Method and device for evaluating adjustable capability of power auxiliary service provided by distributed energy, and computer equipment

Abstract

The invention relates to an adjustable capability evaluation method and device for a distributed energy source to provide an electric power auxiliary service, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring distributed energy data uploaded by a target aggregation unit; inputting the distributed energy data into an operation characteristic analysis model pre-established for different distributed energy sources to obtain an adjustable capability curve of each distributed energy source; determining an adjustable capability curve of the target aggregation unit according to the adjustable capability curve of each distributed energy source; obtaining an adjustable capability curve corresponding to the target section according to the adjustable capability curve of each aggregation unit under the target section; and obtaining an adjustable capability evaluation result of each distributed energy source, an adjustable capability evaluation result of the target aggregation unit and an adjustable capability evaluation result of the target section according to a preset adjustable capability evaluation index. By adopting the method, the accuracy of the adjustable capability of the aggregation unit can be improved, and the credibility of the credible capability is improved.

Description

Technical Field

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

[0002] With the development of the power system, the volatility problem of the power system has become increasingly prominent. In order to further improve the regulation capacity of the power system, third-party energy aggregators as independent energy entities have emerged to provide power auxiliary services to the power system.

[0003] At present, aggregators provide power auxiliary services through the aggregation and collaboration of massive distributed energy resources. However, the physical nodes of massive distributed energy resources are scattered, and the aggregation and adjustable capabilities of massive distributed energy resources sent to the power system by aggregators may be inaccurate. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for evaluating the adjustable capacity of distributed energy to provide power ancillary services, which can accurately evaluate the adjustable capacity of distributed energy to provide power ancillary services, in response to the above-mentioned technical problems.

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

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

[0007] Inputting the distributed energy data into the pre-established operation characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source;

[0008] Aggregating the distributed energy sources according to the adjustable capacity curves of the distributed energy sources, and determining the adjustable capacity curve of the target aggregation unit through the objective function and the constraint conditions;

[0009] Determine other aggregation units except the target aggregation unit under the target section, and obtain adjustable capacity curves of the other aggregation units;

[0010] Inputting 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 a mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section;

[0011] According to the preset adjustable capacity evaluation indicators, the adjustable capacity evaluation results of each distributed energy source, the adjustable capacity evaluation results of the target aggregation unit and the adjustable capacity evaluation results of the target section are obtained; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

[0012] In one of the embodiments, the pre-established operating characteristic analytical models for different distributed energy sources include an energy storage operating characteristic analytical model, a single electric vehicle operating characteristic analytical model, and an air conditioning load cluster operating characteristic analytical model;

[0013] The method further comprises:

[0014] Obtaining historical data and real-time data of each of the distributed energy sources;

[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 parameters of the energy storage operation characteristic analytical model are 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 parameters of the single electric vehicle operating characteristic analytical model are corrected according to the estimated parameters;

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

[0018] In one of the embodiments, the pre-established operating characteristic analytical model includes an energy storage operating characteristic analytical 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] Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the length of the preset research period;

[0022] According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy source is obtained, including the following steps:

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

[0024]

[0025] Where T is the starting time of the research period, and Δt is the preset length of the research period. P(t) ESP For energy storage real-time power, is the maximum charging power of energy storage;

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

[0027]

[0028] in, is the maximum discharge power of energy storage;

[0029] Get the energy storage adjustment duration

[0030]

[0031] Among them, E ESP,max is the maximum charge of energy storage, E ESP,min is the minimum charge of energy storage, E ESP (t) is the real-time charge of energy storage, P′ ESP (t) is the power of energy storage after adjustment, greater than 0 means discharging, less than 0 means charging; η charge is the energy storage charging efficiency; η discharge is the energy storage discharge efficiency.

[0032] In one of the embodiments, the pre-established operating characteristic analytical model further includes an operating characteristic analytical model of a single electric vehicle;

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

[0034]

[0035] Among them, S t is the state of charge of the electric vehicle at time t, P t ch Charging power for electric vehicles, η ch is the charging efficiency of electric vehicles, Δt ch is the charging time period, P t dch is the discharge power of the electric vehicle, η dchis the discharge efficiency of electric vehicles, Δt dch is the discharge time period, E max It is the maximum charge of an electric vehicle after it is fully charged;

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

[0037] When an upward adjustment instruction is received, the electric vehicles in the electric vehicle cluster whose SOC is greater than the upper charge threshold are not adjusted; when a downward adjustment instruction is received, the electric vehicles in the electric vehicle cluster whose SOC is lower than the lower charge threshold are not adjusted;

[0038] Get the maximum upward adjustment range of the electric vehicle cluster

[0039]

[0040] Where T is the starting time of the research period, Δt is the preset length of the research period; P(t) EV Real-time power of electric vehicle clusters, The maximum charging power for the electric vehicle cluster;

[0041] Get the maximum downward adjustment range of the electric vehicle cluster

[0042]

[0043] in, is the maximum discharge power of the electric vehicle cluster;

[0044] Get the duration of electric vehicle cluster regulation

[0045]

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

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

[0048] The analytical model of the air conditioning load cluster operation characteristics is as follows:

[0049]

[0050] Among them, 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;

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

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

[0053]

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

[0055]

[0056] Among them, T Δ is the temperature control margin.

[0057] In one embodiment, the objective function includes a maximum regulation capability objective function and a maximum duration objective function; the constraint conditions include a power balance constraint condition, a tie line constraint condition, and the constraint conditions of each of the distributed energy sources themselves;

[0058] The step of determining the adjustable capacity curve of the target aggregation unit through the objective function and the constraint conditions includes:

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

[0060] In one embodiment, the method further comprises:

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

[0062] Using expert evaluation opinions to subjectively weight the adjustable capability evaluation indicators to obtain subjective weights;

[0063] Determine a comprehensive weight according to the subjective weight and the objective weight, and use the comprehensive weight to perform weighted processing on the adjustable capacity evaluation result of the target aggregation unit to obtain an adjustable capacity evaluation value for the target aggregation unit;

[0064] Obtain the adjustable capacity evaluation values ​​of other aggregation units, sort the adjustable capacity evaluation values ​​of the target aggregation unit and the adjustable capacity evaluation values ​​of the other aggregation units, and display the sorted adjustable capacity evaluation values ​​and the corresponding aggregation units on the power system dispatching interface.

[0065] In one embodiment, the method further comprises:

[0066] Obtaining the reporting adjustable capability corresponding to the target aggregation unit sent by the target aggregation unit;

[0067] The reported adjustable capacity is compared with the adjustable capacity curve of the target aggregation unit. When the comparison result exceeds a preset threshold condition, an adjustable capacity test is initiated for the target aggregation unit, and the comparison result and the test result are displayed on the power system dispatching interface.

[0068] In a second aspect, the present application also provides a device for evaluating the adjustable capability of distributed energy to provide power auxiliary services, including:

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

[0070] The adjustable capacity acquisition module is used to input the data of each distributed energy source into the pre-established operation characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source; according to the adjustable capacity curve of each distributed energy source, aggregate each distributed energy source, and determine the adjustable capacity curve of the target aggregation unit through the objective function and constraint conditions; determine other aggregation units except the target aggregation unit under the target section, and obtain the adjustable capacity curves of other aggregation units; input the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraint conditions of the aggregation unit and the section flow constraint conditions into the mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section;

[0071] The adjustable capacity evaluation module is used to obtain the adjustable capacity evaluation results of each distributed energy source, 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 indicators; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

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

[0073] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

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

[0075] The above-mentioned distributed energy provides ancillary power service adjustable capacity evaluation method, device, computer equipment, computer readable storage medium and computer program product, by obtaining the distributed energy data sent by the target aggregation unit; the energy data includes at least one of the energy storage data, electric vehicle data and air conditioning load data; then each distributed energy data is input into the pre-established operating characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy; then, according to the adjustable capacity curve of each distributed energy, each distributed energy is aggregated, and the adjustable capacity curve of the target aggregation unit is determined through the objective function and constraint conditions; then, other aggregation units except the target aggregation unit under the target section are determined to obtain the adjustable capacity curves of other aggregation units; then, the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraints of the aggregation unit and the section flow constraints are input into the mathematical optimization solution tool to obtain the corresponding Adjustable capacity curve; finally, according to the preset adjustable capacity evaluation index, 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 are obtained; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy; this application obtains the adjustable capacity curve of each distributed energy through the data of each distributed energy and the corresponding operation characteristic analysis model, and then further aggregates to obtain the adjustable capacity curve of the target aggregation unit, which can accurately obtain the adjustable capacity curve of the distributed energy and the target aggregation unit and the adjustable capacity evaluation result; then the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint conditions of the aggregation unit and the section flow constraint conditions are input into the mathematical optimization solution tool, which fully considers the influence of the adjustment capacity of the aggregation units in the same section, and can obtain the accurate adjustable capacity curve of the target section and the adjustable capacity evaluation result. Combining the above characteristics, the solution provided by this application can solve the problem of the credible adjustment capacity after the dynamic aggregation of massive distributed energy in layers and partitions. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 A diagram of an application environment of a method for evaluating the adjustable capability of distributed energy resources to provide power auxiliary services in one embodiment;

[0078] Figure 2 A flow chart of a method for evaluating the adjustable capability of distributed energy to provide power auxiliary services in one embodiment;

[0079] Figure 3 It is a schematic diagram of an adjustable capacity curve of distributed energy in one embodiment;

[0080] Figure 4 A schematic diagram of an adjustable capacity curve of a polymerization unit in one embodiment;

[0081] Figure 5 A flow chart of a method for evaluating the adjustable capability of distributed energy to provide power auxiliary services in another embodiment;

[0082] Figure 6 A flow chart of a method for selecting an adjustable electric vehicle in an adjustable capacity evaluation process for providing power auxiliary services by distributed energy in one embodiment;

[0083] Figure 7 A flow chart of an adjustable capacity evaluation process for providing power auxiliary services by distributed energy resources in another embodiment;

[0084] Figure 8 A structural block diagram of a device for evaluating the adjustable capability of distributed energy resources to provide power auxiliary services in one embodiment;

[0085] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying 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 method for evaluating the adjustable capability of distributed energy to provide power auxiliary services provided by the embodiment of the present application can be applied to Figure 1In the application environment shown. The power system 102 may have power auxiliary services provided by multiple aggregation units, namely, aggregation unit 1 to aggregation unit n. Each aggregation unit may be provided by a different source-load aggregator. The power system 102 has both communication connections and power transmission line connections with each aggregation unit, so that the power system can obtain the power auxiliary services of the aggregation unit and can also interact with the aggregation unit to achieve power transmission. Aggregation unit 104 (aggregation unit 1) is used as an example for explanation here as the target aggregation unit. The power system 102 obtains the distributed energy data sent by the target aggregation unit; the distributed energy data includes at least one of energy storage data, electric vehicle data and air conditioning load data; the power system 102 inputs the data of each distributed energy into the pre-established operating characteristic analysis model for different distributed energy sources 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 constraints; the power system 102 determines Determine other aggregation units except the target aggregation unit under the target section, and obtain the adjustable capacity curves of other aggregation units; the power system 102 inputs the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraints of the aggregation unit, and the section flow constraints into the mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section; the power system 102 obtains the adjustable capacity evaluation results of each distributed energy source, the adjustable capacity evaluation results of the target aggregation unit, and the adjustable capacity evaluation results of the target section according to the preset adjustable capacity evaluation indicators; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration, and adjustment accuracy. The aggregation unit can include a variety of distributed energy sources, such as energy storage, electric vehicles, air conditioners, distributed photovoltaics, wind power, and other energy sources.

[0088] At present, distributed energy mainly participates in the demand-side response market through day-ahead invitations, and rarely participates in the actual application of real-time control of the power grid. Source-load aggregators (aggregation units) are different from traditional generators. Their external characteristics are the representation of the dynamic performance of massive distributed energy aggregation and coordination. They need to rely on high informatization to solve the complementarity and coordination problems between highly uncertain individual units. They are a clean, low-carbon, safe and efficient asset-light solution. However, the physical nodes of large-scale distributed energy are scattered, information acquisition is difficult, and there are many stakeholders. Their participation in the operation of the power system still needs to solve many technical challenges. At the same time, in order to obtain greater resource benefits when participating in the market, the adjustable capacity reported by the aggregation unit may be inaccurate. Therefore, there is an urgent need for an adjustable capacity evaluation method for distributed energy to provide power auxiliary services that can solve the problem of reliable dynamic aggregation and regulation capacity of massive distributed energy layers and partitions.

[0089] In an exemplary embodiment, Figure 2 As shown in the figure, a method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services is provided. Figure 1 Taking the power system 102 in FIG. 1 as an example, the method includes the following steps S202 to S212. Among them:

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

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

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

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

[0094] Step S204: inputting the data of each distributed energy source into the pre-established operation characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source.

[0095] Among them, operation characteristic analysis models can be pre-established for different distributed energy sources, for example, an energy storage operation characteristic analysis model can be pre-established for energy storage, a single electric vehicle operation characteristic analysis model can be pre-established for electric vehicles, and an air conditioning load cluster operation characteristic analysis model can be pre-established for air conditioners. The operation characteristic analysis models pre-established for different distributed energy sources are established based on the working principles and performance characteristics of different types of distributed energy sources. Through the operation characteristic analysis models, the adjustable capacity curves that the corresponding distributed energy sources can provide, such as the maximum capacity adjustment curve, or the maximum power adjustment curve, etc., can be obtained.

[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 operation 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 operation 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 operation characteristic analysis model to obtain the adjustable capacity curve of the air conditioner.

[0097] Step S206, aggregate the distributed energy sources according to the adjustable capacity curves of the distributed energy sources, and determine the adjustable capacity curve of the target aggregation unit through the objective function and constraint conditions.

[0098] Among them, the objective function can refer to a mathematical expression used to quantify and measure adjustable capacity, which can be used to define the goal or purpose that you want to achieve through the aggregation optimization process. For example, the objective function can be the maximum adjustable capacity, maximum duration, fastest response time, fastest adjustment rate, etc.

[0099] The constraints may refer to physical limitations and safety requirements of each distributed energy source.

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

[0101] Step S208, determining other aggregation units except the target aggregation unit under the target section, and obtaining adjustable capacity curves of other aggregation units.

[0102] The section may refer to a specific area in the power grid or a group of transmission lines. In this embodiment, the target section may refer to multiple aggregation units connected to the power grid through the same line, forming a Figure 1 A line containing multiple aggregation units is shown.

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

[0104] Step S210, 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 flow constraint condition into a mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section.

[0105] The mathematical optimization solving tool may be an optimization solver for solving mathematical optimization problems, such as Gurobi / Cplex / GAMS solvers.

[0106] Among them, the adjustable capacity constraint conditions of the aggregation unit can refer to the line constraints of the connection lines of each aggregation unit in the target section, such as the tie line constraints; the adjustable capacity constraint conditions of the aggregation unit can also refer to the output constraints of each aggregation unit in the target section, such as the minimum output and maximum output of different aggregation units.

[0107] The section flow constraint condition may refer to the minimum flow constraint of the section and the maximum flow constraint of the section.

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

[0109] Step S212, according to the preset adjustable capacity evaluation index, obtain the adjustable capacity evaluation result of each distributed energy source, the adjustable capacity evaluation result of the target aggregation unit and the adjustable capacity evaluation result of the target section.

[0110] Among them, the preset adjustable capability evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

[0111] In an exemplary embodiment, after the power system inputs the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraints of the aggregation unit, and the section flow constraints into a determined mathematical optimization solution tool, and obtains the adjustable capacity curve corresponding to the target section, that is, considering the influence of the adjustable capacities of the aggregation units in the same section, the total adjustable capacity curve of the section is obtained, and then the adjustable capacity curve of the target aggregation unit obtained above can be corrected according to the influence of the adjustable capacities of the aggregation units in the same section; and then the target aggregation unit after the corrected 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 between the aggregation units and the influence of the section constraints on the aggregation unit are considered, and the obtained target aggregation unit adjustable capacity evaluation result is more accurate, and the obtained section adjustable capacity evaluation result is also more accurate.

[0112] Specifically, according to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy source can be obtained by referring to the following process. The response capacity of distributed energy sources is mainly reflected in the level, speed and duration of output / absorption power, such as Figure 3 As shown in the figure, it is a schematic diagram of the evaluation index of the adjustable capacity of distributed energy. Taking the process of receiving the upper-level dispatch as an example, t ss The command is issued at this moment, and the resource output is P init , and starts to descend according to the scheduling instruction, at t ds The power reaches the minimum value P at the moment min At t de At this moment, the power starts to recover. The evaluation indicators for the adjustable capacity of distributed energy include:

[0113] Adjustment range: The maximum upward adjustment capacity Pumax refers to the maximum offset value that can be achieved relative to the current operating point after upward adjustment. The maximum downward adjustment capacity Pdmax refers to the maximum offset value that can be achieved relative to the current operating point after downward adjustment, such as Figure 3Middle D.

[0114] Response time: The upward response time Tur refers to the difference between the time when the power generation unit starts to respond to the upward instruction and the time when the upward instruction is issued. The downward response time Tdr refers to the difference between the time when the power generation unit starts to respond to the downward instruction and the time when the downward instruction is issued.

[0115] Adjustment duration: The upward adjustment duration Tud refers to the maximum time that the state can be maintained after the upward adjustment requirements are fully met. The downward adjustment duration Tdd refers to the maximum time that the state can be maintained after the downward adjustment requirements are fully met. Figure 3 Medium T D .

[0116] Adjustment rate: The maximum upward adjustment rate Rumax refers to the change of the operating point per unit time during the upward adjustment process. The maximum downward adjustment rate Rdmax refers to the change of the operating point per unit time during the downward adjustment process, such as Figure 3 Medium A / T R .

[0117] Regulation accuracy: Regulation accuracy Pacur refers to the difference between the final stable output and the target value after distributed energy is adjusted.

[0118] Specifically, the operating characteristics of the aggregation unit can refer to the characteristics of traditional units, including: rated active power, minimum stable output, maximum technical output, active regulation rate, power frequency characteristics, continuous start-stop time, start-stop curve, etc. Referring to the characteristics of traditional units and combining the aforementioned distributed energy adjustable capacity evaluation index, the adjustable capacity evaluation index of the aggregation unit can be obtained. Specifically, the process of the aggregation unit responding to the adjustment instruction can be as follows: Figure 4 As shown, where P meas is the actual output. At T1, the output of the aggregation unit is P1. At this time, the adjustment command is issued, and the aggregation unit adjusts the output in the direction of the command. After a certain response time, at T2, the output of the aggregation unit reaches P2. For the first time, the output of the aggregation unit is greater than the action dead zone and maintained. It is considered that the aggregation unit begins to effectively respond to the adjustment command. After a period of climbing, at T3, the output reaches the maximum value P3 and begins to decline or maintain, and the climbing ends. The output of the aggregation unit can maintain a high level for a period of time thereafter, until the distributed energy is physically constrained, resulting in a power drop, or a new adjustment command is received. Let T scale is the time scale for adjustable capability assessment, starting from time T2, T4 = T2 + T scale The average aggregate unit output P4 between T3 and T4 is taken as the maximum output of the aggregate unit after adjustment. Figure 4 And combined with the above-mentioned distributed energy adjustable capacity evaluation index, the calculation method of the adjustable capacity evaluation index of the aggregation unit can be obtained, which can be specifically as follows:

[0119] Adjustment range: The maximum upward adjustment capacity Pumax can be calculated by abs(P4-P1); the maximum downward adjustment capacity Pdmax can be calculated in the specific downward adjustment process according to the same calculation method as the maximum upward adjustment capacity.

[0120] Response time: The upward adjustment response time Tur is T2-T1; the downward adjustment response time Tdr can be calculated in the specific downward adjustment process according to the same calculation method as the upward adjustment response time.

[0121] Adjustment rate: The maximum adjustment rate Rumax is The maximum downward adjustment rate Rdmax can be calculated in the specific downward adjustment process according to the same calculation method as the maximum upward adjustment rate.

[0122] Adjust duration: Increase duration Tud to T scale The down-regulation duration Tdd can be calculated in the specific down-regulation process using the same calculation method as the up-regulation duration.

[0123] Adjustment accuracy: The formula for adjustment accuracy Pacur is as follows:

[0124]

[0125] It should also be noted that T scale The time scale for evaluating the adjustable capacity is because distributed energy is limited by physical conditions, and its adjustment capacity is different from that of traditional thermal power units. The adjustment capacity of distributed energy cannot be maintained for a long time. For example, distributed energy such as energy storage and electric vehicles are limited by factors such as battery charge and battery capacity. If they are adjusted according to their maximum power, a long time scale may cause the battery to be fully charged or exhausted, resulting in the subsequent adjustment capacity being 0. Therefore, in order to ensure the consistency of energy storage / electric vehicle output within 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 of the adjustable capacity of distributed energy. scale, which can be 15 minutes, 30 minutes, 45 minutes, 1 hour, etc. Based on the distributed energy and aggregation unit adjustable capacity evaluation indicators constructed in the above steps, combined with the proposed different time scales, the adjustable capacity evaluation indicators are calculated at different time scales, realizing the multi-time scale adjustable capacity evaluation of distributed energy and aggregation units; the aforementioned multiple evaluation indicators are used to describe the regulation capacity of distributed energy or aggregation units, and the indicators can be calculated at different time scales to obtain the multi-time scale adjustable capacity evaluation results, and the real-time adjustable capacity of distributed energy and aggregation units can be calculated. The regulation capacity calculation model can be updated in real time based on the measurement data, and the adjustable capacity results of different aggregation units can be calculated for different regulation scenarios. The influence between aggregation units and the section constraints can be considered to obtain the section adjustable capacity evaluation results; among them, the evaluation indicators of the section adjustable capacity can be set with reference to the adjustable capacity evaluation indicators of the aggregation unit.

[0126] In the above-mentioned method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services, the adjustable capacity curve of each distributed energy is obtained through the data of each distributed energy and the corresponding operating characteristic analysis model, and then the adjustable capacity curve of the target aggregation unit is further aggregated, so that the adjustable capacity curve of the distributed energy and the target aggregation unit and the adjustable capacity evaluation result can be accurately obtained; then the adjustable capacity curve of each aggregation unit under the target section, the adjustable capacity constraint conditions of the aggregation unit and the section flow constraint conditions are input into the mathematical optimization solution tool, and the influence of the adjustment capacity of the aggregation units in the same section on each other is fully considered, so that the accurate adjustable capacity curve of the target section and the adjustable capacity evaluation result can be obtained. Combining the above characteristics, the solution provided by this application can solve the problem of the reliability of the adjustment capacity after the dynamic aggregation of massive distributed energy in layers and partitions.

[0127] In an exemplary embodiment, the pre-established operating characteristic analysis models for different distributed energy sources include an energy storage operating characteristic analysis model, a single electric vehicle operating characteristic analysis model, and an air conditioning load cluster operating characteristic analysis model; Figure 5 As shown, the method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services also includes steps S302 to S308.

[0128] in:

[0129] Step S302, obtaining historical data and real-time data of each distributed energy source.

[0130] Among them, historical data can refer to the operating data of distributed energy in a certain period of time in the past; real-time data can refer to the operating data of the current period; the specific period is set by actual demand and is not specifically limited here. The energy storage operating characteristics analysis model can be used to describe the operating characteristics of the energy storage system; the single electric vehicle operating characteristics analysis model can be used to describe the charging and discharging characteristics of a single electric vehicle; the air conditioning load cluster operating characteristics analysis model can be used to describe the load characteristics when a group of air conditioning systems are operating together.

[0131] Step S304, based on 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 using the least squares method, and the parameters of the energy storage operation characteristic analytical model are corrected based on the estimated parameters.

[0132] Specifically, the power system obtains historical data and real-time data corresponding to energy storage, and estimates the key parameters of the energy storage system (effective capacity, charging efficiency, and discharging efficiency) using the least squares method; based on these estimated parameters, the energy storage operation characteristic analytical model is corrected to make it more accurately reflect the actual operation of electric vehicles.

[0133] Step S306, based on 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 using the least squares method, and the parameters of the single electric vehicle operating characteristic analytical model are corrected based on the estimated parameters.

[0134] Specifically, the power system obtains historical data and real-time data corresponding to the electric vehicle cluster, and uses the least squares method to estimate the effective battery capacity, charging efficiency and discharge efficiency of the electric vehicle cluster. Based on these estimated parameters, the parameter correction of the analytical model of the operating characteristics of a single electric vehicle is performed to make it more accurately reflect the actual operation of the electric vehicle.

[0135] Step S308, based on the historical data and real-time data corresponding to the air-conditioning load cluster, the particle swarm optimization algorithm is used to estimate the average building thermal resistance, average building heat capacity and average thermal efficiency parameters of the air-conditioning load cluster, and the parameters of the air-conditioning load cluster operation characteristic analytical model are corrected according to the estimated parameters.

[0136] Specifically, the power system obtains historical data and real-time data corresponding to the air-conditioning load cluster, and uses the particle swarm optimization algorithm to estimate the average building thermal resistance, average building heat capacity and average thermal efficiency of the air-conditioning load cluster. Based on these estimated parameters, the air-conditioning load cluster operating characteristic analytical model is corrected accordingly. The particle swarm optimization algorithm is an optimization technology based on swarm intelligence and can efficiently find the optimal solution.

[0137] For example, for energy storage and electric vehicles, numerical analysis methods are used 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+…+β p x p

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

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

[0141]

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

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

[0144]

[0145] Among them, y i is the ith observation value, is the i-th predicted value, and n represents the total number of observations. Optimization algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA) can be used to solve the model parameters that minimize MSE.

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

[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] Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the preset research period length.

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

[0151] E ESP,min ≤E ESP (t)≤E ESP,max

[0152]

[0153] Among them, E ESP,max , E ESP,min They are the upper and lower limits of the electric energy stored in the energy storage system respectively; They are the upper limits of charge and discharge power respectively.

[0154] Get the maximum upward adjustment range of energy storage

[0155]

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

[0157] Get the maximum downward adjustment range of energy storage

[0158]

[0159] in, is the maximum discharge power of energy storage;

[0160] Get the energy storage adjustment duration

[0161]

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

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

[0164]

[0165] Among them, S t is the state of charge of the electric vehicle at time t, P t ch Charging power for electric vehicles, η ch is the charging efficiency of electric vehicles, Δt ch is the charging time period, P t dch is the discharge power of the electric vehicle, η dch is the discharge efficiency of electric vehicles, Δt dch is the discharge time period, E max It is the maximum charge of an electric vehicle after it is fully charged;

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

[0167] When an upward adjustment instruction is received, electric vehicles in the electric vehicle cluster whose SOC is greater than the upper charge threshold are not adjusted; when a downward adjustment instruction is received, electric vehicles in the electric vehicle cluster whose SOC is lower than the lower charge threshold are not adjusted; the adjustable electric vehicle selection process is as follows Figure 6 As shown, the vehicle status analysis of the electric vehicles in the electric vehicle cluster is started, which is divided into charging status, waiting status and travel status. The electric vehicle in the charging status determines whether its SOC (charge) is greater than SOCu (lower charge threshold). If it is less than SOCu, the electric vehicle can be adjusted up or down. If it is greater than SOCu, it is further determined whether its SOC is less than SOCd (upper charge threshold). If it is less than SOCd, it can be adjusted up, and if it is greater than SOCd, it can be adjusted up or down; the electric vehicle in the waiting status determines whether its SOC is less than SOCd. If it is less than SOCd, it cannot be adjusted, and if it is greater than SOCd, it can be adjusted up or down; the electric vehicle in the travel status cannot be adjusted; the above judgment is performed on the above-mentioned single electric vehicle and the adjustable capacity of the single EV (electric vehicle) is calculated, and then it is determined whether the calculation of all EVs is completed. If not all are completed, it returns to the step of continuing the vehicle status analysis; if the calculation is completed, the comprehensive adjustable capacity of the electric vehicle cluster is calculated; through the above Figure 6 The steps shown can select electric vehicles that can participate in the regulation and calculate the regulated upper power limit and the regulated lower power limit.

[0168] Get the maximum upward adjustment range of the electric vehicle cluster

[0169]

[0170] Where T is the starting time of the research period, Δt is the preset length of the research period; P(t) EV Real-time power of electric vehicle clusters, The maximum charging power for the electric vehicle cluster;

[0171] Get the maximum downward adjustment range of the electric vehicle cluster

[0172]

[0173] in, is the maximum discharge power of the electric vehicle cluster;

[0174] Get the duration of electric vehicle cluster regulation

[0175]

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

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

[0178]

[0179] Among them, 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;

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

[0181]

[0182] Among them, C a is the equivalent specific heat capacity, Q is the cooling capacity, T o is the outside temperature, T i is the indoor gas temperature, R a is the equivalent thermal resistance.

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

[0184] Substituting the air conditioning electric heat conversion model into the thermodynamic model and discretizing it, the recursive formula of indoor temperature and air conditioning power can be obtained as follows:

[0185]

[0186] Among them, T in,t+1 is the indoor temperature at time t+1; T in,t is the indoor temperature at time t; T out,t is the outdoor temperature at time t; P is the average power of the air conditioner between time t and time 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 cooling capacity; △t is the time interval.

[0187] For fixed frequency air conditioners, the COP is a fixed value. For variable frequency air conditioners, the COP is not constant. The relationship between air conditioner power, cooling capacity, and COP relative to the operating frequency of the air conditioner compressor is:

[0188]

[0189] Among them, a, b, k, m, and n are all coefficients. Eliminating the frequency, the relationship between energy efficiency ratio and power is:

[0190]

[0191] Among them, g1, g2, and g3 are all coefficients.

[0192] Air conditioning operation characteristics: For fixed-frequency air conditioners, the energy efficiency ratio is fixed, and the cooling or heating capacity is mainly determined by the working state of the air conditioner. A single fixed-frequency air conditioner has only two operating states during operation: on and off, that is, the power is the rated power and 0, and cannot be adjusted linearly. However, by changing the time of the on and off states within the operating cycle, the average power of the air conditioner within an operating cycle can be linearly adjusted.

[0193] The working state of the fixed-frequency air conditioner load is related to the indoor temperature. Taking the air conditioner load in the cooling state as an example, when the indoor temperature is higher than the maximum value of the temperature setting value, the air conditioner turns on and performs cooling; when the indoor temperature is lower than the minimum value of the temperature setting value, the air conditioner turns off and stops cooling; 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. The working state S of the i-th air conditioner AC It can be expressed by the following formula:

[0194]

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

[0196] For variable frequency air conditioners, the compressor frequency changes with the difference between the indoor temperature and the set temperature during operation. The air conditioner power and the compressor frequency are in a linear function relationship. It can be considered that the variable frequency air conditioner power is the maximum power when the temperature difference is too large, and the minimum power when the temperature difference is too small. When the temperature difference is within the allowable deviation range, the air conditioner power changes based on the current power according to the size of the temperature difference, that is:

[0197]

[0198] Where P(t) is the air conditioning power at time t, T in is the indoor temperature, T set Set the temperature for the air conditioner, ΔT max Maximum temperature deviation allowed for air conditioning.

[0199] Air conditioning cluster model: Fixed-frequency air conditioning load aggregation model: For fixed-frequency air conditioning, the probability p of a single air conditioning load being in the on state on The startup time t of air conditioning load on And shutdown time t off related:

[0200]

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

[0202]

[0203] In the formula, p on,i is the probability that the i-th air conditioner load is in the on state, and the value is between:

[0204]

[0205] Where, T ACset is the temperature setting value of the air conditioning load; δ AC It is the temperature dead zone of air conditioning load.

[0206] The average aggregate power of fixed-frequency air conditioners is:

[0207]

[0208] Variable frequency air conditioner load aggregation model: The total power of variable frequency air conditioner load aggregation can be expressed as:

[0209]

[0210] Among them, P B ACagg (t) is the aggregate power of 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 aggregate power of fixed-frequency air conditioners and considering that variable-frequency air conditioners have a minimum power, the average aggregate power of variable-frequency air conditioners is:

[0211]

[0212] Comprehensive aggregation model of air conditioning load:

[0213]

[0214] Denoted as:

[0215]

[0216] Among them, k1, k2, and k3 are all coefficients.

[0217] Taking into account the differences between fixed-frequency and variable-frequency air conditioners, the total power of the air conditioner cluster is recorded as:

[0218]

[0219] Among them, N is the number of air conditioners, η is the air conditioner energy efficiency ratio, δ is the equivalent thermal resistance of the user's 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 operation characteristics of the air conditioning load cluster can be obtained through the above expression of the total power of the air conditioning cluster.

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

[0221] Get the maximum upward adjustment range of the air conditioning load cluster:

[0222]

[0223] Get the maximum downward adjustment range of the air conditioning load cluster:

[0224]

[0225] Among them, T Δ is the temperature control margin.

[0226] In an exemplary embodiment, the objective function includes a maximum regulation capability objective function and a maximum duration objective function; the constraints include power balance constraints, tie line constraints, and constraints of each distributed energy source; the adjustable capability curve of the target aggregation unit is determined by the objective function and the constraints, including:

[0227] The maximum regulation capacity objective function or the maximum duration objective function, as well as the power balance constraints, the interconnection line constraints and the constraints of each distributed energy source itself are input into the mathematical optimization solution tool to obtain the adjustable capacity curve of the target aggregation unit.

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

[0229] First, determine the adjustable capacity objective function. Different objective functions can be set according to different requirements. VPP The aggregated unit output is equal to the sum of all distributed energy outputs. The formula is as follows:

[0230]

[0231] Where m is the total number of distributed energy resources.

[0232] The objective function for maximum regulation capacity is:

[0233] max P VPP

[0234] If there are requirements for duration, the following restrictions must also be met:

[0235] tT d ≥T r

[0236] Among them, T r The duration of the request.

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

[0238] max T d

[0239] in, is the sustainable duration of the ith distributed energy resource regulation.

[0240] If there is a requirement for adjustment capability, the following restrictions must also be met:

[0241]

[0242] in, The required adjustment capability.

[0243] The constraints on the target aggregation unit are:

[0244] 1. Power balance constraint, that is, the power emitted by the entire network is equal to the power consumed, that is:

[0245]

[0246] 2. Tie line constraints: the power on the tie line must be maintained within the limit, that is:

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

[0248] 3. Distributed energy’s own constraints.

[0249] Finally, the Gurobi / Cplex / GAMS solver is used to solve the above problem and obtain the adjustable capacity curve of the target aggregation unit. In the above steps, multiple objective functions for aggregation are constructed for different adjustment scenarios, such as the objective function for the maximum adjustment capacity and the objective function for the maximum duration, and the aggregation results under different scenarios are obtained by solving them, that is, the adjustable capacity curve of the aggregation unit under different scenarios is obtained.

[0250] In an exemplary embodiment, the method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services also includes: objectively weighting the adjustable capacity evaluation indicators using the entropy weight method to obtain objective weights; subjectively weighting the adjustable capacity evaluation indicators using expert evaluation opinions to obtain subjective weights; determining a comprehensive weight based on the subjective weight and the objective weight, and weighting the adjustable capacity evaluation results of the target aggregation unit using the comprehensive weight to obtain an adjustable capacity evaluation value for the target aggregation unit; obtaining the adjustable capacity evaluation values ​​of other aggregation units, sorting the adjustable capacity evaluation values ​​of the target aggregation unit and the adjustable capacity evaluation values ​​of other aggregation units, and displaying the sorted adjustable capacity evaluation values ​​and the corresponding aggregation units on the power system dispatching interface.

[0251] The subjective weighting method is obtained by the subjective judgment of experts or decision makers, and its interpretability is strong, that is, the rationality of setting weights exists, but it is relatively arbitrary, and different experts will have different opinions. The objective weighting method obtains weights from the changes in the numerical values ​​of the indicators themselves. Since it does not involve the subjective consciousness of the implementers, its objectivity is strong, but it lacks interpretability and the calculated weight results may be contrary to reality. In order to improve the scientificity and rationality of the indicator weights, a combined weighting method is adopted to assign weights, combining the subjective weighting method and the objective weighting method. Specifically, the process of calculating the weights of each evaluation indicator in this embodiment is as follows:

[0252] 1. Calculate objective weights

[0253] (1) Data normalization

[0254] The normalized calculation formula for the positive indicator is:

[0255]

[0256] The normalized calculation formula for the negative indicator is:

[0257]

[0258] Where Y ij It is the result after normalizing the evaluation value data.

[0259] (2) Calculating 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 indicator

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

[0264]

[0265] Where W o i is the final weight of different evaluation indicators.

[0266] 2. Calculate subjective weight

[0267] The subjective weight calculation adopts the expert scoring method, and a small number of consulting experts determine the weight of each indicator from multiple perspectives and take into account the actual operating conditions. The basic steps are as follows:

[0268] (1) Expert selection. Based on the understanding of static safety analysis of power systems, experts are selected from power system dispatching, operation and other departments to develop appropriate and comprehensive evaluation indicators and explain the concept of each indicator;

[0269] (2) List. Give the weight range of each evaluation indicator, which can be expressed in the form of scoring (0 points--100 points);

[0270] (3) Scoring. A list of scores for each indicator is given to each expert. Each expert member scores the weight of each indicator based on his or her understanding of each indicator to obtain the weight score of each evaluation indicator.

[0271] (4) Collect the scoring sheets of each expert, add up the scores of each expert on all indicators to get the total score, and then divide the score of each indicator by the total score to calculate the weight of each evaluation indicator;

[0272] (5) Collect all the scored tables, remove the lowest and highest weights of each indicator, then add up the weights of each remaining indicator and divide by the number of remaining scoring tables to get the final weight of each evaluation indicator;

[0273] (6) List the indicator weights calculated in step (5) and the indicator weights calculated in step (4), and compare the indicator weights calculated in the two times;

[0274] (7) After comparison, if an expert is not satisfied with the weights previously determined by him / her and intends to modify the previous scores, steps 4 to 6 are repeated. If the expert has no objection to the previous weights, the subjective weights based on the expert scoring method are calculated.

[0275] The expert scoring method can be used to determine the degree of attention paid to each indicator by power workers in actual situations. This subjective judgment method can highlight the importance of certain indicators and facilitate adjustments to the weights of each indicator based on the actual operation of the power system.

[0276] 3. Calculate the comprehensive weight

[0277] The weight of the n indicators obtained by objective empowerment is recorded as w o,1 , w o,2 ,......,w o,n , the weight of the n indicators obtained by subjective empowerment is recorded as w s,1 , w s,2 ,......,w s,n , the weights after comprehensive weighting are recorded as w1, w2, ..., w n , w i =a i w o,i +b i w s,i i=1,2,...,n, where a i is the weight factor of the objective weight, b i is the weight factor of subjective weight, which must satisfy a i +b i =1. The distributed energy aggregation indicators are z1, z2, ..., z n , then its comprehensive 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, sort the adjustable capacity evaluation value of the target aggregation unit and the adjustable capacity evaluation values ​​of other aggregation units, and display the sorted adjustable capacity evaluation values ​​and corresponding aggregation units on the power system dispatching interface for reference by relevant staff.

[0280] In an exemplary embodiment, the method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services also includes: obtaining the reported adjustable capacity corresponding to the target aggregation unit sent by the target aggregation unit; comparing the reported adjustable capacity with the adjustable capacity curve of the target aggregation unit, and when the comparison result exceeds a preset threshold condition, initiating an adjustable capacity test on the target aggregation unit, and displaying the comparison result and the test result on the power system dispatching interface.

[0281] Specifically, the power system obtains the reported adjustable capacity corresponding to the target aggregation unit sent by 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 step. When the comparison result exceeds the preset threshold condition, the power system actively tests the target aggregation unit and displays the comparison result and the test result on the power system dispatching interface, which can assist the power system control decision.

[0282] In an exemplary embodiment, Figure 7As shown, a process for evaluating the adjustable capacity of distributed energy to provide power auxiliary services is provided, which may include: the distributed energy adjustable capacity evaluation module calculates the adjustable capacity curve of the distributed energy through the distributed energy regulation model and the corresponding adjustable capacity evaluation index according to the real-time data sent by the distributed energy. The parameter identification module updates the parameters in the model used by the distributed energy adjustable capacity evaluation module based on the historical and stored real-time data, wherein the historical data can be the stored real-time data or the data stored for active testing of the distributed energy, and the parameter identification process includes the numerical analysis of energy storage and charging piles, and the intelligent optimization of air conditioning and ice storage. The aggregation unit adjustable capacity evaluation module calculates the aggregation unit adjustable capacity curve under different scenarios based on the distributed energy adjustable capacity curve, the aggregation unit constraint conditions, the objective functions corresponding to multiple scenarios, and the adjustable capacity evaluation index 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 constraint and the aggregation unit constraint.

[0283] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

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

[0285] In an exemplary embodiment, Figure 8As shown, a device 800 for evaluating the adjustable capacity of distributed energy to provide power auxiliary services is provided, including: 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 used to acquire the distributed energy data sent by the target aggregation unit; the distributed energy data includes at least one of energy storage data, electric vehicle data and air conditioning load data.

[0287] The adjustable capacity acquisition module 802 is used to input the data of each distributed energy source into the operating characteristic analysis model pre-established for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source; aggregate each distributed energy source according to the adjustable capacity curve of each distributed energy source, and determine the adjustable capacity curve of the target aggregation unit through the objective function and constraints; determine other aggregation units except the target aggregation unit under the target section, and obtain the adjustable capacity curves of other aggregation units; input the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraints of the aggregation unit and the section flow constraints into the mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section.

[0288] The adjustable capacity evaluation module 803 is used to obtain the adjustable capacity evaluation results of each distributed energy source, the adjustable capacity evaluation results of the target aggregation unit and the adjustable capacity evaluation results of the target section according to the preset adjustable capacity evaluation indicators; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

[0289] In an exemplary embodiment, the pre-established operating characteristic analytical models for different distributed energy sources include an energy storage operating characteristic analytical model, a single electric vehicle operating characteristic analytical model, and an air conditioning load cluster operating characteristic analytical model; the above-mentioned distributed energy to provide power auxiliary service adjustable capacity evaluation device also includes a model parameter correction module, which is used to obtain 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 discharge efficiency parameters of the energy storage are estimated using the least squares method, and the parameters of the energy storage operating characteristic analytical model are 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 discharge efficiency parameters of the electric vehicle cluster are estimated using the least squares method, and the parameters of the single electric vehicle operating characteristic analytical model are corrected according to the estimated parameters; according to the historical data and real-time data corresponding to the air conditioning load cluster, the average building thermal resistance, average building heat capacity, and average thermal efficiency parameters of the air conditioning load cluster are estimated using the particle swarm optimization algorithm, and the parameters of the air conditioning load cluster operating characteristic analytical model are corrected according to the estimated parameters.

[0290] In an exemplary embodiment, the pre-established operating characteristic analytical model includes an energy storage operating characteristic analytical model;

[0291] The analytical model of energy storage operation characteristics is as follows:

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

[0293] Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the length of the preset research period;

[0294] The adjustable capacity acquisition module 802 is also used to obtain the maximum upward adjustment range of energy storage.

[0295]

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

[0297] Get the maximum downward adjustment range of energy storage

[0298]

[0299] in, is the maximum discharge power of energy storage;

[0300] Get the energy storage adjustment duration

[0301]

[0302] Among them, E ESP,max is the maximum charge of energy storage, E ESP,min is the minimum charge of energy storage, E ESP (t) is the real-time charge of energy storage, P′ ESP (t) is the power of energy storage after adjustment, greater than 0 means discharging, less than 0 means charging; η charge is the energy storage charging efficiency; η discharge is the energy storage discharge efficiency.

[0303] In an exemplary embodiment, the pre-established operating characteristic analytical model further includes a single electric vehicle operating characteristic analytical model;

[0304] The analytical model of the operating characteristics of a single electric vehicle is as follows:

[0305]

[0306] Among them, S t is the state of charge of the electric vehicle at time t, P t ch Charging power for electric vehicles, η ch is the charging efficiency of electric vehicles, Δt ch is the charging time period, P t dch is the discharge power of the electric vehicle, η dch is the discharge efficiency of electric vehicles, Δt dch is the discharge time period, E max It is the maximum charge of an electric vehicle after it is fully charged;

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

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

[0309]

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

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

[0312]

[0313] in, is the maximum discharge power of the electric vehicle cluster;

[0314] Get the duration of electric vehicle cluster regulation

[0315]

[0316] Among them, E EV,max is the maximum charge of the electric vehicle cluster, E EV,min is the minimum charge of the electric vehicle cluster, E EV (t) is the real-time charge of the electric vehicle cluster, P E ' V (t) is the power of the electric vehicle cluster after adjustment, greater than 0 means discharging, less than 0 means charging; η charge Charging efficiency for electric vehicles; η discharge The discharge efficiency of electric vehicles.

[0317] In an exemplary embodiment, the pre-established operating characteristic analytical model further includes an air conditioning load cluster operating characteristic analytical model;

[0318] The analytical model of the air conditioning load cluster operation characteristics is as follows:

[0319]

[0320] Among them, 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 adjustable capacity acquisition module 802 is also used to obtain the maximum upward adjustment range of the air conditioning load cluster:

[0322]

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

[0324]

[0325] Among them, T Δ is the temperature control margin.

[0326] In an exemplary embodiment, the objective function includes a maximum regulation capacity objective function and a maximum duration objective function; the constraints include power balance constraints, interconnection line constraints, and constraints of each distributed energy source itself; the above-mentioned adjustable capacity acquisition module 802 is also used to input the maximum regulation capacity objective function or the maximum duration objective function, as well as the power balance constraints, interconnection line constraints, and constraints of each distributed energy source itself into a mathematical optimization solution tool to obtain the adjustable capacity curve of the target aggregation unit.

[0327] In an exemplary embodiment, the above-mentioned distributed energy provides power auxiliary service adjustable capacity evaluation device also includes an evaluation value acquisition module, which is used to use the entropy weight method to objectively weight the adjustable capacity evaluation index to obtain the objective weight; use expert evaluation opinions to subjectively weight the adjustable capacity evaluation index to obtain the subjective weight; determine the comprehensive weight according to the subjective weight and the objective weight, and use the comprehensive weight to weight the adjustable capacity evaluation result of the target aggregation unit to obtain the adjustable capacity evaluation value for the target aggregation unit; obtain the adjustable capacity evaluation value of other aggregation units, sort the adjustable capacity evaluation value of the target aggregation unit and the adjustable capacity evaluation values ​​of other aggregation units, and display the sorted adjustable capacity evaluation value and the corresponding aggregation unit on the power system dispatching interface.

[0328] In an exemplary embodiment, the adjustable capacity evaluation device for providing power auxiliary services by the above-mentioned distributed energy also includes a reported adjustable capacity comparison module, which is used to obtain the reported adjustable capacity corresponding to the target aggregation unit sent by the target aggregation unit; compare the reported adjustable capacity with the adjustable capacity curve of the target aggregation unit, and when the comparison result exceeds the preset threshold condition, initiate an adjustable capacity test for the target aggregation unit, and display the comparison result and the test result on the power system dispatching interface.

[0329] Each module in the above-mentioned distributed energy source adjustable capacity assessment device for providing power auxiliary services can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0330] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, 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 an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services is implemented.

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

[0332] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0333] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0334] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0335] Those of ordinary skill 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, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the 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. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0336] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0337] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the adjustable capacity of distributed energy to provide power auxiliary services, characterized in that: The method comprises: Obtaining distributed energy data sent by the target aggregation unit; the distributed energy data includes at least one of energy storage data, electric vehicle data and air conditioning load data; Inputting the distributed energy data into the pre-established operation characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source; Aggregating the distributed energy sources according to the adjustable capacity curves of the distributed energy sources, and determining the adjustable capacity curve of the target aggregation unit through the objective function and the constraint conditions; Determine other aggregation units except the target aggregation unit under the target section, and obtain adjustable capacity curves of the other aggregation units; Inputting 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 a mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section; According to the preset adjustable capacity evaluation indicators, the adjustable capacity evaluation results of each distributed energy source, the adjustable capacity evaluation results of the target aggregation unit and the adjustable capacity evaluation results of the target section are obtained; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

2. The method according to claim 1, characterized in that The pre-established operation characteristic analysis models for different distributed energy sources include an energy storage operation characteristic analysis model, a single electric vehicle operation characteristic analysis model, and an air conditioning load cluster operation characteristic analysis model; The method further comprises: Obtaining historical data and real-time data of each of the distributed energy sources; 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 parameters of the energy storage operation characteristic analytical model are 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 parameters of the single electric vehicle operating characteristic analytical model are corrected according to the estimated parameters; According to the historical data and real-time data corresponding to the air-conditioning load cluster, the average building thermal resistance, average building heat capacity and average thermal efficiency parameters of the air-conditioning load cluster are estimated using the particle swarm optimization algorithm, and the parameters of the air-conditioning load cluster operation characteristic analytical model are corrected according to the estimated parameters.

3. The method according to claim 1, characterized in that 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: From b (t+1)=E b (t)-P b (t)η b Δt Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the power of the energy storage battery at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the length of the preset research period; According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy source is obtained, including the following steps: Get the maximum upward adjustment range of energy storage Where T is the starting time of the research period, Δt is the preset length of the research period; P(t) ESP For energy storage real-time power, is the maximum charging power of energy storage; Get the maximum downward adjustment range of energy storage in, is the maximum discharge power of energy storage; Get the energy storage adjustment duration Among them, E ESP,max is the maximum charge of energy storage, E ESP,min is the minimum charge of energy storage, E ESP (t) is the real-time charge of energy storage, P′ ESP (t) is the power of energy storage after adjustment, greater than 0 means discharging, less than 0 means charging; η charge is the energy storage charging efficiency; η discharge is the energy storage discharge efficiency.

4. The method according to claim 1, characterized in that The pre-established operating characteristic analysis model also includes an operating characteristic analysis model of a single electric vehicle; The analytical model of the operating characteristics of a single electric vehicle is as follows: Among them, S t is the state of charge of the electric vehicle at time t, P t ch Charging power for electric vehicles, η ch is the charging efficiency of electric vehicles, Δt ch is the charging time period, P t dch is the discharge power of the electric vehicle, η dch is the discharge efficiency of electric vehicles, Δt dch is the discharge time period, E max It is the maximum charge of an electric vehicle after it is fully charged; According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy source is obtained, including the following steps: When an upward adjustment instruction is received, the electric vehicles in the electric vehicle cluster whose SOC is greater than the upper charge threshold are not adjusted; when a downward adjustment instruction is received, the electric vehicles in the electric vehicle cluster whose SOC is lower than the lower charge threshold are not adjusted; Get the maximum upward adjustment range of the electric vehicle cluster Where T is the starting time of the research period, Δt is the preset length of the research period; P(t) EV Real-time power of electric vehicle clusters, The maximum charging power for the electric vehicle cluster; Get the maximum downward adjustment range of the electric vehicle cluster in, is the maximum discharge power of the electric vehicle cluster; Get the duration of electric vehicle cluster regulation Among them, E EV,max is the maximum charge of the electric vehicle cluster, E EV,min is the minimum charge of the electric vehicle cluster, E EV (t) is the real-time charge of the electric vehicle cluster, P′ EV (t) is the power of the electric vehicle cluster after adjustment, greater than 0 means discharging, less than 0 means charging; η charge Charging efficiency for electric vehicles; η discharge The discharge efficiency of electric vehicles.

5. The method according to claim 1, characterized in that The pre-established operation characteristic analysis model also includes an air conditioning load cluster operation characteristic analysis model; The analytical model of the air conditioning load cluster operation characteristics is as follows: Among them, 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; According to the preset adjustable capacity evaluation index, the adjustable capacity evaluation result of each distributed energy source is obtained, including the following steps: Get the maximum upward adjustment range of the air conditioning load cluster: Get the maximum downward adjustment range of the air conditioning load cluster: Among them, T Δ is the temperature control margin.

6. The method according to claim 1, characterized in that The objective function includes a maximum regulation capability objective function and a maximum duration objective function; the constraint conditions include a power balance constraint condition, a tie line constraint condition and the constraint conditions of each distributed energy source itself; The step of determining the adjustable capacity curve of the target aggregation unit through the objective function and the constraint conditions includes: The maximum regulation capacity objective function or the maximum duration objective function, as well as the power balance constraint condition, the tie line constraint condition and the constraint condition of each distributed energy source itself are input into a mathematical optimization solution tool to obtain the adjustable capacity curve of the target aggregation unit.

7. The method according to claim 1, characterized in that The method further comprises: The adjustable capacity evaluation index is objectively weighted by using the entropy weight method to obtain an objective weight; Using expert evaluation opinions to subjectively weight the adjustable capability evaluation indicators to obtain subjective weights; Determine a comprehensive weight according to the subjective weight and the objective weight, and use the comprehensive weight to perform weighted processing on the adjustable capacity evaluation result of the target aggregation unit to obtain an adjustable capacity evaluation value for 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 adjustable capacity evaluation values ​​of the other aggregation units, and display the sorted adjustable capacity evaluation values ​​and the corresponding aggregation units on the power system dispatching interface.

8. The method according to claim 1, characterized in that The method further comprises: Obtaining the reporting adjustable capability corresponding to the target aggregation unit sent by the target aggregation unit; The reported adjustable capacity is compared with the adjustable capacity curve of the target aggregation unit. When the comparison result exceeds a preset threshold condition, an adjustable capacity test is initiated for the target aggregation unit, and the comparison result and the test result are displayed on the power system dispatching interface.

9. A device for evaluating the adjustable capacity of distributed energy to provide power auxiliary services, characterized in that: The device comprises: An energy data acquisition module, used to acquire distributed energy data sent by a target aggregation unit; the distributed energy data includes at least one of energy storage data, electric vehicle data and air conditioning load data; The adjustable capacity acquisition module is used to input the data of each distributed energy source into the pre-established operation characteristic analysis model for different distributed energy sources to obtain the adjustable capacity curve of each distributed energy source; according to the adjustable capacity curve of each distributed energy source, aggregate each distributed energy source, and determine the adjustable capacity curve of the target aggregation unit through the objective function and constraint conditions; determine other aggregation units except the target aggregation unit under the target section, and obtain the adjustable capacity curves of other aggregation units; input the adjustable capacity curves of each aggregation unit under the target section, the adjustable capacity constraint conditions of the aggregation unit and the section flow constraint conditions into the mathematical optimization solution tool to obtain the adjustable capacity curve corresponding to the target section; The adjustable capacity evaluation module is used to obtain the adjustable capacity evaluation results of each distributed energy source, 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 indicators; the preset adjustable capacity evaluation indicators include adjustment amplitude, response time, adjustment rate, adjustment duration and adjustment accuracy.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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