Method and system for determining application effect of distributed mobile energy storage system

Abstract

The application discloses a kind of distributed mobile energy storage system application effect determination method, including obtaining the data information of the distributed mobile energy storage system to be determined and the data information of new power system;Problem set is constructed to obtain;Determine hybrid problem scene set and adopt function vector to express;The power calculation value and capacity calculation value of distributed mobile energy storage system are calculated;The application effect determination index of distributed mobile energy storage system is calculated, and the application effect determination of distributed mobile energy storage system is completed.The application also discloses a kind of system for realizing the application effect determination method of the distributed mobile energy storage system.The application can not only realize the determination of the application effect of distributed mobile energy storage system, but also has higher reliability, better accuracy, is also more objective and scientific.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electrical automation, and particularly relates to a method and system for judging application effect of a distributed mobile energy storage system. BACKGROUND

[0002] With the development of economy and technology and the improvement of people's living standards, electric energy has become an essential secondary energy in people's production and life, bringing endless convenience to people's production and life. Therefore, guaranteeing the stable and reliable supply of electric energy has become one of the most important tasks of the power system.

[0003] At present, with the rapid growth of conventional power load and electric vehicle charging load and the large-scale access of renewable energy distributed generation systems at the medium and low voltage distribution network level, the power system needs to face challenges such as branch heavy overload, node voltage out-of-limit, local network renewable energy distributed generation near efficient consumption, local network electric vehicle charging load steep increase and local network high reliability power supply demand at the medium and low voltage distribution network level. Moreover, the medium and low voltage distribution network under the condition of new power system, the access situation of its load and renewable energy distributed generation system will change frequently, and the network / equipment reconstruction and new construction will also occur frequently, resulting in the existence / occurrence of the above problems with the characteristics of drift, sparsity, randomness and volatility in time and space distribution. Therefore, the new power system solves part of the problems by using the technical scheme of distribution network reconstruction / new construction according to the established principles / plans, and uses the distributed mobile energy storage system to cope with the remaining problems.

[0004] At present, the new power system has gradually begun to use the distributed mobile energy storage system. However, the current research scheme generally focuses on the site selection and capacity determination of the distributed mobile energy storage system, and does not judge the application effect of the distributed mobile energy storage system. At the same time, the cost of the distributed mobile energy storage system is high, and if the application effect of the distributed mobile energy storage system cannot be scientifically and accurately judged, the effect of the distributed mobile energy storage system may not be reflected, and at the same time, a large amount of resources may be wasted or idle. SUMMARY

[0005] One of the purposes of the present application is to provide a distributed mobile energy storage system application effect judgment method with high reliability, good accuracy and objectivity.

[0006] The second purpose of the present application is to provide a system for realizing the distributed mobile energy storage system application effect judgment method.

[0007] The distributed mobile energy storage system application effect judgment method provided by the present application comprises the following steps:

[0008] S1. Obtain data information of the distributed mobile energy storage system to be determined, and data information of the corresponding new power system;

[0009] S2. According to the data information obtained in step S1, the problem set is constructed according to the problems existing in the new power system and the problems that can be solved by the distributed mobile energy storage system;

[0010] S3. According to the problem set obtained in step S2, the hybrid problem scenario set is determined based on the problems that can be solved by the distributed mobile energy storage system and the mutual relationship between the problems, and is represented by a function vector;

[0011] S4. According to the hybrid problem scenario set obtained in step S3, the power calculation value and the capacity calculation value of the distributed mobile energy storage system are calculated based on the operation requirements of the distributed mobile energy storage system and the new power system;

[0012] S5. Based on the power calculation value and the capacity calculation value of the distributed mobile energy storage system obtained in step S4, the ratio of the power design value and the capacity design value of the distributed mobile energy storage system is calculated to obtain the application effect determination index of the distributed mobile energy storage system;

[0013] S6. According to the application effect determination index of the distributed mobile energy storage system obtained in step S5, the application effect determination of the distributed mobile energy storage system is completed.

[0014] The step S2 comprises the following steps:

[0015] According to the problems existing in the new power system and the technical scheme of the new power system, the total problem set, the planned solution problem set and the energy storage solution problem set are constructed;

[0016] The serial number vector scheme is used to assign values to each element in the energy storage solution problem set;

[0017] According to the specific position of each element in the energy storage solution problem set corresponding to the new power system, the positioning domain of the energy storage solution problem set is determined.

[0018] The step S2 comprises the following steps:

[0019] The total problem set, the planned solution problem set and the energy storage solution problem set are constructed:

[0020] The power supply area of the new power system is divided into n z pieces, wherein the jth piece is represented as Z j , j = 1, 2,..., n z ; for each piece Z j , define the total problem set Planned Problem Solving Set Energy storage solutions

[0021] Set the observation time interval as n d Day, where day d is represented by L d d = 1, 2, ..., n d ;

[0022] For each day within the time interval under consideration, divide it into n time periods. k There are several time periods, where the k-th time period is denoted as T. k k = 1, 2, 3, ..., n k ;

[0023] The power supply area was reviewed:

[0024] If area Z j In time interval L d T k If a branch overload problem exists or occurs during a certain period, the corresponding branch will be formed into the first problem subset.

[0025] If area Z j In time interval L d T k If a node voltage exceeds its limit during a given period, the corresponding node will be grouped into a second subset of the problem.

[0026] If area Z j In time interval L d T k If a localized issue arises regarding the efficient local consumption of distributed renewable energy generation in certain time periods, then the corresponding sub-scenario will be considered as a third problem subset.

[0027] If area Z j In time interval L d T k If a sudden surge in electric vehicle charging load occurs in a localized area of ​​the network during a given period, the corresponding sub-scenario will be classified as the fourth problem subset.

[0028] If area Z j In time interval L d T k If a period of time presents or experiences a localized demand for high-reliability power supply to the network, then the corresponding sub-scenario will be grouped into the fifth problem subset.

[0029] Will and Find the union of the sets to obtain the total problem set.

[0030] In the general problem set In the process of sorting out:

[0031] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k If several severe overload problems exist or occur during a given period, then the corresponding branches will constitute a subset of the planned solutions to the first problem.

[0032]

[0033] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k If several nodes experience voltage exceedance issues during a given time period, then these nodes will form a subset of the planned solutions to the second problem.

[0034] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The localized and efficient consumption of distributed renewable energy generation in certain areas that exist or occur during a given period will constitute a subset of the planned solutions to the third problem.

[0035] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The occurrence of several local network electric vehicle charging load surges during certain periods will constitute a subset of the planned solutions to the fourth problem.

[0036] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k Several local network high-reliability power supply requirements that exist or occur during a certain period will constitute the fifth problem subset, which consists of several corresponding sub-scenarios.

[0037] Will and Find the union of the sets to obtain the set of problems to be solved.

[0038] According to the total problem set And the planned problem set to be solved The energy storage problem set to be solved is calculated by using the following formula

[0039]

[0040] In the formula is the first problem subset to be solved by energy storage; is the second problem subset to be solved by energy storage; is the third problem subset to be solved by energy storage; is the fourth problem subset to be solved by energy storage; is the fifth problem subset to be solved by energy storage; p is the number of the problem subset;

[0041] Determine the sequence number vector of the energy storage problem set to be solved:

[0042] For each element in the energy storage problem set to be solved , give the energy storage problem sequence number vector as shown in the following formula

[0043]

[0044] In the formula is the natural sequence number of the element in ; is the sequence number of the element in the first problem subset to be solved by energy storage; is the sequence number of the element in the second problem subset to be solved by energy storage; is the sequence number of the element in the third problem subset to be solved by energy storage; is the sequence number of the element in the fourth problem subset to be solved by energy storage; is the sequence number of the element in the fifth problem subset to be solved by energy storage;

[0045] Determine the positioning field of the energy storage problem set to be solved:

[0046] The positioning field of the energy storage problem set to be solved is represented by using the following formula:

[0047]

[0048] Wherein, the station name is used to represent the source of the power grid side power supply of the branch, node or sub-scene of the energy storage problem, when the power grid side power supply comes from the medium voltage bus of the transformer substation, the station name uses the corresponding transformer substation name, when the power grid side power supply comes from the low voltage bus of the distribution station, the station name uses the corresponding distribution station name, when the power grid side power supply comes from the bus of the switching station, the station name uses the corresponding switching station name, when the power grid side power supply comes from the distribution transformer, the station name uses the corresponding distribution transformer name, and when the power grid side power supply comes from the distribution line section, the station name uses the corresponding distribution line section name.​

[0049] The step S3 comprises the following steps:

[0050] Defining a mixed problem scenario set;

[0051] According to the topology of the new power system, a general connection model is constructed;

[0052] According to each element in the first problem solved by energy storage to the fifth problem solved by energy storage, the corresponding branch and specific connection in the constructed general connection model are determined, and comparison and fusion are carried out to obtain a mixed problem scenario set;

[0053] For each element in the obtained mixed problem scenario set, the functional vector of the mixed problem scenario is calculated according to the existing or emerging problem.

[0054] The step S3 comprises the following steps:

[0055] Defining a mixed problem scenario set:

[0056] Based on the problem set solved by energy storage , taking connection as the object, setting a piece area Z j , in the T d period of the time interval L k , using the connection of one or more problems in the first problem solved by energy storage to the fifth problem solved by energy storage existing or emerging in the energy storage solution as a mixed problem scenario; the set composed of all mixed problem scenarios of the piece area Z j in the T d period of the time interval L k is taken as a mixed problem scenario set

[0057] Constructing a general connection model:

[0058] The general connection model comprises a main power grid, a main transformer, a distribution line, a distribution transformer, a low-voltage incoming line circuit breaker, a low-voltage bus, a new energy power station, a distributed mobile energy storage system, a conventional load, a high-reliability power supply load and an electric vehicle charging load; the main power grid, the main transformer and the distribution line are connected in series; the distribution transformer is connected to the distribution line and connected to the low-voltage bus through the low-voltage incoming line circuit breaker; the new energy power station, the distributed mobile energy storage system, the conventional load, the high-reliability power supply load and the electric vehicle charging load are all connected to the low-voltage bus;

[0059] The main power grid includes AC transmission networks or AC high-voltage distribution networks; the main transformer includes the main transformer in the substation; the distribution lines include medium-voltage distribution lines; the distribution transformers include step-down transformers that convert medium-voltage AC to low-voltage AC; the low-voltage incoming circuit breaker is the power supply circuit breaker for the low-voltage busbar, used to protect the low-voltage busbar; the conventional load is all loads that have no requirements for power supply reliability, except for electric vehicle charging loads; the high-reliability power supply load is all loads that have power supply reliability requirements greater than a set value.

[0060] Construction of a collection of mixed problem scenarios:

[0061] A. Addressing a subset of the first problem in energy storage Obtain the first problem subset For the i1th element in the general wiring model, obtain the branch corresponding to the i1th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i1th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring If all are different, the i1th element is assigned to the wiring. Subset of wiring problems The value of j1 can be 1 to (i1-1);

[0062] B. Addressing the subset of the second problem related to energy storage Obtain the second problem subset For the i2th element in the general wiring model, obtain the branch corresponding to the i2th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring and all wiring If all are different, the i2th element will be assigned to the wiring. Subset of wiring problems jj1 is valued from 1 to n1, n1 is the number of elements in the first problem subset solved by energy storage; j2 is valued from 1 to (i2-1);

[0063] C. The third problem subset solved by energy storage Obtain the i3th element in the third problem subset, obtain the branch corresponding to the i3th element on the general connection model, determine the connection of the branch on the general connection model, and express the connection as Determine whether is the same as any connection If the connection is the same as any connection , the i3th element is classified into the connection problem subset of the connection If the connection is the same as any connection , the i3th element is classified into the connection problem subset of the connection If the connection is the same as any connection , the i3th element is classified into the connection problem subset of the connection If the connection is different from all connections , all connections , and all connections , the i3th element is classified into the connection problem subset of the connection jj2 is the number of elements in the second problem subset solved by energy storage; j3 is valued from 1 to (i3-1);

[0064] D. The fourth problem subset solved by energy storage Obtain the i4th element in the fourth problem subset, obtain the branch corresponding to the i4th element on the general connection model, determine the connection of the branch on the general connection model, and express the connection as Determine whether is the same as any connection If the connection is the same as any connection , the i4th element is classified into the connection problem subset of the connection If the connection is the same as any connection , the i4th element is classified into the connection problem subset of the connection If the connection is the same as any connection , the i4th element is classified into the connection problem subset of the connection If the connection is different from all connections , all connections , and all connections , the i4th element is classified into the connection problem subset of the connection ​ the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i4th element is classified into the sub-subset of the connection problem of the i3th element If the connection is different from all connections , the i4th element is classified into the sub-subset of the connection problem of the i4th element If the connection is different from all connections , the i4th element is classified into the sub-subset of the connection problem of the i4th element jj3 is the number of elements in the third problem subset solved by energy storage; j4 is 1 to (i4-1);

[0065] E. The fifth problem subset solved by energy storage is obtained The i5th element in the fourth problem subset is obtained, the branch corresponding to the i5th element on the general connection model is determined, the connection of the branch on the general connection model is determined, and the connection is represented as is determined: if the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i3th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is the same as any connection , the i5th element is classified into the sub-subset of the connection problem of the i4th element If the connection is different from all connections , the i5th element is classified into the sub-subset of the connection problem of the i4th element , the i5th element is classified into the sub-subset of the connection problem of the i4th element , the i5th element is classified into the sub-subset of the connection problem of the i4th element , the i5th element is classified into the sub-subset of the connection problem of the i4th element ​​If all are different, the i5th element is assigned to the wiring. Subset of wiring problems Jj4 represents the number of elements in the subset that solves the fourth problem using energy storage; j5 takes values ​​from 1 to (i5-1);

[0066] F. Union all the subsets of wiring problems obtained in steps A to E to obtain the set of mixed problem scenarios.

[0067]

[0068] Calculation of function vectors for mixed problem scenarios:

[0069] Area Z j In time interval L d T k Time period There are n in the middle w A mixed problem scenario, n w To determine the number of mixed problem scenarios, the following formula is used to calculate the 5-dimensional function vector for each mixed problem scenario.

[0070]

[0071] In the formula Let be the dimension element of the function vector for the s-th hybrid problem scenario, corresponding to the functions that the distributed mobile energy storage system needs to configure when eliminating the problems corresponding to the o-th problem subset in the s-th hybrid problem scenario; the value of o is 1 to 5; if the s-th hybrid problem scenario contains or has problems that require energy storage to solve the o-th problem subset, then... otherwise

[0072] The power matrix of the function vector in the mixed problem scenario is used to determine the power of each dimension element of the function vector; the power matrix Represented as

[0073]

[0074] In the formula These are the power variable elements corresponding to the subset of energy storage solutions for the o-th problem;

[0075] Calculate the function vector of the mixed problem scenario for

[0076]

[0077] Step S4 includes the following steps:

[0078] Based on the set of mixed problem scenarios obtained in step S3, and considering the operational requirements of distributed mobile energy storage systems and new power systems, the required power and capacity of mobile energy storage are calculated.

[0079] The required power and capacity of the mobile energy storage are optimized using a proportional coefficient scheme to obtain the calculated power and capacity values ​​of the distributed mobile energy storage system.

[0080] Step S4 specifically includes the following steps:

[0081] Calculation of the required power and capacity of mobile energy storage:

[0082] Based on the mixed problem scenario set obtained in step S3 The region Z is calculated using the following formula. j In time interval L d T k The s-th mixed problem scenario in time period The required power for mobile energy storage

[0083]

[0084] In the formula Let be the power contraction vector, and The power contraction factor for solving the o-th problem subset in energy storage;

[0085] Use the following formula to To retrieve values:

[0086]

[0087] according to The value selection rule for region Z is as follows: j The s-th mixed problem scenario Power required for mobile energy storage within a time interval for:

[0088]

[0089] In the formula, counter[·] represents the count of cases that satisfy the conditions within the square brackets; For area Z j The s-th mixed problem scenario In time interval L d The daily maximum power adjustment factor for mobile energy storage This is used to ensure the cyclical storage and release of energy by the mobile energy storage system within a 24-hour period; n kThis represents the number of time periods in a 24-hour day. If each time period is 1 hour, then n is... k =24. If each time period is 15 minutes, then n k =96; The number of time periods in a 24-hour period where the energy storage capacity is greater than or equal to the maximum energy storage capacity of the day.

[0090] The region Z is calculated using the following formula. j The s-th mixed problem scenario Required capacity for mobile energy storage within a time interval

[0091]

[0092] In the formula for Required mobile energy storage in L d Number of time periods with 25% or more of the daily switching power;

[0093] Calculate the required power for mobile energy storage. for Required capacity for mobile energy storage for

[0094] Calculation of power and capacity values ​​for distributed mobile energy storage systems:

[0095] The power calculation value of the distributed mobile energy storage system is obtained using the following formula. and capacity calculation value

[0096]

[0097] In the formula The power optimization factor for mobile energy storage systems in regional power supply applications; Capacity optimization factor for the systematic application of mobile energy storage in the power supply area.

[0098] Step S5 specifically includes the following steps:

[0099] The application effect of the distributed mobile energy storage system is calculated using the following formulas: first evaluation index a and second evaluation index b.

[0100]

[0101] In the formula This refers to the power design value for a distributed mobile energy storage system. This refers to the capacity design value for a distributed mobile energy storage system.

[0102] The step S6 specifically comprises the following steps:

[0103] According to the application effect determination index of the distributed mobile energy storage system obtained in step S5, the following rules are used to determine the application effect of the distributed mobile energy storage system:

[0104] If it is determined that the application effect of the distributed mobile energy storage system is optimal;

[0105] If it is determined that the application effect of the distributed mobile energy storage system is suboptimal;

[0106] If it is determined that the application effect of the distributed mobile energy storage system is feasible;

[0107] If it is determined that the application effect of the distributed mobile energy storage system is excessive margin;

[0108] If it is determined that the application effect of the distributed mobile energy storage system is excessively high difficulty to realize;

[0109] If it is determined that the application effect of the distributed mobile energy storage system is power-capacity imbalance.

[0110] The application further provides a system for realizing the application effect determination method of the distributed mobile energy storage system, which comprises a data acquisition module, a set construction module, a scene construction module, a data calculation module, an index calculation module and an effect determination module; the data acquisition module, the set construction module, the scene construction module, the data calculation module, the index calculation module and the effect determination module are sequentially connected; the data acquisition module is used for acquiring data information of the distributed mobile energy storage system to be determined and data information of the corresponding new power system, and uploading the data information to the set construction module; the set construction module is used for constructing a problem set according to the received data information, in view of problems existing in the new power system and problems capable of being solved by the distributed mobile energy storage system, and uploading the data information to the scene construction module; the scene construction module is used for determining a mixed problem scene set based on the problems capable of being solved by the distributed mobile energy storage system and the mutual relationship among the problems according to the received data information and the obtained problem set, representing by a function vector, and uploading the data information to the data calculation module; the data calculation module is used for calculating a power calculation value and a capacity calculation value of the distributed mobile energy storage system based on the operation requirements of the distributed mobile energy storage system and the new power system according to the received data information and the obtained mixed problem scene set, and uploading the data information to the index calculation module; the index calculation module is used for calculating an application effect determination index of the distributed mobile energy storage system based on the ratio of the obtained power calculation value and capacity calculation value of the distributed mobile energy storage system to a power design value and a capacity design value of the distributed mobile energy storage system according to the received data information, and uploading the data information to the effect determination module; and the effect determination module is used for completing the application effect determination of the distributed mobile energy storage system according to the received data information and the obtained application effect determination index of the distributed mobile energy storage system.

[0111] The application effect determination method and system of the distributed mobile energy storage system provided by the application are characterized in that the data of the target distributed mobile energy storage system and the new power system are acquired, the corresponding problem set and scene set are constructed, the power value and the capacity value of the distributed mobile energy storage system are calculated based on the problem set and the scene set, and finally the application effect is determined according to the calculation result and the design result of the power value and the capacity value of the distributed mobile energy storage system; therefore, the application effect of the distributed mobile energy storage system can be determined, the reliability is higher, the accuracy is better, and the determination is more objective and scientific. BRIEF DESCRIPTION OF DRAWINGS

[0112] Figure 1 It is a method flowchart of the method of the application.

[0113] Figure 2 It is a schematic diagram of a general wiring model in the method of the application.

[0114] Figure 3 A schematic diagram of a functional module of the system of the present application. DETAILED DESCRIPTION

[0115] As Figure 1 shown is a method flowchart of the method of the present application: the application effect determination method of the distributed mobile energy storage system provided by the present application, comprising the following steps:

[0116] S1. Obtain the data information of the distributed mobile energy storage system to be determined, and the corresponding data information of the new power system;

[0117] S2. According to the data information obtained in step S1, the problem set is constructed according to the problems existing in the new power system and the problems that can be solved by the distributed mobile energy storage system; comprising the following steps:

[0118] According to the problems existing in the new power system and the problems existing in the new power system, the total problem set, the planned problem set and the energy storage problem set are constructed;

[0119] Using a serial number vector scheme, each element in the energy storage problem set is assigned a value;

[0120] According to the specific position of each element in the energy storage problem set corresponding to the new power system, the positioning domain of the energy storage problem set is determined;

[0121] In specific implementation, the following steps can be used:

[0122] Construct the total problem set, the planned problem set and the energy storage problem set:

[0123] Divide the power supply area of the new power system into n z pieces, wherein the jth piece is represented as Z j , j = 1, 2,..., n z ; for each piece Z j , define the total problem set the planned problem set and the energy storage problem set

[0124] Set the observation time interval as n d days, wherein the dth day is represented as L d , d = 1, 2,..., n d ;

[0125] In each day of the observation time interval, the time period is divided into n k periods, wherein the kth period is represented as T k , k = 1, 2, 3,..., nk ;

[0126] Sort the power supply area:

[0127] If the area Z j has or occurs branch overload problem in the T d period of the time interval L k , the corresponding branch forms the first problem subset

[0128] If the area Z j has or occurs node voltage out-of-limit problem in the T d period of the time interval L k , the corresponding node forms the second problem subset

[0129] If the area Z j has or occurs local network renewable energy distributed generation near efficient consumption problem in the T k period of the time interval L d , the corresponding sub-scenario forms the third problem subset

[0130] If the area Z j has or occurs local network electric vehicle charging load steep increase problem in the T k period of the time interval L d , the corresponding sub-scenario forms the fourth problem subset

[0131] If the area Z j has or occurs local network high reliability power supply demand problem in the T k period of the time interval L d , the corresponding sub-scenario forms the fifth problem subset

[0132] The union of and is obtained, and the total problem set

[0133] In the total problem set , sort:

[0134] If the target power system adopts the technical scheme of distribution network reconstruction or new construction, which can solve the several overload problems existing or occurring in the T k period of the time interval L d of the area Z j , the corresponding several branches form the planned solution first problem subset

[0135]

[0136] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k If several nodes experience voltage exceedance issues during a given time period, then these nodes will form a subset of the planned solutions to the second problem.

[0137] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The localized and efficient consumption of distributed renewable energy generation in certain areas of the network, which exists or occurs during a certain period, will constitute a subset of the planned solutions to the third problem.

[0138] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The occurrence of several local network electric vehicle charging load surges during certain periods will constitute a subset of the planned solutions to the fourth problem.

[0139] If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k Several local network high-reliability power supply requirements that exist or occur during a certain period will constitute the fifth problem subset, which consists of several corresponding sub-scenarios.

[0140] Will and Find the union of the sets to obtain the set of problems to be solved.

[0141] Based on the total problem set and plans to solve problems The energy storage problem set is calculated using the following formula.

[0142]

[0143] In the formula Solving the first subset of problems for energy storage; Solving the second subset of the problem for energy storage; Solving a subset of the third problem for energy storage; Solving a subset of the fourth problem for energy storage; p is the number of the problem subset;

[0144] Determine the sequence number vector of the energy storage problem set:

[0145] For each element in the energy storage problem set , the energy storage problem sequence number vector is given as shown in the following formula

[0146]

[0147] In the formula is the natural sequence number of the element in ; p is the number of the problem subset; is the element sequence number in the first energy storage problem subset; is the element sequence number in the second energy storage problem subset; is the element sequence number in the third energy storage problem subset; is the element sequence number in the fourth energy storage problem subset; is the element sequence number in the fifth energy storage problem subset; since the elements in the problem set are listed independently, the sequence number vector has only one element non-zero, and the other four elements are all zero; for example, [0, 6, 0, 0, 0] is the sequence number vector of the 6th element in ; it is easy to know that, because the elements in the problem set are listed independently, there is ;

[0148] Determine the positioning field of the energy storage problem set:

[0149] The positioning field of the energy storage problem set is represented by the following formula:

[0150]

[0151] Among them, the node name is used to represent the positioning field of the third problem to the fifth problem, because the distributed renewable energy power generation system, electric vehicle charging pile / station, and high reliability power supply load are always connected to a certain group of busbars or a certain branch node of the distribution line; the station name is used to represent the source of the grid-side power supply of the branch, node or sub-scenario of the energy storage problem; when the grid-side power supply comes from the medium voltage busbar of the substation, the station name is the name of the corresponding substation; when the grid-side power supply comes from the low voltage busbar of the distribution station, the station name is the name of the corresponding distribution station; when the grid-side power supply comes from the busbar of the switching station, the station name is the name of the corresponding switching station; when the grid-side power supply comes from the distribution transformer, the station name is the name of the corresponding distribution transformer; when the grid-side power supply comes from the distribution line section, the station name is the name of the corresponding distribution line section;

[0152] S3. Based on the problems that can be solved by the distributed mobile energy storage system and the interrelationships between the problems, determine a mixed problem scenario set based on the problem set obtained in step S2, and represent the mixed problem scenario set using a function vector; including the following steps:

[0153] For a specific connection of a medium or low voltage distribution network, there may be no or no occurrence of any of the first to fifth problems, or there may be a certain problem or problems among the first to fifth problems, so the consideration of mixed problem scenarios is needed;

[0154] Define the mixed problem scenario set;

[0155] According to the topology of the new power system, construct a general connection model;

[0156] According to each element in the first to fifth energy storage problem subsets, determine the corresponding branch and specific connection in the constructed general connection model, and compare and fuse to obtain the mixed problem scenario set;

[0157] For each element in the obtained mixed problem scenario set, calculate the function vector of the mixed problem scenario according to the existing or occurring problem;

[0158] In specific implementation, the following steps can be used:

[0159] Define the mixed problem scenario set:

[0160] Based on the energy storage problem set , take the connection as the object, set the area Z j , and use the existing or occurring one or more problems in the first to fifth energy storage problem subsets solved by the energy storage in the T d period of the time interval L k to adopt 1 connection as 1 mixed problem scenario; the set composed of all mixed problem scenarios of the area Z j in the T d period of the time interval L k is the mixed problem scenario set

[0161] Construct a general connection model:

[0162] The general connection model is shown in Figure 2 , including a main grid 1, a main transformer 2, a distribution line 3, a distribution transformer 4, a low-voltage incoming line circuit breaker 5, a low-voltage bus 6, a new energy power station 7 Figure 2The system consists of 8 photovoltaic power plants, 9 distributed mobile energy storage systems, 10 conventional loads, 11 high-reliability power supply loads, and 12 electric vehicle charging loads. The main power grid, main transformer, and distribution lines are connected in series. The distribution transformer is connected to the distribution lines and is connected to the low-voltage busbar through a low-voltage incoming circuit breaker. The new energy power plants, distributed mobile energy storage systems, conventional loads, high-reliability power supply loads, and electric vehicle charging loads are all connected to the low-voltage busbar.

[0163] The main power grid includes AC transmission networks or AC high-voltage distribution networks; the main transformer includes the main transformer in the substation, which may be a two-winding transformer or a three-winding transformer. Figure 2 The diagram uses a double-winding transformer for representation; power distribution lines include medium-voltage power distribution lines, which may be overhead or cable power distribution lines; power distribution transformers include step-down transformers that convert medium-voltage AC to low-voltage AC; low-voltage incoming circuit breakers are the power grid circuit breakers for the low-voltage busbar, used to protect the low-voltage busbar; conventional loads are all loads that have no requirements for power supply reliability, except for electric vehicle charging loads; high-reliability power supply loads are all loads that have power supply reliability requirements greater than a set value; moreover, in general wiring models, new energy power stations, conventional loads, high-reliability power supply loads, and electric vehicle charging loads may not appear simultaneously. If they do not appear, the active and reactive power of the corresponding load or power station can be set to 0.

[0164] Construction of a collection of mixed problem scenarios:

[0165] A. Addressing a subset of the first problem in energy storage Obtain the first problem subset For the i1th element in the general wiring model, obtain the branch corresponding to the i1th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i1th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring If all are different, the i1th element is assigned to the wiring. Subset of wiring problems The value of j1 can be 1 to (i1-1);

[0166] B. Addressing the subset of the second problem related to energy storage Obtain the second problem subset For the i2th element in the general wiring model, obtain the branch corresponding to the i2th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring and all wiring If all are different, the i2th element will be assigned to the wiring. Subset of wiring problems The value of jj1 is 1 to n1, where n1 is the number of elements in the subset that solves the first problem; the value of j2 is 1 to (i2-1);

[0167] C. Addressing the subset of the third problem related to energy storage Obtaining the third problem subset For the i3rd element in the general wiring model, obtain the branch corresponding to the i3rd element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring and all wiring If all are different, the i-th element is assigned to the wiring. Subset of wiring problems jj2 represents the number of elements in the subset that solves the second problem using energy storage; j3 takes values ​​from 1 to (i3-1);

[0168] D. Addressing a subset of the fourth problem related to energy storage Obtaining the fourth problem subset For the i-4th element in the general wiring model, obtain the branch corresponding to the i-4th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring All wiring and all wiring If all are different, the i-th fourth element is assigned to the wiring. Subset of wiring problems jj3 represents the number of elements in the subset that solves the third problem using energy storage; j4 takes values ​​from 1 to (i4-1);

[0169] E. Addressing the subset of the fifth problem in energy storage Obtaining the fourth problem subset For the i5th element in the general wiring model, obtain the branch corresponding to the i5th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring All wiring All wiring and all wiring If all are different, the i5th element is assigned to the wiring. Subset of wiring problems Jj4 represents the number of elements in the subset that solves the fourth problem using energy storage; j5 takes values ​​from 1 to (i5-1);

[0170] F. Union all the subsets of wiring problems obtained in steps A to E to obtain the set of mixed problem scenarios.

[0171]

[0172] Calculation of function vectors for mixed problem scenarios:

[0173] Area Z j In time interval L d T k Time period There are n in the middle w A mixed problem scenario, n w To determine the number of mixed problem scenarios, the following formula is used to calculate the 5-dimensional function vector for each mixed problem scenario.

[0174]

[0175] In the formula Let be the dimension element of the function vector for the s-th hybrid problem scenario, corresponding to the functions that the distributed mobile energy storage system needs to configure when eliminating the problems corresponding to the o-th problem subset in the s-th hybrid problem scenario; the value of o is 1 to 5; if the s-th hybrid problem scenario contains or has problems that require energy storage to solve the o-th problem subset, then... otherwise

[0176] The power matrix of the function vector in the mixed problem scenario is used to determine the power of each dimension element of the function vector; the power matrix is expressed as

[0177]

[0178] wherein is the power variable element corresponding to the o-th problem subset of the energy storage solution;

[0179] In particular implementation, The value of is:

[0180] The first problem corresponds to the branch overload problem, the second problem corresponds to the node voltage out-of-limit problem, the third problem corresponds to the local network renewable energy distributed generation efficient consumption problem, the fourth problem corresponds to the local network electric vehicle charging load steep increase problem, and the fifth problem corresponds to the local network high reliability power supply demand problem.

[0181] For the first problem, let the main transformer 2 be branch2, and branch2=1 indicates that the branch is overloaded, and branch2=0 indicates that the branch is not overloaded; let the distribution line 3 be branch3, and branch3=1 indicates that the branch is overloaded, and branch3=0 indicates that the branch is not overloaded; let the distribution transformer 4 be branch4, and branch4=1 indicates that the branch is overloaded, and branch4=0 indicates that the branch is not overloaded. According to Figure 2 , the first problem is encoded: only the main transformer 2 is overloaded, and the encoding is branch2:branch3:branch4=100; only the distribution line 3 is overloaded, and the encoding is branch2:branch3:branch4=010; only the distribution transformer 4 is overloaded, and the encoding is branch2:branch3:branch4=001; the main transformer 2 and the distribution line 3 are overloaded, and the distribution transformer 4 is not overloaded, and the encoding is branch2:branch3:branch4=110; the main transformer 2 and the distribution transformer 4 are overloaded, and the distribution line 3 is not overloaded, and the encoding is branch2:branch3:branch4=101; the main transformer 2 is not overloaded, and the distribution line 3 and the distribution transformer 4 are overloaded, and the encoding is branch2:branch3:branch4=011; the main transformer 2, the distribution line 3, and the distribution transformer 4 are all overloaded, and the encoding is branch2:branch3:branch4=111;

[0182] For Figure 2It can be known from the analysis that, because the capacity of the main transformer 2 and the capacity of the distribution line 3 are much larger than the capacity of the distribution transformer 4, for the first problem coded as branch2:branch3:branch4=100 / 010 / 110, it is impossible to eliminate the problem by using the distributed mobile energy storage 8 alone, so the distributed mobile energy storage 8 plays an assisting role; at this time, the power of the distributed mobile energy storage 8 is selected to be about 10% of the rated power of the distribution transformer 4 (in specific implementation, the proportion of 10% can be adjusted according to specific circumstances) is more appropriate, considering that the power factor of the distribution network usually reaches above 0.9, so the power of the distributed mobile energy storage 8 is selected to be 0.1x0.9S 4N , S 4N is the rated capacity of the distribution transformer 4 and the unit is kVA; in this way, the unit is kW;

[0183] For the first problem coded as branch2:branch3:branch4=001 / 101 / 011 / 111, focus is on solving the heavy overload problem of the distribution transformer 4, which is also beneficial to the heavy overload problem of the main transformer 2 and the distribution line 3; considering that the distribution transformer can be operated at 120% rated capacity for a short time, the heavy overload of the distribution transformer 4 may overload to 120% rated capacity; at this time, the power of the distributed mobile energy storage 8 is selected to be about 30% of the rated power of the distribution transformer 4 (in specific implementation, the proportion of 30% can be adjusted according to specific circumstances) is more appropriate, considering that the power factor of the distribution network usually reaches above 0.9, so the power of the distributed mobile energy storage 8 is selected to be 0.3x0.9S 4N ; in this way, the unit is kW;

[0184] For the second problem: let the node of the low-voltage side bus of the main transformer 2 (the bus connected at the head of the distribution line 3) be node2, and node2=1 indicates that the node has voltage out-of-limit, and node2=0 indicates that the node has no voltage out-of-limit; let the connection point of the distribution line 3 and the distribution transformer 4 be node3, and node3=1 indicates that the node has voltage out-of-limit, and node3=0 indicates that the node has no voltage out-of-limit; let the low-voltage bus 6 be node4, and node4=1 indicates that the node has voltage out-of-limit, and node4=0 indicates that the node has no voltage out-of-limit. For Figure 1node2:node3:node4=100; only node3 has voltage out-of-limit, encoded as node2:node3:node4=010; only node4 has voltage out-of-limit, encoded as node2:node3:node4=001; node2 and node3 have voltage out-of-limit, while node4 has no voltage out-of-limit, encoded as node2:node3:node4=110; node2 and node4 have voltage out-of-limit, while node3 has no voltage out-of-limit, encoded as node2:node3:node4=101; node2 has no voltage out-of-limit, while node3 and node4 have voltage out-of-limit, encoded as node2:node3:node4=011; node2, node3 and node4 all have voltage out-of-limit, encoded as node2:node3:node4=111;

[0185] For Figure 2 Analysis shows that, because the capacity of main transformer 2 and the capacity of distribution line 3 are much larger than the capacity of distribution transformer 4, for the QA-2 problem encoded as node2:node3:node4=100 / 010 / 110, it is impossible to eliminate the problem by using distributed mobile energy storage 8 alone, so the distributed mobile energy storage 8 plays an assisting role. At this time, whether it is voltage lower limit or voltage upper limit, the power of the distributed mobile energy storage 8 is selected to be about 10% of the rated power of the distribution transformer 4 (in specific implementation, the 10% ratio can be adjusted according to specific circumstances) is appropriate, considering that the power factor of the distribution network usually reaches above 0.9, so the power of the distributed mobile energy storage 8 is selected to be 0.1×0.9S 4N ; in this way, The unit is kW;

[0186] For the second problem encoded as node2:node3:node4=001 / 101 / 011 / 111, focus on solving the voltage out-of-limit problem of node4, and there is also a benefit for alleviating the voltage out-of-limit problem of node2 and node3; at this time, if it is a voltage lower limit problem, it should also be considered that the distribution transformer can operate at 120% rated capacity for a short time, and the heavy overload of the distribution transformer 4 may overload to 120% rated capacity, and the voltage lower limit problem may occur under the condition of heavy overload of the distribution transformer 4, the power of the distributed mobile energy storage 8 is selected to be about 30% of the rated power of the distribution transformer 4 (in specific implementation, the 30% ratio can be adjusted according to specific circumstances) is appropriate, considering that the power factor of the distribution network usually reaches above 0.9, so the power of the distributed mobile energy storage 8 is selected to be 0.3×0.9S 4N , in this way, The unit is kW;

[0187] If the issue is related to voltage exceeding the upper limit, renewable energy power generation systems should be taken into account. Figure 1 The output of the photovoltaic power station 7 significantly exceeds the total load power on the low-voltage bus 6. The distribution transformer 4, by feeding power from the low-voltage side to the high-voltage side, causes the voltage on the low-voltage bus 6 to exceed its upper limit. Simultaneously, it should be considered that the distribution transformer can operate at 120% of its rated capacity for short periods. The heavy overload of distribution transformer 4 could potentially exceed its rated capacity to 120%, and the voltage exceeding the upper limit problem could occur under the condition of reverse power overload of distribution transformer 4. Therefore, under the above circumstances, the power selection for the distributed mobile energy storage 8 is the renewable energy power generation system (…). Figure 1 For the photovoltaic power station (7), approximately 45% of its rated power (this percentage can be adjusted based on specific circumstances) is suitable; that is, the power selection for the distributed mobile energy storage (8) should be 0.45P. 7N P 7N This refers to the rated power of a renewable energy power generation system, expressed in kW; thus... The unit is kW;

[0188] Regarding the third question: For partition Z... j In the Lth time interval under investigation d The Tth of the day k The s-th mixed problem scenario in time period Low-voltage busbar 6, P 7N The rated power of its renewable energy power generation system is expressed in kW; its total load, namely the sum of conventional load 9, high-reliability power supply load 10, and electric vehicle charging load 11, is denoted as... The unit is kW. If Then we take a margin factor of 1.15. otherwise,

[0189] Regarding the fourth question: For partition Z... j In the Lth time interval under investigation d The Tth of the day k The s-th mixed problem scenario in time period Low-voltage busbar 6, P 11N The rated power of the electric vehicle charging load 11 is given in kW, with a sharing factor of 0.65.

[0190] Regarding the fifth question: For partition Z... j In the Lth time interval under investigation d The Tth of the day k The s-th mixed problem scenario in time period Low-voltage busbar 6, P 10NThe rated power of the high-reliability power supply load 10 is denoted as kW, and a margin coefficient 1.2 is taken,

[0191] The function vector of the mixed problem scenario is calculated The rated power of the high-reliability power supply load 10 is denoted as kW, and a margin coefficient 1.2 is taken,

[0192]

[0193] S4. Based on the operation requirements of the distributed mobile energy storage system and the new power system, the power calculation value and the capacity calculation value of the distributed mobile energy storage system are calculated according to the mixed problem scenario set obtained in step S3; including the following steps:

[0194] According to the mixed problem scenario set obtained in step S3, the required power of the mobile energy storage and the required capacity of the mobile energy storage are calculated based on the operation requirements of the distributed mobile energy storage system and the new power system;

[0195] The obtained required power of the mobile energy storage and the required capacity of the mobile energy storage are optimized by using a proportional coefficient scheme to obtain the power calculation value and the capacity calculation value of the distributed mobile energy storage system;

[0196] In specific implementation, the following steps can be used:

[0197] Calculation of the required power of the mobile energy storage and the required capacity of the mobile energy storage:

[0198] According to the mixed problem scenario set obtained in step S3 The following formula is used to calculate the slice Z j In the time interval L d The required power of the mobile energy storage of the s-th mixed problem scenario in the T k hour period

[0199]

[0200] In the formula, is the power contraction vector, and is the power contraction coefficient of the o-th problem subset solved by the energy storage;

[0201] The principle of designing the power contraction vector is that, for the general wiring model shown in Figure 1 If the mobile energy storage is configured to solve multiple problems in QA-1 to QA-5, and if ​The calculated mobile energy storage power may be significantly overestimated because solving one of the five problems may alleviate or even completely resolve other existing or emerging problems. Therefore, for optimizing the technical solution, All elements in the array may be less than 1;

[0202] Considering that this invention provides a systematic application effect evaluation method, and gives a reference feasible solution rather than the optimal solution, the following formula is used to evaluate the system based on the strength of the correlation. To retrieve values:

[0203]

[0204] according to The value selection rule for region Z is as follows: j The s-th mixed problem scenario Power required for mobile energy storage within a time interval for:

[0205]

[0206] In the formula, counter[·] represents the count of cases that satisfy the conditions within the square brackets; For area Z j The s-th mixed problem scenario In time interval L d The daily maximum power adjustment factor for mobile energy storage This is used to ensure the cyclical storage and release of energy by the mobile energy storage system within a 24-hour period; n k This represents the number of time periods in a 24-hour day. If each time period is 1 hour, then n is... k =24. If each time period is 15 minutes, then n k =96; The number of time periods in a 24-hour period where the energy storage capacity is greater than or equal to the maximum energy storage capacity of the day;

[0207] The region Z is calculated using the following formula. j The s-th mixed problem scenario Required capacity for mobile energy storage within a time interval

[0208]

[0209] In the formula for Required mobile energy storage in L d Number of time periods with 25% or more of the daily switching power; The calculation formula of the mobile energy storage system considers that the state of charge (SOC) of the mobile energy storage system is in the effective operation interval from 10% to 90%, and the operation condition is 50% in the ready state. Therefore, the calculated The capacity demand of the mobile energy storage in the time interval is met.

[0210] The required power of the mobile energy storage is calculated The required capacity of the mobile energy storage is The required capacity of the mobile energy storage is The required capacity of the mobile energy storage is

[0211] The calculation of the power calculation value and the capacity calculation value of the distributed mobile energy storage system:

[0212] Considering that there are differences in the network structure, load characteristics and distributed renewable energy generation system output characteristics between different sub-regions in the power supply region in the time interval, the mobile energy storage is applied in the power supply region system, and the scheme can be optimized through optimization design and optimization scheduling. Therefore, the sum of the required power and the sum of the required capacity of the mobile energy storage in each sub-region cannot be directly used as the total power demand and the total capacity demand of the mobile energy storage in the power supply region.

[0213] Therefore, the power calculation value and the capacity calculation value of the distributed mobile energy storage system are calculated by the following formulas

[0214]

[0215] In the formula, The power optimization coefficient of the mobile energy storage system in the power supply region is 0.5-1.0. The capacity optimization coefficient of the mobile energy storage in the power supply region is 0.5-1.

[0216] The upper limit of the values of the above two optimization coefficients is 1.0, that is, the sum of the required power and the sum of the required capacity of the mobile energy storage in each sub-region are directly used as the total power demand and the total capacity demand of the mobile energy storage in the power supply region. The corresponding technical scheme for eliminating the first problem to the fifth problem existing or occurring in the power supply region by using the mobile energy storage is definitely achievable.

[0217] In addition, considering that there are differences in the network structure, load characteristics and distributed renewable energy generation system output characteristics between different sub-regions in the power supply region, a certain degree of complementarity can be obtained, and the best case is that each two sub-regions are completely complementary to each other, so that the lower limit of the values of the above two optimization coefficients is 0.5.

[0218] ​Considering that the application provides a systematic application effect evaluation method of distributed mobile energy storage, a reference feasible solution is given instead of an optimal solution, and therefore the preferred value of the method of the application is

[0219] S5. Based on the power calculation value and the capacity calculation value of the distributed mobile energy storage system obtained in step S4, the ratio of the power design value and the capacity design value of the distributed mobile energy storage system is calculated to obtain an application effect determination index of the distributed mobile energy storage system; specifically including the following steps:

[0220] The following formula is used to calculate the first determination index a and the second determination index b of the application effect of the distributed mobile energy storage system:

[0221]

[0222] In the formula, is the power design value of the distributed mobile energy storage system; is the capacity design value of the distributed mobile energy storage system; wherein the above two design values should be demonstrated by the realizability (including the complete elimination of the first problem to the fifth problem of the power supply area in the time interval under consideration and the realizability of the operation of the distributed energy storage system of each specific connection);

[0223] S6. The application effect determination of the distributed mobile energy storage system is completed according to the application effect determination index of the distributed mobile energy storage system obtained in step S5; specifically including the following steps:

[0224] According to the application effect determination index of the distributed mobile energy storage system obtained in step S5, the following rules are used to determine the application effect of the distributed mobile energy storage system:

[0225] If , it is determined that the application effect of the distributed mobile energy storage system is optimal;

[0226] If , it is determined that the application effect of the distributed mobile energy storage system is suboptimal;

[0227] If , it is determined that the application effect of the distributed mobile energy storage system is feasible;

[0228] If , it is determined that the application effect of the distributed mobile energy storage system is too high;

[0229] If , it is determined that the application effect of the distributed mobile energy storage system is too high to realize;

[0230] If The application effect of the distributed mobile energy storage system is determined as power-capacity imbalance.

[0231] The method of the present application is further described below in connection with an embodiment:

[0232] For example, in a certain power supply area, there are six fragments in which the first problem (denoted as QA-1 below) to the fifth problem (denoted as QA-5 below) exist / occur, i.e., n z = 6. The six fragments are denoted as Z1 to Z6. Among them, the Z1 fragment has two specific connections in which the QA-1 to QA-5 problems exist / occur; the Z2 fragment has three specific connections in which the QA-1 to QA-5 problems exist / occur; the Z3 fragment has one specific connection in which the QA-1 to QA-5 problems exist / occur; the Z4 fragment has three specific connections in which the QA-1 to QA-5 problems exist / occur; the Z5 fragment has one specific connection in which the QA-1 to QA-5 problems exist / occur; and the Z6 fragment has two specific connections in which the QA-1 to QA-5 problems exist / occur. The time interval for investigation is one week, i.e., n d = 7; and each day is divided into 24 time periods, i.e., n k = 24. The time periods in which the QA-1 to QA-5 problems exist / occur in the specific connections of the above fragments are listed in Tables 1 to 6.

[0233] Table 1 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z1 fragment

[0234]

[0235] Table 2 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z2 fragment

[0236]

[0237] Table 3 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z3 fragment

[0238]

[0239] Table 4 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z4 fragment

[0240]

[0241] Table 5 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z5 fragment

[0242]

[0243] Table 6 Time periods in which the QA-1 to QA-5 problems exist / occur in the Z6 fragment

[0244]

[0245] The load power, the distributed renewable energy power output distribution in the time period of each day in the time interval in which the problem exists / occurs for each specific connection in the 6 segments of the example power supply area is the same. For example, if a specific connection has a problem on Saturday and Sunday, it is considered that the load power and the distributed renewable energy power output distribution in the time period on Sunday and Saturday are the same. Therefore, the distribution transformer capacity, the load power, and the distributed renewable energy power output of each specific connection of the example are listed as shown in Tables 7 to 18.

[0246] Table 7 Z1 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0247]

[0248] Table 8 Z1 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0249]

[0250] Table 9 Z2 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0251]

[0252] Table 10 Z2 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0253]

[0254] Table 11 Z2 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0255]

[0256] Table 12 Z3 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0257]

[0258] Table 13 Z4 segment specific connection Power parameters in the time period in which QA-1 to QA-5 problems exist / occur

[0259]

[0260] Table 14 Specific connection of Z4 slice Power parameters of the period when QA-1 to QA-5 problems exist / occur

[0261]

[0262] Table 15 Specific connection of Z4 slice Power parameters of the period when QA-1 to QA-5 problems exist / occur

[0263]

[0264] Table 16 Specific connection of Z5 slice Power parameters of the period when QA-1 to QA-5 problems exist / occur

[0265]

[0266] Table 17 Specific connection of Z6 slice Power parameters of the period when QA-1 to QA-5 problems exist / occur

[0267]

[0268] Table 18 Specific connection of Z6 slice Power parameters of the period when QA-1 to QA-5 problems exist / occur

[0269]

[0270] The following calculates the specific connection of each slice of the power supply area 6 of the embodiment, and the power requirement and capacity requirement of each slice to mobile energy storage, respectively:

[0271] (I) Z1 slice of the power supply area:

[0272] (1) Specific connection of Z1 slice

[0273] Calculation The initial value of the function vector of different periods is

[0274] S 4N = 630 kVA;

[0275] max(P 7N ) = 1300 kW; P 11N = 240 kW; obtain No

[0276] The power matrix of the same period is

[0277]

[0278] Thus, the function vector of different time periods is obtained

[0279]

[0280] Solve The power contraction vector of different time periods is

[0281]

[0282] Thus, the power demand of mobile energy storage in different time periods is obtained

[0283]

[0284] The maximum power adjustment factor of different dates in the time interval is investigated Thus, the specific connection of the slice Z1 is

[0285] The power demand of mobile energy storage in the time interval is

[0286] The specific connection of the slice Z1 is obtained

[0287] The number of exchange power periods of 25% and above per day in the time interval is investigated The corresponding specific connection of the slice Z1 is The capacity demand of mobile energy storage in the time interval is

[0288] (2) The specific connection of Z1 slice

[0289] Solve

[0290] The initial value of the function vector of different time periods is

[0291] S 4N = 500 kVA; P 11N = 260 kW;

[0292] Solve

[0293] The power matrix of different time periods is

[0294]

[0295] ​Therefore, the function vectors for different time periods can be obtained.

[0296]

[0297] Seeking Power contraction vector at different time periods

[0298]

[0299] Thus we can obtain Power demand for mobile energy storage at different times

[0300]

[0301] The maximum power adjustment factor for different dates within the observation period is: Therefore, the specific wiring of segment Z1 is as follows. The power demand for mobile energy storage during the examined time period is:

[0302]

[0303] Please provide the specific wiring diagram for component Z1. The number of periods with 25% or higher switching power per day within the time interval under investigation is: The specific wiring diagram for the corresponding segment Z1 The capacity demand for mobile energy storage during the examined time period is:

[0304]

[0305] (3) Total power and capacity requirements of Z1 segment for mobile energy storage

[0306] The total power and capacity demand for mobile energy storage under all mixed problem scenarios in power supply area Z1 within the examined time interval is:

[0307]

[0308] That is, the total power demand is 1216.6kW, and the total capacity demand is 32696.5kWh;

[0309] (II) Example of power supply area Z2 segmentation

[0310] (1) Specific wiring of Z2 segment

[0311] Seeking The initial values ​​of the function vectors at different time periods are

[0312] S 4N =800kVA;

[0313] The power matrix of different time periods is

[0314]

[0315] Thus, the function vector of different time periods is

[0316]

[0317] The power contraction vector of different time periods is

[0318]

[0319] Thus, the power demand of mobile energy storage in different time periods is

[0320]

[0321] The maximum power adjustment factor of different dates in the time interval is

[0322] Thus, the specific connection of the Z2 slice is The power demand of mobile energy storage in the time interval is

[0323]

[0324] The specific connection of the Z2 slice is obtained The number of exchange power periods of 25% and above per day in the time interval is The corresponding specific connection of the Z2 slice is The capacity demand of mobile energy storage in the time interval is

[0325]

[0326] (2) The specific connection of the Z2 slice

[0327] The initial value of the function vector of different time periods is S 4N = 800 kVA; max(P 7N ) = 980 kW;

[0328] The power matrix of different time periods is

[0329] Thus, the function vector of different time periods is​​​​​

[0330]

[0331] The power shrink vector of different time periods is

[0332]

[0333] Thus, the power demand of mobile energy storage in different time periods can be obtained

[0334]

[0335] The maximum power adjustment factor of different dates in the time interval is

[0336] Thus, the specific connection of the slice Z2 is The power demand of mobile energy storage in the time interval is

[0337]

[0338] The specific connection of the slice Z2 is obtained The number of exchange power periods of 25% and above per day in the time interval is

[0339]

[0340] The corresponding specific connection of the slice Z2 is The capacity demand of mobile energy storage in the time interval is

[0341]

[0342] The specific connection of the slice Z2 is

[0343] The calculation is The initial value of the function vector of different time periods is

[0344] S 4N = 800 kVA;

[0345] P 10N = 160 kW;

[0346] The calculation is The power matrix of different time periods is

[0347]

[0348] Thus, the function vector of different time periods can be obtained

[0349] ​​

[0350] Obtain The power shrinkage vector of different time periods is

[0351]

[0352] Thus, the power demand of the mobile energy storage in different time periods can be obtained The power demand of the mobile energy storage in different time periods is

[0353]

[0354] The maximum power adjustment factor of different dates in the time interval under study is

[0355] Thus, the specific connection of the slice Z2 is The power demand of the mobile energy storage in the time interval under study is

[0356]

[0357] The specific connection of the slice Z2 is obtained The number of exchange power periods of 25% or more per day in the time interval under study is

[0358]

[0359] The specific connection of the slice Z2 is The capacity demand of the mobile energy storage in the time interval under study is

[0360]

[0361] (4) The total power and capacity demand of the Z2 slice to the mobile energy storage

[0362] The total power and capacity demand of the Z2 slice to the mobile energy storage in the time interval under study is

[0363] (Three) Implementation example of the power supply area Z3 slice

[0364] (1) The specific connection of the Z3 slice Obtain The initial value of the function vector of different time periods is

[0365] S 10N = 96kW;

[0366] Obtain The power matrix of different time periods is

[0367]

[0368] Thus, the function vector of different time periods is obtained

[0369]

[0370] Solve The power contraction vector of different time periods is

[0371]

[0372] Thus, the power demand of mobile energy storage in different time periods is obtained The power demand of mobile energy storage in different time periods is

[0373]

[0374] The maximum power adjustment factor of different dates in the time interval is

[0375]

[0376] Thus, the specific connection of the Z3 slice is The power demand of mobile energy storage in the time interval is

[0377]

[0378] The specific connection of the Z3 slice can be obtained The number of exchange power periods of 25% and above per day in the time interval is The corresponding specific connection of the Z3 slice is The capacity demand of mobile energy storage in the time interval is

[0379]

[0380] (2) The total demand of power and capacity of Z3 slice to mobile energy storage

[0381] The total demand of power and capacity of all hybrid problem scenarios of power supply area Z3 slice to mobile energy storage in the time interval is

[0382] (Four) Implementation example of power supply area Z4 slice

[0383] (1) The specific connection of Z4 slice

[0384] Solve The initial value of function vector of different time periods is

[0385] S 4N = 630 kVA;

[0386] Seeking The power matrix for different time periods is

[0387]

[0388] Therefore, the function vectors for different time periods can be obtained as follows:

[0389]

[0390] Seeking The power contraction vector at different time periods is

[0391]

[0392] Thus we can obtain The power demand for mobile energy storage at different times is

[0393]

[0394] The maximum power adjustment factor for different dates within the observation period is:

[0395] Therefore, the specific wiring for segment Z4 is as follows. The power demand for mobile energy storage during the examined time period is:

[0396]

[0397] The specific wiring of component Z4 can be determined. The number of periods with 25% or higher switching power per day within the time interval under investigation is: The specific wiring diagram for the corresponding Z4 segment The capacity demand for mobile energy storage during the examined time period is:

[0398]

[0399] (2) Specific wiring of Z4 segment

[0400] Seeking The initial values ​​of the function vectors at different time periods are

[0401] S 4N =630kVA; calculate No

[0402] The power matrix for the same period is

[0403]

[0404] Thus, the function vector of different time periods can be obtained

[0405]

[0406] Solve The power contraction vector of different time periods is

[0407]

[0408] Thus, the power demand of mobile energy storage of different time periods can be obtained

[0409]

[0410] The maximum power adjustment factor of different dates in the time interval is Thus, the specific connection of the Z4 slice is The power demand of mobile energy storage in the time interval is

[0411]

[0412] The specific connection of the Z4 slice is obtained The number of exchange power periods of 25% and above per day in the time interval is The corresponding specific connection of the Z4 slice is The capacity demand of mobile energy storage in the time interval is

[0413]

[0414] (3) The specific connection of the Z4 slice

[0415] Solve The initial value of the function vector of different time periods is

[0416] S 4N = 630 kVA;

[0417] P 11N = 230 kW;

[0418] Solve The power matrix of different time periods is

[0419]

[0420] Thus, the function vector of different time periods can be obtained

[0421]

[0422] Solve ​The power contraction vector at different time periods is

[0423]

[0424] Thus we can obtain The power demand for mobile energy storage at different times is

[0425]

[0426] The maximum power adjustment factor for different dates within the observation period is:

[0427] Therefore, the specific wiring for segment Z4 is as follows. The power demand for mobile energy storage during the examined time period is:

[0428]

[0429] Please provide the specific wiring diagram for chip Z4. The number of periods with 25% or higher switching power per day within the time interval under investigation is: The specific wiring diagram for the corresponding Z4 segment The capacity demand for mobile energy storage during the examined time period is:

[0430]

[0431] (4) Total power and capacity requirements of Z4 slab for mobile energy storage

[0432] The total power and capacity demand for mobile energy storage under all mixed problem scenarios in power supply area Z4 within the examined time interval is:

[0433] (V) Example of power supply area Z5 segmentation

[0434] Seeking The initial values ​​of the function vectors at different time periods are S 4N =630kVA; max(P 7N ) = 570kW;

[0435] Seeking The power matrix for different time periods is

[0436]

[0437] Therefore, the function vectors for different time periods can be obtained as follows:

[0438]

[0439] Seeking The power shrink vector of different time periods is

[0440]

[0441] Thus, the power demand of the mobile energy storage in different time periods can be obtained The power demand of the mobile energy storage in different time periods is

[0442]

[0443] The maximum power adjustment factor of different dates in the time interval is investigated

[0444] Thus, the specific connection of the slice Z5 is The power demand of the mobile energy storage in the time interval is

[0445]

[0446] The specific connection of the slice Z5 is obtained The number of exchange power periods of 25% and above per day in the time interval under investigation is The corresponding specific connection of the slice Z5 is The capacity demand of the mobile energy storage in the time interval under investigation is

[0447]

[0448] (2) The total power and capacity demand of the mobile energy storage of the Z5 slice

[0449] The total power and capacity demand of the mobile energy storage of all hybrid problem scenarios of the power supply area slice Z5 in the time interval under investigation is

[0450] (Six) Implementation of the power supply area Z6 slice

[0451] (1) The specific connection of the Z6 slice

[0452] The calculation is The initial value of the function vector of different time periods is

[0453] S 4N = 500 kVA;

[0454] The calculation is The power matrix of different time periods is

[0455]

[0456] Thus, the function vector of different time periods can be obtained

[0457]

[0458] Obtain The power contraction vector of different time periods is

[0459]

[0460] Thus, the power demand of mobile energy storage in different time periods can be obtained The power demand of mobile energy storage in different time periods is

[0461]

[0462] The maximum power adjustment factor of different dates in the time interval is Thus, the specific connection of the Z6 slice is The power demand of mobile energy storage in the time interval is

[0463]

[0464] The specific connection of the Z6 slice is obtained The number of exchange power periods of 25% and above per day in the time interval is The corresponding specific connection of the Z6 slice is The capacity demand of mobile energy storage in the time interval is

[0465]

[0466] (2) The specific connection of the Z6 slice

[0467] Obtain The initial value of the function vector of different time periods is S 4N = 630 kVA; max(P 7N ) = 590 kW; P 11N = 270 kW; obtain The power matrix of different time periods is

[0468]

[0469] Thus, the function vector of different time periods can be obtained

[0470]

[0471] Obtain The power contraction vector of different time periods is

[0472]

[0473] Thus, the power demand of the mobile energy storage in the time interval under study is

[0474]

[0475] The maximum power adjustment factor of the different dates in the time interval under study is

[0476] Thus, the specific connection of the slice Z6 is The power demand of the mobile energy storage in the time interval under study is

[0477]

[0478] The specific connection of the slice Z6 is obtained The number of power exchange periods of 25% or more per day in the time interval under study is The corresponding specific connection of the slice Z6 is The capacity demand of the mobile energy storage in the time interval under study is

[0479]

[0480] (3) The total power and capacity demand of the mobile energy storage of the slice Z6

[0481] The total power and capacity demand of the mobile energy storage of the slice Z6 in the time interval under study is

[0482] The total power and capacity demand of the mobile energy storage of the entire power supply area is obtained

[0483] The total power demand of the mobile energy storage of the entire power supply area is obtained

[0484]

[0485] The scheme is evaluated by grading:

[0486] The total power and capacity demand of the mobile energy storage given by the design scheme for eliminating the problems of QA-1 to QA-5 in the power supply area of the embodiment is 2971.6 kW, and the total capacity demand is 82842.4 kWh, so according to the evaluation rules, it is obtained that

[0487]

[0488] Thus, the design scheme of the planning and design personnel of the embodiment belongs to the optimal solution. Therefore, the design scheme for eliminating the problems of QA-1 to QA-5 in the power supply area of the embodiment will have superior systematic application effect. ​

[0489] As Figure 3 The application discloses a system for realizing the application effect judgment method of the distributed mobile energy storage system, and the system comprises a data acquisition module, a set construction module, a scene construction module, a data calculation module, an index calculation module and an effect judgment module. The data acquisition module, the set construction module, the scene construction module, the data calculation module, the index calculation module and the effect judgment module are sequentially connected. The data acquisition module is used for acquiring data information of the distributed mobile energy storage system to be judged and corresponding data information of the new power system, and uploading the data information to the set construction module. The set construction module is used for constructing a problem set according to the received data information, in view of problems existing in the new power system and problems capable of being solved by the distributed mobile energy storage system, and uploading the data information to the scene construction module. The scene construction module is used for determining a mixed problem scene set based on the problems capable of being solved by the distributed mobile energy storage system and the mutual relationship among the problems according to the received data information and the obtained problem set, representing the mixed problem scene set by using a function vector, and uploading the data information to the data calculation module. The data calculation module is used for calculating power calculation values and capacity calculation values of the distributed mobile energy storage system based on operation requirements of the distributed mobile energy storage system and the new power system according to the received data information and the obtained mixed problem scene set, and uploading the data information to the index calculation module. The index calculation module is used for calculating application effect judgment indexes of the distributed mobile energy storage system based on the obtained power calculation values and capacity calculation values of the distributed mobile energy storage system and the ratio of the power calculation values and the capacity calculation values to power design values and capacity design values of the distributed mobile energy storage system according to the received data information, and uploading the data information to the effect judgment module. The effect judgment module is used for completing application effect judgment of the distributed mobile energy storage system according to the received data information and the obtained application effect judgment indexes of the distributed mobile energy storage system.

Claims

1. A method for judging the application effect of a distributed mobile energy storage system, characterized in that... Includes the following steps: S1. Obtain data information of the distributed mobile energy storage system to be judged, as well as data information of the corresponding new power system; S2. Based on the data obtained in step S1, a problem set is constructed, targeting the problems existing in the new power system and the problems that the distributed mobile energy storage system can solve; Includes the following steps: Based on the problems that have emerged or exist in new power systems, as well as the technical solutions for the transformation or construction of new power systems, construct a total problem set, a planned problem set, and an energy storage problem set; A sequence vector scheme is used to assign values ​​to each element in the energy storage problem set; The location domain of the energy storage problem set is determined based on the specific location of the new power system corresponding to each element in the energy storage problem set. S3. Based on the problem set obtained in step S2, and considering the problems that the distributed mobile energy storage system can solve and the relationships between the problems, determine the mixed problem scenario set and represent it using function vectors; S4. Based on the set of mixed problem scenarios obtained in step S3, and based on the operational requirements of distributed mobile energy storage systems and new power systems, calculate the power and capacity of the distributed mobile energy storage system. S5. Based on the ratio of the calculated power and capacity values ​​of the distributed mobile energy storage system obtained in step S4 to the designed power and capacity values ​​of the distributed mobile energy storage system, the application effect evaluation index of the distributed mobile energy storage system is calculated; specifically, the following steps are included: The application effect of the distributed mobile energy storage system is calculated using the following formulas: first evaluation index a and second evaluation index b. In the formula This refers to the power design value for a distributed mobile energy storage system. This refers to the capacity design value for a distributed mobile energy storage system. The calculated power value for a distributed mobile energy storage system; For the calculated capacity of a distributed mobile energy storage system; S6. Based on the application effect evaluation indicators of the distributed mobile energy storage system obtained in step S5, complete the application effect evaluation of the distributed mobile energy storage system; specifically including the following steps: Based on the application effect evaluation index of the distributed mobile energy storage system obtained in step S5, the following rules are used to evaluate the application effect of the distributed mobile energy storage system: like Therefore, the application effect of the distributed mobile energy storage system is determined to be optimal; like Therefore, the application effect of the distributed mobile energy storage system is determined to be suboptimal. like Therefore, the application effect of the distributed mobile energy storage system is deemed feasible. like Therefore, the application effect of the distributed mobile energy storage system is judged to have excessive margin. like Therefore, the application effect of the distributed mobile energy storage system is deemed to be too difficult to achieve. like The application effect of the distributed mobile energy storage system is then determined to be a power-capacity imbalance.

2. The method for determining the application effect of the distributed mobile energy storage system according to claim 1, characterized in that... Step S2 specifically includes the following steps: Construct the overall problem set, the planned problem set, and the energy storage problem set: The power supply area of ​​the new power system is divided into n segments. z There are regions, where the j-th region is denoted as Z. j j = 1, 2, ..., n z For each region Z j Define the total problem set Planned Problem Solving Set Energy storage solutions Set the observation time interval as n d Day, where day d is represented by L d d = 1, 2, ..., n d ; For each day within the time interval under consideration, divide it into n time periods. k There are several time periods, where the k-th time period is denoted as T. k k = 1, 2, 3, ..., n k ; The power supply area was reviewed: If area Z j In time interval L d T k If a branch overload problem exists or occurs during a certain period, the corresponding branch will be formed into the first problem subset. If area Z j In time interval L d T k If a node voltage exceeds its limit during a given period, the corresponding node will be grouped into a second subset of the problem. If area Z j In time interval L d T k If a localized issue arises regarding the efficient local consumption of distributed renewable energy generation in certain time periods, then the corresponding sub-scenario will be considered as a third problem subset. If area Z j In time interval L d T k If a sudden surge in electric vehicle charging load occurs in a localized area of ​​the network during a given period, the corresponding sub-scenario will be classified as the fourth problem subset. If area Z j In time interval L d T k If a period of time presents or experiences a localized demand for high-reliability power supply to the network, then the corresponding sub-scenario will be grouped into the fifth problem subset. Will and Find the union of the sets to obtain the total problem set. In the general problem set In the process of sorting out: If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k If several severe overload problems exist or occur during a given period, then the corresponding branches will constitute a subset of the planned solutions to the first problem. If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k If several nodes experience voltage exceedance issues during a given time period, then these nodes will form a subset of the planned solutions to the second problem. If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The localized and efficient consumption of distributed renewable energy generation in certain areas of the network, which exists or occurs during a certain period, will constitute a subset of the planned solutions to the third problem. If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k The occurrence of several local network electric vehicle charging load surges during certain periods will constitute a subset of the planned solutions to the fourth problem. If the target power system can solve the problem in area Z by adopting a technical solution of distribution network renovation or new construction... j In time interval L d T k Several local network high-reliability power supply requirements that exist or occur during a certain period will constitute the fifth problem subset, which consists of several corresponding sub-scenarios. Will and Find the union of the sets to obtain the set of problems to be solved. Based on the total problem set and plans to solve problems The energy storage problem set is calculated using the following formula. In the formula Solving the first subset of problems for energy storage; Solving the second subset of the problem for energy storage; Solving a subset of the third problem for energy storage; Solving a subset of the fourth problem for energy storage; Solve the fifth subset of problems for energy storage; p is the number of the problem subset; Determine the index vector of the energy storage problem set: Addressing problems related to energy storage Each element in the equation is assigned an energy storage problem-solving index vector as shown in the following formula. In the formula for The natural ordinal number of the element; The element index in the subset that solves the first problem in energy storage; The element index in the subset that solves the second problem for energy storage; The element index in the subset for solving the third problem in energy storage; The element indices in the subset for solving the fourth problem in energy storage; The element index in the subset for solving the fifth problem in energy storage; Determine the location domain of the problem set that energy storage addresses: The location domain of the energy storage problem set is represented by the following formula: The site name is used to indicate the source of the grid-side power supply for the branch, node, or sub-scenario that the energy storage solves. When the grid-side power supply comes from the medium-voltage bus of the substation, the site name is the corresponding substation name. When the grid-side power supply comes from the low-voltage bus of the distribution station, the site name is the corresponding distribution station name. When the grid-side power supply comes from the bus of the switching station, the site name is the corresponding switching station name. When the grid-side power supply comes from the distribution transformer, the site name is the corresponding distribution transformer name. When the grid-side power supply comes from a section of the distribution line, the site name is the corresponding distribution line section name.

3. The method for determining the application effect of the distributed mobile energy storage system according to claim 2, characterized in that... Step S3 includes the following steps: Define the set of mixed problem scenarios; Based on the topology of the new power system, construct a general wiring model; Based on the elements of the first to fifth subsets of energy storage problem solving, the corresponding branches and specific wiring are determined in the constructed general wiring model, and then compared and integrated to obtain a set of mixed problem scenarios. For each element in the obtained set of mixed problem scenarios, the function vector of the mixed problem scenario is calculated based on the problems that exist or occur.

4. The method for judging the application effect of the distributed mobile energy storage system according to claim 3, characterized in that... Step S3 specifically includes the following steps: Define the set of mixed problem scenarios: Solving problems with energy storage Based on this, and taking wiring as the object, define area Z. j In time interval L d T k A single connection of one or more problems from the first to fifth subsets of problems addressed by energy storage during a given time period is considered a mixed problem scenario; this scenario is then applied to area Z. j In time interval L d T k The set of all mixed problem scenarios within a given time period is called the mixed problem scenario set. Construct a general wiring model: A typical wiring model includes the main power grid, main transformer, distribution lines, distribution transformer, low-voltage incoming circuit breaker, low-voltage busbar, new energy power station, distributed mobile energy storage system, conventional loads, high-reliability power supply loads, and electric vehicle charging loads; the main power grid, main transformer, and distribution lines are connected in series; the distribution transformer is connected to the distribution lines and is connected to the low-voltage busbar through the low-voltage incoming circuit breaker; the new energy power station, distributed mobile energy storage system, conventional loads, high-reliability power supply loads, and electric vehicle charging loads are all connected to the low-voltage busbar; The main power grid includes AC transmission networks or AC high-voltage distribution networks; the main transformer includes the main transformer in the substation; the distribution lines include medium-voltage distribution lines; the distribution transformers include step-down transformers that convert medium-voltage AC to low-voltage AC; the low-voltage incoming circuit breaker is the power supply circuit breaker for the low-voltage busbar, used to protect the low-voltage busbar; the conventional load is all loads that have no requirements for power supply reliability, except for electric vehicle charging loads; the high-reliability power supply load is all loads that have power supply reliability requirements greater than a set value. Construction of a collection of mixed problem scenarios: A. Addressing a subset of the first problem in energy storage Obtain the first problem subset For the i1th element in the general wiring model, obtain the branch corresponding to the i1th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i1th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring If all are different, the i1th element is assigned to the wiring. Subset of wiring problems The value of j1 can be 1 to (i1-1); B. Addressing the subset of the second problem related to energy storage Obtain the second problem subset For the i2th element in the general wiring model, obtain the branch corresponding to the i2th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i2th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring and all wiring If all are different, the i2th element will be assigned to the wiring. Subset of wiring problems The value of jj1 is 1 to n1, where n1 is the number of elements in the subset that solves the first problem; the value of j2 is 1 to (i2-1); C. Addressing the subset of the third problem related to energy storage Obtaining the third problem subset For the i3rd element in the general wiring model, obtain the branch corresponding to the i3rd element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring and all the wiring If all are different, the i-th element is assigned to the wiring. Subset of wiring problems jj2 represents the number of elements in the subset that solves the second problem using energy storage; j3 takes values ​​from 1 to (i3-1); D. Addressing a subset of the fourth problem related to energy storage Obtaining the fourth problem subset For the i-4th element in the general wiring model, obtain the branch corresponding to the i-4th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i-th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring All wiring and all wiring If all are different, the i-th fourth element is assigned to the wiring. Subset of wiring problems jj3 represents the number of elements in the subset that solves the third problem using energy storage; j4 takes values ​​from 1 to (i4-1); E. Addressing the subset of the fifth problem in energy storage Obtaining the fourth problem subset For the i5th element in the general wiring model, obtain the branch corresponding to the i5th element, determine the wiring of the branch in the general wiring model, and represent the wiring as follows: right Make a judgment: if the wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With any wiring If they are the same, the i5th element is assigned to the wiring. Subset of wiring problems In the middle, if wiring With all wiring All wiring All wiring All wiring and all wiring If all are different, the i5th element is assigned to the wiring. Subset of wiring problems Jj4 represents the number of elements in the subset that solves the fourth problem using energy storage; j5 takes values ​​from 1 to (i5-1); F. Union all the subsets of wiring problems obtained in steps A to E to obtain the set of mixed problem scenarios. Calculation of function vectors for mixed problem scenarios: Area Z j In time interval L d T k Time period There are n in the middle w A mixed problem scenario, n w To determine the number of mixed problem scenarios, the following formula is used to calculate the 5-dimensional function vector for each mixed problem scenario. In the formula Let be the dimension element of the function vector for the s-th hybrid problem scenario, corresponding to the functions that the distributed mobile energy storage system needs to configure when eliminating the problems corresponding to the o-th problem subset in the s-th hybrid problem scenario; the value of o is 1 to 5; if the s-th hybrid problem scenario contains or has problems that require energy storage to solve the o-th problem subset, then... otherwise The power matrix of the function vector in the mixed problem scenario is set to determine the power of each dimension element of the function vector; Power Matrix Represented as In the formula These are the power variable elements corresponding to the subset of energy storage solutions for the o-th problem; Calculate the function vector of the mixed problem scenario for 5. The method for judging the application effect of the distributed mobile energy storage system according to claim 4, characterized in that... Step S4 includes the following steps: Based on the set of mixed problem scenarios obtained in step S3, and considering the operational requirements of distributed mobile energy storage systems and new power systems, the required power and capacity of mobile energy storage are calculated. The required power and capacity of the mobile energy storage are optimized using a proportional coefficient scheme to obtain the calculated power and capacity values ​​of the distributed mobile energy storage system.

6. The method for judging the application effect of the distributed mobile energy storage system according to claim 5, characterized in that... Step S4 specifically includes the following steps: Calculation of the required power and capacity of mobile energy storage: Based on the mixed problem scenario set obtained in step S3 The region Z is calculated using the following formula. j In time interval L d T k The s-th mixed problem scenario in time period The required power for mobile energy storage In the formula Let be the power contraction vector, and The power contraction factor for solving the o-th problem subset in energy storage; Use the following formula to To retrieve values: according to The value selection rule for region Z is as follows: j The s-th mixed problem scenario Power required for mobile energy storage within a time interval for: In the formula, counter[·] represents the count of cases that satisfy the conditions within the square brackets; For area Z j The s-th mixed problem scenario In time interval L d The daily maximum power adjustment factor for mobile energy storage This is used to ensure the cyclical storage and release of energy by the mobile energy storage system within a 24-hour period; n k This represents the number of time periods in a 24-hour day. If each time period is 1 hour, then n is... k =24. If each time period is 15 minutes, then n k =96; The number of time periods in a 24-hour period where the energy storage capacity is greater than or equal to the maximum energy storage capacity of the day; The region Z is calculated using the following formula. j The s-th mixed problem scenario Required capacity for mobile energy storage within a time interval In the formula for Required mobile energy storage in L d Number of time periods with 25% or more of the daily switching power; Calculate the required power for mobile energy storage. for Required capacity for mobile energy storage for Calculation of power and capacity values ​​for distributed mobile energy storage systems: The power calculation value of the distributed mobile energy storage system is obtained using the following formula. and capacity calculation value In the formula The power optimization factor for mobile energy storage systems in regional power supply applications; Capacity optimization factor for the systematic application of mobile energy storage in the power supply area.

7. A system for determining the application effect of a distributed mobile energy storage system as described in any one of claims 1 to 6, characterized in that... It includes a data acquisition module, a set construction module, a scenario construction module, a data calculation module, an indicator calculation module, and an effect judgment module; the data acquisition module, set construction module, scenario construction module, data calculation module, indicator calculation module, and effect judgment module are connected in series; the data acquisition module is used to acquire data information of the distributed mobile energy storage system to be judged, as well as the data information of the corresponding new power system, and upload the data information to the set construction module; The set construction module is used to construct a set of problems based on the received data information, targeting the problems existing in the new power system and the problems that the distributed mobile energy storage system can solve, and then upload the data information to the scenario construction module. The scenario construction module is used to determine a set of mixed problem scenarios based on the received data information and the obtained problem set, based on the problems that the distributed mobile energy storage system can solve and the interrelationships between the problems, and to represent them using function vectors, and then upload the data information to the data calculation module. The data calculation module is used to calculate the power and capacity of the distributed mobile energy storage system based on the received data information, the obtained mixed problem scenario set, and the operation requirements of the distributed mobile energy storage system and the new power system, and then upload the data information to the index calculation module. The index calculation module is used to calculate the application effect evaluation index of the distributed mobile energy storage system based on the received data information, the ratio of the calculated power and capacity values ​​of the distributed mobile energy storage system to the designed power and capacity values ​​of the distributed mobile energy storage system, and upload the data information to the effect evaluation module; the effect evaluation module is used to complete the application effect evaluation of the distributed mobile energy storage system based on the received data information and the obtained application effect evaluation index of the distributed mobile energy storage system.

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