A multi-station fusion optimization method considering the operating characteristics of different functional modules

Abstract

The present invention provides a multi-station fusion optimization method, device and storage medium taking into account the operation characteristics of different functional modules, and relates to the field of smart grid technology. The optimization method uses a fuzzy comprehensive evaluation method to select target combination forms with high expert recognition by traversing the combination forms of each functional module; introduces a hierarchical analysis method to quantitatively analyze the adaptability of the selected target combination forms to power grids with different characteristics; based on the available resource indicator system, with the optimal system cost as the goal, comprehensively considers the energy-information coupling constraint conditions, constructs a multi-station fusion optimization planning model of mixed integer linear programming, solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and combines the adaptability analysis of the target combination form to power grids with different characteristics to obtain the multi-station fusion optimal planning result. It can maximize the use of the remaining resource value in the substation and optimize the planning layout of multiple fusions.

Description

Technical Field

[0001] The present invention relates to the field of wind power technology, and in particular to a multi-station fusion optimization method, device and storage medium taking into account the operating characteristics of different functional modules. Background Art

[0002] As a key application for the implementation of the energy internet, multi-station integration is a complex integrated system engineering project involving cross-industry, cross-professional and cross-field. Multi-station integration refers to making full use of the surplus resources of substation sites, aggregating resources such as substations, energy storage stations, charging and swapping stations, data center stations, distributed photovoltaic stations, 5G base stations, Beidou ground base stations, etc., optimizing urban resource allocation, improving data perception, analysis and computing efficiency, and absorbing loads locally.

[0003] In the existing technology, in terms of fully leveraging the value of substation site resources, the research content of multi-station integration optimization planning mainly focuses on the separate planning of energy systems and communication systems. There is a lack of methods for optimizing resource integration in multi-station integration planning to maximize the value of remaining resources in the substation and optimize the planning and layout of multiple integrations. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-station fusion optimization method, device and storage medium taking into account the operating characteristics of different functional modules, which can maximize the value of residual resources in the substation and optimize the planning and layout of multiple fusions.

[0005] In a first aspect, an embodiment of the present invention provides a multi-station fusion optimization method taking into account the operating characteristics of different functional modules, the optimization method comprising:

[0006] Traverse the combination forms of each functional module and use the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition;

[0007] The hierarchical analysis method is introduced to quantitatively analyze the adaptability of the selected target combination form to power grids with different characteristics;

[0008] According to the resource requirements of different functional modules, establish the substation available resource indicator system;

[0009] Based on the available resource indicator system, taking the optimal system cost as the goal, and comprehensively considering the energy-information coupling constraints, a multi-station fusion optimization planning model of mixed integer linear programming is constructed;

[0010] The multi-station fusion optimization planning model of the mixed integer linear programming is solved to obtain the multi-station fusion planning layout and capacity configuration, and the adaptability of the power grids with different characteristics is analyzed in combination with the target combination form to obtain the multi-station fusion optimal planning result.

[0011] In a second aspect, an embodiment of the present invention provides a multi-station fusion optimization device taking into account the operating characteristics of different functional modules, the optimization device comprising:

[0012] The first screening module traverses the combination forms of each functional module and uses the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition;

[0013] The second screening module introduces the analytic hierarchy process to quantitatively analyze the adaptability of the selected target combination form to power grids with different characteristics;

[0014] The indicator system module establishes the indicator system of available resources of substations according to the resource requirements of different functional modules;

[0015] The model building module is based on the available resource indicator system, takes the optimal system cost as the goal, comprehensively considers the energy-information coupling constraints, and constructs a multi-station fusion optimization planning model of mixed integer linear programming;

[0016] The calculation module solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and analyzes the adaptability of power grids with different characteristics in combination with the target combination form to obtain the multi-station fusion optimal planning result.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned steps of multi-station fusion optimization taking into account the operating characteristics of different functional modules.

[0018] The present invention provides a multi-station fusion optimization method, device and storage medium taking into account the operation characteristics of different functional modules. The optimization method traverses the combination forms of each functional module, uses the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition; introduces the hierarchical analysis method to quantitatively analyze the adaptability of the selected target combination form to different characteristic power grids; establishes a substation available resource index system for resource requirements of different functional modules; based on the available resource index system, takes the system cost optimization as the goal, comprehensively considers the energy-information coupling constraint conditions, constructs a multi-station fusion optimization planning model of mixed integer linear programming, solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and combines the adaptability analysis of the target combination form to different characteristic power grids to obtain the multi-station fusion optimal planning result. It can maximize the use of the remaining resource value in the substation and optimize the planning layout of multiple fusions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 is a flow chart of a multi-station fusion optimization method taking into account the operating characteristics of different functional modules according to an embodiment of the present invention;

[0021] Figure 2 is a flow chart of a multi-station fusion optimization method taking into account the operating characteristics of different functional modules according to another embodiment of the present invention;

[0022] Figure 3 is a hierarchical analysis structure model diagram according to an embodiment of the present invention;

[0023] Figure 4 It is a structural diagram of a multi-station fusion optimization device taking into account the operating characteristics of different functional modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Figure 1 A multi-station fusion optimization method considering the operating characteristics of different functional modules is shown, such as Figure 1 As shown, the optimization method includes:

[0026] S20, traversing the combination forms of each functional module, and using the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition;

[0027] S40, introducing the analytic hierarchy process to quantitatively analyze the adaptability of the selected target combination form to power grids with different characteristics;

[0028] S60. Establish an indicator system of available resources for substations based on resource requirements of different functional modules;

[0029] S80. Based on the available resource indicator system, taking the optimal system cost as the goal, and comprehensively considering the energy-information coupling constraints, a multi-station fusion optimization planning model of mixed integer linear programming is constructed;

[0030] S100, solving the multi-station fusion optimization planning model of the mixed integer linear programming, obtaining the multi-station fusion planning point layout and capacity configuration, and combining the target combination form to analyze the adaptability of power grids with different characteristics to obtain the multi-station fusion optimal planning result.

[0031] This embodiment provides a multi-station fusion optimization method that takes into account the operating characteristics of different functional modules. The optimization method traverses the combination forms of each functional module and uses the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition; introduces the hierarchical analysis method to quantitatively analyze the adaptability of the selected target combination form to different characteristic power grids; establishes a substation available resource index system based on the resource requirements of different functional modules; based on the available resource index system, takes the system cost optimization as the goal, comprehensively considers the energy-information coupling constraints, constructs a multi-station fusion optimization planning model of mixed integer linear programming, solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and combines the adaptability analysis of the target combination form to different characteristic power grids to obtain the multi-station fusion optimal planning result. It can maximize the use of the remaining resource value in the substation and optimize the planning layout of multiple fusions.

[0032] The beneficial effects of the present invention are described below with a preferred embodiment. This embodiment proposes a multi-station fusion optimization method that takes into account the operating characteristics of different functional modules. The flow chart is as follows: Figure 2 As shown:

[0033] Study the operation characteristics and combination mode analysis of different functional modules involved in multi-station integration:

[0034] Step 1.1, traverse the combination forms of each functional module, and use the fuzzy comprehensive evaluation method to select the combination forms with high expert recognition;

[0035] Step 1.1.1, determine the system evaluation index weight A = [a1, ..., a m ], where m is the number of indicators involved in the evaluation, a k (k=1..m) represents the weight of the kth indicator, satisfying

[0036] Step 1.1.2, select the comment set V. Set the comment set V = {v1, v2, v3}, where v1 indicates high expert recognition; v2 indicates average expert recognition; v3 indicates low expert recognition;

[0037] Step 1.1.3: Use formula (1) to establish the fuzzy relationship matrix R m×3 , where r k1 (k=1..m) represents the percentage of experts who have a high degree of recognition for indicator k; r k2(k=1..m) represents the percentage of experts who generally agree with indicator k; r k3 (k=1..m) represents the percentage of experts with low recognition of indicator k, and its value satisfies This can be obtained by organizing an expert assessment:

[0038]

[0039] Step 1.1.4: Calculate the composite value B of the fuzzy relationship matrix using formula (2), where: is a composite operator, which means taking smaller first and then taking larger. ∧ is the symbol for taking smaller in fuzzy mathematics, and ∨ is the symbol for taking larger in fuzzy mathematics:

[0040]

[0041] In formula (2),

[0042] Step 1.1.5: Weighted comprehensive evaluation. After normalizing the composite value B of the fuzzy relationship matrix, we can get the system evaluation result B'=(b1 / b, b2 / b, b3 / b), where: The combination form with high evaluation results of the screening system;

[0043] Step 1.2: Introduce the analytic hierarchy process to quantitatively analyze the adaptability of the combination selected in step 1 to power grids with different characteristics:

[0044] Step 1.2.1, establish a hierarchical analysis structure model, such as Figure 3 As shown in the figure, the top layer is the purpose of decision-making and the problem to be solved, indicating the ranking of the adaptability of each combination form to power grids with different characteristics; the middle layer is the factors to be considered and the criteria for decision-making, indicating the various criteria required to evaluate each combination form; the bottom layer is the alternative plan for decision-making, indicating the various combinations screened in step 1;

[0045] Step 1.2.2: Use formula (3) and Table 1 to construct the judgment matrix Z:

[0046]

[0047] In formula (3), z ab The numerical expression of the importance of Za to Zb is shown in Table 1.

[0048] Where n is the number of evaluation criteria;

[0049] Table 1 Meaning of scaling method

[0050] Scale meaning 1 Indicates that the two indicators have the same importance. 3 Indicates that compared with the two indicators, the former is slightly more important than the latter 5 Indicates that compared with the two indicators, the former is significantly more important than the latter 7 Indicates that compared with the two indicators, the former is more important than the latter 9 Indicates that compared with the two indicators, the former is extremely important than the latter 2、4、6、8 Indicates the intermediate value of the above phase judgment reciprocal <![CDATA[If the importance ratio of index a to b is z ab , then the importance ratio of index b to a is z ba 1 / z ab >

[0051] Step 1.2.3: The method for determining the weight vector of Z, that is, the weight of each criterion:

[0052] Step 1.2.3.1. Use formula (4) to calculate the product p of all elements in each row of the judgment matrix Z a :

[0053]

[0054] Step 1.2.3.2: Calculate p using formula (5) a The nth root P a :

[0055]

[0056] Step 1.2.3.3: Use formula (6) to calculate P a After normalization, we can get the vector weight W a , construct the feature vector W = [W1,…,W n ]:

[0057]

[0058] Step 1.2.4: Calculate the consistency test of the judgment matrix Z to check the coordination between the importance of each element and avoid logical errors:

[0059] Step 1.2.4.1. Use formula (7) to find the maximum characteristic root λ of the judgment matrix Z max :

[0060]

[0061] In formula (7), ZW is the product of the judgment matrix Z and the eigenvector W, (ZW) a is the ath component of ZW;

[0062] Step 1.2.4.2: Calculate the consistency index CI using formula (8):

[0063]

[0064] Step 1.2.4.3. Calculate the consistency ratio CR using formula (9):

[0065]

[0066] In formula (9), RI is the average random consistency index, and its value is shown in Table 2;

[0067] Table 2 Average random consistency index

[0068]

[0069] Step 1.2.4.4, check consistency and obtain hierarchical single sorting;

[0070] Step 1.2.5, use Table 3 to calculate the combined weights of the elements at each level, i.e. the total ranking of the levels;

[0071] Table 3 Total ranking combination weight calculation table

[0072]

[0073] Step 1.2.6: Use formula (10) to perform a hierarchical total order consistency check:

[0074]

[0075] In formula (10), z1,…,zn is the weight of the combination weight of the upper layer elements Z1,…,Zn; f1 a ,...,f s a , a=1,...n is the single-layer sorting result of the connected elements F1,...,Fs of this layer. If Za and Fc are not related, then f c a =0;

[0076] Study the resource requirements of multi-station integration with different functional modules:

[0077] Step 2.1: Establish a substation available resource indicator system based on the resource requirements of different functional modules:

[0078] Step 2.1.1: Define the indoor space index C1 using formula (11):

[0079] C1=X / S (11)

[0080] In formula (11), X represents the size of the substation's available indoor space, S represents the reference value of the indoor space, and can be selected as the size of the substation's indoor space;

[0081] Step 2.1.2: Define the outdoor space index C2 using the formula:

[0082] C2=Y / O (12)

[0083] In the formula, Y represents the size of the outdoor space available in the substation, O represents the reference value of the outdoor space, which can be selected as the size of the outdoor space of the substation;

[0084] Step 2.1.3: Define the power balance index C3 using formula (13):

[0085] C3=A / B (13)

[0086] In formula (13), A represents the surplus power in the substation, B represents the base value of the substation capacity, and the maximum capacity of the substation can be selected;

[0087] Step 2.1.4: Define the power factor index C4 using formula (14):

[0088]

[0089] In formula (14), when C4 = 1, it means that the current substation can install electrical equipment that has power factor requirements;

[0090] Step 2.1.5: Define the operator's external network access index C5 using formula (15):

[0091] C5=0.1D (15)

[0092] In formula (15), D indicates whether the substation is connected to the operator's external network. If yes, D = 1, otherwise D = 0;

[0093] Step 2.1.6: Define the tower mast installation index C6 using formula (16):

[0094] C6=0.2E (16)

[0095] In formula (16), E indicates whether a tower mast is installed in the substation. If yes, E = 1, otherwise E = 0;

[0096] Step 2.1.7, define the tower mast installation index C7 using formula (17):

[0097] C7=0.4F (17)

[0098] In formula (17), F indicates whether a spare cable core is installed in the substation. If yes, F = 1, otherwise F = 0;

[0099] Step 2.1.8, using equations (11) to (17) and Table 4, propose the substation scalability C expand Calculation formula:

[0100] C expand =w1gC1+w2gC2+w3gC3+w4gC4+w5gC5+w6gC6+w7gC7(18)

[0101] Where C1, C2, C3, C4, C5, C6, and C7 are the index values ​​in equations (11) to (17), and w1, w2, w3, w4, w5, w6, and w7 are the weights corresponding to the index values;

[0102] Table 4 Substation available resource indicator system

[0103]

[0104]

[0105] A multi-station fusion optimization planning strategy based on mixed integer linear programming is proposed:

[0106] Step 3.1: Propose a multi-station fusion optimization planning model based on mixed integer linear programming, and formulate a multi-station fusion planning scheme that comprehensively considers energy-information coupling constraints:

[0107] Step 3.2.1: Use equations (19) to (21) to define the objective function as maximizing the net profit C of the multi-station fusion substation:

[0108] max C=-C inv&op +C benefit (19)

[0109]

[0110] In formula (19)-formula (21), C inv&op is the investment and operation cost of the multi-station integrated substation, C benefit is the income of multi-station integrated substation, α is the ratio of operating cost to investment cost, Ω B is the set of substations, Ω D is the collection of devices participating in multi-station fusion, Ω T,i The set of types in device i, c j,i is the cost of type j in device i, f j,i is the revenue of type j in device i, x j,i,b is a Boolean variable indicating whether type j in device i is installed in substation b;

[0111] Step 3.2.2: Use equation (22) to constrain the power flow:

[0112]

[0113] In formula (22), H is the PTDF matrix, n b is the number of substations, p j,i is the power requirement of type j in device i;

[0114] Step 3.2.3: Use formula (23) to constrain the space:

[0115]

[0116] In formula (23), is the space requirement of type j in device i, is the available space of substation b, where For the specific parameter setting method, please refer to the substation scalability indicator system proposed in step 2;

[0117] Step 3.2.4: Use formula (24) to constrain communication resources:

[0118]

[0119] In formula (24), Ω B,NC is the set of substations without communication resources, DC represents data center station, and FGS represents 5G base station;

[0120] Step 3.2.5, use formula (25) to constrain the photovoltaic power station:

[0121]

[0122] In formula (25), Ω B,NPV It is the set of substations without sufficient light intensity, PV means photovoltaic power station;

[0123] Step 3.2.6: Use formula (26) to constrain the charging and swapping station:

[0124]

[0125] In formula (26), Ω B,NEV It is a collection of substations without sufficient charging demand. CBS stands for Charging and Swapping Station;

[0126] Step 3.2.7: Use equations (27) to (29) to perform additional constraints:

[0127]

[0128] Formula (27) indicates that if a data center or a photovoltaic power station is installed, the energy storage system must be installed at the same time. Formula (28) indicates that if a 5G base station is installed, the data center needs to be installed at the same time. Formula (29) indicates that only one type of each device can be installed on the substation.

[0129] Step 3.4: Call the CPLEX solver to solve the above mixed integer linear programming model and obtain the optimal multi-station fusion planning strategy.

[0130] The main innovations of the present invention are:

[0131] 1. Taking into account the economic compatibility and power complementarity among modules, a combination form of multi-station integration is proposed using the fuzzy comprehensive evaluation method in system evaluation theory.

[0132] 2. The analytic hierarchy process (AHP) is used to comprehensively analyze the adaptability of different fusion modes and the "source-grid-load" characteristic power grid from three aspects: safety, economy and load coordination.

[0133] 3. A substation available resource indicator system is constructed, and a substation expandability assessment method is proposed.

[0134] 4. A multi-station integration planning and capacity configuration model is proposed with the goal of optimizing system cost and taking into account constraints such as power grid operation and communication resources.

[0135] Based on the same inventive concept, the embodiment of the present application also provides a multi-station fusion optimization device taking into account the operating characteristics of different functional modules, which can be used to implement the multi-station fusion optimization method taking into account the operating characteristics of different functional modules described in the above embodiments, as described in the following embodiments. Since the principle of solving the problem by the multi-station fusion optimization device taking into account the operating characteristics of different functional modules is similar to the multi-station fusion optimization method taking into account the operating characteristics of different functional modules, the implementation of the multi-station fusion optimization device taking into account the operating characteristics of different functional modules can refer to the implementation of a multi-station fusion optimization method taking into account the operating characteristics of different functional modules, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0136] like Figure 4 As shown, the optimization device comprises:

[0137] The first screening module 20 traverses the combination forms of each functional module and uses the fuzzy comprehensive evaluation method to screen the target combination forms with high expert recognition;

[0138] The second screening module 40 introduces the analytic hierarchy process to quantitatively analyze the adaptability of the screened target combination form to power grids with different characteristics;

[0139] The indicator system module 60 establishes an indicator system of available resources of the substation according to the resource requirements of different functional modules;

[0140] Model building module 80, based on the available resource indicator system, taking the system cost optimization as the goal, comprehensively considering the energy-information coupling constraint conditions, and building a multi-station fusion optimization planning model of mixed integer linear programming;

[0141] The calculation module 100 solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and analyzes the adaptability of power grids with different characteristics in combination with the target combination form to obtain the multi-station fusion optimal planning result.

[0142] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a different order from the order marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] The present invention also provides a computer-readable storage medium, which may be a computer-readable storage medium included in a multi-station fusion optimization device taking into account the operating characteristics of different functional modules in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into an electronic device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute a multi-station fusion optimization method taking into account the operating characteristics of different functional modules described in the present invention.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-station fusion optimization method taking into account the operating characteristics of different functional modules, It is characterized in that The optimization method comprises: Traverse the combination forms of each functional module and use the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition; The hierarchical analysis method is introduced to quantitatively analyze the adaptability of the selected target combination form to power grids with different characteristics; According to the resource requirements of different functional modules, a substation available resource indicator system is established; specifically: Use formula (11) to define the indoor space index C 1 : C 1 =X / S(11) In formula (11), X represents the size of the substation's available indoor space, S represents the baseline value of the indoor space, and the size of the substation's indoor space; Use formula (12) to define the outdoor space index C 2 : C 2 =Y / O (12) In the formula, Y represents the size of the outdoor space available in the substation, O represents the benchmark value of the outdoor space, and the size of the outdoor space of the substation; The power balance index C is defined using formula (13): 3 : C 3 =A / B (13) In formula (13), A represents the surplus power in the substation, B represents the substation capacity reference value, and the maximum capacity of the substation; Use formula (14) to define the power factor index C 4 : In formula (14), when C 4 =1, it means that the current substation can install electrical equipment that has power factor requirements; Use formula (15) to define the operator's external network access index C 5 : C 5 =0.1D (15) In formula (15), D indicates whether the substation is connected to the operator's external network. If yes, D = 1, otherwise D = 0; Use formula (16) to define the tower mast installation index C 6 : C 6 =0.2E (16) In formula (16), E indicates whether a tower mast is installed in the substation. If yes, E = 1, otherwise E = 0; Use formula (17) to define the tower mast installation index C 7 : C 7 =0.4F (17) In formula (17), F indicates whether a spare cable core is installed in the substation. If yes, F = 1, otherwise F = 0; Using equations (11)-(17), the substation scalability C is proposed expand Calculation formula: C expand =w 1 gC 1 +w 2 gC 2 +w 3 gC 3 +w 4 gC 4 +w 5 gC 5 +w 6 gC 6 +w 7 gC 7 (18) In the formula, C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 are the index values ​​in formula (11) to formula (17), w 1 、w 2 、w 3 、w 4 、w 5 、w 6 、w 7 are the weights corresponding to each indicator value; Based on the available resource indicator system, taking the system cost optimization as the goal, and comprehensively considering the energy-information coupling constraints, a multi-station fusion optimization planning model of mixed integer linear programming is constructed; specifically: Using equations (19) to (21), the objective function is defined as maximizing the net profit C of the multi-station integrated substation: max C=-C inv&op +C benefit (19) In formula (19)-formula (21), C inv&op is the investment and operation cost of the multi-station integrated substation, C benefit is the income of multi-station integrated substation, α is the ratio of operating cost to investment cost, Ω B is the set of substations, Ω D is the collection of devices participating in multi-station fusion, Ω T,i The set of types in device i, c j,i is the cost of type j in device i, f j,i is the revenue of type j in device i, x j,i,b is a Boolean variable indicating whether type j in device i is installed in substation b; Use formula (22) to constrain the power flow: In formula (22), H is the PTDF matrix, n b is the number of substations, p j,i is the power requirement of type j in device i; Use formula (23) to constrain the space: In formula (23), is the space requirement of type j in device i, is the available space of substation b, where For specific parameter setting methods, refer to the substation scalability indicator system; Use formula (24) to constrain communication resources: In formula (24), Ω B,NC is the set of substations without communication resources, DC represents data center station, and FGS represents 5G base station; Formula (25) is used to constrain the photovoltaic power station: In formula (25), Ω B,NPV It is the set of substations without sufficient light intensity, PV means photovoltaic power station; Use formula (26) to constrain the charging and swapping station: In formula (26), Ω B,NEV It is a collection of substations without sufficient charging demand. CBS stands for Charging and Swapping Station; Use equations (27) to (29) to perform additional constraints: Formula (27) indicates that if a data center or a photovoltaic power station is installed, the energy storage system must be installed at the same time. Formula (28) indicates that if a 5G base station is installed, the data center needs to be installed at the same time. Formula (29) indicates that only one type of each device can be installed on the substation. The multi-station fusion optimization planning model of the mixed integer linear programming is solved to obtain the multi-station fusion planning layout and capacity configuration, and the adaptability of the power grids with different characteristics is analyzed in combination with the target combination form to obtain the multi-station fusion optimal planning result.

2. The optimization method according to claim 1, It is characterized in that Traverse the combination forms of each functional module, and use the fuzzy comprehensive evaluation method to select the target combination forms with high expert recognition, including: Determine the system evaluation index weight A = [a 1 ,...,a m ], where m is the number of indicators involved in the evaluation, a k (k=1..m) represents the weight of the kth indicator, satisfying Select a comment set V, set comment set V = {v 1 ,v 2 ,v 3 }, where v 1 Indicates high recognition by experts; 2 Indicates average expert recognition; v 3 It indicates low expert recognition; Establish the fuzzy relationship matrix R m×3 , where r k1 (k=1..m) represents the percentage of experts who have a high degree of recognition for indicator k; r k2 (k=1..m) represents the percentage of experts who generally agree with indicator k; r k3 (k=1..m) represents the percentage of experts with low recognition of indicator k, and its value satisfies Calculate the composite value B of the fuzzy relationship matrix, where o is the composite operator, which means taking the smaller first and then the larger, ∧ is the symbol for taking the smaller in fuzzy mathematics, and ∨ is the symbol for taking the larger in fuzzy mathematics: In formula (2), Weighted comprehensive evaluation, after normalizing the composite value B of the fuzzy relationship matrix, the system evaluation result B'=(b 1 / b,b 2 / b,b 3 / b), where The combination with high evaluation results of the screening system is selected.

3. The optimization method according to claim 1, It is characterized in that The adaptability of the target combination forms selected by introducing the analytic hierarchy process to power grids with different characteristics includes: A hierarchical analysis structure model is established, in which the highest level is the ranking of the adaptability of various combinations to power grids with different characteristics; the middle level is the various criteria required to evaluate each combination; and the lowest level is the alternative plan for decision-making. Construct the judgment matrix Z and judge the weight vector of Z, that is, the weight of each criterion; Calculate the consistency test of the judgment matrix Z; Calculate the combined weights of elements at each level; Perform a hierarchical total ordering consistency check.

4. The optimization method according to claim 3, It is characterized in that Construct the judgment matrix Z as: In formula (3), z ij It represents the numerical representation of the importance of Zi to Zj, where n is the number of evaluation criteria.

5. The optimization method according to claim 3, It is characterized in that Methods for determining the weight vector of Z include: Use formula (4) to calculate the product p of all elements in each row of the judgment matrix Z i : Using formula (5) to calculate p i The nth root P i : Using formula (6) to calculate P i After normalization, we can get the vector weight W i , construct the feature vector W = [W 1 ,…,W n ]:

6. The optimization method according to claim 3, It is characterized in that The consistency check of calculating the judgment matrix Z includes: Using formula (7), we can find the maximum characteristic root λ of the judgment matrix Z: max : In formula (7), ZW is the product of the judgment matrix Z and the eigenvector W, (ZW) i is the i-th component of ZW; The consistency index CI is calculated using formula (8): The consistency index CR is calculated using formula (9): Check consistency and obtain hierarchical single ordering.

7. The optimization method according to claim 3, It is characterized in that The consistency check of hierarchical total ordering includes: Use formula (10) to perform the consistency test of hierarchical total ordering: In formula (10), z 1 ,…,z n is the combined weight of the upper layer elements.

8. The optimization method according to any one of claims 1 to 7, It is characterized in that The CPLEX solver is called to solve the multi-station fusion optimization planning model of the mixed integer linear programming.

9. A multi-station fusion optimization device taking into account the operating characteristics of different functional modules using the method of claim 1, It is characterized in that The optimization device comprises: The first screening module traverses the combination forms of each functional module and uses the fuzzy comprehensive evaluation method to select the target combination form with high expert recognition; The second screening module introduces the analytic hierarchy process to quantitatively analyze the adaptability of the selected target combination form to power grids with different characteristics; The indicator system module establishes the indicator system of available resources of substations according to the resource requirements of different functional modules; The model building module is based on the available resource indicator system, takes the optimal system cost as the goal, comprehensively considers the energy-information coupling constraints, and constructs a multi-station fusion optimization planning model of mixed integer linear programming; The calculation module solves the multi-station fusion optimization planning model of the mixed integer linear programming, obtains the multi-station fusion planning layout and capacity configuration, and analyzes the adaptability of power grids with different characteristics in combination with the target combination form to obtain the multi-station fusion optimal planning result.

10. A computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of multi-station fusion optimization taking into account the operating characteristics of different functional modules as described in any one of claims 1 to 7.

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