CN116316583A - Power distribution network system toughness improving method, device and equipment based on energy storage sharing and storage medium - Google Patents

Power distribution network system toughness improving method, device and equipment based on energy storage sharing and storage medium Download PDF

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CN116316583A
CN116316583A CN202310217488.2A CN202310217488A CN116316583A CN 116316583 A CN116316583 A CN 116316583A CN 202310217488 A CN202310217488 A CN 202310217488A CN 116316583 A CN116316583 A CN 116316583A
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user
power
distribution network
energy storage
power distribution
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宋顺一
秦润
张睿
王亚军
朱昕原
蒋科
钱康
朱东升
何梦雪
马亚林
陈淳
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Shenzhen Energy Nanjing Holding Co ltd
China Energy Engineering Group Jiangsu Power Design Institute Co Ltd
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Shenzhen Energy Nanjing Holding Co ltd
China Energy Engineering Group Jiangsu Power Design Institute Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00004—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the power network being locally controlled
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/008—Circuit arrangements for AC mains or AC distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses a method, a device, equipment and a storage medium for improving toughness of a power distribution network system based on energy storage sharing, wherein the method comprises the following steps: acquiring user load data, real-time electricity price, power grid data and user load loss data after faults; establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions; and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle. The system has the advantages of low cost, flexibility and practicability, stronger toughness of the power distribution system, faster operation speed and easy popularization.

Description

Power distribution network system toughness improving method, device and equipment based on energy storage sharing and storage medium
Technical Field
The invention belongs to the technical field of power distribution network systems, and relates to a power distribution network system toughness improving method, device and equipment based on energy storage sharing and a storage medium.
Background
In recent years, with the increasing popularity of distributed power generation resources and the continuous progress of information communication technologies, the safety and stability of a power system are gradually and widely paid attention to the academia and industry. The electrochemical energy storage system can be installed on the user side to avoid power loss of the user due to flexibility and convenience, and operation safety of the power distribution network system is guaranteed. Then, the unit capacity price of the current electrochemical energy storage system is higher, the place where the power distribution network breaks down is also random, and the large-scale installation of energy storage as a system for standby is difficult.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a method, a device, equipment and a storage medium for improving the toughness of a power distribution network system based on energy storage sharing, which are used for resisting extreme disasters of the power distribution network system, reducing the system fault loss, simultaneously reducing the total energy storage investment requirement and improving the operation efficiency of the power distribution network system.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for improving toughness of a power distribution network system based on energy storage sharing, including:
acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
In some embodiments, the user load data comprises annual load data of users throughout the power distribution network system;
the real-time electricity price adopts the national unified peak Gu Ping three-hour electricity price;
the power grid data comprise the connection relation between the power distribution network system and the upper power grid, the resistance and reactance values of each branch and the upper limit of the transmission power of each branch;
the load loss data of the user after the fault comprises the load variation of the user after the fault occurs.
Further, the user load data and the user load loss data after the fault are acquired at least 15 minutes.
In some embodiments, the objective function of the optimization model of the power distribution network system toughness is:
Figure BDA0004115417400000021
wherein T epsilon T represents the running time of the power distribution network, and b epsilon N representsUser index G e G in distribution network b Indexing of distributed power for user b, C g Unit cost for user distributed generation, P t,b,g For the distributed power generation of the user,
Figure BDA0004115417400000022
for the purchase price of the upper power grid, +.>
Figure BDA0004115417400000023
For the purchase power of the upper power grid, +.>
Figure BDA0004115417400000024
Cost per loss of power for the user, +.>
Figure BDA0004115417400000025
For the electricity demand of user b at time t, p t,b For the actual functional electrical load of the user in the fault state, Δt is the time step, K e K represents the index of the user leasing the stored energy, +.>
Figure BDA0004115417400000026
Price for power storage of a user leasing unit, +.>
Figure BDA0004115417400000027
Stored energy power leased for the user, +.>
Figure BDA0004115417400000028
Price of the capacity of the unit energy store leased for the user, < >>
Figure BDA0004115417400000029
And (5) the energy storage capacity leased for the user.
In some embodiments, constraints of the optimization model of the power distribution network system toughness include charge-discharge state constraints of the shared energy storage, charge-discharge constraints of the shared energy storage, local power balance constraints, power flow constraints of the power distribution network, and reactive power generation constraints of the distributed power source.
Further, the charge-discharge state constraint of the shared energy storage is:
Figure BDA0004115417400000031
wherein:
Figure BDA0004115417400000032
indicating that the shared energy store leased by subscriber b is in a charged state,/->
Figure BDA0004115417400000033
The shared energy storage leased by the user b is in a discharging state, and K epsilon K represents the index of the leased energy storage of the user;
further, the charge-discharge constraint of the shared energy storage is:
Figure BDA0004115417400000034
Figure BDA0004115417400000035
Figure BDA0004115417400000036
Figure BDA0004115417400000037
Figure BDA0004115417400000038
Figure BDA0004115417400000039
wherein:
Figure BDA00041154174000000310
state of charge of stored energy leased for time t, < >>
Figure BDA00041154174000000311
Charging and discharging power of energy storage leased by user respectively, < ->
Figure BDA00041154174000000312
For the lower and upper limit of the state of charge of the energy store k,/>
Figure BDA00041154174000000313
Energy storage capacity leased for the user, +.>
Figure BDA00041154174000000314
Stored energy power leased for the user, +.>
Figure BDA00041154174000000315
Indicating that the shared energy store leased by subscriber b is in a charged state,
Figure BDA00041154174000000316
indicating that the shared energy store leased by user b is in a discharged state, P total To share the total power of the stored energy E total To share the total capacity of the store, K e K represents the index of the user leased store.
Further, the local power balancing constraint is:
Figure BDA00041154174000000317
wherein p is t,b For the actual functional electrical load of the user in the fault condition,
Figure BDA00041154174000000318
charging and discharging power respectively for leased energy storage of user, P t,b,g Distributed generation for a user, +.>
Figure BDA00041154174000000319
The electricity purchasing quantity of the upper power grid is obtained;
further, the power flow constraint of the distribution network is as follows:
Figure BDA0004115417400000041
Figure BDA0004115417400000042
Figure BDA0004115417400000043
Figure BDA0004115417400000044
wherein: l epsilon L is a collection of power transmission lines of the power distribution network, b epsilon N norm User set for normal region, b.epsilon.N fault As a set of users of the failure zone,
Figure BDA0004115417400000045
active and reactive power on line l, respectively, +.>
Figure BDA0004115417400000046
Is a Boolean variable, representing the fault state of the line, S l For the upper limit of the transmission power on line l, < >>
Figure BDA0004115417400000047
Active and reactive power flowing into user b for period t, respectively, +.>
Figure BDA0004115417400000048
Active and reactive power of user b is tapped for period t, respectively, +.>
Figure BDA0004115417400000049
Charging and discharging power, p, respectively of energy storage leased by a user t,b For the actual functional electrical load of the user in the fault state +.>
Figure BDA00041154174000000410
For purchasing electricity of the upper power grid, P t,b,g Distributed generation for a user, +.>
Figure BDA00041154174000000411
Charging and discharging reactive power, q, respectively, of user b at time t t,b For the actual reactive power load of the user in the fault state +.>
Figure BDA00041154174000000412
Reactive power, Q, purchased for consumer b to the upper grid t,b,g Reactive power output for the distributed power supply of user b, < >>
Figure BDA00041154174000000413
Reactive power demand for user b at time t;
further, the reactive power generation constraint of the distributed power supply is as follows:
Figure BDA00041154174000000414
wherein: p (P) t,b,g For the distributed generation of electricity of the user, Q t,b,g Reactive power output, P, for user b's distributed power supply b,g,max ,Q b,g,max The upper active and reactive limits of power generation for the distributed power supply installed at user b, respectively.
In a second aspect, the present invention provides a power distribution network system toughness improving device based on energy storage sharing, including:
a data acquisition module configured to: acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
a model building module configured to: establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
a solution acquisition module configured to: and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
In a third aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
In a fourth aspect, the present invention provides an apparatus comprising,
one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of the first aspect.
The beneficial effects are that: the method, the device, the equipment and the storage medium for improving the toughness of the power distribution network system based on energy storage sharing have the following advantages: the sudden faults in the power distribution network are backed up by utilizing the shared energy storage of the user side, so that the power utilization satisfaction of the energy producers and consumers is improved and the power failure rate of the user is reduced while the safe and stable operation of the whole power distribution network is ensured. The shared energy storage can simultaneously meet the standby requirements of a plurality of users, and has higher use value.
Drawings
FIG. 1 is a schematic diagram of a system application according to an embodiment of the invention;
FIG. 2 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The invention is further described below with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
As shown in fig. 2, a method for improving toughness of a power distribution network system based on energy storage sharing includes:
s1, acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
s2, establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
and S3, inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
In some specific embodiments, referring to fig. 1, which shows a schematic diagram of a power distribution network system based on energy storage sharing in this embodiment, a method for improving toughness of the power distribution network system based on energy storage sharing includes the following steps:
s1, acquiring user load data, real-time electricity price, power grid data and load loss data of a user after faults, wherein the load loss data are used for transmitting the collected data into an optimization model as parameters;
further, the user load data comprises annual load data of users in the whole power distribution network system, and the data acquisition interval is minimum for 15 minutes.
Further, the real-time electricity price adopts the national unified peak Gu Ping three-hour electricity price.
Further, the power grid data comprises connection relation between the power distribution network system and the upper power grid, resistance and reactance values of each branch, and upper limit of transmission power of each branch.
Further, the load loss data of the user after the fault comprises the load change quantity of the user after the fault occurs, and the data acquisition interval is minimum for 15 minutes.
S2, establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
according to the method, the running cost of the power distribution network system under the fault condition is analyzed, and an optimization model and corresponding constraint conditions of the toughness of the power distribution network system are established;
when a sudden fault occurs in a power distribution network system, reducing the fault running cost of the power distribution network system is an important index for measuring the toughness of the power distribution network system. Therefore, the embodiment aims to minimize the running cost of the power distribution network system after the fault, including the power generation cost, the load loss cost and the emergency cost of the shared energy storage. The model is as follows:
Figure BDA0004115417400000071
wherein: t epsilon T represents the running time of the power distribution network, b epsilon N represents the index of users in the power distribution network, G epsilon G b Indexing of distributed power for user b, C g Unit cost for user distributed generation, P t,b,g For the distributed power generation of the user,
Figure BDA0004115417400000081
for the purchase price of the upper power grid, +.>
Figure BDA0004115417400000082
For the purchase power of the upper power grid, +.>
Figure BDA0004115417400000083
Cost per loss of power for the user, +.>
Figure BDA0004115417400000084
For the electricity demand of user b at time t, p t,b For the actual functional electrical load of the user in the fault state, Δt is the time step, K e K represents the index of the user leasing the stored energy, +.>
Figure BDA0004115417400000085
Price for power storage of a user leasing unit, +.>
Figure BDA0004115417400000086
Stored energy power leased for the user, +.>
Figure BDA0004115417400000087
Price of the capacity of the unit energy store leased for the user, < >>
Figure BDA0004115417400000088
And (5) the energy storage capacity leased for the user.
Considering the local energy balance of the user, the present embodiment lists the power balance constraint considering the shared energy storage in the fault state, so the local power balance constraint of the user is:
Figure BDA0004115417400000089
wherein p is t,b For the actual functional electrical load of the user in the fault condition,
Figure BDA00041154174000000810
charging and discharging power respectively for leased energy storage of user, P t,b,g Distributed generation for a user, +.>
Figure BDA00041154174000000811
And the electricity purchasing quantity of the upper power grid is obtained.
Besides the local power generation and consumption energy balance of the user, the power flow constraint of the whole power distribution network also needs to be added into an optimization model, so that the power flow constraint of the power distribution network in the embodiment is as follows:
Figure BDA00041154174000000812
Figure BDA00041154174000000813
Figure BDA00041154174000000814
Figure BDA00041154174000000815
wherein: l epsilon L is a collection of power transmission lines of the power distribution network, b epsilon N norm User set for normal region, b.epsilon.N fault As a set of users of the failure zone,
Figure BDA00041154174000000816
active and reactive power on line l, respectively, +.>
Figure BDA00041154174000000817
Is a Boolean variable, representing the fault state of the line, S l For the upper limit of the transmission power on line l, < >>
Figure BDA00041154174000000818
Active and reactive power flowing into user b for period t, respectively, +.>
Figure BDA00041154174000000819
Respectively for time period t to flow out usersb active and reactive power, +.>
Figure BDA00041154174000000820
Charging and discharging power, p, respectively of energy storage leased by a user t,b For the actual functional electrical load of the user in the fault state +.>
Figure BDA0004115417400000091
For purchasing electricity of the upper power grid, P t,b,g Distributed generation for a user, +.>
Figure BDA0004115417400000092
Charging and discharging reactive power, q, respectively, of user b at time t t,b For the actual reactive power load of the user in the fault state +.>
Figure BDA0004115417400000093
Reactive power, Q, purchased for consumer b to the upper grid t,b,g Reactive power output for the distributed power supply of user b, < >>
Figure BDA0004115417400000094
For the reactive power demand of subscriber b at time t.
Considering the reactive power output of the distributed power supply, the user-installed distributed power supply also has an upper limit of power generation, so the present embodiment models the reactive power generation constraint of the distributed power supply as follows:
Figure BDA0004115417400000095
wherein: p (P) t,b,g For the distributed generation of electricity of the user, Q t,b,g Reactive power output, P, for user b's distributed power supply b,g,max ,Q b,g,max The upper active and reactive limits of power generation for the distributed power supply installed at user b, respectively.
The embodiment analyzes the cost of electricity consumption and leasing shared energy storage of each user in the power distribution network, and in the power distribution network, each user needs to meet own electricity consumption requirement and pay a certain cost of leasing shared energy storage.
The shared energy storage leased by each user cannot be in a charging and discharging state at the same time, so the charging and discharging state of the shared energy storage is constrained as follows:
Figure BDA0004115417400000096
wherein:
Figure BDA0004115417400000097
indicating that the shared energy store leased by subscriber b is in a charged state,/->
Figure BDA0004115417400000098
And (4) indicating that the shared energy storage leased by the user b is in a discharged state, and K epsilon K indicates the index of the leased energy storage of the user.
The energy storage leased by the user cannot exceed the upper limit of the leased energy storage in the charging and discharging process, so that the charging and discharging constraint of the shared energy storage is as follows:
Figure BDA0004115417400000099
Figure BDA0004115417400000101
Figure BDA0004115417400000102
Figure BDA0004115417400000103
Figure BDA0004115417400000104
Figure BDA0004115417400000105
wherein:
Figure BDA0004115417400000106
state of charge of stored energy leased for time t, < >>
Figure BDA0004115417400000107
Charging and discharging power of energy storage leased by user respectively, < ->
Figure BDA0004115417400000108
For the lower and upper limit of the state of charge of the energy store k,/>
Figure BDA0004115417400000109
Energy storage capacity leased for the user, +.>
Figure BDA00041154174000001010
Stored energy power leased for the user, +.>
Figure BDA00041154174000001011
Indicating that the shared energy store leased by subscriber b is in a charged state,
Figure BDA00041154174000001012
indicating that the shared energy store leased by user b is in a discharged state, P total To share the total power of the stored energy E total To share the total capacity of the store, K e K represents the index of the user leased store.
And S3, inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
The method is suitable for toughness improvement of the power distribution network system, the operation toughness of the power distribution network system is improved by applying the investment decision model for sharing energy storage from the power distribution network angle, the energy storage configuration benefits of all the producers and consumers are ensured from the power distribution network production and consumption perspective, a new thought is provided for toughness improvement of the power distribution network, and the power loss rate of the power distribution network in the fault period is effectively reduced.
Example 2
In a second aspect, this embodiment provides a toughness improving device for a power distribution network system based on energy storage sharing, including:
a data acquisition module configured to: acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
a model building module configured to: establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
a solution acquisition module configured to: and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
Example 4
In a fourth aspect, the present invention provides an apparatus comprising,
one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (10)

1. The utility model provides a distribution network system toughness promotes method based on energy storage sharing which characterized in that includes:
acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
2. The energy storage sharing-based power distribution network system toughness improvement method according to claim 1, wherein the user load data comprises annual load data of users in the whole power distribution network system;
the real-time electricity price adopts the national unified peak Gu Ping three-hour electricity price;
the power grid data comprise the connection relation between the power distribution network system and the upper power grid, the resistance and reactance values of each branch and the upper limit of the transmission power of each branch;
the load loss data of the user after the fault comprises the load variation of the user after the fault occurs.
3. The energy storage sharing-based power distribution network system toughness improvement method according to claim 1 or 2, wherein the user load data and the post-fault user load loss data acquisition interval is at least 15 minutes.
4. The energy storage sharing-based power distribution network system toughness improvement method according to claim 1, wherein an objective function of an optimization model of the power distribution network system toughness is:
Figure FDA0004115417390000011
wherein T epsilon T represents the running time of the power distribution network, b epsilon N represents the index of users in the power distribution network, G epsilon G b Indexing of distributed power for user b, C g Unit cost for user distributed generation, P t,b,g For the distributed power generation of the user, C t w For the electricity purchase price of the upper power grid,
Figure FDA0004115417390000012
for the purchase power of the upper power grid, +.>
Figure FDA0004115417390000013
Cost per loss of power for the user, +.>
Figure FDA0004115417390000021
For the electricity demand of user b at time t, p t,b For the actual functional electrical load of the user in the fault state, Δt is the time step, K e K represents the index of the user leasing the stored energy, +.>
Figure FDA00041154173900000215
Price for power storage of a user leasing unit, +.>
Figure FDA0004115417390000022
Stored energy power leased for the user, +.>
Figure FDA00041154173900000216
Price of the capacity of the unit energy store leased for the user, < >>
Figure FDA0004115417390000023
And (5) the energy storage capacity leased for the user.
5. The energy storage sharing-based power distribution network system toughness improvement method according to claim 1, wherein constraint conditions of an optimization model of the power distribution network system toughness comprise a charge and discharge state constraint of the shared energy storage, a charge and discharge constraint of the shared energy storage, a local power balance constraint, a power flow constraint of the power distribution network and a reactive power generation constraint of the distributed power supply.
6. The energy-storage-sharing-based power distribution network system toughness improvement method according to claim 5, wherein the charge and discharge state constraint of the shared energy storage is:
Figure FDA0004115417390000024
wherein:
Figure FDA0004115417390000025
indicating that the shared energy store leased by subscriber b is in a charged state,/->
Figure FDA0004115417390000026
The shared energy storage leased by the user b is in a discharging state, and K epsilon K represents the index of the leased energy storage of the user;
and/or, the charge-discharge constraint of the shared energy storage is:
Figure FDA0004115417390000027
Figure FDA0004115417390000028
Figure FDA0004115417390000029
Figure FDA00041154173900000210
Figure FDA00041154173900000211
Figure FDA00041154173900000212
wherein:
Figure FDA00041154173900000213
state of charge of stored energy leased for time t, < >>
Figure FDA00041154173900000214
Charging and discharging power of energy storage leased by user respectively, < ->
Figure FDA0004115417390000031
For the lower and upper limit of the state of charge of the energy store k,/>
Figure FDA0004115417390000032
The energy storage capacity leased for the user,
Figure FDA0004115417390000033
stored energy power leased for the user, +.>
Figure FDA0004115417390000034
Indicating that the shared energy store leased by subscriber b is in a charged state,/->
Figure FDA0004115417390000035
Indicating that the shared energy store leased by user b is in a discharged state, P total To share the total power of the stored energy E total To share the total capacity of the store, K e K represents the index of the user leased store.
7. The energy storage sharing-based power distribution network system toughness improvement method according to claim 5, wherein the local power balance constraint is:
Figure FDA0004115417390000036
wherein p is t,b For the actual functional electrical load of the user in the fault condition,
Figure FDA0004115417390000037
charging and discharging power respectively for leased energy storage of user, P t,b,g Distributed generation for a user, +.>
Figure FDA0004115417390000038
The electricity purchasing quantity of the upper power grid is obtained;
and/or, the power flow constraint of the distribution network is as follows:
Figure FDA0004115417390000039
Figure FDA00041154173900000310
Figure FDA00041154173900000311
Figure FDA00041154173900000312
wherein: l epsilon L is a collection of power transmission lines of the power distribution network, b epsilon N norm User set for normal region, b.epsilon.N fault As a set of users of the failure zone,
Figure FDA00041154173900000313
active and reactive power on line l, respectively, +.>
Figure FDA00041154173900000314
Is a Boolean variable, representing the fault state of the line, S l For the upper limit of the transmission power on line l, < >>
Figure FDA00041154173900000315
Active and reactive power flowing into user b for period t, respectively, +.>
Figure FDA0004115417390000041
Active and reactive power of user b is tapped for period t, respectively, +.>
Figure FDA0004115417390000042
Charging and discharging power, p, respectively of energy storage leased by a user t,b For the actual functional electrical load of the user in the fault state +.>
Figure FDA0004115417390000043
For purchasing electricity of the upper power grid, P t,b,g Distributed generation for a user, +.>
Figure FDA0004115417390000044
Charging and discharging reactive power, q, respectively, of user b at time t t,b For the actual reactive power load of the user in the fault state +.>
Figure FDA0004115417390000045
Reactive power, Q, purchased for consumer b to the upper grid t,b,g Reactive power output for the distributed power supply of user b, < >>
Figure FDA0004115417390000046
Reactive power demand for user b at time t;
and/or the reactive power generation constraint of the distributed power source is:
Figure FDA0004115417390000047
wherein: p (P) t,b,g For the distributed generation of electricity of the user, Q t,b,g Reactive power output, P, for user b's distributed power supply b,g,max ,Q b,g,max The upper active and reactive limits of power generation for the distributed power supply installed at user b, respectively.
8. Power distribution network system toughness hoisting device based on energy storage sharing, characterized by comprising:
a data acquisition module configured to: acquiring user load data, real-time electricity price, power grid data and user load loss data after faults;
a model building module configured to: establishing an optimization model of the toughness of the power distribution network system, wherein the optimization model of the toughness of the power distribution network system comprises an objective function and constraint conditions;
a solution acquisition module configured to: and inputting the acquired data into an optimization model of the system toughness of the power distribution network, and solving to obtain a scheduling scheme of shared energy storage under the system toughness angle.
9. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 7.
10. An apparatus, characterized in that: comprising the steps of (a) a step of,
one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
CN202310217488.2A 2023-03-08 2023-03-08 Power distribution network system toughness improving method, device and equipment based on energy storage sharing and storage medium Pending CN116316583A (en)

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* Cited by examiner, † Cited by third party
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CN117252034A (en) * 2023-11-14 2023-12-19 山东理工大学 Bi-layer planning model for shared rental energy storage based on robust optimization and demand defense

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117252034A (en) * 2023-11-14 2023-12-19 山东理工大学 Bi-layer planning model for shared rental energy storage based on robust optimization and demand defense
CN117252034B (en) * 2023-11-14 2024-02-02 山东理工大学 Shared leasing energy storage double-layer planning model based on robust optimization and demand defending

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