Electric quantity decomposition method and device of energy storage system, terminal equipment and storage medium

Abstract

The invention discloses an electric quantity decomposition method and device of an energy storage system, terminal equipment and a storage medium, and belongs to the technical field of electric power, and the method comprises the steps: screening a plurality of similar daily net load curves according to a daily net load curve of a target day of an electric quantity decomposition period; according to the similar daily net load curve, constructing a plurality of charging simulation scenes and discharging simulation scenes, and according to the charging simulation scenes and the discharging simulation scenes, determining an energy storage charging demand and an energy storage discharging demand of each charging simulation scene in each time period in the day; generating a daily energy storage charging period, a daily energy storage discharging period, a target charging amount and a target discharging amount of the electric quantity decomposition cycle; and finally, generating an electric quantity decomposition curve of the electric quantity decomposition period. Therefore, by implementing the method, the problem that an existing electric quantity decomposition method is not matched with an energy storage operation scene and operation characteristics can be solved, an energy storage system can play a flexible adjustment value, and supply and demand balance of a power system is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power, and in particular to an electric quantity decomposition method and device of an energy storage system, a terminal device and a storage medium. BACKGROUND

[0002] With the continuous increase of new energy penetration, the power system is facing increasing accommodation pressure and regulation demand. As a new type of power resource with bidirectional regulation ability of charging and discharging, energy storage bears the important function of improving the flexibility of the power system and maintaining the balance of supply and demand. Under this background, reasonable electric quantity decomposition of energy storage, that is, determining the charging and discharging power curve of each period in a day, is of great significance to guide the coordinated operation of energy storage and system and promote the balance of power supply and demand.

[0003] However, under the existing technical framework, the traditional electric quantity decomposition mainly adopts fixed electric quantity curves such as base load, waist load and peak load for decomposition, mainly faces traditional thermal power and other stable output units, does not fully consider the uncertainty of electricity load, new energy output and other variables, and is difficult to adapt to the actual operation characteristics of energy storage such as reversible charging and discharging power, mutual exclusion of charging and discharging state, and does not consider the difference between charging and discharging roles formed by the bidirectional regulation characteristics of energy storage. The result is that the decomposition curve lacks the response ability to the actual operation conditions, therefore, the existing electric quantity decomposition method has the problem of mismatching with the operation scene and operation characteristics of energy storage, which leads to the fact that energy storage cannot play its value of flexible regulation and promote the balance of power supply and demand. SUMMARY

[0004] The present application provides an electric quantity decomposition method, device, terminal device and storage medium of an energy storage system, which can solve the problem of mismatching between the existing electric quantity decomposition method and the operation scene and operation characteristics of energy storage.

[0005] An embodiment of the present application provides an electric quantity decomposition method of an energy storage system, comprising: obtaining a daily net load curve of a target day of an electric quantity decomposition period; According to the daily net load curve, a plurality of similar daily net load curves are selected from a plurality of preset historical daily net load curves; According to the similar daily net load curve, a plurality of charging simulation scenarios and a plurality of discharging simulation scenarios composed of output data of various new energy units and load demand data are constructed; According to the charging simulation scenario and the discharging simulation scenario, the energy storage charging demand of each charging simulation scenario in each period of a day and the energy storage discharging demand of each discharging simulation scenario in each period of a day are determined; generate, according to the energy storage charging demand, the energy storage discharging demand, and a preset energy storage capacity constraint, a daily energy storage charging period, a daily energy storage discharging period, a target charging amount corresponding to each daily energy storage charging period, and a target discharging amount corresponding to each daily energy storage charging period of the electricity decomposition period; generate, according to the target charging amount corresponding to each energy storage charging period and the target discharging amount corresponding to each energy storage charging period, an electricity decomposition curve of the energy storage system in the electricity decomposition period.

[0006] Further, the filtering of the similar daily net load curves from the preset historical daily net load curves according to the daily net load curve comprises: obtaining daily net load data and historical net load data at a plurality of time points from the daily net load curve and the historical daily net load curves according to a preset time interval; calculating the Euclidean distance of the daily net load data and the historical net load data at each time point, and taking the sum of the Euclidean distances at the plurality of time points as the similarity of the corresponding historical daily net load curve and the daily net load curve; sequentially sorting the historical daily net load curves according to the similarity, and obtaining a preset number threshold of historical daily net load curves as the similar daily net load curves.

[0007] Further, the constructing of a plurality of charging simulation scenarios and a plurality of discharging simulation scenarios composed of output data of various types of new energy units and load demand data according to the similar daily net load curves comprises: using a kernel density estimation method, constructing a first marginal probability density function of the output data of various types of new energy units, a second marginal probability density function of the total output data of all new energy units, and a third marginal probability density function of the load demand data according to the plurality of similar daily net load curves; using a Frank-Copula function, constructing a first joint probability distribution model according to the first marginal probability density function and the third marginal probability density function, and constructing a second joint probability distribution model according to the second marginal probability density function and the third marginal probability density function; using a Monte Carlo sampling method, generating a plurality of charging simulation scenarios composed of output data of various types of new energy units and load demand data according to the first joint probability distribution model, and generating a plurality of discharging simulation scenarios composed of total output data of new energy units and load demand data according to the second joint probability distribution model.

[0008] Further, the generating, according to the energy storage charging demand, the energy storage discharging demand, and a preset energy storage capacity constraint, of a daily energy storage charging time period, a daily energy storage discharging time period, a target charging amount corresponding to each daily energy storage charging time period, and a target discharging amount corresponding to each daily energy storage charging time period of the electricity decomposition period comprises: According to the energy storage charging demand of all charging simulation scenarios in each time period within a day, calculating an accommodation space expectation value and a first cumulative probability of the power system in each time period; According to the accommodation space expectation value and the first cumulative probability, determining a daily energy storage charging time period and an initial charging amount of each daily energy storage charging time period under a preset energy storage capacity constraint; According to the energy storage discharging demand of all discharging simulation scenarios in each time period within a day, calculating a second cumulative probability of the power system in each time period; According to the energy storage discharging demand and the second cumulative probability, determining a daily energy storage discharging time period and an initial discharging amount of each daily energy storage discharging time period under a preset energy storage capacity constraint; Cumulating the initial charging amount and the initial discharging amount to calculate the state of charge of the energy storage in each time period; Traversing the state of charge of the energy storage in each time period, judging whether the state of charge of the current traversed time period is in a preset energy storage charge safety interval; If yes, when the current traversed time period is a daily energy storage discharging time period, taking the initial discharging amount of the current traversed time period as a target discharging amount, and when the current traversed time period is a daily energy storage charging time period, taking the initial charging amount of the current traversed time period as a target charging amount; If not, when the current traversed time period is a daily energy storage discharging time period, adjusting the initial discharging amount of the current traversed time period so that the state of charge of the current traversed time period is in the energy storage charge safety interval, and taking the adjusted initial discharging amount as a target discharging amount of the current traversed time period, and when the current traversed time period is a daily energy storage charging time period, adjusting the initial charging amount of the current traversed time period so that the state of charge of the current traversed time period is in the energy storage charge safety interval, and taking the adjusted initial charging amount as a target charging amount of the current traversed time period.

[0009] Further, the generating, according to the energy storage charging demand, the energy storage discharging demand, and a preset energy storage capacity constraint, of a daily energy storage charging time period, a daily energy storage discharging time period, a target charging amount corresponding to each daily energy storage charging time period, and a target discharging amount corresponding to each daily energy storage charging time period of the electricity decomposition period comprises: Taking a time period with a first cumulative probability greater than a preset first probability threshold as a candidate charging time period; According to the accommodation space expectation value, the candidate charging time periods are reversely sorted, and a plurality of first candidate charging time periods with continuous and maximum sum of accommodation space expectation values are selected from the sorted candidate charging time periods as daily energy storage charging time periods; Under the energy storage capacity constraint, initial charging amounts of the daily energy storage charging time periods are calculated according to the accommodation space expectation values of the daily energy storage charging time periods.

[0010] Further, under the preset energy storage capacity constraint, the daily energy storage discharging time periods and initial discharging amounts of the daily energy storage discharging time periods are determined according to the energy storage discharging demand and the second cumulative probability, and the method comprises the steps of: The time period with the second cumulative probability greater than a preset second probability threshold value is taken as a daily energy storage discharging time period. According to the energy storage discharging demand, demand space expectation values of the daily energy storage discharging time periods of each discharging simulation scenario are calculated. Under the energy storage capacity constraint, reference discharging amounts of the daily energy storage discharging time periods are calculated according to the demand space expectation values of the discharging simulation scenarios and preset scenario weights. According to the daily energy storage charging time periods, the daily energy storage discharging time periods and the initial charging amounts, the reference discharging amounts of the daily energy storage discharging time periods are adjusted to meet a preset energy conservation constraint, so as to generate initial discharging amounts of the daily energy storage discharging time periods.

[0011] An embodiment of the present application further provides an electric quantity decomposition device of an energy storage system, which comprises: A load curve acquisition module is configured to acquire a daily net load curve of a target day in an electric quantity decomposition period. A load curve screening module is configured to screen a plurality of similar daily net load curves from a plurality of preset historical daily net load curves according to the daily net load curve. A simulation scenario construction module is configured to construct a plurality of charging simulation scenarios and a plurality of discharging simulation scenarios composed of output data of various new energy units and load demand data according to the similar daily net load curves. An energy storage demand calculation module is configured to determine energy storage charging demands of each charging simulation scenario in each time period in a day and energy storage discharging demands of each discharging simulation scenario in each time period in a day according to the charging simulation scenarios and the discharging simulation scenarios. An energy storage electric quantity calculation module is configured to generate daily energy storage charging time periods, daily energy storage discharging time periods, target charging amounts corresponding to each daily energy storage charging time period and target discharging amounts corresponding to each daily energy storage charging time period of the electric quantity decomposition period according to the energy storage charging demands, the energy storage discharging demands and a preset energy storage capacity constraint. The power decomposition module is configured to generate a power decomposition curve of the energy storage system in the power decomposition period according to the target charging power of each energy storage charging period and the target discharging power of each energy storage charging period.

[0012] Further, the load curve screening module is configured to screen a plurality of similar daily net load curves from a plurality of preset historical daily net load curves according to the daily net load curve, and the screening includes: obtaining daily net load data and historical net load data at a plurality of time points from the daily net load curve and the historical daily net load curves according to a preset time interval; calculating the Euclidean distance of the daily net load data and the historical net load data at each time point, and taking the sum of the Euclidean distances at the plurality of time points as the similarity of the corresponding historical daily net load curve and the daily net load curve; performing a positive order sorting on the historical daily net load curves according to the similarity, and obtaining a preset number threshold of the historical daily net load curves in sequence as the similar daily net load curves.

[0013] The application further provides a terminal device, which comprises: one or more processors; a memory coupled to the processor, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power decomposition method of the energy storage system as described in the above embodiments.

[0014] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the power decomposition method of the energy storage system as described in the above embodiments.

[0015] By implementing the application, the following beneficial effects are achieved: The application provides an energy storage system power decomposition method, device, terminal equipment and storage medium, the method obtains the daily net load curve of the target day of the power decomposition period; according to the daily net load curve, a plurality of similar daily net load curves are screened from a plurality of preset historical daily net load curves; according to the similar daily net load curve, a plurality of charging simulation scenarios and a plurality of discharging simulation scenarios composed of output data of various new energy units and load demand data are constructed, the uncertainty of variables such as power load and new energy output is fully considered to construct a simulation scenario set, and then according to the charging simulation scenario and the discharging simulation scenario, the energy storage charging demand of each charging simulation scenario in each time period within a day and the energy storage discharging demand of each discharging simulation scenario in each time period within a day are determined, the daily energy storage charging time period, the daily energy storage discharging time period, the target charging capacity corresponding to each daily energy storage charging time period and the target discharging capacity corresponding to each daily energy storage charging time period of the power decomposition period are generated; finally, the power decomposition curve of the power decomposition period is generated. The flexibility of charging and discharging of the energy storage, the SOC constraint and the power balance characteristic are fully considered, the limitation of the traditional curve decomposition method which only faces unidirectional output units is broken through, the power decomposition curve has the response ability of the real operation condition, and the energy storage plays a due role in participating in system regulation and maintaining supply and demand balance. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 is a flow diagram of an energy storage system power decomposition method provided by an embodiment of the present application; Figure 2 Figure 2 is a structural diagram of an energy storage system power decomposition device provided by an embodiment of the present application; Figure 3 is a structural diagram of a terminal equipment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the use of the terms "including," "comprising," "having" and "with" in the specification and claims hereof, along with their variants, are intended to be equivalent to the term "consisting of to encompass the inclusion of an element, but not the exclusion of any other element.

[0020] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified.

[0021] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to each other. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0023] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0024] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0025] Reference Figure 1 To solve the problems in the prior art, an embodiment of the present application provides an electric quantity decomposition method of an energy storage system, comprising: S1, obtaining a daily net load curve of a target day of a power decomposition period; In a preferred embodiment of the application, each standard day of each power decomposition period is decomposed according to 96 time periods (15 minutes for one time period) of the next day to determine the power decomposition curve of the next day, and the "next day" here refers to the target day.

[0026] Further, by statistically analyzing the daily power consumption load prediction data of the previous consecutive years (or months) of the target day, a standard daily load prediction data set is formed, which includes the coordinated load prediction values of each time period (such as 96 15-minute time periods) per day .

[0027] Secondly, the historical wind power, photovoltaic, and hydropower daily power prediction data of the target day are statistically analyzed to form a power curve data set of each power source type, which includes the wind power , photovoltaic , and hydropower power prediction values of each time period per day. And integrate and record as the supply data of local power in each time period, the data of sent-out power in each time period according to the sent-out power plan, and the data of non-marketized power generation in each time period, to form curves changing with time, and the corresponding parameters are local power output value , sent-out power value , and non-marketized power value .

[0028] The calculation formula of the daily net load curve is as follows: ; In the formula, represents the net load of the t-th time period (MW, 96 time periods per day, 15 minutes per time period), represents the coordinated load prediction value of the t-th time period, represents the new energy power prediction value, respectively represents the local power, the sent-out power, and the non-marketized power.

[0029] S2, according to the daily net load curve, screening a plurality of similar daily net load curves from a plurality of preset historical daily net load curves; Preferably, according to the daily net load curve, screening a plurality of similar daily net load curves from a plurality of preset historical daily net load curves, comprising: According to a preset time interval, daily net load data and historical net load data at several times are obtained from the daily net load curve and the historical daily net load curve; the Euclidean distance between the daily net load data and the historical net load data at each time is calculated, and the sum of the Euclidean distances at several times is used as the similarity between the corresponding historical daily net load curve and the daily net load curve; according to the similarity, the historical daily net load curves are sorted in ascending order, and historical daily net load curves with a preset number threshold are obtained in sequence as similar daily net load curves.

[0030] In a preferred embodiment of the present invention, daily net load data and historical net load data at several times are obtained from the daily net load curve and the historical daily net load curve according to a preset time interval (15 minutes). Daily net load data: ; Historical net load data: (96 data points for a certain historical day). Given two time series and This study uses two-dimensional Euclidean distance to determine the similarity between the target date and historical dates. Euclidean distance is a metric for measuring the distance between two sample points. The closer the two sample points are, the more similar they are; conversely, the farther apart they are, the less similar they are.

[0031] ; Calculate the Euclidean distance between the target date and all candidate historical dates. ,according to Sort the data from smallest to largest (the smaller the distance, the higher the similarity). Select the top N historical days' net load curves as the net load curves for similar days (e.g., N=10, i.e., the 10 days with the highest net load similarity).

[0032] S3. Based on the similar daily net load curves, construct several charging simulation scenarios and several discharging simulation scenarios composed of output data and load demand data of various new energy units. Preferably, the step of constructing several charging simulation scenarios and several discharging simulation scenarios based on the similar daily net load curves, consisting of output data and load demand data of various new energy units, includes: The first edge probability density function of the output data of each type of new energy unit, the second edge probability density function of the total output data of all new energy units and the third edge probability density function of the load demand data are constructed according to a plurality of the similar day net load curves by using the kernel density estimation method; the first joint probability distribution model is constructed according to the first edge probability density function and the third edge probability density function by using the Frank-Copula function, and the second joint probability distribution model is constructed according to the second edge probability density function and the third edge probability density function; the plurality of charging simulation scenarios composed of the output data of each type of new energy unit and the load demand data are generated according to the first joint probability distribution model by using the Monte Carlo sampling method, and the plurality of discharging simulation scenarios composed of the total output data of new energy units and the load demand data are generated according to the second joint probability distribution model.

[0033] In a preferred embodiment of the present application, for the historical output data of each type of new energy unit in the similar day net load curve, i.e. the historical data of wind power, photovoltaic power, hydropower output and electricity load, the non-parametric kernel density estimation method is used to fit the edge probability density function of each variable respectively, and the first edge probability density function of wind power, photovoltaic power, hydropower and load is obtained and the third edge probability density function .

[0034] Further, the construction of the wind-solar-hydro-load joint probability density function can be divided into two parts: one is to select the first edge probability density function; the other is to determine the connection function, i.e. the first joint probability distribution model (optimal copula function). After obtaining the probability density result by using the kernel density estimation method, the Copula function is further used to solve the first joint probability distribution model of wind-solar-hydro-load. The Copula function is a function used to describe the dependence relationship between multi-dimensional random variables, and the expression of the Copula is: ; In the formula, is the edge distribution function of a single variable; C is the Copula connection function, is the joint distribution function of four variables.

[0035] It can be understood that the common Copula function family includes the elliptical distribution Copula function family and the Archimedean distribution Copula function family. For the multi-variable output characteristics of wind-solar-hydro-load with complex probability correlation, the Frank-Copula function is generally selected for analysis, the joint distribution function of four variables is accurately described through the connection of the edge distribution of each variable, and reliable multi-variable dependence relationship model support is provided for the subsequent power decomposition strategy based on probability distribution.

[0036] The expression for the Frank-Copula function is as follows: ; First joint probability distribution model: ; in For the Frank-Copula function, the parameters are... The correlation parameter (determined through maximum likelihood estimation). , These are the cumulative distribution functions for wind power, photovoltaic power, hydropower output, and load, respectively.

[0037] Based on the optimal Copula joint distribution model (first joint probability distribution model) obtained above, key parameters are input, and Monte Carlo sampling is used to generate an initial scene set for evaluating the absorption space. The main steps are as follows: 1. Generation of initial random variables for Monte Carlo sampling: Independent and identically distributed four-dimensional uniform variables are randomly generated in the interval [0,1]. , The sampling probability values ​​correspond to wind power, photovoltaic power, hydropower, and load, respectively; 2. Solving for marginal distribution probability values: Derive the marginal distribution probability values ​​of each variable using random numbers. , corresponding to the edge probability values ​​of wind power, photovoltaic, hydropower, and load, respectively. Let the edge distribution value of wind power be... The photovoltaic edge distribution function value is solved using the selected Copula function mentioned above. The solution is as follows: ; Based on the joint conditional distribution of the first two variables, the marginal probability of hydropower is solved using the second-order partial derivative equation. : ; Similarly, the load edge probability value can be solved using the third-order partial derivative equation. ; ; Repeat steps 1-2 above. Next, then you can get The marginal distribution function values ​​of four random variables: wind power, photovoltaic power, hydropower, and load.

[0038] Using inverse function operations Marginal distribution probability value The output data of various types of new energy units and the load demand data are converted, and multiple sets of charging simulation scenarios are generated by repeated sampling.

[0039] ; Further, for the historical data of total output data and load demand data in similar daily net load curves, a non-parametric kernel density estimation method is used to fit the probability density functions of new energy and load .

[0040] Further, Frank-Copula function is selected to solve the joint distribution function of new energy and load: ; ; wherein is the Frank-Copula function, and the parameters are determined by maximum likelihood estimation, , are the cumulative distribution functions of new energy output and load, respectively.

[0041] Specifically, the scenario generation based on Monte Carlo sampling includes: 3. Initial random variable generation by Monte Carlo sampling: independent and identically distributed two-dimensional uniform variables are randomly generated in the interval [0, 1] , corresponding to the sampling probability values of new energy and load, respectively; 4. Solving the edge distribution probability value: The edge distribution probability values of the two variables are derived by random numbers , corresponding to the edge probability values of new energy and load, respectively. Let the edge distribution value of new energy be , and the edge distribution function value of load be = , and the solving equation is as follows: ; By numerically solving the equation, the value associated with is obtained, realizing the correlation coupling of the two variables.

[0042] Repeat steps 3-4 above times, and then the edge distribution function values of sets of new energy output and load can be obtained.

[0043] Finally, the edge distribution probability value is converted into the total output data of new energy units and the load demand data by the edge distribution inverse function, and multiple sets of discharge simulation scenarios are generated by repeated sampling.

[0044] .

[0045] S4, determine the energy storage charging demand of each charging simulation scenario in each period of a day and the energy storage discharging demand of each discharging simulation scenario in each period of a day according to the charging simulation scenarios and the discharging simulation scenarios; In a preferred embodiment of the present application, based on a typical scenario set generated by Monte Carlo sampling, the input data includes the period output data (unit: MW) of wind power, photovoltaic power and hydropower and the load demand data (unit: MW) under each charging simulation scenario, the time granularity is 1 hour, and the analysis period is 1 year (8760 periods); The period accommodation space index is defined to quantify the system excess power pressure and reflect the energy storage charging demand: ; In the formula, indicates the accommodation space (energy storage charging demand) of the kth charging simulation scenario in the tth period, indicates that there is excess power in the period and the energy storage charging accommodation is needed; indicates that there is no excess power or gap in the period, and the energy storage charging is not needed, at this time, is 0.

[0046] Based on a typical scenario set generated by Monte Carlo sampling, the input data includes the total new energy output data (unit: MW) and the load demand data (unit: MW) under each discharging simulation scenario, the time granularity is 1 hour, and the analysis period is 1 year (8760 periods); The period bidding space index is defined to quantify the system power gap pressure and reflect the energy storage discharging demand: ; In the formula, indicates the bidding space (energy storage discharging demand) of the kth discharging simulation scenario in the tth period (MW), indicates the regulated load, indicates the new energy power, indicates the local power, the power sent out, and the non-marketized power, respectively. Small, indicates that the bidding space shrinks and the energy storage charging and discharging are needed.

[0047] S5, generate the daily energy storage charging period, the daily energy storage discharging period, the target charging capacity corresponding to each daily energy storage charging period, and the target discharging capacity corresponding to each daily energy storage charging period of the power decomposition period according to the energy storage charging demand, the energy storage discharging demand, and the preset energy storage capacity constraint; Preferably, the generating, according to the energy storage charging demand, the energy storage discharging demand, and a preset energy storage capacity constraint, the daily energy storage charging time period, the daily energy storage discharging time period, the target charging amount corresponding to each daily energy storage charging time period, and the target discharging amount corresponding to each daily energy storage charging time period of the electricity decomposition period comprises the following steps. S51, calculating, according to the energy storage charging demand of all charging simulation scenarios in each time period within a day, the expected value of the accommodation space of the power system in each time period and the first cumulative probability; In a preferred embodiment of the present application, the probability weight of the charging simulation scenario obtained by the backward reduction method is used (satisfying ), the expected value of the accommodation space and the first cumulative probability in each time period are calculated: ; ; wherein, represents the expected value of the accommodation space in the tth time period, K represents the total number of charging simulation scenarios, represents the first cumulative probability of the tth time period in which there is excess power, is an indicator function, represents that there is excess power in the scenario time period.

[0048] S52, under the preset energy storage capacity constraint, determining the daily energy storage charging time period and the initial charging amount of each daily energy storage charging time period according to the expected value of the accommodation space and the first cumulative probability; Preferably, the determining, under the preset energy storage capacity constraint, the daily energy storage charging time period and the initial charging amount of each daily energy storage charging time period according to the expected value of the accommodation space and the first cumulative probability comprises the following steps. The time period in which the first cumulative probability is greater than a preset first probability threshold value is regarded as a candidate charging time period; the candidate charging time periods are sorted in reverse according to the expected value of the accommodation space, and a number of first candidate charging time periods with the largest sum of expected values of the accommodation space are selected from the sorted candidate charging time periods as the daily energy storage charging time period; under the energy storage capacity constraint, the initial charging amount of each daily energy storage charging time period is calculated according to the expected value of the accommodation space of each daily energy storage charging time period.

[0049] In a preferred embodiment of the present application, the first probability threshold value is , the time period satisfying "high probability of excess and maximum accommodation pressure" is screened, the time boundary of the charging window is determined, and the time period is preferentially selected, so as to ensure that there is excess power in the time period in most scenarios (such as 22:00 to 6:00 the next day, a total of 8 time periods per day, and a total of 240 charging time periods for 30 days); among the time periods satisfying the probability constraint, the time period descending order, select the largest continuous time period as the daily energy storage charging time period.

[0050] Further, based on the excess power of the daily energy storage charging time period, the initial charging amount of each daily energy storage charging time period is calculated: ; In the formula, indicates the initial charging amount of the t time period, which is obtained by probability weighting of each scene charging amount, is the maximum charging power of the energy storage, is the time interval, which ensures that the single time period charging amount does not exceed the physical limit of the device.

[0051] Meanwhile, the energy storage capacity constraint is satisfied: ; Among them, indicates the time period set within the charging window, is the rated capacity of the energy storage, is the upper and lower limit of the state of charge.

[0052] S53, according to the energy storage discharge demand of all discharge simulation scenarios in each time period within a day, calculate the second cumulative probability of the power system in each time period; In a preferred embodiment of the present application, the second cumulative probability is calculated: ; In the formula, indicates the second cumulative probability of the t time period appearing in the bidding space of the kth discharge simulation scenario, indicates the total number of discharge simulation scenarios, indicates the occurrence probability of the kth discharge simulation scenario ), is an indicator function (if the t time period appears in the bidding space of the kth scenario, then ); S54, under the preset energy storage capacity constraint, according to the energy storage discharge demand and the second cumulative probability, determine the daily energy storage discharge time period and the initial discharge amount of each daily energy storage discharge time period; Preferably, under the preset energy storage capacity constraint, according to the energy storage discharge demand and the second cumulative probability, the daily energy storage discharge time period and the initial discharge amount of each daily energy storage discharge time period are determined, comprising: The period with the second cumulative probability greater than the preset second probability threshold is taken as a daily energy storage discharging period; the demand space expectation value of each discharging simulation scenario in the daily energy storage discharging period is calculated according to the energy storage discharging demand; the reference discharging electric quantity of each daily energy storage discharging period is calculated according to the demand space expectation value of each discharging simulation scenario and the preset scenario weight under the energy storage capacity constraint; the reference discharging electric quantity of each daily energy storage discharging period is adjusted according to the daily energy storage charging period, the daily energy storage discharging period and the initial charging electric quantity to meet the preset energy conservation constraint, and the initial discharging electric quantity of each daily energy storage discharging period is generated.

[0053] In a preferred embodiment of the present application, the second probability threshold is set to screen the period that meets the condition of “high probability of bidding space shrinkage and maximum power supply pressure”, and the time edge of the discharging window is determined, and the period is preferentially selected to ensure that the period has a risk of insufficient bidding space in most scenarios (for example, 18:00-22:00 every day, a total of 4 periods per day, and 120 discharging periods in 30 days).

[0054] For the K generated discharging simulation scenarios, the period discharging demand of each discharging simulation scenario is calculated in the screened daily energy storage discharging period t (such as 18:00-22:00). ; The period discharging demand is fitted with a continuous probability density curve using kernel density estimation: ; In the formula, represents the probability density of the t period discharging demand d (the higher the value, the greater the possibility of the demand appearing); is the total number of discharging simulation scenarios; is the window width; is the kernel function; is the scenario probability weight of the discharging simulation scenario, which ensures that high-probability scenarios have a greater impact on the distribution.

[0055] The demand space expectation value of the daily energy storage discharging period is calculated as the demand space expectation value of the daily energy storage discharging period: ; In the formula is the period discharging demand of the k scenario t period, is the scenario probability weight of the discharging simulation scenario.

[0056] Based on the power supply gap risk of the daily energy storage discharging period, the energy storage discharging quantity of each period is calculated to ensure that the single-period discharging quantity does not exceed the maximum discharging capacity of the energy storage: ; wherein, represents the energy discharge amount of the tth time period, which is obtained by weighting the discharge demand of each scenario according to the probability , is the maximum discharge power of the energy storage, is the time interval, which ensures that the discharge amount of a single time period does not exceed the physical limit of the device.

[0057] Meanwhile, the energy storage charge-discharge energy balance constraint is satisfied: ; wherein, represents the set of time periods within the discharge window, and are the charge-discharge powers, respectively, which ensure that the total discharge amount does not exceed the effective upper limit of the conversion of the total charge amount.

[0058] S55, the initial charge amount and the initial discharge amount are accumulated to calculate the state of charge of the energy storage in each time period; S56, the state of charge of the energy storage in the current time period is determined whether it is in the preset energy storage charge safety interval by traversing the state of charge of the energy storage in each time period; S57, if yes, the initial discharge amount of the current time period is taken as the target discharge amount when the current time period is a daily energy storage discharge time period, and the initial charge amount of the current time period is taken as the target charge amount when the current time period is a daily energy storage charge time period; S58, if no, the initial discharge amount of the current time period is adjusted to make the state of charge of the energy storage in the current time period in the energy storage charge safety interval when the current time period is a daily energy storage discharge time period, and the initial charge amount of the current time period is adjusted to make the state of charge of the energy storage in the current time period in the energy storage charge safety interval when the current time period is a daily energy storage charge time period, and the adjusted initial discharge amount is taken as the target discharge amount of the current time period.

[0059] In a preferred embodiment of the present application, the daily energy storage medium and long-term decomposition curve needs to satisfy the SOC constraint, and the core boundary constraint of the power limited characteristic.

[0060] The cumulative discharge amount is defined as the time integral of the energy storage power, wherein, is the cumulative discharge amount at the moment t, that is: ; The upper and lower limits of the cumulative discharge amount need to be controlled within the configuration capacity range of the energy storage, that is: ;​ Set the energy storage SOC safety interval (energy storage charge safety interval), establish the dynamic relationship between charging and discharging power and SOC, and control the state of charge range of the energy storage unit to prevent overcharging and overdischarging, and dynamically update the SOC value based on the charging and discharging efficiency. The energy storage state of charge at time t is: When charging: ; When discharging: ; In the formula, The charging and discharging power at time t is positive, that is, charging, and negative, that is, discharging. Any energy storage unit can only be in a single energy conversion state at the same time, that is, the charging state ( rising) and the discharging state ( falling) are mutually exclusive and cannot occur at the same time.

[0061] To prevent overcharging and overdischarging of the energy storage, the energy storage SOC adjustment range is: ; This constraint is the core of the SOC self-recovery, which ensures that the energy storage unit operates in the safe interval for a long time by controlling the cumulative charging and discharging capacity.

[0062] The upper and lower limits of the cumulative discharge capacity are the core boundary constraints of the power limited feature, which determine the medium and long-term capacity configuration and energy balance capability of the energy storage system; the upper and lower limits of the SOC are the real-time state constraints of the power limited feature, which ensure that the power distribution of each period of the decomposition curve meets the physical limit of the device; through the dynamic limiting parameter and real-time update of the SOC, the "medium and long-term feasibility" and "real-time operability" of the decomposition curve are jointly ensured.

[0063] S6, according to the target charging capacity corresponding to each energy storage charging period and the target discharging capacity corresponding to each energy storage charging period, generate an energy decomposition curve of the energy storage system in the energy decomposition period.

[0064] In a preferred embodiment of the present application, the charging power curve is formed by decomposing the charging capacity according to the target charging capacity corresponding to each energy storage charging period, and the cumulative capacity in the charging window is counted monthly to determine the total charging capacity each month: ; In the formula, represents the total charging capacity of the th period (such as the th month), represents the set of daily energy storage charging periods in the th period.

[0065] On the basis of the monthly total, the time-sharing curve is refined according to the proportion of the charging capacity of each period, and the time-sharing capacity is distributed.

[0066] ; In the formula, represents the charging power of the first period. It can be understood that the medium and long-term charging power decomposition of the energy storage is based on the principle of "charging when the new energy large power generation system is insufficient in consumption capacity", and the decision relies on the power consumption load, wind power output, photovoltaic power output, and hydropower output associated with weather and season and having probability correlation with each other. Through probability statistics and joint probability density fitting of these data, Monte Carlo sampling of wind, light, and water load scenarios is performed based on the joint probability density function, and then the charging period of the energy storage is determined by combining the quantitative analysis of the new energy consumption space, and finally the medium and long-term charging power curve decomposition strategy is formed to realize the accurate locking of the charging window and the optimization of the power distribution. It can be understood that the charging power proportion of each period directly reflects the difference in consumption pressure of different periods, so that the energy storage charging power curve can cover high-risk scenarios and also consider economy. In the electricity trading market, low-priced excess power can be locked in advance according to the charging power decomposition to avoid the impact of spot market price fluctuations on revenue.

[0067] According to the target discharge power corresponding to each of the energy storage charging periods, the discharge power is decomposed according to the power decomposition period (monthly / quarterly) to form a discharge curve, and the cumulative power in the discharge window is counted monthly to determine the total discharge power of each month: ; In the formula, represents the total discharge power of the first period (such as the first month), represents the set of daily energy storage discharge periods in the first period.

[0068] On the basis of the monthly total, the time-sharing curve is refined according to the discharge power proportion of each period, and the time-sharing power is distributed: ; In the formula, represents the discharge power proportion of the first period.

[0069] Finally, the and are integrated to generate the power decomposition curve of the power decomposition period.

[0070] It can be understood that the discharge capacity curve of the energy storage is decomposed, the high certainty discharge scene of peak capacity shortage during peak power consumption is focused, the supply and demand rules related to weather and season are combined, the high confidence level scene is screened through joint probability density fitting and Monte Carlo sampling, the minimum fluctuation discharge period of the energy storage is locked based on quantitative analysis of the bidding space. The discharge capacity proportion of each period intuitively reflects the risk difference of power supply gap in different periods. In the power transaction time, the discharge capacity curve of the energy storage can not only preferentially cover the high probability risk period (such as ), but also can match the price peak to maximize the income, lock the income in advance with the stable discharge rhythm, avoid the price fluctuation risk caused by the externality of the spot market, realize the efficient use of the asset and the guarantee of the contract performance.

[0071] The application combines the operation characteristics of the energy storage system, such as reversible charging and discharging, limited capacity, dynamic SOC change and the like, establishes an electric quantity decomposition mechanism conforming to the physical constraint conditions of the energy storage, scientifically identifies the appropriate charging and discharging periods of the energy storage, generates an electric quantity decomposition curve with actual operability, and also provides support for the energy storage as a new market main body to participate in the medium and long-term transaction in a fair and efficient manner in the power market transaction scenario, and improves the income stability and the release ability of the system regulation value.

[0072] It should be noted that the application is also applicable to the participation of the energy storage system in the medium and long-term market transaction, provides a technical path with strong adaptability and high operability for the energy storage in the signing and performance of the medium and long-term market contract, and realizes the standardized participation and income guarantee of the energy storage system in the medium and long-term market.

[0073] A medium and long-term transaction curve decomposition method suitable for the energy storage is proposed according to the operation characteristics and participation mechanism of the energy storage system in the medium and long-term market. The charging and discharging flexibility, SOC constraint and electric quantity balance characteristics of the energy storage are fully considered, the limitation of the traditional curve decomposition method only facing the unidirectional output unit is broken through, the medium and long-term electric quantity decomposition mechanism covering the charging and discharging bidirectional scene is established, the physical constraint of the energy storage is deeply integrated with the market supply and demand rules, the energy storage is promoted to realize the normalized and standardized participation in the medium and long-term market, the flexible value of the energy storage is played, and the supply and demand balance of the power system is promoted.

[0074] Aiming at the characteristics of energy storage with bidirectional power regulation capability, the decomposition strategy of the buyer and the seller is designed respectively. When the energy storage is a medium and long-term charging party, aiming at the strong correlation between core variables such as power load and new energy output and weather and season, the joint probability distribution of wind, light, water and load is constructed by using kernel density estimation and Copula function, the probability correlation between variables is quantified, the 'new energy large power generation period consumption space' is focused on, based on the probability characteristics of new energy output and load valley, the period with minimum new energy consumption space is identified as the charging period, to help local consumption of new energy; and as a medium and long-term discharge side, based on historical load prediction and market bidding space analysis, through joint probability density fitting and Monte Carlo scene sampling, focusing on the 'peak gap' period of power consumption peak and new energy output fluctuation, the discharge period is identified, and the system peak shaving and supply and demand balance are coordinated, the fine power allocation of role adaptation is realized, and the medium and long-term fulfillment curve that fits the actual operation state is constructed.

[0075] Meanwhile, the SOC dynamic constraint mechanism with power limitation characteristics is introduced in the curve decomposition process, the SOC state is updated in real time in the fulfillment curve generation process, the physical constraint conditions such as charge-discharge mutual exclusion, SOC upper and lower limit and cumulative power balance are applied, the decomposition curve has actual operability and sustainable operation ability, and the fulfillment safety and income stability of the energy storage system in the medium and long-term transaction are significantly improved.

[0076] With the continuous rise of new energy penetration rate, the power system is facing increasing consumption pressure and regulation demand. As a new type of power resource with bidirectional regulation capability of charging and discharging, energy storage can be a transaction counterparty in the medium and long-term market. On the one hand, relying on the power decomposition strategy of the market price law, the low-price charging opportunity in the new energy large power generation period is locked in the buyer role, and the bidding demand in the power consumption peak is matched in the seller role, realizing the accurate response to the market price signal; on the other hand, by scientifically identifying the new energy consumption window and the system regulation gap, the charging power and the new energy output surplus period are bound, and the discharging power and the load peak demand are connected, while ensuring the marketization income, the proportion of local consumption of new energy and the system operation flexibility are effectively improved.

[0077] In addition, the present application can also be widely applied to the scenes of perfecting the medium and long-term market rules, optimizing the market operation of new energy storage, etc., to provide core technical support for realizing the coordination and complementation of energy storage and new energy resources, and improving the system regulation capability and operation efficiency.

[0078] Reference Figure 2 The power decomposition device of the energy storage system provided by an embodiment of the present application comprises: A load curve acquisition module is configured to acquire a daily net load curve of a target day in a power decomposition period. a load curve screening module configured to screen a plurality of similar daily net load curves from a plurality of preset historical daily net load curves according to the daily net load curve; a simulation scenario construction module configured to construct a plurality of charging simulation scenarios and a plurality of discharging simulation scenarios composed of output data of various new energy units and load demand data according to the similar daily net load curves; a storage demand calculation module configured to determine storage charging demands of each charging simulation scenario at each time period within a day and storage discharging demands of each discharging simulation scenario at each time period within a day according to the charging simulation scenarios and the discharging simulation scenarios; a storage power calculation module configured to generate daily storage charging time periods, daily storage discharging time periods, target charging powers corresponding to each daily storage charging time period and target discharging powers corresponding to each daily storage charging time period of the power decomposition period according to the storage charging demands, the storage discharging demands and preset storage capacity constraints; a power decomposition module configured to generate a power decomposition curve of a storage system in the power decomposition period according to the target charging powers corresponding to each storage charging time period and the target discharging powers corresponding to each storage charging time period.

[0079] Preferably, the load curve screening module screens a plurality of similar daily net load curves from a plurality of preset historical daily net load curves according to the daily net load curve, and the screening includes: obtaining daily net load data and historical net load data at a plurality of time points from the daily net load curve and the historical daily net load curves according to preset time intervals; calculating Euclidean distances of the daily net load data and the historical net load data at each time point, and taking a sum of the Euclidean distances at the plurality of time points as a similarity of the corresponding historical daily net load curve and the daily net load curve; sequentially sorting the historical daily net load curves in a positive order according to the similarities, and obtaining a preset number threshold of the historical daily net load curves as the similar daily net load curves.

[0080] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present application, and can realize the power decomposition method of the storage system provided by any one of the above-mentioned method item embodiments of the present application.

[0081] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0082] See Figure 3 One embodiment of this application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy decomposition method of an energy storage system as described above.

[0083] The processor controls the overall operation of the terminal device to complete all or part of the steps of the energy decomposition method of the energy storage system described above. The memory stores various types of data to support the operation of the terminal device. This data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0084] In an exemplary embodiment, the terminal device can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements, for executing the power decomposition method of the energy storage system according to any one of the above embodiments and achieving the technical effects consistent with the above method.

[0085] In another exemplary embodiment, a computer readable storage medium including a computer program is also provided, which, when executed by a processor, implements the steps of the power decomposition method of the energy storage system according to any one of the above embodiments. For example, the computer readable storage medium can be the above-mentioned memory including the computer program, which can be executed by the processor of the terminal device to complete the power decomposition method of the energy storage system according to any one of the above embodiments and achieve the technical effects consistent with the above method.

[0086] The above is the preferred embodiment of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for decomposing electricity in an energy storage system, characterized in that, include: Obtain the daily net load curve for the target day of the electricity decomposition cycle; Based on the daily net load curve, select several similar daily net load curves from a set of historical daily net load curves; Based on the similar daily net load curves, several charging simulation scenarios and several discharging simulation scenarios are constructed, consisting of output data and load demand data of various new energy units. Based on the charging simulation scenario and the discharging simulation scenario, determine the energy storage charging demand of each charging simulation scenario at different times of the day, and the energy storage discharging demand of each discharging simulation scenario at different times of the day. Based on the energy storage charging demand, the energy storage discharging demand, and the preset energy storage capacity constraints, the daily energy storage charging period, the daily energy storage discharging period, the target charging amount corresponding to each daily energy storage charging period, and the target discharging amount corresponding to each daily energy storage charging period are generated for the energy decomposition cycle. Based on the target charging amount and the target discharging amount corresponding to each energy storage charging period, an energy decomposition curve of the energy storage system in the energy decomposition cycle is generated.

2. The method for energy decomposition in an energy storage system as described in claim 1, characterized in that, The step of selecting several similar daily net load curves from a preset set of historical daily net load curves based on the daily net load curve includes: Based on a preset time interval, daily net load data and historical net load data at several points are obtained from the daily net load curve and the historical daily net load curve. Calculate the Euclidean distance between the daily net load data and the historical net load data at each time point, and use the sum of the Euclidean distances at several time points as the similarity between the corresponding historical daily net load curve and the daily net load curve. Based on the similarity, the historical daily net load curves are sorted in ascending order, and historical daily net load curves with a preset number threshold are obtained in sequence as similar daily net load curves.

3. The method for energy decomposition in an energy storage system as described in claim 2, characterized in that, Based on the similar daily net load curves, several charging simulation scenarios and several discharging simulation scenarios are constructed, consisting of output data and load demand data of various new energy units, including: Using the kernel density estimation method, based on several similar daily net load curves, a first marginal probability density function for the output data of various new energy units, a second marginal probability density function for the total output data of all new energy units, and a third marginal probability density function for the load demand data are constructed. Using the Frank-Copula function, a first joint probability distribution model is constructed based on the first and third marginal probability density functions, and a second joint probability distribution model is constructed based on the second and third marginal probability density functions. Using the Monte Carlo sampling method, several charging simulation scenarios are generated based on the first joint probability distribution model, consisting of power output data and load demand data of various new energy units. Based on the second joint probability distribution model, several discharging simulation scenarios are generated based on the total power output data and load demand data of new energy units.

4. The method for energy decomposition in an energy storage system as described in claim 3, characterized in that, The step of generating daily energy storage charging periods, daily energy storage discharging periods, target charging amounts for each daily energy storage charging period, and target discharging amounts for each daily energy storage charging period, based on the energy storage charging demand, the energy storage discharging demand, and preset energy storage capacity constraints, includes: Based on the energy storage charging demand of all charging simulation scenarios at different times of the day, calculate the expected value of the power system's absorption capacity and the first cumulative probability at each time period. Under the preset energy storage capacity constraint, the daily energy storage charging period and the initial charging amount of each daily energy storage charging period are determined according to the expected value of the absorption space and the first cumulative probability. Based on the energy storage discharge demand of all discharge simulation scenarios at different times of the day, calculate the second cumulative probability of the power system at each time period; Under the preset energy storage capacity constraint, the daily energy storage discharge period and the initial discharge amount of each daily energy storage discharge period are determined according to the energy storage discharge demand and the second cumulative probability. Accumulate the initial charging amount and the initial discharging amount to calculate the energy storage state of charge in each time period; Iterate through the energy storage charge state in each time period and determine whether the energy storage charge state in the current time period is within the preset energy storage charge safety range. If so, when the current traversed period is the daily energy storage discharge period, the initial discharge amount of the current traversed period is taken as the target discharge amount; when the current traversed period is the daily energy storage charging period, the initial charging amount of the current traversed period is taken as the target charging amount. If not, when the current traversed period is a daily energy storage discharge period, the initial discharge amount of the current traversed period is adjusted to ensure that the energy storage state of charge of the current traversed period is within the safe range of energy storage charge, and the adjusted initial discharge amount is used as the target discharge amount of the current traversed period. When the current traversed period is a daily energy storage charging period, the initial charging amount of the current traversed period is adjusted to ensure that the energy storage state of charge of the current traversed period is within the safe range of energy storage charge, and the adjusted initial charging amount is used as the target charging amount of the current traversed period.

5. The method for energy decomposition in an energy storage system as described in claim 4, characterized in that, Under a preset energy storage capacity constraint, determining the daily energy storage charging period and the initial charging amount for each daily energy storage charging period based on the expected value of the absorption space and the first cumulative probability includes: The time periods in which the first cumulative probability is greater than the preset first probability threshold are selected as candidate charging time periods; Based on the expected value of the absorption space, the candidate charging periods are sorted in reverse order, and from the sorted candidate charging periods, a number of first candidate charging periods that are consecutive and have the largest sum of expected values ​​of absorption space are selected as the daily energy storage charging periods. Under the energy storage capacity constraint, the initial charging amount for each energy storage charging period is calculated based on the expected consumption space value for each daily energy storage charging period.

6. The method for decomposing electricity in an energy storage system as described in claim 5, characterized in that, Under the preset energy storage capacity constraint, the determination of the daily energy storage discharge period and the initial discharge amount for each daily energy storage discharge period, based on the energy storage discharge demand and the second cumulative probability, includes: The period when the second cumulative probability is greater than the preset second probability threshold is taken as the daily energy storage discharge period; Based on the energy storage discharge demand, calculate the expected spatial value of the demand for each discharge simulation scenario during the daily energy storage discharge period; Under the energy storage capacity constraint, the baseline discharge capacity for each day's energy storage discharge period is calculated based on the expected demand space value of each discharge simulation scenario and the preset scenario weights. Based on the daily energy storage charging period, daily energy storage discharging period, and initial charging amount, the baseline discharge amount for each daily energy storage discharging period is adjusted to meet the preset energy conservation constraints, thereby generating the initial discharge amount for each daily energy storage discharging period.

7. A device for decomposing electricity in an energy storage system, characterized in that, include: The load curve acquisition module is used to acquire the daily net load curve of the target day in the power decomposition cycle. The load curve filtering module is used to filter several similar daily net load curves from a number of preset historical daily net load curves based on the daily net load curve. The simulation scenario construction module is used to construct several charging simulation scenarios and several discharging simulation scenarios based on the similar daily net load curves, which consist of output data and load demand data of various new energy units. The energy storage demand calculation module is used to determine the energy storage charging demand of each charging simulation scenario at different times of the day, and the energy storage discharging demand of each discharging simulation scenario at different times of the day, based on the charging simulation scenario and the discharging simulation scenario. The energy storage power calculation module is used to generate the daily energy storage charging period, daily energy storage discharging period, target charging amount corresponding to each daily energy storage charging period, and target discharging amount corresponding to each daily energy storage charging period based on the energy storage charging demand, the energy storage discharging demand, and the preset energy storage capacity constraints. The power decomposition module is used to generate a power decomposition curve of the energy storage system in the power decomposition cycle based on the target charging amount corresponding to each energy storage charging period and the target discharging amount corresponding to each energy storage charging period.

8. The energy decomposition device for an energy storage system as described in claim 7, characterized in that, The load curve screening module, based on the daily net load curve, filters several similar daily net load curves from a preset set of historical daily net load curves, including: Based on a preset time interval, daily net load data and historical net load data at several points are obtained from the daily net load curve and the historical daily net load curve. Calculate the Euclidean distance between the daily net load data and the historical net load data at each time point, and use the sum of the Euclidean distances at several time points as the similarity between the corresponding historical daily net load curve and the daily net load curve. Based on the similarity, the historical daily net load curves are sorted in ascending order, and historical daily net load curves with a preset number threshold are obtained in sequence as similar daily net load curves.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a method for decomposing the energy of an energy storage system as described in any one of claims 1-6.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for decomposing the energy of an energy storage system as described in any one of claims 1-6.

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