Alternating current and direct current power distribution network equipment configuration and SOP site selection double-layer planning method considering cluster division and related device

Abstract

The invention discloses an AC / DC power distribution network equipment configuration and SOP site selection double-layer planning method considering cluster division and a related device. The method comprises the following steps: constructing an upper-layer planning model taking minimization of comprehensive planning cost and optimization of cluster comprehensive performance as objective functions; an improved SRA algorithm is adopted to solve the upper-layer planning model, and collaborative optimization of equipment configuration and cluster division is achieved; according to equipment configuration and a cluster division scheme, constructing a lower-layer planning model taking maximized reduction of transmission loss as an objective function; solving the lower-layer planning model by adopting a second-order cone relaxation method, and outputting an SOP site selection scheme; and inputting the SOP site selection scheme into the upper-layer planning model, solving the upper-layer planning model by adopting an improved SRA algorithm, and outputting an updated equipment configuration and cluster division scheme, so that joint decision and overall optimization of equipment configuration, cluster division and SOP site selection are realized, and the cluster autonomous capability, operation stability and economical efficiency of the AC / DC power distribution network system are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a two-layer planning method and related apparatus for AC / DC power distribution network equipment configuration and SOP site selection that considers cluster partitioning. Background Technology

[0002] With the advancement of "dual-carbon" goals and the accelerated construction of new power systems, the penetration rate of DC loads such as distributed photovoltaic (PV) and electric vehicle charging piles in distribution networks continues to rise, and AC / DC hybrid distribution networks have become a typical form of urban power distribution systems. Against this backdrop, the rational configuration of lines, PV, and energy storage equipment in the distribution network, and the scientific site selection of smart soft-open points (SOPs), have become crucial for improving the economy and operational performance of the distribution network. Existing AC / DC distribution network planning methods typically treat cluster partitioning, line expansion, distributed power source site selection and capacity determination, and energy storage configuration in a fragmented manner, neglecting the coupling relationship between equipment and network topology. For example, the location of PV access affects feeder power flow, thus changing the demand for line expansion; energy storage charging and discharging strategies are highly correlated with PV output and load curves; and the lack of collaborative optimization during the planning stage makes it difficult to achieve global optimization in the planning scheme, often resulting in equipment redundancy or insufficient capacity, which in turn affects the stable operation of the AC / DC distribution network. Summary of the Invention

[0003] This invention provides a two-layer planning method and related apparatus for AC / DC distribution network equipment configuration and SOP location selection that considers cluster partitioning. This method is used to solve the technical problem that existing AC / DC distribution network planning methods use fragmented planning decisions, which makes it difficult to achieve global optimization in the planning scheme.

[0004] This invention provides a two-tier planning method for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning. The method includes:

[0005] Obtain basic and operational data of the AC / DC distribution network to be planned;

[0006] Based on the basic and operational data of the AC / DC distribution network, and under the condition of satisfying the distribution constraints, a higher-level planning model is constructed with the objective function of minimizing the overall planning cost and optimizing the overall performance of the cluster. The improved SRA algorithm is used to solve the higher-level planning model, and the equipment configuration and cluster partitioning scheme are output.

[0007] Based on the equipment configuration and cluster partitioning scheme, and under the condition of satisfying the SOP constraint, a lower-level planning model is constructed with the objective function of maximizing the reduction of transmission loss; the lower-level planning model is solved using the second-order cone relaxation method, and the SOP location scheme is output.

[0008] The SOP site selection scheme is input into the upper-level planning model, and the improved SRA algorithm is used to solve the upper-level planning model, outputting an updated device configuration and cluster partitioning scheme;

[0009] If the preset iteration conditions are not met, the process jumps to the steps of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying the SOP constraint; solving the lower-level planning model using the second-order cone relaxation method and outputting the SOP location scheme; if the preset iteration conditions are met, the device configuration and cluster partitioning scheme and SOP location scheme output by the latest iteration are adopted as the optimal planning scheme and output.

[0010] Optionally, the objective function of the upper-level planning model is expressed as:

[0011]

[0012] In the formula: F is the objective function value that integrates the overall planning cost and the overall performance of the cluster; In order to comprehensively plan operating costs, As a comprehensive indicator of clustering; As an economic weight, Cluster weights;

[0013] in,

[0014]

[0015] In the formula: For the cost of line construction; For photovoltaic investment and construction costs; For energy storage investment and construction costs; Cost of SOP construction; Annual operation and maintenance costs of the power distribution network; To reduce network loss during operation; Main grid electricity purchase cost;

[0016] in,

[0017]

[0018] In the formula: Modularity metric; It is a reactive power balance index; It is an active power balance index; This is an indicator of inter-group power volatility. It is a volatility variance indicator; As a complementary indicator; , , , , and All are indicator weights.

[0019] Optionally, the objective function of the lower-level planning model is expressed as:

[0020]

[0021] In the formula: This represents the total number of lines in the distribution network system. This indicates the change in power flow on branch l of the power grid caused by injecting unit power at node i. This represents the total number of nodes in the distribution network system. and These are the active power and reactive power at the end of the power grid branch l, respectively. and These are the resistance and reactance of the power grid branch l, respectively; Let be the voltage at the end of the power grid branch l.

[0022] Optionally, the step of using the improved SRA algorithm to solve the upper-level planning model and output the device configuration and cluster partitioning scheme includes:

[0023] The decision variables of the upper-level planning model are mixed-encoded to obtain the decision variable structure; the decision variable structure is initialized to obtain the initial population.

[0024] Based on the objective function of the upper-level planning model, the fitness value of each individual in the initial population is calculated, and the optimal individual is determined according to the fitness value.

[0025] An improved SRA algorithm is adopted, which guides individuals in the population to move toward the optimal individual and performs local updates to update the population based on an adaptive step size strategy; the cluster topology in the updated population is checked and repaired, and the repaired population is constrained according to the power distribution constraints.

[0026] Calculate the fitness value of each individual in the constrained population, and select a set of high-quality individuals based on the fitness values ​​to update the optimal individual; determine whether the preset convergence condition is met. If not, use the current set of high-quality individuals as the new population, and jump to the step of using the improved SRA algorithm to guide individuals in the population to move towards the optimal individual based on the adaptive step size strategy to update the population; if the condition is met, generate and output the device configuration and cluster partitioning scheme based on the current optimal individual.

[0027] Optionally, the step of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying SOP constraints, includes:

[0028] Based on the device configuration and cluster partitioning scheme, determine the network topology and device capacity parameters;

[0029] Based on the network topology, a power distribution node association matrix is ​​constructed, and based on the power distribution node association matrix, a power transmission allocation coefficient matrix is ​​constructed.

[0030] Based on the power transmission allocation coefficient matrix and the equipment capacity parameters, a lower-level planning model is constructed with the objective function of maximizing the reduction of transmission loss, while satisfying the SOP constraint.

[0031] Optionally, the step of using the second-order cone relaxation method to solve the lower-level planning model and output the SOP location scheme includes:

[0032] Based on the second-order cone relaxation method, voltage auxiliary variables, current auxiliary variables, and power auxiliary variables are introduced. Based on the voltage auxiliary variables, current auxiliary variables, and power auxiliary variables, the nonlinear constraints of the lower-level planning model are relaxed to second-order cone constraints, and the lower-level planning model is transformed into a convex optimization problem for solution, outputting the SOP location scheme.

[0033] This invention also provides a two-layer planning device for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning, the device comprising:

[0034] The data acquisition module is used to acquire basic and operational data of the AC / DC distribution network to be planned.

[0035] The upper-level planning module is used to construct an upper-level planning model based on the basic data and operation data of the AC / DC distribution network, under the condition of satisfying the power distribution constraints, with the objective function of minimizing the comprehensive planning cost and optimizing the overall performance of the cluster; the improved SRA algorithm is used to solve the upper-level planning model and output the equipment configuration and cluster partitioning scheme.

[0036] The lower-level planning module is used to construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying SOP constraints; the lower-level planning model is solved using the second-order cone relaxation method, and the SOP location scheme is output.

[0037] The joint planning module is used to input the SOP site selection scheme into the upper-level planning model, and use the improved SRA algorithm to solve the upper-level planning model, and output an updated equipment configuration and cluster partitioning scheme.

[0038] The planning scheme output module is used to, if the preset iteration conditions are not met, jump to the steps of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss based on the device configuration and cluster partitioning scheme under the condition of satisfying SOP constraints; solving the lower-level planning model using the second-order cone relaxation method and outputting the SOP location scheme; if the preset iteration conditions are met, then using the latest iteration output device configuration and cluster partitioning scheme and SOP location scheme as the optimal planning scheme and outputting it.

[0039] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the two-level planning method for AC / DC distribution network equipment configuration and SOP location selection as described above.

[0040] This invention also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the steps of the two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection as described above.

[0041] This invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection as described above.

[0042] As can be seen from the above technical solutions, the present invention has the following advantages:

[0043] This invention provides a two-layer planning method and related apparatus for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning. The method includes: acquiring basic and operational data of the AC / DC distribution network to be planned; based on the basic and operational data of the AC / DC distribution network, constructing an upper-layer planning model with the objective function of minimizing the overall planning cost and optimizing the overall cluster performance, under the condition of satisfying distribution constraints; solving the upper-layer planning model using an improved SRA algorithm to output the equipment configuration and cluster partitioning scheme; based on the equipment configuration and cluster partitioning scheme, constructing a lower-layer planning model with the objective function of maximizing the reduction of transmission loss, under the condition of satisfying SOP constraints; and employing a second-order cone... The relaxation method is used to solve the lower-level planning model and output the SOP (Start of Production) location scheme. The SOP location scheme is then input into the upper-level planning model, and the improved SRA (Solution Reduction Algorithm) is used to solve the upper-level planning model, outputting the updated equipment configuration and cluster partitioning scheme. If the preset iteration conditions are not met, the process jumps to the step of constructing a lower-level planning model based on the equipment configuration and cluster partitioning scheme, under the condition of satisfying the SOP constraint, with the objective function of maximizing the reduction of transmission loss. The second-order cone relaxation method is used to solve the lower-level planning model and output the SOP location scheme. If the preset iteration conditions are met, the latest iteration output equipment configuration and cluster partitioning scheme and SOP location scheme are used as the optimal planning scheme and output.

[0044] In this invention, an upper-level planning model is constructed with the objective function of minimizing the overall planning cost and optimizing the overall performance of the cluster, thereby achieving collaborative optimization of equipment configuration and cluster partitioning. Simultaneously, based on a two-layer collaborative optimization framework and a dynamic iteration mechanism, a lower-level planning model is constructed with the objective function of maximizing the reduction of transmission loss. Under the condition of satisfying the equipment configuration and cluster partitioning of the upper layer, the SOP location is optimized, thereby realizing joint decision-making and overall optimization of equipment configuration, cluster partitioning and SOP location, improving the cluster autonomy capability, operational stability and overall operational efficiency and economy of the AC / DC distribution network system. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating the steps of a two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning, provided in an embodiment of the present invention;

[0047] Figure 2The following is a flowchart of the algorithm execution for solving the upper-level planning model using the improved SRA algorithm, provided in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of the basic structure of an AC / DC SOP provided in an embodiment of the present invention;

[0049] Figure 4 This is a structural block diagram of a two-layer planning device for AC / DC distribution network equipment configuration and SOP location selection that considers cluster partitioning, provided in an embodiment of the present invention. Detailed Implementation

[0050] This invention provides a two-layer planning method and related apparatus for AC / DC distribution network equipment configuration and SOP location selection that considers cluster partitioning. This method is used to solve the technical problem that existing AC / DC distribution network planning methods use fragmented planning decisions, which makes it difficult to achieve global optimization in the planning scheme.

[0051] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0052] It should be noted that, in the optional embodiments of the present invention, the data related to object information, etc., requires the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies. Furthermore, the collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of the present invention involve data related to an object, it needs to be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0053] Please see Figure 1 This invention provides a two-tier planning method for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning. The method includes:

[0054] Step 101: Obtain the basic data and operation data of the AC / DC distribution network to be planned.

[0055] It should be noted that acquiring the basic and operational data of the AC / DC distribution network to be planned provides data support for subsequent optimization modeling and analysis. In this embodiment, the basic data may include distribution network topology data, AC / DC network division information, candidate equipment parameters, equipment service life and depreciation parameters, economic parameters, and geographical environment parameters. The operational data may include typical scenario load data, photovoltaic power output data, main grid interconnection branch constraints, and operational constraint parameters.

[0056] The data includes: distribution network topology data (number of nodes, branch connection relationships, substation locations, and existing line parameters (resistance, reactance, capacity limitations); AC / DC network partitioning information (set of AC nodes, set of DC nodes, and AC / DC boundary nodes); candidate equipment parameters (set of lines to be built (AC lines, DC lines) and their cost parameters, photovoltaic candidate nodes and unit investment costs, energy storage candidate nodes and unit investment costs, and SOP unit investment costs); equipment service life and depreciation parameters (design service life and annual discount rate of lines, photovoltaics, energy storage, and SOPs); economic parameters (electricity price, photovoltaic power generation subsidies, and equipment maintenance cost rate); and geographical environment parameters (maintenance cost rate of different geographical environments of different lines).

[0057] Among them, the typical scenario load data is the hourly load power of each node on typical days throughout the year (summer high temperature and high load scenario, winter light load scenario, and spring and autumn stable load scenario); the photovoltaic output data is the hourly photovoltaic output curve of each photovoltaic candidate node on typical days; the main grid interconnection branch constraints include the number of main grid interconnection branches and the maximum allowed backfeed power of each branch; the operation constraint parameters include the allowable upper and lower limits of node voltage, the allowable upper limit of line current, the limit of energy storage charging and discharging power, the upper and lower limits of energy storage state of charge (SOC) and the upper limit of photovoltaic penetration rate.

[0058] Understandably, after acquiring the operational data, it is necessary to perform preprocessing operations on the operational data; specifically, the load data of typical scenarios and photovoltaic output data are normalized; and scenario clustering is performed on the 8760 hours of the year based on the occurrence probability of typical scenarios to extract representative scenarios in order to reduce computational complexity.

[0059] Step 102: Based on the basic and operational data of the AC / DC distribution network, and under the condition of satisfying the distribution constraints, construct an upper-level planning model with the objective function of minimizing the comprehensive planning cost and optimizing the overall performance of the cluster; use the improved SRA algorithm to solve the upper-level planning model and output the equipment configuration and cluster partitioning scheme.

[0060] In this embodiment, an upper-level planning model is constructed based on the basic and operational data of the AC / DC distribution network, aiming to minimize the overall planning and operation costs and optimize the overall performance of the cluster. An improved Sacred Religion Algorithm (SRA) is used to solve the upper-level planning model, outputting equipment configuration and cluster partitioning schemes, thereby achieving collaborative optimization decisions for line expansion, photovoltaic site selection and capacity determination, energy storage site selection and capacity determination, SOP capacity configuration, and cluster partitioning.

[0061] The equipment configuration and cluster division scheme includes an equipment configuration scheme and a cluster division scheme. The equipment configuration scheme includes a line construction scheme (i.e., the specific locations of newly built or expanded AC and DC lines), a power generation and energy storage scheme (i.e., determining which nodes to install photovoltaics and energy storage and their respective installation capacities) and a SOP capacity configuration scheme. The cluster division scheme assigns a cluster number to each node in the AC / DC distribution network (for example, nodes 1, 2, and 3 belong to cluster A, and nodes 4 and 5 belong to cluster B).

[0062] 1. Objective function:

[0063] Traditional planning focuses solely on minimizing overall cost, neglecting the clustering characteristics of distribution networks—a key factor in determining structural rationality. While a single economic objective may result in a cost-optimal solution, cluster structures have inherent flaws, such as large power fluctuations in inter-cluster tie lines, easy fault propagation, and weak regional autonomy. In contrast to traditional planning, the upper-level planning model provided in this embodiment employs a weighted multi-objective function, merging economic and clustering objectives into a single optimization objective. The objective function of the upper-level planning model is expressed as:

[0064] (1)

[0065] In the formula: F is the objective function value that integrates the overall planning cost and the overall performance of the cluster; In order to comprehensively plan operating costs, As a comprehensive indicator of clustering; As an economic weight, The weights are clustered; the weights in equation (1) can be determined through multi-objective analysis using the variable-weight Pareto trajectory method.

[0066] (1) Comprehensive planning and operation costs :

[0067] (2)

[0068] In the formula: For the cost of line construction; For photovoltaic investment and construction costs; For energy storage investment and construction costs; Cost of SOP construction; Annual operation and maintenance costs of the power distribution network; To reduce network loss during operation; This refers to the cost of purchasing electricity from the main grid.

[0069] 1) The calculation method for line construction costs is expressed as follows:

[0070] (3)

[0071] In the formula, The discount rate for that year; It refers to the service life of the line; For the construction cost of AC lines; This refers to the construction cost of DC lines.

[0072] The construction cost of the AC line is expressed as follows:

[0073] (4)

[0074] In the formula, For the selection of communication routes; For the first The decision variable for choosing an AC line is 0, which indicates not to build, and 1 indicates to build. For the first The cost of one AC line.

[0075] The construction cost of DC lines is expressed as follows:

[0076] (5)

[0077] In the formula, This is the candidate set for DC lines; For the first The decision variable for selecting a DC line is 0, which indicates no construction and 1, which indicates construction. For the first The cost of a DC line.

[0078] 2) The calculation method for photovoltaic investment and construction costs is expressed as follows:

[0079] (6)

[0080] In the formula, This refers to the number of system nodes. The unit investment cost of photovoltaic power; Let be the planned photovoltaic capacity for node i; This refers to the operating life of the photovoltaic system.

[0081] 3) The calculation method for energy storage investment and construction costs is expressed as follows:

[0082] (7)

[0083] In the formula, The unit investment cost for energy storage; Plan the energy storage capacity for node i; This refers to the operating life of the energy storage system.

[0084] 4) The calculation method for SOP construction cost is expressed as follows:

[0085] (8)

[0086] In the formula, The number of SOP equipment to be installed; The unit investment cost of SOP; Let be the installation capacity of the s-th SOP; This refers to the operating life of the Standard Operating Procedure (SOP).

[0087] 5) The annual operation and maintenance cost of the distribution network is calculated as follows:

[0088] (9)

[0089] In the formula, For line operation and maintenance costs; For equipment operation and maintenance costs.

[0090] The line operation and maintenance cost is expressed as follows:

[0091] (10)

[0092] In the formula, , These are the maintenance cost rates for the geographical environments of AC and DC lines, respectively.

[0093] The equipment operation and maintenance costs are expressed as follows:

[0094] (11)

[0095] In the formula, T represents the number of hours in a year; and These are the maintenance costs per unit of photovoltaic power generation and per unit of energy storage charge / discharge, respectively. and Let be the photovoltaic output and energy storage charging / discharging power of node i at time t, respectively. The power is positive when discharging and negative when charging. This is the charge / discharge indicator for the energy storage battery. It is 1 when discharging, -1 when charging, and 0 when not in operation. Subsidies for photovoltaic power generation per unit; Maintenance cost per unit of electricity supplied under standard operating procedure (SOP) Let be the operating capacity of the s-th SOP.

[0096] 6) The calculation method for network operation losses is expressed as follows:

[0097] (12)

[0098] In the formula, For electricity price; and These are the resistances of AC and DC lines l, respectively; and Let be the currents flowing through AC and DC line l at time t, respectively; Let be the power loss of the s-th SOP.

[0099] 7) The calculation method for main grid electricity purchase fees is expressed as follows:

[0100] (13)

[0101] In the formula, Let be the net power flowing through the main network connection branch g at time t; Number of main network connection branches.

[0102] (2) Clustering Comprehensive Indicators :

[0103] (14)

[0104] In the formula, Modularity metric; It is a reactive power balance index; It is an active power balance index; This is an indicator of inter-group power volatility. It is a volatility variance indicator; As a complementary indicator; , , , , and All are indicator weights; the weights of each indicator satisfy the following conditions: .

[0105] 1) Regarding the modularity metric:

[0106] First, calculate the power flow sensitivity matrix. :

[0107] (15)

[0108] In the formula: In the formula, , Inject increments of active and reactive power into the nodes; and These are the increments of the node voltage phase angle and magnitude, respectively; Let be the partial derivative of the active power with respect to the phase angle. The partial derivative of active power with respect to amplitude, Let be the partial derivative of reactive power with respect to the phase angle. Let be the partial derivative of reactive power with respect to amplitude; Equation (15) reveals the relationship between the changes in active and reactive power in the power grid flow and the voltage phase angle and amplitude. Subsequently, the voltage amplitude and reactive power sensitivity matrix can be derived based on Equation (15). for:

[0109] (16)

[0110] Then, the AC electrical distance between nodes i and j Defined as:

[0111] (17)

[0112] In the formula, Let be the change in the reactive power of node j and the change in voltage amplitude at node i.

[0113] DC electrical distance between nodes i and j Defined as:

[0114] (18)

[0115] In the formula, This is the conductance matrix of the DC network.

[0116] Combine equations (17) and (18) to determine the final distance based on the combination of node types and define the distance metric. :

[0117] (19)

[0118] In the formula: This indicates that there is no direct electrical connection between AC and DC nodes, and the connection strength is considered to be zero.

[0119] Under the objective of achieving the optimal number of clusters, the modularity index... The formula used to measure the cohesion of a cluster structure is defined as follows:

[0120] (20)

[0121] (twenty one)

[0122] (twenty two)

[0123] (twenty three)

[0124] In the formula: The connection strength between nodes is obtained by transformation based on equation (19); Let $i$ be the sum of the weights of all edges connected to node $i$. The sum of the total connection edge weights of the network; For cluster membership functions, when node i belongs to a cluster and the cluster where node j is located When they are in the same cluster, Otherwise, it is 0.

[0125] 2) Regarding power balance indicators:

[0126] Power balance indicators include reactive power balance. Balance of active and passive power The two types of indicators are represented as follows:

[0127] (twenty four)

[0128] In the formula, and Let represent the supply and demand of cluster k at time t; Total number of time periods; This represents the total number of clusters.

[0129] (25)

[0130] In the formula, and Let represent the supply and demand of cluster k at time t.

[0131] 3) For inter-group power volatility and volatility variance indices:

[0132] Inter-group power volatility index and volatility variance index Used to assess cluster operational stability, two types of metrics are expressed as follows:

[0133] (26)

[0134] (27)

[0135] In the formula, This represents the number of branches for inter-cluster interaction. Let be the active power of tie line l at time t; Let be the active power of tie line l at time t+1.

[0136] 4) Regarding complementarity indicators:

[0137] (28)

[0138] (29)

[0139] (30)

[0140] (31)

[0141] (32)

[0142] In the formula, Let be the complementarity weights of nodes i and j; The correlation coefficient between the net power of nodes i and j; Let be the complementarity strength of node i; M is the total complementarity strength of the network. Let be the net active power of node i during time period t; Let be the average net active power of node i.

[0143] In this embodiment, a six-dimensional evaluation index system for evaluating the overall performance of the distribution network cluster is established, including modularity index, reactive power balance index, active power balance index, inter-cluster power fluctuation index, fluctuation variance index, and complementarity index. The aim is to systematically quantify and guide the rationality of the distribution network cluster structure, realize the transformation from simple economic optimization to economic-safety-autonomous collaborative optimization, thereby significantly improving the regional self-balancing capability and operational stability of the AC / DC distribution network.

[0144] 2. Power distribution constraints:

[0145] In this embodiment, distribution constraints include network radiality and connectivity constraints, distribution network power flow constraints, power balance constraints, node voltage and line current constraints, photovoltaic penetration constraints, photovoltaic active power output constraints, energy storage charging and discharging power and state of charge constraints, and main grid interconnection branch power backfeed and inter-group interaction branch power constraints.

[0146] (1) The radial and connectivity constraints of the network are represented as:

[0147] (33)

[0148] (34)

[0149] In the formula, This indicates whether nodes i and j are connected; 0 indicates no connection, and 1 indicates a connection. Represents the set of communication nodes; This represents the set of DC nodes.

[0150] (2) The power flow constraints of the distribution network are expressed as:

[0151] (35)

[0152] In the formula, and It refers to the active and reactive power injected into node i; and These are the voltage magnitudes at nodes i and j; and These are the conductance and susceptance of the line connecting nodes i and j; This represents the phase angle difference of the node voltage.

[0153] (3) The power balance constraint is expressed as:

[0154] (36)

[0155] In the formula: For energy storage charging and discharging power; This represents the load active power of node i in cluster j at time t; Let be the resistance of line l; Equation (36) represents the resistance at any given time. The power output of the distribution network is equal to the power consumption, which satisfies the law of conservation of energy.

[0156] (4) The node voltage and line current constraints are expressed as follows:

[0157] (37)

[0158] (38)

[0159] In the formula, and These are the upper and lower limits of the allowable voltage for node i; This represents the upper limit of the allowable current for line l.

[0160] (5) The photovoltaic penetration rate constraint is expressed as:

[0161] (39)

[0162] (40)

[0163] In the formula, Let k be the photovoltaic capacity of cluster k; This represents the maximum load demand of node i; This represents the upper limit of the total distributed photovoltaic penetration rate of the system. and Represents a cluster The photovoltaic installed capacity and maximum load demand of inner node i; For cluster The penetration rate of distributed photovoltaic clusters.

[0164] (6) The photovoltaic active power output constraint is expressed as:

[0165] (41)

[0166] In the formula, This indicates the rated output of the distributed photovoltaic system at node i, where the actual output is less than the rated capacity.

[0167] (7) The energy storage charging and discharging power and state of charge constraints are expressed as follows:

[0168] (42)

[0169] In the formula: and These are the maximum power limits for energy storage i-discharge and charging, respectively; and These represent the minimum and maximum states of charge of energy storage, calculated as percentages. Let i be the state of charge of the energy storage device at time t; Let i be the state of charge of the energy storage device at time t-1.

[0170] (8) The power backfeedback of the main network interconnection branch and the power constraint of the inter-group interaction branch are expressed as follows:

[0171] (43)

[0172] In the formula, The maximum allowable backfeed power for the main network connection branch g; The maximum power allowed to pass through the inter-group branch l; Let be the power passing through the inter-group branch l at time t; This represents the number of branches between clusters.

[0173] In one specific implementation, the process of using the improved SRA algorithm to solve the upper-level planning model and output the device configuration and cluster partitioning scheme includes the following steps:

[0174] S11. Perform mixed encoding on the decision variables of the upper-level programming model to obtain the decision variable structure; initialize the decision variable structure to obtain the initial population;

[0175] S12. Based on the objective function of the upper-level planning model, calculate the fitness value of each individual in the initial population, and determine the optimal individual based on the fitness value;

[0176] S13. An improved SRA algorithm is adopted, which guides individuals in the population to move towards the optimal individual based on an adaptive step size strategy and performs local updates to update the population; the cluster topology in the updated population is checked and repaired, and the repaired population is constrained according to power distribution constraints.

[0177] S14. Calculate the fitness value of each individual in the constrained population, and select a set of high-quality individuals based on the fitness values ​​and update the optimal individual; determine whether the preset convergence condition is met. If not, use the current set of high-quality individuals as the new population, and jump to execute the step of using the improved SRA algorithm and guiding individuals in the population to move towards the optimal individual based on the adaptive step size strategy to update the population; if the condition is met, generate the device configuration and cluster partitioning scheme based on the current optimal individual and output it.

[0178] It should be noted that the upper-level planning model is a mixed-integer nonlinear programming problem, containing discrete decision variables (line construction, cluster partitioning) and continuous decision variables (equipment capacity), and involves nonlinear power flow constraints, making it difficult to solve. Therefore, this invention employs an improved Sacred Religion Algorithm (SRA) to solve the upper-level planning model.

[0179] The Sacred Religion Algorithm is a novel swarm intelligence optimization algorithm inspired by pilgrimage and the mechanism of faith propagation in religious beliefs. It analogizes the solutions to optimization problems to believers, simulating their pilgrimage, preaching, and conversion to achieve global search and local development of the solution space. The Sacred Religion Algorithm comprises three core phases: the Pilgrimage Phase, where believers move towards holy sites, corresponding to a search for solutions towards the global optimum; the Preaching Phase, where senior believers spread their faith, corresponding to a search of the local neighborhood of solutions; and the Conversion Phase, where non-believers are converted, corresponding to a mechanism for maintaining solution diversity.

[0180] The upper-level planning model involves decision variables, which include continuous variables (photovoltaic capacity, energy storage capacity, SOP capacity) and discrete variables (line construction decisions, cluster partitioning). This specific embodiment uses a hybrid coding method to encode the above decision variables, resulting in the decision variable structure X. .

[0181] in, Indicates the decision to construct the line (0 - no construction, 1 - construction); Indicates the photovoltaic capacity of each node; Indicates the energy storage capacity of each node; Indicates the capacity of each SOP; This indicates the cluster number to which each node belongs.

[0182] Before solving the problem, it is necessary to initialize the structure of the decision variables to obtain an initial population; for example, initialize the population size. Maximum number of iterations ,initialization A random solution (believer).

[0183] To address the specific characteristics of AC / DC distribution network planning problems, this specific embodiment improves the standard SRA algorithm with an adaptive pilgrimage strategy (adaptive step size strategy), cluster topology constraint repair, and elite preservation and differential mutation:

[0184] (1) Adaptive pilgrimage strategy:

[0185] The standard SRA (Standard Raid Assist) uses a fixed step size, which is prone to getting trapped in local optima. This specific embodiment uses an adaptive step size to improve the step size strategy:

[0186] (44)

[0187] (45)

[0188] (46)

[0189] In the formula, The current position of individual (believer) i; The location of the globally optimal solution (the holy site); The step size coefficient for moving towards the holy site (decreases linearly with iteration, initially...) Later ); The random perturbation coefficient (increases linearly with iteration, initially...) Later ); for Random numbers within an interval.

[0190] (2) Cluster topology constraint repair:

[0191] Cluster partitioning must satisfy radial topological constraints; direct random assignment can easily lead to invalid solutions. This specific embodiment adopts a spanning tree-based repair strategy: 1) Random selection 1) Select one node as the root node of the cluster; 2) Construct the minimum spanning tree based on electrical distance; 3) Assign each node to the cluster where the nearest root node is located; 4) Check the radial constraints, and regenerate if they are violated.

[0192] (3) Elite preservation and differential variation:

[0193] To prevent the loss of high-quality solutions during iteration, an elite retention mechanism is introduced: the top 10% of high-quality solutions in each generation are directly carried over to the next generation. Simultaneously, a differential mutation operator is introduced to enhance population diversity.

[0194] (47)

[0195] In the formula, This is the new solution (mutated individual) generated after differential mutation of individual i. , and Each of the three individuals was randomly selected. This is the scaling factor.

[0196] Specifically, please refer to Figure 2 The execution flow of the improved SRA algorithm in this specific embodiment is as follows:

[0197] (1) Initialize population size Maximum number of iterations ,initialization A random solution (believer);

[0198] (2) Based on the objective function of the upper-level planning model, calculate the fitness value (i.e., the objective function value) of each individual. The individual with the optimal fitness value is selected as the globally optimal individual (solution / holy land). ;

[0199] (3) During the pilgrimage phase, the position of each individual is updated according to the adaptive step size strategy and equation (50), so that it moves towards the direction of the optimal individual; during the evangelism phase, the top 30% of high-quality individuals are selected as evangelists, and new solutions are generated in their neighborhoods: ,in The neighborhood radius decreases with iteration; conversion phase: for the bottom 20% of individuals with poor fitness, differential mutation or random reinitialization is performed to maintain population diversity and update the population;

[0200] (4) Cluster topology repair: perform topology constraint testing and repair on the cluster partitioning variables of all individuals in the population, and punish individuals that violate the power distribution constraints.

[0201] (5) Elite retention: retain the top 10% of high-quality individuals in the current population; update the global optimal solution. and optimal fitness value ;

[0202] (6) Determine whether the convergence condition is met (reaching the maximum number of iterations or the fitness change is less than the threshold). If it is met, output the optimal solution; otherwise, return to step (3).

[0203] In this specific embodiment, the improved SRA algorithm is used to solve the upper-level planning model, which can effectively handle mixed integer programming problems with the coexistence of discrete and continuous variables, complex constraints, and strong nonlinearity. The adaptive search strategy and topology repair mechanism can be used to synergistically optimize the equipment configuration and cluster partitioning, which significantly improves the quality of the generated solution and the computational efficiency, thereby optimizing the global equipment configuration and cluster partitioning scheme in terms of economy and cluster structure.

[0204] Step 103: Based on the equipment configuration and cluster partitioning scheme, and under the condition of satisfying the SOP constraint, construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss; use the second-order cone relaxation method to solve the lower-level planning model and output the SOP location scheme.

[0205] In this embodiment, a lower-level planning model is constructed based on the equipment configuration and cluster partitioning scheme, with the goal of reducing system transmission loss. The lower-level planning model is solved by the second-order cone relaxation (SOCR) method to output the SOP location scheme, that is, to determine the AC and DC access nodes of the SOP.

[0206] In one specific implementation, the process of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying SOP constraints, includes:

[0207] S21. Determine the network topology and equipment capacity parameters based on the equipment configuration and cluster partitioning scheme;

[0208] S22. Construct a distribution node association matrix based on the network topology, and construct a power transmission allocation coefficient matrix based on the distribution node association matrix;

[0209] S23. Based on the power transmission allocation coefficient matrix and equipment capacity parameters, and under the condition of satisfying the SOP constraint, construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss.

[0210] It should be noted that the equipment configuration and cluster partitioning scheme are determined based on the upper-level planning. The network topology and equipment capacity parameters (capacity or power limits of lines, photovoltaics, energy storage and SOPs) are determined based on the equipment configuration and cluster partitioning scheme. The lower-level planning model is then constructed based on the network topology and equipment capacity parameters.

[0211] First, based on the network topology, construct the distribution node association matrix C. When the l-th branch is connected to node i and the power flow exits from node i, the elements of the node association matrix C are... When the current flows in from node i If not connected Therefore, the node association matrix C is represented as:

[0212] (48)

[0213] Then, based on the distribution node correlation matrix, a power transmission allocation coefficient matrix T is constructed, and the elements of the power transmission allocation coefficient matrix T are... This represents the change in power flow on branch l caused by injecting unit power at node i. Let the admittance of branch l be... Establish the construction matrix The admittance can be calculated from the basic data. Middle elements If the balancing node is marked as node 1, then the power transmission allocation coefficient matrix T is:

[0214] (49)

[0215] In the formula: This is the node admittance matrix after removing the balancing nodes.

[0216] right Weighting is performed, and the weights are used to measure the active power on branch l. and reactive power This study aims to quantitatively assess the impact of changes in node unit power on line power transmission losses after the installation of Standard Operating Procedures (SOPs). , The impact.

[0217] Existing SOP (Standard Operating Point) location methods rely solely on node load size or line load intensity, neglecting the impact of node power variations on overall power flow distribution. For instance, a node with a high load may only affect nearby branches due to power disturbances, offering limited improvement to overall system transmission loss; conversely, a node with a lower load, located at a power flow convergence point, can significantly impact the losses of multiple branches due to power variations. This one-sidedness of evaluation metrics leads to inaccurate location decisions.

[0218] Therefore, this invention determines the weighted power transmission distribution factor based on the power transmission allocation coefficient matrix and equipment capacity parameters, and determines the SOP location based on the weighted power transmission distribution factor. The weighted power transmission distribution factor is a physical quantity describing the impact of transmission power on branch power flow, used as a quantitative index to evaluate the degree of influence of different nodes on the total transmission loss of the system. If the power change of a node has a wide impact on the branch, then that node is more suitable for connecting the SOP as a node connecting the AC / DC network to regulate the power flow distribution of the distribution network. Therefore, the SOP location problem can be described as selecting the node that maximizes the reduction of transmission loss, and the objective function of the lower-level planning model is:

[0219] (50)

[0220] In the formula: and These are the active power and reactive power at the end of branch l, respectively; and These are the resistance and reactance of branch l, respectively; Let be the voltage at the end of branch l; This represents the total number of lines in the system. This represents the total number of nodes in the system.

[0221] This embodiment adopts the SOP location method based on weighted power transmission distribution factor, constructs a node association matrix and a power transmission allocation coefficient matrix, quantifies the transmission effect of node power disturbance on the power flow of each branch, and combines loss sensitivity to weighted evaluate the impact of nodes on the overall network loss, and selects the node with the largest transmission loss to access the SOP.

[0222] It is worth noting that the SOP, as a flexible multi-state switch, can flexibly control power flow and regulate node voltage, and can also be used for fault recovery during distribution network faults. In a hybrid AC / DC network, a voltage source converter (VSC) can be transformed into a DC-DC converter based on a traditional two-port SOP. In this case, the common DC bus between the two converters can be opened later to allow more DC equipment to be connected.

[0223] Please see Figure 4 , Figure 4 A basic SOP (Standard Operating Procedure) structure suitable for AC / DC hybrid networks is provided. Based on this, the SOP constraints in the lower-level planning model include power transmission constraints, power loss constraints, and capacity constraints. Considering that in AC / DC hybrid distribution networks, the SOP needs to connect both AC and DC nodes, the power transmission constraint can be expressed as:

[0224] (51)

[0225] The power loss constraint of SOP is expressed as:

[0226] (52)

[0227] Since each port of the SOP is isolated by the DC link, each port only needs to meet its own capacity constraint, that is:

[0228] (53)

[0229] In the formula: and These are the active power injected into AC node i and DC node h of SOP, respectively; The reactive power injected into AC node i of SOP; and These are the active power losses at AC node i and DC node h of SOP, respectively; and These are the capacities of the converters at SOP AC node i and DC node h, respectively. and These are the converter loss factors for AC node i and DC node h of SOP, respectively.

[0230] In one specific implementation, the process of using the second-order cone relaxation method to solve the lower-level planning model and output the SOP location scheme includes the following steps:

[0231] S31. Based on the second-order cone relaxation method, voltage auxiliary variables, current auxiliary variables, and power auxiliary variables are introduced;

[0232] S32. Based on voltage auxiliary variables, current auxiliary variables, and power auxiliary variables, relax the nonlinear constraints of the lower-level planning model into second-order cone constraints, transform the lower-level planning model into a convex optimization problem for solution, and output the SOP location scheme.

[0233] It should be noted that although the lower-level planning model is a nonlinear optimization problem, it can be transformed into a convex optimization problem using the Second-Order Cone Relaxation (SOCR) technique, and then solved efficiently using a mature interior-point method solver.

[0234] The nonlinear constraints involved in the lower-level model mainly originate from the power flow equations of the distribution network, particularly the voltage and current product terms in the nodal power balance and line power constraints. By introducing auxiliary variables and performing appropriate mathematical transformations, these nonlinear constraints can be relaxed into second-order cone constraints, thereby transforming the original nonconvex optimization problem into a convex optimization problem that can be solved efficiently.

[0235] In this specific embodiment, the introduced auxiliary variables include: a current auxiliary variable, let Represents the square of the voltage magnitude at node i; current is an auxiliary variable, let Represents the square of the current magnitude in branch l; power auxiliary variable, introduced variable. , These represent the active power and reactive power of branch l, respectively.

[0236] The nonlinear constraints in the SOP constraint conditions are relaxed to second-order cone constraints based on voltage auxiliary variables, current auxiliary variables, and power auxiliary variables; among them, the second-order cone constraints include node power balance constraints, voltage-current second-order cone constraints, voltage upper and lower limit constraints, line current safety constraints, and SOP equipment operation constraints.

[0237] The node power balance constraint is expressed as:

[0238] (54)

[0239] In the formula, The set of branches connected to the node. , Inject the active and reactive power of node i into the SOP. , Let i be the active and reactive power of the load. , Contribute to photovoltaic and energy storage.

[0240] The voltage-current second-order cone constraint is expressed as:

[0241] (55)

[0242] Voltage upper and lower limit constraints are expressed as follows:

[0243] (56)

[0244] The line current safety constraint is expressed as:

[0245] (57)

[0246] SOP equipment operation constraints are expressed as follows:

[0247] (58)

[0248] (59)

[0249] In the formula, The SOPs given for upper-level planning are based on capacity. This refers to the wear and tear of the SOP itself.

[0250] Step 104: Input the SOP site selection scheme into the upper-level planning model, and use the improved SRA algorithm to solve the upper-level planning model, outputting the updated equipment configuration and cluster partitioning scheme.

[0251] In this embodiment, a coupled iterative mechanism between upper and lower planning models is adopted to achieve collaborative decision-making on equipment configuration, cluster partitioning, and SOP site selection through information interaction and iterative optimization. Specifically, after determining the line construction scheme, photovoltaic site selection and capacity determination, energy storage site selection and capacity determination, SOP capacity configuration, and cluster partitioning by solving the upper-level planning model, the determined network topology and equipment capacity parameters are passed to the lower-level planning model. The lower-level model determines the SOP access node based on the node with the maximum transmission loss and feeds back the SOP site selection scheme (site selection result) to the upper-level planning model. Based on the SOP site selection scheme returned by the lower-level planning model, the upper-level planning model recalculates the overall system planning operating cost and cluster performance indicators to update the objective function value, thereby outputting an updated equipment configuration and cluster partitioning scheme.

[0252] Step 105: If the preset iteration conditions are not met, proceed to the next step: Based on the device configuration and cluster partitioning scheme, under the condition of satisfying the SOP constraint, construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss; solve the lower-level planning model using the second-order cone relaxation method and output the SOP location scheme; if the preset iteration conditions are met, use the latest iteration output device configuration and cluster partitioning scheme and SOP location scheme as the optimal planning scheme and output it.

[0253] Understandably, in the initial stage, SOP (Standard Operating Procedure) location is initialized, and the initial SOP location is used to solve the upper-level planning model, outputting the device configuration and cluster partitioning scheme. Then, based on this device configuration and cluster partitioning scheme, the lower-level planning model is constructed, and an updated SOP location scheme is obtained. This updated location scheme is then fed back to the upper-level model for the next round of optimization. This process iterates until a preset iteration condition is met. The preset iteration condition is that the objective function values ​​of both the upper and lower-level planning models meet a preset threshold.

[0254] Through the coupled iterative mechanism of the upper and lower planning models, dynamic coordination and overall optimization of equipment configuration, cluster partitioning and SOP site selection are achieved, ultimately resulting in a planning scheme that comprehensively improves investment economy, network structure and operating losses.

[0255] The following describes the dual-layer planning device for AC / DC distribution network equipment configuration and SOP location considering cluster partitioning provided in the embodiments of this application. The dual-layer planning device for AC / DC distribution network equipment configuration and SOP location considering cluster partitioning described below can be referred to in correspondence with the dual-layer planning method for AC / DC distribution network equipment configuration and SOP location considering cluster partitioning described above.

[0256] Please see Figure 4 This invention also provides a two-layer planning device for AC / DC distribution network equipment configuration and SOP location selection that considers cluster partitioning. The device includes:

[0257] The data acquisition module 201 is used to acquire the basic data and operation data of the AC / DC distribution network to be planned;

[0258] The upper-level planning module 202 is used to construct an upper-level planning model based on the basic data and operation data of the AC / DC distribution network, under the condition of satisfying the power distribution constraints, with the objective function of minimizing the comprehensive planning cost and optimizing the overall performance of the cluster; the improved SRA algorithm is used to solve the upper-level planning model and output the equipment configuration and cluster partitioning scheme.

[0259] The lower-level planning module 203 is used to construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the equipment configuration and cluster partitioning scheme, while satisfying the SOP constraint; the lower-level planning model is solved using the second-order cone relaxation method, and the SOP location scheme is output.

[0260] The joint planning module 204 is used to input the SOP site selection scheme into the upper-level planning model, and to solve the upper-level planning model using the improved SRA algorithm, and output the updated equipment configuration and cluster partitioning scheme.

[0261] The planning scheme output module 205 is used to, if the preset iteration conditions are not met, jump to execute the following steps: based on the equipment configuration and cluster partitioning scheme, under the condition of satisfying the SOP constraint, construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss; solve the lower-level planning model using the second-order cone relaxation method and output the SOP location scheme; if the preset iteration conditions are met, adopt the latest iteration output equipment configuration and cluster partitioning scheme and SOP location scheme as the optimal planning scheme and output it.

[0262] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of any of the above-described AC / DC distribution network equipment configuration and SOP location two-level planning methods.

[0263] This invention also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the steps of any of the above-mentioned AC / DC distribution network equipment configuration and SOP location two-layer planning methods.

[0264] This invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of any of the above-mentioned AC / DC distribution network equipment configuration and SOP location two-layer planning methods.

[0265] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0266] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not performed. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other may be through some interface, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms. In addition, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0267] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0268] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0269] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0270] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-tier planning method for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning, characterized in that, The method includes: Obtain basic and operational data of the AC / DC distribution network to be planned; Based on the basic and operational data of the AC / DC distribution network, and under the condition of satisfying the distribution constraints, a higher-level planning model is constructed with the objective function of minimizing the overall planning cost and optimizing the overall performance of the cluster. The improved SRA algorithm is used to solve the higher-level planning model, and the equipment configuration and cluster partitioning scheme are output. Based on the device configuration and cluster partitioning scheme, and under the condition of satisfying the SOP constraint, a lower-level planning model is constructed with the objective function of maximizing the reduction of transmission loss; the lower-level planning model is solved using the second-order cone relaxation method, and the SOP location scheme is output. The SOP site selection scheme is input into the upper-level planning model, and the improved SRA algorithm is used to solve the upper-level planning model, outputting an updated device configuration and cluster partitioning scheme; If the preset iteration conditions are not met, the process jumps to the steps of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying the SOP constraint; solving the lower-level planning model using the second-order cone relaxation method and outputting the SOP location scheme; if the preset iteration conditions are met, the device configuration and cluster partitioning scheme and SOP location scheme output by the latest iteration are adopted as the optimal planning scheme and output.

2. The two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection according to claim 1, characterized in that, The objective function of the upper-level planning model is expressed as: In the formula: F is the objective function value that integrates the overall planning cost and the overall performance of the cluster; In order to comprehensively plan operating costs, As a comprehensive indicator of clustering; As an economic weight, Cluster weights; in, In the formula: For the cost of line construction; For photovoltaic investment and construction costs; For energy storage investment and construction costs; Cost of SOP construction; Annual operation and maintenance costs of the power distribution network; To reduce network loss during operation; Main grid electricity purchase cost; in, In the formula: Modularity metric; It is a reactive power balance index; It is an active power balance index; This is an indicator of inter-group power volatility. It is a volatility variance indicator; As a complementary indicator; , , , , and All are indicator weights.

3. The two-tier planning method for AC / DC distribution network equipment configuration and SOP location selection according to claim 1, characterized in that, The objective function of the lower-level planning model is expressed as: In the formula: This represents the total number of lines in the distribution network system. This indicates the change in power flow on branch l of the power grid caused by injecting unit power at node i. This represents the total number of nodes in the distribution network system. and These are the active power and reactive power at the end of the power grid branch l, respectively. and These are the resistance and reactance of the power grid branch l, respectively; Let be the voltage at the end of the power grid branch l.

4. The two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection according to claim 1, characterized in that, The steps of using the improved SRA algorithm to solve the upper-level planning model and output the device configuration and cluster partitioning scheme include: The decision variables of the upper-level planning model are mixed-encoded to obtain the decision variable structure; the decision variable structure is initialized to obtain the initial population. Based on the objective function of the upper-level planning model, the fitness value of each individual in the initial population is calculated, and the optimal individual is determined according to the fitness value. An improved SRA algorithm is adopted, which guides individuals in the population to move toward the optimal individual and performs local updates to update the population based on an adaptive step size strategy; the cluster topology in the updated population is checked and repaired, and the repaired population is constrained according to the power distribution constraints. Calculate the fitness value of each individual in the constrained population, and select a set of high-quality individuals based on the fitness values ​​to update the optimal individual; determine whether the preset convergence condition is met. If not, use the current set of high-quality individuals as the new population, and jump to the step of using the improved SRA algorithm to guide individuals in the population to move towards the optimal individual based on the adaptive step size strategy to update the population; if the condition is met, generate and output the device configuration and cluster partitioning scheme based on the current optimal individual.

5. The two-layer planning method for AC / DC distribution network equipment configuration and SOP location selection according to claim 1, characterized in that, The step of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying SOP constraints, includes: Based on the device configuration and cluster partitioning scheme, determine the network topology and device capacity parameters; Based on the network topology, a power distribution node association matrix is ​​constructed, and based on the power distribution node association matrix, a power transmission allocation coefficient matrix is ​​constructed. Based on the power transmission allocation coefficient matrix and the equipment capacity parameters, a lower-level planning model is constructed with the objective function of maximizing the reduction of transmission loss, while satisfying the SOP constraint.

6. The two-tier planning method for AC / DC distribution network equipment configuration and SOP location selection according to claim 1, characterized in that, The steps of solving the lower-level planning model using the second-order cone relaxation method and outputting the SOP site selection scheme include: Based on the second-order cone relaxation method, voltage auxiliary variables, current auxiliary variables, and power auxiliary variables are introduced. Based on the voltage auxiliary variables, current auxiliary variables, and power auxiliary variables, the nonlinear constraints of the lower-level planning model are relaxed to second-order cone constraints, and the lower-level planning model is transformed into a convex optimization problem for solution, outputting the SOP location scheme.

7. A two-layer planning device for AC / DC distribution network equipment configuration and SOP location selection considering cluster partitioning, characterized in that, The device includes: The data acquisition module is used to acquire basic and operational data of the AC / DC distribution network to be planned. The upper-level planning module is used to construct an upper-level planning model based on the basic data and operation data of the AC / DC distribution network, under the condition of satisfying the power distribution constraints, with the objective function of minimizing the comprehensive planning cost and optimizing the overall performance of the cluster; the improved SRA algorithm is used to solve the upper-level planning model and output the equipment configuration and cluster partitioning scheme. The lower-level planning module is used to construct a lower-level planning model with the objective function of maximizing the reduction of transmission loss, based on the device configuration and cluster partitioning scheme and under the condition of satisfying SOP constraints; the lower-level planning model is solved using the second-order cone relaxation method, and the SOP location scheme is output. The joint planning module is used to input the SOP site selection scheme into the upper-level planning model, and use the improved SRA algorithm to solve the upper-level planning model, and output an updated equipment configuration and cluster partitioning scheme. The planning scheme output module is used to, if the preset iteration conditions are not met, jump to the steps of constructing a lower-level planning model with the objective function of maximizing the reduction of transmission loss based on the device configuration and cluster partitioning scheme under the condition of satisfying SOP constraints; solving the lower-level planning model using the second-order cone relaxation method and outputting the SOP location scheme; if the preset iteration conditions are met, then using the latest iteration output device configuration and cluster partitioning scheme and SOP location scheme as the optimal planning scheme and outputting it.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the two-level planning method for AC / DC distribution network equipment configuration and SOP location selection as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the two-level planning method for AC / DC distribution network equipment configuration and SOP location selection as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the two-level planning method for AC / DC distribution network equipment configuration and SOP location selection as described in any one of claims 1-6.

Images