CN116307511A

CN116307511A - Energy storage configuration method, device, equipment and medium for park power grid

Info

Publication number: CN116307511A
Application number: CN202310094925.6A
Authority: CN
Inventors: 陈凤超; 段孟雍; 周立德; 邱泽坚; 饶欢; 何毅鹏; 刘沛林; 徐睿烽; 赵俊炜; 张锐; 刘铮
Original assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-02-07
Filing date: 2023-02-07
Publication date: 2023-06-23

Abstract

The invention discloses a park power grid energy storage configuration method, a device, equipment and a medium, wherein the method comprises the following steps: determining a net load curve of the target park on the history M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the history M days; other distributed power sources are power sources other than distributed photovoltaics; clustering the historical M-day payload curves to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers; determining an energy storage payload curve configured in the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes; and determining the rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park.

Description

Energy storage configuration method, device, equipment and medium for park power grid

Technical Field

The invention relates to the technical field of energy storage planning, in particular to a park power grid energy storage configuration method, device, equipment and medium.

Background

With the continued development of renewable energy sources and distributed power sources, a large number of distributed photovoltaics and other distributed power sources in addition to distributed photovoltaics are connected to the campus grid. The distributed photovoltaic has volatility, intermittence and unpredictability, and the access of other distributed power sources besides the distributed photovoltaic brings challenges to the configuration of energy storage of a park power grid.

The existing park power grid energy storage configuration method is based on power data of a park power grid single payload scene, the park power grid energy storage configuration is realized by establishing constraint conditions such as power constraint, state of charge constraint, energy constraint and regulation rate constraint, scientificity is lacking, model establishment is complex, and operability is low.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for energy storage configuration of a park power grid, which are used for improving the scientificity of the energy storage configuration in the park power grid, simplifying the establishment of a model and improving the operability of the energy storage configuration in the park power grid.

According to one aspect of the invention, there is provided a method for energy storage configuration of a campus network, comprising:

determining a net load curve of the target park on the history M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the history M days; other distributed power sources are power sources other than distributed photovoltaics;

Clustering the historical M-day payload curves to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers;

determining an energy storage payload curve configured in the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes;

and determining the rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park.

According to another aspect of the invention there is provided a campus network energy storage configuration device comprising:

the first determining module is used for determining a net load curve of the target park on the historical M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the historical M days; other distributed power sources are power sources other than distributed photovoltaics;

the second determining module is used for clustering the net load curves of the historical M days to obtain net load curves of N net load scenes and days corresponding to the N net load scenes; m and N are positive integers;

a third determining module, configured to determine a stored energy payload curve configured by the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes;

And the fourth determining module is used for determining the rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park and controlling the energy storage consumption in the target park.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the campus network energy storage configuration method of any of the embodiments of the present invention.

According to another aspect of the invention there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the method of park grid energy storage configuration of any of the embodiments of the invention.

According to the technical scheme, according to the time sequence curves of the distributed photovoltaic, load and other distributed power sources of the target park on the history M days, the net load curve of the target park on the history M days is determined; other distributed power sources are power sources other than distributed photovoltaics; clustering the historical M-day payload curves to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers; determining an energy storage payload curve configured in the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes; and determining the rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park. According to the technical scheme, the net load curves of N net load scenes and the days corresponding to the N net load scenes are obtained in a clustering mode by combining the electric power data of the target park for M days, the net load curves of the energy storage configured by the target park are determined by utilizing the net load curves of the N net load scenes and the days corresponding to the N net load scenes, the characteristics of the electric power data corresponding to the N net load scenes are comprehensively considered, and the accuracy and scientificity of the energy storage configuration in the power grid of the target park are improved; and meanwhile, the rated power and rated capacity of the energy storage configured in the target park are determined according to the net load curve of the energy storage configured in the target park, the model is simple, and the factors and variables to be considered are less than those of the existing park power grid energy storage configuration method, so that the operability of the energy storage configuration in the park power grid is improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for energy storage configuration of a campus network according to a first embodiment of the present invention;

fig. 2 is a flowchart of a method for energy storage configuration of a campus network according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a campus network energy storage configuration device according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device for implementing a method for energy storage configuration of a campus network according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "object," "first," and "second," and the like in the description and claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In addition, it should be noted that, in the technical scheme of the invention, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the electric power data of the target park history M days all conform to the regulations of the related laws and regulations and do not violate the popular regulations of the public order.

Example 1

Fig. 1 is a flowchart of a method for configuring energy storage of a campus network according to an embodiment of the present invention, where the method may be applied to a case of configuring energy storage of an electric power system, and particularly to a case of configuring energy storage of a campus network, and the method may be performed by a device for configuring energy storage of a campus network, where the device may be implemented in a form of hardware and/or software, and may be configured in an electronic device, where the electronic device may be one of a desktop computer, a notebook computer, a server, and the like. As shown in fig. 1, the method includes:

s101, determining a net load curve of a target park on a history M day according to time sequence curves of distributed photovoltaics, loads and other distributed power supplies of the target park on the history M day; other distributed power sources are power sources other than distributed photovoltaics.

The target campus may be a campus where energy storage needs to be configured. The value of M is determined according to personal study requirements, and M is a positive integer, such as m=100. For each of the historical M days, the time series curve for the day's distributed photovoltaic may be plotted against the distributed photovoltaic output of the target campus over time units in the day. Similarly, the time sequence curve of the daily load can be drawn according to the load of the target park in each time unit in the day; the time sequence curve of other distributed power sources in the day can be drawn according to the output of other distributed power sources in the target park in each time unit in the day. For each day of the history M days, one time unit of the day may be 1 hour or 15 minutes, and is not particularly limited. Other distributed power sources are power sources other than distributed photovoltaics, such as distributed wind power and gas turbines.

Specifically, for each of the historical M days, a net load profile for the target campus on that day is determined from the time-series profile of the distributed photovoltaic, the time-series profile of the load, and the time-series profiles of other distributed power sources for that day. Similarly, a net load profile for the target campus over a history of M days may be obtained. It will be appreciated that from the power data of the target campus over the historical M days, M net load curves of the target campus over the historical M days may be obtained. The power data of the target park on the historical M days may include power data such as distributed photovoltaic output, load and other distributed power output of the target park on each time unit of each day in the historical M days.

Alternatively, for each of the historical M days, the net load profile for that day is obtained using the time-series profile of the day load minus the time-series profile of the day's distributed photovoltaic minus the time-series profiles of the other distributed power sources for that day.

Specifically, for each day in the history M days, on each time unit of the day, subtracting the distributed photovoltaic output corresponding to the time sequence curve of the distributed photovoltaic from the load corresponding to the time sequence curve of the load of the day, and subtracting the other distributed power outputs corresponding to the time sequence curves of the other distributed power supplies of the day, to obtain the net load on each time unit of the day; the payload curve for the day is plotted against the payload over time units of the day. Similarly, a net load profile for the target campus over a history of M days may be obtained.

Illustratively, for each of the historical M days, one time unit for that day is 1 hour, then that day has 24 time units, and each time unit is 1 hour of equal length. If the distributed photovoltaic output at the jth (j=1, 2,3, …, M) day x (x=1, 2,3, …, 24) of the historical M days is PV _{j_x} Load is L _{j_x} Other distributed power sources output PO _{j_x} The payload of the jth day, x hours, of the historical M days can be found by the following formula:

PN _{j_x} ＝L _{j_x} -PV _{j_x} -PO _{j_x} ；

further obtaining the net load on 24 time units of the j-th day in the historical M days; the payload curves for day j of history M are plotted against the payload over 24 time units of day j of history M. Similarly, a net load profile for the target campus over a history of M days may be obtained.

It can be appreciated that, for each of the historical M days, the same calculation method is used to obtain the payload curve of each of the historical M days, so that interference of other historical power data in the historical M days of the target park can be avoided, and further accuracy and scientificity in determining the payload curve of the target park in the historical M days are improved. Other historical power data may refer to other historical power data in the target park historical M-day power data, in addition to distributed photovoltaic output, load, and other distributed power source outputs.

S102, clustering the payload curves of the historical M days to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers.

N is a positive integer, N < M, N represents the number of payload scenes and the number of clustering algorithms, and can be determined according to personal research requirements, such as weather conditions of each day in a history M days, at least one condition of electricity consumption conditions of a target park power grid user, electricity generation conditions of the target park power grid and the like, and the number of clustering algorithms is determined according to seasons of the target park in the history M days. For example, determining N according to weather conditions of each of the historical M days, classifying the payload curves of cloudy or rainy days as one payload scene; and classifying the payload curves on sunny days as one payload scene, and obtaining 2 payload scenes, namely N=2, wherein the number of clustering algorithms is 2.

Specifically, taking a net load curve of the history M days as input of a k-means clustering algorithm, taking each time unit of each day in the history M days as dimensions of the k-means clustering algorithm, and clustering the net load curve of the history M days according to the distribution condition of the net load curve of the history M days on each dimension of the k-means clustering algorithm to obtain net load curves of N net load scenes and days corresponding to the N net load scenes. It will be appreciated that the sum of the corresponding days for each payload scenario is equal to M. Accordingly, it can be expressed by the following formula:

num ₁ +num ₂ +…+num _k ＝M。

Wherein num is _k The number of days corresponding to the kth (k=1, 2, …, N) payload scenario is indicated.

Illustratively, for each of the historical M days, one time unit for that day is 1 hour, then that day has 24 time units, and each time unit is 1 hour of equal length. If m=100, 2400 (i.e. 100×24=2400) pieces of power data are input into the k-means clustering algorithm, and the k-means clustering algorithm has 24 dimensions; if the number of k-means clustering algorithms is determined according to the seasons of the target park on the historical M days, n=4; and clustering the payload curves of the historical M days according to the distribution condition of the payload curves of the historical M days on each dimension of the k-means clustering algorithm to obtain payload curves of 4 payload scenes and days corresponding to the 4 payload scenes.

It can be appreciated that clustering the historical M day payload curves can reduce the amount of data of the power data in the subsequent target campus configuration energy storage process, and improve the efficiency of subsequently determining the target campus configured energy storage payload curves.

S103, determining the energy storage payload curve configured in the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes.

Illustratively, the duty cycle of the number of days corresponding to the N payload scenes in the historical M days is calculated; comparing each duty ratio with a preset duty ratio threshold value, and accumulating the net load curves of the net load scenes corresponding to the duty ratios larger than the preset duty ratio threshold value, so as to obtain the energy storage net load curve configured in the target park. Wherein the preset duty cycle threshold may be determined according to personal research needs, such as 20% of the preset duty cycle threshold.

Illustratively, the duty cycle of the number of days corresponding to the N payload scenes in the historical M days is calculated; and determining the energy storage payload curve configured in the target park according to the obtained duty ratio of the days corresponding to the N payload scenes in the historical M days and the payload curves of the N payload scenes.

And S104, determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park.

The rated power may refer to the maximum output power of the energy storage energy output capacity configured by the target park. The rated capacity may refer to the maximum capacity of the energy storage capacity of the target campus configuration.

Illustratively, according to the peak value of the net load curve of the energy storage configured in the target park, determining the maximum value of the net load of the energy storage configured in the target park, taking the power corresponding to the maximum value as the rated power of the energy storage configured in the target park, and taking the maximum value as the rated capacity of the energy storage configured in the target park for controlling the energy storage consumption in the target park.

Example two

Fig. 2 is a flowchart of a method for configuring energy storage of a campus network according to a second embodiment of the present invention, where on the basis of the foregoing embodiment, an alternative implementation manner is provided for further optimizing a "determining a net load curve of energy storage configured in a target campus according to net load curves of N net load scenes and days corresponding to the N net load scenes". In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 2, the method includes:

s201, determining a net load curve of the target park on the historical M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the historical M days; other distributed power sources are power sources other than distributed photovoltaics.

S202, clustering the payload curves of the historical M days to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers.

S203, calculating the ratio of the number of days corresponding to the N payload scenes to the historical M days respectively to obtain the probability of the payload curves of the N payload scenes.

It is to be understood that the payload curve of one payload scenario is actually the payload curve of the j (j=1, 2,3, …, M) th day of the history M days; correspondingly, the number of days corresponding to one payload scene is j.

Specifically, if the number of days corresponding to the kth (k=1, 2,3, …, N) payload scene is num _k The probability of the payload curve of the kth payload scene can be obtained by the following formula:

wherein eta _k The probability of the payload curve representing the kth payload scene. Similarly, the probability of the payload curves for N payload scenarios can be obtained.

S204, determining the energy storage payload curve configured by the target park according to the payload curves of the N payload scenes and the probability of the payload curves of the N payload scenes.

Illustratively, a maximum value of probabilities of payload curves of the N payload scenes is obtained, and the payload curve of the payload scene corresponding to the maximum value is used as the stored energy payload curve configured in the target park.

Optionally, for each time unit in the payload curves of the N payload scenes, respectively obtaining the payloads of the payload curves of the N payload scenes on the time unit, and calculating the product of the payload and the probability of the payload curve of the payload scene corresponding to the payload; accumulating products corresponding to N net load scenes in each time unit to obtain the net load of the energy storage configured in the target park in each time unit; and obtaining a net load curve of the energy storage configured in the target park according to the net load of the energy storage configured in the target park in each time unit.

Specifically, the kth (k=1, 2,3, …, N) net negativeThe payload of the payload profile of the payload scene at the ith time unit is noted as P _{k_i} I is a positive integer; the probability of the payload curve of the kth (k=1, 2,3, …, N) payload scene is noted as η _k The product of the probabilities of the payload and the payload profile of the payload scene to which the payload corresponds (denoted as a):

A＝η _k *P _{k_i} ；

and accumulating products corresponding to the N payload scenes in the time unit to obtain the payload of the energy storage configured in the target park in the time unit. The net load of the energy store of the target park configuration over the time unit (denoted P) can be found in particular by the following formula _{st_i} )：

Similarly, the net load of the energy storage configured in the target park on each time unit can be obtained; and drawing a net load curve of the energy storage configured in the target park according to the net load of the energy storage configured in the target park in each time unit.

Illustratively, for each of the N payload scenarios, if one time unit in the payload curve of the payload scenario is 1 hour, the payload curve of the payload scenario includes a payload over 24 time units; the payload of the payload profile of the kth (k=1, 2,3, …, N) payload scene at the ith (i=1, 2, …, 24) hour is denoted as P _{k_i} The probability of the payload curve of the kth (k=1, 2,3, …, N) payload scene is noted as η _k The method comprises the steps of carrying out a first treatment on the surface of the The net load of the stored energy on the i-th hour (denoted as P) of the target campus configuration is determined by the following formula _{st_i} )：

For example, the net load of the energy storage of the target campus configuration on hour 3 is:

similarly, the net load of the energy storage configured by the target park on 24 time units can be obtained; and drawing a net load curve of the energy storage configured by the target park according to the net load of the energy storage configured by the target park in 24 time units. Accordingly, the net load curve of the stored energy configured for the target campus can be expressed by the following formula:

P _st ＝{P _{st_1} ,P _{st_2} ,…,P _{st_24} }。

wherein P is _st A net load curve representing stored energy for the target campus configuration.

It can be understood that, on each time unit, according to the net load corresponding to the net load curves of the N net load scenes and the probability of the net load curves of the N net load scenes, the net load curve of the energy storage configured by the target park is determined, and the influence of the characteristics of the electric power data corresponding to the N net load scenes of the target park on the net load configured by the target park in the history M days is comprehensively considered, so that the configuration process of the energy storage of the target park is more scientific, the precision of the net load of the energy storage configured by the target park is improved, and the precision of the net load curve of the energy storage configured by the target park is further improved.

And S205, determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park.

According to the technical scheme provided by the embodiment of the invention, the net load curves of the energy storage configured in the target park are determined according to the net load curves of N net load scenes and the days corresponding to the N net load scenes, the influence of the electric power data corresponding to each net load scene on the net load curves of the energy storage configured in the target park is comprehensively considered, and the scientificity and the precision of the net load curves of the energy storage configured in the target park are improved.

On the basis of the foregoing embodiments, as an optional manner of the embodiment of the present invention, the determining, according to the net load curve of the energy storage configured by the target campus, the rated power and the rated capacity of the energy storage configured by the target campus may be: and determining the rated power and rated capacity of the energy storage configured in the target park according to the net load curve, the preset power deviation coefficient and the preset capacity deviation coefficient of the energy storage configured in the target park. Compared with the existing energy storage configuration method of the park power grid, the rated power and rated capacity of the energy storage configured in the target park can be determined only according to the net load curve, the preset power deviation coefficient and the preset capacity deviation coefficient of the energy storage configured in the target park, the model is simple, the factors and the variables to be considered are few, and the operability of the energy storage configuration in the park power grid is improved.

Wherein the preset power deviation coefficient is epsilon ₁ And 0.ltoreq.ε ₁ Is less than or equal to 1; the preset capacity deviation coefficient is epsilon ₂ And 0.ltoreq.ε ₂ ≤1。

Optionally, the rated power of the energy storage configured in the target park is determined according to the net load curve of the energy storage configured in the target park and a preset power deviation coefficient. Illustratively, a maximum value of a negative value of the net load of the stored energy configured by the target campus is obtained in each time unit; and determining the rated power of the energy storage configured in the target park according to the maximum value and the preset power deviation coefficient.

Specifically, the net load curve of the stored energy configured in the target park is denoted as P _st Obtaining the maximum value of the net load negative value of the energy storage net load curve configured in the target park on each time unit, comparing the maximum value with 0, if the maximum value is larger than 0, calculating the product of the maximum value and a preset power deviation coefficient, and using the product as the rated power of the energy storage configured in the target park; if the maximum value is less than or equal to 0, the rated power of the stored energy configured in the target park is 0. Accordingly, the power rating of the stored energy configured for the target campus may be determined by the following formula:

P _rated ＝ε ₁ max(0,-P _st )；

wherein P is _rated Representing the rated power of the stored energy for the target campus configuration. For example, if the target park One time unit in the configured stored energy payload profile is 1 hour, then the target campus configured stored energy payload profile includes a payload over 24 time units. Accordingly, the net load profile (i.e., P _st ) The method comprises the following steps:

P _st ＝{P _{st_1} ,P _{st_2} ,…,P _{st_24} }，

rated power of energy storage configured in the target park (i.e. P _rated ) The method comprises the following steps:

P _rated ＝ε ₁ max(0,-P _{st_1} ,…,-P _{st_24} )。

optionally, the rated capacity of the energy storage configured in the target park is determined according to the net load curve of the energy storage configured in the target park and a preset capacity deviation coefficient. Exemplary, if one time unit in the target campus-configured stored energy payload profile is 1 hour, the target campus-configured stored energy payload profile includes a payload over 24 time units, and the corresponding target campus-configured stored energy payload profile (i.e., P _st ) The method comprises the following steps:

P _st ＝{P _{st_1} ,P _{st_2} ,P _{st_3} ,…,P _{st_24} }；

based on the net load profile of the stored energy of the target park configuration and a predetermined capacity deviation coefficient (i.e. ε ₂ ) The rated capacity of the stored energy configured for the target campus is determined by the following formula:

Erated＝ε2*max[0，-Pst_b，-(Pst_b+Pst_(b+1))，-(Pst_b+Pst_(b+1)+Pst_(b+2))，…，-(Pst_b+Pst_(b+1)+……+Pst_e)}

wherein E is _rated Representing the rated capacity of the energy storage of the target park configuration, b representing the net load profile P of the energy storage of the target park configuration _st At a time when the medium payload is less than 0 for the first time, e represents the stored energy payload profile P of the target campus configuration _st Time when the last time the medium payload was less than 0.

It can be understood that after the net load curve of the energy storage configured in the target park is determined, the rated power and rated capacity of the energy storage configured in the target park are further determined according to the net load curve of the energy storage configured in the target park, so that the whole energy storage configuration process of the target park is more complete, the energy storage consumption in the target park is conveniently controlled according to the net load curve, the rated power and the rated capacity of the energy storage configured in the target park, the waste rate of waste generated energy is reduced, and the economical efficiency, the reliability and the safety of the operation of the power grid in the target park are improved.

Example III

Fig. 3 is a schematic structural diagram of a storage configuration device for a campus network according to a third embodiment of the present invention, where the embodiment is applicable to a storage configuration of an electric power system, and is particularly applicable to a storage configuration of a campus network, and the device may be implemented in hardware and/or software, and may be configured in an electronic device, where the electronic device may be one of a desktop computer, a notebook computer, a server, and the like. As shown in fig. 3, the apparatus includes:

a first determining module 301, configured to determine a payload curve of the target campus on a historical M day according to a timing curve of the distributed photovoltaic, load and other distributed power sources of the target campus on the historical M day; other distributed power sources are power sources other than distributed photovoltaics;

A second determining module 302, configured to cluster the payload curves of the historical M days to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers;

a third determining module 303, configured to determine a stored energy payload curve configured by the target campus according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes;

a fourth determining module 304 is configured to determine a rated power and a rated capacity of the energy storage configured in the target campus according to the net load curve of the energy storage configured in the target campus, and is configured to control the amount of energy storage in the target campus.

According to the technical scheme, a first determining module is used for determining a net load curve of a target park in a history of M days; determining, by the second determining module, a payload curve of the N payload scenes and days corresponding to the N payload scenes; determining, by a third determination module, a net load curve of the stored energy configured by the target campus; and determining the rated power and rated capacity of the energy storage configured by the target park through a fourth determining module. According to the technical scheme, the net load curves of N net load scenes and the days corresponding to the N net load scenes are obtained in a clustering mode by combining the electric power data of the target park for M days, the net load curves of the energy storage configured by the target park are determined by utilizing the net load curves of the N net load scenes and the days corresponding to the N net load scenes, the characteristics of the electric power data corresponding to the N net load scenes are comprehensively considered, and the accuracy and scientificity of the energy storage configuration in the power grid of the target park are improved; and meanwhile, the rated power and rated capacity of the energy storage configured in the target park are determined according to the net load curve of the energy storage configured in the target park, the model is simple, and the factors and variables to be considered are less than those of the existing park power grid energy storage configuration method, so that the operability of the energy storage configuration in the park power grid is improved.

Optionally, the first determining module 301 is specifically configured to:

for each of the historical M days, the net load curve for that day is obtained using the time series curve for that day load minus the time series curve for that day's distributed photovoltaic minus the time series curves for the other distributed power sources for that day.

Optionally, the third determining module 303 includes:

the probability determining unit is used for respectively calculating the ratio of the days corresponding to the N payload scenes to the historical M days to obtain the probability of the payload curves of the N payload scenes;

and the payload curve determining unit is used for determining the payload curve of the energy storage configured by the target park according to the payload curves of the N payload scenes and the probability of the payload curves of the N payload scenes.

Optionally, the payload curve determining unit is specifically configured to:

for each time unit in the payload curves of the N payload scenes, respectively acquiring the payloads of the payload curves of the N payload scenes on the time unit, and calculating the product of the payload and the probability of the payload curve of the payload scene corresponding to the payload; accumulating products corresponding to N net load scenes in each time unit to obtain the net load of the energy storage configured in the target park in each time unit; and obtaining a net load curve of the energy storage configured in the target park according to the net load of the energy storage configured in the target park in each time unit.

Optionally, the fourth determining module 304 includes:

and the power and capacity determining unit is used for determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve, the preset power deviation coefficient and the preset capacity deviation coefficient of the energy storage configured in the target park.

Optionally, the power and capacity determining unit includes:

the maximum value obtaining subunit is used for obtaining the maximum value of the net load negative value of the energy storage net load curve configured in the target park in each time unit;

and the power determination subunit is used for determining the rated power of the energy storage configured in the target park according to the maximum value and the preset power deviation coefficient.

Optionally, the power and capacity determining unit further includes:

and the capacity determining subunit is used for determining the rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park and a preset capacity deviation coefficient.

The energy storage configuration device for the park power grid provided by the embodiment of the invention can execute the energy storage configuration method for the park power grid provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the energy storage configuration method for the park power grid.

Example IV

Fig. 4 shows a schematic diagram of an electronic device 400 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 400 includes at least one processor 401, and a memory communicatively connected to the at least one processor 401, such as a Read Only Memory (ROM) 402, a Random Access Memory (RAM) 403, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 401 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 402 or the computer program loaded from the storage unit 408 into the Random Access Memory (RAM) 403. In the RAM403, various programs and data required for the operation of the electronic device 400 may also be stored. The processor 401, the ROM402, and the RAM403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

Various components in electronic device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Processor 401 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of processor 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 401 performs the various methods and processes described above, such as the campus network energy storage configuration method.

In some embodiments, the campus network energy storage configuration method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM402 and/or the communication unit 409. When loaded into RAM403 and executed by processor 401, one or more steps of the campus network energy storage configuration method described above may be performed. Alternatively, in other embodiments, the processor 401 may be configured to perform the campus network energy storage configuration method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method for energy storage configuration of a campus network, comprising:

determining a net load curve of the target park on the history M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the history M days; the other distributed power sources are power sources other than distributed photovoltaics;

clustering the payload curves of the historical M days to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers;

Determining the energy storage payload curves configured by the target park according to the payload curves of the N payload scenes and the days corresponding to the N payload scenes;

and determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park, and controlling the energy storage consumption in the target park.

2. The method of claim 1, wherein determining a net load profile of the target campus over historical M days based on timing profiles of photovoltaic, load, and other distributed power sources distributed by the target campus over historical M days, comprises:

3. The method of claim 1, wherein the determining the target campus configured stored energy payload profile based on the N payload scenarios payload profiles and the N payload scenarios corresponding days comprises:

calculating the ratio of the number of days corresponding to the N payload scenes to the historical M days respectively to obtain the probability of the payload curves of the N payload scenes;

And determining the energy storage payload curves of the target park configuration according to the payload curves of the N payload scenes and the probability of the payload curves of the N payload scenes.

4. The method of claim 3, wherein said determining a target campus configured stored energy payload profile based on the N payload scenarios payload profiles and probabilities of the N payload scenarios payload profiles comprises:

for each time unit in the payload curves of the N payload scenes, respectively acquiring the payloads of the payload curves of the N payload scenes on the time unit, and calculating the product of the probabilities of the payload and the payload curves of the payload scenes corresponding to the payload;

accumulating products corresponding to the N net load scenes in each time unit to obtain the net load of the energy storage configured in the target park in each time unit;

and obtaining a net load curve of the energy storage configured in the target park according to the net load of the energy storage configured in the target park in each time unit.

5. The method of claim 1, wherein determining the rated power and rated capacity of the target campus configured energy store based on the target campus configured energy store payload profile comprises:

And determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve, the preset power deviation coefficient and the preset capacity deviation coefficient of the energy storage configured in the target park.

6. The method of claim 5, wherein determining the rated power and rated capacity of the energy storage configured for the target campus based on the net load profile, the preset power deviation coefficient, and the preset capacity deviation coefficient of the energy storage configured for the target campus comprises:

acquiring the maximum value of the net load negative value of the energy storage net load curve configured by the target park in each time unit;

and determining the rated power of the energy storage configured in the target park according to the maximum value and the preset power deviation coefficient.

7. The method of claim 5, wherein determining the rated power and rated capacity of the energy storage configured for the target campus based on the net load profile, the preset power deviation coefficient, and the preset capacity deviation coefficient of the energy storage configured for the target campus, further comprises:

and determining the rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park and the preset capacity deviation coefficient.

8. A campus grid energy storage configuration device, comprising:

the first determining module is used for determining a net load curve of the target park on the historical M days according to the time sequence curves of the distributed photovoltaics, loads and other distributed power supplies of the target park on the historical M days; the other distributed power sources are power sources other than distributed photovoltaics;

the second determining module is used for clustering the payload curves of the historical M days to obtain payload curves of N payload scenes and days corresponding to the N payload scenes; m and N are positive integers;

a third determining module, configured to determine a stored energy payload curve configured by the target park according to the payload curves of the N payload scenes and days corresponding to the N payload scenes;

and the fourth determining module is used for determining rated power and rated capacity of the energy storage configured in the target park according to the net load curve of the energy storage configured in the target park and controlling the energy storage consumption in the target park.

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the campus network energy storage configuration method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to implement the campus network energy storage configuration method of any one of claims 1-7 when executed.