Power grid operation reserve capacity demand evaluation method and system

Abstract

The invention discloses a power grid operation reserve capacity demand evaluation method and system, and relates to the technical field of power dispatching, and the method comprises the steps: constructing a new energy power generation output prediction distribution function according to the historical distribution characteristics of a new energy power generation output prediction deviation rate, determining an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function; calculating a power grid operation reserve capacity demand by using the initial new energy power generation output prediction value, and constructing a low-carbon comprehensive benefit evaluation index according to the power grid operation reserve capacity demand; and if the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, adjusting the new energy power generation output prediction value and repeating the tentative process until the convergence condition is met, and outputting the new energy power generation output prediction value and the corresponding power grid operation reserve capacity demand. The internal relation between the new energy power generation output prediction value and the prediction deviation is fully considered, so that the power grid operation reserve is reasonably reserved, and the power grid operation reserve capacity demand benefit is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a method and system for evaluating the demand for spare capacity of power grid operation. Background Art

[0002] The grid operation reserve is an auxiliary service regulation resource necessary for grid dispatching and operation. Its function is to deal with power shortages caused by factors such as power load forecast deviation, new energy power generation output forecast deviation, and power generation and transmission equipment failure during grid operation. The demand for operation reserve capacity is an important basis for the organization and dispatching plan of the grid operation reserve market. In the traditional mode, the grid operation reserve capacity demand mainly adopts the uncertainty factor assessment method, that is, screening the influencing factors that may cause power shortages, and evaluating their operation reserve capacity demand, and summing them up to form the total demand for grid operation reserve capacity.

[0003] The current grid operation reserve capacity demand is the sum of 2-5% of the expected maximum load on the operating day and the maximum power shortage that may be caused by the failure of a single component. This evaluation method focuses on the impact of two factors: power load forecast deviation and power generation and transmission equipment failure on the grid operation reserve capacity demand.

[0004] With the rapid development of new energy sources such as wind power and photovoltaic power, the output of new energy power generation has become another important factor affecting the balance of supply and demand in the power grid. To this end, in recent years, some scholars have begun to study the method of evaluating the demand for grid operation reserve capacity considering the forecast deviation of new energy power generation output. On the whole, the demand for grid operation reserve capacity caused by the uncertainty factors of new energy power generation output is the product of its predicted maximum power generation output and the forecast deviation. After considering the uncertainty factors of new energy power generation output, the evaluation of grid operation reserve capacity demand is more in line with the current grid operation regulation requirements under the large-scale development of new energy. However, it must be pointed out that this method does not change the overall framework of analyzing and evaluating the demand for grid operation reserve capacity based on uncertainty factors in the traditional mode. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a power grid operating reserve capacity demand assessment method and system to solve the problems of low operating reserve call rate and insufficient utilization of operating reserve resources during actual use in traditional one-way assessment methods based on uncertainty factor analysis.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for evaluating the demand for reserve capacity of power grid operation, comprising: constructing a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determining an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function; using the initial new energy power generation output prediction value to calculate the demand for reserve capacity of power grid operation, and constructing a low-carbon comprehensive benefit evaluation index according to the demand for reserve capacity of power grid operation; if the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, adjusting the new energy power generation output prediction value and repeating the trial process until the convergence condition is met, and outputting the new energy power generation output prediction value and the corresponding power grid operation reserve capacity demand.

[0009] As a preferred solution of the method for evaluating the demand for spare capacity of power grid operation described in the present invention, the construction of a new energy power generation output prediction distribution function includes:

[0010] The daily new energy power generation output forecast deviation rates are counted for no less than 3 years, and the new energy power generation output forecast deviation rates are sorted in ascending order, and the new energy power generation output forecast deviation rates are divided into multiple intervals within the variation range of the new energy power generation output forecast deviation rate. The probability of occurrence of the new energy power generation output forecast deviation rate in each interval is counted as the historical distribution characteristics of the new energy power generation output forecast deviation rate, and a new energy power generation output forecast distribution function is constructed according to the historical distribution characteristics of the new energy power generation output forecast deviation rate.

[0011] As a preferred scheme of the method for evaluating the demand for spare capacity of power grid operation described in the present invention, the maximum predicted value of the new energy power generation output with a prediction error of 0 in the new energy power generation output prediction distribution function is used as the initial new energy power generation output prediction value.

[0012] As a preferred solution of the method for evaluating the power grid operation reserve capacity demand of the present invention, the method for calculating the power grid operation reserve capacity demand includes:

[0013] After considering the influence of uncertainty of renewable energy power generation output, the initial renewable energy power generation output forecast value is used to calculate the grid operation reserve capacity demand. The grid operation reserve capacity demand is the influence of power load forecast deviation, power generation and transmission equipment failure, and renewable energy power generation output forecast deviation on the grid operation reserve capacity demand. The formula is expressed as follows:

[0014]

[0015] in, represents the grid operation reserve capacity requirement on operation day d, P Loss Indicates the maximum power shortfall that may be caused by a single component failure. represents the predicted maximum power load on operation day d, αPL Indicates the operating reserve demand coefficient corresponding to the power load, represents the predicted maximum renewable energy power generation output on operation day d, α NG It indicates the demand coefficient of operating reserve capacity corresponding to the output of new energy power generation.

[0016] As a preferred solution of the method for evaluating the demand for power grid operation reserve capacity of the present invention, evaluating the operation reserve transaction fee and the output consumption capacity of new energy power generation according to the demand for power grid operation reserve capacity includes:

[0017] The evaluation of the operating reserve transaction fee includes taking the lowest operating reserve transaction fee as the optimization goal, considering the operating reserve capacity demand constraint and the operating reserve declared capacity constraint to construct an operating reserve clearing model, and using the Cplex software package or the simplex method to solve the operating reserve clearing model to obtain the winning bid value of the unit operating reserve capacity and the operating reserve transaction fee;

[0018] The evaluation of the new energy power generation output absorption capacity includes the evaluation of the wind and photovoltaic power abandonment caused by insufficient operation reserve based on the power grid operation reserve capacity demand as an evaluation indicator of the new energy power generation output absorption capacity, wherein the expected wind and photovoltaic power abandonment is the expected cumulative value of the power that exceeds the predicted level of new energy power generation output, expressed as:

[0019]

[0020] Among them, E NEL Indicates the expected amount of wind and solar power abandoned. It represents the maximum value of the new energy power generation output when the prediction error first reaches 100% in the new energy power generation output prediction distribution function. It indicates the prediction of new energy power generation output. It indicates the probability of occurrence of the maximum output of renewable energy power generation. It indicates the expected abandoned wind and photovoltaic power under the selected renewable energy power generation output, which is the part of the prediction curve that is higher than the maximum predicted renewable energy power generation output, expressed as:

[0021]

[0022] Among them, NT and ΔT are the number of optimization periods and time interval respectively. Represents the renewable energy power generation output during period t.

[0023] As a preferred solution of the method for evaluating the demand for spare capacity of power grid operation described in the present invention, it also includes:

[0024] The reciprocal of the sum of the operating reserve transaction fee and the expected corresponding fee of the abandoned wind and photovoltaic power is used as a low-carbon comprehensive benefit evaluation index. If the change range of the low-carbon comprehensive benefit evaluation index before and after the trial does not exceed the given threshold, the convergence condition is met, and the predicted value of the new energy power generation output after the trial and the corresponding operating reserve capacity demand are output. Otherwise, the convergence condition is not met, the predicted value of the new energy power generation output is readjusted and the trial process is repeated. The convergence condition is expressed as:

[0025] |I EL (n)-I EL (n-1)|≤ε

[0026] Among them, I EL (n), I EL (n-1) represents the low-carbon comprehensive benefit evaluation index of the nth round and the n-1th round respectively, and ε represents the given threshold.

[0027] As a preferred solution of the method for evaluating the demand for spare capacity of power grid operation described in the present invention, the step of adjusting the predicted value of the power generation output of renewable energy and repeating the trial process until the convergence condition is met includes:

[0028] Given a maximum number of trials, the trial step is the ratio of the difference between the maximum value of the new energy power generation output when the prediction error first reaches 100% in the new energy power generation output prediction distribution function and the maximum value of the new energy power generation output corresponding to the prediction error reaching 0% to the maximum number of trials;

[0029] When the change range of the low-carbon comprehensive benefit evaluation index before and after the trial exceeds a given threshold, the convergence condition is not met. On the basis of the previous round of trials, the new energy power generation output corresponding to one unit of the trial step is increased, and the trial process is repeated until the convergence condition is met, and the predicted value of the new energy power generation output after the trial and the corresponding grid operation reserve capacity demand are output.

[0030] In a second aspect, the present invention provides a system for evaluating power grid operation reserve capacity demand, comprising:

[0031] An initial prediction value determination unit is used to construct a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determine an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function;

[0032] An evaluation index construction unit, used to calculate the power grid operation reserve capacity demand by using the initial new energy power generation output forecast value, and to construct a low-carbon comprehensive benefit evaluation index according to the power grid operation reserve capacity demand;

[0033] The adjustment judgment unit is used to adjust the predicted value of the new energy power generation output and repeat the trial process until the convergence condition is met when the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, and output the predicted value of the new energy power generation output and the corresponding grid operation reserve capacity demand.

[0034] In a third aspect, the present invention provides a computing device, comprising:

[0035] Memory and processor;

[0036] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method are implemented.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the method when executed by a processor.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a method and system for evaluating the demand for power grid operation reserve capacity, which fully considers the intrinsic relationship between the predicted value of new energy power generation output and its estimated deviation, and constructs a power grid operation reserve capacity demand evaluation model to obtain a power grid operation reserve capacity demand evaluation result that meets the low-carbon requirements; the present invention can comprehensively consider the power grid operation reserve transaction costs brought about by large-scale new energy access and the low-carbon benefits generated by reserving operation reserves to promote new energy consumption, thereby reasonably reserving power grid operation reserves and improving the benefits of power grid operation reserve capacity demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 This is an overall flow chart of a method for evaluating power grid operation reserve capacity demand according to an embodiment of the present invention;

[0041] Figure 2 This is a flowchart of an implementation of a method for evaluating power grid operation reserve capacity demand according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a distribution function for predicting the output of renewable energy power generation according to a method for evaluating the demand for spare capacity of power grid operation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] Reference Figure 1 to Figure 3 In one embodiment of the present invention, a method and system for evaluating the demand for spare capacity of power grid operation are provided, including a method for evaluating the demand for spare capacity of power grid operation and a system for evaluating the demand for spare capacity of power grid operation. The method for evaluating the demand for spare capacity of power grid operation specifically includes the following steps:

[0046] S100: constructing a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determining an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function;

[0047] In the embodiment of the present application, constructing a new energy power generation output prediction distribution function includes: counting the daily new energy power generation output prediction deviation rate for no less than 3 years, and sorting the new energy power generation output prediction deviation rate in ascending order, dividing the new energy power generation output prediction deviation rate into no less than 10 intervals within the new energy power generation output prediction deviation rate variation range, counting the probability of occurrence of the new energy power generation output prediction deviation rate in each interval, that is, the new energy power generation output prediction deviation rate historical distribution characteristics, and constructing a new energy power generation output prediction distribution function according to the new energy power generation output prediction deviation rate historical distribution characteristics;

[0048] It should be noted that the forecast deviation rate of renewable energy power generation output is the ratio of the actual maximum output to the forecast maximum output, expressed as:

[0049]

[0050] in, They represent the actual and predicted maximum power generation output of renewable energy on the operation day, It indicates the forecast deviation rate of renewable energy power generation output on the operation day d;

[0051] It should be noted that the probability of occurrence of the deviation rate of the forecast of new energy power generation output in each interval is the ratio of the number of occurrences of the deviation rate of the forecast of new energy power generation output in the historical data in this interval to the total number of occurrences, expressed as:

[0052]

[0053] in, N D They represent the number of occurrences of forecast deviations of renewable energy power generation output in interval n and the total number of historical data statistics, respectively. It represents the probability of occurrence of the forecast deviation rate of renewable energy power generation output in interval n;

[0054] It should be noted that the probability of occurrence of the deviation rate of new energy power generation output forecast essentially reflects the distribution characteristics of the deviation rate of new energy power generation output forecast based on the existing new energy power generation output forecast technology, and its characteristic is that the deviation rate of new energy power generation output forecast takes a value of 0 when it is lower or higher than a certain value;

[0055] Preferably, the new energy power generation output forecast distribution function is essentially a cumulative function of the probability of occurrence of the new energy power generation output forecast deviation rate, which is used to reflect the cumulative effect of the new energy power generation output forecast deviation. Figure 3 The forecast distribution function of renewable energy power generation output shown has the following characteristics, that is, when the forecast maximum renewable energy power generation output is lower than a certain level, the forecast error will drop to 0, because after it is lower than a certain level, the forecast requirement can be met by limiting the renewable energy power generation output; after it exceeds a certain level, the forecast error shows a trend of gradual increase, and increases rapidly with the increase of the forecast maximum renewable energy power generation output, and eventually approaches 100%, because after exceeding a certain level, the actual power generation output may be different from the forecast value, resulting in an error, and as the forecast value increases, the error increases rapidly, and eventually tends to 100%.

[0056] In the embodiment of the present application, the maximum value of the predicted output of new energy power generation with a prediction error of 0 in the prediction distribution function of the new energy power generation output is used as the initial predicted output value of new energy power generation;

[0057] It should be noted that the position corresponding to the maximum predicted output of renewable energy power generation with a prediction error of 0 in the renewable energy power generation output prediction distribution function is selected as the starting point for two reasons:

[0058] ① The corresponding operating reserve capacity at this position is special. Below this position, the forecast error of new energy power generation output is 0, and there is no need to configure operating reserve for this. Above this position, the forecast error of new energy power generation output gradually increases, and the difference in the demand for operating reserve will gradually emerge.

[0059] ② The distribution function of the forecast output of renewable energy power generation is a monotonically increasing function. Starting from this position, it is helpful to gradually search for the optimal forecast point according to the changes in the forecast results of renewable energy power generation output.

[0060] S200: Use the initial forecast value of renewable energy power generation output to calculate the grid operation reserve capacity demand, and construct a low-carbon comprehensive benefit evaluation index based on the grid operation reserve capacity demand;

[0061] In the embodiment of the present application, after considering the influence of uncertainty of renewable energy power generation output, the initial renewable energy power generation output forecast value is used to calculate the grid operation reserve capacity demand, which is the influence of power load forecast deviation, power generation and transmission equipment failure, and renewable energy power generation output forecast deviation on the grid operation reserve capacity demand, and the formula is expressed as follows:

[0062]

[0063] in, represents the grid operation reserve capacity requirement on operation day d, P Loss Indicates the maximum power shortfall that may be caused by a single component failure. represents the predicted maximum power load on operation day d, α PL Indicates the operating reserve demand coefficient corresponding to the power load, represents the predicted maximum renewable energy power generation output on operation day d, α NG It indicates the operating reserve capacity demand coefficient corresponding to the new energy power generation output, that is, the prediction deviation rate corresponding to the predicted value of the new energy power generation output.

[0064] Further, such as Figure 2 As shown, the operating reserve transaction fee and the new energy power generation output absorption capacity are evaluated according to the grid operating reserve capacity demand;

[0065] Specifically, the evaluation of the operating reserve transaction fee includes taking the lowest operating reserve transaction fee as the optimization goal, building an operating reserve clearing model by considering the operating reserve capacity demand constraint and the operating reserve declared capacity constraint, and using the Cplex software package or the simplex method to solve the operating reserve clearing model to obtain the winning bid value of the unit operating reserve capacity and the operating reserve transaction fee. The calculation of the operating reserve clearing model is:

[0066]

[0067] Among them, F RC represents the running standby transaction fee, They represent the declared operating reserve capacity and the winning bid value of the operating reserve capacity of unit g respectively. It represents the function of the price of the unit g declaring the operating reserve, and NG represents the number of units;

[0068] It should be noted that there is a strong coupling characteristic between operating reserve and electric energy, and the evaluation of operating reserve transaction costs is closely related to the operating reserve market clearing mode, which does not affect the main innovation of the present invention. Only the operating reserve clearing mode is discussed, and other clearing modes only need to replace the corresponding clearing model;

[0069] Specifically, the assessment of the capacity to absorb new energy power generation output includes the assessment of the amount of wind and photovoltaic power abandoned due to insufficient operation reserve based on the grid operation reserve capacity demand as an evaluation indicator of the capacity to absorb new energy power generation output, where the expected amount of wind and photovoltaic power abandoned is the expected cumulative value of the amount of power that exceeds the predicted level of new energy power generation output, expressed as:

[0070]

[0071] Among them, E NEL Indicates the expected amount of wind and solar power abandoned. It represents the maximum value of the new energy power generation output when the prediction error first reaches 100% in the new energy power generation output prediction distribution function. It indicates the prediction of new energy power generation output. It indicates the probability of occurrence of the maximum output of renewable energy power generation. It indicates the expected abandoned wind and photovoltaic power under the selected renewable energy power generation output, which is the part of the prediction curve that is higher than the maximum predicted renewable energy power generation output, expressed as:

[0072]

[0073] Among them, NT and ΔT are the number of optimization periods and time interval respectively. It represents the renewable energy power generation output in period t;

[0074] It should be noted that the demand for operating reserve capacity fully takes into account the grid regulation requirements corresponding to the predicted level of renewable energy power generation output, that is, it can ensure that renewable energy does not cause wind and solar power abandonment due to insufficient operating reserve capacity when the predicted level is not exceeded. However, if the predicted level is exceeded, the problem of wind and solar power abandonment will be faced.

[0075] Furthermore, the inverse of the sum of the operating reserve transaction fees and the corresponding fees for the expected wind and photovoltaic power abandonment based on the green certificate price is used as a low-carbon comprehensive benefit evaluation indicator. The reason is that the higher the operating reserve transaction fee, the lower the practicality of the operating reserve; and the greater the expected wind and photovoltaic power abandonment, the worse the output absorption capacity of new energy power generation. The green certificate price represents the price that the region is willing to pay for the low-carbon transformation. The inverse of the sum of the operating reserve transaction fees and the corresponding fees for the expected wind and photovoltaic power abandonment based on the green certificate price can be used as a low-carbon comprehensive benefit evaluation indicator. The larger the value, the better the low-carbon comprehensive benefit. This indicator is expressed as:

[0076]

[0077] Among them, I EL represents the low-carbon comprehensive benefit evaluation index, p GC Indicates the price of green certificate.

[0078] S300: If the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, the predicted value of the new energy power generation output is adjusted and the trial process is repeated until the convergence condition is met, and the predicted value of the new energy power generation output and the corresponding grid operation reserve capacity demand are output;

[0079] In the embodiment of the present application, if the change range of the low-carbon comprehensive benefit evaluation index before and after the trial does not exceed a given threshold, the convergence condition is met, and the predicted value of the new energy power generation output after the trial and the corresponding operating reserve capacity demand are output; otherwise, the convergence condition is not met, the predicted value of the new energy power generation output is readjusted, and the trial process is repeated, wherein the convergence condition is expressed as:

[0080] |I EL (n)-I EL (n-1)|≤ε

[0081] Among them, I EL (n), I EL (n-1) represents the low-carbon comprehensive benefit evaluation index of the nth round and the n-1th round, respectively, and ε represents the given threshold;

[0082] Further, adjusting the predicted value of renewable energy power generation output and repeating the trial process until the convergence conditions are met include:

[0083] Given the maximum number of trials, the trial step is Figure 3 The ratio of the difference between the maximum value of the new energy power generation output when the prediction error reaches 100% for the first time in the new energy power generation output prediction distribution function shown and the maximum number of trials corresponding to the maximum value of the new energy power generation output when the prediction error reaches 0%;

[0084] When the change range of the low-carbon comprehensive benefit evaluation index before and after the trial exceeds a given threshold, the convergence condition is not met. On the basis of the previous round of trials, the new energy power generation output corresponding to one unit trial step is increased, and the trial process is repeated until the convergence condition is met. The predicted value of the new energy power generation output after the trial and the corresponding grid operation reserve capacity demand are output.

[0085] From the above, it can be seen that the present invention provides a method and system for evaluating the demand for power grid operation reserve capacity, which fully considers the intrinsic relationship between the predicted value of new energy power generation output and its estimated deviation, and constructs a power grid operation reserve capacity demand evaluation model to obtain a power grid operation reserve capacity demand evaluation result that meets the low-carbon requirements; the present invention can comprehensively consider the power grid operation reserve transaction costs brought about by large-scale new energy access and the low-carbon benefits generated by reserving operation reserves to promote new energy consumption, thereby reasonably reserving power grid operation reserves and improving the benefits of power grid operation reserve capacity demand.

[0086] The above is a schematic scheme of a method for evaluating the demand for spare capacity of power grid operation in this embodiment. It should be noted that the technical scheme of the system for evaluating the demand for spare capacity of power grid operation and the technical scheme of the method for evaluating the demand for spare capacity of power grid operation described above belong to the same concept. For details not described in detail in the technical scheme of the system for evaluating the demand for spare capacity of power grid operation in this embodiment, please refer to the description of the technical scheme of the method for evaluating the demand for spare capacity of power grid operation described above.

[0087] This embodiment also provides a power grid operation reserve capacity demand assessment system, including:

[0088] An initial prediction value determination unit is used to construct a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determine an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function;

[0089] An evaluation index construction unit is used to calculate the power grid operation reserve capacity demand using the initial new energy power generation output forecast value, and to construct a low-carbon comprehensive benefit evaluation index based on the power grid operation reserve capacity demand;

[0090] The adjustment judgment unit is used to adjust the predicted value of renewable energy power generation output and repeat the trial process until the convergence conditions are met when the low-carbon comprehensive benefit evaluation index does not meet the convergence conditions, and output the predicted value of renewable energy power generation output and the corresponding grid operation reserve capacity demand.

[0091] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0092] This embodiment also provides a computing device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for evaluating the demand for spare capacity of power grid operation is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0093] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0094] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0095] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0096] Example 2

[0097] Reference Figure 3 Based on the previous embodiment, this embodiment provides an application example of a method and system for evaluating the demand for spare capacity of power grid operation, in order to verify and illustrate the technical effects adopted in this method.

[0098] In order to verify the effectiveness of the method proposed in this embodiment, an implementation case will be constructed based on the actual data of a provincial power grid. The probability and distribution probability of the deviation rate of the forecast of the output of new energy power generation in the provincial power grid are shown in the attached Figure 3 .

[0099] According to the method proposed in the present invention, the trial evaluation process is shown in Table 1. The predicted value of the initial position of new energy power generation output is 760MW, the corresponding grid operation reserve capacity demand is 1304MW, the operation reserve market transaction fee is 590,000 yuan, and the corresponding expected wind and photovoltaic power abandonment is 430MWh, and its low-carbon comprehensive benefit is 0.010. Within the range of the new energy power generation output forecast deviation rate, the new energy power generation output forecast value is increased, and the optimal position is gradually explored. When the new energy power generation output forecast value is 768MW, the low-carbon comprehensive benefit reaches the maximum value and meets the convergence conditions.

[0100] Table 1: Implementation process of the present invention.

[0101]

[0102] Further compared with the traditional method, the predicted value of new energy power generation output using the traditional new energy power generation output prediction method on this operating day is 800MW, and the corresponding grid operation reserve capacity demand is 1920MW. Due to the increase in the grid operation reserve capacity, the transaction fee of its operation reserve market has surged to 1.152 million yuan. Although the expected wind and photovoltaic power abandonment in this case is only 301MWh, the low-carbon comprehensive benefit is only 0.008, which is significantly lower than the implementation results of the present invention, indicating that the traditional method does not take into account the distribution characteristics of new energy power generation output, which may cause a decrease in the low-carbon comprehensive benefit.

[0103] Therefore, it can be seen from the above that the intrinsic relationship between the predicted value of new energy power generation output and its estimated deviation is fully considered, and a power grid operation reserve capacity demand assessment model is constructed to obtain a power grid operation reserve capacity demand assessment result that meets the low-carbon requirements; the present invention can comprehensively consider the power grid operation reserve transaction costs brought about by large-scale new energy access and the low-carbon benefits generated by reserving operation reserves to promote new energy consumption, thereby reasonably reserving power grid operation reserves and improving the benefits of power grid operation reserve capacity demand.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0105] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0106] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0110] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating the demand for spare capacity of power grid operation, characterized in that: include: Constructing a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determining an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function; Calculating the power grid operation reserve capacity demand using the initial new energy power generation output forecast value, and constructing a low-carbon comprehensive benefit evaluation index based on the power grid operation reserve capacity demand; If the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, the predicted value of the new energy power generation output is adjusted and the trial process is repeated until the convergence condition is met, and the predicted value of the new energy power generation output and the corresponding grid operation reserve capacity demand are output.

2. The method for evaluating the power grid operation reserve capacity demand according to claim 1, characterized in that: The construction of the new energy power generation output prediction distribution function includes: The daily new energy power generation output forecast deviation rates are counted for no less than 3 years, and the new energy power generation output forecast deviation rates are sorted in ascending order, and the new energy power generation output forecast deviation rates are divided into multiple intervals within the variation range of the new energy power generation output forecast deviation rate. The probability of occurrence of the new energy power generation output forecast deviation rate in each interval is counted as the historical distribution characteristics of the new energy power generation output forecast deviation rate, and a new energy power generation output forecast distribution function is constructed according to the historical distribution characteristics of the new energy power generation output forecast deviation rate.

3. The method for evaluating the demand for spare capacity of power grid operation according to claim 2, characterized in that: The maximum predicted value of the new energy power generation output with a prediction error of 0 in the new energy power generation output prediction distribution function is taken as the initial new energy power generation output prediction value.

4. The method for evaluating the demand for spare capacity of power grid operation according to claim 3, characterized in that: The calculation of the grid operation reserve capacity requirement includes: After considering the influence of uncertainty of renewable energy power generation output, the initial renewable energy power generation output forecast value is used to calculate the grid operation reserve capacity demand. The grid operation reserve capacity demand is the influence of power load forecast deviation, power generation and transmission equipment failure, and renewable energy power generation output forecast deviation on the grid operation reserve capacity demand. The formula is expressed as follows: in, represents the grid operation reserve capacity requirement on operation day d, P Loss Indicates the maximum power shortfall that may be caused by a single component failure. represents the predicted maximum power load on operation day d, α PL Indicates the power load corresponding to the operating reserve demand coefficient, represents the predicted maximum renewable energy power generation output on operation day d, α NG It indicates the demand coefficient of operating reserve capacity corresponding to the output of new energy power generation.

5. The method for evaluating the demand for spare capacity of power grid operation according to claim 4, characterized in that: According to the grid operation reserve capacity demand, the operation reserve transaction fee and the new energy power generation output consumption capacity are evaluated, including: The evaluation of the operating reserve transaction fee includes building an operating reserve clearing model by considering the operating reserve capacity demand constraint and the operating reserve declared capacity constraint, and solving the operating reserve clearing model by using Cplex software package or simplex method to obtain the winning bid value of the unit operating reserve capacity and the operating reserve transaction fee; The evaluation of the new energy power generation output absorption capacity includes the evaluation of the wind and photovoltaic power abandonment caused by insufficient operation reserve based on the power grid operation reserve capacity demand as an evaluation indicator of the new energy power generation output absorption capacity, wherein the expected wind and photovoltaic power abandonment is the expected cumulative value of the power that exceeds the predicted level of new energy power generation output, expressed as: Among them, E NEL Indicates the expected amount of wind and solar power abandoned, It represents the maximum value of the new energy power generation output when the prediction error first reaches 100% in the new energy power generation output prediction distribution function. It indicates the prediction of new energy power generation output. It indicates the probability of occurrence of the maximum output of renewable energy power generation. It indicates the expected abandoned wind and photovoltaic power under the selected renewable energy power generation output, which is the part of the prediction curve that is higher than the maximum predicted renewable energy power generation output, expressed as: Among them, NT and ΔT are the number of optimization periods and time interval respectively. Represents the renewable energy power generation output during period t.

6. The method for evaluating the demand for spare capacity of power grid operation according to claim 5, characterized in that: Also includes: The reciprocal of the sum of the operating reserve transaction fee and the expected corresponding fee of the abandoned wind and photovoltaic power is used as a low-carbon comprehensive benefit evaluation index. If the change range of the low-carbon comprehensive benefit evaluation index before and after the trial does not exceed the given threshold, the convergence condition is met, and the predicted value of the new energy power generation output after the trial and the corresponding operating reserve capacity demand are output. Otherwise, the convergence condition is not met, the predicted value of the new energy power generation output is readjusted and the trial process is repeated. The convergence condition is expressed as: I EL (n)-I EL (n-1)|≤ε Among them, I EL (n), I EL (n-1) represents the low-carbon comprehensive benefit evaluation index of the nth round and the n-1th round respectively, and ε represents the given threshold.

7. The method for evaluating the demand for spare capacity of power grid operation according to claim 6, characterized in that: The step of adjusting the predicted value of the new energy power generation output and repeating the trial process until the convergence condition is met includes: Given a maximum number of trials, the trial step is the ratio of the difference between the maximum value of the new energy power generation output when the prediction error first reaches 100% in the new energy power generation output prediction distribution function and the maximum value of the new energy power generation output corresponding to the prediction error reaching 0% to the maximum number of trials; When the change range of the low-carbon comprehensive benefit evaluation index before and after the trial exceeds a given threshold, the convergence condition is not met. On the basis of the previous round of trials, the new energy power generation output corresponding to one unit of the trial step is increased, and the trial process is repeated until the convergence condition is met, and the predicted value of the new energy power generation output after the trial and the corresponding grid operation reserve capacity demand are output.

8. A system using the method for evaluating the power grid operation reserve capacity demand according to any one of claims 1 to 7, characterized in that: include: An initial prediction value determination unit is used to construct a new energy power generation output prediction distribution function according to the historical distribution characteristics of the new energy power generation output prediction deviation rate, and determine an initial new energy power generation output prediction value based on the new energy power generation output prediction distribution function; An evaluation index construction unit, used to calculate the power grid operation reserve capacity demand by using the initial new energy power generation output forecast value, and to construct a low-carbon comprehensive benefit evaluation index according to the power grid operation reserve capacity demand; The adjustment judgment unit is used to adjust the predicted value of the new energy power generation output and repeat the trial process until the convergence condition is met when the low-carbon comprehensive benefit evaluation index does not meet the convergence condition, and output the predicted value of the new energy power generation output and the corresponding grid operation reserve capacity demand.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer program instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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