Charge and Discharge Method of Energy Storage System and Energy Storage System

Abstract

The present application provides a charge-discharge method and an energy storage system for an energy storage system. The method includes: predicting based on the collected photovoltaic data to obtain a first photovoltaic power, and predicting based on the collected load data to obtain a first load power; determining a reserved power based on the first photovoltaic power and the first load power, where the reserved power is a standby power set to ensure the stable operation of the energy storage system; and determining a charge-discharge strategy based on the reserved power and one or more preset targets. The method provided by the present application helps to improve the charge-discharge efficiency, reduce the operating cost, and improve the economic benefit.

Description

Technical Field

[0001] The present application relates to the field of energy storage, and particularly to a charge and discharge method for an energy storage system and an energy storage system. Background Art

[0002] In the integrated photovoltaic and energy storage scenario, the charge and discharge scheduling strategy of the energy storage system is usually based on photovoltaic power generation and load demand. However, the uncertainty of photovoltaic power generation and load demand will lead to the inefficiency of the charge and discharge scheduling strategy. For example, the charge and discharge scheduling strategy lacks the adaptability to uncertainty factors such as photovoltaic power generation and load demand. Especially in the scenario where photovoltaic power generation fluctuates greatly or the load demand changes rapidly, the scheduling effect of the energy storage system is significantly reduced. Therefore, there is an urgent need for an efficient charge and discharge method for an energy storage system to solve the above problems. Summary of the Invention

[0003] The embodiments of the present application provide a charge and discharge method, device and energy storage system for an energy storage system, which helps to improve the charge and discharge efficiency, reduce the operating cost and improve the economic benefit.

[0004] In a first aspect, the embodiments of the present application provide a charge and discharge method for an energy storage system, including: predicting based on the collected photovoltaic data to obtain a first photovoltaic power, and predicting based on the collected load data to obtain a first load power; determining a reserved power based on the first photovoltaic power and the first load power, where the reserved power is a standby power set to ensure the stable operation of the energy storage system; determining a charge and discharge strategy based on the reserved power and one or more preset goals, where the one or more preset goals at least include a revenue goal and / or an efficiency goal, the revenue goal is used to maximize the revenue, and the efficiency goal is used to maximize the energy storage operation efficiency;

[0005] The revenue goal is characterized by the following expression:

[0006] ;

[0007] Where T is a preset time span, L discharge (t) is the discharge amount of the energy storage system at time t, L charge (t) is the charge amount of the energy storage system at time t, E(t) is the electricity price at time t, C charge (t) is the charging operation cost of the energy storage system at time t, C discharge (t) is the discharge operation cost of the energy storage system at time t, C pv (t) is used to characterize the negative revenue caused by the prediction error of the photovoltaic power at time t, C load (t) is used to characterize the negative revenue caused by the prediction error of the load power at time t;

[0008] The said C pv (t) is calculated through the following calculation formula:

[0009] ;

[0010] The said λ pv (t) is the weight coefficient of the photovoltaic at time t, and the said λ pv (t) is determined by the photovoltaic power probability interval, and the said P pv-actual (t) is the true power of the photovoltaic at time t, and the said P pv-forecast (t) is the predicted power of the photovoltaic at time t;

[0011] The said C load (t) is calculated through the following calculation formula:

[0012] ;

[0013] The said λ load (t) is the weight coefficient of the load at time t, and the said λ load (t) is determined by the load power probability interval, and the said P load-actual (t) is the true power of the load at time t, and the said P load-forecast (t) is the predicted power of the load at time t;

[0014] The said efficiency target is characterized by the following expression:

[0015] .

[0016] In one possible implementation, the photovoltaic power probability interval is obtained by calculating the first photovoltaic power through a preset Frank copula function, and the load power probability interval is obtained by calculating the first load power through the preset Frank copula function.

[0017] In one possible implementation, the determining the charge-discharge strategy based on the reserved power and one or more preset targets includes: determining the charge-discharge strategy based on the reserved power, one or more preset targets and constraint conditions; wherein, the constraint conditions include a storage capacity balance constraint condition, a site power balance constraint condition and a remaining power constraint condition;

[0018] The storage capacity balance constraint condition is characterized by the following expression:

[0019] ;

[0020] The said L reserve (t) is the reserved power at time t, and the said Lrated (t) is the rated capacity of the energy storage system at time t;

[0021] The site power balance constraint condition is characterized by the following expression:

[0022] ;

[0023] The P grid (t) is the grid power at time t, the P ess (t) is the energy storage power at time t, the P ess (t) is used to represent charging or discharging;

[0024] The remaining charge constraint condition is characterized by the following expression:

[0025] ;

[0026] The Soc(t) is the remaining charge of the energy storage system at time t, the η charge is the charging efficiency, the η discharge is the discharging efficiency.

[0027] In one possible implementation, the predicting based on the collected photovoltaic data to obtain the first photovoltaic power, and the predicting based on the collected load data to obtain the first load power include: inputting the collected photovoltaic data into a preset prediction model for prediction to obtain the first photovoltaic power, and inputting the collected load data into the preset prediction model for prediction to obtain the first load power.

[0028] In one possible implementation, the preset prediction model is the FEDformer model.

[0029] In one possible implementation, the inputting the collected photovoltaic data into a preset prediction model for prediction to obtain the first photovoltaic power, and the inputting the collected load data into the preset prediction model for prediction to obtain the first load power include: inputting the collected photovoltaic data into the preset prediction model to obtain the second photovoltaic power, and inputting the collected load data into the preset prediction model to obtain the second load power; performing time correction on the second photovoltaic power, and performing the time correction on the second load power; determining the time-corrected photovoltaic power prediction value as the first photovoltaic power, and determining the time-corrected load power prediction value as the first load power; wherein, the time correction is used to adjust the sensitivity of the prediction value to time;

[0030] The time correction of the second photovoltaic power is realized through the following calculation formula:

[0031] ;

[0032] The P pv-corrected (t) is the predicted value of the photovoltaic power after the time correction, and the P pv (t) is the second photovoltaic power at time t, and the β pv is the correction coefficient of the photovoltaic, and the W pv (t) is the time weight factor of the photovoltaic at time t;

[0033] The time correction of the second load power is achieved through the following calculation formula:

[0034] ;

[0035] The P load-corrected (t) is the predicted value of the load power after the time correction, and the P load (t) is the second load power at time t, and the β load is the correction coefficient of the load, and the W load (t) is the time weight factor of the load at time t.

[0036] In one possible implementation, before determining the predicted value of the photovoltaic power after the time correction as the first photovoltaic power and determining the predicted value of the load power after the time correction as the first load power, the method further includes: performing correlation correction based on the predicted value of the photovoltaic power after the time correction and the first parameter correlation to obtain the first photovoltaic power; and performing correlation correction based on the predicted value of the load power after the time correction and the second parameter correlation to obtain the first load power; wherein, the first parameter correlation and the second parameter correlation are used to adjust the sensitivity of the predicted value to the correlation between the parameters.

[0037] In one possible implementation, the first parameter correlation is characterized by a first correlation matrix, the first correlation matrix includes the correlation coefficients between the parameters in the photovoltaic data, and the second parameter correlation is characterized by a second correlation matrix, the second correlation matrix includes the correlation coefficients between the parameters in the load data;

[0038] The first correlation matrix is characterized by the following expression:

[0039] ;

[0040] The R pv is the first correlation matrix, and the C pv (r i,pv , r j,pv ) is the i-th photovoltaic parameter r i,pvThe correlation coefficient with the j-th photovoltaic parameter r j,pv ;

[0041] The second correlation matrix is characterized by the following expression:

[0042] ;

[0043] The R laod is the second correlation matrix, and the C load (r i,load , r j,load ) is the correlation coefficient between the i-th load parameter r i,load and the j-th load parameter r j,load .

[0044] In one possible implementation, the correlation coefficient in the first correlation matrix is calculated by the following calculation formula:

[0045] ;

[0046] The cov pv (r i,pv , r j,pv ) is the covariance between the i-th photovoltaic parameter r i,pv and the j-th photovoltaic parameter r j,pv , the Var pv (r i,pv ) is the variance of the i-th photovoltaic parameter r i,pv , and the Var pv (r j,pv ) is the variance of the j-th photovoltaic parameter r j,pv ;

[0047] The correlation coefficient in the second correlation matrix is calculated by the following calculation formula:

[0048] ;

[0049] The cov load (r i,load , r j,load ) is the covariance between the i-th load parameter r i,load and the j-th load parameter r j,load , the Var load (r i,load ) is the variance of the i-th load parameter r i,load , and the Var load (r j,load ) is the variance of the j-th load parameter r j,load .

[0050] In a second aspect, an embodiment of the present application provides a charging and discharging device for an energy storage system, including one or more functional modules, and the one or more functional modules are used to execute the charging and discharging method as described in the first aspect.

[0051] In a third aspect, an embodiment of the present application provides an energy storage system, including: a processor and a memory, the memory is used to store a computer program; the processor is used to run the computer program to implement the charging and discharging method as described in the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which a program is stored. When the program runs on an energy storage system, the energy storage system is enabled to implement the charging and discharging method as described in the first aspect.

[0053] In a fifth aspect, an embodiment of the present application provides a program. When the above program runs on a processor of an energy storage system, the energy storage system is enabled to execute the charging and discharging method as described in the first aspect.

[0054] In a possible design, the program in the fifth aspect can be stored in whole or in part on a storage medium packaged together with the processor, or can be stored in whole or in part on a memory not packaged together with the processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic flowchart of the charging and discharging method for the energy storage system provided by the embodiment of the present application;

[0056] Figure 2 It is a schematic structural diagram of the charging and discharging device for the energy storage system provided by the embodiment of the present application;

[0057] Figure 3 It is a schematic structural diagram of the energy storage system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0058] In the embodiments of the present application, unless otherwise specified, the character " / " indicates that the related objects before and after are in an "or" relationship. For example, A / B may represent A or B. "And / or" describes the association relationship of the related objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0059] It should be noted that the terms "first", "second", etc. involved in the embodiments of the present application are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features, nor can they be understood as indicating or implying an order.

[0060] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. In addition, "at least one (item)" or its similar expression means any combination of these items, which may include any combination of single item or plural items. For example, at least one (item) of A, B, or C may represent: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C itself may be an element or a set containing one or more elements.

[0061] In the embodiments of the present application, "exemplary", "in some embodiments", "in another embodiment", etc. are used to represent examples, illustrations, or explanations. Any embodiment or design described as "exemplary" in the present application should not be construed as more preferred or more advantageous than other embodiments or design solutions. Rather, the use of the term "exemplary" is intended to present concepts in a specific manner.

[0062] In the embodiments of the present application, "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the intended meanings are the same. In the embodiments of the present application, communication and transmission can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings expressed are the same. For example, transmission may include sending and / or receiving, and can be a noun or a verb.

[0063] In the embodiments of the present application, the equality involved can be used in combination with greater than, applicable to the technical solutions adopted when greater than, or can be used in combination with less than, applicable to the technical solutions adopted when less than. It should be noted that when equality is used in combination with greater than, it cannot be used in combination with less than; when equality is used in combination with less than, it is not used in combination with greater than.

[0064] In the integrated solar and energy storage scenario, the charge and discharge scheduling strategy of the energy storage system is usually based on photovoltaic power generation and load demand. However, the uncertainties of photovoltaic power generation and load demand will lead to the inefficiency of the charge and discharge scheduling strategy. For example, the charge and discharge scheduling strategy lacks the adaptability to uncertainty factors such as photovoltaic power generation and load demand. Especially in the scenario where photovoltaic power generation fluctuates greatly or the load demand changes rapidly, the scheduling effect of the energy storage system is significantly reduced. Therefore, there is an urgent need for an efficient charge and discharge method for the energy storage system to solve the above problems.

[0065] Based on the above problems, the present application provides a charge and discharge method, which helps to improve the charge and discharge efficiency, reduce the operating cost, and improve the economic benefits.

[0066] Figure 1 It is a schematic flowchart of an embodiment of the charge and discharge method provided by the present application, including the following steps:

[0067] Step 101: Collect photovoltaic data and load data.

[0068] Specifically, the photovoltaic data may be photovoltaic power generation data. For the convenience of description, photovoltaic power generation is simply referred to as PV in this article.

[0069] The load data may be load demand data. For the convenience of description, load demand is simply referred to as load in this article.

[0070] In some alternative embodiments, the photovoltaic data may include, but is not limited to, data such as timestamp, cumulative electrical energy of the electricity meter, solar irradiance, ambient temperature, photovoltaic module temperature, wind speed, atmospheric pressure, weather category code, and active power.

[0071] In some alternative embodiments, the load data may include, but is not limited to, timestamp, ambient temperature, weather category code, electricity consumption, cumulative electrical energy of the electricity meter, peak-valley data, and number of people.

[0072] In some alternative embodiments, the energy storage system status data may also be collected. The energy storage system status data may be collected through a metering electricity meter, a local Energy Management System (EMS), a System site Control Unit (SCU), or a Battery Management Unit (BMU), or the energy storage system status data may be collected by other means. The embodiments of the present application do not make special limitations on this.

[0073] Among them, the energy storage system status data may include, but is not limited to, data such as State of Charge (SOC) status, power of the Power Convert System (PCS), current, and voltage.

[0074] It can be understood that the above energy storage system status data may be uploaded to the energy storage big data cloud platform through the Message Queuing Telemetry Transport (MQTT) protocol or other protocols at a preset period (for example, 30 seconds) to provide support for the charge and discharge scheduling decision-making and real-time control of the energy storage system.

[0075] Step 102: Perform prediction based on the collected photovoltaic data to obtain the first photovoltaic power, and perform prediction based on the collected load data to obtain the first load power.

[0076] In some alternative embodiments, the above prediction may be performed through a preset prediction model.

[0077] For example, the collected photovoltaic data can be input into a preset prediction model for prediction to obtain the first photovoltaic power. Also, the collected load data can be input into a preset prediction model for prediction to obtain the first load power.

[0078] Among them, the preset prediction model can be trained using an improved FEDformer model, or the preset prediction model can be trained using other models. The embodiments of this application do not make special limitations in this regard.

[0079] The preset prediction model can be trained with a photovoltaic dataset and a load dataset as inputs.

[0080] Among them, the above-mentioned photovoltaic dataset and load dataset can be a dataset composed of data collected at a preset time granularity (1 minute) within a historical time period (for example, 1 year). This photovoltaic dataset and load dataset contain the characteristics of photovoltaic and load. Combining the time series characteristics of photovoltaic and load, the prediction accuracy and efficiency can be significantly improved through training.

[0081] It can be understood that the above historical event segment and time granularity can also be other values, and the embodiments of this application do not make special limitations in this regard.

[0082] In some alternative embodiments, the photovoltaic data can be predicted through a preset prediction model to obtain the second photovoltaic power, the second photovoltaic power can be corrected in terms of time to obtain the time-corrected photovoltaic power prediction value, and the time-corrected photovoltaic power prediction value can be determined as the first photovoltaic power. Also, the load data can be predicted through a preset prediction model to obtain the second load power, the second load power can be corrected in terms of time to obtain the time-corrected load power prediction value, and the time-corrected load power prediction value can be determined as the first load power.

[0083] Among them, time correction can be used to adjust the sensitivity of the prediction value (for example, this prediction value can be the second photovoltaic power or the second load power) to time.

[0084] Exemplarily, the above method for correcting the second photovoltaic power in terms of time can be implemented through the following calculation formula:

[0085] ;

[0086] Among them, P pv-corrected (t) is the photovoltaic power after correcting the second photovoltaic power in terms of time. In this embodiment, P pv-corrected (t) can be used as the first photovoltaic power at time t, P pv (t) is the second photovoltaic power at time t, and β pv is the correction coefficient of photovoltaic, Wpv (t) is the time weight factor of photovoltaic.

[0087] Through time correction, the sensitivity of the predicted value of photovoltaic power to the time weight can be dynamically adjusted, improving the dynamic adaptability and robustness of the prediction result.

[0088] The above method for time correction of the second load power can be realized by the following calculation formula:

[0089] ;

[0090] Among them, P load-corrected (t) is the load power after time correction of the second load power. In this embodiment, P load-corrected (t) can be used as the first load power at time t, P load (t) is the second load power at time t, β load is the correction coefficient of the load, W load (t) is the time weight factor of the load.

[0091] Through time correction, the sensitivity of the predicted value of load power to the time weight can be dynamically adjusted, improving the dynamic adaptability and robustness of the prediction result.

[0092] In some alternative embodiments, after obtaining the photovoltaic power after time correction, correlation correction can also be performed based on the photovoltaic power after time correction and the first parameter correlation to obtain the first photovoltaic power, and the first parameter correlation can be characterized by the first correlation matrix. And, after obtaining the load power after time correction, correlation correction can also be performed based on the load power after time correction and the second parameter correlation to obtain the first load power, and the second parameter correlation can be characterized by the second correlation matrix.

[0093] Exemplarily, the first correlation matrix can be characterized by the following expression:

[0094] ;

[0095] Among them, R pv is the first correlation matrix, C pv (r i,pv , r j,pv ) is the correlation coefficient between the i-th photovoltaic parameter r i,pv and the j-th photovoltaic parameter r j,pv .

[0096] The correlation coefficient in the first correlation matrix can be calculated by the following calculation formula:

[0097] ;

[0098] Among them, cov pv (r i,pv , r j,pv ) is the covariance between the i-th photovoltaic parameter r i,pv and the j-th photovoltaic parameter r j,pv . Var pv (r i,pv ) is the variance of the i-th photovoltaic parameter r i,pv , and Var pv (r j,pv ) is the variance of the j-th photovoltaic parameter r j,pv .

[0099] The second correlation matrix can be characterized by the following expression:

[0100] ;

[0101] Among them, R laod is the second correlation matrix, and C load (r i,load , r j,load ) is the correlation coefficient between the i-th load parameter r i,load and the j-th load parameter r j,load .

[0102] The correlation coefficients in the second correlation matrix can be calculated by the following calculation formula:

[0103] ;

[0104] Among them, cov load (r i,load , r j,load ) is the covariance between the i-th load parameter r i,load and the j-th load parameter r j,load , Var load (r i,load ) is the variance of the i-th load parameter r i,load , and Var load (r j,load ) is the variance of the j-th load parameter r j,load .

[0105] Quantifying the correlation between different parameters by the ratio of covariance and variance can improve the accuracy of the predicted value.

[0106] In the embodiments of the present application, the correlation matrix (for example, the first correlation matrix and the second correlation matrix) stores the correlation coefficients between different parameters. Through the correlation matrix, the most relevant input features can be identified. For example, the relationship between temperature and light intensity, thereby improving the accuracy of the predicted value.

[0107] Step 103: Determine the reserved power based on the first photovoltaic power and the first load power. The reserved power is the standby power set to ensure the stable operation of the energy storage system.

[0108] Specifically, the reserved power can be determined by the difference between the first photovoltaic power and the first load power.

[0109] Step 104: Determine the charge-discharge strategy based on the reserved power and one or more preset goals.

[0110] Specifically, the charge-discharge strategy can be optimized with multiple goals through one or more preset goals.

[0111] Among them, one or more preset goals can include, but are not limited to, a revenue goal and / or an efficiency goal. The revenue goal is used to maximize the revenue, and the efficiency goal is used to maximize the energy storage operation efficiency.

[0112] It can be understood that through the above one or more preset goals and combined with real-time constraint conditions, an optimal energy storage charge-discharge strategy can be generated.

[0113] Exemplarily, the revenue goal can be characterized by the following expression:

[0114] ;

[0115] Where T is the preset time span, L discharge (t) is the discharge amount of the energy storage system at time t, L charge (t) is the charge amount of the energy storage system at time t, E(t) is the electricity price at time t, C charge (t) is the charging operation cost of the energy storage system at time t, C discharge (t) is the discharging operation cost of the energy storage system at time t, C pv (t) is used to characterize the negative revenue caused by the photovoltaic power prediction error at time t, C load (t) is used to characterize the negative revenue caused by the load power prediction error at time t;

[0116] C pv (t) can be calculated through the following calculation formula:

[0117] ;

[0118] Where λ pv (t) is the weight coefficient of the photovoltaic at time t, λ pv (t) is determined by the photovoltaic power probability interval, P pv-actual (t) is the true power of the photovoltaic at time t, P pv-forecast (t) is the predicted power of the photovoltaic at time t;

[0119] It can be understood that the photovoltaic power probability interval can be obtained by calculating through the Frank-Copula correlation function. For example, the photovoltaic prediction value at time t can be input into the Frank-Copula correlation function to obtain the photovoltaic power probability interval. Alternatively, the photovoltaic power probability interval can be obtained by calculating through other functions, and the embodiments of the present application do not make special limitations on this.

[0120] C load (t) can be obtained by calculating through the following calculation formula:

[0121] ;

[0122] Among them, λ load (t) is the weight coefficient of the load at time t, and λ load (t) is determined by the load power probability interval, P load-actual (t) is the actual power of the load at time t, and the P load-forecast (t) is the predicted power of the load at time t.

[0123] It can be understood that the load power probability interval can be obtained by calculating through the Frank-Copula correlation function. For example, the load prediction value at time t can be input into the Frank-Copula correlation function to obtain the load power probability interval. Alternatively, the load power probability interval can be obtained by calculating through other functions, and the embodiments of the present application do not make special limitations on this.

[0124] It should be noted that charging and discharging can satisfy the constraint conditions shown in the following formula, and this constraint condition can be the energy storage capacity balance constraint condition:

[0125] ;

[0126] Among them, L reserve (t) is the reserved power at time t, and L rated (t) is the rated capacity of the energy storage system at time t.

[0127] The efficiency target can be characterized by the following expression:

[0128] .

[0129] It can be understood that in order to achieve the above efficiency target, the constraint conditions shown in the following formula can be satisfied, and this constraint condition can include the site power balance constraint condition and the remaining power constraint condition.

[0130] Exemplarily, the site power balance constraint condition can be characterized by the following expression:

[0131] ;

[0132] Among them, P grid (t) is the grid power at time t, and P ess (t) is the energy storage power at time t, and P ess (t) is used to represent the charging power or discharging power at time t.

[0133] It should be noted that the maximum value of the photovoltaic power is limited by the inverter, the maximum values of the grid power and the energy storage power are limited by the PCS, and the maximum value of the load power is based on the demand of the site transformer.

[0134] The remaining power constraint condition is characterized by the following expression:

[0135] ;

[0136] Among them, Soc(t) is the remaining power of the energy storage system at time t, and η charge is the charging efficiency, and η discharge is the discharging efficiency.

[0137] It can be understood that the remaining power can be determined from the state data of the energy storage system.

[0138] Figure 2 FIG. is a schematic structural diagram of the charging and discharging device of the energy storage system provided by the embodiment of the present application. As Figure 2 shown, the charging and discharging device 20 of the above-mentioned energy storage system may include: a prediction module 21 and a determination module 22; among them,

[0139] The prediction module 21 is configured to perform prediction based on the collected photovoltaic data to obtain a first photovoltaic power, and perform prediction based on the collected load data to obtain a first load power;

[0140] The determination module 22 determines a reserved power based on the first photovoltaic power and the first load power, where the reserved power is a standby power set to ensure the stable operation of the energy storage system; and determines a charging and discharging strategy based on the reserved power and one or more preset goals;

[0141] Among them, the one or more preset goals at least include a revenue goal and / or an efficiency goal. The revenue goal is used to maximize the revenue, and the efficiency goal is used to maximize the energy storage operation efficiency;

[0142] The revenue goal is characterized by the following expression:

[0143] ;

[0144] where T is a preset time span, and L dischargeThe discharge amount of the energy storage system at time t is (t), and the L charge The charge amount of the energy storage system at time t is (t), the electricity price at time t is E(t), and the C charge The charging operation cost of the energy storage system at time t is C discharge The discharging operation cost of the energy storage system at time t is C pv C(t) is used to characterize the negative profit brought by the photovoltaic power prediction error at time t, and the C load C(t) is used to characterize the negative profit brought by the load power prediction error at time t;

[0145] The C pv C(t) is obtained through the following calculation formula:

[0146] ;

[0147] The λ pv The weight coefficient of the photovoltaic at time t is λ(t), and the λ pv λ(t) is determined by the photovoltaic power probability interval, and the P pv-actual The true power of the photovoltaic at time t is P(t), and the P pv-forecast The predicted power of the photovoltaic at time t is P(t);

[0148] The C load C(t) is obtained through the following calculation formula:

[0149] ;

[0150] The λ load The weight coefficient of the load at time t is λ(t), and the λ load λ(t) is determined by the load power probability interval, and the P load-actual The true power of the load at time t is P(t), and the P load-forecast The predicted power of the load at time t is P(t);

[0151] The efficiency target is characterized by the following expression:

[0152] .

[0153] In one possible implementation, the photovoltaic power probability interval is obtained by calculating the first photovoltaic power through a preset Frank copula function, and the load power probability interval is obtained by calculating the first load power through the preset Frank copula function.

[0154] In one possible implementation, the determining module 22 is further configured to determine a charge-discharge strategy based on the reserved power, one or more preset targets, and constraint conditions;

[0155] Among them, the constraint conditions include an energy storage capacity balance constraint condition, a site power balance constraint condition, and a remaining power constraint condition;

[0156] The energy storage capacity balance constraint condition is characterized by the following expression:

[0157] ;

[0158] The L reserve (t) is the reserved power at time t, and the L rated (t) is the rated capacity of the energy storage system at time t;

[0159] The site power balance constraint condition is characterized by the following expression:

[0160] ;

[0161] The P grid (t) is the grid power at time t, the P ess (t) is the energy storage power at time t, and the P ess (t) is used to represent charging or discharging;

[0162] The remaining power constraint condition is characterized by the following expression:

[0163] ;

[0164] The Soc(t) is the remaining power of the energy storage system at time t, the η charge is the charging efficiency, and the η discharge is the discharging efficiency.

[0165] In one possible implementation, the prediction module 21 is further configured to input the collected photovoltaic data into a preset prediction model for prediction to obtain a first photovoltaic power, and input the collected load data into the preset prediction model for prediction to obtain a first load power.

[0166] In one possible implementation, the preset prediction model is the FEDformer model.

[0167] In one possible implementation, the prediction module 21 is further configured to input the collected photovoltaic data into the preset prediction model to obtain a second photovoltaic power, and input the collected load data into the preset prediction model to obtain a second load power;

[0168] Perform time correction on the second photovoltaic power and perform the time correction on the second load power;

[0169] Determine the predicted value of the photovoltaic power after time correction as the first photovoltaic power, and determine the predicted value of the load power after time correction as the first load power;

[0170] Wherein, the time correction is used to adjust the sensitivity of the predicted value to time;

[0171] The time correction on the second photovoltaic power is achieved through the following calculation formula:

[0172] ;

[0173] The P pv-corrected (t) is the predicted value of the photovoltaic power after time correction, the P pv (t) is the second photovoltaic power at time t, the β pv is the correction coefficient of the photovoltaic, and the W pv (t) is the time weight factor of the photovoltaic at time t;

[0174] The time correction on the second load power is achieved through the following calculation formula:

[0175] ;

[0176] The P load-corrected (t) is the predicted value of the load power after time correction, the P load (t) is the second load power at time t, the β load is the correction coefficient of the load, and the W load (t) is the time weight factor of the load at time t.

[0177] In one possible implementation, the prediction module 21 is further configured to perform correlation correction based on the predicted value of the photovoltaic power after time correction and the first parameter correlation to obtain the first photovoltaic power; and perform correlation correction based on the predicted value of the load power after time correction and the second parameter correlation to obtain the first load power;

[0178] Wherein, the first parameter correlation and the second parameter correlation are used to adjust the sensitivity of the predicted value to the correlation between parameters.

[0179] In one possible implementation, the first parameter correlation is characterized by a first correlation matrix, and the first correlation matrix includes the correlation coefficients between the parameters in the photovoltaic data. The second parameter correlation is characterized by a second correlation matrix, and the second correlation matrix includes the correlation coefficients between the parameters in the load data;

[0180] The first correlation matrix is characterized by the following expression:

[0181] ;

[0182] The R pv is the first correlation matrix, and the C pv (r i,pv , r j,pv ) is the correlation coefficient between the i-th photovoltaic parameter r i,pv and the j-th photovoltaic parameter r j,pv ;

[0183] The second correlation matrix is characterized by the following expression:

[0184] ;

[0185] The R laod is the second correlation matrix, and the C load (r i,load , r j,load ) is the correlation coefficient between the i-th load parameter r i,load and the j-th load parameter r j,load ;

[0186] In one possible implementation, the correlation coefficients in the first correlation matrix are calculated by the following calculation formula:

[0187] ;

[0188] The cov pv (r i,pv , r j,pv ) is the covariance between the i-th photovoltaic parameter r i,pv and the j-th photovoltaic parameter r j,pv , and the Var pv (r i,pv ) is the variance of the i-th photovoltaic parameter r i,pv , and the Var pv (r j,pv ) is the variance of the j-th photovoltaic parameter r j,pv ;

[0189] The correlation coefficients in the second correlation matrix are calculated by the following calculation formula:

[0190] ;

[0191] The cov load (r i,load , r j,load ) is the covariance between the i-th load parameter r i,load and the j-th load parameter r j,load . The Var load (r i,load ) is the variance of the i-th load parameter r i,load . The Var load (r j,load ) is the variance of the j-th load parameter r j,load .

[0192] Figure 2 The charge and discharge device 20 of the energy storage system provided by the illustrated embodiment can be used to execute the technical solution of the method embodiment shown in the present application. Its implementation principle and technical effect can be further referred to the relevant description in the method embodiment.

[0193] It should be understood that the division of each module of the above charge and discharge device 20 of the energy storage system is only a logical function division. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. And these modules can all be implemented in the form of software called by a processing element; they can also all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the detection module can be a separately established processing element, or can be integrated in a certain chip of the terminal device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together or can be independently implemented. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit or software-form instruction in the processor element.

[0194] For example, the above modules can be one or more integrated circuits configured to implement the above method, such as: one or more application specific integrated circuits (ASIC); hereinafter referred to as ASIC), or, one or more digital signal processors (Digital Signal Processor; hereinafter referred to as DSP), or, one or more field programmable gate arrays (Field Programmable Gate Array; hereinafter referred to as FPGA), etc. Again, these modules can be integrated together to be implemented in the form of a system-on-a-chip (hereinafter referred to as SOC).

[0195] Figure 3 FIG. 1 is a schematic structural diagram of an energy storage system 300 provided by an embodiment of the present application. The energy storage system 300 may include: at least one processor; and at least one memory communicatively connected to the processor. The memory stores program instructions executable by the processor, and the processor in the energy storage system 300 can execute the actions performed in the storage access method provided by the embodiment of the present application by invoking the program instructions.

[0196] As Figure 3 shown, the energy storage system 300 is presented in the form of a general-purpose computing device. The components of the energy storage system 300 may include, but are not limited to: one or more processors 310, a memory 320, a communication bus 340 connecting different system components (including the memory 320 and the processor 310), and a communication interface 330.

[0197] The communication bus 340 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (hereinafter referred to as: ISA) bus, Micro Channel Architecture (hereinafter referred to as: MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (hereinafter referred to as: VESA) local bus, and Peripheral Component Interconnection (hereinafter referred to as: PCI) bus.

[0198] The energy storage system 300 typically includes a variety of computer system-readable media. These media can be any available media accessible by the terminal device, including volatile and non-volatile media, removable and non-removable media.

[0199] The memory 320 may include computer system-readable media in the form of volatile memory, such as random access memory (hereinafter referred to as: RAM) and / or cache memory. The terminal device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Although Figure 3Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a Compact Disc Read Only Memory (hereinafter referred to as CD-ROM), a Digital Video Disc Read Only Memory (hereinafter referred to as DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the communication bus 340 through one or more data medium interfaces. The memory 320 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present application.

[0200] A program / utility having a set (at least one) of program modules may be stored in the memory 320. Such program modules include - but are not limited to - an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules generally perform the functions and / or methods in the embodiments described in the present application.

[0201] The energy storage system 300 may also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and may also communicate with one or more devices that enable a user to interact with the terminal device, and / or communicate with any device that enables the terminal device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication may be carried out through the communication interface 330. And, the energy storage system 300 may also communicate with one or more networks (such as a Local Area Network (hereinafter referred to as LAN), a Wide Area Network (hereinafter referred to as WAN) and / or a public network, such as the Internet) through a network adapter ( Figure 3 not shown in the figure). The above network adapter may communicate with other modules of the terminal device through the communication bus 340. It should be understood that although Figure 3 not shown in the figure, other hardware and / or software modules may be used in combination with the energy storage system 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Drives (hereinafter referred to as RAID) systems, tape drives, and data backup storage systems, etc.

[0202] The processor 310 executes various functional applications and data processing by running the programs stored in the memory 320, for example, implementing the method provided by the embodiments of the present application.

[0203] It can be understood that the interface connection relationships between the modules illustrated in the embodiments of the present application are only illustrative descriptions and do not constitute a structural limitation on the energy storage system 300. In other embodiments of the present application, the energy storage system 300 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0204] In the above embodiments, the processor involved may include, for example, a CPU, a DSP, a microcontroller or a digital signal processor, and may also include a GPU, an embedded neural network processor (Neural-network Process Units; hereinafter referred to as: NPU) and an image signal processor (Image Signal Processing; hereinafter referred to as: ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as an ASIC, or one or more integrated circuits for controlling the execution of the technical solution program of the present application. In addition, the processor may have the function of operating one or more software programs, and the software programs may be stored in a storage medium.

[0205] The embodiments of the present application also provide a readable storage medium, in which a program is stored. When it runs on a system, the system is enabled to execute the method provided by the embodiments shown in the present application.

[0206] The embodiments of the present application also provide a program product, which includes a program. When it runs on a system, the system is enabled to execute the method provided by the embodiments shown in the present application.

[0207] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent the situation of A existing alone, A and B existing simultaneously, or B existing alone. Where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c may be single or multiple.

[0208] Those of ordinary skill in the art will realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0209] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0210] In several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROMs), random access memories (hereinafter referred to as RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0211] The above is only the specific implementation manner of this application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application and should be covered by the protection scope of this application. The protection scope of this application shall be subject to the protection scope of the claimed rights.

Claims

1. A charging and discharging method for an energy storage system, characterized in that: The method comprises: Predicting based on the collected photovoltaic data to obtain a first photovoltaic power, and predicting based on the collected load data to obtain a first load power; Determine a reserved power based on the first photovoltaic power and the first load power, wherein the reserved power is a backup power set to ensure stable operation of the energy storage system; Determining a charging and discharging strategy based on the reserved power and one or more preset targets; The one or more preset targets include at least a revenue target and / or an efficiency target, wherein the revenue target is used to maximize the revenue, and the efficiency target is used to maximize the energy storage operation efficiency; The profit target is represented by the following expression: ； Where T is the preset time span, L discharge (t) is the discharge capacity of the energy storage system at time t, L charge (t) is the charge amount of the energy storage system at time t, E(t) is the electricity price at time t, C charge (t) is the charging operation cost of the energy storage system at time t, C discharge (t) is the discharge operation cost of the energy storage system at time t, C pv (t) is used to represent the negative benefits caused by the photovoltaic power prediction error at time t, C load (t) is used to represent the negative benefits caused by the load power forecast error at time t; The C pv (t) is calculated by the following formula: ； In the formula, λ pv (t) is the weight coefficient of photovoltaic at time t, and the λ pv (t) is determined by the photovoltaic power probability interval, P pv-actual (t) is the real power of the photovoltaic at time t, P pv-forecast (t) is the predicted power of photovoltaic at time t; The C load (t) is calculated by the following formula: ； In the formula, λ load (t) is the weight coefficient of the load at time t, and the load (t) is determined by the load power probability interval, P load-actual (t) is the real power of the load at time t, P load-forecast (t) is the predicted power of the load at time t; The efficiency target is represented by the following expression: 。 2. The method according to claim 1, characterized in that The photovoltaic power probability interval is obtained by calculating the first photovoltaic power using a preset Frankcopula function, and the load power probability interval is obtained by calculating the first load power using the preset Frankcopula function.

3. The method according to claim 1, characterized in that Determining the charging and discharging strategy based on the reserved power and one or more preset targets includes: Determining a charging and discharging strategy based on the reserved power, one or more preset targets and constraints; The constraints include energy storage capacity balance constraints, site power balance constraints and remaining power constraints; The energy storage capacity balance constraint condition is represented by the following expression: ； Where, L reserve (t) is the reserved power at time t, L rated (t) is the rated capacity of the energy storage system at time t; The site power balance constraint is represented by the following expression: ； Where P grid (t) is the power of the power grid at time t, P ess (t) is the energy storage power at time t, and P ess (t) used to characterize charging or discharging; The remaining power constraint condition is represented by the following expression: ； Where Soc(t) is the remaining power of the energy storage system at time t, η charge is the charging efficiency, η discharge is the discharge efficiency.

4. The method according to claim 1, characterized in that: The method of performing prediction based on the collected photovoltaic data to obtain the first photovoltaic power and performing prediction based on the collected load data to obtain the first load power includes: The collected photovoltaic data is input into a preset prediction model for prediction to obtain a first photovoltaic power, and the collected load data is input into the preset prediction model for prediction to obtain a first load power.

5. The method according to claim 4, characterized in that The preset prediction model is the FEDformer model.

6. The method according to claim 4, characterized in that The step of inputting the collected photovoltaic data into a preset prediction model for prediction to obtain a first photovoltaic power, and the step of inputting the collected load data into the preset prediction model for prediction to obtain a first load power comprises: Inputting the collected photovoltaic data into the preset prediction model to obtain a second photovoltaic power, and inputting the collected load data into the preset prediction model to obtain a second load power; Performing time correction on the second photovoltaic power, and performing the time correction on the second load power; Determine the time-corrected photovoltaic power prediction value as the first photovoltaic power, and determine the time-corrected load power prediction value as the first load power; Wherein, the time correction is used to adjust the sensitivity of the predicted value to time; The time correction of the second photovoltaic power is achieved by the following calculation formula: ； Where P pv-corrected (t) is the photovoltaic power prediction value after correction at the time, P pv (t) is the second photovoltaic power at time t, β pv is the photovoltaic correction factor, W pv (t) is the time weight factor of PV at time t; The time correction of the second load power is achieved by the following calculation formula: ； Where P load-corrected (t) is the load power forecast value after the correction of the time, P load (t) is the second load power at time t, β load is the load correction factor, W load (t) is the time weight factor of the load at time t.

7. The method according to claim 6, characterized in that Before determining the time-corrected photovoltaic power prediction value as the first photovoltaic power and determining the time-corrected load power prediction value as the first load power, the method further includes: Performing correlation correction based on the time-corrected photovoltaic power prediction value and the first parameter correlation to obtain the first photovoltaic power; and performing correlation correction based on the time-corrected load power prediction value and the second parameter correlation to obtain the first load power; The first parameter correlation and the second parameter correlation are used to adjust the sensitivity of the predicted value to the correlation between parameters.

8. The method according to claim 7, characterized in that The first parameter correlation is represented by a first correlation matrix, the first correlation matrix includes correlation coefficients between parameters in the photovoltaic data, and the second parameter correlation is represented by a second correlation matrix, the second correlation matrix includes correlation coefficients between parameters in the load data; The first correlation matrix is ​​represented by the following expression: ； In the formula, R pv is the first correlation matrix, C pv (r i,pv , r j,pv ) is the ith photovoltaic parameter r i,pv and the jth photovoltaic parameter r j,pv The correlation coefficient between The second correlation matrix is ​​represented by the following expression: ； In the formula, R laod is the second correlation matrix, C load (r i,load , r j,load ) is the i-th load parameter r i,load and the jth load parameter r j,load The correlation coefficient between .

9. The method according to claim 8, characterized in that The correlation coefficient in the first correlation matrix is ​​calculated by the following calculation formula: ； In the formula, cov pv (r i,pv , r j,pv ) is the ith photovoltaic parameter r i,pv and the jth photovoltaic parameter r j,pv The covariance between pv (r i,pv ) is the ith photovoltaic parameter r i,pv The variance of pv (r j,pv ) is the jth photovoltaic parameter r j,pv The variance of The correlation coefficient in the second correlation matrix is ​​calculated by the following calculation formula: ； In the formula, cov load (r i,load , r j,load ) is the i-th load parameter r i,load and the jth load parameter r j,load The covariance between load (r i,load ) is the i-th load parameter r i,load The variance of load (r j,load ) is the jth load parameter r j,load The variance of .

10. An energy storage system, characterized in that: include: A processor and a memory, wherein the memory is used to store a program; and the processor is used to run the program to implement the charging and discharging method of the energy storage system as described in any one of claims 1 to 9.

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