A transition method for a honeycomb power distribution network based on SOP optimization configuration

Abstract

The application discloses a kind of based on the transition method of honeycomb power distribution network of SOP optimization configuration, comprising: constructing the upper layer SOP site selection model based on comprehensive performance index SOP optimization configuration, comprehensive performance index includes: power-voltage sensitivity index, node voltage fluctuation index, honeycomb unit interconnection weakness index;SOP optimization configuration lower layer fixed capacity model is constructed.The lower layer fixed capacity model includes the comprehensive operation target and constraint condition of active power distribution network, the operation reliability of active power distribution network comprehensive operation target has considered cost and honeycomb power distribution network ESS, SOP simultaneously;Based on the improved sparrow algorithm to upper layer SOP site selection model and lower layer fixed capacity model are solved, determine the optimal access scheme of SOP, realize in active power distribution network optimization configuration SOP equipment and construct honeycomb unit, finally transition into honeycomb power distribution network.

Description

Technical Field

[0001] This invention relates to the field of distribution network control technology, and in particular to a transition method for cellular distribution networks based on SOP optimized configuration. Background Technology

[0002] Currently, the large-scale application of distributed generation (DG) such as photovoltaics and wind power is being promoted, while bidirectional loads such as energy storage and electric vehicles are widely connected to the distribution network, driving the transformation of traditional passive distribution networks into active distribution networks with power electronics. However, traditional distribution networks, with their radial topology and unidirectional power flow, have inherent defects such as insufficient centralized control and regulation flexibility. They are ill-suited to the volatility and randomness of distributed energy sources and the complexity of new loads, leading to frequent problems such as voltage exceeding limits, reverse power flow, excessive harmonic content, and wind and solar curtailment. This not only affects power quality and reliability but also restricts the efficient absorption of distributed clean energy, becoming an obstacle to the development of high-proportion clean energy power electronic distribution networks. Currently, the distribution network is at a critical stage of transformation from a traditional distribution network to an active distribution network, facing new situations and challenges such as large-scale integration of distributed sources and loads, widespread integration of power electronic equipment, insufficient planning and operation coordination, and the participation of multiple entities in the distribution-side electricity market. How to transition from a traditional distribution network to a cellular distribution network has become an urgent problem to be solved.

[0003] The construction of cellular power distribution networks follows a transitional strategy of "from point to area, gradual expansion," such as... Figure 2 and Figure 3As shown in the diagram. Initially, construction begins with individual honeycomb units, gradually forming a complete honeycomb topology through modifications or the addition of key nodes. The honeycomb distribution network uses modular, distributed "honeycomb units" as its basic building blocks, presenting an scalable, hexagonal honeycomb topology that expands from points to surfaces. A single honeycomb unit consists of a hexagonal power supply area and a central base station. The power supply area can cover diverse entities such as distributed photovoltaics, wind power, and various loads. The base station is the core hub for power interaction and regulation within and outside the unit. Flexible interconnection between different honeycomb units is achieved through flexible power electronic devices such as smart soft switches (SOPs) installed inside the base station, supporting cross-unit power balance and power flow optimization. Furthermore, an energy storage system (ESS) is synchronously configured within the base station, which can smooth out fluctuations in distributed power output and load disturbances in real time, achieving power self-balancing within the unit and quickly supporting voltage stability and frequency regulation. Ultimately, this forms a new type of distribution network characterized by unit autonomy, cross-regional interconnection, and coordinated regulation. The key equipment in a cellular unit is the soft open point (SOP) and energy storage system (ESS) inside the base station. The ESS is directly connected to the SOP via the PCC. The flexible power electronic devices and energy storage system inside the base station directly determine the internal energy flow characteristics and external interconnection capabilities of the cellular unit. The location of the SOP can be understood as the location of the base station. Therefore, the transition of a cellular distribution network is essentially a matter of site selection and capacity configuration of in-station components. In other words, by optimizing the construction of base stations and determining the location of SOPs, and then determining the construction path and structural layout of the cellular distribution network, phased optimization and global coordination of the system can be achieved during the gradual expansion process, thereby effectively supporting the smooth transition of the cellular distribution network towards integration and intelligence. Summary of the Invention

[0004] Technical Objective: To address the shortcomings of existing technologies, this invention discloses a transition method for cellular distribution networks based on SOP optimized configuration. In the upper-level SOP location model, SOP location is selected through a comprehensive performance sensitivity index, and in the lower-level capacity model, the capacity of SOPs is configured through an improved sparrow algorithm. This enables the addition of new SOP devices to construct cellular units in a microgrid, ultimately forming a cellular distribution network.

[0005] Technical solution: To achieve the above technical objectives, the present invention adopts the following technical solution.

[0006] A transition method for cellular distribution networks based on SOP optimized configuration, characterized by the following steps: Step 1: Construct an upper-level SOP location model based on comprehensive performance indicators for SOP optimization configuration. The comprehensive performance indicators include: power-voltage sensitivity index, node voltage fluctuation index, and cellular cell interconnection weakness index. Several SOP location schemes are formed by sorting them according to the magnitude of the comprehensive performance indicators. Step 2: Construct a lower-level capacity model based on SOP capacity quantization; the lower-level capacity model includes the comprehensive operation objectives and constraints of the active distribution network. The comprehensive operation objectives of the active distribution network simultaneously consider cost and the operational reliability of the ESS and SOP of the cellular distribution network; the constraints include system power flow constraints, line power constraints, branch current constraints, node voltage constraints, and SOP operation constraints. Step 3: Based on the location schemes of all SOPs, solve the lower-level capacity model using the improved sparrow algorithm to determine the optimal access scheme for the SOP, including the location of the SOP, the capacity of the SOP, and the capacity of the ESS.

[0007] Beneficial Effects: This invention constructs a two-layer optimization framework of "upper-layer comprehensive performance index site selection and lower-layer cost and reliability coordinated capacity determination." At the upper layer, it accurately identifies key weak interconnection nodes and strengthens topological defects with minimal equipment investment. At the lower layer, it simultaneously considers comprehensive operating costs, SOPs, and the operational reliability of energy storage, achieving economical and efficient configuration of equipment capacity. Furthermore, by improving the sparrow algorithm to solve the two-layer model, it effectively enhances the algorithm's global exploration and local convergence capabilities, avoiding getting trapped in local optima. This method can orderly construct interconnected, autonomous, and scalable cellular units, forming a flexible interconnected cellular distribution network structure. This significantly improves the cross-unit power interaction capability, voltage support level, local renewable energy consumption capability, and power supply reliability of the distribution network, while reducing network losses and wind and solar curtailment costs. It provides an efficient and reliable technical solution for the transitional construction and orderly expansion of distribution networks under the new power system. Attached Figure Description

[0008] Figure 1 This is a flowchart of a cellular distribution network transition method based on SOP optimized configuration according to the present invention; Figure 2 This is a schematic diagram illustrating the transition strategy for the gradual expansion of cellular distribution networks in existing technologies. Figure 3 This is a schematic diagram of the structure of a honeycomb unit in the prior art. Detailed Implementation

[0009] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0010] Example: As attached Figure 1 As shown in this embodiment, a cellular distribution network transition method based on SOP optimized configuration includes the following steps: Step 1: Construct an upper-level SOP location model based on comprehensive performance indicators for SOP optimization configuration. The comprehensive performance indicators include: power-voltage sensitivity index, node voltage fluctuation index, and cellular cell interconnection weakness index. Several SOP location schemes are formed by sorting them according to the magnitude of the comprehensive performance indicators. The power-voltage sensitivity index is calculated using a power-voltage sensitivity index model. The calculation process includes: To solve for the maximum power-voltage sensitivity, the following power-voltage sensitivity index model needs to be established and solved: In a power system with n nodes, for any node i, the injected active power is... and reactive power The power flow equations can be expressed in polar coordinates; that is, the formula for calculating the power equations is: , in, , Let be the voltage magnitudes at nodes i and j, respectively. Let be the phase angle difference of the voltage between node i and node j. and These are conductivity and susceptance, respectively.

[0011] For each node, apply this power equation to the voltage magnitude U and phase angle. Performing a first-order Taylor expansion and neglecting higher-order terms, the change in active power is... and reactive power change Between with state variables and The relationship between state variables can be represented in matrix form, i.e., the Jacobian matrix. and Represents the voltage magnitude and vector difference between different nodes; change in active power. and reactive power change The calculation formula is as follows: , in, P represents active power, Q represents reactive power, and U represents voltage amplitude. This refers to the voltage phase angle; Inverting the above formula, we get: , in, This is the sensitivity matrix of active power to voltage. This is the sensitivity matrix of reactive power to voltage. This is the sensitivity matrix of active power to voltage phase angle. This is the sensitivity matrix of reactive power to voltage phase angle.

[0012] To comprehensively consider power-voltage sensitivity, a weighted coefficient method is used to combine active and reactive power-voltage sensitivity to obtain power-voltage sensitivity index parameters. It is calculated by weighting the sensitivity matrices of active power to voltage and reactive power to voltage, and the calculation formula is as follows: , in, , These are the weighting coefficients for active and reactive power sensitivity, respectively. In this embodiment, the same weighting coefficients are used for both active and reactive power sensitivity. and All values ​​are 0.5.

[0013] The power-voltage sensitivity index represents the impact of node power changes on node voltage, which base stations can use to flexibly adjust power among multiple cellular units. Therefore, a higher power-voltage sensitivity index value indicates a greater need to install a Standard Operating Procedure (SOP) at that node.

[0014] The calculation process for the node voltage fluctuation index includes: The integration of large-scale distributed power sources exacerbates the randomness and volatility of power distribution in cellular distribution networks. Standard Operating Procedures (SOPs) can enable flexible power regulation, thereby suppressing node variations and node voltage fluctuation indicators. The calculation formula is: , in, For node i in The node voltage fluctuation index at any given time, through node voltage With rated voltage The variance was calculated over 24 hours.

[0015] The cellular unit interconnection weakness index is constructed based on the cellular distribution network's regional topology, unit boundary network characteristics, and inter-regional topological connectivity. It is used to quantitatively evaluate a node's regional affiliation, boundary topological status, and inter-cell power channel connectivity within a multi-cellular unit topology network. It characterizes the necessity for nodes to achieve flexible inter-cell interconnection and reinforce topological defects through Standard Operating Procedures (SOPs). Cellular Unit Interconnection Weakness Index The calculation formulas include: , in, Indicates the number of cross-cell boundary link branches connected to node i; This represents the total number of branches connected to node i in the entire network. Let be the electrical distance from node i to the other nodes in the honeycomb cell. The set of all cross-honeycomb boundary branches associated with node i; It is the topology correction factor. Topology bottleneck coefficient; topology correction factor With topological bottleneck coefficient Used to characterize the topological margin and link congestion level of boundary branches. Let m be the apparent power of the m-th branch. This represents the maximum apparent power of the m-th branch.

[0016] The comprehensive performance index of upper-level SOP location is obtained by weighting coefficient method. The calculation formula is: , in, For comprehensive performance indicators, These are the weighting coefficients for the power-voltage sensitivity index, the node voltage fluctuation index, and the weak interconnection index of the cellular unit, respectively. The specific values ​​are set according to the actual situation.

[0017] Step 2: Construct a lower-level capacity model based on SOP optimized configuration; the lower-level capacity model includes the comprehensive operation objectives and constraints of the active distribution network. The comprehensive operation objectives of the active distribution network simultaneously consider cost and the operational reliability of the ESS and SOP of the cellular distribution network; the constraints include system power flow constraints, line power constraints, branch current constraints, node voltage constraints, and SOP operation constraints.

[0018] To determine the size of the SOP and ESS access capacity, this invention comprehensively considers the economy and reliability of the cellular unit after the transition, and establishes a comprehensive operating target for the active distribution network that takes into account both economy and reliability. The calculation formula for the comprehensive operating target of the active distribution network is as follows: , Where F represents the overall operational target of the active distribution network; F1 represents the total economic cost of the active distribution network; and F2 represents the reliability of the SOP and ESS. , These represent the investment cost and annual operating cost during the planning period of the SOP, respectively. For distribution network loss costs; Costs associated with curtailing wind and solar power; For the reliability indicators of the ESS (Emergency Shared Distribution System) in a cellular distribution network; Reliability indicators for SOPs in cellular distribution networks; , These are the total economic cost of the active distribution network and the weighted coefficients for the reliability of SOP and ESS, respectively.

[0019] The specific calculation formulas for each part are as follows: (1) Investment costs during the planning period of the SOP before normalization: The investment cost during the planning period of the SOP represents the comprehensive construction cost of the cellular distribution network base station components. To facilitate economic evaluation, the initial investment is converted into the equivalent annual cost during the planning period using a capital recovery factor. The calculation formula is as follows: , in, The total initial construction investment representing the SOP (Standard Operating Procedure) is the benchmark discount rate; N is the expected useful life of the equipment. Let be the capacity of the b-th SOP, which is also the key decision variable in this model. This represents the cost factor for installing one Standard Operating Procedure (SOP). This represents the cost factor for a fixed installation of one ESS; M represents the number of SOP units installed; L represents the number of ESS units installed. The unit cost of capacity for differentiated SOPs; The unit cost of differentiated gradient capacity for ESS; This is the correction factor for SOP based on the honeycomb topology; This is the correction factor for the honeycomb topology to the ESS; Cost per unit capacity of SOP; Cost per unit capacity of ESS; The capacity discount factor for SOP; Here is the capacity discount factor for ESS; where The capacity of the b-th SOP; For the first The capacity of the ESS (Easy Scale) and these two parameters are the decision variables to be output by the lower-level capacitive model.

[0020] (2) Annual operating costs during the planning period of the SOP before normalization: The annual operating cost during the planning period of a Standard Operating Procedure (SOP) refers to the equipment operation and maintenance cost. Throughout the equipment's life cycle, routine inspection and maintenance expenses are typically related to the installed capacity. The annual operating cost during the planning period of an SOP before normalization can be expressed as: , in, This represents the average annual operation and maintenance cost rate per unit capacity of the Standard Operating Procedure (SOP). This represents the average annual operation and maintenance cost per unit capacity of the ESS; Calculate the maintenance unit price for fixed power conversion losses at SOP; The active power of the b-th SOP; Let Y be the reactive power of the b-th SOP, and Y be the total duration of the planning cycle. In this embodiment, the value ranges from 25 to 30.

[0021] (3) Distribution network loss cost before normalization: Distribution network loss cost refers to the system network loss cost. After accessing the Standard Operating Procedure (SOP), it is necessary to quantify the active power loss cost of the active distribution network caused by line thermal effects. The expression for calculating the distribution network loss cost before normalization is as follows: , in, and These represent the current amplitude and resistance value of the m-th branch at time y, respectively; The unit of measurement is the pricing standard for unit power loss, expressed in yuan / kWh; Y is the total duration of the planning period; this integral term reflects the total active power loss of the system during the planning period.

[0022] (4) Costs of wind and solar power curtailment before normalization: Due to the strong volatility and randomness of distributed power generation, and the limited peak-shaving capacity of modern power systems, the distribution network cannot absorb all the output of distributed power generation in real time. The resulting costs of wind and solar power curtailment are linearly positively correlated with the corresponding amount of curtailed power. Under a market-based trading mechanism, these costs essentially reflect the lost revenue from electricity trading due to output limitations of renewable energy generation units. Their specific quantitative calculation can be achieved through a linear cost function. The costs of wind and solar power curtailment before normalization are: , in, and Let represent the curtailed solar power and wind power of all distributed power sources under the cellular unit at time y, respectively. and These represent the unit power generation costs of distributed photovoltaic and distributed wind power, respectively.

[0023] (5) Reliability indicators of the ESS (Electronic Power Supply) network before normalization: The energy storage system (ESS) exhibits the highest reliability when its state of charge (SOC) deviates from 0.5, simultaneously meeting the charging and discharging needs of the distribution network. Its reliability is quantified by the degree to which the ESS deviates from 0.5, calculated using the following formula: , in, Let represent the state of charge of the i-th ESS at time y.

[0024] (6) Reliability indicators of the SOP of the cellular distribution network before normalization: The device operates with the highest stability when the SOP port power remains balanced and the load rate is within a reasonable range, simultaneously meeting the power flow control and fault support requirements of the cellular distribution network. Its reliability is quantified by the degree of SOP port power imbalance and the extent to which the load rate deviates from the optimal range: , in, , Let be the active power of the two ports of the b-th SOP at time y; The rated power of the b-th SOP; Let b be the actual load rate of the SOP at time y; M represents the optimal load factor for the SOP, and M represents the number of SOPs installed.

[0025] The units of the various costs and indicators established differ, and therefore the expected values ​​during optimization also differ. Therefore, they need to be normalized. for , During iteration, we want them to be as small as possible, so we use the following normalization method to obtain the normalized processed values: , for During iteration, it is desirable for them to be as large as possible, but the final objective function seeks to minimize them. Therefore, the following normalization method is used to obtain the normalized processed value: , Where F is the normalized objective function; This represents the maximum value of the objective function before normalization. This represents the minimum value of the objective function before normalization. Let be the objective function to be normalized.

[0026] The formulas for calculating constraints are as follows: (1) System power flow constraints: , in, It is the set of nodes directly connected to node i. and The active power and reactive power flowing from node j to node i, respectively; It is the set of nodes connected to node i through SOP; and These are the active and reactive power of the load at node i. , These represent the active power and reactive power injected by the b-th SOP into each port of node j, respectively.

[0027] (2) Power constraints of the line: , in, and These represent the active power flowing from node j to node i and the active power flowing from node i to node j, respectively. and These are the reactive power flowing from node j to node i and the reactive power flowing from node i to node j, respectively. It is the voltage amplitude at node i. R is the magnitude of the current flowing from node j to node i. ij X is the equivalent resistance of the line. ij This is the equivalent inductance of the line.

[0028] (3) Branch current constraint: , in, Let be the magnitude of the current flowing from node i to node j. This represents the maximum value of the branch current.

[0029] (4) Node voltage constraints: , in, Let represent the voltage at node i at time y. and These represent the upper and lower limits of the voltage, respectively.

[0030] (5) SOP operating constraints: , in, , These represent the active power and reactive power injected by the b-th SOP into each port of node j, respectively. This represents the upper limit of the apparent power for the b-th SOP. These represent the upper and lower limits of capacity for all SOPs.

[0031] Step 3: Based on the location schemes of all SOPs, solve the lower-level capacity model using the improved sparrow algorithm to determine the optimal access scheme for the SOP, including the location of the SOP, the capacity of the SOP, and the capacity of the ESS.

[0032] To address the multi-objective optimization problem involving power flow calculation mentioned above, this invention proposes a heuristic capacity determination method suitable for SOPs based on the sparrow search method. Specifically, the improved sparrow algorithm is used to solve the upper-level SOP location model and the lower-level capacity determination model. Through iterative solutions, the SOP capacity that enables the modified honeycomb cells to meet self-balancing requirements and achieve global economic optimization is obtained.

[0033] The following objective function is constructed based on the lower-level capacity model using SOP and ESS: , The Sparrow Search Algorithm (SSA) is a typical swarm intelligence optimization algorithm. Its core design idea originates from the simulation and abstraction of the foraging and anti-predation defense behaviors of sparrow populations in nature. Compared with traditional intelligent optimization methods such as particle swarm optimization, genetic algorithms, and gravity search algorithms, the Sparrow Search Algorithm has more significant advantages in local optimization ability, global search performance, and iterative convergence speed, enabling it to more efficiently approach the optimal solution in complex high-dimensional optimization problems.

[0034] In the foraging behavior mechanism constructed by the algorithm, there is a clear division of roles and a cooperative pattern within the sparrow population. Explorers, as the core guiding individuals in the population, are primarily responsible for searching for foraging areas, determining the population's movement direction and search range, and providing efficient foraging paths for the entire group. Followers, relying on the location information and search guidance provided by the explorers, obtain food in the vicinity. Simultaneously, some followers also monitor the explorers' location and foraging status in real time, using competitive strategies to seize food resources and improve their own energy acquisition efficiency.

[0035] In the simulation experiment, virtual sparrows perform food-hunting tasks, and the entire group can be described by the following matrix: , Where n is the population size and d is the dimension of the decision variable.

[0036] In this invention, the decision variables for determining the SOP capacity are only the capacity of the SOP and the capacity of the ESS, so the population matrix can be represented by two column vectors: , in, Indicates the population associated with SOP; This indicates a population associated with ESS. This is the Mth SOP individual in the population; This is the Lth ESS individual in the population.

[0037] In the SSA (Search and Search) mechanism, explorers with higher fitness have priority in obtaining food and are responsible for guiding the foraging direction of the entire group, thus their search range is wider than that of followers. The location updates of explorers follow these rules: , in, This represents the coordinates of the c-th individual in the h-th dimension; Here, ST is the warning parameter; ST is the safety threshold (usually set to 0.8); Q is a random variable following a normal distribution; E is... A matrix of all 1s; This represents the maximum number of iterations.

[0038] Followers continuously track the explorer's movements, and once the explorer discovers a better food resource, the follower will quickly move to compete for it. Their location update mechanism is as follows: , in, The optimal coordinates for the current explorer; A represents the position of the individual with the worst fitness in the group; A is... A dimensional matrix whose elements randomly take values ​​of 1 or -1; Let be the pseudo-inverse matrix of A.

[0039] The scouts are responsible for environmental monitoring and detecting potential threats. Their location update rules are as follows: , in, This is the optimal solution for the current group; Let K be a random variable that follows a standard normal distribution (mean 0, variance 1); K is the adjustment coefficient. Let be the fitness of the c-th individual; and These are the globally optimal and globally worst fitness values, respectively. It is a very small positive number to avoid division by zero errors. K is also the step size control parameter in the sparrow search method.

[0040] The improved sparrow algorithm in this invention includes: improving the step size control parameters of the sparrow search method during the iteration process by introducing an adaptive decay mechanism that is aware of population diversity to adjust the step size control parameters. The step size control parameter K is adjusted by using an elite-guided Gaussian perturbation strategy.

[0041] During the iteration process, due to the step size control parameter Since both K and are random numbers, the objective function may get trapped in a local optimum, therefore these two parameters need to be improved. During the iteration process, the step size control parameter of the traditional sparrow search algorithm... Since K only monotonically decays with the number of iterations and is unaware of population diversity and search status, it is prone to insufficient exploration in the early stages and overexploitation in the later stages, thus getting trapped in local optima. Furthermore, the tangent function used in the original parameter K is prone to numerical oscillations in the later stages of iteration, affecting the algorithm's stability. Therefore, this invention significantly improves the above two parameters: (1) Improved parameters Introducing an adaptive decay mechanism based on population diversity perception Based on the original nonlinear decay strategy, a diversity feedback term based on the standard deviation of population fitness is introduced. When the population fitness distribution tends to be concentrated and the algorithm is prone to getting trapped in local optima, the step size parameter is automatically increased, forcing the algorithm to expand the search range; when the population is still in the effective exploration stage, the step size decays at a preset rhythm to ensure convergence efficiency. The improved parameters... It combines exploratory and convergent aspects; the calculation formula is as follows: , in, and These are the globally optimal and globally worst fitness values, respectively. t represents the maximum number of iterations; t represents the current number of iterations. For diversity feedback adjustment coefficient; This represents the standard deviation of the current population fitness. The maximum standard deviation of fitness; To provide a minimum value to resolve division by zero errors. This is the decay exponent coefficient.

[0042] (2) Improved parameter K: Adopt an elite-guided Gaussian perturbation strategy: The original tangent function, which is prone to numerical oscillations, is abandoned in favor of a more stable exponentially decaying form, and a zero-mean Gaussian perturbation is introduced to replace the purely random perturbation. Simultaneously, an individual fitness guiding term is added, dynamically linking the perturbation intensity to the individual's fitness: individuals with better fitness experience greater perturbation intensity, which is beneficial for fine-tuning the search around the optimal solution; perturbations to individuals with poor fitness are suppressed, avoiding ineffective searches. The improved parameter K retains the bidirectional perturbation capability while achieving differentiated search intensity control for different individuals, effectively improving the algorithm's ability to escape local optima and its solution stability. The calculation formula is as follows: , in, and These are the globally optimal and globally worst fitness values, respectively. t represents the maximum number of iterations; t represents the current number of iterations. The exponential decay intensity coefficient; This is the shape factor of the attenuation curve; Indicates that it follows a normal distribution Random numbers; This represents the fitness of the c-th individual; It is a local minimum.

[0043] This invention proposes a two-layer model for the optimized configuration of base stations in a cellular distribution network. The upper layer determines the basic site selection scheme for SOPs and ESS access within the station through comprehensive performance indicators. During the construction and gradual expansion of cellular units, topological connectivity, voltage support capacity, power interaction potential, and distributed generation absorption level must be considered. Therefore, this invention constructs a multi-dimensional comprehensive performance index considering the topological characteristics, voltage stability characteristics, and power regulation requirements of the cellular distribution network to optimize the nodes for SOP and ESS access. The comprehensive performance index for cellular distribution network base station site selection is constructed with the cellular unit interconnection weakness index as the core. The lower layer, based on the access scheme proposed in the upper layer, determines the access capacity of SOPs and ESS through an improved sparrow algorithm, constructing a comprehensive operational objective function for the cellular distribution network considering both economy and reliability. Since the sparrow algorithm uses random step-size control parameters for position updates, it is prone to getting trapped in local optima; therefore, the step-size control parameters of the sparrow algorithm are improved. An adaptive decay mechanism based on population diversity perception is introduced, and an elite-guided Gaussian perturbation strategy is adopted for the step size control parameter K.

[0044] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A transition method for a cellular distribution network based on SOP optimized configuration, characterized in that, Includes the following steps: Step 1: Construct an upper-level SOP location model based on comprehensive performance indicators for SOP optimization configuration. The comprehensive performance indicators include: power-voltage sensitivity index, node voltage fluctuation index, and cellular cell interconnection weakness index. Several SOP location schemes are formed by sorting them according to the magnitude of the comprehensive performance indicators. Step 2: Construct a lower-level capacity model based on SOP optimized configuration; the lower-level capacity model includes the comprehensive operation objectives and constraints of the active distribution network. The comprehensive operation objectives of the active distribution network simultaneously consider cost and the operational reliability of the ESS and SOP of the cellular distribution network; the constraints include system power flow constraints, line power constraints, branch current constraints, node voltage constraints, and SOP operation constraints. Step 3: Based on the location schemes of all SOPs, solve the lower-level capacity model using the improved sparrow algorithm to determine the optimal access scheme for the SOP, including the location of the SOP, the capacity of the SOP, and the capacity of the ESS.

2. The transition method for a cellular distribution network based on SOP optimized configuration according to claim 1, characterized in that: The power-voltage sensitivity index is calculated by weighting the active power-voltage sensitivity matrix and the reactive power-voltage sensitivity matrix; the node voltage fluctuation index is calculated by the variance of the node voltage and the rated voltage over 24 hours; the cellular unit interconnection weakness index is used to quantitatively evaluate the node's partition affiliation, boundary topological status, and cross-cell power channel unobstructedness in a multi-cellular unit topology network.

3. The transition method for a cellular distribution network based on SOP optimized configuration according to claim 2, characterized in that: Cellular cell interconnection weakness index Calculation formula include: ， in, Indicates the number of cross-cell boundary link branches connected to node i; This represents the total number of branches connected to node i in the entire network. Let be the electrical distance from node i to the other nodes in the honeycomb cell. The set of all cross-honeycomb boundary branches associated with node i; It is the topology correction factor. Topology bottleneck coefficient; topology correction factor With topological bottleneck coefficient Used to characterize the topological margin and link congestion level of boundary branches. Let m be the apparent power of the m-th branch. This represents the maximum apparent power of the m-th branch.

4. The transition method for a cellular distribution network based on SOP optimized configuration according to claim 1, characterized in that: The formulas for calculating the comprehensive operation targets of active distribution networks include: ， Where F represents the overall operational target of the active distribution network; F1 represents the total economic cost of the active distribution network; and F2 represents the reliability of the SOP and ESS. , These represent the investment cost and annual operating cost during the planning period of the SOP, respectively. For distribution network loss costs; Costs associated with curtailing wind and solar power; For the reliability indicators of the ESS (Emergency Shared Distribution System) in a cellular distribution network; Reliability indicators for SOPs in cellular distribution networks; , These are the total economic cost of the active distribution network and the weighted coefficients for the reliability of SOP and ESS, respectively.

5. A transition method for a cellular distribution network based on SOP optimized configuration according to claim 4, characterized in that: The reliability index of the cellular distribution network ESS is obtained through normalization. The calculation formula for the reliability index of the cellular distribution network ESS before normalization includes: ， in, Let L represent the state of charge of the i-th ESS at time y, where L represents the number of ESS installed and Y represents the total duration of the planning period.

6. A transition method for a cellular distribution network based on SOP optimized configuration according to claim 4, characterized in that: The reliability index of the SOP in a cellular distribution network is obtained through normalization. The calculation formula for the reliability index of the SOP in a cellular distribution network before normalization includes: ， in, , Let be the active power of the two ports of the b-th SOP at time y; The rated power of the b-th SOP; Let b be the actual load rate of the SOP at time y; M represents the optimal load factor for the SOP, M represents the number of SOPs installed, and Y represents the total duration of the planning period.

7. A transition method for a cellular distribution network based on SOP optimized configuration according to claim 1, characterized in that: The improved sparrow algorithm includes: improving the step size control parameters of the sparrow search method during the iteration process, and introducing a population diversity-aware adaptive decay mechanism to adjust the step size control parameters. The step size control parameter K is adjusted by using an elite-guided Gaussian perturbation strategy.

8. A transition method for a cellular distribution network based on SOP optimized configuration according to claim 7, characterized in that: Introducing an adaptive decay mechanism based on population diversity awareness to adjust step size control parameters The calculation formula includes: ， in, and These are the globally optimal and globally worst fitness values, respectively. t represents the maximum number of iterations; t represents the current number of iterations. For diversity feedback adjustment coefficient; This represents the standard deviation of the current population fitness. The maximum standard deviation of fitness; It is the minimum value. This is the decay exponent coefficient.

9. A transition method for a cellular distribution network based on SOP optimized configuration according to claim 7, characterized in that: The step size control parameter K is adjusted using an elite-guided Gaussian perturbation strategy. The calculation formula includes: ， in, and These are the globally optimal and globally worst fitness values, respectively. t represents the maximum number of iterations; t represents the current number of iterations. The exponential decay intensity coefficient; This is the shape factor of the attenuation curve; Indicates that it follows a normal distribution Random numbers; This represents the fitness of the c-th individual; It is a local minimum.

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