the maximum output of hydropower.

[0041] System equilibrium constraints: (9) in, Number of delivery channels This refers to the power of the external transmission channel.

[0042] Energy storage charging and discharging power constraints: (10) (11) in, For energy storage charging state variables; For energy storage discharge state variables; both energy storage charging state variables and energy storage discharge state variables are Boolean variables and cannot be 1 at the same time. This represents the maximum charging power for energy storage. This represents the maximum discharge power of the energy storage.

[0043] Constraints on the energy storage charging and discharging process: (12) in, For energy storage capacity, In the state of energy storage charge, and These represent the energy storage charging and discharging efficiency, respectively.

[0044] Power range constraints for thermal power units: (13) Among them, Boolean variables This setting indicates the start / stop status of the generating unit. Units that must be turned on during the heating season should be set to 1. This refers to the unit's maximum output, typically the installed capacity. To ensure the minimum technical output of the unit, heating units are greatly affected by seasonal factors. During the heating season, the unit's output is generally 60% of its installed capacity, while during the non-heating season it is 35% of its installed capacity. Therefore, it is necessary to set the output differently for the heating and non-heating seasons.

[0045] Power ramping constraints for thermal power units: (14) (15) External power transmission level constraints: (16) in, The capacity of the transmission channel varies in different seasons; The power transmission level coefficient needs to be set differently for the heating season and the non-heating season, taking into account the supply and demand situation at both the sending and receiving ends.

[0046] Step 102: Obtain the actual output of new energy at each moment in each typical weekly scenario by solving the time-series optimization model.

[0047] In step 102 above, the CPLEX solver is called based on the MATLAB platform to perform calculations, which can obtain the actual wind power output at each time point in three typical scenarios during the heating season and three typical scenarios during the non-heating season. Actual output of photovoltaic power .

[0048] Step 103: Using the probability of occurrence of each typical weekly scenario during the heating season as weights, perform weighted averages on the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario to obtain the weighted average of actual output and predicted output during the heating season. Divide the weighted average of actual output and predicted output during the heating season to obtain the new energy consumption rate during the heating season.

[0049] In step 103 above, the renewable energy absorption rate during the heating season includes: the wind power absorption rate during the heating season; the actual renewable energy output includes: the actual wind power output; the probability of occurrence of each typical weekly scenario during the heating season is used as a weight to calculate the weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario, and the results of the weighted average are divided to obtain the renewable energy absorption rate during the heating season, which is achieved through the following formula: (17) in, Wind power absorption rate during the heating season; , , These represent the probability of occurrence for typical weekly scenarios during the heating season. This represents the total number of time periods included in a typical weekly scenario. To provide the actual wind power output at each moment in each typical scenario; It contributes to wind power prediction at various times in various typical scenarios.

[0050] Step 104: Using the probability of occurrence of each typical weekly scenario during the non-heating season as a weight, perform a weighted average of the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario to obtain the weighted average of the actual output and the weighted average of the predicted output during the non-heating season. Divide the weighted average of the actual output and the weighted average of the predicted output during the non-heating season to obtain the new energy consumption rate during the non-heating season.

[0051] In step 104 above, the renewable energy absorption rate during the non-heating season includes: the wind power absorption rate during the non-heating season; the probability of occurrence of each typical weekly scenario during the non-heating season is used as a weight to calculate the weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario, and the results of the weighted average are divided to obtain the renewable energy absorption rate during the non-heating season, which is achieved through the following formula: (18) in, Wind power absorption rate during the non-heating season; , as well as These represent the probability of occurrence for each typical weekly scenario during the non-heating season.

[0052] By changing the numerator of formula (17) to the actual electricity consumption of the six scenarios of heating season plus non-heating season, and the denominator to the predicted electricity consumption of the six scenarios of heating season plus non-heating season, the annual wind power consumption rate can be obtained.

[0053] The annual wind power absorption rate is achieved using the following formula: (19) in, This represents the annual wind power absorption rate.

[0054] The photovoltaic absorption rate during the heating season, the photovoltaic absorption rate during the non-heating season, and the annual photovoltaic absorption rate can be calculated by adaptive substitution of formulas (17), (18), and (19), respectively.

[0055] Optionally, embodiments of the present invention further include: The occurrence probability of each typical weekly scenario is used as a weight to calculate the weighted average of the actual output and the predicted output of new energy at multiple times in the corresponding typical weekly scenario, so as to obtain the annual weighted average of the actual output and the annual weighted average of the predicted output. The annual new energy consumption rate is obtained by dividing the annual weighted average of the actual output and the annual weighted average of the predicted output.

[0056] This invention proposes a method for calculating the renewable energy absorption rate of provincial power grids, taking into account seasonal differences. Based on renewable energy load characteristic data, a clustering method is used to determine typical weekly scenarios for multiple heating seasons and multiple typical weekly scenarios for multiple non-heating seasons, and to determine the occurrence probability of each typical weekly scenario and the predicted renewable energy output at multiple times within each typical weekly scenario. A time-series optimization model is used to solve for the actual renewable energy output at each time point within each typical weekly scenario. The occurrence probability of each typical weekly scenario during the heating season is used as a weight to calculate a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times within the corresponding typical weekly scenario, resulting in a weighted average of the actual output during the heating season and the power consumption. The heating season's renewable energy absorption rate is obtained by averaging the predicted power output during the heating season and dividing the actual power output during the heating season by the predicted power output. Similarly, the non-heating season's renewable energy absorption rate is obtained by averaging the actual and predicted power output at multiple times within each typical weekly scenario, using the probability of occurrence of each scenario as weights. This is then divided by the predicted and actual power output during the non-heating season to obtain the renewable energy absorption rate during the non-heating season. Compared to existing power system time-series operation simulation methods that are universal across clustering scenarios and fail to reflect seasonal differences in wind and solar resource conditions, thermal power regulation capacity, and source-load characteristics, this invention performs scenario clustering analysis separately for the heating and non-heating seasons, reflecting the seasonal differences in renewable energy. Based on the probabilities of each typical weekly scenario, weighted predicted and actual power generation are calculated across all scenarios. The absorption rate of renewable energy for each period and type can be accurately calculated by dividing the power output by the predicted power output.

[0057] The following describes the grid renewable energy absorption rate calculation device considering seasonal differences provided by the present invention. The grid renewable energy absorption rate calculation device considering seasonal differences described below can be referred to in correspondence with the grid renewable energy absorption rate calculation method considering seasonal differences described above.

[0058] Figure 2 This is a schematic diagram of the power grid renewable energy absorption rate calculation device that takes into account seasonal differences, as provided by the present invention. Figure 2 As shown, the device includes the following: The typical weekly scenario determination module 201 is used to determine multiple typical weekly scenarios for heating seasons and multiple typical weekly scenarios for non-heating seasons based on new energy load characteristic data through clustering methods, and to determine the occurrence probability of each typical weekly scenario and the predicted output of new energy at multiple times in each typical weekly scenario. The new energy actual output module 202 is used to obtain the actual output of new energy at each moment in each typical weekly scenario by solving the time-series operation optimization model; The heating season renewable energy absorption rate calculation module 203 is used to take the probability of occurrence of each typical weekly scenario during the heating season as a weight to calculate the weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario, so as to obtain the weighted average of the actual output and the weighted average of the predicted output during the heating season. The module then divides the weighted average of the actual output and the weighted average of the predicted output during the heating season to obtain the renewable energy absorption rate during the heating season. The non-heating season renewable energy absorption rate calculation module 204 is used to use the probability of occurrence of each typical weekly scenario during the non-heating season as a weight to perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario, and divide the weighted average result to obtain the non-heating season renewable energy absorption rate.

[0059] Optionally, the device further includes: The objective function setting submodule is used to determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function of multiple thermal power units. The total system operating cost function is determined by adding the cost functions. The objective function is obtained by setting the value of the total system operating cost function to the first preset value. The constraint setting submodule is used to obtain constraint conditions by separately constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons. The model building submodule is used to build a time-series optimization model based on the objective function and constraints.

[0060] This invention provides a power grid renewable energy absorption rate calculation device that considers seasonal differences. Based on renewable energy load characteristic data, it uses a clustering method to determine multiple typical weekly scenarios during the heating season and multiple typical weekly scenarios during the non-heating season, and determines the occurrence probability of each typical weekly scenario and the predicted renewable energy output at multiple times within each typical weekly scenario. The actual renewable energy output at each time point within each typical weekly scenario is obtained by solving a time-series optimization model. The occurrence probability of each typical weekly scenario during the heating season is used as a weight to perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times within the corresponding typical weekly scenario. The heating season's renewable energy absorption rate is obtained by dividing the weighted average of the actual and predicted power output during the heating season by the weighted average of the actual and predicted power output. Similarly, the non-heating season's renewable energy absorption rate is obtained by weighting the actual and predicted power output at multiple times within each typical weekly scenario using the probability of occurrence of each scenario as weights. This is then divided by the non-heating season's weighted average of the actual and predicted power output, yielding the non-heating season's renewable energy absorption rate. Compared to existing power system time-series operation simulation methods that are universal across clustering scenarios and fail to reflect seasonal differences in wind and solar resource conditions, thermal power regulation capacity, and source-load characteristics, this invention performs scenario clustering analysis separately for the heating and non-heating seasons, reflecting the seasonal differences in renewable energy. Based on the probability of each typical weekly scenario, the weighted predicted power generation and actual power generation are calculated by traversing all scenarios. The absorption rate of new energy sources for each period and type can be accurately calculated by dividing the power generation.

[0061] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logic instructions from the memory 830 to execute a method for calculating the grid renewable energy absorption rate that takes into account seasonal differences.

[0062] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the grid renewable energy absorption rate calculation method considering seasonal differences provided by the above methods.

[0064] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the grid renewable energy absorption rate calculation method considering seasonal differences provided by the above methods.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the grid renewable energy absorption rate considering seasonal differences, characterized in that, include: Based on the characteristics of renewable energy loads, clustering methods are used to determine typical weekly scenarios for multiple heating seasons and multiple typical weekly scenarios for non-heating seasons. The probability of occurrence of each typical weekly scenario and the predicted renewable energy output at multiple times in each typical weekly scenario are then determined. The actual output of new energy at each moment in each typical weekly scenario is obtained by solving the time-series optimization model. Using the probability of occurrence of each typical weekly scenario during the heating season as weights, the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario are weighted averaged to obtain the weighted average of the actual output and the weighted average of the predicted output during the heating season. The new energy consumption rate during the heating season is obtained by dividing the weighted average of the actual output and the weighted average of the predicted output during the heating season. Using the probability of occurrence of each typical weekly scenario during the non-heating season as weights, the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario are weighted averaged to obtain the weighted average of actual output and predicted output during the non-heating season. The non-heating season new energy consumption rate is obtained by dividing the weighted average of actual output and predicted output during the non-heating season.

2. The method for calculating the grid renewable energy absorption rate considering seasonal differences according to claim 1, characterized in that, Before obtaining the actual output of new energy at each moment in each typical weekly scenario through the time-series optimization model, the method further includes: Determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function for multiple thermal power units, and add up the cost functions to determine the total system operation cost function; The objective function is obtained by setting the value of the total system operating cost function to a first preset value; The constraints are obtained by constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons respectively. Construct a time-series optimization model based on the objective function and the constraints.

3. The method for calculating the grid renewable energy absorption rate considering seasonal differences according to claim 2, characterized in that, The determination of the thermal power cost function, hydropower operating cost function, and new energy equivalent cost function for multiple thermal power units includes: The thermal power cost function of multiple thermal power units is determined based on the coal consumption cost and start-up and shutdown cost of multiple thermal power units in multiple scheduling periods; The hydropower operating cost function is determined based on the hydropower consumption coefficient and hydropower output. The equivalent cost function of new energy is determined based on the predicted and actual output of new energy at multiple times in each typical weekly scenario.

4. The method for calculating the grid renewable energy absorption rate considering seasonal differences according to claim 1, characterized in that, The renewable energy absorption rate during the heating season includes: the wind power absorption rate during the heating season; the actual renewable energy output includes: the actual wind power output; the renewable energy absorption rate during the heating season is obtained by weighting the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenarios using the probability of occurrence of each typical weekly scenario as weights, and dividing the weighted average by the result. This is achieved through the following formula: in, Wind power absorption rate during the heating season; , as well as These represent the probability of occurrence for typical weekly scenarios during the heating season. This represents the total number of time periods included in a typical weekly scenario. To provide the actual wind power output at each moment in each typical scenario; It contributes to wind power forecasting at various times in various typical scenarios; The non-heating season renewable energy absorption rate includes: non-heating season wind power absorption rate; the non-heating season renewable energy absorption rate is obtained by weighting the actual renewable energy output and predicted renewable energy output at multiple times in the corresponding typical weekly scenarios using the probability of occurrence of each typical weekly scenario as weights, and dividing the weighted average results by the sum of the results. This is achieved through the following formula: in, Wind power absorption rate during the non-heating season; , as well as These represent the probability of occurrence for each typical weekly scenario during the non-heating season.

5. The method for calculating the grid renewable energy absorption rate considering seasonal differences according to claim 1, characterized in that, Also includes: Using the probability of occurrence of each typical weekly scenario as weights, the actual output and predicted output of new energy at multiple times in the corresponding typical weekly scenario are weighted averaged to obtain the annual weighted average of actual output and the annual weighted average of predicted output. The annual new energy consumption rate is obtained by dividing the annual weighted average of actual output and the annual weighted average of predicted output.

6. A device for calculating the grid renewable energy absorption rate considering seasonal differences, characterized in that, include: The typical weekly scenario determination module is used to determine multiple typical weekly scenarios for heating seasons and multiple typical weekly scenarios for non-heating seasons based on new energy load characteristic data through clustering methods, and to determine the occurrence probability of each typical weekly scenario and the predicted output of new energy at multiple times in each typical weekly scenario. The actual output module of new energy is used to obtain the actual output of new energy at each moment in each typical weekly scenario by solving the time-series operation optimization model. The heating season renewable energy absorption rate calculation module is used to take the probability of occurrence of each typical weekly scenario during the heating season as a weight and perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario to obtain the weighted average of the actual output and the weighted average of the predicted output during the heating season. The module then divides the weighted average of the actual output and the weighted average of the predicted output during the heating season to obtain the renewable energy absorption rate during the heating season. The module for calculating the renewable energy absorption rate during the non-heating season is used to take the probability of occurrence of each typical weekly scenario during the non-heating season as a weight and perform a weighted average of the actual renewable energy output and the predicted renewable energy output at multiple times in the corresponding typical weekly scenario to obtain the weighted average of the actual output and the weighted average of the predicted output during the non-heating season. The module then divides the weighted average of the actual output and the weighted average of the predicted output during the non-heating season to obtain the renewable energy absorption rate during the non-heating season.

7. The power grid renewable energy absorption rate calculation device considering seasonal differences according to claim 6, characterized in that, The device further includes: The objective function setting submodule is used to determine the thermal power cost function, hydropower operation cost function, new energy equivalent cost function, and energy storage operation loss cost function of multiple thermal power units. The total system operating cost function is determined by adding the cost functions. The objective function is obtained by setting the value of the total system operating cost function to the first preset value. The constraint setting submodule is used to obtain constraint conditions by separately constructing system balance constraints, energy storage constraints, hydropower constraints, and new energy constraints for heating and non-heating seasons. The model building submodule is used to build a time-series optimization model based on the objective function and the constraints.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the grid renewable energy absorption rate calculation method that takes into account seasonal differences as described in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the grid renewable energy absorption rate calculation method that takes into account seasonal differences as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the grid renewable energy absorption rate calculation method that takes into account seasonal differences as described in any one of claims 1 to 5.

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