A flexibility assessment method for integrated energy systems based on multi-type energy storage

Abstract

The present invention belongs to the field of energy assessment technology, and in particular, relates to a flexibility assessment method for an integrated energy system based on multi-type energy storage. The present invention analyzes the regulation characteristics of flexibility resources under the background of high penetration of renewable energy, and considers the relationship between the flexibility response capabilities of different flexibility resources and the time scale and output level; starting from quantitative assessment, relevant research is conducted on the types and potential of flexibility resources available to the system, and flexibility assessment work is carried out under the framework of probabilistic methods. The supply and demand of flexibility are regarded as random variables that obey a specific distribution, and probability density is introduced to describe them, thereby constructing a corresponding mathematical model. Finally, the flexibility quantitative index is introduced into the integrated energy system planning work, and the optimal configuration scheme under different flexibility constraint thresholds is optimized. The present invention realizes the quantitative assessment of the system flexibility balance, and ensures the flexible and stable operation of the system under the conditions of grid connection of a high proportion of renewable energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy assessment, and in particular relates to a flexibility assessment method for an integrated energy system based on multi-type energy storage, and specifically to a quantitative assessment method for flexibility balance. Background Art

[0002] Under the background of high penetration of renewable energy, the energy structure has undergone unprecedented major changes, and it is of great significance to carry out special planning work on energy system flexibility. Supply-side resources and energy storage are the main sources of flexibility at present, ensuring the sufficient level of flexibility supply. The flexible resources on the supply side are the flexible adjustment of conventional power sources; energy storage provides flexibility by giving play to its energy migration characteristics, thereby effectively matching the random and fluctuating characteristics of renewable energy power sources and ensuring the safe and stable operation of the entire system. Summary of the invention

[0003] In view of the shortcomings of the above-mentioned prior art, the present invention provides a method for evaluating the flexibility of an integrated energy system based on multi-type energy storage. Its purpose is to achieve a detailed description of the supply and demand balance mechanism under a probabilistic framework, and to achieve a quantitative evaluation of the system flexibility balance by establishing a system flexibility supply and demand probability model and flexibility evaluation indicators. At the same time, the flexibility quantitative index is introduced into the integrated energy system planning work to optimize the optimal configuration plan under different flexibility constraint thresholds, thereby ensuring the flexible and stable operation of the system under the conditions of grid connection with a high proportion of renewable energy.

[0004] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0005] A method for evaluating the flexibility of an integrated energy system based on multiple types of energy storage comprises the following steps:

[0006] Step 1. Under a specific time scale, the unit flexibility adjustment capability is related to the output level, and the relationship is expressed as:

[0007]

[0008] Among them, P h represents the unit output; τ represents the time scale, which is usually 15 minutes, 4 hours and 24 hours, corresponding to the research work of frequency regulation, ramp climbing and peak regulation respectively, x h It represents the functional relationship between the unit's flexibility adjustment capability and output level at a specific time scale;

[0009] Step 2. The functional relationship between the flexibility supply and output of conventional units is:

[0010]

[0011]

[0012] Among them, x + 、x - Respectively represent the upward and downward peak regulation flexibility of conventional units; R h + , R h - Respectively represent the up and down climbing rates of the unit; P hmam , P hmin They represent the maximum and minimum output values ​​of the unit respectively, and dt represents the integral over time;

[0013] On the basis of establishing a multi-time scale and multi-state flexibility supply mathematical model, according to the selected time scale and the probability distribution function of the operating state, the flexibility supply probability distribution function is constructed as follows:

[0014]

[0015]

[0016] Among them, x s + 、x s - Respectively represent the upward and downward flexibility of the energy storage device; P out , P in Respectively represent output power and input power; P in,e , P out,e Respectively represent the rated input and output power; E s 、E smin 、E smax Respectively represent real-time capacity, minimum capacity, and maximum capacity;

[0017] Step 3. Considering the characteristics of load-side uncertainty, assume that the load-side demand is a random variable Y that obeys conditional distribution. The relationship is expressed as:

[0018]

[0019] Among them, τ is the time scale, which is usually selected from 15 minutes, 4 hours, and 24 hours, corresponding to the time scales of frequency regulation, ramp climbing, and peak regulation related research respectively; 24 hours is selected as the time scale, L represents the load level, and L max Indicates the maximum load that may occur within the time scale, L imn represents the minimum load that may occur within the time scale, is the functional relationship between load side demand and time, f L is the function expression of load level, y is a random variable subject to conditional distribution, and dL represents the integral of load level;

[0020] Step 4. The flexibility demand caused by the randomness and volatility of new energy is expressed by a random variable that satisfies the conditional distribution as follows:

[0021]

[0022] Among them, K represents the output level of new energy units, K max Indicates the maximum possible operating value within the time scale, K min Indicates the minimum possible operating value within the time scale, represents the flexibility requirement, f K is the functional expression of the output level of the new energy unit, y is a random variable that obeys conditional distribution, and dK represents the integral of the output level;

[0023] Step 5. Under the framework of the probability method, it is effective to characterize the flexible supply and demand relationship in the form of probability distribution;

[0024] Step 6. Quantitative evaluation indicators of flexibility, by statistically analyzing the size of flexibility resources, to obtain corresponding evaluation results; in contrast, insufficient attention is paid to the impact of the imbalance in the binary relationship between flexibility supply and demand;

[0025] Step 7. Coordinated planning of various types of flexibility resources. By quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement costs of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of minimum unit improvement cost, iteratively solving, determine the flexibility investment plan for comprehensive utilization of multiple resources;

[0026] Step 8. Introduce flexibility quantitative indicators into the integrated energy system planning work, and optimize the optimal configuration plan under different flexibility constraint thresholds.

[0027] Furthermore, in step 5, under the framework of the probability method, it is effective and feasible to characterize the flexible supply and demand relationship in the form of probability distribution, which specifically includes:

[0028] Step 51. The probability distribution characteristics of flexible supply and demand are not considered;

[0029] Step 52. Consider the probability distribution characteristics of flexible supply and demand.

[0030] Furthermore, the probability distribution characteristics of supply and demand without considering flexibility include:

[0031] The difference Z between the flexibility supply X and the flexibility demand Y is defined as the system flexibility margin. The probability distribution of the system supply being less than the demand is used as the criterion to characterize whether the system flexibility is sufficient or not. Its probability form is:

[0032]

[0033] Among them, X i Represents the supply of a single flexible resource; Y i Represents a single flexibility demand; R and D represent the set of flexibility resources and demands respectively, ε represents the flexibility abundance level threshold, P represents the probability that system supply is less than demand, and Z represents the system flexibility margin.

[0034] Furthermore, the probability distribution characteristics of flexible supply and demand are considered, including:

[0035] Considering the probability distribution characteristics of flexible supply and demand, the flexibility margin also satisfies the conditional distribution and is expressed as

[0036]

[0037] Among them, s i It represents the system operating status, including unit output, energy storage status, and load level; S represents the operating status set, P represents the probability distribution of flexibility supply and demand, Z represents the system flexibility margin, τ is the time constant, and ε represents the flexibility margin threshold.

[0038] Furthermore, the flexibility margin probability distribution includes:

[0039] (1) Sufficient flexibility;

[0040] (2) There may be a lack of flexibility.

[0041] Furthermore, the flexibility evaluation index includes the following steps:

[0042] Step 1. The probability density expression of flexibility margin is:

[0043]

[0044] Among them, φ Xi represents the probability distribution of a single supply resource; φ Yi represents the probability density of a single demand, φ X ,φ Y Represent the probability density functions of total flexibility supply and total demand respectively: X ,φ Y By performing reverse convolution, we can obtain the probability density function of the flexibility margin. represents the probability distribution function of flexibility margin, i represents the number of convolution integrals, and di represents the integral of the number;

[0045] Step 2. When the time scale is selected, the above formula is expressed as:

[0046]

[0047] Among them, S max , S min They correspond to the upper and lower limits of the system operating state, Z represents the system flexibility margin, s represents the operating state set, dz represents the integration of the flexibility margin, and ds represents the integration of the operating state;

[0048] Step 3. Based on the above probability density expression of flexibility margin, a set of evaluation indicators for quantitative analysis of system flexibility level is proposed, and its expression is:

[0049]

[0050]

[0051]

[0052] The above formulas are the probability distribution of flexibility margin, the probability of insufficient flexibility and the expected value of insufficient flexibility, respectively. dz represents the integral of flexibility margin, and ds represents the integral of operating status.

[0053] Furthermore, by quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement costs of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of minimum unit improvement cost, iteratively solving, determine the flexibility investment plan for comprehensive utilization of multiple resources, including the following steps:

[0054] Step 1. According to the output information of existing flexible units and new energy units, the initial configuration cost is given;

[0055] Step 2: Take the initial configuration cost as the iteration starting point, combine it with the flexibility evaluation index, perform iterative calculation, and then obtain the flexibility resource optimization configuration result.

[0056] The present invention has the following beneficial effects and advantages:

[0057] This paper first analyzes the regulation characteristics of flexibility resources under the background of high penetration of renewable energy, and considers the relationship between the flexibility response capacity of different flexibility resources and the time scale and output level; secondly, starting from quantitative evaluation, relevant research is conducted on the types and potential of flexibility resources available to the system, and flexibility evaluation is carried out under the framework of probabilistic methods. The supply and demand of flexibility are regarded as random variables subject to specific distributions, and probability density is introduced to describe them, thereby constructing a corresponding mathematical model. Finally, the flexibility quantitative index is introduced into the integrated energy system planning work, and the optimal configuration scheme under different flexibility constraint thresholds is optimized.

[0058] With the rational configuration of multiple types of energy storage equipment, the flexibility index is linearly correlated with the level of new energy consumption, and the optimal resource combination that meets the system flexibility level requirements is constructed. The problem of insufficient flexibility under the background of high penetration of renewable energy can be properly solved, and the comprehensive flexibility optimization under the complex scenario of source-grid-load-storage can be effectively achieved based on the system flexibility balance mechanism.

[0059] The present invention achieves quantitative evaluation of system flexibility balance by establishing a system flexibility supply and demand probability model and flexibility evaluation index. At the same time, the flexibility quantitative index is introduced into the comprehensive energy system planning work to optimize the optimal configuration scheme under different flexibility constraint thresholds, thereby ensuring the flexible and stable operation of the system under the condition of high proportion of renewable energy grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0061] Figure 1 It is the probability distribution diagram of the flexibility supply and demand of the present invention;

[0062] Figure 2 is a probability distribution diagram of the flexibility margin of the present invention;

[0063] Figure 3 This is a flow chart of the flexibility resource optimization configuration algorithm of the present invention. DETAILED DESCRIPTION

[0064] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0066] Refer to the following Figure 1-Figure 3 The technical solutions of some embodiments of the present invention are described.

[0067] Example 1

[0068] The present invention is a method for evaluating the flexibility of an integrated energy system based on multiple types of energy storage, which specifically includes the following steps:

[0069] Step 1. Under a specific time scale, the unit flexibility adjustment capability is only related to the output level, and the relationship can be expressed as:

[0070]

[0071] Among them, P h represents the unit output; τ represents the time scale, which is usually 15 minutes, 4 hours and 24 hours, corresponding to the research work of frequency regulation, ramp climbing and peak regulation respectively, x h It represents the functional relationship between the unit's flexibility adjustment capability and output level at a specific time scale.

[0072] Step 2. The functional relationship between the flexibility supply and output of conventional units is:

[0073]

[0074]

[0075] Among them, x + 、x - Respectively represent the peak regulation flexibility of conventional units; R h + , R h - Respectively represent the up and down climbing rates of the unit; P hmam , P hmin They represent the maximum and minimum output of the unit respectively, and dt represents the integral over time.

[0076] On the basis of establishing a multi-time scale and multi-state flexibility supply mathematical model, according to the selected time scale and the probability distribution function of the operating state, the flexibility supply probability distribution function is constructed as follows:

[0077]

[0078]

[0079] Among them, x s + 、x s - Respectively represent the upward and downward flexibility of the energy storage device; P out , P in Respectively represent output power and input power; P in,e , P out,e Respectively represent the rated input and output power; E s 、E smin 、E smax Respectively represent the real-time capacity, minimum capacity, and maximum capacity.

[0080] Step 3. At present, the main reasons for the emergence of a large number of flexibility demands are the ever-changing renewable energy sources represented by wind power, the volatility and uncertainty of the load side. Considering the characteristics of uncertainty on the load side, the load side demand is assumed to be a random variable Y that obeys conditional distribution, and the relationship is expressed as:

[0081]

[0082] Among them, τ is the time scale, which is usually selected from 15 minutes, 4 hours, and 24 hours, corresponding to the time scales of frequency modulation, ramp climbing, and peak regulation. 24 hours is selected as the time scale of the study, L represents the load level, and L max Indicates the maximum load that may occur within the time scale, L imn represents the minimum load that may occur within the time scale, is the functional relationship between load side demand and time, f L is the function expression of load level, y is a random variable that obeys conditional distribution, and dL represents the integral of load level.

[0083] Step 4. Similarly, the flexibility demand caused by the randomness and volatility of new energy can also be expressed by a random variable that satisfies the conditional distribution as follows:

[0084]

[0085] Among them, K represents the output level of new energy units, K max Indicates the maximum possible operating value within the time scale, K min Indicates the minimum possible operating value within the time scale, represents the flexibility requirement, f K is the function expression of the output level of the new energy unit, y is a random variable that obeys the conditional distribution, and dK represents the integral of the output level.

[0086] Step 5. In the renewable energy high penetration system, flexibility supply and flexibility demand are both random variables subject to conditions. Therefore, within the framework of the probabilistic method, it is very effective to characterize the flexible supply and demand relationship in the form of probability distribution.

[0087] Step 51. The probability distribution characteristics of flexible supply and demand are not considered.

[0088] The difference Z between the flexibility supply X and the flexibility demand Y is defined as the system flexibility margin. The probability distribution of the system supply being less than the demand is used as the criterion to characterize whether the system flexibility is sufficient or not. Its probability form is:

[0089]

[0090] Among them, X i Represents the supply of a single flexible resource; Y i Represents a single flexibility demand; R and D represent the set of flexibility resources and demands respectively, ε represents the flexibility abundance level threshold, P represents the probability that system supply is less than demand, and Z represents the system flexibility margin.

[0091] Step 52. Consider the probability distribution characteristics of flexible supply and demand.

[0092] Considering the probability distribution characteristics of flexible supply and demand, the flexibility margin also satisfies the conditional distribution and can be expressed as

[0093]

[0094] Among them, s i It represents the system operating status, including unit output, energy storage status, and load level; S represents the operating status set, P represents the probability distribution of flexibility supply and demand, Z represents the system flexibility margin, τ is the time constant, and ε represents the flexibility margin threshold.

[0095] Step 6. Flexibility quantitative evaluation indicators. Existing quantitative analysis of system flexibility is mostly based on certain evaluation indicators, and the corresponding evaluation results are obtained by statistically analyzing the size of flexibility resources. In contrast, there is a lack of sufficient attention to the impact of the imbalance in the binary relationship between flexibility supply and demand.

[0096] Step 7. Coordinated planning of various types of flexibility resources. By quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement cost of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of minimum unit improvement cost, iteratively solve and determine the flexibility investment plan for comprehensive utilization of multiple resources.

[0097] Step 8. At the same time, introduce the flexibility quantitative index into the integrated energy system planning work to optimize the optimal configuration plan under different flexibility constraint thresholds.

[0098] Example 2

[0099] like Figure 1 As shown, Figure 1 This is a probability distribution diagram of the flexibility supply and demand of the present invention.

[0100] In the shaded area in the figure, the system may have an imbalance in flexibility, which may easily lead to load shedding, renewable energy power restrictions, etc. To ensure that the system has sufficient flexibility, corresponding measures should be taken to change the probability distribution characteristics of flexibility supply and demand, effectively reduce the shaded area in the above figure, and ensure flexible and stable operation of the system.

[0101] like Figure 2 As shown, Figure 2 This is a probability distribution diagram of the flexibility margin of the present invention.

[0102] In the first quadrant, the flexibility Z≥0 means that the flexibility is sufficient; in the second quadrant, the flexibility margin Z≤0 means that the flexibility may be insufficient. Existing quantitative analysis of system flexibility is mostly based on certain evaluation indicators, and the corresponding evaluation results are obtained by statistically analyzing the size of flexibility resources. In contrast, there is a lack of sufficient attention to the impact of the imbalance in the binary relationship between flexibility supply and demand.

[0103] The physical meaning of the flexibility evaluation index can be explained by the probability distribution of the flexibility margin. By analyzing the changing characteristics of the flexibility margin probability distribution function and obtaining its corresponding value, the quantitative analysis between the flexibility demand and flexibility supply capacity on both sides of the source and load can be achieved, which specifically includes the following steps:

[0104] Step 1. The probability density expression of flexibility margin can be obtained by convolution of the random variables of flexibility supply and demand, referring to the relevant theorem of probability convolution. Its expression is:

[0105]

[0106] Among them, φ Xi represents the probability distribution of a single supply resource; φ Yi represents the probability density of a single demand, φ X ,φ Y Represent the probability density functions of total flexibility supply and total demand respectively: X ,φ Y By performing reverse convolution, we can obtain the probability density function of the flexibility margin. represents the probability distribution function of flexibility margin, i represents the number of convolution integrals, and di represents the integral of the number.

[0107] Step 2. When the time scale is selected, the above formula can be expressed as:

[0108]

[0109] Among them, S max , S min They correspond to the upper and lower limits of the system operating state respectively, Z represents the system flexibility margin, s represents the operating state set, dz represents the integral of the flexibility margin, and ds represents the integral of the operating state.

[0110] Step 3. Based on the above probability density expression of flexibility margin, a set of evaluation indicators for quantitative analysis of system flexibility level is proposed, and its expression is:

[0111]

[0112]

[0113]

[0114] The above formulas are the probability distribution of flexibility margin, the probability of insufficient flexibility and the expected value of insufficient flexibility, respectively. dz represents the integral of flexibility margin, and ds represents the integral of operating status.

[0115] like Figure 3 As shown, Figure 3 This is a flow chart of the flexibility resource optimization configuration algorithm of the present invention.

[0116] By quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement cost of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of minimum unit improvement cost, iteratively solving, and determining the flexibility investment plan for comprehensive utilization of multiple resources. First, the adjustment resource allocation capacity is used as a coupling variable, and second, the power balance constraint and flexibility balance constraint are satisfied, and the solution process is divided into two stages.

[0117] More specifically, the flexibility resource optimization configuration algorithm process includes the following steps:

[0118] Step 1. Give the initial configuration cost based on the output information of existing flexible units and new energy units.

[0119] Step 2: Take the initial configuration cost as the iteration starting point, combine it with the flexibility evaluation index, perform iterative calculation, and then obtain the flexibility resource optimization configuration result.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the flexibility of an integrated energy system based on multi-type energy storage, characterized by: The following steps are involved: Step 1. Under a specific time scale, the unit flexibility adjustment capability is related to the output level, and the relationship is expressed as: Among them, P h Indicates the unit output; τ indicates the time scale, which is 15min, 4h, and 24h, corresponding to the time scales of frequency regulation, ramp climbing, and peak regulation, respectively. It represents the functional relationship between the unit's flexibility adjustment capability and output level at a specific time scale; Step 2. The functional relationship between the flexibility supply and output of conventional units is: Among them, x + 、x - Respectively represent the peak regulation flexibility of conventional units; R h + , R h - Respectively represent the up and down climbing rates of the unit; P hmam , P hmin They represent the maximum and minimum output values ​​of the unit respectively, and dt represents the integral over time; On the basis of establishing a multi-time scale and multi-state flexibility supply mathematical model, according to the selected time scale and the probability distribution function of the operating state, the flexibility supply probability distribution function is constructed as follows: Among them, x s + 、x s - Respectively represent the upward and downward flexibility of the energy storage device; P out , P in Respectively represent output power and input power; P in,e , P out,e Respectively represent the rated input and output power; E s 、E smin 、E smax Respectively represent real-time capacity, minimum capacity, and maximum capacity; Step 3. Considering the characteristics of load-side uncertainty, assume that the load-side demand is a random variable Y that obeys conditional distribution. The relationship is expressed as: Among them, τ is the time scale, and 15min, 4h, and 24h are selected to correspond to the time scales of frequency regulation, ramp climbing, and peak regulation, respectively; 24h is selected as the time scale of the study, L represents the load level, and L max Indicates the maximum load that may occur within the time scale, L imn represents the minimum load that may occur within the time scale, f Y L is the functional relationship between load side demand and time, f L is the function expression of load level, y is a random variable subject to conditional distribution, and dL represents the integral of load level; Step 4. The flexibility demand caused by the randomness and volatility of new energy is expressed by a random variable that satisfies the conditional distribution as follows: Among them, K represents the output level of new energy units, K max Indicates the maximum possible operating value within the time scale, K min Indicates the minimum possible operating value within the time scale, represents the flexibility requirement, f K is the functional expression of the output level of the new energy unit, y is a random variable that obeys conditional distribution, and dK represents the integral of the output level; Step 5. Under the framework of the probability method, it is effective to characterize the flexible supply and demand relationship in the form of probability distribution, including: Step 51. The probability distribution characteristics of flexibility supply and demand are not considered, including: defining the difference Z between flexibility supply X and flexibility demand Y as the system flexibility margin, and taking the probability distribution of system supply being less than demand as the criterion for characterizing whether the system flexibility is sufficient or not, and its probability form is: Among them, X i Represents the supply of a single flexible resource; Y i represents a single flexibility demand; R and D represent the set of flexibility resources and demands respectively, ε represents the threshold of flexibility abundance level, P represents the probability that system supply is less than demand, and Z represents the system flexibility margin; Step 52: Considering the probability distribution characteristics of flexibility supply and demand, including: considering the probability distribution characteristics of flexibility supply and demand, the flexibility margin also satisfies the conditional distribution, which is expressed as: Among them, s i It represents the system operation status, including unit output, energy storage status, and load level; S represents the set of operation status, P represents the probability distribution of flexibility supply and demand, Z represents the system flexibility margin, τ is the time constant, and ε represents the flexibility margin threshold; The flexibility margin probability distribution includes: (1) a situation where flexibility is sufficient; (2) a situation where flexibility may be insufficient; Step 6. Quantitative evaluation indicators of flexibility, by statistically analyzing the size of flexibility resources, to obtain corresponding evaluation results; in contrast, insufficient attention is paid to the impact of the imbalance in the binary relationship between flexibility supply and demand; Step 7. Coordinated planning of various types of flexibility resources. By quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement costs of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of minimum unit improvement cost, iteratively solving, determine the flexibility investment plan for comprehensive utilization of multiple resources; Step 8. Introduce the flexibility quantitative index into the integrated energy system planning work, and optimize the optimal configuration scheme under different flexibility constraint thresholds; the flexibility evaluation index includes the following steps: Step 1. The probability density expression of the flexibility margin is: Among them, φ Xi represents the probability distribution of a single supply resource; φ Yi represents the probability density of a single demand, φ X ,φ Y Represent the probability density functions of total flexibility supply and total demand respectively: X ,φ Y Performing reverse convolution can obtain the probability density function of the flexibility margin, φ M represents the probability distribution function of flexibility margin, i represents the number of convolution integrals, and di represents the integral of the number; Step 2. When the time scale is selected, the above formula is expressed as: Among them, S max , S min They correspond to the upper and lower limits of the system operating state, Z represents the system flexibility margin, s represents the operating state set, dz represents the integration of the flexibility margin, and ds represents the integration of the operating state; Step 3. Based on the above probability density expression of flexibility margin, a set of evaluation indicators for quantitative analysis of system flexibility level is proposed, and its expression is: The above formulas are respectively the probability distribution of flexibility margin, the probability of occurrence of insufficient flexibility and the expected value of insufficient flexibility, dz represents the integral of flexibility margin, and ds represents the integral of operation status; By quantitatively comparing the specific adjustment levels of the three types of flexibility adjustment means, taking the unit improvement cost of different types of flexibility supply sources as the construction standard, introducing the idea of ​​maximum net benefit increment ratio, and adhering to the principle of lowest unit improvement cost, iterative solution is performed to determine the flexibility investment plan for comprehensive utilization of multiple resources, including the following steps: Step 1. According to the output information of existing flexibility units and new energy units, the initial configuration cost is given; Step 2. Taking the initial configuration cost as the iteration starting point, combined with the flexibility evaluation index, iterative calculation is performed to obtain the optimal configuration result of flexibility resources.

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