Method, device and computer equipment for evaluating adjustable capacity of distributed energy for power auxiliary service

Abstract

The application relates to a distributed energy adjustable capacity evaluation method and device for providing power auxiliary services, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining distributed energy data sent by a target aggregation unit; inputting each distributed energy data into a pre-established operation characteristic analysis model for different distributed energies to obtain an adjustable capacity curve of each distributed energy; determining an adjustable capacity curve of the target aggregation unit according to the adjustable capacity curve of each distributed energy; obtaining an adjustable capacity curve corresponding to a target section according to the adjustable capacity curves of each aggregation unit under the target section; and obtaining an adjustable capacity evaluation result of each distributed energy, an adjustable capacity evaluation result of the target aggregation unit and an adjustable capacity evaluation result of the target section according to a preset adjustable capacity evaluation index. The method can improve the accuracy of the adjustable capacity of the aggregation unit and the credibility of the credibility.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a method and device for evaluating adjustable capacity of distributed energy providing power auxiliary services, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] With the development of power systems, the volatility of power systems is increasingly prominent. In order to further improve the regulation capacity of power systems, third-party energy aggregators as independent energy subjects provide power auxiliary services for power systems.

[0003] At present, aggregators provide power auxiliary services by aggregating and coordinating massive distributed energy. However, the physical nodes of massive distributed energy are dispersed, and the aggregated adjustable capacity of massive distributed energy sent by the aggregator to the power system may be inaccurate. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for evaluating adjustable capacity of distributed energy providing power auxiliary services, computer equipment, computer readable storage medium and computer program product, which can accurately evaluate the adjustable capacity of distributed energy providing power auxiliary services.

[0005] In a first aspect, the present application provides a method for evaluating adjustable capacity of distributed energy providing power auxiliary services, comprising:

[0006] obtaining distributed energy data sent by a target aggregation unit, wherein the distributed energy data at least includes one of energy storage data, electric vehicle data and air conditioner load data;

[0007] inputting each of the distributed energy data into an operation characteristic analysis model pre-established for different distributed energy to obtain adjustable capacity curves of each of the distributed energy;

[0008] aggregating each of the distributed energy according to the adjustable capacity curves of each of the distributed energy, and determining an adjustable capacity curve of the target aggregation unit through a target function and a constraint condition;

[0009] determining other aggregation units except the target aggregation unit under a target section, and obtaining adjustable capacity curves of the other aggregation units;

[0010] inputting the adjustable capacity curves of each of the aggregation units under the target section, aggregation unit adjustable capacity constraint conditions and section power flow constraint conditions into a mathematical optimization solving tool to obtain an adjustable capacity curve corresponding to the target section;

[0011] According to a preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy, an adjustable capacity evaluation result of the target aggregation unit and an adjustable capacity evaluation result of the target section are obtained; the preset adjustable capacity evaluation index includes an adjustment range, a response time, an adjustment rate, an adjustment duration and an adjustment accuracy.

[0012] In one of the embodiments, the pre-established operation characteristic analysis model for different distributed energies includes an energy storage operation characteristic analysis model, a single electric vehicle operation characteristic analysis model and an air conditioner load cluster operation characteristic analysis model.

[0013] The method further includes:

[0014] The historical data and real-time data of each distributed energy are obtained;

[0015] According to the historical data and real-time data corresponding to the energy storage, the effective capacity, charging efficiency and discharging efficiency parameters of the energy storage are estimated by using the least square method, and the energy storage operation characteristic analysis model is parameter corrected according to the estimated parameters;

[0016] According to the historical data and real-time data corresponding to the electric vehicle cluster, the effective battery capacity, charging efficiency and discharging efficiency parameters of the electric vehicle cluster are estimated by using the least square method, and the single electric vehicle operation characteristic analysis model is parameter corrected according to the estimated parameters;

[0017] According to the historical data and real-time data corresponding to the air conditioner load cluster, the average building thermal resistance, average building thermal capacity and average thermal efficiency parameters of the air conditioner load cluster are estimated by using the particle swarm optimization algorithm, and the air conditioner load cluster operation characteristic analysis model is parameter corrected according to the estimated parameters.

[0018] In one of the embodiments, the pre-established operation characteristic analysis model includes an energy storage operation characteristic analysis model.

[0019] The energy storage operation characteristic analysis model is as follows:

[0020] E b (t+1)=E b (t)-P b (t)η b Δt

[0021] Wherein, E b (t) is the electric energy stored by the energy storage system at t moment; P b (t) is the power of the energy storage battery at t moment, and the discharging power is positive; η b is the charging and discharging efficiency; and Δt is a preset research period length.

[0022] Based on the preset adjustable capability assessment indicators, the adjustable capability assessment results of each of the distributed energy sources are obtained, including the following steps:

[0023] Obtain the maximum upward adjustment range of energy storage

[0024]

[0025] Where T represents the start time of the study period, and Δt represents the preset length of the study period. P(t) ESP For real-time energy storage power, This represents the maximum charging power for energy storage.

[0026] Obtain the maximum downward adjustment range of energy storage

[0027]

[0028] in, This represents the maximum discharge power of the energy storage.

[0029] Obtain the duration of energy storage regulation

[0030]

[0031] Among them, E ESP,max To store the maximum energy charge, E ESP,min For the minimum charge capacity of energy storage, E ESP (t) represents the real-time energy storage charge, P′ ESP (t) represents the power of the stored energy after adjustment; a value greater than 0 indicates discharging, and a value less than 0 indicates charging; η charge For energy storage charging efficiency; η discharge This refers to the energy storage and discharge efficiency.

[0032] In one embodiment, the pre-established operational characteristic analysis model further includes a single electric vehicle operational characteristic analysis model;

[0033] The analytical model for the operating characteristics of a single electric vehicle is as follows:

[0034]

[0035] Among them, S t Let P be the state of charge of the electric vehicle at time t. t ch For electric vehicle charging power, η ch For electric vehicle charging efficiency, Δt ch For the charging period, P t dch For the discharge power of electric vehicles, η dchis the discharging efficiency of the electric vehicle, and Δt dch is the discharging time period, and E max is the maximum state of charge of the electric vehicle cluster after being fully charged;

[0036] According to the preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy source is obtained, including the following steps:

[0037] When receiving the upward adjustment instruction, the electric vehicle whose SOC is greater than the upper limit of the state of charge threshold in the electric vehicle cluster does not perform adjustment; when receiving the downward adjustment instruction, the electric vehicle whose SOC is less than the lower limit of the state of charge threshold in the electric vehicle cluster does not perform adjustment;

[0038] The maximum upward adjustment range of the electric vehicle cluster is obtained

[0039]

[0040] wherein, T is the starting time of the research time period, and Δt is the preset research time period length; P(t) EV is the real-time power of the electric vehicle cluster, is the maximum charging power of the electric vehicle cluster;

[0041] The maximum downward adjustment range of the electric vehicle cluster is obtained

[0042]

[0043] wherein, is the maximum discharging power of the electric vehicle cluster;

[0044] The adjustment duration of the electric vehicle cluster is obtained

[0045]

[0046] wherein, E EV,max is the maximum state of charge of the electric vehicle cluster, E EV,min is the minimum state of charge of the electric vehicle cluster, E EV (t) is the real-time state of charge of the electric vehicle cluster, P E ' V (t) is the power of the electric vehicle cluster after adjustment, greater than 0 is discharging, and less than 0 is charging; η charge is the charging efficiency of the electric vehicle; η discharge is the discharging efficiency of the electric vehicle.

[0047] In one of the embodiments, the pre-established operation characteristic analysis model further includes an air conditioner load cluster operation characteristic analysis model;

[0048] The air conditioning load cluster operation characteristic analysis model is as follows:

[0049]

[0050] wherein N is the number of air conditioners, η is the equivalent air conditioner energy efficiency ratio, δ is the equivalent thermal resistance of the house, is the average outdoor temperature, is the average air conditioner setting temperature, and k is a constant;

[0051] According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy is obtained, including the following steps:

[0052] Obtaining the maximum upward adjustment range of the air conditioning load cluster:

[0053]

[0054] Obtaining the maximum downward adjustment range of the air conditioning load cluster:

[0055]

[0056] wherein T Δ is the temperature control margin.

[0057] In one of the embodiments, the objective function includes a maximum adjustment capacity objective function and a maximum duration objective function; the constraint conditions include a power balance constraint condition, a tie line constraint condition, and a constraint condition of each distributed energy itself;

[0058] The determination of the adjustable capacity curve of the target aggregation unit through the objective function and the constraint condition includes:

[0059] The maximum adjustment capacity objective function or the maximum duration objective function, and the power balance constraint condition, the tie line constraint condition, and the constraint condition of each distributed energy itself are input into a mathematical optimization solving tool to obtain the adjustable capacity curve of the target aggregation unit.

[0060] In one of the embodiments, the method further includes:

[0061] The entropy weight method is used to objectively weight the adjustable capacity evaluation index to obtain an objective weight;

[0062] The expert evaluation opinion is used to subjectively weight the adjustable capacity evaluation index to obtain a subjective weight;

[0063] The comprehensive weight is determined according to the subjective weight and the objective weight, the adjustable capacity evaluation result of the target aggregation unit is weighted processed by using the comprehensive weight, and the adjustable capacity evaluation value of the target aggregation unit is obtained.

[0064] obtaining an adjustable capacity evaluation value of the target aggregation unit and an adjustable capacity evaluation value of the other aggregation units, sorting the adjustable capacity evaluation values and corresponding aggregation units, and displaying the sorted adjustable capacity evaluation values and corresponding aggregation units on a power system dispatch interface.

[0065] In one of the embodiments, the method further comprises:

[0066] obtaining a reported adjustable capacity of the target aggregation unit sent by the target aggregation unit;

[0067] comparing the reported adjustable capacity with an adjustable capacity curve of the target aggregation unit, initiating an adjustable capacity test of the target aggregation unit if the comparison result exceeds a preset threshold condition, and displaying the comparison result and test result on a power system dispatch interface.

[0068] In a second aspect, the application further provides an adjustable capacity evaluation device for distributed energy providing power auxiliary services, comprising:

[0069] an energy data acquisition module configured to acquire distributed energy data sent by a target aggregation unit, wherein the distributed energy data at least includes one of energy storage data, electric vehicle data, and air conditioner load data;

[0070] an adjustable capacity acquisition module configured to input each of the distributed energy data into an operation characteristic analysis model pre-established for different distributed energy, to obtain an adjustable capacity curve of each of the distributed energy, to aggregate each of the distributed energy according to the adjustable capacity curve of each of the distributed energy, and to determine an adjustable capacity curve of the target aggregation unit through a target function and a constraint condition, to determine other aggregation units except the target aggregation unit under a target section, to obtain an adjustable capacity curve of each of the aggregation units, to input the adjustable capacity curve of each of the aggregation units, an aggregation unit adjustable capacity constraint condition, and a section power flow constraint condition into a mathematical optimization solving tool, and to obtain an adjustable capacity curve corresponding to the target section;

[0071] an adjustable capacity evaluation module configured to obtain an adjustable capacity evaluation result of each of the distributed energy, an adjustable capacity evaluation result of the target aggregation unit, and an adjustable capacity evaluation result of the target section according to a preset adjustable capacity evaluation index, wherein the preset adjustable capacity evaluation index includes a regulation amplitude, a response time, a regulation rate, a regulation duration, and a regulation accuracy.

[0072] In a third aspect, the present application also 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 method in the first aspect when executing the computer program.

[0073] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method in the first aspect when executed by a processor.

[0074] In a fifth aspect, the present application also provides a computer program product comprising a computer program, and the computer program implements the steps of the method in the first aspect when executed by a processor.

[0075] The above method, device, computer device, computer readable storage medium and computer program product for evaluating adjustable capacity of distributed energy providing power auxiliary services, by obtaining distributed energy data sent by a target aggregation unit; the energy data at least includes one of energy storage data, electric vehicle data and air conditioner load data; then inputting each distributed energy data into a pre-established operation characteristic analysis model for different distributed energy to obtain adjustable capacity curves of each distributed energy; then aggregating each distributed energy according to the adjustable capacity curves of each distributed energy, and determining the adjustable capacity curve of the target aggregation unit through a target function and a constraint condition; then determining other aggregation units except the target aggregation unit under a target section, and obtaining the adjustable capacity curves of the other aggregation units; then inputting the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit and the section power flow constraint condition into a mathematical optimization solving tool to obtain the adjustable capacity curve corresponding to the target section; finally, obtaining the adjustable capacity evaluation results of each distributed energy, the adjustable capacity evaluation results of the target aggregation unit and the adjustable capacity evaluation results of the target section according to preset adjustable capacity evaluation indexes; the preset adjustable capacity evaluation indexes include adjustment range, response time, adjustment rate, adjustment duration and adjustment accuracy; the present application obtains the adjustable capacity curves of each distributed energy through the data of each distributed energy and the corresponding operation characteristic analysis model, and further aggregates to obtain the adjustable capacity curve of the target aggregation unit, so that the adjustable capacity curves and the adjustable capacity evaluation results of the distributed energy and the target aggregation unit can be accurately obtained; then inputting the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit and the section power flow constraint condition into the mathematical optimization solving tool, so that the influence of the adjustment capacity of the aggregation units in the same section on each other is fully considered, and the accurate adjustable capacity curve and the adjustable capacity evaluation results of the target section can be obtained. In summary, the scheme provided by the present application can solve the problem of reliable adjustment capacity of massive distributed energy after hierarchical and partitioned dynamic aggregation. BRIEF DESCRIPTION OF DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other related drawings can be obtained without creative labor.

[0077] Figure 1 An application environment diagram of the adjustable capacity evaluation method for the distributed energy to provide power auxiliary services in an embodiment;

[0078] Figure 2 A flowchart of the adjustable capacity evaluation method for the distributed energy to provide power auxiliary services in an embodiment;

[0079] Figure 3 A schematic diagram of the adjustable capacity curve of the distributed energy in an embodiment;

[0080] Figure 4 A schematic diagram of the adjustable capacity curve of the aggregation unit in an embodiment;

[0081] Figure 5 A flowchart of the adjustable capacity evaluation method for the distributed energy to provide power auxiliary services in another embodiment;

[0082] Figure 6 A flowchart of the adjustable electric vehicle selection method in the adjustable capacity evaluation process for the distributed energy to provide power auxiliary services in an embodiment;

[0083] Figure 7 A flowchart of the adjustable capacity evaluation process for the distributed energy to provide power auxiliary services in another embodiment;

[0084] Figure 8 A structural block diagram of the adjustable capacity evaluation device for the distributed energy to provide power auxiliary services in an embodiment;

[0085] Figure 9 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0086] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0087] The adjustable capacity evaluation method for the distributed energy to provide power auxiliary services provided by the embodiments of the present application can be applied to, for example Figure 1The application environment shown. The power system 102 can have multiple aggregation units providing power ancillary services, i.e. aggregation unit 1 to aggregation unit n, each of which can be provided by a different source load aggregator, and the power system 102 has both a communication connection and a power transmission line connection with each aggregation unit, so that the power system can obtain the power ancillary services of the aggregation unit and interact with the aggregation unit to realize power transmission. Here, aggregation unit 104 (aggregation unit 1) is taken as an example to illustrate the target aggregation unit, and the power system 102 obtains the distributed energy data sent by the target aggregation unit; the distributed energy data at least includes one of the energy storage data, the electric vehicle data and the air conditioner load data; the power system 102 inputs each distributed energy data into the operation characteristic analysis model pre-established for different distributed energy, to obtain the adjustable capacity curve of each distributed energy; the power system 102 aggregates each distributed energy according to the adjustable capacity curve of each distributed energy, and determines the adjustable capacity curve of the target aggregation unit through the objective function and the constraint condition; the power system 102 determines other aggregation units except the target aggregation unit under the target section, and obtains the adjustable capacity curve of the other aggregation units; the power system 102 inputs the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit and the section power flow constraint condition into the mathematical optimization solving tool, to obtain the adjustable capacity curve corresponding to the target section; the power system 102 obtains the adjustable capacity evaluation result of each distributed energy, the adjustable capacity evaluation result of the target aggregation unit and the adjustable capacity evaluation result of the target section according to the preset adjustable capacity evaluation index; the preset adjustable capacity evaluation index includes the adjustment range, the response time, the adjustment rate, the adjustment duration and the adjustment accuracy. The aggregation unit can include various distributed energy, such as energy storage, electric vehicles, air conditioners, distributed photovoltaic and wind power, etc.

[0088] Currently, distributed energy mainly participates in the day-ahead invitation of the demand side response market, and there are few actual applications of participating in real-time control of the power grid. The source load aggregator (aggregation unit) is different from the traditional generator unit, and its external characteristics are the representation of the dynamic performance of the massive distributed energy aggregation and cooperation, which needs to rely on high informationization to solve the complementarity and cooperation problems between individual units with strong uncertainty, and is a clean, low-carbon, safe and efficient light asset solution. However, the physical nodes of large-scale distributed energy are scattered, information acquisition is difficult, and there are many benefit subjects, and its participation in power system operation also needs to solve many technical challenges. At the same time, when participating in the market, the aggregation unit may have inaccurate adjustable capacity for greater resource income. Therefore, there is an urgent need for an adjustable capacity evaluation method for distributed energy providing power ancillary services that can solve the hierarchical and partitioned dynamic aggregation of massive distributed energy and the credibility of the adjustable capacity.

[0089] In one exemplary embodiment, as shown in Figure 2 a method for evaluating adjustable capacity of distributed energy providing power auxiliary services is provided. The method is applied to the power system 102 in Figure 1 for example, and includes the following steps S202 to S212. Among them:

[0090] Step S202, obtaining distributed energy data sent by a target aggregation unit.

[0091] Among them, the distributed energy data at least includes one of energy storage data, electric vehicle data and air conditioning load data.

[0092] Among them, the aggregation unit can refer to a collection of one or more distributed energy combined by a source-load aggregator.

[0093] Exemplarily, the power system obtains distributed energy data sent by the target aggregation unit, for example, obtains the state of the energy storage system, the charging / discharging condition of the electric vehicle, and the operating condition information of the air conditioning load.

[0094] Step S204, inputting each distributed energy data into a pre-established operating characteristic analysis model for different distributed energy to obtain the adjustable capacity curve of each distributed energy.

[0095] Among them, the operating characteristic analysis model can be pre-established for different distributed energy, for example, the energy storage operating characteristic analysis model can be pre-established for energy storage, the single electric vehicle operating characteristic analysis model can be pre-established for electric vehicle, and the air conditioning load cluster operating characteristic analysis model can be pre-established for air conditioning. The operating characteristic analysis model pre-established for different distributed energy is established based on the working principle and performance characteristics of different types of distributed energy. Through the operating characteristic analysis model, the adjustable capacity curve of the corresponding distributed energy can be obtained, for example, the maximum capacity adjustment curve or the maximum power adjustment curve.

[0096] Exemplarily, the distributed energy data includes energy storage data, electric vehicle data and air conditioning load data. The power system inputs the energy storage data into the energy storage operating characteristic analysis model to obtain the adjustable capacity curve of the energy storage. The power system inputs the electric vehicle data into the single electric vehicle operating characteristic analysis model to obtain the adjustable capacity curve of the electric vehicle. The power system inputs the air conditioning load data into the air conditioning load cluster operating characteristic analysis model to obtain the adjustable capacity curve of the air conditioning.

[0097] Step S206, aggregating each distributed energy according to the adjustable capacity curve of each distributed energy, and determining the adjustable capacity curve of the target aggregation unit through the objective function and the constraint condition.

[0098] The objective function can be a mathematical expression used to quantify and measure the adjustable capacity, and can be used to define a goal or objective that is desired to be achieved through the aggregation optimization process. For example, the objective function can be a maximum adjustment capacity, a maximum duration, a minimum response time, a maximum adjustment rate, etc.

[0099] The constraint condition can be a physical limit of each distributed energy source and a safety requirement, etc.

[0100] For example, the power system obtains the optimal distributed energy source capacity combination mode through the objective function and the constraint condition according to the adjustable capacity curve of each distributed energy source, so that the entire aggregation unit as a whole has the highest adjustability defined by the objective function.

[0101] In step S208, the adjustable capacity curve of each aggregation unit other than the target aggregation unit under the target section is determined.

[0102] The section can be a specific area or a group of transmission lines in the power grid. The target section in the embodiment can be a group of aggregation units connected to the power grid through the same line, forming a line containing a plurality of aggregation units as shown in FIG. 2. Figure 1

[0103] Specifically, the power system determines the target section where the target aggregation unit is located, and determines the other aggregation units under the target section, and obtains the adjustable capacity curve of the other aggregation units from the other aggregation units.

[0104] In step S210, the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit, and the section flow constraint condition are input into a mathematical optimization solver to obtain the adjustable capacity curve corresponding to the target section.

[0105] The mathematical optimization solver can be an optimization solver used to solve mathematical optimization problems, such as Gurobi / Cplex / GAMS, etc.

[0106] The adjustable capacity constraint condition of the aggregation unit can be a line constraint of the connection line of each aggregation unit in the target section, such as a tie line constraint condition. The adjustable capacity constraint condition of the aggregation unit can also be a constraint condition of the output of each aggregation unit in the target section, such as the minimum output and the maximum output of different aggregation units.

[0107] The section flow constraint condition can be a minimum section flow constraint and a maximum section flow constraint.

[0108] ​Exemplarily, the power system inputs the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit, and the section flow constraint condition into the determined mathematical optimization solving tool to obtain the adjustable capacity curve corresponding to the target section.

[0109] In step S212, the adjustable capacity evaluation results of each distributed energy, the adjustable capacity evaluation result of the target aggregation unit, and the adjustable capacity evaluation result of the target section are obtained according to the preset adjustable capacity evaluation index.

[0110] The preset adjustable capacity evaluation index includes adjustment amplitude, response time, adjustment rate, adjustment duration, and adjustment accuracy.

[0111] In an exemplary embodiment, after the power system inputs the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit, and the section flow constraint condition into the determined mathematical optimization solving tool to obtain the adjustable capacity curve corresponding to the target section, that is, the influence of the adjustment capacity of the aggregation unit in the same section on each other is considered, the total adjustable capacity curve of the section is obtained, and then the adjustable capacity curve of the target aggregation unit obtained before can be corrected through the influence of the adjustment capacity of the aggregation unit in the same section on each other; further, the target aggregation unit after the correction of the adjustable capacity curve is evaluated according to the preset adjustable capacity evaluation index to obtain the final adjustable capacity evaluation result of the target aggregation unit; in this way, the influence of the aggregation units on each other is considered, and the influence of the section constraint on the aggregation unit is considered, so that the adjustable capacity evaluation result of the target aggregation unit is more accurate, and the adjustable capacity evaluation result of the section is also more accurate.

[0112] Specifically, the adjustable capacity evaluation result of each distributed energy can be obtained according to the preset adjustable capacity evaluation index. The response capacity of the distributed energy mainly reflects the level, speed, and sustainable time of the output / absorption power, as shown in Figure 3 FIG. 4 is a schematic diagram of the adjustable capacity evaluation index of the distributed energy. Taking a process of receiving a superior dispatching instruction as an example, the instruction is issued at t ss , and the resource output is P init , and starts to decrease according to the dispatching instruction. At t ds , the power reaches the minimum value P min . At t de , the power starts to rise. The adjustable capacity evaluation index of the distributed energy includes:

[0113] Adjustment amplitude: maximum up-regulation capacity Pumax, which refers to the maximum deviation value that can be reached relative to the current operating point after up-regulation. Maximum down-regulation capacity Pdmax, which refers to the maximum deviation value that can be reached relative to the current operating point after down-regulation, as shown in Figure 3Medium D.

[0114] Response time: Up-regulation response time Tur refers to the difference between the time when the power generation unit starts to respond to the up-regulation instruction and the time when the up-regulation instruction is issued. Down-regulation response time Tdr refers to the difference between the time when the power generation unit starts to respond to the down-regulation instruction and the time when the down-regulation instruction is issued.

[0115] Adjustment duration: Up-regulation duration Tud refers to the longest time that the power generation unit can maintain the state after fully meeting the up-regulation requirement. Down-regulation duration Tdd refers to the longest time that the power generation unit can maintain the state after fully meeting the down-regulation requirement, such as Figure 3 Medium T D .

[0116] Adjustment rate: Maximum up-regulation rate Rumax refers to the amount of change in the operating point per unit time during the up-regulation process. Maximum down-regulation rate Rdmax refers to the amount of change in the operating point per unit time during the down-regulation process, such as Figure 3 Medium A / T R .

[0117] Adjustment accuracy: Adjustment accuracy Pacur refers to the difference between the final stable output of the distributed energy after adjustment and the target value.

[0118] Specifically, the operating characteristics of the aggregation unit can refer to the characteristics of the traditional unit, including: rated active power, minimum stable output, maximum technical output, active regulation rate, power frequency characteristic, continuous start-stop time, start-stop curve, etc. In combination with the aforementioned adjustable capacity evaluation index of the distributed energy, the adjustable capacity evaluation index of the aggregation unit can be obtained by referring to the characteristics of the traditional unit. Specifically, the process of the aggregation unit responding to the regulation instruction can be as shown in Figure 4 meas , where P is the actual output. At T1, the output of the aggregation unit is P1, at this time the regulation instruction is issued, and the aggregation unit adjusts the output according to the instruction direction. After a certain response time, at T2, the output of the aggregation unit reaches P2, and the aggregation unit output first exceeds the action dead zone and is maintained, which is considered as the aggregation unit starting to effectively respond to the regulation instruction. After a period of climbing, the output reaches the maximum value P3 at T3 and starts to decline or maintain, and the climbing ends. The output of the aggregation unit can maintain a high level for a period of time until the power is reduced due to physical constraints of the distributed energy, or a new regulation instruction is received. Let T scale be the time scale of adjustable capacity evaluation, which starts from T2, and T4 = T2 + T scale . Take the average output P4 of the aggregation unit between T3 and T4 as the maximum output after regulation of the aggregation unit. Referring to Figure 4 and in combination with the aforementioned adjustable capacity evaluation index of the distributed energy, the calculation method of the adjustable capacity evaluation index of the aggregation unit can be obtained, which can be as follows:​

[0119] Regulation amplitude: the maximum up-regulation capacity Pumax can be calculated by abs(P4-P1); the maximum down-regulation capacity Pdmax can be calculated according to the same calculation method of the maximum up-regulation capacity in the specific down-regulation process.

[0120] Response time: the up-regulation response time Tur is T2-T1; the down-regulation response time Tdr can be calculated according to the same calculation method of the up-regulation response time in the specific down-regulation process.

[0121] Regulation rate: the maximum up-regulation rate Rumax is The maximum down-regulation rate Rdmax can be calculated according to the same calculation method of the maximum up-regulation rate in the specific down-regulation process.

[0122] Regulation duration: the up-regulation duration Tud is T scale ; the down-regulation duration Tdd can be calculated according to the same calculation method of the up-regulation duration in the specific down-regulation process.

[0123] Regulation accuracy: the formula of the regulation accuracy Pacur is as follows:

[0124]

[0125] It should be further supplemented that T scale is the time scale of adjustable capacity evaluation, because the distributed energy is limited by physical conditions, its regulation capacity is different from that of traditional thermal power units, and the regulation capacity of distributed energy cannot be maintained for a long time; for example, distributed energy such as energy storage and electric vehicles is limited by factors such as battery charge and battery capacity, if it is adjusted according to its maximum power, the time scale is too long, which may lead to full or empty of the power, and the subsequent regulation capacity is 0. Therefore, in order to ensure the output consistency of the energy storage / electric vehicle in the research period, it is necessary to reduce its adjustable capacity according to different time scales, that is, to determine the calculation time scale T scaleThe time scale can be 15 minutes, 30 minutes, 45 minutes, 1 hour, etc. Based on the evaluation indexes of the adjustable capacity of the distributed energy and the aggregation unit constructed in the above steps, the adjustable capacity evaluation indexes are calculated at different time scales in combination with the different time scales proposed, and the multi-time scale adjustable capacity evaluation of the distributed energy and the aggregation unit is realized. The above evaluation indexes are used to describe the adjustable capacity of the distributed energy or the aggregation unit, the indexes can be calculated at different time scales to obtain the adjustable capacity evaluation results at the multi-time scale, the real-time adjustable capacity of the distributed energy and the aggregation unit can be calculated, the adjustable capacity calculation model can be updated in real time based on the measurement data, the adjustable capacity results of different aggregation units can be calculated for different regulation scenarios, and the cross-section adjustable capacity evaluation results can be obtained by considering the influence between the aggregation units and the cross-section constraints. The evaluation indexes of the cross-section adjustable capacity can be set by referring to the adjustable capacity evaluation indexes of the aggregation unit.

[0126] In the adjustable capacity evaluation method of the distributed energy providing power auxiliary services, the adjustable capacity curves of the distributed energy and the target aggregation unit can be accurately obtained by obtaining the adjustable capacity curves of the distributed energy through the data of each distributed energy and the corresponding operation characteristic analysis model, and further aggregating the adjustable capacity curves of the target aggregation unit. The adjustable capacity curves and the adjustable capacity evaluation results of the distributed energy and the target aggregation unit can be accurately obtained. The adjustable capacity curves of each aggregation unit under the target cross-section, the adjustable capacity constraints of the aggregation unit, and the cross-section power flow constraints are input into a mathematical optimization solving tool, the influence between the adjustable capacities of the aggregation units in the same cross-section is fully considered, and the accurate adjustable capacity curves and the adjustable capacity evaluation results of the target cross-section can be obtained. In combination with the above features, the scheme provided by the application can solve the problem of reliable regulation capacity of the hierarchical and partitioned dynamic aggregation of a large number of distributed energies.

[0127] In one exemplary embodiment, the pre-established operation characteristic analysis model for different distributed energies includes an energy storage operation characteristic analysis model, a single electric vehicle operation characteristic analysis model, and an air conditioner load cluster operation characteristic analysis model. Figure 5 As shown in FIG. 6, the adjustable capacity evaluation method of the distributed energy providing power auxiliary services further includes steps S302 to S308.

[0128] Among them:

[0129] In step S302, the historical data and real-time data of each distributed energy are obtained.

[0130] The historical data can refer to operation data of the distributed energy in a certain period of time in the past, and the real-time data can refer to operation data in a current period of time. The specific period of time is set according to actual needs, and is not limited herein. The energy storage operation characteristic analysis model can be used to describe the operation characteristic of the energy storage system. The single electric vehicle operation characteristic analysis model can be used to describe the charging and discharging characteristics of a single electric vehicle. The air conditioner load cluster operation characteristic analysis model can be used to describe the load characteristics of a group of air conditioner systems when they are operated together.

[0131] In step S304, the effective capacity, charging efficiency and discharging efficiency parameters of the energy storage are estimated by using the least square method according to the historical data and real-time data corresponding to the energy storage, and the energy storage operation characteristic analysis model is parameter-modified according to the estimated parameters.

[0132] Specifically, the power system acquires the historical data and real-time data corresponding to the energy storage, estimates the key parameters (effective capacity, charging efficiency and discharging efficiency) of the energy storage system by using the least square method, and parameter-modifies the energy storage operation characteristic analysis model based on the estimated parameters, so that the model can more accurately reflect the actual operation of the electric vehicle.

[0133] In step S306, the effective battery capacity, charging efficiency and discharging efficiency parameters of the electric vehicle cluster are estimated by using the least square method according to the historical data and real-time data corresponding to the electric vehicle cluster, and the single electric vehicle operation characteristic analysis model is parameter-modified according to the estimated parameters.

[0134] Specifically, the power system acquires the historical data and real-time data corresponding to the electric vehicle cluster, estimates the effective battery capacity, charging efficiency and discharging efficiency of the electric vehicle cluster by using the least square method, and parameter-modifies the single electric vehicle operation characteristic analysis model based on the estimated parameters, so that the model can more accurately reflect the actual operation of the electric vehicle.

[0135] In step S308, the average building thermal resistance, average building thermal capacity and average thermal efficiency parameters of the air conditioner load cluster are estimated by using the particle swarm optimization algorithm according to the historical data and real-time data corresponding to the air conditioner load cluster, and the air conditioner load cluster operation characteristic analysis model is parameter-modified according to the estimated parameters.

[0136] Specifically, the power system acquires the historical data and real-time data corresponding to the air conditioner load cluster, estimates the average building thermal resistance, average building thermal capacity and average thermal efficiency of the air conditioner load cluster by using the particle swarm optimization algorithm, and parameter-modifies the air conditioner load cluster operation characteristic analysis model based on the estimated parameters. The particle swarm optimization algorithm is an optimization technology based on swarm intelligence, and can efficiently perform optimization.

[0137] Exemplarily, for energy storage and electric vehicles, numerical analysis method is adopted to identify and correct the parameters of energy storage and electric vehicles. For a general linear regression model, when there are multiple independent variables, the model can be written as:

[0138] y = β0+ β1x1+ β2x2+ … + βp+1xp+1 p x p

[0139] The corresponding matrix form is: y = Xβ + ∈. Wherein, y is an n x 1 observation value vector, X is an n x (p + 1) design matrix, the first column is 1 (corresponding to the intercept term), and the remaining columns are the values of the independent variables. β is a (p + 1) x 1 parameter vector. ∈ is an n x 1 error vector.

[0140] The matrix solution of the least square method is:

[0141]

[0142] For energy storage and electric vehicles, the adjustable capacity calculation formula is relatively simple, which can be approximated as a linear relationship, and the numerical analysis method can be used to correct the model parameters.

[0143] For air conditioning load, intelligent optimization algorithm is adopted to identify and correct the parameters of air conditioning load, and mean squared error (MSE) is used to measure the difference between the predicted value and the actual value of the air conditioning load cluster operation characteristic analysis model. The calculation method is:

[0144]

[0145] Wherein, y i is the i th observation value, is the i th predicted value, and n represents the total number of observation values. Genetic algorithm (GA), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA) and other optimization algorithms can be used to solve the model parameters that make the MSE minimum.

[0146] In this embodiment, the corresponding operation characteristic analysis model is updated and corrected in real time through historical data and real-time data, which can improve the calculation accuracy of the operation characteristic analysis model and improve the prediction accuracy of the distributed energy adjustable capacity curve.

[0147] In an exemplary embodiment, the pre-established operation characteristic analysis model includes an energy storage operation characteristic analysis model; the energy storage operation characteristic analysis model is as follows:

[0148] E b (t+1) = E b (t) - P b (t) η b Δt

[0149] wherein, E b (t) is the power of the energy storage battery at time t, positive for discharging power; η b (t) is the power of the energy storage battery at time t, positive for discharging power; η b is the charging and discharging efficiency; and Δt is the preset research period length.

[0150] According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation results of each distributed energy are obtained, including the following steps: wherein, the constraint condition for the energy storage is:

[0151] E ESP,min ≤E ESP (t)≤E ESP,max

[0152]

[0153] wherein, E ESP,max , E ESP,min are the upper and lower limits of the energy storage system storage energy respectively; are the upper limits of the charging and discharging power respectively.

[0154] The maximum upward adjustment range of the energy storage is obtained

[0155]

[0156] wherein, T is the starting time of the research period, and Δt is the preset research period length. P(t) ESP is the real-time power of the energy storage, is the maximum charging power of the energy storage;

[0157] The maximum downward adjustment range of the energy storage is obtained

[0158]

[0159] wherein, is the maximum discharging power of the energy storage;

[0160] The adjustment duration of the energy storage is obtained

[0161]

[0162] wherein, E ESP,max is the maximum charge capacity of the energy storage, E ESP,min is the minimum charge capacity of the energy storage, E ESP (t) is the real-time charge capacity of the energy storage, P′ ESP (t) is the power of the energy storage after adjustment, greater than 0 for discharging and less than 0 for charging; η charge is the charging efficiency of the energy storage; and ηdischarge for energy storage discharge efficiency.

[0163] In an exemplary embodiment, the pre-established operation characteristic analysis model further comprises a single electric vehicle operation characteristic analysis model; the single electric vehicle operation characteristic analysis model is as follows:

[0164]

[0165] wherein S t is the state of charge of the electric vehicle at time t, P t ch is the charging power of the electric vehicle, η ch is the charging efficiency of the electric vehicle, Δt ch is the charging time period, P t dch is the discharging power of the electric vehicle, η dch is the discharging efficiency of the electric vehicle, Δt dch is the discharging time period, E max is the maximum charge capacity of the electric vehicle after being fully charged;

[0166] According to the preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy is obtained, including the following steps:

[0167] Upon receiving an upward adjustment instruction, the electric vehicles in the electric vehicle cluster whose SOC is greater than the upper limit charge threshold do not perform adjustment; upon receiving a downward adjustment instruction, the electric vehicles in the electric vehicle cluster whose SOC is lower than the lower limit charge threshold do not perform adjustment; the adjustable electric vehicle selection process is as shown in Figure 6 , vehicle state analysis of the electric vehicles in the electric vehicle cluster is started, which is divided into a charging state, a waiting state and a travel state; the electric vehicle in the charging state is judged whether its SOC (state of charge) is greater than SOCu (lower limit charge threshold); if less, the electric vehicle can be adjusted upward and downward; if greater, it is further judged whether its SOC is less than SOCd (upper limit charge threshold); if less, it can be adjusted upward; if greater than SOCd, it can be adjusted upward and downward; the electric vehicle in the waiting state is judged whether its SOC is less than SOCd; if less, it cannot be adjusted; if greater than SOCd, it can be adjusted upward and downward; the electric vehicle in the travel state cannot be adjusted; the above single electric vehicle is judged and single EV (electric vehicle) adjustable capacity calculation is performed; then it is judged whether all EVs are calculated; if not, the step of vehicle state analysis is returned to continue; if calculated, electric vehicle cluster comprehensive adjustable capacity calculation is performed; through the above steps as shown in Figure 6 , the electric vehicles that can participate in regulation and control can be selected, and the adjusted upper limit power and the adjusted lower limit power are calculated.

[0168] Obtaining maximum upward adjustment range of electric vehicle cluster

[0169]

[0170] wherein, T is a starting time of a research time period, Δt is a preset research time period length; P(t) EV is a real-time power of the electric vehicle cluster, is a maximum charging power of the electric vehicle cluster;

[0171] Obtaining maximum downward adjustment range of electric vehicle cluster

[0172]

[0173] wherein, is a maximum discharging power of the electric vehicle cluster;

[0174] Obtaining adjustment duration of electric vehicle cluster

[0175]

[0176] wherein, E EV,max is a maximum state of charge of the electric vehicle cluster, E EV,min is a minimum state of charge of the electric vehicle cluster, E EV (t) is a real-time state of charge of the electric vehicle cluster, P E ' V (t) is a power of the electric vehicle cluster after adjustment, greater than 0 is discharging, less than 0 is charging; η charge is a charging efficiency of the electric vehicle; η discharge is a discharging efficiency of the electric vehicle.

[0177] In an exemplary embodiment, the pre-established operation characteristic analysis model further comprises an air conditioner load cluster operation characteristic analysis model; the air conditioner load cluster operation characteristic analysis model is as follows:

[0178]

[0179] wherein, N is the number of air conditioners, η is an equivalent air conditioner energy efficiency ratio, δ is a house equivalent thermal resistance, is an average outdoor temperature, is an average air conditioner setting temperature, k is a constant;

[0180] Specifically, an equivalent thermal parameter (ETP) modeling method based on circuit simulation is used to model a thermodynamic model of a building to which the air conditioner belongs, and a first-order thermodynamic equivalent thermal parameter model is as follows:

[0181]

[0182] wherein C a is the equivalent heat capacity, Q is the refrigeration capacity, T o is the outside temperature, T i is the indoor air temperature, R a is the equivalent thermal resistance.

[0183] Electric-thermal conversion model: The electric-thermal conversion between the electric power of the air conditioner and the refrigeration (heating) capacity conforms to the following form: Q = P · COP, wherein Q is the refrigeration capacity, P is the air conditioner power, and COP is the air conditioner energy efficiency ratio / thermal efficiency.

[0184] Substituting the electric-thermal conversion model of the air conditioner into the thermodynamic model and discretizing, the recursive formula of the indoor temperature and the air conditioner power is obtained as follows:

[0185]

[0186] wherein T in,t+1 is the indoor temperature at t + 1; T in,t is the indoor temperature at t; T out,t is the outdoor temperature at t; P is the average power of the air conditioner between t and t + 1; R is the indoor equivalent thermal resistance; C is the indoor equivalent heat capacity; COP is the air conditioner energy efficiency ratio; Q is the refrigeration capacity; and △t is the time interval.

[0187] For a fixed-frequency air conditioner, the energy efficiency ratio COP is a fixed value. For a variable-frequency air conditioner, the energy efficiency ratio is not constant. The relationship of the air conditioner power, the refrigeration capacity, and the energy efficiency ratio with respect to the working frequency of the air conditioner compressor is as follows:

[0188]

[0189] wherein a, b, k, m, and n are coefficients. Eliminating the frequency, the relationship of the energy efficiency ratio and the power is as follows:

[0190]

[0191] wherein g1, g2, and g3 are coefficients.

[0192] Air conditioner operation characteristics: For a fixed-frequency air conditioner, the energy efficiency ratio is fixed, and the refrigeration or heating capacity is mainly determined by the working state of the air conditioner. A single fixed-frequency air conditioner has only two running states of opening and closing in the running process, i.e., the power is the rated power and 0, and cannot be linearly adjusted, but the linear adjustment of the average power of the air conditioner in a running cycle can be realized by changing the time of the opening state and the closing state in the running cycle.

[0193] The working state of the fixed frequency air conditioner load is related to the indoor temperature. Taking the refrigeration state of the air conditioner load as an example, when the indoor temperature is higher than the maximum value of the temperature setting value, the air conditioner is turned on and performs refrigeration work; when the indoor temperature is lower than the minimum value of the temperature setting value, the air conditioner is turned off and stops refrigeration; when the indoor temperature value is between the upper and lower limits of the temperature setting value, the air conditioner continues to maintain the working state of the previous moment unchanged. The working state S AC The working state S

[0194]

[0195] In the formula, S i AC (t), S i AC (t+1) are the working states of the i-th air conditioner at time t and time t+1 respectively, 0 represents that the air conditioner stops refrigeration, 1 represents that the air conditioner performs refrigeration and is in the working state; T i AC (t) is the indoor temperature at time t; T i ACmax , and T i ACmin are the upper and lower limits of the temperature setting value of the i-th air conditioner load respectively.

[0196] For the variable frequency air conditioner, the frequency of the compressor changes with the difference between the indoor temperature and the setting temperature during operation. The air conditioner power has a linear relationship with the frequency of the compressor, so the variable frequency air conditioner power can be considered as the maximum power when the temperature difference is too large, the minimum power when the temperature difference is too small, and the air conditioner power changes according to the size of the temperature difference on the basis of the current power when the temperature difference is within the allowable deviation range, that is:

[0197]

[0198] Where P(t) is the air conditioner power at time t, T in is the indoor temperature, T set is the air conditioner setting temperature, and ΔT max is the maximum temperature deviation allowed by the air conditioner.

[0199] Air conditioner cluster model: fixed frequency air conditioner load aggregation model: for the fixed frequency air conditioner, the probability p on of a single air conditioner load being in the on state is related to the on time t on and the off time t off of the air conditioner load:

[0200]

[0201] Each air conditioner load operates independently, and the aggregated real-time power of the air conditioner load can be represented as:

[0202]

[0203] where p on,i is the probability of the i-th air conditioner load being in the on state, and the value is between 0 and 1.

[0204]

[0205] where T ACset is the temperature set value of the air conditioner load; and δ AC is the temperature dead zone of the air conditioner load.

[0206] The average value of the aggregated power of the fixed-frequency air conditioner is:

[0207]

[0208] The aggregated model of the variable-frequency air conditioner load is:

[0209]

[0210] where P B ACagg (t) is the aggregated power of the variable-frequency air conditioner load at time t, N2 is the number of fixed-frequency air conditioners, P ACi is the average power consumption of the i-th air conditioner. Referring to the aggregated power of the fixed-frequency air conditioner and considering that the variable-frequency air conditioner has a minimum power, the average value of the aggregated power of the variable-frequency air conditioner is:

[0211]

[0212] The comprehensive aggregated model of the air conditioner load is:

[0213]

[0214] denoted as:

[0215]

[0216] where k1, k2, and k3 are coefficients.

[0217] Considering the differences between the fixed-frequency and variable-frequency air conditioners, the total power of the air conditioner cluster is denoted as:

[0218]

[0219] where N is the number of air conditioners, η is the energy efficiency ratio of the air conditioner, δ is the equivalent thermal resistance of the user room, Tin is the indoor temperature, Tout is the outdoor temperature, k is a constant, and E() is the mathematical expectation. The analytical model of the operating characteristics of the air conditioner load cluster can be obtained through the above expression of the total power of the air conditioner cluster.

[0220] According to the preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy is obtained, including the following steps:

[0221] Obtaining the maximum upward adjustment range of the air conditioner load cluster:

[0222]

[0223] Obtaining the maximum downward adjustment range of the air conditioner load cluster:

[0224]

[0225] Wherein, T Δ is a temperature control margin.

[0226] In an exemplary embodiment, the objective function includes a maximum adjustment capacity objective function and a maximum duration objective function; the constraint conditions include a power balance constraint condition, a tie line constraint condition and a constraint condition of each distributed energy itself; the adjustable capacity curve of the target aggregation unit is determined through the objective function and the constraint condition, including:

[0227] The maximum adjustment capacity objective function or the maximum duration objective function, and the power balance constraint condition, the tie line constraint condition and the constraint condition of each distributed energy itself are input into a mathematical optimization solving tool to obtain the adjustable capacity curve of the target aggregation unit.

[0228] Specifically, the adjustable capacity curve acquisition process of the target aggregation unit can be as follows:

[0229] First, the adjustable capacity objective function is determined, and different objective functions can be set according to different requirements. Let P VPP be the output of the aggregated aggregation unit, which is equal to the sum of the outputs of all distributed energies, and the formula is as follows:

[0230]

[0231] Wherein, m is the total number of distributed energies.

[0232] The objective function for the maximum adjustment capacity is:

[0233] max P VPP

[0234] If there is a requirement for the duration, the limit condition also needs to be met:

[0235] s.t.T d ≥ T r

[0236] Wherein, T r is the required duration.

[0237] The objective function for the maximum duration is:

[0238] max T d

[0239] wherein, is the sustainable duration of the i-th distributed energy.

[0240] If there is a requirement for the regulation capacity at the same time, the limit condition also needs to be met:

[0241]

[0242] wherein, is the required regulation capacity.

[0243] The constraint conditions for the target aggregation unit are:

[0244] 1. Power balance constraint, i.e., the power emitted by the whole network is equal to the power consumed, i.e.:

[0245]

[0246] 2. Tie line constraint, the power on the tie line needs to be maintained within the limit, i.e.:

[0247] p tie,min ≤p tie (t)≤p tie,max t∈ψ

[0248] 3. Distributed energy itself constraint.

[0249] Finally, the above problem is solved by using Gurobi / Cplex / GAMS solver to obtain the adjustable capacity curve of the target aggregation unit. The above steps, by constructing multiple aggregation target functions for different regulation scenarios, for example, the target function for the maximum regulation capacity, the target function for the maximum duration, etc. The aggregation results under different scenarios are obtained, i.e., the adjustable capacity curve of the aggregation unit under different scenarios.

[0250] In an example embodiment, the adjustable capacity evaluation method of the distributed energy providing power auxiliary services further comprises: objectively weighting the adjustable capacity evaluation indexes by using an entropy weight method to obtain objective weights; subjectively weighting the adjustable capacity evaluation indexes by using expert evaluation opinions to obtain subjective weights; determining a comprehensive weight according to the subjective weights and the objective weights, weighting the adjustable capacity evaluation result of the target aggregation unit by using the comprehensive weight to obtain an adjustable capacity evaluation value for the target aggregation unit; obtaining adjustable capacity evaluation values of other aggregation units, sorting the adjustable capacity evaluation value of the target aggregation unit and the adjustable capacity evaluation values of the other aggregation units, and displaying the sorted adjustable capacity evaluation values and the corresponding aggregation units on a power system dispatching interface.

[0251] The subjective weight weighting method is obtained by subjective judgment of experts or decision makers, and has strong interpretability, i.e., the rationality of setting a weight is reasonable, but it has great randomness, and different experts have different opinions. The objective weighting method obtains the weight from the change of the index value itself, and has strong objectivity because it does not involve the subjective consciousness of the implementer, but lacks interpretability and the calculated weight result may be contrary to reality. In order to improve the scientificity and rationality of the index weight, a combined weight weighting method is used for weighting, which combines the subjective weight weighting method and the objective weight weighting method. Specifically, the process of calculating the weight of each evaluation index in the embodiment is as follows:

[0252] 1. Calculate the objective weight

[0253] (1) Data normalization

[0254] The normalization calculation formula of the positive index is:

[0255]

[0256] The normalization calculation formula of the negative index is:

[0257]

[0258] Y ij is the result of the normalized processing of the evaluation value data.

[0259] (2) Calculate the information entropy

[0260]

[0261] In the formula, n is the number of evaluation objects, and j is the index number.

[0262] (3) Determine the weight of each index

[0263] On the basis of calculating the information entropy, the objective weight of each evaluation index is calculated by the following formula:

[0264]

[0265] wherein W o i is the maximum weight of the different evaluation indexes.

[0266] 2. Calculate the subjective weight

[0267] The subjective weight is calculated by using the expert scoring method, in which a few consulting experts determine the weight of each index from multiple angles and considering the actual operating conditions. The basic steps are as follows:

[0268] (1) Select experts. According to the understanding of static security analysis of power system, select experts from power system dispatching and operation departments, develop appropriate and comprehensive evaluation indexes, and explain the concept of each index;

[0269] (2) List. Give the weight value range of each evaluation index, which can be expressed by scoring (0-100);

[0270] (3) Scoring. Give each evaluation expert a list for scoring each index, and each expert member scores the weight of each index on the premise of understanding the index, to obtain the weight score of each evaluation index;

[0271] (4) Collect the scoring table of each expert, add the scores of all indexes given by each expert to obtain the total score, and then divide the score of each index by the total score to calculate the weight of each evaluation index;

[0272] (5) Collect all scoring tables, remove the minimum weight and maximum weight of each index, then add the weights of the remaining indexes, and divide by the number of remaining scoring lists to obtain the maximum weight of each evaluation index;

[0273] (6) List the index weight calculated in step (5) and the index weight calculated in step (4), and compare the index weights calculated in the two steps;

[0274] (7) After comparison, if an expert is not satisfied with the weight determined by himself previously and intends to modify the previous score, repeat steps 4-6, if the expert has no objection to the previous weight, the subjective weight calculation based on the expert scoring method is completed.

[0275] The expert scoring method can determine the attention degree of each index in actual power work by power staff, and this subjective judgment method can highlight the importance of some indexes, and facilitate the adjustment of the weight of each index according to the actual power system operation.

[0276] 3. Calculate the comprehensive weight

[0277] Let w be the weight of the n indicators obtained by objective weighting. o,1 w o,2 , ......, w o,n The weights of the n indicators obtained through subjective weighting are denoted as w. s,1 w s,2 , ......, w s,n The weights after comprehensive weighting are denoted as w1, w2, ..., w n w i =a i w o,i +b i w s,i i=1,2,...,n, where a i b is the weighting factor for objective weights. i For subjective weighting factors, it is necessary to satisfy a i +b i =1. Let the aggregated distributed energy resources be indices z1, z2, ..., z n Therefore, its overall score is:

[0278]

[0279] The power system can obtain the adjustable capacity evaluation value of the target aggregation unit according to the above process, and then obtain the adjustable capacity evaluation values ​​of other aggregation units. The adjustable capacity evaluation values ​​of the target aggregation unit and other aggregation units are sorted, and the sorted adjustable capacity evaluation values ​​and the corresponding aggregation units are displayed on the power system dispatch interface for relevant personnel to refer to.

[0280] In an exemplary embodiment, the method for assessing the adjustability of distributed energy resources to provide power auxiliary services further includes: obtaining the reported adjustable capability of the target aggregation unit; comparing the reported adjustable capability with the adjustable capability curve of the target aggregation unit; if the comparison result exceeds a preset threshold condition, initiating an adjustable capability test for the target aggregation unit; and displaying the comparison result and the test result on the power system dispatch interface.

[0281] Specifically, the power system obtains the reported adjustable capacity of the target aggregation unit, and then compares the reported adjustable capacity with the adjustable capacity curve of the target aggregation unit obtained in the previous steps. If the comparison result exceeds the preset threshold condition, the power system actively tests the target aggregation unit and displays the comparison result and test result on the power system dispatch interface, which can assist the power system in control decision-making.

[0282] In one exemplary embodiment, such as Figure 7As shown, a distributed energy power auxiliary service adjustable capacity evaluation process is provided, which can include: a distributed energy adjustable capacity evaluation module calculates the adjustable capacity curve of the distributed energy according to the real-time data uploaded by the distributed energy, through a distributed energy adjustment model and corresponding adjustable capacity evaluation indexes. The parameter identification module updates the parameters in the model used by the distributed energy adjustable capacity evaluation module based on historical and stored real-time data, wherein the historical data can be stored real-time data or data stored by actively testing the distributed energy. The parameter identification process includes numerical analysis of energy storage and charging piles, and intelligent optimization of air conditioners and ice storage. The aggregation unit adjustable capacity evaluation module calculates the adjustable capacity curve of the aggregation unit in different scenarios based on the adjustable capacity curve of the distributed energy, the constraints of the aggregation unit, the objective functions corresponding to multiple scenarios, and the adjustable capacity evaluation indexes corresponding to the aggregation unit. The section adjustable capacity evaluation module obtains the section adjustable capacity and the final aggregation unit adjustable capacity curve based on the aggregation unit adjustable capacity curve, considering the section constraints and the aggregation unit constraints.

[0283] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0284] Based on the same inventive concept, the embodiments of the present application also provide a distributed energy power auxiliary service adjustable capacity evaluation device for implementing the above-mentioned distributed energy power auxiliary service adjustable capacity evaluation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more distributed energy power auxiliary service adjustable capacity evaluation device embodiments provided below can refer to the limitations of the distributed energy power auxiliary service adjustable capacity evaluation method in the above text, which will not be repeated here.

[0285] In one exemplary embodiment, as Figure 8As shown, the adjustable capacity evaluation device 800 for distributed energy providing power auxiliary service is provided, comprising: an energy data acquisition module 801, an adjustable capacity acquisition module 802 and an adjustable capacity evaluation module 803, wherein:

[0286] The energy data acquisition module 801 is configured to acquire distributed energy data sent by a target aggregation unit; the distributed energy data at least includes one of energy storage data, electric vehicle data and air conditioner load data.

[0287] The adjustable capacity acquisition module 802 is configured to input each distributed energy data into an operation characteristic analysis model pre-established for different distributed energy to obtain an adjustable capacity curve of each distributed energy; aggregate each distributed energy according to the adjustable capacity curve of each distributed energy, and determine an adjustable capacity curve of the target aggregation unit through a target function and a constraint condition; determine other aggregation units except the target aggregation unit under a target section, obtain an adjustable capacity curve of the other aggregation units; input the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint condition of the aggregation unit and the section power flow constraint condition into a mathematical optimization solving tool to obtain an adjustable capacity curve corresponding to the target section.

[0288] The adjustable capacity evaluation module 803 is configured to obtain an adjustable capacity evaluation result of each distributed energy, an adjustable capacity evaluation result of the target aggregation unit and an adjustable capacity evaluation result of the target section according to a preset adjustable capacity evaluation index; the preset adjustable capacity evaluation index includes a regulation amplitude, a response time, a regulation rate, a regulation duration and a regulation accuracy.

[0289] In an exemplary embodiment, the pre-established operation characteristic analysis model for different distributed energy sources includes an energy storage operation characteristic analysis model, a single electric vehicle operation characteristic analysis model, and an air conditioner load cluster operation characteristic analysis model; the adjustable capacity evaluation device of the distributed energy source providing power auxiliary services further includes a model parameter correction module, configured to acquire historical data and real-time data of each distributed energy source; according to the historical data and real-time data corresponding to the energy storage, the effective capacity, charging efficiency, and discharging efficiency parameters of the energy storage are estimated by using the least square method, and the energy storage operation characteristic analysis model is parameter corrected according to the estimated parameters; according to the historical data and real-time data corresponding to the electric vehicle cluster, the effective battery capacity, charging efficiency, and discharging efficiency parameters of the electric vehicle cluster are estimated by using the least square method, and the single electric vehicle operation characteristic analysis model is parameter corrected according to the estimated parameters; according to the historical data and real-time data corresponding to the air conditioner load cluster, the average building thermal resistance, average building thermal capacity, and average thermal efficiency parameters of the air conditioner load cluster are estimated by using the particle swarm optimization algorithm, and the air conditioner load cluster operation characteristic analysis model is parameter corrected according to the estimated parameters.

[0290] In an exemplary embodiment, the pre-established operation characteristic analysis model includes an energy storage operation characteristic analysis model.

[0291] The energy storage operation characteristic analysis model is as follows:

[0292] E b (t+1)=E b (t)-P b (t)η b Δt

[0293] Wherein, E b (t) is the electric energy stored by the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, and the discharging power is positive; η b is the charging and discharging efficiency; and Δt is the preset research period length.

[0294] The adjustable capacity acquisition module 802 is further configured to acquire the maximum upward adjustment range of the energy storage

[0295]

[0296] Wherein, T is the starting time of the research time period, and Δt is the preset research period length. P(t) ESP is the real-time power of the energy storage, is the maximum charging power of the energy storage;

[0297] The maximum downward adjustment range of the energy storage is acquired

[0298]

[0299] wherein, is the maximum discharging power of the energy storage;

[0300] acquiring the energy storage adjustment duration

[0301]

[0302] wherein, E ESP,max is the maximum charge amount of the energy storage, E ESP,min is the minimum charge amount of the energy storage, E ESP (t) is the real-time charge amount of the energy storage, P' ESP (t) is the power of the energy storage after adjustment, greater than 0 is discharging, less than 0 is charging; η charge is the charging efficiency of the energy storage; η discharge is the discharging efficiency of the energy storage.

[0303] In an exemplary embodiment, the pre-established operation characteristic analysis model further comprises a single electric vehicle operation characteristic analysis model;

[0304] The single electric vehicle operation characteristic analysis model is as follows:

[0305]

[0306] wherein, S t is the state of charge of the electric vehicle at time t, P t ch is the charging power of the electric vehicle, η ch is the charging efficiency of the electric vehicle, Δt ch is the charging time period, P t dch is the discharging power of the electric vehicle, η dch is the discharging efficiency of the electric vehicle, Δt dch is the discharging time period, E max is the maximum charge amount of the electric vehicle after full charging;

[0307] The above adjustable capacity acquisition module 802 is further configured to, when receiving an upward adjustment instruction, not adjusting the electric vehicles in the electric vehicle cluster whose SOC is greater than the upper limit charge threshold; and when receiving a downward adjustment instruction, not adjusting the electric vehicles in the electric vehicle cluster whose SOC is lower than the lower limit charge threshold.

[0308] acquiring the maximum upward adjustment range of the electric vehicle cluster

[0309]

[0310] wherein, T is the starting time of the research time period, Δt is the preset research time period length; P(t) EV is the real-time power of the electric vehicle cluster, is the maximum charging power of the electric vehicle cluster;

[0311] obtaining the maximum downward adjustment range of the electric vehicle cluster

[0312]

[0313] wherein, is the maximum discharging power of the electric vehicle cluster;

[0314] obtaining the adjustment duration of the electric vehicle cluster

[0315]

[0316] wherein, E EV,max is the maximum state of charge of the electric vehicle cluster, E EV,min is the minimum state of charge of the electric vehicle cluster, E EV (t) is the real-time state of charge of the electric vehicle cluster, P E V (t) is the power of the electric vehicle cluster after adjustment, greater than 0 is discharging, less than 0 is charging; η charge is the charging efficiency of the electric vehicle; η discharge is the discharging efficiency of the electric vehicle.

[0317] In an exemplary embodiment, the pre-established operation characteristic analysis model further comprises an air conditioning load cluster operation characteristic analysis model;

[0318] The air conditioning load cluster operation characteristic analysis model is as follows:

[0319]

[0320] wherein, N is the number of air conditioners, η is the equivalent air conditioner energy efficiency ratio, δ is the equivalent thermal resistance of the house, is the average outdoor temperature, is the average air conditioning setting temperature, k is a constant;

[0321] The above adjustable capacity obtaining module 802 is also used to obtain the maximum upward adjustment range of the air conditioning load cluster:

[0322]

[0323] obtaining the maximum downward adjustment range of the air conditioning load cluster:

[0324] ​

[0325] wherein T Δ is a temperature control margin.

[0326] In an example embodiment, the objective function includes a maximum adjustment capacity objective function and a maximum duration objective function; the constraint condition includes a power balance constraint condition, a tie-line constraint condition and a constraint condition of each distributed energy source itself; the adjustable capacity obtaining module 802 is further configured to input the maximum adjustment capacity objective function or the maximum duration objective function, and the power balance constraint condition, the tie-line constraint condition and the constraint condition of each distributed energy source itself into a mathematical optimization solving tool to obtain the adjustable capacity curve of the target aggregation unit.

[0327] In an example embodiment, the adjustable capacity evaluation device of the distributed energy source providing power auxiliary services further includes an evaluation value obtaining module configured to objectively weight the adjustable capacity evaluation indexes by using an entropy weight method to obtain objective weights; subjectively weight the adjustable capacity evaluation indexes by using expert evaluation opinions to obtain subjective weights; determine comprehensive weights according to the subjective weights and the objective weights; and weight the adjustable capacity evaluation result of the target aggregation unit by using the comprehensive weights to obtain the adjustable capacity evaluation value of the target aggregation unit; obtain the adjustable capacity evaluation values of other aggregation units; sort the adjustable capacity evaluation values of the target aggregation unit and the other aggregation units; and display the sorted adjustable capacity evaluation values and the corresponding aggregation units on a power system dispatching interface.

[0328] In an example embodiment, the adjustable capacity evaluation device of the distributed energy source providing power auxiliary services further includes a reporting adjustable capacity comparing module configured to obtain the reporting adjustable capacity corresponding to the target aggregation unit reported by the target aggregation unit; compare the reporting adjustable capacity with the adjustable capacity curve of the target aggregation unit; in a case where the comparison result exceeds a preset threshold condition, initiate an adjustable capacity test on the target aggregation unit, and display the comparison result and the test result on the power system dispatching interface.

[0329] Each module in the adjustable capacity evaluation device of the distributed energy source providing power auxiliary services can be realized by software, hardware and a combination thereof in whole or in part. Each module can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0330] In an example embodiment, a computer device is provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a kind of distributed energy to provide power auxiliary service adjustable capacity evaluation method.

[0331] Those skilled in the art can understand that, Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or less components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0332] In one embodiment, a computer device is also provided, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps in each method embodiment described above.

[0333] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to realize the steps in each method embodiment described above.

[0334] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to realize the steps in each method embodiment described above.

[0335] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0336] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0337] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for evaluating adjustable capacity of distributed energy resources providing ancillary services, the method comprising: The method comprises: acquiring distributed energy data sent by a target aggregation unit; the distributed energy data at least comprises one of energy storage data, electric vehicle data and air conditioner load data; inputting each of the distributed energy data into a pre-established operation characteristic analysis model for different distributed energy to obtain an adjustable capacity curve of each of the distributed energy; aggregating each of the distributed energy according to the adjustable capacity curve of each of the distributed energy, and determining an adjustable capacity curve of the target aggregation unit through a target function and a constraint condition; wherein the target function comprises a maximum adjustment capacity target function and a maximum duration target function; the constraint condition comprises a power balance constraint condition, a tie line constraint condition and a constraint condition of each of the distributed energy itself; the determination of the adjustable capacity curve of the target aggregation unit through the target function and the constraint condition comprises inputting the maximum adjustment capacity target function or the maximum duration target function, and the power balance constraint condition, the tie line constraint condition and the constraint condition of each of the distributed energy itself into a mathematical optimization solving tool to obtain the adjustable capacity curve of the target aggregation unit; determining other aggregation units under a target section except the target aggregation unit, and acquiring an adjustable capacity curve of the other aggregation units; inputting the adjustable capacity curve of each of the aggregation units under the target section, an aggregation unit adjustable capacity constraint condition and a section power flow constraint condition into the mathematical optimization solving tool to obtain an adjustable capacity curve corresponding to the target section; obtaining an adjustable capacity evaluation result of each of the distributed energy, an adjustable capacity evaluation result of the target aggregation unit and an adjustable capacity evaluation result of the target section according to a preset adjustable capacity evaluation index; the preset adjustable capacity evaluation index comprises an adjustment range, a response time, an adjustment rate, an adjustment duration and an adjustment accuracy.

2. The method of claim 1, wherein, The pre-established operation characteristic analysis model for different distributed energy comprises an energy storage operation characteristic analysis model, a single electric vehicle operation characteristic analysis model and an air conditioner load cluster operation characteristic analysis model; The method further comprises: acquiring historical data and real-time data of each of the distributed energy; estimating an effective capacity, a charging efficiency and a discharging efficiency parameter of the energy storage by using a least square method according to the historical data and the real-time data corresponding to the energy storage, and performing parameter correction on the energy storage operation characteristic analysis model according to the estimated parameter; estimating an effective battery capacity, a charging efficiency and a discharging efficiency parameter of the electric vehicle cluster by using a least square method according to the historical data and the real-time data corresponding to the electric vehicle cluster, and performing parameter correction on the single electric vehicle operation characteristic analysis model according to the estimated parameter; estimating an average building thermal resistance, an average building thermal capacity and an average thermal efficiency parameter of the air conditioner load cluster by using a particle swarm optimization algorithm according to the historical data and the real-time data corresponding to the air conditioner load cluster, and performing parameter correction on the air conditioner load cluster operation characteristic analysis model according to the estimated parameter.

3. The method of claim 1, wherein, The pre-established operation characteristic analysis model comprises an energy storage operation characteristic analysis model; The energy storage operation characteristic analysis model is as follows: E b (t+1) = E b (t) - P b (t) η b Δt wherein E b (t) is the energy stored in the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, positive for discharging; η b is the charge and discharge efficiency; and Δt is the length of the predetermined study period. According to a preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy is obtained, comprising the following steps: Obtaining maximum upward adjustment range of energy storage Wherein, T is the starting time of the research period, and Δt is the preset research period length; P(t) ESP is the real-time power of the energy storage, is the maximum charging power of the energy storage; Obtaining maximum downward adjustment range of energy storage wherein, Pmax is the maximum discharge power for the stored energy; Acquiring energy storage regulation duration Wherein, E ESP,max is the maximum energy storage charge, E ESP,min is the minimum energy storage charge, E ESP (t) is the real-time energy storage charge, P' ESP (t) is the adjusted energy storage power, greater than 0 is discharging, less than 0 is charging; η charge is the energy storage charging efficiency; η discharge is the energy storage discharging efficiency.

4. The method of claim 1, wherein, The pre-established operation characteristic analysis model further comprises a single electric vehicle operation characteristic analysis model; The single electric vehicle operation characteristic analysis model is as follows: Where S t is the state of charge of the electric vehicle at time t, P t ch is the charging power of the electric vehicle, η ch is the charging efficiency of the electric vehicle, Δt ch is the charging time period, P t dch is the discharging power of the electric vehicle, η dch is the discharging efficiency of the electric vehicle, Δt dch is the discharging time period, E max is the maximum charge capacity of the electric vehicle after being fully charged; According to a preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy is obtained, comprising the following steps: When receiving an upward adjustment instruction, an electric vehicle in the electric vehicle cluster whose SOC is greater than an upper limit charging threshold value does not perform adjustment; when receiving a downward adjustment instruction, an electric vehicle in the electric vehicle cluster whose SOC is lower than a lower limit charging threshold value does not perform adjustment; Obtaining maximum upward adjustment range for electric vehicle cluster Wherein, T is the starting time of the research period, and Δt is the preset research period length; P(t) EV is the real-time power of the electric vehicle cluster, is the maximum charging power of the electric vehicle cluster; Obtaining maximum down-regulation range for electric vehicle fleet wherein, Pmax is the maximum discharge power of the cluster of electric vehicles; Obtaining electric vehicle fleet regulation duration Among them, E EV,max E represents the maximum charge capacity of the electric vehicle cluster. EV,min E represents the minimum charge capacity of the electric vehicle cluster. EV (t) represents the real-time charge of the electric vehicle cluster, E b (t) represents the electrical energy stored in the energy storage system at time t; P' EV (t) represents the adjusted power of the electric vehicle cluster; a value greater than 0 indicates discharging, and a value less than 0 indicates charging; η charge Electric vehicle charging efficiency; η discharge For the discharge efficiency of electric vehicles.

5. The method of claim 1, wherein, The pre-established operation characteristic analysis model further comprises an air conditioner load cluster operation characteristic analysis model; The air conditioner load cluster operation characteristic analysis model is as follows: where N is the number of air conditioners, η is the equivalent air conditioner energy efficiency ratio, δ is the equivalent thermal resistance of the house, is the average outdoor temperature, is the average air conditioner setting temperature, and k is a constant; According to a preset adjustable capacity evaluation index, an adjustable capacity evaluation result of each distributed energy is obtained, comprising the following steps: An air conditioner load cluster maximum upward adjustment range is obtained: An air conditioner load cluster maximum downward adjustment range is obtained: where T Δ is the temperature control margin.

6. The method of claim 1, wherein, The method further comprises: An objective weight is obtained by using an entropy weight method to objectively weight the adjustable capacity evaluation index; A subjective weight is obtained by using expert evaluation opinions to subjectively weight the adjustable capacity evaluation index; A comprehensive weight is determined according to the subjective weight and the objective weight, and the adjustable capacity evaluation result of the target aggregation unit is weighted and processed by using the comprehensive weight to obtain an adjustable capacity evaluation value of the target aggregation unit; An adjustable capacity evaluation value of other aggregation units is obtained, and the adjustable capacity evaluation value of the target aggregation unit and the adjustable capacity evaluation values of the other aggregation units are sorted, and the sorted adjustable capacity evaluation values and the corresponding aggregation units are displayed on a power system dispatching interface.

7. The method of claim 1, wherein, The method further comprises: The target aggregation unit corresponding to the target aggregation unit is obtained by sending the reportable adjustable capacity sent by the target aggregation unit; The reportable adjustable capacity is compared with the adjustable capacity curve of the target aggregation unit, and in the case that the comparison result exceeds a preset threshold condition, the target aggregation unit is initiated for adjustable capacity testing, and the comparison result and the test result are displayed on a power system dispatching interface.

8. An adjustable capability assessment device for distributed energy resources providing ancillary services, characterized by, The device comprises: An energy data acquisition module is configured to acquire distributed energy data sent by a target aggregation unit; the distributed energy data at least comprises one of energy storage data, electric vehicle data and air conditioner load data; An energy data acquisition module is configured to acquire distributed energy data sent by a target aggregation unit; the distributed energy data at least comprises one of energy storage data, electric vehicle data and air conditioner load data; The adjustable capacity obtaining module is configured to input each distributed energy data into an operation characteristic analysis model pre-established for different distributed energies respectively to obtain an adjustable capacity curve of each distributed energy; aggregate each distributed energy according to the adjustable capacity curve of each distributed energy, and determine an adjustable capacity curve of the target aggregation unit through a target function and a constraint condition; determine other aggregation units except the target aggregation unit under a target section, and obtain an adjustable capacity curve of each aggregation unit; input the adjustable capacity curve of each aggregation unit under the target section, an adjustable capacity constraint condition of the aggregation unit, and a section power flow constraint condition into a mathematical optimization solving tool to obtain an adjustable capacity curve corresponding to the target section; the target function includes a maximum adjustment capacity target function and a maximum duration target function; the constraint condition includes a power balance constraint condition, a tie line constraint condition, and a constraint condition of each distributed energy itself; the determination of the adjustable capacity curve of the target aggregation unit through the target function and the constraint condition includes inputting the maximum adjustment capacity target function or the maximum duration target function, the power balance constraint condition, the tie line constraint condition, and the constraint condition of each distributed energy itself into the mathematical optimization solving tool to obtain the adjustable capacity curve of the target aggregation unit; The adjustable capacity evaluation module is configured to obtain an adjustable capacity evaluation result of each distributed energy, an adjustable capacity evaluation result of the target aggregation unit, and an adjustable capacity evaluation result of the target section according to a preset adjustable capacity evaluation index; the preset adjustable capacity evaluation index includes an adjustment range, a response time, an adjustment rate, an adjustment duration, and an adjustment accuracy. 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 implement the steps of the method in any one of claims 1 to 6.

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