A method and device for optimizing the planning of a coordinated distribution network

Abstract

The present invention discloses an optimization method and device for coordinating the planning of a distribution network framework. The optimization method specifically includes the following steps: based on the planning of a distribution network framework that coordinates the primary grid and secondary equipment, a first evaluation model, a second evaluation model and multiple topological structures for the planning of the distribution network framework are constructed; in combination with the first evaluation model and the second evaluation model, a first objective function corresponding to the planning of the primary grid is given; based on the second evaluation model, a second objective function corresponding to the planning of the secondary equipment is given; in combination with a modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the planning of the distribution network framework is optimized in multiple topological structures; according to the optimization situation, the target topological structure is determined, and a planning for the distribution network framework is given. The present invention considers the complementary functions of the secondary equipment to the primary grid, and coordinates the planning of the distribution network framework, improves the reliability of the operation of the distribution network, and optimizes the construction cost and operation efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network planning, and in particular relates to an optimization method and device for coordinating distribution network framework planning. Background Art

[0002] The distribution network is an important part of the power system, distributing electricity from the high-voltage transmission grid to end users safely, reliably and efficiently.

[0003] The effective planning and management of distribution networks play an irreplaceable role in meeting the growing demand for electricity, adapting to changes in the energy structure, improving system resilience and promoting social and economic development.

[0004] Generally speaking, the primary grid structure of the distribution network is the foundation of the entire distribution network power system, including the layout of lines, substation location and capacity, etc. Secondary equipment plays a key role in measurement, protection and control in the distribution network power system, including protection equipment, automation equipment, etc.

[0005] The idea of ​​distribution network planning is usually to plan the grid structure first, and then configure the corresponding secondary equipment. For example, patent CN118095765A provides a two-stage planning method, device, equipment and storage medium for distribution network, the method includes: gridding the planning area to obtain a number of grids, and based on the several grids, spatial load prediction is performed on the load nodes in the planning area to obtain spatial load prediction values; output characteristics of distributed power sources in the planning area are predicted to obtain node load prediction values; according to the spatial load prediction values ​​and the node load prediction values, a grid expansion planning model is established; based on the grid expansion planning model, a flexible upgrade planning model is established; based on the standard particle swarm, the two-stage planning model consisting of the grid expansion planning model and the flexible upgrade planning model is solved to obtain the planning results, and the planning results include the optimal grid expansion structure, as well as the ESS-SOP site selection plan and the ESS site selection plan. This scheme can effectively improve the accuracy of distribution network planning, thereby building a reliable and stable distribution system.

[0006] However, the above-mentioned sequential planning method has considerable limitations. For example, the planning process must first adapt to the stringent requirements of the primary grid structure, and the functional attributes of the secondary equipment cannot be fully demonstrated through the planning method.

[0007] Therefore, how to coordinate the configuration of secondary equipment in the distribution network to achieve coordinated planning of the distribution network grid and improve the operational efficiency of the distribution network on the basis of optimizing construction costs and reliability constraints is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0008] In view of the defects existing in the above-mentioned prior art, the present invention provides an optimization method and device for coordinating the planning of the distribution network framework. The optimization method specifically includes the following steps: based on the distribution network framework planning that coordinates the primary grid and the secondary equipment, construct the first evaluation model, the second evaluation model and multiple topological structures of the distribution network framework planning; combining the first evaluation model and the second evaluation model, give the first objective function corresponding to the primary grid planning; based on the second evaluation model, give the second objective function corresponding to the secondary equipment planning; combining the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, optimize the distribution network framework planning in multiple topological structures; according to the optimization situation, determine the target topological structure, and give the planning for the distribution network framework. The present invention takes into account the complementary functions of the secondary equipment to the primary grid, and conducts collaborative planning of the distribution network framework, improves the reliability of the distribution network operation, and optimizes the construction cost and operation efficiency.

[0009] In a first aspect, the present invention provides an optimization method for coordinating distribution network framework planning, which specifically comprises the following steps:

[0010] Based on the distribution network framework planning of coordinating the primary grid and the secondary equipment, a first evaluation model, a second evaluation model and a plurality of topological structures of the distribution network framework planning are constructed, wherein the distribution network framework planning of coordinating the primary grid and the secondary equipment is to embed a secondary equipment configuration module in the primary grid planning of the distribution network;

[0011] Combining the first evaluation model and the second evaluation model, a first objective function corresponding to a grid planning is given;

[0012] Based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given;

[0013] Combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the distribution network framework planning is optimized in multiple topological structures;

[0014] According to the optimization situation, the target topology is determined and a plan for the distribution network is given.

[0015] Furthermore, multiple topological structures of the distribution network framework planning are constructed, which specifically include the following steps:

[0016] Obtain and traverse a variety of pre-planned initial topologies, and divide the distribution network into feeder zones according to the headend and terminal types of the distribution network;

[0017] The connection relationship between each feeder partition and other feeder partitions is traversed to form multiple topological structures of distribution network grid planning.

[0018] Furthermore, combining the first evaluation model and the second evaluation model, a first objective function corresponding to a grid planning is given, specifically including:

[0019] Combining the first evaluation model and the second evaluation model, based on the annual comprehensive cost of the primary grid and secondary equipment layout, a first objective function corresponding to the primary grid planning is given;

[0020] Among them, the annual comprehensive cost includes the annual total cost of the primary grid and the annual total cost of the secondary equipment;

[0021] The first evaluation model is specifically expressed as:

[0022]

[0023] in, is the net power of load node k, is the power flow from load node k to other load nodes l, is the power flow from other load nodes m to load node k, L and M are the number of outgoing and incoming edges connected to load node k, respectively. , are the minimum and maximum allowable powers of the load node, respectively;

[0024] The second evaluation model is specifically expressed as:

[0025]

[0026] in, is the average power supply availability of the distribution network, The number of load nodes in the distribution network, is the annual average power outage time of load node k, is the number of users connected to load node k.

[0027] Furthermore, based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given, specifically including:

[0028] Combined with the analysis of the load nodes in the evaluation model, the second objective function corresponding to the secondary equipment planning is given based on the annual total cost of the secondary equipment and the annual power outage cost of the primary grid;

[0029] The second objective function is specifically expressed as:

[0030]

[0031] in, is the total annual cost of secondary equipment, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. is the average load of load node k, The unit power outage loss costs.

[0032] Furthermore, combined with the modified particle swarm optimization algorithm and based on the analysis of the first objective function and the second objective function, the optimization of the distribution network framework planning is performed in multiple topological structures, specifically including the following steps:

[0033] Based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function;

[0034] Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed;

[0035] Alternate the optimization analysis of the first objective function and the second objective function until convergence.

[0036] Further, based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function, which specifically includes the following steps:

[0037] Determine an initial target topological structure, and provide an initial first matrix value corresponding to a first physical parameter group;

[0038] Perform optimization analysis on the initial first matrix value through the first objective function to give an initial result;

[0039] Update the first matrix value and repeat the optimization analysis step through the first objective function;

[0040] Iterate the above update and optimization analysis process;

[0041] The first physical parameter group includes feeder partitions and connection relationships between feeder partitions.

[0042] Furthermore, the first matrix value is specifically expressed as:

[0043]

[0044] in, is the set of connection relationships between feeder partitions. is the connection relationship of the first feeder partition, and d is the total number of connection relationships between each feeder partition.

[0045] Further, based on the type and quantity of the secondary equipment, the second objective function is optimized and analyzed, specifically including the following steps:

[0046] According to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is provided;

[0047] Perform optimization analysis on the initial second matrix value through the second objective function to give an initial evaluation result;

[0048] Update the second matrix value and repeat the optimization analysis step through the second objective function;

[0049] Iterate the above update and optimization analysis process;

[0050] The second physical parameter group includes the type of secondary devices and the number of secondary devices.

[0051] Furthermore, the second matrix value is specifically expressed as:

[0052]

[0053] in, is a collection of various secondary equipment types, is the number of secondary devices corresponding to the first feeder partition connection relationship, and d is the total number of connection relationships between each feeder partition.

[0054] Further, according to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is given, specifically including:

[0055] Based on the initial target topology and the connection relationship nodes between each feeder partition, the distance from each secondary device to the connection relationship node between each feeder partition is given.

[0056] Further, updating the second matrix value includes:

[0057] The connection relationship nodes between each secondary device and each feeder partition form a distance matrix;

[0058] Select the minimum distance between each secondary device and each feeder partition connection relationship node from the distance matrix, and combine them to form a minimum distance vector;

[0059] Extract at least one element from the minimum distance vector until all combinations of elements in the minimum distance vector are extracted, and give all extracted element combinations and the corresponding minimum distance sub-vectors;

[0060] Based on the distance matrix, determine the maximum value of the minimum distances among all elements of each minimum distance subvector;

[0061] Compare the maximum values ​​of the minimum distances corresponding to the minimum distance sub-vectors with the same number of elements, and give the minimum distance sub-vector with the smallest maximum value among the minimum distance sub-vectors with the same number of elements;

[0062] Sort the minimum distance sub-vectors with the smallest maximum value according to the number of elements from small to large, and process the maximum value difference of the minimum distance sub-vector corresponding to the minimum distance in order to obtain the change amount of the adjacent minimum distance sub-vectors;

[0063] Compare the change amount of the adjacent minimum distance sub-vectors with the change amount threshold range, and give the minimum distance sub-vector that meets the change amount threshold range and has the least number of elements;

[0064] According to the minimum distance sub-vector that meets the change threshold range and has the least number of elements, the secondary device corresponding to each element of the minimum distance sub-vector in the initial target topology structure is determined.

[0065] In a second aspect, the present invention further provides an optimization device for coordinating the planning of a distribution network framework, which adopts the above-mentioned optimization method for coordinating the planning of a distribution network framework, and specifically includes:

[0066] A construction unit is used to construct a first evaluation model, a second evaluation model and a plurality of topological structures for the distribution network framework planning based on the distribution network framework planning of coordinating the primary grid and the secondary equipment, wherein the distribution network framework planning of coordinating the primary grid and the secondary equipment is to embed a secondary equipment configuration module in the primary grid planning of the distribution network;

[0067] The analysis unit is used to combine the first evaluation model and the second evaluation model to provide a first objective function corresponding to the primary grid planning; based on the second evaluation model, provide a second objective function corresponding to the secondary equipment planning;

[0068] The optimization unit is used to combine the modified particle swarm optimization algorithm and optimize the distribution network framework planning in multiple topological structures based on the analysis of the first objective function and the second objective function; according to the optimization situation, the target topological structure is determined and the planning for the distribution network framework is given.

[0069] Furthermore, multiple topological structures of distribution network planning are constructed, including:

[0070] Obtain and traverse a variety of pre-planned initial topologies, and divide the distribution network into feeder zones according to the headend and terminal types of the distribution network;

[0071] The connection relationship between each feeder partition and other feeder partitions is traversed to form multiple topological structures of distribution network grid planning.

[0072] Furthermore, combining the first evaluation model and the second evaluation model, a first objective function corresponding to a grid planning is given, specifically including:

[0073] Combining the first evaluation model and the second evaluation model, based on the annual comprehensive cost of the primary grid and secondary equipment layout, a first objective function corresponding to the primary grid planning is given;

[0074] Among them, the annual comprehensive cost includes the annual total cost of the primary grid and the annual total cost of the secondary equipment;

[0075] The first evaluation model is specifically expressed as:

[0076]

[0077] in, is the net power of load node k, is the power flow from load node k to other load nodes l, is the power flow from other load nodes m to load node k, L and M are the number of outgoing and incoming edges connected to load node k, respectively. , are the minimum and maximum allowable powers of the load node, respectively;

[0078] The second evaluation model is specifically expressed as:

[0079]

[0080] in, is the average power supply availability of the distribution network, The number of load nodes in the distribution network, is the annual average power outage time of load node k, is the number of users connected to load node k.

[0081] Furthermore, based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given, specifically including:

[0082] Combined with the analysis of the load nodes in the evaluation model, the second objective function corresponding to the secondary equipment planning is given based on the annual total cost of the secondary equipment and the annual power outage cost of the primary grid;

[0083] The second objective function is specifically expressed as:

[0084]

[0085] in, is the total annual cost of secondary equipment, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. is the average load of load node k, The unit power outage loss costs.

[0086] Furthermore, combined with the modified particle swarm optimization algorithm and based on the analysis of the first objective function and the second objective function, the optimization of the distribution network framework planning is carried out in multiple topological structures, specifically including:

[0087] Based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function;

[0088] Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed;

[0089] Alternate the optimization analysis of the first objective function and the second objective function until convergence.

[0090] Furthermore, based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function, specifically including:

[0091] Determine an initial target topological structure, and provide an initial first matrix value corresponding to a first physical parameter group;

[0092] Perform optimization analysis on the initial first matrix value through the first objective function to give an initial result;

[0093] Update the first matrix value and repeat the optimization analysis step through the first objective function;

[0094] Iterate the above update and optimization analysis process;

[0095] The first physical parameter group includes feeder partitions and connection relationships between feeder partitions.

[0096] Furthermore, the first matrix value is specifically expressed as:

[0097]

[0098] in, is the set of connection relationships between feeder partitions. is the connection relationship of the first feeder partition, and d is the total number of connection relationships between each feeder partition.

[0099] Furthermore, based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed, including:

[0100] According to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is provided;

[0101] Perform optimization analysis on the initial second matrix value through the second objective function to give an initial evaluation result;

[0102] Update the second matrix value and repeat the optimization analysis step through the second objective function;

[0103] Iterate the above update and optimization analysis process;

[0104] The second physical parameter group includes the type of secondary devices and the number of secondary devices.

[0105] Furthermore, the second matrix value is specifically expressed as:

[0106]

[0107] in, is a collection of various secondary equipment types, is the number of secondary devices corresponding to the first feeder partition connection relationship, and d is the total number of connection relationships between each feeder partition.

[0108] Further, according to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is given, specifically including:

[0109] Based on the initial target topology and the connection relationship nodes between each feeder partition, the distance from each secondary device to the connection relationship node between each feeder partition is given.

[0110] Further, updating the second matrix value includes:

[0111] The connection relationship nodes between each secondary device and each feeder partition form a distance matrix;

[0112] Select the minimum distance between each secondary device and each feeder partition connection relationship node from the distance matrix, and combine them to form a minimum distance vector;

[0113] Extract at least one element from the minimum distance vector until all combinations of elements in the minimum distance vector are extracted, and give all extracted element combinations and the corresponding minimum distance sub-vectors;

[0114] Based on the distance matrix, determine the maximum value of the minimum distances among all elements of each minimum distance subvector;

[0115] Compare the maximum values ​​of the minimum distances corresponding to the minimum distance sub-vectors with the same number of elements, and give the minimum distance sub-vector with the smallest maximum value among the minimum distance sub-vectors with the same number of elements;

[0116] Sort the minimum distance sub-vectors with the smallest maximum value according to the number of elements from small to large, and process the maximum value difference of the minimum distance sub-vector corresponding to the minimum distance in order to obtain the change amount of the adjacent minimum distance sub-vectors;

[0117] Compare the change amount of the adjacent minimum distance sub-vectors with the change amount threshold range, and give the minimum distance sub-vector that meets the change amount threshold range and has the least number of elements;

[0118] According to the minimum distance sub-vector that meets the change threshold range and has the least number of elements, the secondary device corresponding to each element of the minimum distance sub-vector in the initial target topology structure is determined.

[0119] The present invention provides an optimization method and device for coordinating distribution network framework planning, which at least includes the following beneficial effects:

[0120] (1) The present invention takes into account the complementary functions of secondary equipment to the primary grid, conducts coordinated planning of the distribution network grid, improves the operational reliability of the distribution network, and optimizes construction costs and operating efficiency.

[0121] (2) In the optimization of the coordinated distribution network framework planning, the modified particle swarm optimization algorithm uses the feeder partitions, the connection relationship between each feeder partition, the type of secondary equipment, and the number of secondary equipment as particle dimensions for the first objective function and the second objective function that are interrelated in the present invention, thereby reducing the number of redundancies and improving the optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 A schematic diagram of a flow chart of an optimization method for coordinating distribution network framework planning provided by the present invention;

[0123] Figure 2 A schematic diagram of a process of constructing multiple topological structures of a distribution network framework planning according to an embodiment of the present invention;

[0124] Figure 3 A schematic diagram of a process flow for optimizing distribution network framework planning in multiple topological structures according to a certain embodiment of the present invention;

[0125] Figure 4 A schematic diagram of a process of performing optimization analysis on a first objective function in a certain embodiment provided by the present invention;

[0126] Figure 5 A schematic diagram of a process of performing optimization analysis on a second objective function in a certain embodiment provided by the present invention;

[0127] Figure 6 A schematic structural diagram of an optimization device for coordinating distribution network framework planning provided by the present invention. DETAILED DESCRIPTION

[0128] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0129] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0130] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity 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 commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0131] Fine-tuning the primary grid structure of the distribution network will have an important impact on the configuration of the secondary equipment. Similarly, the configuration of the secondary equipment will also affect the function of the primary grid structure. Therefore, from the perspective of secondary equipment configuration, coordinating the distribution network grid planning and fully considering the cooperation of the secondary equipment in the primary grid structure fault handling process can take into account both reliability and economy, making the management of the distribution network more intelligent.

[0132] like Figure 1 As shown, the present invention provides an optimization method for coordinating distribution network framework planning, which specifically includes the following steps:

[0133] Based on the distribution network framework planning of coordinating the primary grid and the secondary equipment, a first evaluation model, a second evaluation model and a plurality of topological structures of the distribution network framework planning are constructed, wherein the distribution network framework planning of coordinating the primary grid and the secondary equipment is to embed a secondary equipment configuration module in the primary grid planning of the distribution network;

[0134] Combining the first evaluation model and the second evaluation model, a first objective function corresponding to a grid planning is given;

[0135] Based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given;

[0136] Combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the distribution network framework planning is optimized in multiple topological structures;

[0137] According to the optimization situation, the target topology is determined and a plan for the distribution network is given.

[0138] Among them, the primary grid is the physical structure of the distribution network system, usually including substations, transmission lines, distribution lines, transformers, etc. Secondary equipment is the equipment and system used to control, protect, measure and monitor the operation of the primary grid, usually including relay protection devices, automation controllers, measuring equipment, etc.

[0139] The collaborative distribution network planning of the present invention is mainly to coordinate the planning of the primary grid and the secondary equipment. For the planning of the distribution network, the focus is still on the planning of the primary grid, that is, the secondary equipment configuration module is embedded in the primary grid planning of the distribution network, and the layout and connection of the primary grid and secondary equipment are optimized during the planning process. The topological structure is the connection mode and layout of each component after coordinating the primary grid and the secondary equipment.

[0140] The first evaluation model is the node power model in the topology structure, and the second evaluation model is the average power supply availability model of the distribution network in the topology structure. The first and second evaluation models jointly constrain the planning of the primary grid, and the second evaluation model constrains the planning of the secondary equipment. On the basis of the first and second evaluation models, combined with the first objective function and the second objective function to achieve the target constraint, under the modified particle swarm optimization algorithm, multiple possible topologies are optimized, and finally the optimal distribution network grid planning is given.

[0141] The present invention takes into account the complementary functions of secondary equipment to the primary grid, conducts collaborative planning of the distribution network grid, improves the operational reliability of the distribution network, and optimizes construction costs and operational efficiency.

[0142] For the construction of topological structures, such as Figure 2 As shown in the figure, multiple topological structures of distribution network planning are constructed, which specifically includes the following steps:

[0143] Obtain and traverse a variety of pre-planned initial topologies, and divide the distribution network into feeder zones according to the headend and terminal types of the distribution network;

[0144] The connection relationship between each feeder partition and other feeder partitions is traversed to form multiple topological structures of distribution network grid planning.

[0145] In coordinating secondary equipment to build a topology, it is important to consider optimizing the grid structure to ensure and improve power supply reliability and management efficiency. Therefore, after obtaining a variety of pre-planned initial topologies, the topology needs to be divided into feeder zones.

[0146] Feeder zoning is the process of dividing the topology into several relatively independent power supply areas. This power supply area division process needs to be determined based on the type, location, characteristics and other information of the head end (such as substation) and terminal (such as load node, user, etc.) in the distribution network. On the basis of ensuring the basic power supply capacity, reliability, economy, etc. of the distribution network after planning, multiple topologies are formed.

[0147] Generally speaking, the components with common inlet switches are divided into a feeder partition, and the feeder partition without switch components is the smallest isolation zone. Using the smallest isolation zone to distinguish topological structures can reduce the complexity of distribution network system reliability assessment, reduce the workload of analysis, and improve analysis efficiency while ensuring the accuracy of analysis.

[0148] In order to optimize the obtained multiple topological structures, constraints and objective functions are set for the primary grid and secondary equipment respectively. Furthermore, the first objective function corresponding to the primary grid planning is given by combining the first evaluation model and the second evaluation model, which specifically includes:

[0149] Combining the first evaluation model and the second evaluation model, based on the annual comprehensive cost of the primary grid and secondary equipment layout, a first objective function corresponding to the primary grid planning is given;

[0150] Among them, the annual comprehensive cost includes the annual total cost of the primary grid and the annual total cost of the secondary equipment.

[0151] From the perspective of safety and efficiency of coordinated distribution network planning, the constraints on primary grid planning are first considered from the node power aspect. Therefore, the first evaluation model is specifically expressed as:

[0152]

[0153] in, is the net power of load node k, is the power flow from load node k to other load nodes l, is the power flow from other load nodes m to load node k, L and M are the number of outgoing and incoming edges connected to load node k, respectively. , are the minimum and maximum allowable powers of the load node, respectively.

[0154] By limiting the inflow and outflow power of each load node, load balancing can be achieved under different conditions. Of course, the node power constraint alone cannot achieve the optimization and iteration of distribution network planning, and it is also necessary to consider the operational reliability.

[0155] For the second evaluation model, it is specifically expressed as:

[0156]

[0157] in, is the average power supply availability of the distribution network, is the number of load nodes in the distribution network, is the annual average power outage time of load node k, is the number of users connected to load node k, It is the average power supply availability benchmark value required by the distribution network.

[0158] The average power supply availability of the distribution network is a comprehensive indicator for the planning, construction and operation of the distribution network, and is an important evaluation indicator for measuring the reliability of the distribution network system.

[0159] In the above, the constraints on the primary grid planning are jointly realized through the first evaluation model and the second evaluation model.

[0160] The objective function can achieve the purpose and optimization direction of distribution network planning. After coordinating the secondary equipment, the system reliability of the corresponding distribution network grid planning will be improved, which will of course also lead to an increase in costs. On the basis of satisfying the constraints of the first and second evaluation models, the first objective function will be mainly cost-oriented, considering the primary grid cost and the cost of secondary equipment.

[0161] Therefore, the first objective function is specifically expressed as:

[0162]

[0163] Among them, F is the annual comprehensive cost of primary grid and secondary equipment, is the cost ratio of distribution line operation and maintenance to investment, r is the discount rate, m is the service life of the planned line, i and j are the numbers of the feeder partitions, respectively. is a Boolean variable, A 0 / 1 variable indicating whether a connection is established between feeder partition i and feeder partition j. Indicates the connection construction between feeder partition i and feeder partition j. If Indicates that no connection is constructed between feeder partition i and feeder partition j. is the line length between feeder partition i and feeder partition j, is the line construction cost per unit length, is the total annual cost of secondary equipment.

[0164] The second objective function is specifically expressed as:

[0165]

[0166] in, is the total annual cost of secondary equipment, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. is the average load of load node k, The total annual cost of power outages.

[0167] For the second objective function, the annual comprehensive cost of secondary equipment and the loss cost caused by power outage under the current secondary equipment layout are mainly considered. Among them, the annual comprehensive cost of secondary equipment is divided into the annual investment cost of the secondary equipment intelligent terminal and the annual operation and maintenance cost of the secondary equipment intelligent terminal. Specifically expressed as:

[0168]

[0169] in, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. For the return on investment, To plan the service life of secondary equipment, is the number of first-class secondary equipment, The unit price of investment cost for the first type of secondary equipment, is the number of secondary devices of type n, is the unit price of investment cost for the nth type of secondary equipment, It is the cost ratio of secondary equipment operation and maintenance to investment.

[0170] For secondary equipment that guarantees and improves the reliability of the distribution network, different types of secondary equipment will be set according to different feeder partitions. For example, "two-shake" terminals, "three-shake" terminals, etc. The location, quantity, cost, etc. of different types of secondary equipment configurations will affect the first and second evaluation models and objective functions. Therefore, in the second objective function, it is necessary to classify and optimize the secondary equipment.

[0171] After obtaining multiple topological structures, under the constraints of the first and second evaluation models, the optimization in the first and second objective functions can be performed by the modified particle swarm optimization algorithm. For the first and second objective functions that are interrelated, the modified particle swarm optimization algorithm uses the feeder partitions, the connection relationship between each feeder partition, the type of secondary equipment, and the number of secondary equipment as particle dimensions for optimization, which can reduce the number of redundancies and improve the optimization efficiency.

[0172] like Figure 3As shown, combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the optimization of the distribution network framework planning is performed in multiple topological structures, specifically including the following steps:

[0173] Based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function;

[0174] Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed;

[0175] Alternate the optimization analysis of the first objective function and the second objective function until convergence.

[0176] Specifically, if Figure 4 As shown, based on the feeder partitions and the connection relationship between the feeder partitions, the first objective function is optimized and analyzed, specifically including the following steps:

[0177] Determine an initial target topological structure, and provide an initial first matrix value corresponding to a first physical parameter group;

[0178] Perform optimization analysis on the initial first matrix value through the first objective function to give an initial result;

[0179] Update the first matrix value and repeat the optimization analysis step through the first objective function;

[0180] Iterate the above update and optimization analysis process;

[0181] The first physical parameter group includes feeder partitions and connection relationships between feeder partitions.

[0182] The first matrix value is specifically expressed as:

[0183]

[0184] in, is the set of connection relationships between feeder partitions. is the connection relationship of the first feeder partition, and d is the total number of connection relationships between each feeder partition.

[0185] like Figure 5 As shown, based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed, which specifically includes the following steps:

[0186] According to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is provided;

[0187] Perform optimization analysis on the initial second matrix value through the second objective function to give an initial evaluation result;

[0188] Update the second matrix value and repeat the optimization analysis step through the second objective function;

[0189] Iterate the above update and optimization analysis process;

[0190] The second physical parameter group includes the type of secondary devices and the number of secondary devices.

[0191] The second matrix value is specifically expressed as:

[0192] ;

[0193] in, is a collection of various secondary equipment types, is the number of secondary devices corresponding to the first feeder partition connection relationship, and d is the total number of connection relationships between each feeder partition.

[0194] According to the initial target topology structure, an initial second matrix value corresponding to the second physical parameter group is given, specifically including: based on the initial target topology structure and the connection relationship nodes between each feeder partition, the distance from each secondary device to the connection relationship nodes between each feeder partition is given.

[0195] The update of the second matrix value includes:

[0196] The connection relationship nodes between each secondary device and each feeder partition form a distance matrix;

[0197] Select the minimum distance between each secondary device and each feeder partition connection relationship node from the distance matrix, and combine them to form a minimum distance vector;

[0198] Extract at least one element from the minimum distance vector until all combinations of elements in the minimum distance vector are extracted, and give all extracted element combinations and the corresponding minimum distance sub-vectors;

[0199] Based on the distance matrix, determine the maximum value of the minimum distances among all elements of each minimum distance subvector;

[0200] Compare the maximum values ​​of the minimum distances corresponding to the minimum distance sub-vectors with the same number of elements, and give the minimum distance sub-vector with the smallest maximum value among the minimum distance sub-vectors with the same number of elements;

[0201] Sort the minimum distance sub-vectors with the smallest maximum value according to the number of elements from small to large, and process the maximum value difference of the minimum distance sub-vector corresponding to the minimum distance in order to obtain the change amount of the adjacent minimum distance sub-vectors;

[0202] Compare the change amount of the adjacent minimum distance sub-vectors with the change amount threshold range, and give the minimum distance sub-vector that meets the change amount threshold range and has the least number of elements;

[0203] According to the minimum distance sub-vector that meets the change threshold range and has the least number of elements, the secondary device corresponding to each element of the minimum distance sub-vector in the initial target topology structure is determined.

[0204] In one embodiment, after obtaining the initial second matrix value determined by the second physical parameter group, the optimization process of the optimal configuration is considered next. As the number of placed secondary devices increases from 1, the maximum distance from the connection relationship node between each feeder partition to the nearest secondary device first decreases and then remains unchanged.

[0205] The optimization in the first objective function through the modified particle swarm optimization algorithm is mainly carried out through the optimization in the first matrix value. The first matrix value provides the possibility of feeder partitions and feeder partition connection relationships covering multiple topological structures of the distribution network grid planning. The optimization based on the first matrix value can complete the optimization process quickly and efficiently.

[0206] The optimization of the second objective function by the modified particle swarm optimization algorithm is to optimize the second matrix value. Similarly, because the second matrix value covers feasible solutions for the type and quantity of secondary equipment layout in multiple topologies, the target topology can be quickly determined.

[0207] In the process of optimizing the target topology, the first objective function and the second objective function are iterated alternately until all the data calculations of the first matrix value and the second matrix value are completed, which means convergence is achieved. According to the numerical calculations of each topology, the optimization result is determined and the final target topology is given.

[0208] like Figure 6 As shown, the present invention also provides an optimization device for coordinating the planning of the distribution network framework, which adopts the optimization method for coordinating the planning of the distribution network framework as mentioned above, and specifically includes:

[0209] A construction unit is used to construct a first evaluation model, a second evaluation model and a plurality of topological structures for the distribution network framework planning based on the distribution network framework planning of coordinating the primary grid and the secondary equipment, wherein the distribution network framework planning of coordinating the primary grid and the secondary equipment is to embed a secondary equipment configuration module in the primary grid planning of the distribution network;

[0210] The analysis unit is used to combine the first evaluation model and the second evaluation model to provide a first objective function corresponding to the primary grid planning; based on the second evaluation model, provide a second objective function corresponding to the secondary equipment planning;

[0211] The optimization unit is used to combine the modified particle swarm optimization algorithm and optimize the distribution network framework planning in multiple topological structures based on the analysis of the first objective function and the second objective function; according to the optimization situation, the target topological structure is determined and the planning for the distribution network framework is given.

[0212] Construct multiple topological structures for distribution network planning, including:

[0213] Obtain and traverse a variety of pre-planned initial topologies, and divide the distribution network into feeder zones according to the headend and terminal types of the distribution network;

[0214] The connection relationship between each feeder partition and other feeder partitions is traversed to form multiple topological structures of distribution network grid planning.

[0215] Combining the first evaluation model and the second evaluation model, the first objective function corresponding to a grid planning is given, which specifically includes:

[0216] Combining the first evaluation model and the second evaluation model, based on the annual comprehensive cost of the primary grid and secondary equipment layout, a first objective function corresponding to the primary grid planning is given;

[0217] Among them, the annual comprehensive cost includes the annual total cost of the primary grid and the annual total cost of the secondary equipment;

[0218] The first evaluation model is specifically expressed as:

[0219] ;

[0220] in, is the net power of load node k, is the power flow from load node k to other load nodes l, is the power flow from other load nodes m to load node k, L and M are the number of outgoing and incoming edges connected to load node k, respectively. , are the minimum and maximum allowable powers of the load node, respectively;

[0221] The second evaluation model is specifically expressed as:

[0222] ;

[0223] in, is the average power supply availability of the distribution network, The number of load nodes in the distribution network, is the annual average power outage time of load node k, is the number of users connected to load node k.

[0224] Based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given, specifically including:

[0225] Combined with the analysis of the load nodes in the evaluation model, the second objective function corresponding to the secondary equipment planning is given based on the annual total cost of the secondary equipment and the annual power outage cost of the primary grid;

[0226] The second objective function is specifically expressed as:

[0227] ;

[0228] in, is the total annual cost of secondary equipment, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. is the average load of load node k, The unit power outage loss costs.

[0229] Combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the optimization of the distribution network framework planning is carried out in multiple topological structures, specifically including:

[0230] Based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function;

[0231] Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed;

[0232] Alternate the optimization analysis of the first objective function and the second objective function until convergence.

[0233] Based on the feeder partitions and the connection relationship between the feeder partitions, the first objective function is optimized and analyzed, specifically including:

[0234] Determine an initial target topological structure, and provide an initial first matrix value corresponding to a first physical parameter group;

[0235] Perform optimization analysis on the initial first matrix value through the first objective function to give an initial result;

[0236] Update the first matrix value and repeat the optimization analysis step through the first objective function;

[0237] Iterate the above update and optimization analysis process;

[0238] The first physical parameter group includes feeder partitions and connection relationships between feeder partitions.

[0239] The first matrix value is specifically expressed as:

[0240]

[0241] in, is the set of connection relationships between feeder partitions. is the connection relationship of the first feeder partition, and d is the total number of connection relationships between each feeder partition.

[0242] Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed, including:

[0243] According to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is provided;

[0244] Perform optimization analysis on the initial second matrix value through the second objective function to give an initial evaluation result;

[0245] Update the second matrix value and repeat the optimization analysis step through the second objective function;

[0246] Iterate the above update and optimization analysis process;

[0247] The second physical parameter group includes the type of secondary devices and the number of secondary devices.

[0248] The second matrix value is specifically expressed as:

[0249]

[0250] in, is a collection of various secondary equipment types, is the number of secondary devices corresponding to the first feeder partition connection relationship, and d is the total number of connection relationships between each feeder partition.

[0251] According to the initial target topology structure, an initial second matrix value corresponding to the second physical parameter group is given, specifically including:

[0252] Based on the initial target topology and the connection relationship nodes between each feeder partition, the distance from each secondary device to the connection relationship node between each feeder partition is given.

[0253] Further, updating the second matrix value includes:

[0254] The connection relationship nodes between each secondary device and each feeder partition form a distance matrix;

[0255] Select the minimum distance between each secondary device and each feeder partition connection relationship node from the distance matrix, and combine them to form a minimum distance vector;

[0256] Extract at least one element from the minimum distance vector until all combinations of elements in the minimum distance vector are extracted, and give all extracted element combinations and the corresponding minimum distance sub-vectors;

[0257] Based on the distance matrix, determine the maximum value of the minimum distances among all elements of each minimum distance subvector;

[0258] Compare the maximum values ​​of the minimum distances corresponding to the minimum distance sub-vectors with the same number of elements, and give the minimum distance sub-vector with the smallest maximum value among the minimum distance sub-vectors with the same number of elements;

[0259] Sort the minimum distance sub-vectors with the smallest maximum value according to the number of elements from small to large, and process the maximum value difference of the minimum distance sub-vector corresponding to the minimum distance in order to obtain the change amount of the adjacent minimum distance sub-vectors;

[0260] Compare the change amount of the adjacent minimum distance sub-vectors with the change amount threshold range, and give the minimum distance sub-vector that meets the change amount threshold range and has the least number of elements;

[0261] According to the minimum distance sub-vector that meets the change threshold range and has the least number of elements, the secondary device corresponding to each element of the minimum distance sub-vector in the initial target topology structure is determined.

[0262] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the above-mentioned optimization methods for coordinated distribution network framework planning is implemented.

[0263] Specifically, a computer device may include one or more processing devices, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. A computer device may also include any storage resource for storing any type of information such as code, settings, data, etc. Without limitation, for example, a storage resource may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, etc. More generally, any storage resource may use any technology to store information.

[0264] Further, any storage resource may provide volatile or non-volatile retention of information. Further, any storage resource may represent a fixed or removable component of a computer device. In one case, when a processing device executes an associated instruction stored in any storage resource or a combination of storage resources, the computer device may perform any operation of the associated instruction. The computer device also includes one or more drive mechanisms for interacting with any storage resource, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0265] The computer device may also include an input / output module (I / O) for receiving various inputs (via input devices) and for providing various outputs (via output devices). A specific output mechanism may include a presentation device and an associated graphical user interface (GUI). In other embodiments, the input / output module (I / O), input devices, and output devices may not be included, and the computer device may be used only as a computer device in a network. The computer device may also include one or more network interfaces for exchanging data with other devices via one or more communication links. One or more communication buses couple the components described above together.

[0266] The communication link may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0267] The beneficial effects obtained by the above-mentioned equipment and devices are consistent with the beneficial effects obtained by the above-mentioned methods, and are not described in detail in the embodiments of this specification.

[0268] The present invention provides an optimization method and device for coordinating distribution network framework planning, which at least includes the following beneficial effects:

[0269] (1) The present invention takes into account the complementary functions of secondary equipment to the primary grid, conducts coordinated planning of the distribution network grid, improves the operational reliability of the distribution network, and optimizes construction costs and operating efficiency.

[0270] (2) In the optimization of the coordinated distribution network framework planning, the modified particle swarm optimization algorithm uses the feeder partitions, the connection relationship between each feeder partition, the type of secondary equipment, and the number of secondary equipment as particle dimensions for the first objective function and the second objective function that are interrelated in the present invention, thereby reducing the number of redundancies and improving the optimization efficiency.

[0271] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An optimization method for coordinating distribution network framework planning, characterized in that: The specific steps include: Based on the distribution network framework planning that coordinates the primary grid and the secondary equipment, the first evaluation model, the second evaluation model and multiple topological structures of the distribution network framework planning are constructed, wherein the distribution network framework planning that coordinates the primary grid and the secondary equipment is to embed the secondary equipment configuration module in the primary grid planning of the distribution network, and the first evaluation model is specifically expressed as: ； in, is the net power of load node k, is the power flow from load node k to other load nodes l, is the power flow from other load nodes m to load node k, L and M are the number of outgoing and incoming edges connected to load node k, respectively. , are the minimum and maximum allowable powers of the load node, respectively; The second evaluation model is specifically expressed as: ； in, is the average power supply availability of the distribution network, is the number of load nodes in the distribution network, is the annual average power outage time of load node k, is the number of users connected to load node k; Combining the first evaluation model and the second evaluation model, a first objective function corresponding to the primary grid planning is given, specifically including: combining the first evaluation model and the second evaluation model, based on the annual comprehensive cost of the primary grid and the secondary equipment layout, a first objective function corresponding to the primary grid planning is given; wherein the annual comprehensive cost includes the annual total cost of the primary grid and the annual total cost of the secondary equipment; Based on the second evaluation model, a second objective function corresponding to the secondary equipment planning is given, specifically including: combining the analysis of the load nodes in the evaluation model, taking the annual total cost of the secondary equipment and the annual power outage cost of the primary grid, giving the second objective function corresponding to the secondary equipment planning; Combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the distribution network framework planning is optimized in multiple topological structures; According to the optimization situation, the target topology is determined and a plan for the distribution network is given.

2. The optimization method for coordinating the distribution network framework planning according to claim 1 is characterized in that: Constructing multiple topological structures for distribution network planning includes the following steps: Obtain and traverse a variety of pre-planned initial topologies, and divide the distribution network into feeder zones according to the headend and terminal types of the distribution network; The connection relationship between each feeder partition and other feeder partitions is traversed to form multiple topological structures of distribution network grid planning.

3. The method for optimizing the coordinated distribution network framework planning according to claim 1, characterized in that: The second objective function is specifically expressed as: ； in, is the total annual cost of secondary equipment, is the annual investment cost of the secondary equipment intelligent terminal, The annual operation and maintenance cost of the secondary equipment intelligent terminal. is the average load of load node k, The unit power outage loss costs.

4. The method for optimizing the coordinated distribution network framework planning according to claim 1, characterized in that: Combined with the modified particle swarm optimization algorithm, and based on the analysis of the first objective function and the second objective function, the optimization of the distribution network framework planning is carried out in multiple topological structures, which specifically includes the following steps: Based on the feeder partitions and the connection relationship between the feeder partitions, an optimization analysis is performed on the first objective function; Based on the type and quantity of secondary equipment, the second objective function is optimized and analyzed; Alternate the optimization analysis of the first objective function and the second objective function until convergence.

5. The method for optimizing the coordinated distribution network framework planning according to claim 4, characterized in that: Based on the feeder partitions and the connection relationship between the feeder partitions, the first objective function is optimized and analyzed, which specifically includes the following steps: Determine an initial target topological structure, and provide an initial first matrix value corresponding to a first physical parameter group; Perform optimization analysis on the initial first matrix value through the first objective function to give an initial result; Update the first matrix value and repeat the optimization analysis step through the first objective function; Iterate the above update and optimization analysis process; The first physical parameter group includes feeder partitions and connection relationships between feeder partitions.

6. The method for optimizing the coordinated distribution network framework planning according to claim 5, characterized in that: The first matrix value is specifically expressed as: ； in, is the set of connection relationships between feeder partitions. is the connection relationship of the first feeder partition, and d is the total number of connection relationships between each feeder partition.

7. The method for optimizing the coordinated distribution network framework planning according to claim 4, characterized in that: Based on the type and quantity of secondary equipment, the optimization analysis of the second objective function is carried out, which specifically includes the following steps: According to the initial target topological structure, an initial second matrix value corresponding to the second physical parameter group is provided; Perform optimization analysis on the initial second matrix value through the second objective function to give an initial evaluation result; Update the second matrix value and repeat the optimization analysis step through the second objective function; Iterate the above update and optimization analysis process; The second physical parameter group includes the type of secondary devices and the number of secondary devices.

8. The method for optimizing the coordinated distribution network framework planning according to claim 7, characterized in that: The second matrix value is specifically expressed as: ； in, is a collection of various secondary equipment types, is the number of secondary devices corresponding to the first feeder partition connection relationship, and d is the total number of connection relationships between each feeder partition.

9. An optimization device for coordinating distribution network grid planning, characterized in that: The optimization method for coordinating the planning of the distribution network framework as claimed in any one of claims 1 to 8 specifically comprises: A construction unit is used to construct a first evaluation model, a second evaluation model and a plurality of topological structures for the distribution network framework planning based on the distribution network framework planning of coordinating the primary grid and the secondary equipment, wherein the distribution network framework planning of coordinating the primary grid and the secondary equipment is to embed a secondary equipment configuration module in the primary grid planning of the distribution network; The analysis unit is used to combine the first evaluation model and the second evaluation model to provide a first objective function corresponding to the primary grid planning; based on the second evaluation model, provide a second objective function corresponding to the secondary equipment planning; The optimization unit is used to combine the modified particle swarm optimization algorithm and optimize the distribution network framework planning in multiple topological structures based on the analysis of the first objective function and the second objective function; according to the optimization situation, the target topological structure is determined and the planning for the distribution network framework is given.

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