Operation reserve classification clearing method, system and equipment based on call distribution characteristic analysis and medium

Abstract

The invention discloses an operation reserve classification clearing method, system and device based on calling distribution characteristic analysis and a medium, and belongs to the field of power dispatching and power market cross technologies, and the method comprises the steps: analyzing operation reserve calling characteristics, calculating an initial common reserve coefficient, building a joint clearing model considering the participation of an adjusting power supply, and solving the joint clearing model; and constructing and solving a common operation standby joint clearing model without considering the participation of the adjusting power supply, constructing and solving a non-common operation standby clearing model oriented to the participation of the adjusting power supply, judging a cost change convergence requirement, and adjusting a common standby coefficient. The method can effectively optimize the operation standby classification of the power system, and improves the economical efficiency and reliability of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power dispatch and power market cross, in particular to a method, system, device and medium for operation reserve classification clearing based on calling distribution characteristic analysis. BACKGROUND

[0002] With the large-scale grid connection of new energy and the improvement of flexible regulation demand of power system, the optimization of operation reserve capacity clearing has become one of the core problems of power market operation. The traditional operation reserve clearing method usually adopts a unified capacity demand model, optimizes the joint of electric energy and reserve, and ignores the probability difference of reserve calling and the heterogeneity of power regulation characteristics. In recent years, a hierarchical clearing mechanism based on probability reserve evaluation has been proposed, such as statistically analyzing the reserve calling frequency based on historical data, or dynamically adjusting the reserve demand based on risk indicators. In addition, the participation of regulating power sources (such as energy storage and demand response) provides new flexible resources for the reserve market, and the complementarity of fast response characteristics and conventional units has become a research hotspot. However, the existing methods mainly focus on economic optimization under a single clearing model, lack quantitative analysis of reserve calling distribution characteristics, and do not fully consider the differentiated pricing mechanism of regulating power sources in normal and abnormal reserve scenarios, resulting in a difficult balance between robustness and economy in the clearing result.

[0003] The deficiencies of the prior art mainly manifest in the following three aspects: first, the joint clearing model regards reserve capacity as a homogeneous commodity, without distinguishing between normal and abnormal reserve according to the calling probability, resulting in over-calling of high-cost regulating resources in low-probability calling scenarios; second, the fast response characteristics of regulating power sources are usually handled as fixed capacity constraints in existing models, resulting in low resource utilization efficiency; third, the existing reserve coefficient determination methods mainly rely on empirical thresholds or static probability distribution, lack of dynamic feedback mechanism based on historical calling data, and are difficult to adapt to the changes in reserve demand caused by new energy output fluctuations. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that it is difficult to consider the complex coupling characteristics between operation reserve and electric energy in detail, and since operation reserve and electric energy spot clearing are carried out in sequence, the clearing result is difficult to guarantee to meet the requirement of maximizing overall benefit, the joint clearing model is relatively complex, and the solving efficiency is difficult to guarantee.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a method for operation reserve classification clearing based on calling distribution characteristic analysis, comprising the following steps, The initial common backup coefficient is calculated by analyzing the backup calling characteristics of the operation backup, a joint clearing model considering the participation of the regulating power is constructed and solved, a common operation backup joint clearing model not considering the participation of the regulating power is constructed and solved, a non-common operation backup clearing model facing the participation of the regulating power is constructed and solved, and the common backup coefficient is adjusted according to the convergence requirement of the cost change.

[0007] As a preferred scheme of the operation backup classification clearing method based on the calling distribution characteristic analysis, the initial common backup coefficient is calculated by analyzing the backup calling characteristics of the operation backup, which comprises the following steps: analyzing the historical data of the backup calling situation of the operation backup, analyzing the backup calling characteristics of the operation backup, and calculating the initial common backup coefficient index.

[0008] The backup calling coefficient of the operation backup is calculated, that is, the ratio of the actual calling maximum operation backup capacity of a running day to the operation backup capacity demand of the running day is calculated, which is represented as: , Wherein, is the backup calling coefficient of the running day d, is the operation backup capacity of the running day d, is the actual calling maximum operation backup capacity of the running day d, and d is the running day.

[0009] As a preferred scheme of the operation backup classification clearing method based on the calling distribution characteristic analysis, the initial common backup coefficient is calculated by analyzing the backup calling characteristics of the operation backup, which comprises the following steps: analyzing the historical data of the backup calling situation of the operation backup, analyzing the backup calling characteristics of the operation backup, and calculating the initial common backup coefficient index. , Wherein, is the comprehensive cost after the total of the electricity purchase cost and the operation backup transaction cost, NT is the number of optimization periods, is the optimization time interval, NG is the number of power units, and NS is the number of regulating powers, is the power unit is the declared electricity price function, is the power unit is the declared operation backup price function, is the declared operation backup price function of the regulating power s, is the power unit g is the power unit is the winning operation backup, is the winning operation backup of the regulating power s, g, s and t are variable indexes. The constraints of the joint clearing model considering the participation of regulating power sources include power balance constraints, operating reserve capacity constraints, network transmission capacity constraints, maximum power generation capacity constraints of power source units, minimum power generation capacity constraints of power source units, ramping capacity constraints of power source units, operating reserve capacity constraints of regulating power source units, and are expressed as: , , , , , , , wherein NN is the number of new energy power stations, NB is the number of load nodes, is the power generation output of the new energy power station n at the time period t, is the power consumption of the load node b at the time period t, is the current operating day operating reserve capacity demand, is the upper limit of the operating section s power flow limit, is the lower limit of the operating section s power flow limit, is the power flow transfer distribution factor of the power source unit g and the operating section s, is the power flow transfer distribution factor of the new energy power station n and the operating section s, is the power flow transfer distribution factor of the load node b and the operating section s, is the maximum power generation capacity of the power source unit g, is the minimum power generation capacity of the power source unit g, is the maximum ramping capacity of the power source unit, is the minimum ramping capacity of the power source unit, is the upper limit of the operating reserve capacity of the regulating power source s, is the lower limit of the operating reserve capacity of the regulating power source s, , n, b and s are variable indexes.

[0010] As a preferred scheme of the operating reserve classification clearing method based on the calling distribution characteristic analysis according to the present application, the constructing a common operating reserve joint clearing model not considering the participation of regulating power sources and solving includes that the clearing model optimization objective includes two parts of power energy purchase cost and operating reserve transaction cost, and is expressed as: , wherein, The total comprehensive cost of the electricity purchase cost and the operation reserve transaction cost is considered in the common operation reserve joint clearing model without considering the participation of the regulating power supply, NT is the number of optimization time intervals, NG is the number of power supply units for the optimization time interval, The power supply unit is The electricity price function is declared, The power supply unit is The operation reserve price function is declared, The power supply unit is The electricity generation output of the time interval t is The power supply unit is The winning operation reserve is And t is the variable index; The constraint conditions of the common operation reserve joint clearing model without considering the participation of the regulating power supply include the power balance constraint, the operation reserve capacity constraint, the network transmission capability constraint, the maximum power generation capability constraint of the power supply unit, the minimum power generation capability constraint of the power supply unit and the ramping capability constraint of the power supply unit; The operation reserve capacity constraint of the common operation reserve joint clearing model without considering the participation of the regulating power supply is represented as: , , Wherein, The current operation day common operation reserve capacity demand is The common reserve coefficient is The power supply unit is The winning operation reserve is NG, and NG is the number of power supply units, The current operation day operation reserve capacity demand is The variable index is t.

[0011] As a preferred scheme of the operation reserve classification clearing method based on the calling distribution characteristic analysis, wherein: the common operation reserve clearing model facing the participation of the regulating power supply is constructed and solved, including: the optimization objective of the common operation reserve clearing model facing the participation of the regulating power supply is constructed, and the optimization objective only includes the operation reserve transaction cost and is represented as: , Wherein, The operation reserve transaction cost of the common operation reserve clearing model facing the participation of the regulating power supply is NS, and NS is the number of regulating power supplies, The operation reserve price function declared by the regulating power supply s is The winning operation reserve of the regulating power supply s is s, and s is the variable index; The constraint conditions of the common operation reserve clearing model facing the participation of the regulating power supply are constructed, including the operation reserve capacity constraint and represented as: , , wherein, is the current operating day non-essential operating reserve capacity requirement, is the regulating power s in the mark operating reserve, is the current operating day essential operating reserve capacity requirement, current operating day operating reserve capacity requirement, NS is the number of regulating power, and s is the variable index.

[0012] As a preferred scheme of the operating reserve classification and clearing method based on the analysis of the calling distribution characteristics, wherein: the solution of the non-essential operating reserve clearing model considering the participation of the regulating power includes that the non-essential operating reserve clearing model considering the participation of the regulating power is solved by using Cplex software, and the optimization result of the model is obtained.

[0013] As a preferred scheme of the operating reserve classification and clearing method based on the analysis of the calling distribution characteristics, wherein: the judgment of the convergence requirement of the cost change and the adjustment of the essential reserve coefficient include that the cost of the classification and clearing model and the joint clearing model is judged. The convergence requirement of the classification and clearing model is that the cost of the essential reserve capacity clearing obtained by the classification and clearing model and the cost of the non-essential capacity clearing are compared with the cost obtained by the joint clearing model, and the cost does not exceed the specified limit value, and the formula is expressed as: , wherein, is the comprehensive cost after the total of the electricity purchase cost and the operating reserve transaction cost is considered, is the operating reserve transaction cost of the non-essential operating reserve clearing model considering the participation of the regulating power, is the comprehensive cost after the total of the electricity purchase cost and the operating reserve transaction cost is considered in the essential operating reserve joint clearing model without considering the participation of the regulating power, is the convergence control threshold value given by the dispatch personnel; When the convergence requirement is met, the optimization result of the non-essential operating reserve joint clearing model without considering the participation of the regulating power and the non-essential operating reserve clearing model considering the participation of the regulating power is output; and when the convergence requirement is not met, the essential reserve coefficient level is adjusted.

[0014] Another object of the present application is to provide an operating reserve classification and clearing system based on the analysis of the calling distribution characteristics.

[0015] To solve the above technical problems, the application provides the following technical scheme: a running backup classification clearing system based on calling distribution characteristic analysis, comprising: A backup coefficient calculation module is configured to analyze the running backup calling characteristics and calculate the initial common backup coefficient.

[0016] A joint clearing model construction module is configured to construct a joint clearing model considering the participation of regulating power sources, solve the model, construct a common running backup joint clearing model without considering the participation of regulating power sources, and solve the model, and construct a very rare running backup clearing model facing the participation of regulating power sources and solve the model.

[0017] An adjustment module is configured to judge the convergence requirement of cost change and adjust the common backup coefficient.

[0018] The application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the running backup classification clearing method based on calling distribution characteristic analysis when executing the computer program.

[0019] The application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the running backup classification clearing method based on calling distribution characteristic analysis when executed by a processor.

[0020] The application has the following beneficial effects: by analyzing the running backup calling characteristics, the initial common backup coefficient can be more accurately calculated, and more reasonable basic data can be provided for the subsequent clearing model; by constructing the clearing model and solving the model, the running backup demand under different conditions can be comprehensively considered, and more refined management can be realized; by judging the convergence requirement of cost change and adjusting the common backup coefficient, the stability and economy of the clearing result can be ensured; in addition, by analyzing the historical data and the characteristics, the actual demand of the running backup can be more accurately reflected, and the accuracy of the initial common backup coefficient can be improved; by considering the participation of the regulating power source, the running backup demand of the power system can be more comprehensively considered, and the rationality and economy of the clearing result can be improved; the consideration of multiple constraint conditions ensures the feasibility and stability of the clearing result, and the application can effectively optimize the running backup classification clearing of the power system, and improve the economy and reliability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 The overall flow chart of the running backup classification clearing method based on the analysis of the calling distribution characteristics is provided for an embodiment of the present application.

[0023] Figure 2 The system scheme module diagram of the running backup classification clearing system based on the analysis of the calling distribution characteristics is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0025] Embodiment 1, reference Figure 1 For the first embodiment of the present application, the embodiment provides a running backup classification clearing method based on the analysis of the calling distribution characteristics, which comprises: S1: analyzing the running backup calling characteristics and calculating the initial frequently-used backup coefficient.

[0026] Further, the running backup calling coefficient is calculated, that is, the ratio of the actual calling maximum running backup capacity of a running day to the running backup capacity demand of the running day is calculated, which is represented as: , wherein, is the running backup calling coefficient of the running day d, is the running backup capacity of the running day d, is the actual calling maximum running backup capacity of the running day d, d is the running day, the running backup calling coefficient corresponding to the 80% probability of the running backup calling coefficient is taken as the frequently-used backup coefficient, and the remaining backup calling coefficients are taken as the infrequently-used backup coefficients.

[0027] It should be noted that the distribution rule of the running backup calling coefficient is counted, the running backup calling coefficients of not less than one year are counted and arranged in order from small to large. In the distribution range of the running backup calling coefficient, 10 intervals are divided at equal intervals, and the occurrence times of the running backup calling coefficients in each interval are counted. The occurrence times correspond to the occurrence rate of the running backup coefficient in the interval range, and the interval running backup occurrence probability can be represented as: , wherein, is the occurrence probability of the running backup calling coefficient interval division i, is the occurrence times of the interval i, Total number of occurrences for interval i.

[0028] Further, the common reserve coefficient refers to the most common level of actual call of the operating reserve, since the calculation of the demand of the operating reserve capacity is based on the prediction of the uncertainty level of the output of new energy power generation and the uncertainty level of the power load, and the actual call of the operating reserve is determined by the actual uncertainty fluctuation, therefore, the call amount of the demand of the operating reserve capacity is lower than the demand of the operating reserve capacity, and the common reserve coefficient is determined from the perspective of the analysis of the common level of the call capacity of the operating reserve under the common reserve coefficient, the common reserve coefficient can be determined by the dispatching operation personnel according to the actual operation experience, and can also be obtained by analyzing the distribution rule of the call coefficient of the operating reserve, the call coefficient of the operating reserve corresponding to the occurrence probability of 80% of the operating reserve is taken as the common reserve coefficient in the application, and the value can be obtained by statistical analysis of the distribution rule of the call coefficient of the operating reserve.

[0029] Based on historical data statistics (such as 80% call probability threshold), the common and non-common reserve are scientifically divided to avoid the problem of “one-size-fits-all” of the reserve capacity in the traditional method, so that the reserve demand is more in line with the actual call rule; the optimized resource allocation provides data support for the subsequent differentiated clearing model, ensures that the reserve with high probability is borne by the conventional unit with higher economic efficiency, and the reserve with low probability is flexibly responded by the regulating power (such as energy storage), and the overall reserve cost is reduced.

[0030] S2: build a joint clearing model considering the participation of regulating power and solve.

[0031] Further, the optimization objective of the joint clearing model considering the participation of the regulating power includes two parts of the electricity purchase cost and the operating reserve transaction cost, and is expressed as: , Among them, is the comprehensive cost after considering the total of the electricity purchase cost and the operating reserve transaction cost, NT is the number of optimization periods, is the optimization time interval, NG is the number of power units, and NS is the number of regulating powers, is the declared electricity price function of the power unit g, is the declared operating reserve price function of the power unit g, is the declared operating reserve price function of the regulating power s, is the declared operating reserve price function of the regulating power s, is the power unit g electricity generation output at period t, is the winning operating reserve of the power unit g, is the winning operating reserve of the regulating power s, g, s and t are variable indexes. is the winning operating reserve of the regulating power s, g, s and t are variable indexes. ​The constraint conditions of the joint clearing model considering the participation of the regulating power source include power balance constraints, operating reserve capacity constraints, network transmission capacity constraints, maximum power generation capacity constraints of the power source unit, minimum power generation capacity constraints of the power source unit, ramping capacity constraints of the power source unit, operating reserve capacity constraints of the regulating power source unit, and are expressed as: , , , , , , , wherein NN is the number of new energy power stations, NB is the number of load nodes, is the power generation output of the new energy power station n at the time period t, is the power consumption load of the load node b at the time period t, is the current operating day operating reserve capacity demand, is the upper limit of the operating section s power flow limit, is the lower limit of the operating section s power flow limit, is the power flow transfer distribution factor of the power source unit g and the operating section s, is the power flow transfer distribution factor of the new energy power station n and the operating section s, is the power flow transfer distribution factor of the load node b and the operating section s, is the maximum power generation capacity of the power source unit g, is the minimum power generation capacity of the power source unit g, is the maximum ramping capacity of the power source unit, is the minimum ramping capacity of the power source unit, is the upper limit of the operating reserve capacity of the regulating power source s, is the lower limit of the operating reserve capacity of the regulating power source s, , n, b and s are variable indexes.

[0032] The regulating power source (such as energy storage and demand response) is included in the reserve market, the system flexibility is enhanced by using the fast response characteristics, and it is especially suitable for fluctuation suppression of the high-proportion new energy power grid; through the power balance, network transmission, ramping capacity and other constraints, it is ensured that the clearing result is within the range of technical feasibility, and the reliability of the power grid operation is improved.

[0033] S3: build a commonly used operating reserve joint clearing model without considering the participation of the regulating power source and solve it.

[0034] Further, the optimization objective of the clearing model includes two parts of electricity purchase cost and operating reserve transaction cost, expressed as: , wherein, is the comprehensive cost after considering the total sum of electricity purchase cost and operating reserve transaction cost of the conventional operating reserve joint clearing model without considering the participation of regulating power sources, and NT is the number of optimization periods, is the optimization time interval, and NG is the number of power source units, is the power source unit the declared electricity price function, is the power source unit the declared operating reserve price function, is the power source unit the electricity generation output of period t, is the power source unit the winning operating reserve, and t is the variable index.

[0035] The constraint conditions of the conventional operating reserve joint clearing model without considering the participation of regulating power sources include power balance constraint, operating reserve capacity constraint, network transmission capability constraint, maximum power generation capability constraint of power source units, minimum power generation capability constraint of power source units, and power source unit ramping capability constraint; The operating reserve capacity constraint of the conventional operating reserve joint clearing model without considering the participation of regulating power sources is expressed as: , , wherein, is the conventional operating reserve capacity demand of the current operating day, is the conventional reserve coefficient, is the power source unit the winning operating reserve, and NG is the number of power source units, the operating reserve capacity demand of the current operating day, is the variable index.

[0036] Conventional units (such as thermal power and hydropower) usually have lower bidding prices and bear high call probability of reserve, which can reduce the total expenditure of the reserve market; since regulating power sources do not participate in the conventional reserve market, the number of model variables is reduced, the calculation complexity is lowered, the clearing speed is accelerated, and the model is suitable for real-time market dispatching; the conventional reserve is provided by stable power sources, which reduces the market fluctuations caused by frequent charging and discharging of regulating power sources.

[0037] S4: constructing a non-conventional operating reserve clearing model oriented to the participation of regulating power sources and solving the model.

[0038] Further, the implementation purpose of the step is to construct a very-usage operation reserve clearing model oriented to the participation of regulating power sources, and to obtain a very-usage operation reserve clearing result by solving the model.

[0039] The optimization objective of the very-usage operation reserve clearing model oriented to the participation of regulating power sources is constructed, and the optimization objective only includes operation reserve transaction fees and is expressed as: Among them, is the operation reserve transaction fee of the very-usage operation reserve clearing model oriented to the participation of regulating power sources, and NS is the number of regulating power sources, is the declared operation reserve price function of the regulating power source s, is the winning operation reserve of the regulating power source s, and s is the variable index; The constraint condition of the very-usage operation reserve clearing model oriented to the participation of regulating power sources is constructed, including operation reserve capacity constraints and being expressed as: Among them, is the very-usage operation reserve capacity demand of the current operation day, is the winning operation reserve of the regulating power source s, is the normal-usage operation reserve capacity demand of the current operation day, is the operation reserve capacity demand of the current operation day, NS is the number of regulating power sources, and s is the variable index.

[0040] Further, the solving of the very-usage operation reserve clearing model oriented to the participation of regulating power sources includes that the joint clearing model considering the participation of regulating power sources, the normal-usage operation reserve joint clearing model not considering the participation of regulating power sources, and the very-usage operation reserve clearing model oriented to the participation of regulating power sources are solved by using Cplex software to obtain the optimization result of the model.

[0041] It should be noted that the regulating power source (such as energy storage) provides low-call-probability reserve, avoids the increase of opportunity cost of conventional units due to long-term reservation of reserve capacity, and obtains reasonable reserve income through separate clearing of very-usage reserve, thereby promoting the development of flexible resources. The very-usage reserve model can quickly respond to extreme situations (such as sudden drop of new energy) and improve the resilience of the power grid.

[0042] S5: Determine the convergence requirement of the fee change, and adjust the normal-usage reserve coefficient.

[0043] It should be noted that the convergence requirement of the classification clearing model is that the fees obtained by clearing the normal-usage reserve capacity and the very-usage capacity under the classification clearing model are not more than the fees obtained by the joint clearing model within a specified limit, and the formula is expressed as: ​​​, wherein, is the comprehensive cost considering the total of the electricity purchase cost and the operating reserve transaction cost, is the operating reserve transaction cost of the exceptional operating reserve dispatching model facing the participation of the regulating power, is the comprehensive cost considering the total of the electricity purchase cost and the operating reserve transaction cost of the normal operating reserve joint dispatching model without considering the participation of the regulating power, is the convergence control threshold given by the dispatchers.

[0044] When the convergence requirement is met, the application involves that the classified dispatching result meets the requirement, and the S3, S4 dispatching optimization result is output, otherwise, the normal reserve coefficient level is adjusted, which causes the classified dispatching cost to exceed the joint dispatching model factor due to the fact that the normal reserve coefficient is too low, causing the dispatching of the regulating power to correspond to the operating reserve transaction cost which is too high, and the application recommends using the trial method to adjust the normal capacity coefficient, that is, increasing the distribution rate level by 1%, repeating S2, S3, and re-performing the cost convergence requirement judgment until the requirement is met.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and are not limiting. Although the application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the application, and they should be covered in the scope of the claims of the application.

[0046] Embodiment 2, referring to Figure 2 is a second embodiment of the application, which provides an operating reserve classified dispatching system based on calling distribution characteristic analysis, comprising: a reserve coefficient calculation module for analyzing the operating reserve calling characteristics and calculating the initial normal reserve coefficient.

[0047] a joint dispatching model construction module for constructing and solving the joint dispatching model considering the participation of the regulating power, and constructing and solving the normal operating reserve joint dispatching model without considering the participation of the regulating power, and constructing and solving the exceptional operating reserve dispatching model facing the participation of the regulating power.

[0048] an adjustment module for judging the cost change convergence requirement and adjusting the normal reserve coefficient.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and are not limiting. Although the application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the application, and they should be covered in the scope of the claims of the application.

[0050] Embodiment 3, which is an embodiment of the present application, provides a method for classifying and clearing operation backup based on calling distribution characteristic analysis, and simulation experiments are performed to scientifically demonstrate the beneficial effects of the present application.

[0051] The present application analyzes the operation backup calling condition, and the ratio of the maximum operation backup capacity actually called in a historical operation day to the operation backup capacity in the operation day is the operation day operation backup calling coefficient. The operation backup calling coefficients of not less than one year are counted, the commonly used backup coefficient is calculated according to a probability of not less than 80%, and the commonly used backup coefficient of the regional power grid is 68.5% after calculation.

[0052] For the operation day to be evaluated, the comprehensive cost under the joint clearing of electric energy and operation backup is calculated by using the joint clearing method, and the comprehensive cost is 147.82 million yuan, wherein the electric energy cost is 141.02 million yuan, and the operation backup cost is 6.8 million yuan.

[0053] The operation backup capacity demand in the operation day is 2000 MW, and the commonly used operation backup capacity is 1370 MW, and the uncommon operation backup capacity is 630 MW. For the commonly used operation backup capacity, an operation backup clearing model is constructed without considering the participation of regulating power supply; and for the uncommon operation backup capacity, an operation backup clearing model is constructed for the participation of regulating power supply. The comprehensive cost of electric energy and operation backup obtained by twice optimization decision is 149.85 million yuan, which increases by 1.37% compared with the joint clearing method and does not exceed the given limit value.

[0054] The clearing results of the regulating power supply in the traditional method and the proposed method can be compared, as shown in Table 1. Since the cost of the regulating power supply is higher than that of the conventional power supply, only a small number of regulating power supplies with relatively low cost can be successful in the operation backup market, and the proposed method can be successful in the operation backup market.

[0055] Table 1 Comparison of regulating power supply operation backup bidding , It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application 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 application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

[0056] Embodiment 4, which is the fourth embodiment of the present application, is different from the first three embodiments in that: If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the technical solutions that essentially contribute to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0057] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions execution systems, apparatuses, or devices. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatuses, or devices.

[0058] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways to be electronically obtained, and then stored in the computer memory.

[0059] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technology, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

Claims

1. A method for classifying out-of-service based on call distribution characteristics analysis, characterized in that: The method comprises the following steps: analyzing the operating reserve calling characteristics to calculate the initial common reserve coefficient; constructing a joint clearing model considering the participation of regulating power and solving the model; constructing a common operating reserve joint clearing model without considering the participation of regulating power and solving the model; constructing a very common operating reserve clearing model facing the participation of regulating power and solving the model; judging the convergence requirement of the cost change and adjusting the common reserve coefficient.

2. The method of claim 1, wherein the method is based on a call distribution characteristic analysis. The step of analyzing the operating reserve calling characteristics to calculate the initial common reserve coefficient comprises the following steps: analyzing the historical data of operating reserve calling, analyzing the operating reserve calling characteristics, and calculating the initial common reserve coefficient index. The operating reserve calling coefficient is calculated, that is, the ratio of the actual calling maximum operating reserve capacity to the operating reserve capacity demand of an operating day is calculated, and is expressed as: ， wherein, is the operating day d operating reserve call factor, is the operating day d operating reserve capacity, is the operating day d actual call maximum operating reserve capacity, d is the operating day, the operating reserve call factor corresponding to the probability of 80% operating reserve call factor is taken as the common reserve factor, and the remaining operating reserve call factor is taken as the uncommon reserve factor.

3. The method of claim 2, wherein the method is based on a call distribution characteristic analysis. The step of constructing a joint clearing model considering the participation of regulating power and solving the model comprises the following steps: the optimization objective of the joint clearing model considering the participation of regulating power comprises two parts of electric energy purchase cost and operating reserve transaction cost, and is expressed as: ， wherein, is the total cost of electricity purchase and operating reserve transaction, NT is the number of optimization periods, is the optimization time interval, NG is the number of power units, and NS is the number of regulating power sources, is the power unit g, is the declared electricity price function, is the power unit g, is the declared operating reserve price function, is the declared operating reserve price function of the regulating power source s, is the electricity generation output of the power unit g at the time interval t, is the power unit g, is the winning operating reserve, is the winning operating reserve of the regulating power source s, and g, s, and t are variable indices. The constraint conditions of the joint clearing model considering the participation of regulating power comprise power balance constraint, operating reserve capacity constraint, network transmission capability constraint, maximum power generation capability constraint of power source unit, minimum power generation capability constraint of power source unit, power source unit climbing capability constraint, and operating reserve capacity constraint of regulating power unit, and are expressed as: ， ， ， ， ， ， ， where NN is the number of new energy power plants, NB is the number of load nodes, is the power output of new energy power plant n at period t, is the power consumption of load node b at period t, is the current operating day operating reserve capacity requirement, is the upper limit of the operating section s power flow limit, is the lower limit of the operating section s power flow limit, is the power flow transfer distribution factor of power supply unit g and operating section s, is the power flow transfer distribution factor of new energy power plant n and operating section s, is the power flow transfer distribution factor of load node b and operating section s, is the maximum power generation capacity of power supply unit g, is the minimum power generation capacity of power supply unit g, is the maximum ramping capacity of power supply unit, is the minimum ramping capacity of power supply unit, is the upper limit of the regulating power s operating reserve capacity, is the lower limit of the regulating power s operating reserve capacity, , n, b and s are variable indices.

4. The method of claim 3, wherein the method is based on a call distribution characteristic analysis. The step of constructing a common operating reserve joint clearing model without considering the participation of regulating power and solving the model comprises the following steps: the optimization objective of the clearing model comprises two parts of electric energy purchase cost and operating reserve transaction cost, and is expressed as: ， wherein, is the total cost of the combined cost of electricity purchase and operating reserve transaction cost for the common operation reserve joint clearing model without considering the participation of the regulating power, NT is the number of optimization periods, is the optimization time interval, NG is the number of power units, is the power unit is the declared electricity price function, is the power unit is the declared operating reserve price function, is the power unit is the electricity generation output of period t, is the power unit is the winning operating reserve, and t is the variable index; The constraint conditions of the common operating reserve joint clearing model without considering the participation of regulating power comprise power balance constraint, operating reserve capacity constraint, network transmission capability constraint, maximum power generation capability constraint of power source unit, minimum power generation capability constraint of power source unit, and power source unit climbing capability constraint. The operating reserve capacity constraint of the common operating reserve joint clearing model without considering the participation of regulating power is expressed as: ， ， wherein, is the current operating daily routine operating reserve capacity requirement, is the routine reserve factor, is the power unit is the bid operating reserve, NG is the number of power units, is the current operating daily routine operating reserve capacity requirement, is the variable index.

5. The method of claim 4, wherein the method is based on a call distribution characteristic analysis. The step of constructing a very common operating reserve clearing model facing the participation of regulating power and solving the model comprises the following steps: the optimization objective of the very common operating reserve clearing model facing the participation of regulating power comprises only operating reserve transaction cost, and is expressed as: ， wherein, is the reserve market clearing price for the reserve market with the participation of the regulating power, NS is the number of regulating powers, is the reserve market clearing price function declared by the regulating power s, is the winning reserve market of the regulating power s, s is the variable index. The constraint conditions of the very common operating reserve clearing model facing the participation of regulating power comprise operating reserve capacity constraint, and are expressed as: ， ， wherein, is the current operating day normal operation reserve capacity requirement, is the regulating power s in the mark operation reserve, is the current operating day normal operation reserve capacity requirement, is the current operating day operation reserve capacity requirement, NS is the number of regulating power, and s is the variable index.

6. The method of claim 4, wherein the method is based on a call distribution characteristic analysis. The solving of the very common operating reserve clearing model facing the participation of regulating power comprises the following steps: the joint clearing model considering the participation of regulating power, the common operating reserve joint clearing model without considering the participation of regulating power, and the very common operating reserve clearing model facing the participation of regulating power are solved by using Cplex software to obtain the optimization result of the model.

7. The method of claim 4, wherein the method is based on a call distribution characteristic analysis. The step of judging the convergence requirement of the cost change and adjusting the common reserve coefficient comprises the following steps: judging the cost of the classified clearing model and the joint clearing model; The convergence requirement of the classified clearing model is that the cost obtained by clearing the common reserve capacity under the classified clearing model and the cost obtained by clearing the very common capacity are compared with the cost obtained by the joint clearing model, and the comparison result does not exceed the specified limit value, and is expressed by a formula as: ， wherein, a total of the electricity purchase cost and the operating reserve transaction cost, an operating reserve transaction cost of the very use operating reserve dispatch model facing the participation of the regulating power source, a total of the electricity purchase cost and the operating reserve transaction cost considered by the common use operating reserve joint dispatch model without considering the participation of the regulating power source, a convergence control threshold value given by a dispatcher; When the convergence requirement is met, the dispatching optimization results of the common operation reserve joint dispatching model not considering the participation of the regulating power and the unusual operation reserve dispatching model oriented to the participation of the regulating power are outputted; when the convergence requirement is not met, the common reserve coefficient level is adjusted.

8. A system for categorizing and purging operational spares based on call distribution analysis, applying the method for categorizing and purging operational spares based on call distribution analysis according to any one of claims 1 to 7, characterized in that, The method comprises the steps of: a reserve coefficient calculation module, which is configured to analyze the operation reserve calling characteristics and calculate an initial common reserve coefficient; a joint dispatching model construction module, which is configured to construct and solve the joint dispatching model considering the participation of the regulating power, construct and solve the common operation reserve joint dispatching model not considering the participation of the regulating power, and construct and solve the unusual operation reserve dispatching model oriented to the participation of the regulating power; an adjustment module, which is configured to judge the cost change convergence requirement and adjust the common reserve coefficient. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the operation reserve classification dispatching method based on calling distribution characteristic analysis in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the operation reserve classification dispatching method based on calling distribution characteristic analysis in any one of claims 1 to 7.

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