Energy storage resource regulation methods, devices and computer equipment

Abstract

This application relates to an energy storage resource regulation method, apparatus, and computer equipment, belonging to the field of resource regulation technology. The method includes: acquiring a first set of relevant parameters; inputting the first set of relevant parameters into a pre-constructed target electricity consumption assessment model to determine the required electricity consumption in a future time period; acquiring a first environmental parameter and determining a first power generation fluctuation probability based on the first environmental parameter; determining the power generation in the future time period based on the first power generation fluctuation probability; determining the electricity price in the future time period; and regulating the charging or discharging of the energy storage device based on the electricity price in the future time period, the power generation in the future time period, and the required electricity consumption in the future time period. This application can improve the dynamic response capability of the power grid while improving resource utilization efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource regulation, in particular to a method and device for regulating energy storage resources and a computer device. BACKGROUND

[0002] With the transformation of energy structure and the intelligent development of power systems, the scale of power grids is continuously expanding, and a large number of new power resources such as distributed energy (e.g. photovoltaic, wind power), energy storage systems, etc. are connected to the grid, making the operating environment of the grid increasingly complex. Traditional energy storage resource regulation methods are mainly based on centralized scheduling and static models, which are difficult to adapt to the volatility and uncertainty under high proportion of renewable energy access, resulting in low regulation efficiency, delayed response, and even stability problems of energy storage devices.

[0003] In the prior art, energy storage resource regulation usually relies on artificial experience or offline optimization algorithms, such as load distribution based on fixed thresholds or simple priority scheduling strategies. Such methods lack dynamic perception ability for real-time operating data and cannot effectively respond to sudden load changes or random output of distributed resources. In addition, traditional regulation systems often use isolated control modules, which lack coordination between subsystems, resulting in low resource utilization and increased redundant configuration. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for regulating energy storage resources and a computer device to improve the dynamic response capability and resource utilization efficiency of the power grid.

[0005] In a first aspect, a method for regulating energy storage resources is provided, the method comprising:

[0006] obtaining a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period;

[0007] obtaining a first environmental parameter and determining a first power generation fluctuation probability based on the first environmental parameter;

[0008] determining the power generation in the future time period based on the first power generation fluctuation probability;

[0009] determining the electricity price in the future time period, and regulating the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0010] Optionally, before obtaining the first set of related parameters, the method further comprises:

[0011] constructing the target power consumption evaluation model.

[0012] Optionally, the constructing the target electricity consumption evaluation model comprises:

[0013] obtaining a second set of related parameters;

[0014] preprocessing the second set of related parameters to obtain a third set of related parameters;

[0015] training and testing the constructed initial electricity consumption evaluation model based on the third set of related parameters to obtain the target electricity consumption evaluation model, the model parameters of the target electricity consumption evaluation model being obtained by training the model parameters of the initial electricity consumption evaluation model based on the third set of related parameters.

[0016] Optionally, the preprocessing the second set of related parameters to obtain a third set of related parameters comprises:

[0017] performing data cleaning processing on the second set of related parameters to obtain cleaned parameters;

[0018] performing normalization processing on the cleaned parameters to obtain normalized parameters;

[0019] defining the normalized parameters as the third set of related parameters.

[0020] Optionally, after obtaining the third set of related parameters, the method further comprises:

[0021] classifying and saving the parameters in the third set of related parameters;

[0022] dividing the classified parameters into a training set and a test set according to a preset ratio.

[0023] Optionally, the third set of related parameters at least comprises electrical parameters, second environmental parameters and electricity prices, and the training and testing of the pre-constructed initial electricity consumption evaluation model based on the third set of related parameters to obtain the target electricity consumption evaluation model comprises:

[0024] determining electricity consumption in a target time period based on the electrical parameters;

[0025] obtaining a plurality of second environmental parameters affecting electricity consumption fluctuation, and determining a second electricity consumption fluctuation probability based on the second environmental parameters;

[0026] determining a third electricity consumption fluctuation probability based on the electricity prices;

[0027] determining the initial electricity consumption evaluation model based on the electricity consumption in the target time period, the third electricity consumption fluctuation probability and the second electricity consumption fluctuation probability;

[0028] training the initial electricity consumption evaluation model based on the training set, and testing the trained initial electricity consumption evaluation model based on the test set;

[0029] outputting the target electricity consumption evaluation model in response to the test result meeting a preset standard.

[0030] Optionally, the determining the power generation in the future time period based on the first power generation fluctuation probability comprises:

[0031] determining the power generation in the future time period based on the first power generation fluctuation probability;

[0032] wherein, represents the power generation of a power generation device i in a time period t , i represents a power generation device identifier, t represents a time period identifier, represents a second adjustable parameter, represents a controllable power generation, represents a time period number, represents a regression coefficient of a power generation variable, represents a fluctuation power generation in a time period, represents a time period identifier, represents a first environmental parameter number, represents a regression coefficient of a first environmental parameter, represents a probability that c a first environmental parameter affects power generation fluctuation, c represents a first environmental parameter identifier, represents a first environmental parameter influence degree assignment, c represents a second random error term.

[0033] Optionally, the charging or discharging regulation of the energy storage device based on the electricity price in the future time period, the power generation in the future time period, and the required electricity consumption in the future time period comprises:

[0034] determining whether to perform the charging or discharging regulation based on an objective function and a constraint condition, the objective function comprising:

[0035] ;

[0036] the constraint condition comprising:

[0037] a supply-demand balance constraint:

[0038] ​​ ;

[0039] Generation equipment constraints:

[0040] ;

[0041] Energy storage equipment constraints, including charge-discharge power constraints, power dynamics and capacity constraints, wherein:

[0042] Charge-discharge power constraints:

[0043] ;

[0044] Power dynamics:

[0045] ;

[0046] Capacity constraints:

[0047] ;

[0048] Consumption equipment constraints:

[0049] ;

[0050] Priority constraints:

[0051] ;

[0052] Line transmission constraints:

[0053] ;

[0054] wherein, H represents the objective function calculation value, T represents a set of time periods, I represents a set of generation equipment, J represents a set of consumption equipment, K represents a set of energy storage equipment, L represents a grid node or line, represents the electricity price at time period t , t represents a time period identifier, represents the grid purchase power at time period t , represents the unit generation cost of generation equipment i , i represents a generation equipment identifier, represents the generation amount of generation equipment i at time period t , represents consumption equipmentj Priority weight coefficient, j Indicates the identification of electrical equipment. Indicates electrical equipment j In time period t The required electricity consumption, Indicates the time period t For electrical equipment j The actual power supply, Indicates energy storage devices k In time period t The remaining battery power, k Indicates the identification of energy storage equipment. To prevent simultaneous charging and discharging, Indicates electrical equipment j In time period t Is it powered at any time? This represents the set of highest priority electrical devices. These represent energy storage devices. k In time period t The charging power and discharging power, They represent power generation equipment. i In time period t The upper and lower limits of power generation, These represent energy storage devices. k The discharge efficiency and charging efficiency, These represent energy storage devices. k The lower and upper limits of capacity, These represent energy storage devices. k The upper limit of charging power and the upper limit of discharging power, Indicates the upper limit of transmission capacity of a power grid node or line. They respectively represent the connections in Power generation equipment and electrical equipment on the premises;

[0055] In response to the calculated value of the objective function being less than a first preset threshold, the energy storage device is charged;

[0056] In response to the objective function calculation value being greater than a second preset threshold, the energy storage device is discharged.

[0057] In a second aspect, a device for regulating energy storage resources is provided, comprising: [determining whether to perform charging or discharging regulation].

[0058] The first determining module is used to obtain a first relevant parameter set and input the first relevant parameter set into a pre-built target electricity consumption assessment model to determine the electricity consumption required in future time periods.

[0059] a second determining module, configured to acquire a first environmental parameter, and determine a first power generation fluctuation probability according to the first environmental parameter;

[0060] a third determining module, configured to determine power generation in a future time period based on the first power generation fluctuation probability;

[0061] a regulating module, configured to determine an electricity price in the future time period, and regulate charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0062] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0063] acquiring a first set of related parameters, and inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine required power consumption in a future time period;

[0064] acquiring a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter;

[0065] determining power generation in a future time period based on the first power generation fluctuation probability;

[0066] determining an electricity price in the future time period, and regulating charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0067] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the following steps when executed by a processor:

[0068] acquiring a first set of related parameters, and inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine required power consumption in a future time period;

[0069] acquiring a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter;

[0070] determining power generation in a future time period based on the first power generation fluctuation probability;

[0071] determining an electricity price in the future time period, and regulating charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0072] In a fifth aspect, a computer program product is provided, which comprises a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0073] obtaining a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period;

[0074] obtaining a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter;

[0075] determining the power generation in the future time period based on the first power generation fluctuation probability;

[0076] determining the electricity price in the future time period, and performing charging or discharging regulation and control on the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0077] The above energy storage resource regulation method, device and computer equipment, the method comprises: obtaining a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period; obtaining a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter; determining the power generation in the future time period based on the first power generation fluctuation probability; determining the electricity price in the future time period, and performing charging or discharging regulation and control on the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period. The present application can improve the dynamic response capability of the power grid while improving the resource utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present application, the drawings required in the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0079] Figure 1 An application environment diagram of the energy storage resource regulation method in an embodiment;

[0080] Figure 2 A flowchart of the energy storage resource regulation method in an embodiment;

[0081] Figure 3 A structural block diagram of the energy storage resource regulation device in an embodiment;

[0082] Figure 4An internal structure diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0083] For the purposes of the present application, the technical solutions and advantages of the present application will be more clearly apparent from the following description of the embodiments of the present application, which will be described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0084] It should be understood that in the description of the present application, unless the context clearly requires otherwise, the terms "comprise", "comprise", and the like in the entire specification mean the inclusive meaning and not the exclusive or exhaustive meaning; that is, the meaning of "including but not limited to".

[0085] It should also be understood that the terms "first", "second", and the like are only for the purpose of description and should not be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise stated, the meaning of "multiple" is two or more.

[0086] It should be noted that the terms "S1", "S2" and the like are only for the purpose of describing the steps and do not specifically refer to the order or position, nor are they used to limit the present application. They are only used to facilitate the description of the method of the present application and should not be understood as indicating the order of the steps. In addition, the technical solutions of various embodiments can be combined with each other, but must be based on the realization of those of ordinary skill in the art. When the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0087] The energy storage resource regulation method provided by the present application can be applied to the application environment as shown in Figure 1 . Among them, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0088] In one embodiment, as shown in Figure 2 , an energy storage resource regulation method is provided. Taking the terminal in Figure 1 as an example, the method includes the following steps:

[0089] S1: obtaining a first set of relevant parameters, inputting the first set of relevant parameters into a pre-constructed target electricity consumption evaluation model to determine the required electricity consumption in a future time period.

[0090] In some embodiments, before obtaining the first set of relevant parameters, the method further comprises:

[0091] constructing the target electricity consumption evaluation model.

[0092] In some embodiments, the constructing the target electricity consumption evaluation model comprises:

[0093] obtaining a second set of relevant parameters of the target region affecting the regulation of the energy storage resource in different historical time periods, the second set of relevant parameters comprising at least electrical parameters, environmental parameters and electricity prices;

[0094] preprocessing the second set of relevant parameters to obtain a third set of relevant parameters;

[0095] training and testing the pre-constructed initial electricity consumption evaluation model based on the third set of relevant parameters to obtain the target electricity consumption evaluation model, the model parameters of the target electricity consumption evaluation model being obtained by training the model parameters of the initial electricity consumption evaluation model based on the third set of relevant parameters.

[0096] In some embodiments, preprocessing the second set of relevant parameters to obtain a third set of relevant parameters comprises:

[0097] performing data cleaning processing on the second set of relevant parameters to obtain cleaned parameters, wherein the data cleaning processing method is a common method and the specific process is not described here;

[0098] performing normalization processing on the cleaned parameters to obtain normalized parameters, and defining the normalized parameters as the third set of relevant parameters, wherein the normalization processing method is a common method and the specific process is not described here.

[0099] In some embodiments, after obtaining the third set of relevant parameters, the method further comprises:

[0100] classifying and saving the parameters in the third set of relevant parameters and determining the classification marker value corresponding to each classification category, wherein the classification marker value can represent the data attributes included in each category, for example, 1 represents an environmental parameter, so the classification marker value is 1, and so on. By classifying and saving the parameters and marking, different categories of parameters can be quickly extracted in the subsequent process, improving the running efficiency.

[0101] dividing the classified parameters into a training set and a test set according to a preset ratio, wherein the preset ratio can be set according to actual needs, for example, 8:2.

[0102] It should be noted that the training set is used to train the pre-constructed initial power consumption evaluation model, and the test set is used to test the pre-constructed initial power consumption evaluation model.

[0103] In some embodiments, based on the third set of related parameters, the pre-constructed initial power consumption evaluation model is trained and tested to obtain a target power consumption evaluation model, which includes:

[0104] Based on the electrical parameters, the power consumption in the target time period is determined, wherein the target time period refers to the next time period corresponding to the current time period, and the power consumption can be determined by comprehensively calculating the current, voltage, time and other parameters;

[0105] Obtain a plurality of second environmental parameters affecting power consumption fluctuation Such as temperature, humidity, etc., according to the second environmental parameters, the second power consumption fluctuation probability is determined Wherein, the probability and the value are 1, wherein the second environmental parameters and the second power consumption fluctuation probability exist a mapping relationship, which is determined by multiple experiments, for example, when the temperature rises from 25 degrees Celsius to 35 degrees Celsius, some users need to start air conditioning and other cooling equipment, and the power consumption fluctuation probability is 50%, when the humidity rises from 80% to 95%, some users need to start dehumidification equipment, and the power consumption fluctuation probability is 10%, when the temperature drops from 25 degrees Celsius to 10 degrees Celsius, some users need to start air conditioning and other heating equipment, and the power consumption fluctuation probability is 35%, and so on, which will not be repeated here;

[0106] Based on the electricity price, the third power consumption fluctuation probability is determined That is, based on the fluctuation of the electricity price, the probability of the electricity price affecting the power consumption fluctuation in the target time period is determined, wherein the probability and the value are 1, wherein the electricity price and the third power consumption fluctuation probability exist a mapping relationship, which is determined by multiple experiments, for example, when the current time is at the valley time, such as 22 o'clock, the valley time electricity price is used, at this time, users are more willing to start electrical equipment, such as electric vehicle charging, starting air conditioning, etc., at this time, the third power consumption fluctuation probability is 60%, when the current time is at the valley time, such as 3 o'clock, the valley time electricity price is used, at this time, more users are in a resting state, and the starting amount of electrical equipment is less, and the fluctuation probability is 20%, and so on, which will not be repeated here;

[0107] Based on the power consumption in the target time period, the third power consumption fluctuation probability and the second power consumption fluctuation probability, the initial power consumption evaluation model is determined;

[0108] The initial power consumption evaluation model is trained based on the training set, and the trained initial power consumption evaluation model is tested by using the test set.

[0109] In response to the test results meeting a preset standard, the target electricity consumption assessment model is output. The preset standard can be an assessment accuracy rate greater than a preset value, which can be set according to actual needs, such as 95%. The expression of the target electricity consumption assessment model includes:

[0110] ;

[0111] in, Indicates time period t Electricity consumption within the room t Indicates a time period identifier. This indicates the first adjustable parameter. The regression coefficients represent the variables of electricity consumption. This represents the first random error term. express Electricity consumption during the time period Indicates a time period identifier. Indicates the number of time periods. Indicates the number of second environmental parameters. This represents the regression coefficient of the second environmental parameter. Indicates the first b The probability that a second environmental parameter affects fluctuations in electricity consumption. b Indicates the second environmental parameter identifier. Indicates the first b Assign a value to the degree of influence of the second environmental parameter. s Indicates the quantity of electricity price. The regression coefficient representing the electricity price. No. r The probability that individual electricity prices affect fluctuations in electricity consumption. Indicates the first r Assigning values ​​to the degree of impact on electricity prices. r This indicates the electricity price.

[0112] It should be noted that the first set of relevant parameters includes at least electrical parameters, environmental parameters, and electricity price. The target area can refer to urban areas or areas divided according to actual needs, such as Shanghai. The time period can refer to the peak-valley time-of-use period or the peak and off-peak electricity consumption periods. The specific time period, including the time step, can be set according to actual needs. Electrical parameters can include voltage, current, power, frequency, and efficiency, etc. Environmental parameters can include temperature, humidity, etc. Electricity price includes peak-valley time-of-use electricity prices. For example, the peak-valley time-of-use electricity price in Shanghai includes: peak period: 6:00 to 22:00 daily, electricity price is 0.641 yuan per kilowatt-hour (including...). The data includes a renewable energy surcharge of 0.1 fen per kilowatt-hour. During off-peak hours (10 PM to 6 AM the following day), the electricity price is approximately 0.331 yuan per kilowatt-hour. The methods used to obtain this data are common and will not be detailed here. Preprocessing methods generally include data cleaning and data transformation. Data cleaning includes removing duplicate values, handling missing values, and handling outliers, such as filling missing values ​​with the mean or median, or estimating using interpolation methods. Data transformation involves converting the data to suit specific analytical methods, such as normalization. This application uses normalization as the data transformation method.

[0113] S2: Obtain the first environmental parameters and determine the first power generation fluctuation probability based on the first environmental parameters.

[0114] S3: Based on the first power generation fluctuation probability, determine the power generation within the future time period.

[0115] It should be noted that the above steps specifically include:

[0116] Obtain the first environmental parameters that affect power generation fluctuations over future time periods. Data such as temperature, typhoons, heavy rain, and solar radiation intensity can be obtained through weather forecasts, and the probability of each primary environmental parameter affecting power generation fluctuations can be determined. This refers to the first power generation fluctuation probability. A mapping relationship exists between the first environmental parameter and the first power generation fluctuation probability. This mapping relationship was determined through multiple experiments. For example, when the temperature rises from 25 degrees Celsius to 35 degrees Celsius, the efficiency of the photovoltaic module decreases by approximately -0.35% / ℃, and its power generation fluctuation probability is 3.5%. When heavy rain occurs, leading to damage to transmission facilities or reduced power generation from photovoltaic and other new energy sources, the power generation fluctuation probability is 40%, and so on. Further details are omitted. Combining the first power generation fluctuation probability and... To determine the amount of electricity generated in the future time period;

[0117] in, Indicates power generation equipment iIn time period t Electricity generation, i Indicates the identification of power generation equipment. t Indicates a time period identifier. This indicates the second adjustable parameter. This indicates adjustable power generation. Indicates the number of time periods. Represents the regression coefficients of the power generation variable. express Fluctuating power generation within a time period Indicates a time period identifier. Indicates the number of the first environmental parameters. This represents the regression coefficient of the first environmental parameter. Indicates the first c The probability that a primary environmental parameter affects fluctuations in power generation. c Indicates the first environmental parameter identifier. Indicates the first c Assign a value to the degree of influence of the first environmental parameter. This represents the second random error term. The degree of influence of the above-mentioned environmental parameters can be determined through multiple experiments. For example, if the degree of influence of temperature on electricity consumption is high, then the value is high. If the levels are low, medium, and high, the values ​​are 1, 3, and 5 respectively, then the degree of influence of temperature is assigned a value of 5. Similarly, if the influence of new energy power generation on power generation is medium, and the levels are low, medium, and high, the values ​​are 2, 4, and 6 respectively, then the degree of influence of new energy power generation is assigned a value of 4. Adjustable power generation refers to the power generation obtained through traditional methods, such as power generation by burning coal and oil. Fluctuating power generation refers to the power generation determined by renewable energy methods, such as power generation through solar and wind power.

[0118] S4: Determine the electricity price for the future time period, and regulate the charging or discharging of the energy storage device based on the electricity price for the future time period, the power generation for the future time period, and the required power consumption for the future time period.

[0119] It should be noted that the future time period is the next time period after the current time period.

[0120] In some specific implementations, the electricity price for a future time period is determined, and the charging or discharging of the energy storage device is regulated based on the electricity price for the future time period, the power generation for the future time period, and the required electricity consumption for the future time period.

[0121] Based on the objective function and constraints, it is determined whether to perform charging or discharging regulation. The objective function includes:

[0122] ;

[0123] The constraint includes:

[0124] Supply and demand balance constraint:

[0125] ;

[0126] Power generation equipment constraint:

[0127] ;

[0128] Energy storage equipment constraint, the energy storage equipment constraint includes charge and discharge power constraint, power dynamic and capacity constraint, wherein:

[0129] Charge and discharge power constraint:

[0130] ;

[0131] Power dynamic:

[0132] ;

[0133] Capacity constraint:

[0134] ;

[0135] Power consumption equipment constraint:

[0136] ;

[0137] Priority constraint:

[0138] ;

[0139] Line transmission constraint:

[0140] ;

[0141] Wherein, H The target function calculation value is represented by f, T The time period set is represented by T, I The power generation equipment set is represented by G, J The power consumption equipment set is represented by D, K The energy storage equipment set is represented by S, L The power grid node or line is represented by L, The electricity price at time period T is represented by p, t The time period identifier is represented by t, t The power grid purchase power at time period T is represented by P, The unit power generation cost of power generation equipment G is represented by c, t The unit power consumption cost of power consumption equipment D is represented by b, The unit charge and discharge power cost of energy storage equipment S is represented by a, i The unit transmission cost of line L is represented by d,i represents a power generation device identifier, represents a power generation device i in a time period t of power generation, represents a power consumption device j priority weight coefficient, j represents a power consumption device identifier, represents a power consumption device j in a time period t of required power consumption, represents the actual power supply to the power consumption device t in a time period j , represents a storage device k remaining power in a time period t , k represents a storage device identifier, to prevent simultaneous charging and discharging, represents a power consumption device j whether it is powered in a time period t , represents a set of highest priority power consumption devices, respectively represent the charging power and discharging power of the storage device k in a time period t , respectively represent the upper limit and lower limit of the power generation of the power generation device i in a time period t , respectively represent the discharging efficiency and charging efficiency of the storage device k , respectively represent the lower limit and upper limit of the capacity of the storage device k , respectively represent the upper limit of the charging power and the upper limit of the discharging power of the storage device k , represents the upper limit of the transmission capacity of the grid node or line, respectively represent the power generation device and the power consumption device connected to ;

[0142] in response to the target function calculation value being less than a first preset threshold, charging the storage device;

[0143] in response to the target function calculation value being greater than a second preset threshold, discharging the storage device.

[0144] wherein the first preset threshold and the second preset threshold can be set according to actual needs.

[0145] In the energy storage resource regulation method, the method comprises: acquiring a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine required power consumption in a future time period; acquiring a first environmental parameter and determining a first power generation fluctuation probability according to the first environmental parameter; determining power generation in the future time period based on the first power generation fluctuation probability; determining an electricity price in the future time period, and regulating charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period and the required power consumption in the future time period. The application can improve the dynamic response capability of the power grid and improve the resource utilization efficiency.

[0146] It should be understood that, although Figure 2 The steps in the flowchart of the application are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps in the application can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0147] In one embodiment, as shown in Figure 3 An energy storage resource regulation device is provided, comprising: a first determination module, a second determination module, a third determination module and a regulation module, wherein:

[0148] The first determination module is configured to acquire a first set of related parameters, input the first set of related parameters into a pre-constructed target power consumption evaluation model to determine required power consumption in a future time period;

[0149] The second determination module is configured to acquire a first environmental parameter and determine a first power generation fluctuation probability according to the first environmental parameter;

[0150] The third determination module is configured to determine power generation in the future time period based on the first power generation fluctuation probability;

[0151] The regulation module is configured to determine an electricity price in the future time period, and regulate charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period and the required power consumption in the future time period.

[0152] The specific limitations of the energy storage resource regulation device for determining whether to perform charging or discharging regulation can refer to the limitations of the energy storage resource regulation method described above, which will not be repeated here. Each module in the above energy storage resource regulation device can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor calls and executes the operations corresponding to each of the above modules.

[0153] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 4 The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement an energy storage resource regulation method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball, or touchpad provided on the shell of the computer device, or can be an external keyboard, touchpad, or mouse, etc.

[0154] Those skilled in the art can understand that Figure 4 The structure shown in the above

[0155] In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0156] S1: obtaining a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period;

[0157] S2: obtaining a first environmental parameter and determining a first power generation fluctuation probability based on the first environmental parameter;

[0158] S3: determining the power generation in the future time period based on the first power generation fluctuation probability;

[0159] S4: determining the electricity price in the future time period, and regulating the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0160] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium has stored thereon a computer program, and the computer program is executed by a processor to implement the following steps:

[0161] S1: obtaining a first set of related parameters, and inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period;

[0162] S2: obtaining a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter;

[0163] S3: determining the power generation in the future time period based on the first power generation fluctuation probability;

[0164] S4: determining the electricity price in the future time period, and regulating the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0165] In one embodiment, a computer program product is provided, and the computer program product includes a computer program, and the computer program is executed by a processor to implement the following steps:

[0166] S1: obtaining a first set of related parameters, and inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period;

[0167] S2: obtaining a first environmental parameter, and determining a first power generation fluctuation probability according to the first environmental parameter;

[0168] S3: determining the power generation in the future time period based on the first power generation fluctuation probability;

[0169] S4: determining the electricity price in the future time period, and regulating the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0171] The technical features of the above embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0172] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application.

Claims

1. A method for regulating energy storage resources, characterized in that, The method comprises: obtaining a first set of related parameters, inputting the first set of related parameters into a pre-constructed target power consumption evaluation model to determine the required power consumption in a future time period, the first set of related parameters at least comprising electrical parameters, environmental parameters and electricity prices; obtaining a first environmental parameter and determining a first power generation fluctuation probability according to the first environmental parameter; determining the power generation in the future time period based on the first power generation fluctuation probability; determining the electricity price in the future time period, and regulating the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period and the required power consumption in the future time period; The expression of the target power consumption evaluation model comprises: ； in, Indicates time period t Electricity consumption within the room t Indicates a time period identifier. This indicates the first adjustable parameter. The regression coefficients represent the variables of electricity consumption. This represents the first random error term. express Electricity consumption during the time period Indicates a time period identifier. Indicates the number of time periods. Indicates the number of second environmental parameters. This represents the regression coefficient of the second environmental parameter. Indicates the first b The probability that a second environmental parameter affects fluctuations in electricity consumption. b Indicates the second environmental parameter identifier. Indicates the first b Assign a value to the degree of influence of the second environmental parameter. s Indicates the quantity of electricity price. The regression coefficient representing the electricity price. No. r The probability that individual electricity prices affect fluctuations in electricity consumption. Indicates the first r Assigning values ​​to the degree of impact on electricity prices. r Indicates electricity price; The determination of the power generation in the future time period based on the first power generation fluctuation probability comprises: Based on the first power generation fluctuation probability, based on To determine the amount of electricity generated in the future time period; wherein, represents a power generation facility i in a time period t of power generation, i represents a power generation facility identification, t represents a time period identification, represents a second adjustable parameter, represents a controllable power generation, represents a number of time periods, represents a regression coefficient of a power generation variable, represents a fluctuation power generation in a time period, represents a time period identification, represents a first environmental parameter number, represents a regression coefficient of a first environmental parameter, represents a probability that c a first environmental parameter affects a power generation fluctuation, c represents a first environmental parameter identification, represents a first environmental parameter impact degree assignment, c represents a first environmental parameter impact degree assignment, represents a second random error term.

2. The energy storage resource regulation method of claim 1, wherein, Before obtaining the first set of related parameters, the method further comprises: constructing the target power consumption evaluation model.

3. The energy storage resource regulation method of claim 2, wherein, The construction of the target power consumption evaluation model comprises: obtaining a second set of related parameters; preprocessing the second set of related parameters to obtain a third set of related parameters; training and testing the constructed initial power consumption evaluation model based on the third set of related parameters to obtain the target power consumption evaluation model, the model parameters of the target power consumption evaluation model being obtained by training the model parameters of the initial power consumption evaluation model based on the third set of related parameters.

4. The energy storage resource regulation method of claim 3, wherein, The preprocessing of the second set of related parameters to obtain the third set of related parameters comprises: performing data cleaning processing on the second set of related parameters to obtain cleaned parameters; performing normalization processing on the cleaned parameters to obtain normalized parameters; defining the normalized parameters as the third set of related parameters.

5. The energy storage resource regulation method of claim 4, wherein, After obtaining the third set of related parameters, the method further comprises: classifying and saving the parameters in the third set of related parameters; dividing the classified parameters into a training set and a test set according to a preset ratio.

6. The energy storage resource regulation method of claim 5, wherein, The third set of related parameters at least comprises electrical parameters, second environmental parameters and electricity prices, and the training and testing of the pre-constructed initial power consumption evaluation model based on the third set of related parameters to obtain the target power consumption evaluation model comprises: determining the power consumption in a target time period based on the electrical parameters; obtaining a plurality of second environmental parameters affecting power consumption fluctuation, and determining a second power consumption fluctuation probability based on the second environmental parameters; determining a third power consumption fluctuation probability based on the electricity price; determining the initial power consumption evaluation model based on the power consumption in the target time period, the second power consumption fluctuation probability and the third power consumption fluctuation probability; training the initial power consumption evaluation model based on the training set, and testing the trained initial power consumption evaluation model based on the test set; in response to the test result meeting a preset standard, outputting the target power consumption evaluation model.

7. The energy storage resource arbitrage method of claim 1, wherein, The regulation of the charging or discharging of the energy storage device according to the electricity price in the future time period, the power generation in the future time period and the required power consumption in the future time period comprises: Based on the objective function and the constraint conditions, it is determined whether to perform charging or discharging regulation, the objective function comprising: ； The constraint conditions comprise: Supply-demand balance constraint: ； Power generation equipment constraint: ； Energy storage equipment constraint, comprising charging and discharging power constraint, power dynamic and capacity constraint, wherein: Charging and discharging power constraint: ； Power dynamic: ； Capacity constraint: ； Power consumption equipment constraint: ； Priority constraint: ； Line transmission constraint: ； wherein, H represents a target function calculation value, T represents a time period set, I represents a power generation facility set, J represents a power consumption facility set, K represents an energy storage facility set, L represents a grid node or line, represents an electricity price at a time period t t represents a time period identifier, represents a grid power purchase at a time period t represents a unit power generation cost of a power generation facility i i represents a power generation facility identifier, represents a power generation amount of a power generation facility i at a time period t represents a priority weight coefficient of a power consumption facility j j represents a power consumption facility identifier, represents a required power consumption amount of a power consumption facility j at a time period t represents an actual power supply amount to a power consumption facility t at a time period j represents a remaining power amount of an energy storage facility k at a time period t k represents an energy storage facility identifier, to prevent simultaneous charging and discharging, represents whether a power consumption facility j is powered at a time period t represents a highest priority power consumption facility set, represents a charging power and a discharging power of an energy storage facility k at a time period t represents a power generation upper limit and a power generation lower limit of a power generation facility i at a time period t represents a discharging efficiency and a charging efficiency of an energy storage facility k represents a capacity lower limit and a capacity upper limit of an energy storage facility k represents a charging power upper limit and a discharging power upper limit of an energy storage facility k ​​​​​​​​​​​​​​ an upper limit of the transmission capacity of a grid node or line, respectively a power generation device and a power consumption device connected to a power grid. In response to the objective function calculation value being less than a first preset threshold, the energy storage equipment is charged; In response to the objective function calculation value being greater than a second preset threshold, the energy storage equipment is discharged.

8. A device for implementing the energy storage resource regulation method of claim 1, wherein, The device comprises: A first determination module configured to obtain a first set of related parameters, input the first set of related parameters into a pre-constructed target power consumption evaluation model, and determine required power consumption in a future time period; A second determination module configured to obtain a first environmental parameter and determine a first power generation fluctuation probability according to the first environmental parameter; A third determination module configured to determine power generation in the future time period based on the first power generation fluctuation probability; A regulation module configured to determine an electricity price in the future time period, and regulate charging or discharging of an energy storage equipment according to the electricity price in the future time period, the power generation in the future time period, and the required power consumption in the future time period.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1 to 7 when executing the computer program.

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