CN118014164A - Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements - Google Patents

Abstract

The present invention discloses a double-layer optimization method and system for energy storage capacity configuration considering flexibility requirements, the method comprising: constructing an inner model of energy storage capacity configuration with the minimum operating cost of short-time-scale flexibility resource output as the first objective function under a preset first constraint; calculating the flexibility resource abundance under a short-time scale, and determining a second constraint according to the flexibility resource abundance; constructing an outer model of energy storage capacity configuration with the minimum operating cost of the energy storage system as the second objective function under the second constraint; iteratively solving the inner model of energy storage capacity configuration and the outer model of energy storage capacity configuration according to an improved quantum particle swarm algorithm to obtain the final energy storage capacity configuration plan. The interactive decision-making of the double-layer model is realized, and the optimal plan for flexibility resource configuration in the region is quickly obtained.

Description

A dual-layer optimization method and system for energy storage capacity configuration considering flexibility requirements

Technical Field

The present invention belongs to the technical field of energy storage capacity optimization, and in particular relates to a double-layer optimization method and system for energy storage capacity configuration considering flexibility requirements.

Background technique

Flexibility resources include distributed energy storage, demand response loads, and adjustable power sources, which are widely distributed and dispersed. During peak electricity consumption, flexibility resources can achieve the effect of peak load regulation or frequency regulation by reducing load or increasing output, improving the safety and stability of the power grid and reducing redundant investment. However, the response degree of various types of flexibility resources is different. Therefore, how to quantify the flexibility response degree according to the characteristics of various types of flexibility resources, optimize the configuration and scheduling of flexibility resources in the region, and improve the economy of the power grid is an unresolved problem. In current research, different types of indicators are often used for different types of flexibility needs. There is a lack of a quantitative indicator that can systematically describe the degree of flexibility supply for different types of resources, and there is a lack of an indicator system that can quickly calculate the configuration of flexibility resources.

The configuration optimization of flexibility resources needs to consider the dispatching cost, operation cost, and construction cost supply of each flexibility resource, as well as the investment and operation cost of the system. Different dispatching schemes and different capacity configuration schemes have different flexibility adjustment costs. Therefore, achieving flexible and stable operation of regional power grids is an urgent problem to be solved.

Summary of the invention

The present invention provides a double-layer optimization method and system for energy storage capacity configuration taking flexibility requirements into consideration, which are used to solve the technical problem that flexible and stable operation of a regional power grid cannot be achieved.

In a first aspect, the present invention provides a two-layer optimization method for energy storage capacity configuration considering flexibility requirements, comprising:

Under the preset first constraint condition, the inner model of energy storage capacity configuration is constructed with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods;

Calculating the flexibility resource sufficiency in a short time scale, and determining a second constraint condition according to the flexibility resource sufficiency;

Under the second constraint condition, the outer model of energy storage capacity configuration is constructed with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The energy storage capacity configuration inner model and the energy storage capacity configuration outer model are iteratively solved according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

In a second aspect, the present invention provides a two-layer optimization system for energy storage capacity configuration considering flexibility requirements, comprising:

The first construction module is configured to construct an inner model of energy storage capacity configuration under a preset first constraint condition with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources, /> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods;

A calculation module, configured to calculate the flexibility resource margin in a short time scale, and determine a second constraint condition according to the flexibility resource margin;

The second construction module is configured to construct an outer model of energy storage capacity configuration under the second constraint condition with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The solution module is configured to iteratively solve the energy storage capacity configuration inner model and the energy storage capacity configuration outer model according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the two-layer optimization method for energy storage capacity configuration considering flexibility requirements of any embodiment of the present invention.

In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of a two-layer optimization method for energy storage capacity configuration considering flexibility requirements according to any embodiment of the present invention.

The double-layer optimization method and system for energy storage capacity configuration considering flexibility demand in the present application calculates the flexibility resource abundance in a short time scale, determines the second constraint condition according to the flexibility resource abundance, considers both the system investment and operating costs and the output and operating costs of the flexibility resources, constructs a double-layer capacity configuration model, realizes interactive decision-making of the double-layer model, and quickly obtains the optimal solution for flexibility resource allocation in the region.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is a flow chart of a dual-layer optimization method for energy storage capacity configuration considering flexibility requirements provided by an embodiment of the present invention;

FIG2 is a structural block diagram of a dual-layer optimization system for energy storage capacity configuration considering flexibility requirements provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Please refer to FIG1 , which shows a flow chart of a double-layer optimization method for energy storage capacity configuration considering flexibility requirements of the present application.

As shown in Figure 1, the two-layer optimization method for energy storage capacity configuration considering flexibility requirements specifically includes the following steps:

Step S101, under a preset first constraint condition, an inner model of energy storage capacity configuration is constructed with the minimization of the operating cost of the short-time-scale flexible resource output as the first objective function.

In this step, the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods.

It should be noted that the first constraint condition includes power generation-load balance constraint, electrochemical energy storage charge and discharge constraint, and power constraint;

The expression of the generation-load balance constraint is:

,

In the formula, is the power generation of all generator sets at time t, /> is the discharge power of electrochemical energy storage i at time t, /> is the load at time t, /> is the charging power of electrochemical energy storage i at time t, /> is the number of electrochemical energy storages;

The expression of electrochemical energy storage charge and discharge constraint is:

,

,

,

In the formula, is the minimum discharge power of electrochemical energy storage i, /> is the discharge power of electrochemical energy storage i at time t, /> is the shutdown state of the electrochemical energy storage i at time t, /> is the maximum discharge power of electrochemical energy storage i, /> is the minimum charging power of electrochemical energy storage i, /> is the charging power of electrochemical energy storage i at time t, /> is the starting state of electrochemical energy storage i at time t, /> is the maximum charging power of electrochemical energy storage i;

The expression of power constraint is:

,

,

,

In the formula, is the minimum output of adjustable hydropower i,/> is the maximum output of adjustable hydropower i,/> The upper limit of the downward ramp rate of adjustable hydropower, /> is the upper limit of the upward ramp rate of adjustable hydropower,/> is the actual output of adjustable hydropower station i at time t-1, /> is the number of monthly periods, /> is the monthly maximum power generation of the adjustable hydropower station i.

Step S102, calculate the flexibility resource margin in a short time scale, and determine the second constraint condition according to the flexibility resource margin.

In this step, the expression for calculating the flexibility resource sufficiency in the short time scale is:

,

,

,

In the formula, is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> is the power generation of all generator sets at time t, /> The flexibility of the hydropower supply at time t is adjustable,/> is the degree of flexible supply of electrochemical energy storage at time t,/> is the degree of flexible supply that is adjusted upward in response to demand load at time t,/> The flexibility of the hydropower supply at time t can be adjusted. is the degree of flexible supply of electrochemical energy storage at time t, is the degree of flexible supply that responds to demand by adjusting the load downward at time t.

It should be noted that the expression for the flexible supply degree of adjustable hydropower is:

,

In the formula, is the upper limit of the upward ramp rate of adjustable hydropower,/> is the time scale,/> is the maximum output power of adjustable hydropower,/> The upper limit of the downward ramp rate of adjustable hydropower, /> is the minimum output power of adjustable hydropower;

The expression for the degree of flexibility of electrochemical energy storage is:

,

In the formula, is the maximum discharge power, /> is the rated capacity of the electrochemical energy storage i, /> is the discharge efficiency of electrochemical energy storage, /> is the real-time value of the electrochemical energy storage charge state,/> is the minimum value of the electrochemical energy storage charge state,/> is the maximum charging power, /> is the maximum value of the electrochemical energy storage state of charge, /> is the charging efficiency of electrochemical energy storage;

The expression of the flexible supply degree of demand response load is:

,

In the formula, The additional power consumption due to demand response load,/> The power removed by the demand response load,/> is the number of demand response loads.

Step S103: constructing an outer model of energy storage capacity configuration under the second constraint condition with the minimum operation cost of the energy storage system as the second objective function.

In this step, the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

It should be noted that the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The expression for calculating the investment cost of adjustable hydropower units is:

,

In the formula, is the total number of adjustable hydroelectric units, /> is the unit investment cost of adjustable hydropower unit i, /> is the capacity of the adjustable hydropower unit i;

The expression for calculating the investment cost of electrochemical energy storage is:

,

In the formula, is the total amount of electrochemical energy storage, /> is the unit investment cost of electrochemical energy storage i, /> is the capacity of electrochemical energy storage i.

Furthermore, the second constraint condition is to consider the flexibility resource abundance constraint. The expression of considering the flexibility resource abundance constraint is: , where / > It is the limit of flexibility resource abundance.

Step S104, iteratively solving the energy storage capacity configuration inner model and the energy storage capacity configuration outer model according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

In this step, the random initial position and speed of the particle swarm are set, that is, the position and capacity of the flexibility resource;

Input the first objective function and the first constraint condition of the inner model of energy storage capacity configuration to optimize the output of flexible resources in each period;

Feeding back the optimization result of the output of the flexible resources to the outer model of the energy storage capacity configuration, calculating the second objective function and the second constraint condition of the outer model of the energy storage capacity configuration, and optimizing the location and capacity of the flexible resources;

Calculate the fitness function and update the particle swarm;

Perform a chaotic search for the top 20% of the best particles in the particle swarm and update their historical optimal speed and position;

Perform chaotic search on the remaining particles and update their historical optimal speed and position;

Calculate the local attraction point of the particle and the average optimal position of the population, and update the particle position;

If the convergence conditions are met, the final energy storage capacity configuration plan is obtained.

In summary, the method of the present application calculates the abundance of flexibility resources in a short time scale, and determines the second constraint condition based on the abundance of flexibility resources. It considers both the system investment and operating costs and the output and operating costs of flexibility resources, and constructs a two-layer capacity configuration model. It realizes interactive decision-making of the two-layer model and quickly obtains the optimal solution for the configuration of flexibility resources in the region.

Please refer to FIG. 2 , which shows a structural block diagram of a two-layer optimization system for energy storage capacity configuration taking flexibility requirements into consideration in the present application.

As shown in FIG. 2 , the energy storage capacity configuration dual-layer optimization system 200 includes a first construction module 210 , a calculation module 220 , a second construction module 230 and a solution module 240 .

The first construction module 210 is configured to construct an inner model of energy storage capacity configuration under a preset first constraint condition with the operation cost of the short-time scale flexible resource output being minimized as the first objective function, wherein the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources, /> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods;

A calculation module 220, configured to calculate the flexibility resource margin in a short time scale, and determine a second constraint condition according to the flexibility resource margin;

The second construction module 230 is configured to construct an outer layer model of energy storage capacity configuration under the second constraint condition with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The solution module 240 is configured to iteratively solve the energy storage capacity configuration inner model and the energy storage capacity configuration outer model according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

It should be understood that the modules recorded in Figure 2 correspond to the steps in the method described with reference to Figure 1. Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in Figure 2 and will not be repeated here.

In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the double-layer optimization method for energy storage capacity configuration considering flexibility requirements in any of the above method embodiments;

As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

Under the preset first constraint condition, the inner model of energy storage capacity configuration is constructed with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i, is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, /> is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, /> is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i, is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods;

Calculating the flexibility resource sufficiency in a short time scale, and determining a second constraint condition according to the flexibility resource sufficiency;

Under the second constraint condition, the outer model of energy storage capacity configuration is constructed with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The energy storage capacity configuration inner model and the energy storage capacity configuration outer model are iteratively solved according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the energy storage capacity configuration dual-layer optimization system considering flexibility requirements, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the energy storage capacity configuration dual-layer optimization system considering flexibility requirements via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

FIG3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. As shown in FIG3 , the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means, and FIG3 takes the bus connection as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the double-layer optimization method of energy storage capacity configuration considering flexibility requirements of the above-mentioned method embodiment is implemented. The input device 330 may receive input digital or character information, and generate key signal input related to user settings and function control of the double-layer optimization system for energy storage capacity configuration considering flexibility requirements. The output device 340 may include display devices such as display screens.

The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

As an implementation mode, the electronic device is applied to a two-tier optimization system for energy storage capacity configuration considering flexibility requirements, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

Under the preset first constraint condition, the inner model of energy storage capacity configuration is constructed with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is:

,

In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods;

Calculating the flexibility resource sufficiency in a short time scale, and determining a second constraint condition according to the flexibility resource sufficiency;

Under the second constraint condition, the outer model of energy storage capacity configuration is constructed with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is:

,

In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system;

Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is:

,

In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or/> ,/> Indicates the degree of inflexibility;

The energy storage capacity configuration inner model and the energy storage capacity configuration outer model are iteratively solved according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A two-layer optimization method for energy storage capacity configuration considering flexibility requirements, characterized by comprising: Under the preset first constraint condition, the inner model of energy storage capacity configuration is constructed with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is: , In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, /> is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, /> is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods; Calculating the flexibility resource sufficiency in a short time scale, and determining a second constraint condition according to the flexibility resource sufficiency; Under the second constraint condition, the outer model of energy storage capacity configuration is constructed with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is: , In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system; The expression for calculating the risk loss of the system due to insufficient flexibility resources is: , In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or ,/> Indicates the degree of inflexibility; The energy storage capacity configuration inner model and the energy storage capacity configuration outer model are iteratively solved according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution. 2. A two-level optimization method for energy storage capacity configuration considering flexibility requirements according to claim 1, characterized in that the first constraint condition includes power generation-load balance constraint, electrochemical energy storage charge and discharge constraint, and power constraint; The expression of the power generation-load balance constraint is: , In the formula, is the power generation of all generator sets at time t, /> is the discharge power of electrochemical energy storage i at time t, /> is the load at time t, /> is the charging power of electrochemical energy storage i at time t, /> is the number of electrochemical energy storages; The expression of the electrochemical energy storage charge and discharge constraint is: , , , In the formula, is the minimum discharge power of electrochemical energy storage i, /> is the discharge power of electrochemical energy storage i at time t, /> is the shutdown state of the electrochemical energy storage i at time t, /> is the maximum discharge power of electrochemical energy storage i, /> is the minimum charging power of electrochemical energy storage i, /> is the charging power of electrochemical energy storage i at time t, is the starting state of electrochemical energy storage i at time t, /> is the maximum charging power of electrochemical energy storage i; The expression of the power constraint is: , , , In the formula, is the minimum output of adjustable hydropower i,/> is the maximum output of adjustable hydropower i,/> The upper limit of the downward ramp rate of adjustable hydropower, /> is the upper limit of the upward ramp rate of adjustable hydropower,/> is the actual output of adjustable hydropower station i at time t-1,/> is the number of monthly periods, /> is the monthly maximum power generation of the adjustable hydropower station i. 3. According to a two-level optimization method for energy storage capacity configuration considering flexibility requirements according to claim 1, it is characterized in that, wherein, the expression for calculating the flexibility resource abundance under a short time scale is: , , , In the formula, is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> is the power generation of all generator sets at time t, /> The flexibility of the hydropower supply at time t is adjustable,/> is the degree of flexible supply of electrochemical energy storage at time t,/> is the degree of flexible supply that is adjusted upward in response to demand load at time t,/> The flexibility of the hydropower supply at time t can be adjusted. is the degree of flexible supply of electrochemical energy storage at time t,/> is the degree of flexible supply that responds to demand by adjusting the load downward at time t. 4. A two-layer optimization method for energy storage capacity configuration considering flexibility requirements according to claim 1, characterized in that, wherein, the expression for calculating the investment cost of the adjustable hydropower unit is: , In the formula, is the total number of adjustable hydropower units, /> is the unit investment cost of adjustable hydropower unit i, /> is the capacity of the adjustable hydropower unit i; The expression for calculating the investment cost of electrochemical energy storage is: , In the formula, is the total amount of electrochemical energy storage, /> is the unit investment cost of electrochemical energy storage i, /> is the capacity of electrochemical energy storage i. 5. According to a two-layer optimization method for energy storage capacity configuration considering flexibility requirements as described in claim 1, it is characterized in that the inner layer model of energy storage capacity configuration and the outer layer model of energy storage capacity configuration are iteratively solved according to the improved quantum particle swarm algorithm to obtain the final energy storage capacity configuration scheme, which includes: Set the random initial position and speed of the particle swarm, that is, the position and capacity of the flexibility resource; Input the first objective function and the first constraint condition of the inner model of energy storage capacity configuration to optimize the output of flexible resources in each period; Feeding back the optimization result of the output of the flexible resources to the outer model of the energy storage capacity configuration, calculating the second objective function and the second constraint condition of the outer model of the energy storage capacity configuration, and optimizing the location and capacity of the flexible resources; Calculate the fitness function and update the particle swarm; Perform a chaotic search for the top 20% of the best particles in the particle swarm and update their historical optimal speed and position; Perform chaotic search on the remaining particles and update their historical optimal speed and position; Calculate the local attraction point of the particle and the average optimal position of the population, and update the particle position; If the convergence conditions are met, the final energy storage capacity configuration plan is obtained. 6. A two-layer optimization system for energy storage capacity configuration considering flexibility requirements, characterized by comprising: The first construction module is configured to construct an inner model of energy storage capacity configuration under a preset first constraint condition with the minimum operating cost of the short-time scale flexible resource output as the first objective function, wherein the expression of the first objective function is: , In the formula, Adjusting costs for flexible resources,/> is the unit start-up and shutdown cost of adjustable hydropower station i,/> is the start-stop capacity of the adjustable hydropower station i at time t,/> is the unit load shedding cost of demand response load unit i, /> is the load shedding amount of demand response load i at time t, /> is the increased output cost of demand response load unit i, /> is the increased output of demand response load unit i,/> is the unit power generation cost of adjustable hydropower station i,/> is the actual output of adjustable hydropower station i at time t, /> They are adjustable hydropower station, demand response load,/> is the number of time periods; A calculation module, configured to calculate the flexibility resource margin in a short time scale, and determine a second constraint condition according to the flexibility resource margin; The second construction module is configured to construct an outer model of energy storage capacity configuration under the second constraint condition with the minimum operation cost of the energy storage system as the second objective function, wherein the expression of the second objective function is: , In the formula, The system investment and operating costs, The investment cost of adjustable hydropower unit is is the investment cost of electrochemical energy storage,/> is the system operating cost,/> Risk loss caused by insufficient flexibility resources in the system; Among them, the expression for calculating the risk loss of the system due to insufficient flexibility resources is: , In the formula, 、/> They are all auxiliary variables for calculating CvaR values./> is the confidence level, /> 、/> The loss cost caused by insufficient upward and downward flexibility resources of the unit,/> is the power generation of all generator sets at time t, /> is the probability of a typical scenario n occurring, /> For a typical scenario, /> The total supply of flexible resources is adjusted downward,/> The total supply of flexible resources is adjusted upward,/> is the load at time t, /> ,/> For/> or ,/> Indicates the degree of inflexibility; The solution module is configured to iteratively solve the energy storage capacity configuration inner model and the energy storage capacity configuration outer model according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration solution. 7. An electronic device, characterized in that it comprises: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 5. 8. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.