Energy storage optimization configuration method and device based on simulation

Abstract

The present invention proposes a method and device for optimizing energy storage configuration based on simulation. The method is based on the method for optimizing energy storage configuration based on simulation. The energy storage parameter range set by the user is modified by a numerical calculation method to obtain a variety of configuration combination parameters. Under each set of energy storage configuration parameters, a sequential Monte Carlo sampling method is used to randomly generate a charge and discharge power curve for simulation. When generating the charge and discharge power curve, parallel calculation is performed to greatly save operating costs and time. The final energy storage configuration parameters are obtained by maximizing the average daily return on investment as an evaluation indicator. The present invention overcomes the problem in existing research that the contradiction between the leveling effect and the economic benefit cannot be fully coordinated, avoids the discrepancy between the model and the actual operation, realizes the coordinated optimization configuration of the capacity and power of the energy storage system, can be directly used in actual industrial production applications, and assists wind power plants in determining the optimal combination of energy storage capacity and power.

Description

Technical Field

[0001] The present invention relates to the field of energy storage optimization technology, and in particular to a method, device, equipment, and storage medium for energy storage optimization configuration based on simulation. Background Art

[0002] For wind farms, the uncertainty, unpredictability, and poor dispatchability of wind power output inherently lead to missed profit opportunities in electricity markets. For example, insufficient power generation during periods of high prices and excessive power generation during periods of low prices can lead to losses and low economic returns. This also creates a certain degree of volatility in the power system.

[0003] Currently, to address these two issues, the prevailing practice both domestically and internationally is to deploy a certain scale of energy storage system for wind farms. This allows wind and storage to participate in the spot market, mitigating losses and gains, while also smoothing wind power output fluctuations. However, determining the size of the energy storage system remains a pressing challenge. The most commonly used methods for sizing energy storage systems are frequency domain analysis and probabilistic statistics. The frequency domain analysis method uses a Fourier transform to perform spectral analysis on the intermittent power output data series. This determines the smoothing target and the frequency band in which the energy storage component resides. The accumulated energy value at each sampling moment is calculated, along with the difference between the maximum and minimum accumulated energy storage system energy value during the sampling period. Assuming that the energy storage system's charge and discharge power and state of charge meet the requirements and can ensure continuous and stable operation, the energy storage system capacity is determined from the perspective of smoothing wind power fluctuations. The probabilistic statistics method first determines the minimum-level component of wind power fluctuations that the energy storage system smoothes, then describes the distribution patterns of the minimum-level charge and discharge capacity of the energy storage system. The energy storage system capacity is then determined using interval estimation theory from a statistical perspective.

[0004] Currently available mainstream methods, such as frequency domain analysis and probability statistics, have chosen between stabilization effects and economic benefits. Most studies primarily consider the effectiveness of stabilization without properly coordinating the two. Some methods, such as the eigenvector method, optimize capacity under the premise of a fixed energy storage system power, resulting in limited optimization effects. Research based on genetic algorithms is still in the theoretical stage due to factors such as technological level, and cannot be widely implemented and deployed in power systems. In addition to the aforementioned shortcomings, the aforementioned methods also suffer from the problem of inconsistency between optimized configuration and actual operation. They only analyze the laws and characteristics of wind farm operation and do not place the configured energy storage in a real control and operation environment for dynamic analysis.

[0005] The above approach only considers the energy storage system's stabilization effect in energy storage configuration, without considering the economic benefits of storage configuration. The relationship between the two is not linear; a good stabilization effect necessarily corresponds to high investment costs, but not necessarily good economic benefits. Therefore, achieving a balance between stabilization effect and economic benefits requires further research. Summary of the Invention

[0006] The present invention provides a method, device, equipment, and storage medium for optimizing energy storage configuration based on simulation, which aims to comprehensively consider the leveling effect and economic benefits, coordinate and balance the leveling effect and economic benefits based on maximizing the actual operating return on investment, conduct simulation experiments through historical data, and coordinately optimize the configuration of energy storage capacity and power.

[0007] To this end, the first object of the present invention is to propose a method for optimizing energy storage configuration based on simulation, comprising:

[0008] Collect the required number of days of wind power operation history sample data and perform preprocessing;

[0009] Utilize statistical methods combined with pre-processed historical wind power operation sample data to revise the energy storage configuration parameter range set by the user;

[0010] According to the corrected energy storage parameter range, a grid search method is used to obtain different energy storage parameter combinations, and N groups of energy storage configuration combination parameters that meet the preset conditions are screened out;

[0011] For N groups of energy storage configuration combination parameters, combined with the actual physical operating conditions of the energy storage system, a sequential Monte Carlo simulation sampling method is used to randomly generate N*M different charge and discharge power curves;

[0012] Based on market settlement rules, taking into account settlement income and excess profit recovery losses, the average daily settlement income and average daily energy storage operating costs corresponding to each energy storage configuration combination parameter are calculated, and the ratio of the two is calculated to obtain the average daily return on investment;

[0013] A simulation experiment was conducted using the maximization of the average daily return on investment as an evaluation indicator to screen out the optimal combination of energy storage system capacity and power.

[0014] Among them, in the step of collecting the required number of days of wind power operation history sample data and performing preprocessing, the sampling frequency is determined according to Shannon sampling theorem, wherein the wind power operation history sample data is based on days as the basic unit, and the sampling point time interval is set at the same time.

[0015] The step of using a statistical method in combination with pre-processed historical wind power operation sample data to correct the energy storage configuration parameter range set by the user includes:

[0016] Obtain the daily maximum values ​​of wind power data and real-time electricity data from historical sample data of wind power operation;

[0017] Perform arithmetic average calculation on the daily maximum values ​​of wind power data and real-time electricity data to obtain the average value of maximum wind power and the average value of maximum real-time electricity;

[0018] According to "the ratio of energy storage capacity to power is not less than the maximum charge and discharge rate ", the energy storage configuration parameter range is modified based on the calculated maximum wind power average value and maximum real-time power average value to obtain the final configuration parameter range;

[0019] Among them, the maximum wind power average value is set and the maximum real-time power average , the final configuration parameter range is expressed as:

[0020]

[0021]

[0022]

[0023]

[0024] in, The lower and upper limits of energy storage power set by the user in the historical sample data of wind power operation are: The lower and upper limits of energy storage capacity set by the user in the historical sample data of wind power operation are: are the lower and upper limits of the corrected energy storage power, are the lower and upper limits of the revised energy storage capacity, is the installed capacity of the wind farm.

[0025] The steps of obtaining different energy storage parameter combinations using a grid search method based on the corrected energy storage parameter range and screening out N groups of energy storage configuration combination parameters that meet preset conditions include:

[0026] According to the grid search method, the minimum interval fragments allowed are divided into Energy storage parameter combination methods; among them,

[0027] Rated capacity of the energy storage system With rated power Satisfy respectively and , Indicates rounding down;

[0028] The ratio of energy storage capacity to power is not less than the maximum charge and discharge rate Constraints from N groups of energy storage configuration combination parameters that meet the conditions are selected from the energy storage parameter combination methods.

[0029] The step of randomly generating N*M different charge and discharge power curves using a sequential Monte Carlo simulation sampling method for N groups of energy storage configuration combination parameters in combination with actual physical operating conditions of the energy storage system includes:

[0030] Set the known parameters in the energy storage configuration combination parameters to the rated capacity of the energy storage system With rated power ;

[0031] Based on the same set of energy storage configuration combination parameters, within the set rated power range, a charge-discharge power curve is randomly generated using the Monte Carlo simulation sampling method as a minimum granularity. For each set of energy storage configuration combination parameters, the minimum granularity generation operation is performed M times to obtain M charge-discharge power curves.

[0032] For all energy storage configuration combination parameters, N*M times are executed in total to obtain N*M charge and discharge power curves;

[0033] Filter out points that meet the conditions in each charge and discharge power curve based on market rules and physical constraints , forming a new charge and discharge power curve;

[0034] Get the corresponding charge and discharge capacity based on the new charge and discharge power curve And the remaining capacity of the energy storage system ; Among them, the conditions that should be met are as follows:

[0035] Energy storage system charging and discharging power Not exceeding the rated power of the energy storage device ,Right now

[0036]

[0037] Energy storage system charging and discharging power It should also meet the upper and lower limits of energy storage capacity and power. The specific formula is as follows:

[0038]

[0039] Among them, it is assumed that the initial capacity of energy storage for , is the charge-discharge conversion efficiency of the energy storage system, for The actual power generation at the time, is the installed capacity of the wind farm, is the time interval between sampling points;

[0040] Charging and discharging capacity of the energy storage system No more than actual power generation , and is smaller than the installed capacity of the wind farm , as follows:

[0041]

[0042] The remaining available capacity of the energy storage system should satisfy the following formula:

[0043]

[0044] in, for The remaining available energy storage capacity at the moment.

[0045] The steps of calculating the average daily settlement income and average daily energy storage operating cost corresponding to each energy storage configuration combination parameter based on market settlement rules, taking into account settlement income and excess profit recovery losses, and then calculating the ratio of the two to obtain the average daily investment return rate include:

[0046] Each charge and discharge power curve is used as the actual charge and discharge power curve after energy storage intervention, and is then substituted into the settlement income formula to calculate the settlement income after energy storage intervention. Market settlement income takes into account both spot settlement income and excess profit recovery losses. The income is the settlement income minus the excess profit recovery losses.

[0047] The settlement income formula is as follows:

[0048]

[0049] Where:

[0050] For the Spot settlement income at the time;

[0051] For the The base electricity price at the time, also known as the benchmark electricity price;

[0052] for The weighted electricity price of the medium and long-term contracts at the time;

[0053] For the The spot day-ahead electricity price forecast data at the time;

[0054] For the Real-time spot electricity price forecast data at all times;

[0055] For the Base power at the moment;

[0056] For the Total electricity consumption of medium and long-term contracts at the moment;

[0057] For the The day-ahead electricity consumption at the time;

[0058] For the Actual wind power generation at the time;

[0059]

[0060] For the The charge and discharge capacity after the energy storage is involved at the moment;

[0061]

[0062] in, For each charge and discharge curve The charging and discharging power at each moment, is the charge-discharge conversion efficiency of the energy storage system, is the time interval between sampling points;

[0063] Excess profit recovery loss:

[0064] When the power is predicted Higher than actual power When it is necessary to discharge, And benchmark electricity price Greater than real-time price When the recovery loss Equivalent to the product of the portion exceeding the power limit and the price difference;

[0065]

[0066] When the power is predicted Lower than actual power When charging is required, And benchmark electricity price Less than real-time price When the recovery loss Equivalent to the product of exceeding the lower limit of electricity consumption and the price difference;

[0067]

[0068] The settlement proceeds are:

[0069]

[0070] Since each charge and discharge curve has a settlement benefit, the same set of energy storage configuration combination parameters can be obtained. In order to effectively evaluate the profitability of each set of energy storage configuration parameters, the average profit value is obtained by balancing them. As settlement income after energy storage intervention:

[0071]

[0072] Calculate the settlement income of all historical sampling data before the energy storage system is involved, using the method for calculating the settlement income after the energy storage system is involved. ;

[0073] For the same set of energy storage configuration parameters, in order to reflect whether there is benefit after energy storage intervention, the profit difference before and after energy storage intervention is further calculated. ,Right now

[0074]

[0075] Since the calculated settlement profit difference includes the profit difference of all days, in order to reflect the profit situation, the average daily profit value is calculated. :

[0076]

[0077] Calculate the average daily cycle times of the energy storage system for all charge and discharge curves under the same energy storage configuration combination parameters :

[0078]

[0079] Calculate the energy storage operating cost under the same energy storage configuration combination parameters :

[0080]

[0081] in, Indicates the unit power cost, Indicates the unit capacity cost, Indicates other costs, is the rated power, is the rated capacity of the energy storage system;

[0082] Calculate the average daily operating cost of energy storage ,Right now

[0083]

[0084]

[0085] in, The service life days corresponding to the energy storage configuration combination parameters, is the number of cycles over the entire service life of the energy storage system under the corresponding energy storage configuration combination parameters, The average daily cycle times of the energy storage system.

[0086] In the simulation experiment, maximizing the daily average return on investment was used as an evaluation indicator to screen out the optimal energy storage system capacity and power combination. The following operations were performed for each set of energy storage configuration parameters:

[0087]

[0088] For all energy storage configuration combination parameters, the test evaluation indicator is to maximize the average return on investment for all days of historical data:

[0089]

[0090] Among them, the energy storage configuration combination parameters that maximize the average daily return on investment are obtained according to numerical calculations: and , which is the final energy storage configuration parameter.

[0091] A second object of the present invention is to provide an energy storage optimization configuration device based on simulation, comprising:

[0092] Data acquisition module, used to collect historical sample data of wind power operation for the required number of days and perform preprocessing;

[0093] The energy storage configuration parameter correction module is used to use statistical methods combined with pre-processed wind power operation historical sample data to correct the energy storage configuration parameter range set by the user;

[0094] The energy storage configuration parameter screening module is used to obtain different energy storage parameter combinations based on the corrected energy storage parameter range using a grid search method, and screen out N groups of energy storage configuration combination parameters that meet preset conditions;

[0095] The charge and discharge power curve generation module is used to randomly generate N*M different charge and discharge power curves based on N groups of energy storage configuration combination parameters and the actual physical operating conditions of the energy storage system using the sequential Monte Carlo simulation sampling method;

[0096] The daily average investment return rate calculation module is used to calculate the daily average settlement income and daily average energy storage operating cost corresponding to each energy storage configuration combination parameter based on market settlement rules, taking into account settlement income and excess profit recovery losses, and then calculate the ratio of the two to obtain the daily average investment return rate;

[0097] The energy storage optimization configuration module is used to conduct simulation experiments using the maximization of the average daily return on investment as an evaluation indicator to screen out the optimal combination of energy storage system capacity and power.

[0098] The third object of the present invention is to provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute each step in the method of the aforementioned technical solution.

[0099] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute each step in the method according to the aforementioned technical solution.

[0100] Different from the existing technology, the energy storage optimization configuration method based on simulation provided by the present invention amends the energy storage parameter range set by the user through a numerical calculation method, and obtains a variety of configuration combination parameters through a combination method; under each set of energy storage configuration parameters, a sequential Monte Carlo sampling method is used to randomly generate a charge and discharge power curve for simulation, wherein parallel calculation is performed when generating the charge and discharge power curve, which greatly saves operating costs and time; the final energy storage configuration parameters are obtained by maximizing the average daily return on investment as an evaluation indicator. The present invention uses the maximization of the average return on investment as an evaluation indicator, which overcomes the problem that the contradiction between the leveling effect and the economic benefit cannot be fully coordinated in existing research. At the same time, it approaches the actual energy storage operation through simulation experiments, avoids the difference between the model and the actual operation, and realizes the coordinated optimization configuration of the capacity and power of the energy storage system. Moreover, the algorithm model of the present invention is simple in design and does not involve a large amount of modeling and calculation. It can be directly used in actual industrial production applications to assist wind power plants in determining the optimal combination of energy storage capacity and power. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] The present invention and / or additional aspects and advantages will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0102] Figure 1 It is a flow chart of a method for optimizing energy storage configuration based on simulation provided by the present invention.

[0103] Figure 2It is a structural schematic diagram of an energy storage optimization configuration device based on simulation provided by the present invention.

[0104] Figure 3 It is a structural schematic diagram of a non-transitory computer-readable storage medium storing computer instructions provided by the present invention. DETAILED DESCRIPTION

[0105] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but are not to be construed as limiting the present invention.

[0106] like Figure 1 As shown, an energy storage optimization configuration method based on simulation provided by an embodiment of the present invention includes:

[0107] S110: Collecting wind power operation history sample data for the required number of days and performing preprocessing.

[0108] The wind power operation history sample data of a specified number of days is collected from the database. The data types involved in this invention mainly include three categories of data: medium- and long-term market data (quantity, price), day-ahead market data (quantity, price), and real-time market data (quantity, price). In order to avoid over-reliance on data samples, the sampling time interval for data collection cannot be too long, otherwise the data characteristics cannot be reflected, nor can it be too short, otherwise the data volume will be too large and the operating cost will increase. Therefore, the sampling frequency is determined according to Shannon's sampling theorem, where the data is based on days and the sampling frequency is based on days. is the sampling point time interval.

[0109] S120: Utilizing statistical methods in combination with pre-processed historical wind power operation sample data, the energy storage configuration parameter range set by the user is corrected.

[0110] Specifically include:

[0111] include:

[0112] S121: Obtain daily maximum values ​​of wind power data and real-time electricity data in historical sample data of wind power operation.

[0113] S122: Perform arithmetic mean calculation on the daily maximum values ​​of the wind power data and the real-time electricity data to obtain the maximum wind power average value and the maximum real-time electricity average value.

[0114] S123: According to the “ratio of energy storage capacity to power is not less than the maximum charge and discharge rate ", the energy storage configuration parameter range is modified based on the calculated maximum wind power average value and maximum real-time power average value to obtain the final configuration parameter range.

[0115] Among them, the maximum wind power average value is set and the maximum real-time power average , the final configuration parameter range is expressed as:

[0116]

[0117]

[0118]

[0119]

[0120] in, The lower and upper limits of energy storage power set by the user in the historical sample data of wind power operation are: The lower and upper limits of energy storage capacity set by the user in the historical sample data of wind power operation are: are the lower and upper limits of the corrected energy storage power, are the lower and upper limits of the revised energy storage capacity, is the installed capacity of the wind farm.

[0121] S130: According to the corrected energy storage parameter range, a grid search method is used to obtain different energy storage parameter combinations, and N groups of energy storage configuration combination parameters that meet preset conditions are screened out.

[0122] Specifically include:

[0123] S131: According to the grid search method, the minimum interval segments allowed are divided to obtain Energy storage parameter combination methods; among them,

[0124] Rated capacity of the energy storage system With rated power Satisfy respectively and , Indicates rounding down.

[0125] S132: The ratio of energy storage capacity to power is not less than the maximum charge and discharge rate Constraints from N groups of energy storage configuration combination parameters that meet the conditions are selected from the energy storage parameter combination methods.

[0126] S140: For N groups of energy storage configuration combination parameters, combined with the actual physical operating conditions of the energy storage system, a sequential Monte Carlo simulation sampling method is used to randomly generate N*M different charge and discharge power curves.

[0127] Specifically include:

[0128] S141: Set the known parameters in the energy storage configuration combination parameters to the rated capacity of the energy storage system With rated power .

[0129] S142: Based on the same set of energy storage configuration combination parameters, within the set rated power range, a charge-discharge power curve is randomly generated using the Monte Carlo simulation sampling method as a minimum granularity; for each set of energy storage configuration combination parameters, the minimum granularity generation operation is performed M times to obtain M charge-discharge power curves.

[0130] S143: Execute N*M times for all energy storage configuration combination parameters to obtain N*M charge and discharge power curves.

[0131] For all combination parameters, the operation process of generating the charge and discharge power curve N*M times is calculated in parallel using a multi-threading method without affecting each other, thus saving cost and running time.

[0132] S144: Filter out points that meet the conditions in each charge and discharge power curve based on market rules and physical constraints , forming a new charge and discharge power curve.

[0133] S145: Obtain the corresponding charge and discharge power according to the new charge and discharge power curve And the corresponding remaining capacity of the energy storage system Among them, the conditions that should be met are as follows:

[0134] Energy storage system charging and discharging power Not exceeding the rated power of the energy storage device ,Right now

[0135]

[0136] Energy storage system charging and discharging power It should also meet the upper and lower limits of energy storage capacity and power. The specific formula is as follows:

[0137]

[0138] Among them, it is assumed that the initial capacity of energy storage for , is the charge-discharge conversion efficiency of the energy storage system, for The actual power generation at the time, is the installed capacity of the wind farm, is the time interval between sampling points;

[0139] Charging and discharging capacity of the energy storage system No more than actual power generation , and is smaller than the installed capacity of the wind farm , as follows:

[0140]

[0141] The remaining available capacity of the energy storage system should satisfy the following formula:

[0142]

[0143] in, for The remaining available energy storage capacity at the moment.

[0144] S150: Based on market settlement rules, taking into account settlement income and excess profit recovery losses, calculate the average daily settlement income and average daily energy storage operating cost corresponding to each energy storage configuration combination parameter, and calculate the ratio of the two to obtain the average daily investment return rate.

[0145] Specifically include:

[0146] S151: Each charge and discharge power curve is used as the actual charge and discharge power curve after energy storage intervention, and is substituted into the settlement income formula to calculate the settlement income after energy storage intervention. Among them, the market settlement income takes into account two aspects: spot settlement income and excess profit recovery loss. The income is the settlement income minus the excess profit recovery loss.

[0147] The settlement income formula is as follows:

[0148]

[0149] Where:

[0150] For the Spot settlement income at the time;

[0151] For the The base electricity price at the time, also known as the benchmark electricity price;

[0152] for The weighted electricity price of the medium and long-term contracts at the time;

[0153] For the The spot day-ahead electricity price forecast data at the time;

[0154] For the Real-time spot electricity price forecast data at all times;

[0155] For the Base power at the moment;

[0156] For the Total electricity consumption of medium and long-term contracts at the moment;

[0157] For the The day-ahead electricity consumption at the time;

[0158] For the Actual wind power generation at the time;

[0159]

[0160] For the The charge and discharge capacity after the energy storage is involved at the moment;

[0161]

[0162] in, For each charge and discharge curve The charging and discharging power at each moment, is the charge-discharge conversion efficiency of the energy storage system, is the time interval between sampling points;

[0163] Excess profit recovery loss:

[0164] When the power is predicted Higher than actual power When it is necessary to discharge, And benchmark electricity price Greater than real-time price When the recovery loss Equivalent to the product of the portion exceeding the power limit and the price difference;

[0165]

[0166] When the power is predicted Lower than actual power When charging is required, And benchmark electricity price Less than real-time price The recovery loss generated Equivalent to the product of exceeding the lower limit of electricity consumption and the price difference;

[0167]

[0168] The settlement proceeds are:

[0169]

[0170] S152: Since each charge and discharge curve has a settlement income, the same set of energy storage configuration combination parameters is obtained. In order to effectively evaluate the profitability of each set of energy storage configuration parameters, the average profit value is obtained by balancing them. As settlement income after energy storage intervention:

[0171]

[0172] S153: Calculate the settlement benefits of all historical sampling data before the energy storage system is involved, using the method for calculating the settlement benefits after the energy storage system is involved. ;

[0173] S154: For the same set of energy storage configuration parameters, to reflect whether the energy storage intervention will generate benefits, further calculate the difference in benefits before and after the energy storage intervention. ,Right now

[0174]

[0175] Since the calculated settlement profit difference includes the profit difference of all days, in order to reflect the profit situation, the average daily profit value is calculated. :

[0176]

[0177] S155: Calculate the average daily cycle times of the energy storage system for all charge and discharge curves under the same energy storage configuration combination parameters. :

[0178]

[0179] S156: Calculate the energy storage operating cost under the same energy storage configuration combination parameters :

[0180]

[0181] in, Indicates the unit power cost, Indicates the unit capacity cost, Indicates other costs, is the rated power, is the rated capacity of the energy storage system;

[0182] S157: Calculate the average daily operating cost of energy storage ,Right now

[0183]

[0184]

[0185] in, The service life days corresponding to the energy storage configuration combination parameters, is the number of cycles over the entire service life of the energy storage system under the corresponding energy storage configuration combination parameters, The average daily cycle times of the energy storage system.

[0186] S160: Use the maximization of the average daily return on investment as an evaluation indicator to conduct simulation experiments and screen out the optimal combination of energy storage system capacity and power.

[0187] In each set of energy storage configuration parameters, perform the following operations:

[0188]

[0189] For all energy storage configuration combination parameters, the test evaluation indicator is to maximize the average return on investment for all days of historical data:

[0190]

[0191] Among them, the energy storage configuration combination parameters that maximize the average daily return on investment are obtained according to numerical calculations: and , which is the final energy storage configuration parameter.

[0192] like Figure 2 As shown, the present invention provides an energy storage optimization configuration device 300 based on simulation, comprising:

[0193] The data collection module 310 is used to collect the required number of days of wind power operation history sample data and perform preprocessing;

[0194] The energy storage configuration parameter correction module 320 is used to correct the energy storage configuration parameter range set by the user by using statistical methods combined with pre-processed wind power operation historical sample data;

[0195] The energy storage configuration parameter screening module 330 is used to obtain different energy storage parameter combinations based on the corrected energy storage parameter range using a grid search method, and screen out N groups of energy storage configuration combination parameters that meet preset conditions;

[0196] The charge-discharge power curve generation module 340 is used to randomly generate N*M different charge-discharge power curves using a sequential Monte Carlo simulation sampling method based on N groups of energy storage configuration combination parameters and the actual physical operating conditions of the energy storage system;

[0197] The daily average investment return calculation module 350 is used to calculate the daily average settlement income and daily average energy storage operating costs corresponding to each energy storage configuration combination parameter based on market settlement rules, taking into account settlement income and excess profit recovery losses. The daily average investment return is calculated by calculating the ratio of the two.

[0198] The energy storage optimization configuration module 360 ​​is used to perform simulation experiments using the maximization of the average daily return on investment as an evaluation indicator to screen out the optimal combination of energy storage system capacity and power.

[0199] In order to implement the embodiment, the present invention also proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute each step in the energy storage optimization configuration method based on simulation of the aforementioned technical solution.

[0200] like Figure 3 As shown, a non-transitory computer-readable storage medium 800 includes a memory 810 of instructions and an interface 830. The instructions can be executed by a processor 820 based on a simulation-based energy storage optimization configuration to complete the method. Alternatively, the storage medium can be a non-transitory computer-readable storage medium, for example, a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0201] In order to implement the embodiment, the present invention further proposes a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the simulation-based energy storage optimization configuration according to the embodiment of the present invention is implemented.

[0202] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction 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 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 appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0203] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0204] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0205] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0206] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gates for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gates, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0207] Those skilled in the art will understand that all or part of the steps of the method for implementing the embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0208] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0209] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the embodiments are exemplary and are not to be construed as limiting the present invention. Those skilled in the art may make changes, modifications, substitutions, and variations to the embodiments within the scope of the present invention.

Claims

1. A method for optimizing energy storage configuration based on simulation, characterized in that: include: Collect the required number of days of wind power operation history sample data and perform preprocessing; Utilize statistical methods combined with pre-processed historical wind power operation sample data to revise the energy storage configuration parameter range set by the user; According to the corrected energy storage parameter range, a grid search method is used to obtain different energy storage parameter combinations, and N groups of energy storage configuration combination parameters that meet the preset conditions are screened out; For N groups of energy storage configuration combination parameters, combined with the actual physical operating conditions of the energy storage system, a sequential Monte Carlo simulation sampling method is used to randomly generate N*M different charge and discharge power curves; Based on market settlement rules, taking into account settlement income and excess profit recovery losses, the average daily settlement income and average daily energy storage operating costs corresponding to each energy storage configuration combination parameter are calculated, and the ratio of the two is calculated to obtain the average daily return on investment; Using the maximization of the daily average return on investment as an evaluation indicator, simulation experiments were conducted to screen out the optimal combination of energy storage system capacity and power; The steps of obtaining different energy storage parameter combinations using a grid search method based on the corrected energy storage parameter range and screening out N groups of energy storage configuration combination parameters that meet preset conditions include: According to the grid search method, the minimum interval fragments allowed are divided into Energy storage parameter combination methods; among them, Rated capacity of the energy storage system With rated power Satisfy respectively and , Indicates rounding down; The ratio of energy storage capacity to power is not less than the maximum charge and discharge rate Constraints from N groups of energy storage configuration combination parameters that meet the conditions are selected from the energy storage parameter combination methods.

2. The energy storage optimization configuration method based on simulation according to claim 1 is characterized in that: In the step of collecting the required number of days of wind power operation history sample data and performing preprocessing, the sampling frequency is determined according to Shannon sampling theorem, wherein the wind power operation history sample data is based on days as the basic unit, and the sampling point time interval is set.

3. The energy storage optimization configuration method based on simulation according to claim 1 is characterized in that: The step of using a statistical method in combination with pre-processed historical wind power operation sample data to correct the energy storage configuration parameter range set by the user includes: Obtain the daily maximum values ​​of wind power data and real-time electricity data from historical sample data of wind power operation; Perform arithmetic average calculation on the daily maximum values ​​of wind power data and real-time electricity data to obtain the average value of maximum wind power and the average value of maximum real-time electricity; According to the "ratio of energy storage capacity to power is not less than the maximum charge and discharge rate ", the energy storage configuration parameter range is modified based on the calculated maximum wind power average value and maximum real-time power average value to obtain the final configuration parameter range; Among them, the maximum wind power average value is set and the maximum real-time power average , the final configuration parameter range is expressed as: in, The lower and upper limits of energy storage power set by the user in the historical sample data of wind power operation are: The lower and upper limits of energy storage capacity set by the user in the historical sample data of wind power operation are: are the lower and upper limits of the corrected energy storage power, are the lower and upper limits of the revised energy storage capacity, is the installed capacity of the wind farm.

4. The energy storage optimization configuration method based on simulation according to claim 3 is characterized in that: The steps of randomly generating N*M different charge and discharge power curves using a Monte Carlo simulation sampling method for N groups of energy storage configuration combination parameters in combination with actual physical operating conditions of the energy storage system include: Set the known parameters in the energy storage configuration combination parameters to the rated capacity of the energy storage system With rated power ; Based on the same set of energy storage configuration combination parameters, within the set rated power range, a charge-discharge power curve is randomly generated using the Monte Carlo simulation sampling method as a minimum granularity. For each set of energy storage configuration combination parameters, the minimum granularity generation operation is performed M times to obtain M charge-discharge power curves. For all energy storage configuration combination parameters, N*M times are executed in total to obtain N*M charge and discharge power curves; Filter out points that meet the conditions in each charge and discharge power curve based on market rules and physical constraints , forming a new charge and discharge power curve; Get the corresponding charge and discharge capacity based on the new charge and discharge power curve And the corresponding remaining capacity of the energy storage system ; The conditions that should be met are as follows: Energy storage system charging and discharging power Not exceeding the rated power of the energy storage device ,Right now Energy storage system charging and discharging power It is also necessary to consider whether the wind farm capacity constraints after configuration are met, and the operating constraints of the energy storage discharge power in the initial state. The specific formula is as follows: Among them, it is assumed that the initial capacity of energy storage for , is the charge-discharge conversion efficiency of the energy storage system, for The actual power generation at the time, is the installed capacity of the wind farm, is the time interval between sampling points; Charging and discharging capacity of the energy storage system No more than actual power generation , and is smaller than the installed capacity of the wind farm , as follows: The remaining available capacity of the energy storage system should satisfy the following formula: in, for The remaining available energy storage capacity at the moment.

5. The energy storage optimization configuration method based on simulation according to claim 4 is characterized in that: The steps of calculating the daily average settlement income and daily average energy storage operating cost corresponding to each energy storage configuration combination parameter based on market settlement rules, taking into account settlement income and excess profit recovery losses, and then calculating the ratio of the two to obtain the daily average investment return rate include: Each charge and discharge power curve is used as the actual charge and discharge power curve after energy storage intervention, and is then substituted into the settlement income formula to calculate the settlement income after energy storage intervention. Market settlement income takes into account both spot settlement income and excess profit recovery losses. The income is the settlement income minus the excess profit recovery losses. The settlement income formula is as follows: Where: For the Spot settlement income at the time; For the The base electricity price at the time, also known as the benchmark electricity price; for The weighted electricity price of the medium and long-term contracts at the time; For the The spot day-ahead electricity price forecast data at the time; For the Real-time spot electricity price forecast data at all times; For the Base power at the moment; For the Total electricity consumption of medium and long-term contracts at the moment; For the The day-ahead electricity consumption at the time; For the Actual wind power generation at the time; For the The charge and discharge capacity after the energy storage is involved at the moment; in, The first charge / discharge curve The charge and discharge power at each moment, is the charge-discharge conversion efficiency of the energy storage system, is the time interval between sampling points; Excess profit recovery loss: When the power is predicted Higher than actual power When it is necessary to discharge, And benchmark electricity price Greater than real-time price The recovery loss generated Equivalent to the product of the portion exceeding the power limit and the price difference; When the power is predicted Lower than actual power When charging is required, And benchmark electricity price Less than real-time price The recovery loss generated Equivalent to the product of exceeding the lower limit of electricity consumption and the price difference; The settlement proceeds are: Since each charge and discharge curve has a settlement benefit, the same set of energy storage configuration combination parameters can be obtained. In order to effectively evaluate the profitability of each set of energy storage configuration parameters, the average profit value is obtained by balancing them. As settlement income after energy storage intervention: Calculate the settlement income of all historical sampling data before the energy storage system is involved, using the method for calculating the settlement income after the energy storage system is involved. ; For the same set of energy storage configuration parameters, in order to reflect whether there is benefit after energy storage intervention, the difference in benefits before and after energy storage intervention is further calculated. ,Right now Since the calculated settlement income difference includes the income difference of all days, in order to reflect the income situation, the average daily income value is calculated. : Calculate the average daily cycle times of the energy storage system for all charge and discharge curves under the same energy storage configuration combination parameters : Calculate the energy storage operating cost under the same energy storage configuration combination parameters : in, Indicates the unit power cost, Indicates the unit capacity cost, Indicates other costs, is the rated power, is the rated capacity of the energy storage system; Calculate the average daily operating cost of energy storage ,Right now in, The service life days corresponding to the energy storage configuration combination parameters, is the number of cycles over the entire service life of the energy storage system under the corresponding energy storage configuration combination parameters, The average daily cycle times of the energy storage system.

6. The energy storage optimization configuration method based on simulation according to claim 5 is characterized in that: In the simulation experiment using the maximization of the daily average return on investment as the evaluation indicator to screen out the optimal energy storage system capacity and power combination, the following operations are performed for each set of energy storage configuration combination parameters: For all energy storage configuration combination parameters, the test evaluation indicator is to maximize the average return on investment for all days of historical data: Among them, the energy storage configuration combination parameters that maximize the average daily return on investment are obtained according to numerical calculations: and , which is the final energy storage configuration parameter.

7. A device for optimizing energy storage configuration based on simulation, characterized in that: include: Data acquisition module, used to collect historical sample data of wind power operation for the required number of days and perform preprocessing; The energy storage configuration parameter correction module is used to use statistical methods combined with pre-processed wind power operation historical sample data to correct the energy storage configuration parameter range set by the user; The energy storage configuration parameter screening module is used to obtain different energy storage parameter combinations based on the corrected energy storage parameter range using a grid search method, and screen out N groups of energy storage configuration combination parameters that meet preset conditions; The charge and discharge power curve generation module is used to randomly generate N*M different charge and discharge power curves based on N groups of energy storage configuration combination parameters and the actual physical operating conditions of the energy storage system using the sequential Monte Carlo simulation sampling method; The daily average investment return rate calculation module is used to calculate the daily average settlement income and daily average energy storage operating cost corresponding to each energy storage configuration combination parameter based on market settlement rules, taking into account settlement income and excess profit recovery losses, and then calculate the ratio of the two to obtain the daily average investment return rate; The energy storage optimization configuration module is used to conduct simulation experiments using the maximum daily average return on investment as an evaluation indicator to screen out the optimal combination of energy storage system capacity and power; The steps of obtaining different energy storage parameter combinations using a grid search method based on the corrected energy storage parameter range and screening out N groups of energy storage configuration combination parameters that meet preset conditions include: According to the grid search method, the minimum interval fragments allowed are divided into Energy storage parameter combination methods; among them, Rated capacity of the energy storage system With rated power Satisfy respectively and , Indicates rounding down; The ratio of energy storage capacity to power is not less than the maximum charge and discharge rate Constraints from N groups of energy storage configuration combination parameters that meet the conditions are selected from the energy storage parameter combination methods.

8. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform each step in the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute each step of the method according to any one of claims 1 to 6.

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