CN105653832B - The appraisal procedure and device of the energy-storage units power distribution of energy-accumulating power station - Google Patents

Abstract

The present invention provides a method and device for evaluating the power distribution of energy storage units of an energy storage power station, wherein the method includes: calculating the power-energy state SOE correlation degree of battery strings, and evaluating the energy storage according to the power-SOE correlation degree The power distribution of the unit to each battery string during operation; calculate the battery string power-SOE correlation standard deviation coefficient according to the power-SOE correlation degree, and evaluate the energy storage unit during operation according to the standard deviation coefficient The degree of dispersion of the battery string power-SOE correlation at each moment; calculate the cumulative value of the battery string power-SOE correlation standard deviation coefficient according to the standard deviation coefficient, and evaluate the power distribution of the energy storage unit to the battery string according to the cumulative value Trends over run time. The invention solves the technical problem that the power distribution of the battery string cannot be effectively evaluated in the prior art, and achieves the technical effect of effectively evaluating the power distribution of the battery string.

Description

Evaluation method and device for energy storage unit power distribution of energy storage power station

technical field

The invention relates to the technical field of evaluating the performance of an energy storage unit, in particular to an evaluation method and device for power distribution of an energy storage unit of an energy storage power station.

Background technique

With the continuous increase of wind power and photovoltaic projects, energy storage technology, as an effective means to suppress the volatility and intermittency of new energy power generation, has also developed rapidly. The energy storage unit is the basic unit of the energy storage power station. The energy storage unit is composed of multiple battery packs connected in series and parallel. The specific structure is shown in Figure 1. The state of energy (SOE) is the most important performance index of the energy storage unit. The SOE of the energy storage unit is determined by the battery string with the worst energy state. Therefore, the SOE consistency of the battery string is an important parameter for evaluating the SOE of the energy storage unit.

The SOE consistency of the battery strings has a lot to do with the power distribution of the battery strings. If the power distribution of the energy storage unit to the battery strings is reasonable, the SOE of the battery strings will tend to be consistent during operation. If the string power distribution is unreasonable, the difference in SOE of the battery strings will continue to increase. Therefore, evaluating the power distribution of energy storage units to battery strings is of great significance for evaluating the SOE of energy storage units and grasping the operation status of energy storage units.

However, there is currently no effective method for evaluating the power allocation of energy storage units.

Contents of the invention

An embodiment of the present invention provides a method for evaluating the power distribution of energy storage units of an energy storage power station to solve the technical problem in the prior art that the power distribution of battery pack energy storage units cannot be effectively evaluated. The method includes:

Calculate the battery string power-energy state SOE correlation, and evaluate the power distribution of the energy storage unit to each battery string during operation according to the power-SOE correlation;

Calculate the battery string power-SOE correlation standard deviation coefficient according to the power-SOE correlation degree, and evaluate the dispersion degree of the battery string power-SOE correlation degree at each moment during the operation of the energy storage unit according to the standard deviation coefficient;

According to the standard deviation coefficient, the battery string power-SOE correlation standard deviation coefficient cumulative value is calculated, and according to the cumulative value, the power distribution trend of the energy storage unit to the battery string changes with the running time is evaluated.

In one embodiment, calculating the battery string power-SOE correlation includes:

Calculate the power-SOE correlation during charging according to the following formula:

Among them, γ _ich represents the power-SOE correlation of the i-th battery string during charging, P _chi represents the charging power of the i-th battery string, SOE _i represents the SOE of the i-th battery string, and n represents the power of the i-th battery string Number of groups;

Calculate the power-SOE correlation during discharge according to the following formula:

Among them, γ _idis represents the power-SOE correlation of the i-th battery string during discharge, and P _disi represents the discharge power of the i-th battery string.

In one embodiment, the battery string power-SOE correlation standard deviation coefficient is calculated according to the following formula:

Among them, γ _δ represents the standard deviation coefficient of battery string power-SOE correlation, γ _j represents the jth battery string power-SOE correlation, Indicates the average value of battery string power-SOE correlation.

In one embodiment, the cumulative value of the battery string power-SOE correlation standard deviation coefficient is calculated according to the following formula:

Among them, m represents m moments when the energy storage unit is running, Indicates the value of the battery string power-SOE correlation standard deviation coefficient at time t _k , Indicates the cumulative value of the battery string power-SOE correlation standard deviation coefficient at time t _m .

In one embodiment, evaluating the power distribution of the energy storage unit to each battery string during operation according to the power-SOE correlation degree includes: drawing power-SOE correlation curves of different battery strings, and according to the correlation Evaluate the power distribution of the energy storage unit to each battery string during operation using the degree curve;

According to the standard deviation coefficient, the degree of dispersion of the battery string power-SOE correlation degree is evaluated at each moment during the operation of the energy storage unit, including: drawing a standard deviation coefficient curve of the battery string power-SOE correlation degree, and according to the standard deviation coefficient The curve evaluates the degree of dispersion of the battery string power-SOE correlation at each moment during the operation of the energy storage unit;

According to the accumulated value, the energy storage unit is used to evaluate the trend of power distribution of the energy storage unit to the battery string with the running time, including: drawing the battery string power-SOE correlation standard deviation coefficient cumulative value change trend curve, according to the cumulative value change trend curve Evaluate the trend of battery string power allocation over runtime.

The embodiment of the present invention also provides an evaluation device for the power distribution of the energy storage unit of the energy storage power station, so as to realize the effective evaluation of the power distribution of the battery pack, the device includes:

A correlation degree determination module, configured to calculate the battery string power-energy state SOE correlation degree, and evaluate the power allocation of the energy storage unit to each battery string during operation according to the power-SOE correlation degree;

The standard deviation coefficient determination module is used to calculate the battery string power-SOE correlation standard deviation coefficient according to the power-SOE correlation degree, and evaluate the battery string power of the energy storage unit at each moment during the operation process- The degree of dispersion of SOE correlation;

The cumulative value determination module is used to calculate the cumulative value of the battery string power-SOE correlation standard deviation coefficient according to the standard deviation coefficient, and to evaluate the trend of the power allocation of the energy storage unit to the battery string with the running time according to the cumulative value.

In one embodiment, the correlation determination module is specifically used for:

Calculate the power-SOE correlation during charging according to the following formula:

Among them, γ _ich represents the power-SOE correlation of the i-th battery string during charging, P _chi represents the charging power of the i-th battery string, SOE _i represents the SOE of the i-th battery string, and n represents the power of the i-th battery string Number of groups;

Calculate the power-SOE correlation during discharge according to the following formula:

Among them, γ _idis represents the power-SOE correlation of the i-th battery string during discharge, and P _disi represents the discharge power of the i-th battery string.

In one embodiment, the standard deviation coefficient determination module is specifically configured to calculate the battery string power-SOE correlation standard deviation coefficient according to the following formula:

Among them, γ _δ represents the standard deviation coefficient of battery string power-SOE correlation, γ _j represents the jth battery string power-SOE correlation, Indicates the average value of battery string power-SOE correlation.

In one embodiment, the cumulative value determination module is specifically configured to calculate the cumulative value of the battery string power-SOE correlation standard deviation coefficient according to the following formula:

Among them, m represents m moments when the energy storage unit is running, Indicates the value of the battery string power-SOE correlation standard deviation coefficient at time t _k , Indicates the cumulative value of the battery string power-SOE correlation standard deviation coefficient at time t _m .

In one embodiment, the correlation determination module is specifically used to draw different battery string power-SOE correlation curves, and evaluate the power allocation of the energy storage unit to each battery string during operation according to the correlation curve ;

The standard deviation coefficient determination module is specifically used to evaluate the dispersion degree of the battery string power-SOE correlation degree at each moment during the operation of the energy storage unit according to the standard deviation coefficient, including: drawing the battery string power-SOE correlation standard The difference coefficient curve, according to the standard deviation coefficient curve, evaluates the degree of dispersion of the battery string power-SOE correlation degree at each moment during the operation of the energy storage unit;

The cumulative value determination module is specifically used to evaluate the trend of power distribution of the energy storage unit to the battery string with the running time according to the cumulative value, including: drawing a trend curve of the cumulative value of the battery string power-SOE correlation standard deviation coefficient cumulative value , according to the cumulative value change trend curve, evaluate the change trend of the battery string power distribution with the running time.

In the embodiment of the present invention, by analyzing the battery string power-SOE correlation, it is possible to effectively evaluate whether the power allocation of the energy storage unit to the battery string is reasonable, and to evaluate whether the energy storage unit maintains the SOE balance of the battery string during operation. The ability to maintain the SOE balance control capability of battery strings with the extension of operating time, and the effective evaluation of battery power distribution. Further, the evaluation results are useful for judging the health status of energy storage units, control performance, and operation. The situation has very important reference significance.

Description of drawings

The drawings described here are used to provide further understanding of the present invention, constitute a part of the application, and do not limit the present invention. In the attached picture:

Fig. 1 is a schematic diagram of an energy storage unit according to an embodiment of the present invention;

Fig. 2 is a method flowchart of an evaluation method for power distribution of energy storage units of an energy storage power station according to an embodiment of the present invention;

Fig. 3 is a schematic diagram of power and SOE curves of 6 sets of battery strings according to an embodiment of the present invention;

Fig. 4 is a schematic diagram of power-SOE correlation curves of 6 sets of battery strings according to an embodiment of the present invention;

5 is a schematic diagram of a standard deviation coefficient curve of battery string power-SOE correlation in an embodiment of the present invention;

Fig. 6 is a schematic diagram of the variation trend curve of the battery string power-SOE correlation standard deviation coefficient according to an embodiment of the present invention;

Fig. 7 is a structural block diagram of an evaluation device for power distribution of energy storage units of an energy storage power station according to an embodiment of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the embodiments and accompanying drawings. Here, the exemplary embodiments and descriptions of the present invention are used to explain the present invention, but not to limit the present invention.

In this example, an evaluation method for power distribution of energy storage units of an energy storage power station is provided, as shown in Figure 2, including the following steps:

Step 201: Calculate the battery string power-SOE correlation degree, and evaluate the power distribution of the energy storage unit to each battery string during operation according to the power-SOE correlation degree;

Step 202: Calculate the battery string power-SOE correlation standard deviation coefficient according to the power-SOE correlation degree, and evaluate the dispersion of the battery string power-SOE correlation degree at each moment during the operation of the energy storage unit according to the standard deviation coefficient degree;

Step 203: Calculate the battery string power-SOE correlation standard deviation coefficient cumulative value according to the standard deviation coefficient, and evaluate the power distribution trend of the energy storage unit to the battery string with the running time according to the cumulative value.

In the above embodiment, by analyzing the power-SOE correlation of battery strings, it is possible to effectively evaluate whether the power allocation of the energy storage unit to the battery strings is reasonable, and evaluate the ability of the energy storage unit to maintain the SOE balance of the battery strings during operation. ability, as well as the change trend of maintaining the battery string SOE balance control ability with the extension of running time, to realize the effective evaluation of the power distribution of the battery pack. It has very important reference significance.

Specifically, in the above step 201, the power-SOE correlation during charging can be calculated according to the following formula:

Among them, γ _ich represents the power-SOE correlation of the i-th battery string during charging, P _chi represents the charging power of the i-th battery string, SOE _i represents the SOE of the i-th battery string, and n represents the power of the i-th battery string Number of groups;

Calculate the power-SOE correlation during discharge according to the following formula:

Among them, γ _idis represents the power-SOE correlation of the i-th battery string during discharge, and P _disi represents the discharge power of the i-th battery string.

Specifically, in the above step 202, the battery string power-SOE correlation standard deviation coefficient can be calculated according to the following formula:

Among them, γ _δ represents the standard deviation coefficient of battery string power-SOE correlation, γ _j represents the jth battery string power-SOE correlation, Indicates the average value of battery string power-SOE correlation.

Specifically, in the above step 203, the cumulative value of the battery string power-SOE correlation standard deviation coefficient can be calculated according to the following formula:

Among them, m represents m moments when the energy storage unit is running, Indicates the value of the battery string power-SOE correlation standard deviation coefficient at time t _k , Indicates the cumulative value of the battery string power-SOE correlation standard deviation coefficient at time t _m .

In order to evaluate the power allocation of battery strings more effectively, you can use the method of drawing curves, because the curves are more intuitive to observe, for example: you can draw different battery string power-SOE correlation curves, and evaluate according to the correlation curves For the power distribution of each battery string, draw the standard deviation coefficient curve of the battery string power-SOE correlation degree, and evaluate the dispersion degree of the battery string power-SOE correlation degree at each moment during the operation process according to the standard deviation coefficient curve; draw the battery string power-SOE correlation degree curve; The string power-SOE correlation standard deviation coefficient cumulative value change trend curve, according to the cumulative value change trend curve, evaluates the battery string power distribution trend with the running time.

The evaluation method of the power distribution of the energy storage unit of the present invention will be described in detail below in conjunction with a specific embodiment. However, it should be noted that this specific embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention. .

In this example, by analyzing the battery string power-SOE correlation, an evaluation method for energy storage unit power distribution is proposed, in order to evaluate the power control of the energy storage unit to the battery string, and to evaluate the battery string SOE consistency , it provides an effective method to master the SOE and operating status of the energy storage unit, which mainly includes:

Step 1: First, the calculation formula of battery string power-SOE correlation is proposed, and different battery string power-SOE correlation curves are drawn to evaluate the power allocation of energy storage units to each battery string during operation Happening;

Step 2: Then the formula for calculating the standard deviation coefficient of the battery string power-SOE correlation is proposed, and the standard deviation coefficient curve of the battery string power-SOE correlation is drawn to evaluate the dispersion of the battery string power-SOE correlation degree;

Step 3: Finally, the formula for calculating the cumulative value of the battery string power-SOE correlation standard deviation coefficient is proposed, and the trend curve of the cumulative value of the battery string power-SOE correlation standard deviation coefficient is drawn to evaluate the energy storage unit Changes in power distribution of battery strings with operating time.

Through the above three steps, a comprehensive and effective evaluation of the power distribution of the energy storage unit is realized. The above three steps are described in detail below:

1) Evaluation of battery string power distribution

Battery string power-SOE correlation refers to the degree of correlation between the power of different battery strings and their SOE during the operation of the energy storage unit. ability to balance. Specifically, the power-SOE correlation calculation can be performed according to the following formula 1 and formula 2, wherein the power-SOE correlation calculation during charging is shown in formula 1, and the power-SOE correlation calculation during discharging is shown in formula 2:

Among them, γ _ich represents the power-SOE correlation of the i-th battery string during charging, P _chi represents the charging power of the i-th battery string, SOE _i represents the SOE of the i-th battery string, and n represents the power of the i-th battery string Number of groups.

Among them, γ _idis represents the power-SOE correlation of the i-th battery string during discharge, and P _disi represents the discharge power of the i-th battery string.

It can be seen from the above formula 1 and formula 2 that when the power-SOE correlation is 100%, it is an ideal situation. When the power-SOE correlation is >100%, it indicates that the power allocation of the battery string is more, which will cause the SOE of the battery string to be larger than that of other battery strings. When the power-SOE correlation of the battery string is <100%, It indicates that the power distribution of the battery string is insufficient, which will cause the SOE of the battery string to be smaller than that of other battery strings.

By drawing the power-SOE correlation curves of different battery strings during operation, it is possible to evaluate whether the power allocation of battery strings at each moment during operation is reasonable, or to evaluate the ability of each battery string to maintain SOE balance.

2) Evaluation of dispersion degree of battery string power-SOE correlation distribution

The standard deviation coefficient of the battery string power-SOE correlation is mainly used to evaluate the dispersion degree of each battery string power-SOE correlation, or to evaluate the ability of the energy storage unit to maintain the SOE balance between the battery strings, which can be calculated according to the following formula 3 Calculate the battery string power-SOE correlation standard deviation coefficient:

Among them, γ _δ represents the standard deviation coefficient of battery string power-SOE correlation, γ _j represents the jth battery string power-SOE correlation, Indicates the average value of battery string power-SOE correlation.

It can be seen from formula 3 that the smaller the standard deviation coefficient of the battery string power-SOE correlation is, the more consistent the battery string power-SOE correlation is.

By drawing the standard deviation coefficient curve of the battery string power-SOE correlation degree during operation, it can be used to evaluate the dispersion degree of the battery string power-SOE correlation degree at each moment during the operation process, which is similar to the battery string power-SOE correlation degree curve. Since there is only one curve for the standard deviation coefficient of the battery string power-SOE correlation, it can more intuitively reflect the ability of the energy storage unit to maintain the SOE balance of the battery string at each moment.

3) Evaluation of the change trend of the power distribution of the energy storage unit to the battery string

The cumulative value of the battery string power-SOE correlation standard deviation coefficient can reflect the trend of the dispersion of the battery string power-SOE correlation over time, or the ability of the energy storage unit to maintain the SOE balance between the battery strings changes over time The cumulative value of the battery string power-SOE correlation standard deviation coefficient can be calculated according to the following formula 4:

Among them, m represents m moments when the energy storage unit is running, Indicates the value of the battery string power-SOE correlation standard deviation coefficient at time t _k , Indicates the cumulative value of the battery string power-SOE correlation standard deviation coefficient at time t _m .

The energy storage unit composed of 6 battery strings is taken as an example to illustrate the energy storage unit power distribution evaluation method described in the present invention. The power and SOE curves of the 6 battery strings are shown in Figure 3, where the battery The string power is between -60kW and 50kW, and the SOE is between 60% and 80%.

1) Evaluation of battery string power distribution

From the power and SOE data in Figure 3, the power-SOE correlation of the six battery strings can be obtained, and the drawn power-SOE correlation curves of the six battery strings are shown in Figure 4. It can be seen from Figure 4 that in At time tl, the battery string power-SOE correlation is poor. At this time, the battery string power, SOE, and power-SOE correlation data are shown in Table 1. From Table 1, it can be seen that the battery string SOE is all At 70%, but the power is quite different, so the power-SOE correlation is poor.

Table 1

Battery string number 1# 2# 3# 4# 5# 6# Power(kW) -21.8 -21.9 -13.9 -13.9 -14.1 -16.7 SOE(%) 71.0 70.0 71.0 69.0 72.0 70.0 Power-SOE Correlation (%) 130.0 130.4 82.6 83.0 84.3 99.6

Combining with Figure 3, it can be seen that the state of the battery string suddenly decreases from a large charging power to 0 at time t1, so there will be a large deviation in the power-SOE correlation, indicating that the energy storage unit maintains The ability of battery string SOE balance is not good.

2) Evaluation of dispersion degree of battery string power-SOE correlation distribution

Calculate the battery string power-SOE correlation standard deviation coefficient according to the data in Figure 4, and draw the battery string power-SOE correlation standard deviation coefficient curve as shown in Figure 5. It can be seen from Fig. 5 that the standard deviation coefficient of the power-SOE correlation at time t1 is very large, which can more intuitively reflect the poor SOE balance ability of the energy storage unit to the battery string at this time compared with Fig. 4 .

3) Evaluation of the change trend of the power distribution of the energy storage unit to the battery string

From the data shown in Figure 5, the cumulative value of the battery string power-SOE correlation standard deviation coefficient can be calculated, and thus the battery string power-SOE correlation standard deviation coefficient change trend curve shown in Figure 6 can be drawn. It can be seen from Figure 5 and Figure 6 that although the standard deviation coefficient at time t1 is very large, its cumulative value does not increase significantly, indicating that the control ability of the energy storage unit to maintain the SOE balance of the battery string has not deteriorated, and the standard deviation coefficient The mutation is mainly caused by the power mutation.

In this example, by analyzing the power-SOE correlation of the battery strings, it is possible to effectively evaluate whether the power allocation of the energy storage unit to the battery strings is reasonable, evaluate the ability of the energy storage unit to maintain the SOE balance of the battery strings during operation, and As the running time prolongs the change trend of its ability to maintain the battery string SOE balance control, the evaluation results have important reference significance for judging the health status, control performance and operation of the energy storage unit.

Based on the same inventive concept, an embodiment of the present invention also provides an evaluation device for power distribution of energy storage units of an energy storage power station, as described in the following embodiments. Since the problem-solving principle of the evaluation device for power distribution of energy storage units in energy storage power stations is similar to the evaluation method for power distribution of energy storage units in energy storage power stations, the implementation of the evaluation device for power distribution of energy storage units in energy storage power stations can be found in The implementation of the evaluation method for the power distribution of the energy storage unit of the energy station will not be repeated here. As used below, the term "unit" or "module" may be a combination of software and/or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated. Fig. 7 is a structural block diagram of an evaluation device for energy storage unit power distribution of an energy storage power station according to an embodiment of the present invention, as shown in Fig. 7, including: a correlation degree determination module 701, a standard deviation coefficient determination module 702 and an accumulation value determination module Module 703, the structure will be described below.

The correlation determination module 701 is used to calculate the battery string power-energy state SOE correlation, and evaluate the power allocation of the energy storage unit to each battery string during operation according to the power-SOE correlation;

The standard deviation coefficient determination module 702 is configured to calculate the battery string power-SOE correlation standard deviation coefficient according to the power-SOE correlation degree, and evaluate the battery string power at each moment during the operation of the energy storage unit according to the standard deviation coefficient - degree of dispersion of SOE correlation;

The cumulative value determination module 703 is used to calculate the cumulative value of the battery string power-SOE correlation standard deviation coefficient according to the standard deviation coefficient, and to evaluate the trend of the power distribution of the energy storage unit to the battery string with the running time according to the cumulative value .

In one embodiment, the correlation determination module 701 is specifically configured to: calculate the power-SOE correlation during charging according to the following formula:

Among them, γ _ich represents the power-SOE correlation of the i-th battery string during charging, P _chi represents the charging power of the i-th battery string, SOE _i represents the SOE of the i-th battery string, and n represents the power of the i-th battery string Number of groups;

Calculate the power-SOE correlation during discharge according to the following formula:

Among them, γ _idis represents the power-SOE correlation of the i-th battery string during discharge, and P _disi represents the discharge power of the i-th battery string.

In one embodiment, the standard deviation coefficient determination module 702 is specifically configured to calculate the battery string power-SOE correlation standard deviation coefficient according to the following formula:

Among them, γ _δ represents the standard deviation coefficient of battery string power-SOE correlation, γ _j represents the jth battery string power-SOE correlation, Indicates the average value of battery string power-SOE correlation.

In one embodiment, the cumulative value determination module 703 is specifically configured to calculate the cumulative value of the battery string power-SOE correlation standard deviation coefficient according to the following formula:

Among them, m represents m moments when the energy storage unit is running, Indicates the value of the battery string power-SOE correlation standard deviation coefficient at time t _k , Indicates the cumulative value of the battery string power-SOE correlation standard deviation coefficient at time t _m .

In one embodiment, the correlation determination module 701 is specifically configured to draw power-SOE correlation curves of different battery strings, and evaluate the power allocation of the energy storage unit to each battery string during operation according to the correlation curves Situation; the standard deviation coefficient determination module 702 is specifically used to evaluate the degree of dispersion of the battery string power-SOE correlation degree at each moment during the operation of the energy storage unit according to the standard deviation coefficient, including: drawing the battery string power-SOE correlation degree Standard deviation coefficient curve, according to the standard deviation coefficient curve to evaluate the degree of dispersion of the battery string power-SOE correlation degree of the energy storage unit at each moment during operation; the cumulative value determination module 703 is specifically used to evaluate the energy storage unit according to the cumulative value The trend of unit-to-battery string power distribution with running time, including: drawing the cumulative value change trend curve of the battery string power-SOE correlation standard deviation coefficient, and evaluating the battery string power distribution with the operating time according to the cumulative value change trend curve Trends over time.

In another embodiment, software is also provided, and the software is used to implement the technical solutions described in the above embodiments and preferred implementation manners.

In another embodiment, there is also provided a storage medium, in which the software is stored, the storage medium includes but not limited to: optical discs, floppy disks, hard disks, rewritable memories, and the like.

From the above description, it can be seen that the embodiment of the present invention achieves the following technical effects: by analyzing the battery string power-SOE correlation, it is possible to effectively evaluate whether the power allocation of the energy storage unit to the battery string is reasonable, and evaluate the The ability of the energy storage unit to maintain the SOE balance of the battery strings during operation, and the change trend of the SOE balance control ability of the battery strings as the operating time prolongs, realize the effective evaluation of the power distribution of the battery strings, and further, the evaluation The results have important reference significance for judging the health status, control performance and operation of the energy storage unit.

Obviously, those skilled in the art should understand that each module or each step of the above-mentioned embodiments of the present invention can be implemented by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed among multiple computing devices. Optionally, they may be implemented in program code executable by a computing device, thereby, they may be stored in a storage device to be executed by a computing device, and in some cases, may be implemented in a code different from that described herein The steps shown or described are executed in sequence, or they are fabricated into individual integrated circuit modules, or multiple modules or steps among them are fabricated into a single integrated circuit module for implementation. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, various modifications and changes may be made to the embodiments of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. a kind of appraisal procedure of the energy-storage units power distribution of energy-accumulating power station, which is characterized in that including：

Battery string formation power-energy state SOE degrees of correlation are calculated, are being transported according to the power-SOE correlation evaluations energy-storage units To the power allocation case of each battery string formation during row；

According to the power-SOE relatedness computations battery string formation power-SOE degree of correlation coefficients of standard deviation, according to the standard deviation Coefficient assesses the dispersion degree of energy-storage units each moment battery string formation power-SOE degrees of correlation in the process of running；

Battery string formation power-SOE degree of correlation coefficient of standard deviation aggregate-values are calculated according to the coefficient of standard deviation, according to described accumulative The trend that value assessment energy-storage units change battery string formation power distribution with run time.

2. the method as described in claim 1, which is characterized in that calculating the battery string formation power-SOE degrees of correlation includes：

Power-SOE degrees of correlation when charging are calculated according to following formula：

Wherein, γ_ichIndicate i-th of battery string formation power-SOE degree of correlation when charging, P_chiIndicate the charging of i-th of battery string formation Power, SOE_iIndicate that the SOE of i-th of battery string formation, n indicate the group number of battery string formation；

Power-SOE degrees of correlation when discharging are calculated according to following formula：

Wherein, γ_idisIndicate i-th of battery string formation power-SOE degree of correlation when electric discharge, P_disiIndicate putting for i-th battery string formation Electrical power.

3. method as claimed in claim 2, which is characterized in that calculate the battery string formation power-SOE degrees of correlation according to following formula Coefficient of standard deviation：

Wherein, γ_δIndicate battery string formation power-SOE degree of correlation coefficients of standard deviation, γ_jIndicate j-th of battery string formation power-SOE The degree of correlation,Indicate battery string formation power-SOE degree of correlation average values.

4. method as claimed in claim 3, which is characterized in that calculate the battery string formation power-SOE degrees of correlation according to following formula Coefficient of standard deviation aggregate-value：

Wherein, m indicates the m moment of energy-storage units operation,Indicate that battery string formation power-SOE degree of correlation coefficients of standard deviation exist t_kThe value at moment,Indicate battery string formation power-SOE degree of correlation coefficients of standard deviation in t_mThe aggregate-value at moment.

5. method according to any one of claims 1 to 4, it is characterised in that：

According to the power-SOE correlation evaluations energy-storage units in the process of running to the power allocation case of each battery string formation, Including：Different battery string formation power-SOE degree of correlation curves are drawn, are being run according to the degree of correlation curve assessment energy-storage units In the process to the power allocation case of each battery string formation；

Energy-storage units each moment battery string formation power-SOE degrees of correlation in the process of running are assessed according to the coefficient of standard deviation Dispersion degree, including：Battery string formation power-SOE degree of correlation coefficient of standard deviation curves are drawn, according to the coefficient of standard deviation curve Assess the dispersion degree of energy-storage units each moment battery string formation power-SOE degrees of correlation in the process of running；

The trend that energy-storage units change battery string formation power distribution with run time is assessed according to the aggregate-value, including：It paints Battery string formation power-SOE degree of correlation coefficient of standard deviation aggregate-value change trend curves processed, according to aggregate-value variation tendency song The trend that line assessment battery string formation power distribution changes with run time.

6. a kind of apparatus for evaluating of the energy-storage units power distribution of energy-accumulating power station, which is characterized in that including：

Degree of correlation determining module, for calculating battery string formation power-energy state SOE degrees of correlation, according to the power-SOE phases Guan Du assesses energy-storage units in the process of running to the power allocation case of each battery string formation；

Coefficient of standard deviation determining module, for according to the power-SOE relatedness computations battery string formation power-SOE correlation scales Quasi- difference coefficient, assessing energy-storage units according to the coefficient of standard deviation, each moment battery string formation power-SOE is related in the process of running The dispersion degree of degree；

Aggregate-value determining module, for calculating battery string formation power-SOE degree of correlation coefficients of standard deviation according to the coefficient of standard deviation Aggregate-value assesses the trend that energy-storage units change battery string formation power distribution with run time according to the aggregate-value.

7. device as claimed in claim 6, which is characterized in that the degree of correlation determining module is specifically used for：

Power-SOE degrees of correlation when charging are calculated according to following formula：

Wherein, γ_ichIndicate i-th of battery string formation power-SOE degree of correlation when charging, P_chiIndicate the charging of i-th of battery string formation Power, SOE_iIndicate that the SOE of i-th of battery string formation, n indicate the group number of battery string formation；

Power-SOE degrees of correlation when discharging are calculated according to following formula：

Wherein, γ_idisIndicate i-th of battery string formation power-SOE degree of correlation when electric discharge, P_disiIndicate putting for i-th battery string formation Electrical power.

8. device as claimed in claim 7, which is characterized in that the coefficient of standard deviation determining module is specifically used for according to following Formula calculates battery string formation power-SOE degree of correlation coefficients of standard deviation：

Wherein, γ_δIndicate battery string formation power-SOE degree of correlation coefficients of standard deviation, γ_jIndicate j-th of battery string formation power-SOE The degree of correlation,Indicate battery string formation power-SOE degree of correlation average values.

9. device as claimed in claim 8, which is characterized in that the aggregate-value determining module is specifically used for according to following formula Calculate battery string formation power-SOE degree of correlation coefficient of standard deviation aggregate-values：

Wherein, m indicates the m moment of energy-storage units operation,Indicate that battery string formation power-SOE degree of correlation coefficients of standard deviation exist t_kThe value at moment,Indicate battery string formation power-SOE degree of correlation coefficients of standard deviation in t_mThe aggregate-value at moment.

10. the device as described in any one of claim 6 to 9, it is characterised in that：

The degree of correlation determining module is specifically used for drawing different battery string formation power-SOE degree of correlation curves, according to the correlation Curve assessment energy-storage units are spent in the process of running to the power allocation case of each battery string formation；

The coefficient of standard deviation determining module is specifically used for assessing energy-storage units in the process of running according to the coefficient of standard deviation The dispersion degree of each moment battery string formation power-SOE degrees of correlation, including：Draw battery string formation power-SOE degree of correlation standard deviations Coefficient curve, according to the coefficient of standard deviation curve assessment energy-storage units each moment battery string formation power-SOE in the process of running The dispersion degree of the degree of correlation；

The aggregate-value determining module be specifically used for according to the aggregate-value assess energy-storage units to battery string formation power distribution with The trend of run time variation, including：It is bent to draw battery string formation power-SOE degree of correlation coefficient of standard deviation aggregate-values variation tendency Line assesses the trend that battery string formation power distribution changes with run time according to the aggregate-value change trend curve.