Wind storage constant volume planning method based on double-layer optimization

Abstract

The invention relates to a wind storage constant volume planning method based on double-layer optimization. The method is suitable for the technical field of new energy system optimization. The method comprises the following steps: S1, acquiring unit power cost and unit capacity cost of an energy storage system, and wind power output, electrical load and electricity purchase price at each moment in at least one typical day; s2, based on the unit power cost, the unit capacity cost, and the wind power output, the electrical load and the electricity purchase price at each moment, solving a target function of the upper-layer model to obtain the rated capacity and the rated power of the energy storage system and the electricity purchase power at each moment; s3, based on the rated capacity and the rated power of the energy storage system obtained in the step S2, and the wind power output and the electrical load at each moment, solving an objective function of a lower-layer model, and obtaining the electricity purchase power and the average electricity purchase power at each moment; and S4, the steps S2 and S3 are repeated, and the objective function of the upper-layer model is solved again in combination with the average electricity purchase power obtained in the step S3 until a preset iterative calculation condition is met.

Description

Technical Field

[0001] The present invention relates to a wind energy storage fixed-capacity planning method based on double-layer optimization, which is applicable to the technical field of new energy system optimization. Background Art

[0002] With the advancement of global energy transformation, wind power, as a clean and renewable energy source, has become an important part of the power system. However, the output of wind power has significant randomness, intermittency, and volatility, which pose challenges to the consumption and utilization of wind power. Especially when the fluctuation of wind power output does not match the power load demand, the phenomenon of wind curtailment often occurs. To solve this problem, wind farms usually operate in conjunction with energy storage systems. By adjusting the volatility of wind power through the charge and discharge characteristics of the energy storage system, the effects of peak shaving, valley filling, and output smoothing can be achieved, thereby improving the wind power consumption capacity and the economic efficiency of system operation.

[0003] Currently, the research on the capacity configuration of wind energy storage systems mainly focuses on single-layer optimization models and heuristic algorithms. Among them, single-layer optimization models usually take the energy storage capacity as the decision variable, and the optimization goal is to minimize the energy storage investment cost or maximize the wind power consumption capacity. Although these methods are intuitive and easy to implement, it is difficult to simultaneously consider the coupling characteristics of long-term capacity planning and short-term operation scheduling. In addition, although heuristic algorithms (such as genetic algorithms, particle swarm algorithms, etc.) have solved the computational efficiency of complex optimization problems to a certain extent, they lack globality and robustness when facing various uncertain factors such as wind power and load. The optimization results are easily affected by the fluctuations of input parameters and are difficult to meet the actual operation requirements.

[0004] The application of double-layer optimization models in the capacity configuration of energy storage systems has gradually received attention. Its characteristic lies in solving the capacity configuration problem through hierarchical optimization and coordinated iteration of the upper and lower layer models: the upper layer model focuses on the long-term investment planning of the energy storage system, and the goal is to minimize the investment cost by reasonably configuring the energy storage capacity and power; the lower layer model focuses on short-term operation scheduling, and the goal is to optimize the charge and discharge strategy of the energy storage, smooth the wind power output fluctuation, and minimize the wind curtailment amount or operation cost while meeting the power balance constraint. The double-layer optimization model can not only effectively coordinate the long-term planning and short-term operation of the system, but also capture the dynamic relationship between wind power output and load demand through iterative solution, thereby significantly improving the economic efficiency and robustness of the optimization results.

[0005] However, most current double-layer optimization methods still have deficiencies in uncertainty modeling. The uncertainty of wind power output usually adopts static scenario analysis or probability distribution assumption, and this simplification may lead to poor adaptability of the optimization results to the actual operation situation. In addition, the application of multi-objective optimization is also less, and it is difficult to simultaneously take into account performance indicators in multiple dimensions such as investment cost, wind power consumption capacity, and system stability. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: in view of the above problems, to provide a wind-storage fixed-capacity planning method based on double-layer optimization.

[0007] The technical solution adopted by the present invention is: a wind-storage fixed-capacity planning method based on double-layer optimization, including:

[0008] S1. Obtain the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, electricity load, and electricity purchase price at each moment within at least one typical day.

[0009] S2. Based on the unit power cost, unit capacity cost, and the wind power output, electricity load, and electricity purchase price at each moment, solve the objective function of the upper-layer model to obtain the rated capacity, rated power of the energy storage system, and the electricity purchase power at each moment.

[0010] The objective function of the upper-layer model includes equipment investment cost, operation cost, and variance of electricity purchase power, where the equipment investment cost is determined based on the rated power, rated capacity, unit power cost, and unit capacity cost of the energy storage system, and the operation cost is determined based on the charge and discharge power losses of the energy storage system and the electricity purchase price.

[0011] S3. Based on the rated capacity and rated power of the energy storage system obtained in step S2, and the wind power output and electricity load at each moment, solve the objective function of the lower-layer model to obtain the electricity purchase power and average electricity purchase power at each moment.

[0012] The objective function of the lower-layer model includes variance of electricity purchase power.

[0013] S4. Repeat steps S2 and S3, and re-solve the objective function of the upper-layer model in combination with the average electricity purchase power obtained in step S3 until the preset iterative calculation conditions are met.

[0014] The obtaining of the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, electricity load, and electricity purchase price at each moment within at least one typical day, includes:

[0015] Obtain historical data, which contains intraday basic data for several days, and the intraday basic data includes the wind power output, electricity load, and electricity purchase price at each moment of that day.

[0016] Perform clustering analysis on the historical data based on the wind power output, electricity load, and electricity purchase price at each moment, with the number of clusters being D, to obtain D classifications.

[0017] Select one day from each classification as the typical day of that classification, and use the wind power output, electricity load, and electricity purchase price at each moment of that day as the wind power output, electricity load, and electricity purchase price at each moment within the typical day.

[0018] By calculating the covariance matrices between and within classes, the CH index and the silhouette coefficient are used to select the number of clusters D.

[0019] Based on the proportion of the number of days within each classification in the historical data, the weight coefficients of each classification are determined and used as the weight coefficients of the typical days in each classification.

[0020] The objective function of the upper-layer model includes the equipment investment cost, the operating cost, and the variance of the purchased power, including:

[0021] min C t +C r +α·f t

[0022] Where: C t is the equipment investment cost; C r is the operating cost; α is the weight coefficient; f t is the variance of the purchased power.

[0023] The equipment investment cost is determined based on the rated power, rated capacity, unit power cost, and unit capacity cost of the energy storage system, including:

[0024]

[0025] Where: C t is the equipment investment cost, k esp and k esE are the unit power cost and unit capacity cost of the energy storage system respectively; P esN is the rated power of the energy storage system; E esN is the rated capacity of the energy storage system; l is the discount rate; n is the life of the energy storage system.

[0026] The operating cost is determined based on the charge and discharge power losses of the energy storage system and the purchased electricity price, including:

[0027]

[0028] Where: C r is the operating cost; is the charging power of the energy storage system at time t on the typical day d; is the discharging power of the energy storage system at time t on the typical day d; η chr is the charging efficiency of the energy storage system; η dis is the discharging efficiency of the energy storage system; D is the number of typical days; T is the number of time periods in a day; is the purchased electricity price at time t; w d is the weight coefficient of the typical day.

[0029] The constraints of the upper-layer model objective function include: power purchase power constraint, charge-discharge mutual exclusion constraint, energy conservation constraint, and energy storage dynamic energy balance constraint.

[0030] The objective function of the lower-layer model includes the variance of the power purchase power, including:

[0031]

[0032] In the formula: f t is the variance of the power purchase power; is the power purchase power at time t of typical day d; D is the number of typical days; T is the number of time periods in a day; w d is the weight coefficient of the typical day.

[0033] The constraints of the lower-layer model objective function include: rated capacity constraint, rated power constraint, energy conservation constraint, and energy storage dynamic energy balance constraint.

[0034] A wind energy storage fixed-capacity planning device based on double-layer optimization includes:

[0035] A basic data acquisition module, configured to acquire the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, power consumption load, and power purchase price at each moment within at least one typical day;

[0036] An upper-layer optimization module, configured to solve the objective function of the upper-layer model based on the unit power cost, unit capacity cost, and the wind power output, power consumption load, and power purchase price at each moment, so as to obtain the rated capacity, rated power of the energy storage system, and the power purchase power at each moment;

[0037] The objective function of the upper-layer model includes equipment investment cost, operation cost, and variance of power purchase power, wherein the equipment investment cost is determined based on the rated power, rated capacity, unit power cost, and unit capacity cost of the energy storage system, and the operation cost is determined based on the charge and discharge power losses of the energy storage system and the power purchase price;

[0038] A lower-layer optimization module, based on the rated capacity and rated power of the energy storage system obtained by the upper-layer optimization module, and the wind power output and power consumption load at each moment, solves the objective function of the lower-layer model to obtain the power purchase power at each moment and the average power purchase power;

[0039] The objective function of the lower-layer model includes the variance of the power purchase power;

[0040] An iterative calculation module, configured to re-solve the objective function of the upper-layer model by combining the average power purchase power obtained by the lower-layer optimization module until the preset iterative calculation condition is met.

[0041] A storage medium stores a computer program executable by a processor. When the computer program is executed, the steps of the wind energy storage fixed-capacity planning method based on two-layer optimization are implemented.

[0042] A wind energy storage fixed-capacity planning device based on two-layer optimization has a memory and a processor. The memory stores a computer program executable by the processor. When the computer program is executed, the steps of the wind energy storage fixed-capacity planning method based on two-layer optimization are implemented.

[0043] The beneficial effects of the present invention are as follows: The present invention constructs the objective function of the upper-layer model through the equipment investment cost, operation cost, and variance of the purchased power. The objective function of the lower-layer model is constructed through the variance of the purchased power. The upper and lower-layer model objective functions are combined for phased optimization. The planning result of the first phase generally affects the operation objectives and constraints of the second phase, and the operation result of the second phase is fed back to the first phase to verify the accuracy and reliability of the first-phase planning, realizing the interaction between the first phase and the second phase. Thus, the balance between the capacity configuration and operation optimization of the energy storage system is achieved, the cost is reduced as much as possible, and the purchased power is smoothed.

[0044] The typical-day method adopted by the present invention is based on the clustering analysis of historical data, and selects the wind power output curves that can represent the whole year or a certain period (such as different seasons), rather than relying only on static scenarios or single probability distribution models. Since the typical day can cover various fluctuation modes of the wind power output (such as daytime fluctuations, nighttime stability, etc.), the optimization scheme can better adapt to the actual operation situation and avoid the local optimum problem caused by static scenario analysis or probability distribution assumptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following introduces the drawings required to be used in the embodiments of the present invention. It should be understood that the drawings introduced below only conveniently and clearly represent some embodiments of the technical solutions in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic flowchart of the embodiment.

[0047] Figure 2 It is the electricity load curve graph of the typical day in the embodiment.

[0048] Figure 3 It is the wind power output curve graph of the typical day in the embodiment.

[0049] Figure 4 It is the purchased electricity price curve graph of the typical day in the embodiment.

[0050] Figure 5 It is the operation optimization diagram of energy storage charging and discharging in the embodiment.

[0051] Figure 6 It is the curve graph of energy storage capacity in the embodiment. Specific implementation manners

[0052] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0053] In the description of the present invention, the meaning of "a plurality of" is two or more. If the first and second are described, it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0054] As Figure 1 shown, this embodiment is a wind energy storage fixed-capacity planning method based on double-layer optimization, which specifically includes the following steps:

[0055] S1. Obtain the unit power cost k esp and the unit capacity cost k esE of the energy storage system, as well as the wind power output, power consumption load and electricity purchase price at each moment within at least one typical day (or the wind power output curve, power consumption load curve and electricity purchase price curve of the typical day, see Figure 2 , 3 , 4).

[0056] S1-1. Obtain historical data, which includes the intraday basic data of each day within at least one year, and the intraday basic data includes the wind power output, power consumption load and electricity purchase price at each moment of that day.

[0057] S1-2. Perform clustering analysis on the historical data based on the wind power output, power consumption load and electricity purchase price at each moment. The number of clusters is D, and D classifications are obtained.

[0058] In this embodiment, the covariance matrix between classes and within classes is calculated, and the CH index and silhouette coefficient are used to select the optimal number of clusters D.

[0059] S1-3. Select the most representative day from each category (such as the centroid of each category) as the typical day of the category, and use the wind power output, power load and electricity purchase price at each moment of the day as the wind power output, power load and electricity purchase price at each moment of the typical day.

[0060] S2. Based on the unit power cost, unit capacity cost, wind power output, power load and electricity purchase price at each moment, the objective function of the upper model is solved by optimization methods such as genetic algorithm to obtain the rated capacity, rated power and purchased power of the energy storage system at each moment.

[0061] In this embodiment, the objective function of the upper model mainly includes three items. The first item is the equipment investment cost, also known as the equivalent life cycle cost of the energy storage system; the second item is the operating cost, which is mainly considered from the charging and discharging loss of the energy storage equipment; the third item is the power purchase variance, which is consistent with the objective function of the lower model, that is:

[0062] min C t +C r +α·f t (1)

[0063] Where: C t is the equipment investment cost; C r is the operating cost; α is the weight coefficient; f t is the electricity purchase variance.

[0064] In this embodiment, the value of α depends on the focus of optimization. When α is small, the optimization process focuses more on minimizing investment costs and operating costs, which may lead to large fluctuations in purchased power and affect the stability of the power grid. When α is large, the optimization process tends to reduce the fluctuation of purchased power, that is, smooth the purchase curve, thereby reducing the economic losses or dispatching pressure caused by power grid fluctuations, but may lead to higher energy storage investment costs.

[0065] The equivalent full-cycle life cost of an energy storage system is the one-time investment cost of the energy storage system minus the residual value recovered at the end of the period, and then the cost is amortized to the daily cost during the life of the energy storage system.

[0066]

[0067] Where: k esp and k esE are the unit power cost and unit capacity cost of the energy storage system respectively; P esN is the rated power of the energy storage system; E esN is the rated capacity of the energy storage system; l is the discount rate; n is the life of the energy storage system.

[0068] In this example, the operating cost is determined based on the charging and discharging power losses of the energy storage system and the electricity purchase price, so as to increase the attention to charging and discharging behaviors, avoid over-discharging or frequent charging and discharging of the energy storage system, and reduce battery aging. At the same time, through scheduling optimization, inefficient charging and discharging behaviors can be arranged during low electricity price periods, thereby reducing the operating cost.

[0069]

[0070] In the formula: is the charging power of the energy storage device at time t on the typical day d; is the discharging power of the energy storage device at time t on the typical day d; η chr is the charging efficiency of the energy storage device; η dis is the discharging efficiency of the energy storage device; D is the number of typical days; T is the number of time periods within a day; is the electricity price at time t; w d is the weight coefficient of the typical day.

[0071] In this embodiment, the weight coefficient w of the typical day d is determined according to the proportion of the number of days in the corresponding classification of each typical day in the historical data. For example, if there are 365 days of intraday basic data in the historical data and 10 days of intraday basic data in the corresponding classification of a certain typical day, then the weight coefficient of this certain typical day is 10 / 365.

[0072] In this embodiment, the constraints of the upper-layer model objective function include: capacity and power upper limit constraints, capacity and power relationship constraints, power purchase constraints, charge-discharge mutual exclusion constraints, energy conservation constraints, and energy storage dynamic energy balance constraints.

[0073] Capacity and power upper limit constraints. Constraints on the rated capacity and rated power of the energy storage system, and their upper limits are usually determined by the physical characteristics of the energy storage device or the investment budget.

[0074] 0 ≤ E esN ≤ E max (4)

[0075] 0 ≤ P esN ≤ P max (5)

[0076] In the formula: E max is the upper limit of the energy storage system capacity; P max is the upper limit of the energy storage system power.

[0077] Capacity and power relationship constraints. In the actual design of the energy storage system, there is usually a fixed proportional relationship between the capacity and the power. "Two-hour energy storage" is a common industry experience value, indicating that the energy storage system can work continuously for 2 hours during full-power charging and discharging.

[0078] EesN = 2P esN (6)

[0079] Where: E esN is the rated capacity of the energy storage system; P esN is the rated power of the energy storage system.

[0080] Purchased power constraint. The power supply capacity of the power supply side is limited and cannot meet the user's needs without limit, especially during peak loads. Moreover, equipment such as transmission lines, transformers, and circuit breakers in the power grid all have a rated power upper limit, and exceeding this range may cause equipment overload, overheating, and even damage.

[0081]

[0082] Where: is the minimum purchased power; is the maximum purchased power.

[0083] Charge-discharge mutual exclusion constraint. The charge-discharge mutual exclusion constraint is set to conform to the physical characteristics and actual operation of the energy storage system, avoiding efficiency losses, economic waste, and equipment losses caused by simultaneous charge and discharge. This constraint can improve the clarity and optimization efficiency of system scheduling, ensure that the energy storage plays a role in scenarios such as peak shaving and valley filling, reducing wind curtailment, etc., contribute to extending the equipment life, and ensure the economy and reliability of the operation of the energy storage system.

[0084]

[0085] Where: is the discharge power of the energy storage device at time t on typical day d; is the charge power of the energy storage device at time t on typical day d.

[0086] Energy conservation constraint. The energy in the power system always remains conserved, and the total supplied power (wind power, energy storage charging, grid power supply) must be equal to the total demanded power (user load, energy storage discharge). Through such a balance relationship, it can be ensured that the operation of the power system is feasible at any time t and any typical day d, without the situation of power supply shortage or overload.

[0087]

[0088] Where: is the wind power output at time t on typical day d; is the charge power of the energy storage device at time t on typical day d; is the purchased power at time t on typical day d; is the discharge power of the energy storage device at time t on typical day d; is the load at time t on typical day d.

[0089] Dynamic energy balance constraint of energy storage. The dynamic energy balance constraint of energy storage describes the operation law of the energy storage system through the dynamic energy conservation equation. Combined with the charging and discharging efficiency losses, it reflects the change of the energy storage state over time and ensures the safe operation of the energy storage system in practice (preventing overcharging or over-discharging). It provides a basis for optimizing the energy storage dispatching, supports the realization of goals such as peak shaving and valley filling, minimizing curtailment of wind power, and improves the economy and operation efficiency of the energy storage system.

[0090]

[0091] In the formula: is the energy state of the energy storage system at time t and scenario d; is the charging power of the energy storage device at time t on the typical day d; is the discharging power of the energy storage device at time t on the typical day d; η chr is the charging efficiency of the energy storage device; η dis is the discharging efficiency of the energy storage device.

[0092] S3. Based on the rated capacity and rated power of the energy storage system obtained in step S2, as well as the wind power output and electricity load at each moment, solve the objective function of the lower-layer model to obtain the electricity purchase power at each moment and the average electricity purchase power at all moments.

[0093] In this embodiment, the objective function of the lower-layer model is constructed based on the variance of the electricity purchase power and includes:

[0094]

[0095] In the formula: is the electricity purchase power at time t on the typical day d; D is the number of typical days; T is the number of time periods in a day; w d is the weight coefficient of the typical day.

[0096] The decision variable of the electricity purchase variance in the upper-layer model is different from that in the lower-layer model. The average electricity purchase power solved by the lower-layer model can be transmitted to the upper-layer model to update the electricity purchase variance of the upper-layer model, and then the upper-layer model can re-find the optimal solution.

[0097] In this embodiment, the constraints of the objective function of the lower-layer model include: rated capacity constraint, rated power constraint, energy conservation constraint, and dynamic energy balance constraint of energy storage.

[0098] Rated capacity constraint. The rated capacity obtained by the upper-layer model can constrain the energy state of the lower-layer model to prevent overcharging or over-discharging of the energy storage system.

[0099]

[0100] In the formula: is the energy state of the energy storage system at time t and scenario d; E esN is the rated capacity of the energy storage system.

[0101] Rated power constraint. The charging power and discharging power of the energy storage system should be constrained by the rated power to ensure the operation safety and rationality of the energy storage system in all scenarios and time steps.

[0102]

[0103] Where: is the charging power of the energy storage device at time t on the typical day d; is the discharging power of the energy storage device at time t on the typical day d; P esN is the rated power of the energy storage system.

[0104] Energy storage dynamic energy balance constraint:

[0105]

[0106] Where: is the energy state of the energy storage system at time t and scenario d; is the charging power of the energy storage device at time t on the typical day d; is the discharging power of the energy storage device at time t on the typical day d; η chr is the charging efficiency of the energy storage device; η dis is the discharging efficiency of the energy storage device.

[0107] Energy conservation constraint (power balance constraint):

[0108]

[0109] Where: is the wind power output at time t on the typical day d; is the charging power of the energy storage device at time t on the typical day d; is the power purchase at time t on the typical day d; is the discharging power of the energy storage device at time t on the typical day d; is the load at time t on the typical day d.

[0110] The operation optimization of the lower-layer model is as Figure 5 、 Figure 6As shown, the charging power is mainly concentrated around 15 o'clock, with a relatively high peak, and the charging process is intermittent. The discharging power appears during periods of high power demand, and the charging and discharging processes cooperate with each other to ensure the stable operation of the system. The energy storage capacity increases significantly during charging and decreases rapidly during discharging, reflecting the energy storage and release process of the energy storage device throughout the day. Generally speaking, through reasonable charging and discharging strategies, the energy storage system effectively regulates the system load, reduces the grid pressure, and optimizes the power usage efficiency.

[0111] S4. Return to step S2, and recompute the objective function of the upper-layer model by combining the average purchased power obtained in step S3 until the preset iterative calculation condition is satisfied, and take the latest obtained rated capacity and rated power of the energy storage system as the final planning solution.

[0112] In this example, the preset iterative calculation condition is that the number of iterative calculations or the difference between the average purchased power obtained in step S2 and the average purchased power obtained in step S3 is less than a preset value.

[0113] This embodiment also provides a wind energy storage fixed-capacity planning device based on double-layer optimization, including: a basic data acquisition module, an upper-layer optimization module, a lower-layer optimization module, an iterative calculation module, etc.

[0114] In this example, the basic data acquisition module is used to acquire the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, power consumption load, and purchased electricity price at each moment within at least one typical day.

[0115] In this embodiment, the upper-layer optimization module is used to solve the objective function of the upper-layer model based on the unit power cost, unit capacity cost, and the wind power output, power consumption load, and purchased electricity price at each moment, so as to obtain the rated capacity, rated power of the energy storage system, and the purchased power at each moment.

[0116] The objective function of the upper-layer model includes equipment investment cost, operation cost, and variance of purchased power. Among them, the equipment investment cost is determined based on the rated power, rated capacity, unit power cost, and unit capacity cost of the energy storage system, and the operation cost is determined based on the charging and discharging power losses of the energy storage system and the purchased electricity price;

[0117] In this example, the lower-layer optimization module solves the objective function of the lower-layer model based on the rated capacity and rated power of the energy storage system obtained by the upper-layer optimization module, as well as the wind power output and power consumption load at each moment, so as to obtain the purchased power at each moment and the average purchased power. The objective function of the lower-layer model includes the variance of purchased power.

[0118] In this embodiment, the iterative calculation module is used to recompute the objective function of the upper-layer model by combining the average purchased power obtained by the lower-layer optimization module until the preset iterative calculation condition is satisfied.

[0119] This embodiment also provides a storage medium, on which a computer program executable by a processor is stored. When the computer program is executed, the steps of the above-mentioned wind-storage fixed-capacity planning method based on double-layer optimization are implemented.

[0120] This embodiment also provides a wind-storage fixed-capacity planning device based on double-layer optimization, which has a memory and a processor. A computer program executable by the processor is stored on the memory. When the computer program is executed, the steps of the above-mentioned wind-storage fixed-capacity planning method based on double-layer optimization are implemented.

[0121] An embodiment of the present invention also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, the computer device executes Figure 1 the method shown.

[0122] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously, or the above-mentioned blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are foreseeable, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0123] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, and the scope of the present invention is determined by the full scope of the appended claims and their equivalents.

[0124] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs and other various media that can store program codes.

[0125] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0126] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, the computer-readable medium can even be paper or other suitable media on which the above program can be printed, because the above program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways when necessary, and then storing it in a computer memory.

[0127] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0128] In the above description of this specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0129] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0130] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A wind storage capacity planning method based on double-layer optimization, characterized in that: include: S1. Obtain the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, power load and power purchase price at each time in at least one typical day; S2. Based on the unit power cost, unit capacity cost, wind power output, power load and power purchase price at each moment, solve the objective function of the upper model to obtain the energy storage system's rated capacity, rated power and power purchase power at each moment; The objective function of the upper model includes equipment investment cost, operating cost and purchased electricity power variance, wherein the equipment investment cost is determined based on the rated power, rated capacity, unit power cost and unit capacity cost of the energy storage system, and the operating cost is determined based on the charging and discharging power loss of the energy storage system and the purchased electricity price; S3, based on the rated capacity and rated power of the energy storage system obtained in step S2, as well as the wind power output and power load at each moment, solve the objective function of the lower model to obtain the purchased power and average purchased power at each moment; The objective function of the lower model includes the purchased power variance; S4. Repeat steps S2 and S3, and re-solve the objective function of the upper model in combination with the average purchased electricity power obtained in step S3 until the preset iterative calculation conditions are met.

2. The wind storage capacity planning method based on double-layer optimization according to claim 1 is characterized in that: The unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, power load and power purchase price at each time in at least one typical day, include: Obtain historical data, which includes daily basic data for several days, and the daily basic data includes wind power output, power load and power purchase price at each time of the day; Based on the wind power output, power load and electricity purchase price at each moment, the historical data is clustered and analyzed. The number of clusters is D, and D categories are obtained. One day is selected from each category as the typical day of the category, and the wind power output, power load and purchased electricity price at each moment of the day are used as the wind power output, power load and purchased electricity price at each moment of the typical day.

3. The wind storage capacity planning method based on double-layer optimization according to claim 2 is characterized in that: The number of clusters D is selected by calculating the covariance matrix between and within classes and using the CH index and silhouette coefficient.

4. The wind storage capacity planning method based on double-layer optimization according to claim 2 is characterized in that: The weight coefficient of each category is determined based on the proportion of days in each category in the historical data, and is used as the weight coefficient of the typical day in each category.

5. The wind storage capacity planning method based on double-layer optimization according to claim 1 is characterized in that: The objective function of the upper model includes equipment investment cost, operating cost and purchased power variance, including: min C t +C r +a·f t Where: C t is the equipment investment cost; C r is the operating cost; α is the weight coefficient; f t is the purchased power variance.

6. The wind storage capacity planning method based on double-layer optimization according to claim 1 is characterized in that: The equipment investment cost is determined based on the rated power, rated capacity, unit power cost, and unit capacity cost of the energy storage system, including: Where: C t is the equipment investment cost, k esp and k esE are the unit power cost and unit capacity cost of the energy storage system respectively; P esN is the rated power of the energy storage system; E esN is the rated capacity of the energy storage system; l is the discount rate; n is the life of the energy storage system.

7. The wind storage capacity planning method based on double-layer optimization according to claim 4 is characterized in that: The operating cost is determined based on the charging and discharging power loss of the energy storage system and the electricity purchase price, including: Where: C r For operating costs; is the charging power of the energy storage system at time t on a typical day d; is the discharge power of the energy storage system at time t on a typical day d; η chr is the charging efficiency of the energy storage system; η dis is the discharge efficiency of the energy storage system; D is the number of typical days; T is the number of time periods in a day; is the electricity purchase price at time t; w d is the typical day weight coefficient.

8. The wind storage capacity planning method based on double-layer optimization according to claim 1 is characterized in that: The constraints of the objective function of the upper model include: power purchase constraint, charge and discharge mutual exclusion constraint, energy conservation constraint and energy storage dynamic energy balance constraint.

9. The method for planning wind storage capacity based on double-layer optimization according to claim 4 is characterized in that: The objective function of the lower model includes the purchased power variance, including: Where: f t is the purchased power variance; is the power purchased at time t on a typical day d; D is the number of typical days; T is the number of time periods in a day; w d is the typical day weight coefficient.

10. The wind storage capacity planning method based on double-layer optimization according to claim 1 is characterized in that: The constraints of the objective function of the lower model include: rated capacity constraints, rated power constraints, energy conservation constraints and energy storage dynamic energy balance constraints.

11. A wind storage capacity planning device based on double-layer optimization, characterized in that: include: A basic data acquisition module is used to obtain the unit power cost and unit capacity cost of the energy storage system, as well as the wind power output, power load and power purchase price at each time in at least one typical day; The upper optimization module is used to solve the objective function of the upper model based on the unit power cost, unit capacity cost, wind power output, power load and power purchase price at each moment, and obtain the rated capacity, rated power and power purchase power of the energy storage system at each moment; The objective function of the upper model includes equipment investment cost, operating cost and purchased electricity power variance, wherein the equipment investment cost is determined based on the rated power, rated capacity, unit power cost and unit capacity cost of the energy storage system, and the operating cost is determined based on the charging and discharging power loss of the energy storage system and the purchased electricity price; The lower optimization module solves the objective function of the lower model based on the rated capacity and rated power of the energy storage system obtained by the upper optimization module, as well as the wind power output and power load at each moment, and obtains the purchased power and average purchased power at each moment; The objective function of the lower model includes the purchased power variance; The iterative calculation module is used to re-solve the objective function of the upper model in combination with the average purchased power obtained by the lower optimization module until the preset iterative calculation conditions are met.

12. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the wind storage capacity planning method based on double-layer optimization described in any one of claims 1 to 10 are implemented.

13. A wind storage capacity planning device based on double-layer optimization, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that: When the computer program is executed, the steps of the wind storage capacity planning method based on double-layer optimization described in any one of claims 1 to 10 are implemented.

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