Distribution network planning method and storage medium considering microgrid flexibility resource attributes

Abstract

The present invention provides a distribution network planning method and storage medium that considers the flexibility resource attributes of a microgrid, and the steps include: planning substations in consideration of the flexibility resource attributes of the microgrid; planning the distribution network grid in consideration of the flexibility resource attributes of the microgrid; establishing a two-layer planning model for the distribution network in consideration of the flexibility resource attributes of the microgrid; solving the grid planning part of the distribution network planning model using a genetic algorithm, and using a line crossing determination method and establishing a planning model for the phenomenon of crossing of the grid structure. The planning method that considers the flexibility resources of the microgrid proposed in the present invention will slightly increase the cost of grid planning, but will increase the transmission margin of the entire distribution network, so that in the future, when the distributed power source fluctuates or the load forecast is inaccurate during the operation of the distribution network, the line will have enough flexibility to allow the main grid or microgrid to transmit power.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a distribution network planning method taking into account the flexibility resource attributes of a microgrid. Background Art

[0002] Considering that in recent years, with the development of the economy, energy consumption has continued to grow, and load-side resources have appeared in the distribution network in multiple forms, including distributed power sources, electric vehicles with dual source-load characteristics, and energy storage devices. A series of flexible resources. Except for the controllable energy storage devices, other flexible resources have a greater impact on the power grid due to their uncertainty. As an electrical interconnected system that aggregates flexible resources, microgrids are responsible for the use and dispatch of flexible resources, and can improve the level of renewable energy utilization and improve economic and environmental benefits while ensuring the safety and stability of the main grid.

[0003] Microgrid is a new type of network structure, which is a system unit composed of a group of micro power sources, loads, energy storage systems and control devices. It is an autonomous system that can achieve self-control, protection and management. It can be connected to the external power grid or operate in isolation. For traditional large power grids, microgrid refers to a network composed of multiple distributed power sources and their related loads in a certain topological structure, and is connected to the conventional power grid through static switches. The development and extension of microgrids can fully promote the large-scale access of distributed power sources and renewable energy, realize the high-reliability supply of various energy forms to loads, and is an effective way to realize active distribution networks. Microgrid is an important part of the realization of smart grids. It can realize the integrated operation of internal power sources and loads, coordinate and control with the main power grid, smoothly connect to the main power grid or operate independently, and meet or even improve users' requirements for power quality, power reliability and safety. At present, most of the research on microgrid flexibility resources focuses on the operation optimization level, but does not fully involve flexibility resources in the planning stage, that is, the problem of insufficient consideration of flexibility resources in the substation site selection and capacity determination and grid planning appears. This may result in insufficient available transmission margin of the line during actual operation if the output of distributed power sources fluctuates greatly or the load forecast is inaccurate, requiring the main grid or microgrid to change its output to meet power balance. This will increase the difficulty of intraday scheduling and even lead to wind and solar power abandonment or load shedding. Summary of the invention

[0004] The present invention proposes a distribution network planning method that takes into account the flexibility resource attributes of a microgrid, which can solve the above technical problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A distribution network planning method considering the flexibility resource attributes of microgrids includes:

[0007] Substation planning considering the flexibility resource attributes of microgrids;

[0008] Planning the distribution network framework considering the flexibility resource attributes of microgrids;

[0009] A two-layer planning model for distribution network is established considering the flexibility resource attributes of microgrids;

[0010] The genetic algorithm is used to solve the grid planning part of the distribution network planning model. In view of the crossing phenomenon of the grid structure, the line crossing determination method is adopted.

[0011] Furthermore, the substation planning considering the microgrid flexibility resource attributes includes:

[0012] Step 1.1: The number and capacity of substations are determined based on the capacity-load ratio formula. The traditional capacity-load ratio model is established using formula (1) and the traditional capacity-load ratio model is established using formula (2).

[0013] Establish a distribution network capacity-load ratio model with microgrid flexibility resources:

[0014]

[0015]

[0016] In formula (1) to formula (2), R s Indicates the load ratio; S i represents the capacity of the i-th transformer; P max represents the peak load value of the planned area; m is the number of substations; n is the number of transformers used in each substation; λ is the load simultaneity rate; P mgeq is the equivalent capacity of the microgrid, including the output of distributed generation P dgeq and microgrid flexibility resource output Fl mg ;

[0017] Step 1.2: According to the principle of equal load moment minimum, use formula (3) to establish the substation site selection model, and use formula (4) to establish the single substation coordinate model:

[0018]

[0019]

[0020] In equations (3) and (4), C1 and C2 are the common node load moments and microgrid node load moments; m is the number of substations; Φ l , Φ mg is the set of common nodes and microgrid nodes in the planning area; l , mg is the load simultaneity rate of common nodes and microgrid nodes; bij 、b ik is a binary variable. When it is 1, it means that the substation supplies power to the node, and when it is 0, it means that the substation does not supply power to the node. j , P k is the load power of common nodes and microgrid nodes; CO l,j / k Indicates the load node coordinates; CO t,i Indicates the coordinates of the substation;

[0021] Step 1.3: After deriving the coordinate calculation formula, use the iteration method to find the final substation coordinates. During the iteration process, the initial value is the average value of the coordinates of each load point, and the iteration stops when the difference between the k+1th result and the kth result meets the accuracy range.

[0022] Furthermore, the steps of planning the distribution network framework taking into account the microgrid flexibility resource attributes include:

[0023] Step 2.1: In the grid planning stage, constraints are imposed on the line transmission margin to ensure the flexibility of the area to be planned. Formula (5) is used to establish a flexibility constraint model in grid planning:

[0024] 0≤FL l ≤βFL max (5)

[0025] In formula (5), FL l is the used flexibility adjustment capability of the lth line, that is, the transmission capacity used by the line; β is the margin coefficient, and its value range is [0,1]; FL max Provides maximum flexibility in line adjustment capabilities;

[0026] Step 2.2: In addition to satisfying the flexibility constraint, the node voltage constraint model for grid planning is established using equation (6):

[0027] V i,min ≤V i ≤V i,max (6)

[0028] In formula (6), V i is the voltage of node i; V i,min and V i,max Respectively represent the lower and upper limits of the voltage at node i;

[0029] Step 2.3: In addition to satisfying the flexibility constraint, the power balance constraint model for grid planning is established using formula (7):

[0030]

[0031] In formula (7), P i and Qi is the active power and reactive power injected at node i; U i and U j is the voltage amplitude of node i and node j; G ij and B ij is the conductance and susceptance of branch ij; δ ij is the voltage phase angle difference between nodes i and j;

[0032] Step 2.4: In addition to establishing the power balance constraint model for grid planning, the reverse power flow constraint model for grid planning is also established using formula (8):

[0033] |P mg,i -P load,i +j(Q mg,i -Q load,i )|≤αS i,max (8)

[0034] In formula (8), P mg,i represents the active output of the i-th microgrid; P load,i represents the active power demand of the ith load in the distribution network; Q mg,i represents the reactive power output of the i-th microgrid; Q load,i represents the reactive power demand of the i-th load; α represents the allowable reverse capacity ratio; S i,max represents the maximum capacity of the i-th substation;

[0035] Step 2.5: In addition to establishing the reverse power flow constraint model for grid planning, the radial network constraint model for grid planning is also established using formula (9):

[0036] m=n+1 (9)

[0037] In formula (9), m is the total number of system points; n is the total number of branches after planning.

[0038] Furthermore, the step considers the flexibility resource attributes of the microgrid and uses equations (10), (11) and (12) to establish a two-layer planning model for the distribution network:

[0039] min C=C p +C r (10)

[0040]

[0041]

[0042] In formula (23) to formula (25), C is the total cost, including the planned investment cost C p and operating cost C rThe first item of the investment planning cost is the substation investment cost, and the second item is the grid construction planning cost, where C Tp (S i ) is the substation investment cost function under the corresponding capacity, r is the discount rate, m is the depreciation period of the substation, and is the cost of building a new power supply line; the items in the operating cost represent the substation operation and maintenance cost and the line network loss cost respectively, where C rT (S i ) is the substation operation and maintenance cost function under the corresponding capacity, n year is the simulation period; x i is a binary decision variable, indicating whether line i is selected; c loss is the unit network loss cost; ΔP i is the active power loss of the ith line.

[0043] Furthermore, the step adopts a genetic algorithm to solve the grid planning part in the distribution network planning model. Aiming at the phenomenon of grid structure crossing, a line crossing determination method is adopted, and a new fitness function model is established using formula (13):

[0044]

[0045] In formula (13), C is the original planning cost objective function; K j is the penalty function of the conventional constraints in the planning process; a linecross is a binary variable for determining line crossing; K' is a penalty function corresponding to line crossing.

[0046] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above method.

[0047] It can be seen from the above technical solutions that the distribution network planning method considering the flexibility resource attributes of microgrids of the present invention proposes flexibility resource evaluation indicators based on microgrid flexibility resources, and establishes a two-layer two-stage model for distribution network planning and operation considering the flexibility resource attributes of microgrids. In the first stage, the model considers the impact of microgrid flexibility resources on substation capacity, and in the second stage, constrains the line flexibility indicators, fully considering the volatility brought about by the flexibility resources after being connected to the distribution network.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] 1. The present invention considers the flexibility resource attributes of the microgrid to plan the substation, mainly considering the address planning and capacity planning. The selection of the substation address mainly considers reducing the cable cost and transmission loss, the convenience of outgoing lines, the influence of policy and environmental factors, and the influence of the flexible resource access to the distribution network. The capacity of the substation is determined by considering the power supply demand and the capacity-load ratio guidelines.

[0050] 2. The present invention considers the flexibility resource attributes of the microgrid to plan the distribution network grid. It considers whether the flexibility resources will be unavailable in the actual operation of the system after planning, that is, when the actual value of the distributed power source or load deviates from the predicted value, whether other flexibility resources in the distribution network can make up for the difference, and whether the transmission network has enough margin to transmit the source-attributed flexibility resources such as energy storage to the target node. Therefore, in the grid planning stage, constraints are imposed on the line transmission margin to ensure the flexibility of the area to be planned.

[0051] 3. The present invention aims at both investment and operation costs, and establishes a two-layer planning model for the distribution network that takes into account the flexibility resource attributes of the microgrid. The upper model plans the distribution network substations and grid respectively, and the lower model calculates the operation cost of the distribution network.

[0052] 4. The present invention adopts a line crossing determination method to solve the problem that when using genetic algorithm to solve grid planning, the output grid structure may have crossing, which is not allowed in the actual planning operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention obtains a schematic diagram of the load partitioning and substation planning results of the distribution network in the planned area;

[0054] Figure 2 It is a schematic flow chart of the line crossing determination method of the present invention;

[0055] Figure 3 The present invention is a schematic diagram of grid planning based on genetic algorithm and taking minimization of investment and operation cost as objective function and considering the flexibility resources of microgrid. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0057] like Figure 1As shown, the distribution network planning method considering the flexibility resource attributes of the microgrid described in this embodiment takes a 35kV distribution network connected to multiple microgrids as an example to verify the distribution network planning method considering the flexibility resource attributes of the microgrid. First, the substation is planned under the consideration of the flexibility resource attributes of the microgrid; secondly, the distribution network grid is planned under the consideration of the flexibility resource attributes of the microgrid; then, a two-layer planning model of the distribution network is established under the consideration of the flexibility resource attributes of the microgrid; finally, a genetic algorithm is used to solve the grid planning part in the distribution network planning model, and a line crossing determination method is used for the phenomenon of crossing of the grid structure; specifically, the following steps are followed:

[0058] Step 1: Plan the substation considering the flexibility resource attributes of the microgrid. Since the voltage level of the distribution network in the planned area is 35kV, the substation uses a 35 / 10kV transformer. According to the relevant guidelines, the number of main transformers in the substation is determined to be 3, and the capacity-load ratio is determined to be 1.9. The load zoning and substation planning results of the distribution network in the planned area are as follows: Figure 1 As shown;

[0059] Step 1.1: The number and capacity of substations are determined mainly based on the capacity-load ratio formula. The traditional capacity-load ratio model can be established using formula (14), and the distribution network capacity-load ratio model containing microgrid flexibility resources can be established using formula (15):

[0060]

[0061]

[0062] In formula (14) to formula (15), R s Indicates the load ratio; S i represents the capacity of the i-th transformer; P max represents the peak load value of the planned area; m is the number of substations; n is the number of transformers used in each substation; λ is the load simultaneity rate; P mgeq is the equivalent capacity of the microgrid, including the output of distributed generation P dgeq and microgrid flexibility resource output Fl mg ;

[0063] Step 1.2: According to the principle of equal load moment minimum, use formula (16) to establish the substation site selection model, and use formula (17) to establish the single substation coordinate model:

[0064]

[0065]

[0066] In equations (16) and (17), C1 and C2 are the common node load moments and microgrid node load moments; m is the number of substations; Φ l , Φ mg is the set of common nodes and microgrid nodes in the planning area; l , mg is the load simultaneity rate of common nodes and microgrid nodes; b ij 、b ik is a binary variable. When it is 1, it means that the substation supplies power to the node, and when it is 0, it means that the substation does not supply power to the node. j , P k is the load power of common nodes and microgrid nodes; CO l,j / k Indicates the load node coordinates; CO t,i Indicates the coordinates of the substation;

[0067] Step 1.3: After deriving the coordinate calculation formula, the final substation coordinates can be obtained using the iteration method. During the iteration process, the initial value can be selected as the average value of the coordinates of each load point, and the iteration is stopped when the difference between the k+1th result and the kth result meets the accuracy range.

[0068] Step 2 plans the distribution network grid considering the flexibility resource attributes of the microgrid. Select power cables for the grid planning lines in the area to be planned. According to the "Guidelines for Urban Power Grid Planning and Design", cables of 220kV and below should preferably use cross-linked polyethylene insulated copper core cables. Therefore, this embodiment selects cross-linked polyethylene insulated YJV22-21 / 353*400 three-core copper cables. During the grid planning process, the unit resistance is 0.05Ω / km; the unit reactance is 0.09Ω / km; the current load is 600A; the investment cost per kilometer is 800,000 yuan; the conversion coefficient is 0.1; and the network loss cost is 0.37 yuan per kilowatt-hour. ;

[0069] Step 2.1: In the grid planning stage, constraints are imposed on the line transmission margin to ensure the flexibility of the area to be planned. The flexibility constraint model in grid planning is established using formula (18):

[0070] 0≤FL l ≤βFL max (18)

[0071] In formula (18), FL l is the used flexibility adjustment capability of the lth line, that is, the transmission capacity used by the line; β is the margin coefficient, and its value range is [0,1]; FL max Provides maximum flexibility in line adjustment capabilities;

[0072] Step 2.2: In addition to satisfying the flexibility constraint, it is also necessary to use formula (19) to establish the node voltage constraint model for grid planning:

[0073] V i,min ≤V i ≤V i,max (19)

[0074] In formula (19), V i is the voltage of node i; V i,min and V i,max Respectively represent the lower and upper limits of the voltage at node i;

[0075] Step 2.3: In addition to satisfying the flexibility constraint, it is also necessary to use formula (20) to establish the power balance constraint model for grid planning:

[0076]

[0077] In formula (20), P i and Q i is the active power and reactive power injected at node i; U i and U j is the voltage amplitude of node i and node j; G ij and B ij is the conductance and susceptance of branch ij; δ ij is the voltage phase angle difference between nodes i and j;

[0078] Step 2.4: In addition to establishing the power balance constraint model for grid planning, it is also necessary to use formula (21) to establish the reverse power flow constraint model for grid planning:

[0079] |P mg,i -P load,i +j(Q mg,i -Q load,i )|≤αS i,max (twenty one)

[0080] In formula (21), P mg,i represents the active output of the i-th microgrid; P load,i represents the active power demand of the ith load in the distribution network; Q mg,i represents the reactive power output of the i-th microgrid; Q load,i represents the reactive power demand of the i-th load; α represents the allowable reverse capacity ratio; S i,max represents the maximum capacity of the i-th substation;

[0081] Step 2.5: In addition to establishing the reverse power flow constraint model for grid planning, it is also necessary to use formula (22) to establish the radial network constraint model for grid planning:

[0082] m=n+1 (22)

[0083] In formula (22), m is the total number of system points; n is the total number of branches after planning.

[0084] Step 3: Considering the flexibility resource attributes of the microgrid, a two-layer planning model for the distribution network is established using equations (23), (24) and (25):

[0085] min C=C p +C r (twenty three)

[0086]

[0087]

[0088] In formula (23) to formula (25), C is the total cost, including the planned investment cost C p and operating cost C r The first item of the investment planning cost is the substation investment cost, and the second item is the grid construction planning cost, where C Tp (S i ) is the substation investment cost function under the corresponding capacity, r is the discount rate, m is the depreciation period of the substation, and is the cost of building a new power supply line; the items in the operating cost represent the substation operation and maintenance cost and the line network loss cost respectively, where C rT (S i ) is the substation operation and maintenance cost function under the corresponding capacity, n year is the simulation period; x i is a binary decision variable, indicating whether line i is selected; c loss is the unit network loss cost; ΔP i is the active power loss of the i-th line;

[0089] Step 4: Genetic algorithm is used to solve the grid planning part of the distribution network planning model. In view of the cross phenomenon of the grid structure, a line cross determination method is adopted. The flow chart of the line cross determination method is as follows: Figure 2 As shown in Figure 26, a new fitness function model is established using formula (26). The grid planning results based on the genetic algorithm with the minimum investment and operating costs as the objective function considering the microgrid flexibility resources are as follows: Figure 3 As shown. The K value of the fitness function should be positive and much larger than the cost of the feasible grid planning scheme. Therefore, in this embodiment, the K value is 10 million yuan:

[0090]

[0091] In formula (26), C is the original planning cost objective function; K j is the penalty function of the conventional constraints in the planning process; a linecrossis a binary variable for determining line crossing; K' is a penalty function corresponding to line crossing.

[0092] The experiment to verify the "distribution network planning method considering the flexibility resource attributes of microgrids" of the present invention mainly includes the following steps:

[0093] 1) A 35kV distribution network with multiple microgrids is used as the test system. There are 3 microgrid nodes and 32 common load nodes in the planned area.

[0094] The basic load data in the region are shown in Table 1:

[0095] Table 1 Basic data of regional load

[0096]

[0097]

[0098] 2) Considering the flexibility resources of microgrids for distribution network partitioning and substation planning

[0099] 3) Considering microgrid flexibility resources for grid planning

[0100] 4) Analyze the impact of microgrids and flexibility resources on distribution network planning

[0101] The following schemes are set for comparative analysis: ① Scheme 1 does not consider microgrids, and assumes that all the loads in the planned area are ordinary loads; ② Scheme 2 considers microgrids, but does not consider flexibility resources in the planning process, that is, conventional distribution network planning of microgrids; ③ Scheme 3 is the distribution network planning considering microgrid flexibility resources proposed in this paper, in which the line flexibility margin is set to 10%. For the substation planning part, the depreciation period is 20, the discount rate is 5%, and the unit capacity cost is 600,000 yuan / MW. The planning costs of each scheme are shown in Tables 2 to 4:

[0102] Table 2 Economic cost of scheme 1

[0103]

[0104] Table 3 Economic cost of scheme 2

[0105]

[0106]

[0107] Table 4 Economic cost of scheme 3

[0108]

[0109] As for the line investment cost and network loss cost, by comparing Scheme 1 and Scheme 3, it can be found that due to the existence of microgrids, reverse flow will appear in the distribution network, and reverse flow constraints need to be performed during the planning process, which will increase the total length of the line, increase network losses, and increase investment costs. The line investment cost is positively correlated with the capacity of the microgrid. However, when the reverse flow generated by the microgrid does not exceed the limit in all time periods, part of the load can be directly supplied by the microgrid, thereby reducing the total length of the system line and reducing the line investment cost, such as Area 1 of Scheme 2.

[0110] As for the grid planning considering the flexibility resources of microgrids, the lines sacrifice a small part of the cost in exchange for the transmission capacity margin, as shown in Scheme 2 and Scheme 3. Since Scheme 3 leaves a 10% flexibility margin in the planning process, the line investment costs in the three regions have increased, but the increase is not large. The advantage of grid planning considering the flexibility resources of microgrids is that it improves the robustness of the actual operation process by sacrificing a small amount of cost in the planning process, that is, it better copes with the fluctuation of distributed power sources and the uncertainty of load forecasting, reduces the occurrence of wind and solar power abandonment and load shedding due to insufficient line transmission capacity, and avoids line overload, indirectly extending the service life of the grid and making the distribution network stronger.

[0111] In summary, the distribution network planning method proposed in the present invention that considers the flexibility resource attributes of the microgrid considers the flexibility resources from the two stages of substation planning and grid planning and the two levels of investment and operation. This method will slightly increase the cost of grid planning, but will increase the transmission margin of the entire distribution network, so that in the future, when the distributed power source fluctuates or the load forecast is inaccurate during the operation of the distribution network, the line will have enough flexibility to enable the main grid or microgrid to transmit power.

[0112] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0113] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0114] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any distribution network planning method that considers the flexibility resource attributes of the microgrid in the above-mentioned embodiments.

[0115] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0116] The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0117] Memory, used to store computer programs;

[0118] A processor, configured to implement the distribution network planning method considering the flexibility resource attributes of the microgrid when executing the program stored in the memory;

[0119] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0120] The communication interface is used for communication between the above electronic device and other devices.

[0121] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0122] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.

[0123] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0124] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0125] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0126] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network planning method considering the flexibility resource attributes of a microgrid, characterized in that: Substation planning considering the flexibility resource attributes of microgrids; Planning the distribution network framework considering the flexibility resource attributes of microgrids; A two-layer planning model for distribution network is established considering the flexibility resource attributes of microgrids; Genetic algorithm is used to solve the grid planning part of the distribution network planning model. In view of the phenomenon of grid structure crossing, the line crossing determination method is adopted. The substation planning considering the microgrid flexibility resource attribute includes: Step 1.1: The number and capacity of substations are determined based on the capacity-load ratio formula. The traditional capacity-load ratio model is established using formula (1) and the traditional capacity-load ratio model is established using formula (2). Establish a distribution network capacity-load ratio model with microgrid flexibility resources: (1) (2) In formula (1) to formula (2), Indicates the capacity ratio; Indicates i The capacity of each transformer; Indicates the peak load value of the area to be planned; m is the number of substations; n The number of transformers used in each substation; is the load simultaneity rate; is the equivalent capacity of the microgrid, including the output of distributed generation and microgrid flexibility resource output ; Step 1.2: Based on the principle of minimum equal load moment, use formula (3) to establish the substation site selection model, and use formula (4) to establish the single substation coordinate model: (3) (4) In formula (3) to formula (4), 、 is the common node load moment and microgrid node load moment; m is the number of substations; , It is the set of common nodes and microgrid nodes in the planning area; , is the load simultaneity rate of common nodes and microgrid nodes; , It is a binary variable. When it is 1, it means that the substation supplies power to the node, and when it is 0, it means that the substation does not supply power to the node. , is the load power of common nodes and the load power of microgrid nodes; represents the load node coordinates; Indicates the coordinates of the substation; Step 1.3: After the coordinate calculation formula is derived, the final substation coordinates are obtained using the iteration method. During the iteration process, the initial value is the average value of the coordinates of each load point, and the average value is used in the first step. k+1 The results and k Stop when the difference between the results meets the accuracy range; The steps are to plan the distribution network framework taking into account the flexibility resource attributes of the microgrid, including: Step 2.1: In the grid planning stage, constraints are imposed on the line transmission margin to ensure the flexibility of the area to be planned. Formula (5) is used to establish a flexibility constraint model in grid planning: (5) In formula (5), For the l The used flexibility of the line, i.e. the used transmission capacity of the line; is the margin coefficient, and its value range is [0,1]; Provides maximum flexibility in line adjustment capabilities; Step 2.2: In addition to satisfying the flexibility constraint, the node voltage constraint model for grid planning is established using equation (6): (6) In formula (6), For Node i Voltage; and Respectively represent nodes i The lower and upper voltage limits; Step 2.3: In addition to satisfying the flexibility constraint, the power balance constraint model for grid planning is established using formula (7): (7) In formula (7), and For Node i The active power and reactive power injected at and For Node i and nodes j The voltage amplitude of and For branch ij Conductance and susceptance values; For Node i , j The voltage phase angle difference between Step 2.4: In addition to establishing the power balance constraint model for grid planning, the reverse power flow constraint model for grid planning is also established using formula (8): (8) In formula (8), Indicates i The active power output of each microgrid; Indicates the distribution network i Active power demand of each load; Indicates i Reactive power output of each microgrid; Indicates i Reactive power demand of each load; Indicates the allowable backfeed capacity ratio; Indicates i The maximum capacity of a substation; Step 2.5: In addition to establishing the reverse power flow constraint model for grid planning, the radial network constraint model for grid planning is also established using formula (9): (9) In formula (9), m Summarize points for the system; n is the total number of branches after planning; The steps take into account the flexibility resource attributes of the microgrid and use equations (10), (11) and (12) to establish a two-layer planning model for the distribution network: (10) (11) (12) In formula (23) to formula (25), C is the total cost, including planning investment cost and operating costs The first item in the investment planning cost is the substation investment cost, and the second item is the grid construction planning cost. is the substation investment cost function under the corresponding capacity, r is the discount rate, m is the depreciation period of the substation, is the cost of building a new power supply line; the items in the operating cost represent the substation operation and maintenance cost and the line network loss cost, among which is the substation operation and maintenance cost function under the corresponding capacity, is the simulation period; is a binary decision variable, representing the route i whether it is selected; is the unit network loss cost; For the i Active power loss of the line.

2. The distribution network planning method considering the microgrid flexibility resource attributes according to claim 1 is characterized by: The step adopts a genetic algorithm to solve the grid planning part in the distribution network planning model. Aiming at the phenomenon of grid structure crossing, a line crossing determination method is adopted, and a new fitness function model is established using formula (13): (13) In formula (13), C is the original planning cost objective function; is the penalty function for the conventional constraints in the planning process; Binary variable for line crossing determination; is the penalty function corresponding to line crossing.

3. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 2.

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