Double-layer multi-time scale rolling optimization method and system for electric-hydrogen hybrid energy storage system

Abstract

The present disclosure provides a double-layer multi-time-scale rolling optimization method and system for an electric-hydrogen hybrid energy storage system, which relates to the field of optimized scheduling of an electric-hydrogen hybrid energy storage system, including: constructing a fuzzy set of wind-solar forecast errors; constructing an upper-layer long-time-scale energy scheduling model, and solving to obtain the long-time-scale hydrogen storage capacity and power imbalance; under a generated typical scenario, constructing a lower-layer short-time-scale power scheduling model, and solving to obtain the short-time-scale hydrogen storage capacity; using the short-time-scale hydrogen storage capacity solved by the lower layer as the initial hydrogen storage capacity of the long-time-scale energy scheduling model, and performing rolling optimization to achieve double-layer multi-time-scale rolling optimization; the present invention takes into account the influence of different time scales and wind-solar uncertainty on the optimized scheduling of the electric-hydrogen hybrid energy storage system, constructs a double-layer scheduling model, and performs double-layer multi-time-scale rolling optimization to provide a scheduling scheme that takes into account both economy and robustness for the flexible scheduling of the electric-hydrogen hybrid energy storage system before the week and before the day.

Description

Technical Field

[0001] The present disclosure relates to the field of optimization and scheduling of electric-hydrogen hybrid energy storage systems, and in particular to a double-layer multi-time scale rolling optimization method and system for electric-hydrogen hybrid energy storage systems. Background Art

[0002] As a flexible regulation resource, energy storage plays an important role in smoothing the fluctuations of renewable energy. At present, most energy storage technologies, mainly battery energy storage, have limited storage capacity and short duration (mostly concentrated within a day), and are not very applicable to solving the problem of source-load imbalance on a long time scale.

[0003] With the development of electricity-to-hydrogen technology, hydrogen storage has gained wide attention as a new energy storage method. Hydrogen energy is an ideal clean energy with flexible storage scale and can achieve long-term (daytime) energy storage. Electricity and hydrogen energy complement each other and play an important role in the energy structure. The electric-hydrogen hybrid energy storage system can realize the interconnection of electricity and hydrogen energy. However, the differences in the scheduling time of the two energy forms and the uncertainty of wind and solar power output brought about by the access of high-penetration renewable energy have increased the difficulty of optimizing the scheduling of the electric-hydrogen hybrid energy storage system.

[0004] At present, the operation and dispatch of the electric-hydrogen hybrid energy storage system is mostly concentrated on the short time scale of 24 hours as the dispatch cycle, which fails to give full play to the advantages of hydrogen energy as a long-term energy storage and can be stored across days, and fails to coordinate the short-time scale optimization of electricity before the day and the long-time scale optimization of hydrogen energy before the week. For the long-term hydrogen storage dispatch, the timing characteristics of wind and solar output forecasts have not been fully explored, and the uncertainty of the typical characteristics of wind and solar forecasts has not been characterized; therefore, the existing operation and dispatch technology of the electric-hydrogen hybrid energy storage system cannot take into account the hydrogen storage cycle dispatch of wind and solar forecast information and the short-term electricity storage dispatch before the day, and the dispatch reliability and practicality are insufficient.

[0005] In addition, uncertainty optimization models include two types: stochastic optimization and robust optimization, in order to characterize the uncertainty of renewable energy sources such as wind and solar. However, the probability distribution of uncertain parameters is actually unknown or difficult to estimate, or a large number of scenarios need to be sampled, so that the uncertainty processing through random optimization cannot cover all the complex change characteristics of uncertainty factors, the out-of-sample performance is poor, and the solution efficiency is low. Although robust optimization has strong risk resistance, the frequency of the worst scenarios in practice is relatively low, resulting in overly conservative results. Distributed robust optimization takes into account the advantages of robust optimization and stochastic optimization. There is no need to assume that the wind and solar prediction errors meet the precise probability distribution, which can solve the problem of difficulty in obtaining accurate probability distribution parameters in random optimization; at the same time, by combining probability distribution information, it improves conservatism on the basis of ensuring robustness; therefore, for the problem of different timing accuracy of wind and solar output uncertainty over long time scales, the robustness of existing technologies is insufficient.

[0006] Therefore, existing technologies mostly focus on the short time scale of 24 hours a day, failing to fully utilize the advantages of long-term storage of hydrogen energy. At the same time, they fail to fully consider the timing characteristics of the prediction when describing the uncertainty of wind and solar power over a long period of time. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a double-layer multi-time scale rolling optimization method and system for an electric-hydrogen hybrid energy storage system. Considering the influence of different time scales and wind and solar uncertainties on the optimal scheduling of the electric-hydrogen hybrid energy storage system, a double-layer scheduling model is constructed, and double-layer multi-time scale rolling optimization is performed to provide a scheduling solution that takes into account both economy and robustness for the flexible scheduling of the electric-hydrogen hybrid energy storage system before the week and before the day.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] The double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system includes:

[0010] Using Wasserstein distance, the fuzzy set of wind power prediction error is constructed;

[0011] Based on the fuzzy set of wind and solar forecast errors and with the goal of maximizing the self-balancing of electricity, an upper-level long-time scale energy dispatch model is constructed to solve the long-time scale hydrogen storage capacity and electricity imbalance.

[0012] Based on the fuzzy set of wind and solar prediction errors, a typical scenario is generated for the potential distribution of short-time-scale wind and solar output. In the typical scenario, the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer are received. With the goal of comprehensive economic optimization, the short-time-scale power dispatch model of the lower layer is constructed to solve the short-time-scale hydrogen storage.

[0013] The short-time scale hydrogen storage capacity obtained by solving the lower layer is used as the initial hydrogen storage capacity of the long-time scale energy scheduling model for rolling optimization to achieve double-layer multi-time scale rolling optimization.

[0014] According to some embodiments, the present disclosure adopts the following technical solutions:

[0015] The double-layer multi-time scale rolling optimization system of the electric-hydrogen hybrid energy storage system includes:

[0016] The fuzzy set construction module is configured to: construct the fuzzy set of wind power prediction error using Wasserstein distance;

[0017] The energy dispatch module is configured as follows: based on the fuzzy set of wind and solar forecast errors, with the goal of maximizing the self-balancing of electricity, an upper-layer long-time scale energy dispatch model is constructed to solve the long-time scale hydrogen storage capacity and electricity imbalance;

[0018] The power dispatch module is configured to: generate typical scenarios for the potential distribution of short-time-scale wind and solar power output based on the fuzzy set of wind and solar power prediction errors; in typical scenarios, receive the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer, and build a short-time-scale power dispatch model for the lower layer with the goal of comprehensive economic optimization to solve for the short-time-scale hydrogen storage;

[0019] The rolling optimization module is configured to use the short-time scale hydrogen storage capacity obtained by the lower layer solution as the initial hydrogen storage capacity of the long-time scale energy scheduling model, and perform rolling optimization to achieve double-layer multi-time scale rolling optimization.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A computer program product includes a computer program, and when the computer program is executed by a processor, the double-layer multi-time scale rolling optimization method of the electric-hydrogen hybrid energy storage system is implemented.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the double-layer multi-time scale rolling optimization method of the electric-hydrogen hybrid energy storage system is implemented.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device implements the double-layer multi-time scale rolling optimization method of the electric-hydrogen hybrid energy storage system.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention takes into account the uncertainty of wind and solar power output at multiple time scales in the electric-hydrogen hybrid energy storage system, and proposes a double-layer multi-time scale rolling optimization method, constructs a distributed robust optimization model on a long time scale to perform energy scheduling, and performs power optimization on a short time scale, thereby improving the economy and flexibility of system scheduling.

[0028] The present invention constructs a fuzzy set based on the Wasserstein distance, sets the fuzzy set radius and confidence level corresponding to different prediction days according to the actual wind and solar power output prediction characteristics, handles the uncertainty of long-time wind and solar power output, and adopts distributed robust optimization to solve the long-time energy scheduling model, realizes the cross-day transfer of hydrogen energy, reduces the daily unbalanced electricity, and smoothes the long-time wind and solar power output fluctuations.

[0029] The double-layer multi-time-scale rolling optimization method of the present invention provides decision-making guidance for short-time-scale power optimization by receiving long-time-scale energy scheduling results, and feeds back the short-time-scale scheduling results to the next stage for continued rolling optimization on the long-time scale, thereby realizing the coordinated optimization of energy and power corresponding to multi-time-scale hydrogen storage and electricity storage, and achieving a balance between economy and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0031] Figure 1 This is a structural diagram of the electric-hydrogen hybrid energy storage system of Example 1.

[0032] Figure 2 This is a double-layer multi-time-scale rolling optimization framework for the electric-hydrogen hybrid energy storage system of Example 1.

[0033] Figure 3 This is the rolling optimization model structure of Example 1. DETAILED DESCRIPTION

[0034] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0037] Example 1

[0038] In one embodiment of the present disclosure, a double-layer multi-time scale rolling optimization method for an electric-hydrogen hybrid energy storage system is provided, comprising:

[0039] Using Wasserstein distance, the fuzzy set of wind power prediction error is constructed;

[0040] Based on the fuzzy set of wind and solar forecast errors and with the goal of maximizing the self-balancing of electricity, an upper-level long-time scale energy dispatch model is constructed to solve the long-time scale hydrogen storage capacity and electricity imbalance.

[0041] Based on the fuzzy set of wind and solar prediction errors, a typical scenario is generated for the potential distribution of short-time-scale wind and solar output. In the typical scenario, the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer are received. With the goal of comprehensive economic optimization, the short-time-scale power dispatch model of the lower layer is constructed to solve the short-time-scale hydrogen storage.

[0042] The short-time scale hydrogen storage capacity obtained by solving the lower layer is used as the initial hydrogen storage capacity of the long-time scale energy scheduling model for rolling optimization to achieve double-layer multi-time scale rolling optimization.

[0043] As an embodiment, the disclosed double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system considers the influence of different time scales and wind-solar uncertainty on the optimal scheduling of the electric-hydrogen hybrid energy storage system, constructs a double-layer scheduling model, and performs double-layer multi-time scale rolling optimization. Figure 1 The flexible scheduling of the electric-hydrogen hybrid energy storage system before the week and before the day provides a scheduling solution that takes into account both economy and robustness, such as Figure 2 The specific implementation process is as follows:

[0044] Step 1: Constructing long-time-scale (week-ahead) Wasserstein fuzzy sets

[0045] Specifically, the robust optimization is combined with the stochastic optimization theory to establish a fuzzy set, which is used to obtain the uncertainty set of the potential true distribution. The corresponding set contains the worst case of the true distribution.

[0046] The Wasserstein distance can be expressed as:

[0047] (1)

[0048] In the formula, is the infimum function; and They represent the actual prediction error and the empirical prediction error of wind and solar power output respectively; Represents the empirical distribution and the true distribution The joint distribution of The entire collection of; Represents the norm function.

[0049] In order to facilitate model solving, this embodiment adopts 1-norm, and the Wasserstein distance can be rewritten into the desired form:

[0050] (2)

[0051] In the formula, is the base distance of the Wasserstein distance expressed in 1-norm.

[0052] This embodiment introduces Wasserstein distance into fuzzy set construction and obtains empirical distribution from historical forecast data. , the fuzzy set can be regarded as the true distribution Distribution around experience The radius is The sphere contains the true distribution of all possible wind and solar output prediction errors, then the Wasserstein fuzzy set It is expressed as:

[0053] (3)

[0054] In the formula, For the support set All probability distributions in the support set satisfy:

[0055] (4)

[0056] In the formula, is an auxiliary variable, and They represent the upper and lower bounds of the wind and solar prediction error respectively. The specific formula is:

[0057] (5)

[0058] In the formula, is the future forecast value of the distribution, Take a value for the confidence level.

[0059] Different prediction error intervals can be obtained by setting different confidence levels.

[0060] For example, different values ​​are taken for the fuzzy set radius of the first 3 days, 4th, 5th, 6th, and 7th days of the long time scale. According to the different prediction accuracy of the corresponding days of wind and solar power, different confidence levels are taken. Thus, by controlling the radius and confidence level of the fuzzy set in different days, the potential real distribution of wind and solar power output on a long time scale can be obtained. and ; Potential real distribution of wind and solar power output and In the period Sum the time period The potential real distributed power generation of indoor wind turbines and photovoltaics is the corresponding and .

[0061] Step 2: Construct an upper-level long-time scale energy scheduling model

[0062] The long-time scale energy scheduling model takes minimizing the imbalanced electricity over a long time scale as its optimization goal, that is, maximizing the self-balance of electricity. The constraints include hydrogen energy balance constraints, hydrogen storage device model, power-to-hydrogen (P2H) device and fuel cell (FC) model.

[0063] (1) Objective function

[0064] Electric-hydrogen hybrid energy storage needs to be Sub-long time scale energy scheduling, The objective function of the suboptimal scheduling model is:

[0065] (6)

[0066] In the formula, Indicates the unbalanced electricity in the dispatch cycle; and It is the time period Potential real distributed power generation of photovoltaic and wind turbines; and It is the time period Internal FC power generation and P2H device power consumption; It is the time period Internal electrical load; Indicates the total duration of the long-time scheduling cycle.

[0067] (2) Hydrogen energy balance constraints

[0068] The long-term hydrogen energy balance constraint is:

[0069] (7)

[0070] In the formula, and It is the time period The hydrogen production of the internal P2H device and the hydrogen consumption of the FC; and It is the time period The amount of hydrogen charged and discharged in the internal hydrogen storage device; It is the time period Internal hydrogen loading.

[0071] (3) Hydrogen storage device model

[0072] During operation, the hydrogen storage device needs to charge and discharge hydrogen according to the working characteristics of the equipment. At the same time, it is necessary to consider the timing characteristics of hydrogen storage and establish an operation model of the hydrogen storage device.

[0073] (8)

[0074] In the formula, for The amount of hydrogen stored in the hydrogen storage tank during the period; and is the amount of hydrogen charged and discharged, and Represents the efficiency of hydrogen charging and discharging; and They are the upper and lower limits of the total amount of hydrogen stored in the hydrogen energy storage device, respectively; and is a 0-1 variable. Taking the hydrogen release state as an example, when When it is 1, it means the hydrogen storage device releases hydrogen; and express The upper and lower limits of the amount of hydrogen charged and discharged within a time period. The hydrogen storage capacity of the hydrogen storage device at the beginning and end of the scheduling cycle is equal.

[0075] (4) P2H device and FC model

[0076] The constraints of the long-time optimization model also include P2H device and FC constraints, which are expressed as

[0077] (9)

[0078] (10)

[0079] In the formula, is the hydrogen production of the P2H device using electricity to produce hydrogen, are the minimum and maximum values ​​of hydrogen production, respectively; is the amount of hydrogen consumed by FC to generate electricity using hydrogen, are the minimum and maximum values ​​of hydrogen consumption respectively; and It is the time period Internal FC power generation and P2H device power consumption; It is the time period Internal electrical load; and It is the electricity-hydrogen energy conversion efficiency of the P2H device and FC.

[0080] Furthermore, for the long-time scale energy scheduling model, the objective function can be rewritten as:

[0081] (11)

[0082] In the formula, is the decision variable, that is, and ; is the coefficient matrix corresponding to the decision variables in the objective function; represents the expected function; is the supremum function; is the uncertainty variable, that is, and The objective function corresponds to the fuzzy set Under the worst conditions, the inner layer expectation function is minimized and the unbalanced electricity is minimized on a long time scale.

[0083] Among them, the uncertain variables The inner function changes to:

[0084] (12)

[0085] In the formula, is the supremum function; is the infimum function; is the decision variable, that is, and ; represents the expected function; is the fuzzy set radius; is the uncertainty variable, that is, and ; is the dual variable; It is Historical forecast samples; is the number of error samples; Represents auxiliary variables.

[0086] The corresponding inner layer distribution robust optimization function model, i.e. (12), can be obtained through dual transformation to obtain the final mixed integer linear programming form, which can be solved as follows:

[0087] (13)

[0088] In the formula, Corresponding to the constraints in the long-time scale energy scheduling model; is an abstract function; is the dual variable; It is Historical forecast samples; and is the maximum and minimum value in the prediction sample set; is the number of error samples; Represents auxiliary variables; is the decision variable, that is, and ; is the uncertainty variable, that is, and .

[0089] Step 3: Generate typical scenarios of wind and solar power output in short time scales

[0090] In layman's terms, the scenario here refers to the potential real distribution of wind and solar power output in the 24 hours before the day obtained by the upper model, that is, the 24-hour wind and solar power. A large number of wind and solar power output arrays are obtained through Latin hypercube sampling, and then K-means clustering is performed to obtain several typical 24-hour wind and solar power arrays. This typical wind and solar power output array is a typical scenario, corresponding to a scenario generation probability. This process is the typical scenario generation process.

[0091] Specifically, the error distribution of random variables has a great impact on the rationality of random scene generation. The kernel density estimation method is used to verify that the error of wind and solar output meets the actual distribution. Latin hypercube (LHS) sampling uses stratified sampling to make the sample points evenly and comprehensively cover the variable distribution range. Compared with the Monte Carlo method, it can improve the phenomenon of sample accumulation and uneven distribution, and achieve higher sampling accuracy in a smaller sampling scale. The nature of sampling is to perform an inverse function transformation on each sample value based on the probability density function without changing the original density function, and finally obtain the sampling result.

[0092] When generating typical wind and solar power output scenarios, Latin hypercube sampling is used to obtain a large number of wind and solar power output scenarios, and kernel density estimation is used to evaluate and verify the rationality of these scenarios. Specifically, kernel density estimation is used to estimate the error distribution of wind and solar power output, thereby ensuring that a large number of scenarios generated by Latin hypercube sampling are closer to the actual distribution in statistical characteristics. In other words, kernel density estimation provides a way to test and adjust the scenarios obtained by Latin hypercube sampling.

[0093] After obtaining a large number of scenarios through LHS sampling, the improved K-means clustering is used to obtain typical wind and solar output scenarios. The main clustering steps are as follows:

[0094] Step 1: Assume the number of clusters is , randomly select a scene as a cluster center .

[0095] Step 2: Calculate the remaining scenes and The distance between them (using Euclidean distance) is used to select the scene with the farthest distance as the second cluster center. Then calculate the sum of the distances between the remaining scenes and the two cluster centers after removing the two cluster centers, and find the scene with the largest distance sum as the third cluster center , and so on, we get Initial cluster centers.

[0096] Step 3: Calculate the distance between the remaining scenes and each cluster center, classify the remaining scenes into the cluster center with the closest distance, and re-calculate the cluster center of each category.

[0097] Step 4: Define the sum of the squares of the distances between each scene and its cluster center as the clustering error , when the clustering error of the cluster center of the second iteration is When the difference is less than a certain convergence accuracy, stop the iteration and output the cluster center of the last iteration, otherwise return to Step 3.

[0098] Step 4: Construct the lower-level short-time-scale power scheduling model

[0099] Specifically, the lower-level short-time-scale power dispatch takes the comprehensive economic optimization as the optimization goal, and the constraints include the electric-hydrogen power balance constraint, the power storage device model, the power purchase and sales model, the P2H device and FC model, and the short-time-scale supplementary constraints of the hydrogen storage device.

[0100] (1) Objective function

[0101] The short-time scale power dispatch objective function includes the cost of purchasing and selling electricity, the cost of wind and solar power abandonment, the cost of energy storage, the operating cost of P2H devices and FC, the cost of carbon emission and the penalty cost of power imbalance, which can be expressed as follows:

[0102] (14)

[0103] Respectively expressed as:

[0104] (15)

[0105] In the formula, and , and Respectively The power and time-of-use electricity price purchased from and sold to the external power grid at any given moment; and Indicates the probability of different wind and solar power output scenarios; and , and They represent the penalty cost coefficients for wind and solar power abandonment, Moment Scene of abandoned wind and solar power; and is the energy storage cost coefficient of hydrogen and electricity storage devices; and , and yes The hydrogen storage device is charged and discharged at all times, and the electricity storage device is fully charged at all times; and , and It is the charging and discharging efficiency of hydrogen storage and electricity storage devices; and are the operating cost coefficients of the P2H unit and FC, respectively; and yes The operating power of the P2H device and FC at the moment; is the carbon emission coefficient corresponding to the unit electricity purchase; is the environmental penalty cost coefficient per unit of carbon dioxide emissions; Penalty price for system power imbalance; The unbalanced electricity volume of the day for 24 hours is obtained by optimizing the upper layer over a long time scale.

[0106] (2) Electric-hydrogen power balance constraints

[0107] For the short-term 24-hour power dispatch, the power balance constraint of electricity and hydrogen is expressed as:

[0108] (16)

[0109] In the formula, and yes Electric hydrogen load power at every moment; and yes The hydrogen production power of the P2H device and the hydrogen consumption power of the FC at each moment.

[0110] (3) Energy storage device model

[0111] The power storage device is represented by a battery in this paper. The battery stores electric energy by charging when the power supply is sufficient and discharges when there is a power shortage. Its output constraint is expressed as:

[0112] (17)

[0113] In the formula, yes The storage capacity of the power storage device at all times; and It is the upper and lower limits of the storage capacity of the power storage device; and is a 0-1 variable, Taking the discharge state as an example, when it is 1, it means the battery is discharged; and Respectively represent the maximum value of battery charging and discharging power.

[0114] (4) Electricity purchasing and sales model

[0115] (18)

[0116] In the formula, and It is a 0-1 variable. Taking the electricity purchasing status as an example, 1 means the system purchases electricity from the external power grid; and It is the upper limit of the power that the system can purchase from and sell to the external power grid.

[0117] (5) P2H device and FC model

[0118] (19)

[0119] (20)

[0120] In the formula, and yes The maximum hydrogen production power of the P2H device and the maximum power generation power of the FC at that moment; and is the electricity-to-hydrogen conversion efficiency of the P2H device and FC; and Indicates the maximum climbing power of the P2H device and FC.

[0121] (6) Supplementary constraints on short-term hydrogen storage devices

[0122] In the short-time scale power dispatch, the hydrogen storage device receives the hydrogen storage capacity of the day transmitted by the upper long-time scale and relaxes it. The short-time scale model of the hydrogen storage device is expressed as:

[0123] (twenty one)

[0124] In the formula, yes The hydrogen storage capacity of the hydrogen storage device at any time; and It is a 0-1 variable. Taking the hydrogen release state as an example, When it is 1, it means the hydrogen storage device releases hydrogen; and They respectively represent the maximum value of hydrogen charging and discharging power of the hydrogen storage device; Indicates the initial hydrogen storage capacity of the next cycle during short-term scheduling of the hydrogen storage device, which is the 25th hour in this embodiment; Represents the long time scale Suboptimal scheduling of the initial hydrogen storage volume on the second day; and It is the relaxation coefficient for upward and downward adjustment of the hydrogen storage capacity of the hydrogen storage device.

[0125] Furthermore, the short-time-scale power scheduling model is a single-level linear programming problem, which can be directly solved using commercial solvers such as Gurobi, and is expressed in a compact form as:

[0126] (twenty two)

[0127] In the formula, is the decision variable in the short-time-scale power dispatch stage; and is the constant matrix of the objective function; and is the constant matrix of constraints.

[0128] Furthermore, the lower power model is solved to obtain the short-time scale optimization scheduling strategy. As the The initial hydrogen storage capacity of the sub-long time scale energy scheduling is transmitted back to the upper energy scheduling model and then Figure 3 Scrolling optimization shown.

[0129] Example 2

[0130] In one embodiment of the present disclosure, a double-layer multi-time scale rolling optimization system for an electric-hydrogen hybrid energy storage system is provided, comprising:

[0131] The fuzzy set construction module is configured to: construct the fuzzy set of wind power prediction error using Wasserstein distance;

[0132] The energy dispatch module is configured as follows: based on the fuzzy set of wind and solar forecast errors, with the goal of maximizing the self-balancing of electricity, an upper-layer long-time scale energy dispatch model is constructed to solve the long-time scale hydrogen storage capacity and electricity imbalance;

[0133] The power dispatch module is configured to: generate typical scenarios for the potential distribution of short-time-scale wind and solar power output based on the fuzzy set of wind and solar power prediction errors; in typical scenarios, receive the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer, and build a short-time-scale power dispatch model for the lower layer with the goal of comprehensive economic optimization to solve for the short-time-scale hydrogen storage;

[0134] The rolling optimization module is configured to use the short-time scale hydrogen storage capacity obtained by the lower layer solution as the initial hydrogen storage capacity of the long-time scale energy scheduling model, and perform rolling optimization to achieve double-layer multi-time scale rolling optimization.

[0135] Example 3

[0136] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the double-layer multi-time-scale rolling optimization method for the electric-hydrogen hybrid energy storage system.

[0137] Example 4

[0138] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the double-layer multi-time-scale rolling optimization method of the electric-hydrogen hybrid energy storage system is implemented.

[0139] Example 5

[0140] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the double-layer multi-time-scale rolling optimization method for the electric-hydrogen hybrid energy storage system.

[0141] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A double-layer multi-time scale rolling optimization method for an electric-hydrogen hybrid energy storage system, characterized in that: include: The Wasserstein distance is used to construct a fuzzy set of wind and solar power prediction errors. The fuzzy set of wind and solar power prediction errors is used to set the fuzzy set radius and confidence level corresponding to different prediction days according to the actual wind and solar power output prediction characteristics, so as to deal with the uncertainty of wind and solar power output on a long time scale. Based on the fuzzy set of wind and solar forecast errors, with the goal of maximizing the self-balancing of electricity, an upper-level long-time scale energy scheduling model is constructed to solve the long-time scale hydrogen storage and electricity imbalance. The long-time scale energy scheduling model also includes constraints, specifically hydrogen energy balance constraints, hydrogen storage device models, power-to-hydrogen devices, and fuel cell models. Based on the fuzzy set of wind and solar prediction errors, a typical scenario is generated for the potential distribution of short-time-scale wind and solar output. In the typical scenario, the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer are received, and the short-time-scale power dispatch model of the lower layer is constructed with the goal of comprehensive economic optimization to solve the short-time-scale hydrogen storage. The typical scenario generation adopts Latin hypercube scenario generation and K-means clustering to obtain the short-time-scale wind and solar output typical scenario. The short-time-scale power dispatch model also includes constraints, specifically, electric-hydrogen power balance constraints, power storage device constraints, power purchase and sale constraints, power-to-hydrogen device and fuel cell model constraints, and short-time-scale supplementary constraints for hydrogen storage devices. The short-time scale hydrogen storage capacity obtained by solving the lower layer is used as the initial hydrogen storage capacity of the long-time scale energy scheduling model for rolling optimization to achieve double-layer multi-time scale rolling optimization.

2. The double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system according to claim 1, characterized in that: The objective is to maximize the self-balancing of electricity, which can be expressed as the objective function: In the formula, Indicates the unbalanced electricity in the dispatching period; and It is the time period Potential real distributed power generation of photovoltaic and wind turbines; and It is the time period Internal FC power generation and P2H device power consumption; It is the time period Internal electrical load; Indicates the total duration of the long-time scheduling cycle.

3. The double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system according to claim 1, characterized in that: The above-mentioned objective is to optimize the comprehensive economic performance, which is represented by an objective function consisting of the cost of purchasing and selling electricity, the cost of curtailing wind and solar power, the cost of energy storage, the operating cost of P2H devices and FC, the cost of carbon emission and the penalty cost of electricity imbalance.

4. The double-layer multi-time scale rolling optimization system of the electric-hydrogen hybrid energy storage system is characterized by: include: The fuzzy set construction module is configured to: construct a wind-solar forecast error fuzzy set using Wasserstein distance, wherein the wind-solar forecast error fuzzy set is used to set the fuzzy set radius and confidence level corresponding to different forecast days according to actual wind-solar output forecast characteristics, and to handle the uncertainty of wind-solar output on a long time scale; The energy dispatch module is configured to: construct an upper-layer long-time scale energy dispatch model based on the fuzzy set of wind and solar forecast errors and aim at maximizing the self-balancing of electricity, and solve the long-time scale hydrogen storage and electricity imbalance. The long-time scale energy dispatch model also includes constraints, specifically hydrogen energy balance constraints, hydrogen storage device models, power-to-hydrogen devices, and fuel cell models; The power dispatch module is configured to: generate typical scenarios for the potential distribution of short-time-scale wind and solar output based on the fuzzy set of wind and solar prediction errors; in typical scenarios, receive the long-time-scale hydrogen storage and power imbalance transmitted by the upper layer, and build a short-time-scale power dispatch model of the lower layer with the goal of comprehensive economic optimization to solve and obtain the short-time-scale hydrogen storage; the typical scenario generation adopts Latin hypercube scenario generation and K-means clustering to obtain the short-time-scale wind and solar output typical scenario; the short-time-scale power dispatch model also includes constraints, specifically, electric-hydrogen power balance constraints, power storage device constraints, power purchase and sale constraints, power-to-hydrogen device and fuel cell model constraints, and short-time-scale supplementary constraints for hydrogen storage devices; The rolling optimization module is configured to use the short-time scale hydrogen storage capacity obtained by the lower layer solution as the initial hydrogen storage capacity of the long-time scale energy scheduling model, and perform rolling optimization to achieve double-layer multi-time scale rolling optimization.

5. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system according to any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the double-layer multi-time-scale rolling optimization method of the electric-hydrogen hybrid energy storage system as described in any one of claims 1-3 is implemented.

7. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the double-layer multi-time scale rolling optimization method for the electric-hydrogen hybrid energy storage system as described in any one of claims 1-3.

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