Multi-voltage-class power distribution network light storage and charging collaborative optimization configuration method, system, equipment and medium

Abstract

The invention discloses a multi-voltage-class power distribution network optical storage and charging collaborative optimization configuration method, system, equipment and medium, and belongs to the technical field of power systems and automation thereof, and the method comprises the steps: collecting power distribution network data, preprocessing the collected original data, building a medium-voltage-low-voltage power distribution network hierarchical coupling model considering the constraint of a distribution transformer, and carrying out the optimization of the power distribution network. Performing optical storage and charging optimization configuration of the upper-layer medium-voltage and lower-layer low-voltage power distribution networks, and setting an optimization target; executing double-layer coordination solution to obtain an optical storage and charging optimal configuration scheme of the medium-voltage and low-voltage power distribution networks; continuous loading capacity evaluation is carried out on the optical storage and charging optimal configuration scheme, and the maximum transfer load capable of being supported by the configuration scheme is determined; and a complete optical storage and charging optimal configuration scheme is generated. According to the method, the problem of low resource configuration efficiency caused by local optimization of a traditional method is solved, cross-voltage-level global optimization configuration is realized, the voltage quality and the operation efficiency of the power distribution network are improved, and the power supply reliability and the safety margin of the power distribution network are enhanced.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, specifically to a method, system, equipment, and medium for the coordinated optimization of photovoltaic, energy storage, and charging configurations in multi-voltage level distribution networks. Background Technology

[0002] With the rapid popularization of electric vehicles and the large-scale integration of distributed photovoltaic power, the penetration rate of charging piles, photovoltaic power sources, and energy storage systems (referred to as PV-storage-charging) in power distribution networks is constantly increasing. Existing PV-storage-charging configuration methods mainly suffer from the following technical shortcomings: Ignoring the coupling effects of multi-voltage distribution networks, the configuration scheme lacks global perspective: Traditional photovoltaic-storage-charging configuration methods typically optimize only for a single voltage level distribution network, with 10kV medium-voltage distribution networks and 380V low-voltage distribution networks planned independently. This method neglects the coupling effect of distribution transformers as the connecting hub between medium-voltage and low-voltage distribution networks.

[0003] The existing configuration methods do not adequately consider the differences in access characteristics of photovoltaic (PV) storage and charging (PSC) devices at different voltage levels: The access scenarios for PSC and PSC devices differ significantly between medium-voltage and low-voltage distribution networks. In 10kV medium-voltage distribution networks, PSC and PSC devices are typically centrally connected, with large individual capacities (megawatts) and a wide impact on the grid. In 380V low-voltage distribution networks, PSC and PSC devices are mostly distributed, with smaller individual capacities (kilowatts), but numerous and randomly distributed. Existing configuration methods use a uniform optimization model and fail to establish differentiated configuration strategies for the access characteristics at different voltage levels.

[0004] There is a lack of a safety assessment mechanism that considers the continuous load-carrying capacity of photovoltaic, energy storage, and charging systems: Existing configuration methods mainly focus on the static capacity configuration of photovoltaic, energy storage, and charging systems, lacking a dynamic assessment of their continuous load-carrying capacity. When a fault occurs in the distribution network or planned maintenance causes a change in the network topology, whether photovoltaic, energy storage, and charging systems can continue to supply power to important loads in islanded mode or power transfer mode depends on their actual continuous load-carrying capacity. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, this invention aims to achieve coordinated optimization of photovoltaic, energy storage, and charging configuration across voltage levels, establish differentiated multi-voltage-level photovoltaic, energy storage, and charging configuration strategies, and construct a photovoltaic, energy storage, and charging safety assessment system that considers continuous load-carrying capacity.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for coordinated optimization of photovoltaic, energy storage, and charging configuration in multi-voltage level distribution networks, comprising, Data from the distribution network is collected, and the raw data is preprocessed and clustered for different scenarios. A hierarchical coupling model of the medium-voltage and low-voltage distribution network considering the constraints of distribution transformers is established. Optimization of the photovoltaic, energy storage, and charging (PV-SGC) configuration in the upper-level medium-voltage distribution network is performed, and a multi-objective optimization function is established to determine the total PV-SGC capacity for each distribution area. Optimization of the PV-SGC configuration in the lower-level low-voltage distribution network is performed, and under the constraints of the upper-level optimization, the configuration of PV-SGC at each node and phase of the low-voltage feeder is optimized, setting optimization objectives. A two-layer coordinated solution is executed to obtain the optimized PV-SGC configuration schemes for the medium-voltage and low-voltage distribution networks. The continuous load-carrying capacity of the optimized PV-SGC configuration schemes is evaluated to determine the maximum load that the configuration schemes can support. A complete optimized PV-SGC configuration scheme is generated, and an orderly charging control strategy is given based on the current scheme.

[0008] As a preferred embodiment of the multi-voltage level distribution network photovoltaic-storage-charging collaborative optimization configuration method described in this invention, the preprocessing and scenario clustering include collecting medium-voltage distribution network data and low-voltage distribution network data. The collected raw data is quality checked and preprocessed. Clustering algorithms are used to cluster the running data into scenarios and extract representative running scenarios.

[0009] As a preferred embodiment of the multi-voltage level distribution network photovoltaic-storage-charging collaborative optimization configuration method described in this invention, the medium-voltage-low-voltage distribution network hierarchical coupling model includes establishing a medium-voltage-low-voltage distribution network hierarchical coupling model that considers the constraints of distribution transformers. The medium-voltage distribution network adopts a radial topology, starting from the bus and supplying power to each distribution substation through the main line. For each distribution substation, the nodal power balance equations on the medium-voltage side, the power transmission relationship of the distribution transformer, the relationship between transformer loss and transmission power are established, and the capacity constraints of the distribution transformer are defined. The low-voltage distribution network adopts a three-phase four-wire topology. A three-phase power flow model is established, the power balance equation on the low-voltage side is established, and the three-phase unbalance index is defined.

[0010] The beneficial effects of the preferred technical solution in this embodiment of the invention are as follows: A hierarchical coupling model of the medium-voltage and low-voltage distribution networks is established, clarifying the constraint role of the distribution transformer as a connection hub. A two-layer optimization framework is used to achieve coordinated optimization of the photovoltaic, energy storage, and charging (PV-SGC) configuration between the 10kV medium-voltage and 380V low-voltage distribution networks. The upper-layer optimization determines the total PV-SGC capacity configuration of each distribution substation on the medium-voltage side, while the lower-layer optimization determines the specific access locations and capacity allocation of PV-SGC on each feeder in the low-voltage distribution network. Information exchange and coordinated solutions are achieved between the two layers through the capacity constraints and adjustment capabilities of the distribution transformer, ensuring that the configuration scheme is optimal globally and maximizing the overall PV-SGC acceptance capacity of the distribution network.

[0011] As a preferred embodiment of the multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method described in this invention, the above-level medium-voltage distribution network photovoltaic-storage-charging optimization configuration includes: the above-level optimization takes the medium-voltage distribution network as the object, optimizes the total photovoltaic-storage-charging capacity configuration of each distribution substation, and the above-level optimization objectives include minimizing network loss and optimizing voltage quality, and establishing a multi-objective optimization function; The decision variable for upper-level optimization is determined to be the total photovoltaic, energy storage, and charging capacity configuration of each distribution area. Upper-level optimization must meet the operational constraints of the medium-voltage distribution network. The total photovoltaic, energy storage, and charging capacity of each distribution area determined by the upper-level optimization results is used as the input boundary condition for the lower-level optimization, and is coupled with the lower-level optimization through the capacity constraint of the distribution transformer.

[0012] As a preferred embodiment of the multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method described in this invention, the lower-level low-voltage distribution network photovoltaic-storage-charging optimization includes optimizing the configuration of photovoltaic-storage-charging at low-voltage feeder nodes and each phase under the constraints of the upper-level optimization, with the low-voltage distribution network as the object. The lower-level optimization objectives include minimizing three-phase imbalance and optimizing terminal voltage quality: In the formula, To optimize the objective function at the lower level, and These are the weighting coefficients. This refers to the number of nodes in a low-voltage distribution network. Let be the three-phase imbalance at node j. Let ph be the phase voltage at node j. Here, j is the reference voltage and j is the variable index. The decision variables for lower-level optimization are the optical-storage-charging configuration of each phase at each node: In the formula, The photovoltaic, energy storage, and charging pile capacities configured for phase ph of low-voltage side node j are respectively. Lower-level optimization needs to meet the operational constraints of the low-voltage distribution network, including three-phase power balance constraints, three-phase unbalance constraints, low-voltage line capacity constraints, voltage constraints, consistency constraints between total capacity and upper-level results, and distribution transformer capacity constraints.

[0013] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: by optimizing the distribution of photovoltaic energy storage and charging on the low-voltage side, the three-phase imbalance is effectively reduced and the end voltage quality is improved.

[0014] As a preferred embodiment of the multi-voltage level distribution network photovoltaic-storage-charging coordinated optimization configuration method described in this invention, the two-layer coordinated solution includes using the alternating direction multiplier method for coordinated solution of upper and lower layer optimization, and defining the augmented Lagrangian function: In the formula, L is the augmented Lagrangian function. Let Lagrange multiplier vectors be used. For coupling constraint functions, For penalty parameters, To optimize the objective function for the upper layer; The iterative process of the two-level coordinated solution is as follows: in the k-th iteration, the lower-level decision variables are fixed. and the vehicle Solve the upper-level optimization problem to obtain the upper-level optimization solution for the k-th iteration. The upper-level optimization is solved using the particle swarm optimization algorithm, with the upper-level decision variables fixed. and the vehicle Solve the lower-level optimization problem, and find the lower-level optimization solution in the k-th iteration. Update the Lagrange multipliers: Determine the convergence condition. In the formula, The threshold is used for convergence. If the convergence condition is met, the iteration terminates and the optimal solution is output; otherwise, let k = k + 1 and continue iterating until convergence. and This refers to the optimized configuration scheme of photovoltaic, energy storage, and charging for medium-voltage and low-voltage distribution networks.

[0015] As a preferred embodiment of the multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method of the present invention, the continuous load-carrying capacity assessment includes assessing the continuous load-carrying capacity of the obtained photovoltaic-storage-charging configuration scheme to verify whether it can continuously supply power to important loads under distribution network fault or maintenance scenarios, including assessing the islanded operation mode and assessing the joint power transfer capacity of multiple transformer areas. Assuming a fault occurs in any main line of the medium-voltage distribution network and needs to be disconnected, the current transformer area switches to islanded operation mode relying on its local photovoltaic, energy storage, and charging system. Calculate the power balance in islanded mode: In the formula, The actual output of photovoltaic power at time t. For energy storage discharge power, For load power, For the charging pile load; Calculate the dynamic changes in the state of charge of the energy storage system under the condition of satisfying the state of charge constraints: In the formula, SOC(t) represents the state of charge of the stored energy at time t. For the simulation time step, For discharge efficiency, This represents the total energy capacity of the energy storage system. Under the condition of satisfying voltage constraints, through voltage deviation To assess and determine voltage stability in islanded mode, the formula is as follows: This represents the actual voltage at time t in islanded mode; The continuous load-carrying capacity index is defined as the longest time that a photovoltaic-storage-charging system can maintain power balance, qualified state of charge, and stable voltage. According to power supply reliability requirements, the continuous power supply duration for critical loads should not be less than 4 hours. If the calculated... If the load capacity is less than 4 hours, the current configuration is deemed insufficient, and the photovoltaic-storage-charging capacity configuration needs to be readjusted, either by increasing the energy storage capacity or optimizing the photovoltaic placement, until the load capacity is met. Safety requirements for the hour; The assessment of the joint power transfer capacity of multiple transformer substations includes the following: when any transformer substation fails, adjacent substations transfer power to it via a low-voltage tie switch. The constraints of the power transfer capacity assessment include tie line capacity and voltage drop constraints. In the formula, For the connection line current, For the capacity of the connection lines, The voltage drop of the power transfer path is considered; by evaluating the power transfer capacity under all fault scenarios, the maximum power transfer load that the configuration scheme can support is determined, which serves as an important evaluation basis for the safety of the configuration scheme.

[0016] Another objective of this invention is to provide a multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration system.

[0017] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a multi-voltage level distribution network photovoltaic-storage-charging collaborative optimization configuration system, comprising: a data acquisition and preprocessing module, a medium-voltage-low-voltage coupling modeling module, an upper-level medium-voltage distribution network optimization module, a lower-level low-voltage distribution network optimization module, a two-layer coordination solution module, a continuous load-carrying capacity assessment module, and a configuration scheme output module; The data acquisition and preprocessing module collects power distribution network data and performs preprocessing and scenario clustering on the collected raw data. The medium-voltage-low-voltage coupling modeling module establishes a hierarchical coupling model of the medium-voltage-low-voltage distribution network that takes into account the constraints of the distribution transformer. The upper medium-voltage distribution network optimization module performs photovoltaic, energy storage and charging optimization configuration of the upper medium-voltage distribution network, and establishes a multi-objective optimization function to determine the total photovoltaic, energy storage and charging capacity of each distribution area. The lower-level low-voltage distribution network optimization module optimizes the photovoltaic, energy storage, and charging configuration of the lower-level low-voltage distribution network. Under the constraints of the upper-level optimization, it optimizes the configuration of photovoltaic, energy storage, and charging at each node and phase of the low-voltage feeder and sets optimization targets. The dual-layer coordinated solution module performs dual-layer coordinated solution to obtain the optimal configuration scheme of photovoltaic, energy storage and charging for medium-voltage and low-voltage distribution networks; The continuous load capacity assessment module assesses the continuous load capacity of the photovoltaic-storage-charging optimized configuration scheme and determines the maximum transfer load that the configuration scheme can support. The configuration scheme output module generates a complete optimized configuration scheme for photovoltaic energy storage and charging, and provides an orderly charging control strategy based on the current scheme.

[0018] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned method for coordinated optimization configuration of photovoltaic, energy storage and charging in a multi-voltage level distribution network.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the aforementioned method for coordinated optimization configuration of photovoltaic, energy storage and charging systems in a multi-voltage-level distribution network.

[0020] The beneficial effects of this invention are as follows: By establishing a medium-voltage-low-voltage distribution network coupling model and a two-layer optimization framework, this invention comprehensively considers the configuration of photovoltaic, energy storage and charging at two voltage levels of 10kV and 380V, overcomes the problem of low resource allocation efficiency caused by local optimization in traditional methods, and realizes global optimization configuration across voltage levels.

[0021] This invention significantly improves the voltage quality and operating efficiency of the distribution network through a differentiated optimization strategy.

[0022] This invention, by embedding a continuous load capacity assessment module, ensures that the configuration scheme can maintain continuous power supply to important loads under fault scenarios, thereby enhancing the reliability and safety margin of the power distribution network. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0024] Figure 1This is a flowchart illustrating the overall process of a multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method, as provided in one embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for coordinated optimization of photovoltaic, energy storage, and charging configuration in multi-voltage level distribution networks, including: S100: Collect data from the power distribution network, and perform preprocessing and scenario clustering on the collected raw data; S200. Establish a hierarchical coupling model of medium-voltage and low-voltage distribution networks that considers the constraints of distribution transformers. S300: Perform optimal configuration of photovoltaic, energy storage and charging in the upper medium-voltage distribution network, and establish a multi-objective optimization function to determine the total capacity of photovoltaic, energy storage and charging in each distribution area; S400: Optimize the photovoltaic, energy storage, and charging configuration of the lower-level low-voltage distribution network. Under the constraints of the upper-level optimization, optimize the configuration of photovoltaic, energy storage, and charging at each node and phase of the low-voltage feeder, and set optimization targets. S500: Perform a two-layer coordinated solution to obtain the optimal configuration scheme of photovoltaic, energy storage and charging for medium-voltage and low-voltage distribution networks; S600: Conduct a continuous load capacity assessment of the optimized configuration scheme for photovoltaic storage and charging to determine the maximum transfer load that the configuration scheme can support; S700 generates a complete optimized configuration scheme for photovoltaic, energy storage, and charging, and provides an orderly charging control strategy based on the current scheme; It should be noted that existing technologies have technical shortcomings such as insufficient global coverage, lack of differentiated configuration, and imperfect security assessment mechanisms.

[0027] Therefore, to address the aforementioned problems, the global optimization of photovoltaic (PV), energy storage, and charging (ESC) configuration in the medium- and low-voltage distribution networks is achieved through steps S100-S700, combining two-layer optimization with hierarchical coordinated control. First, operational data from multiple voltage levels of the distribution network are collected, including line parameters, load distribution, and current status of PV charging piles in the medium-voltage distribution network, as well as topology, user load, and three-phase imbalance information in the low-voltage distribution network. Then, a hierarchical model of the medium- and low-voltage distribution networks considering the coupling effect of distribution transformers is established, clarifying the power transfer relationship and constraints between the two voltage levels. Based on this, a two-layer optimization model is constructed. The upper layer focuses on the medium-voltage distribution network, aiming to minimize network loss and optimize voltage quality, optimizing the total PV, ESC, and charging capacity of each distribution substation. The lower layer focuses on the low-voltage distribution network, aiming for optimal three-phase balance and qualified terminal voltage, optimizing the specific configuration of PV, ESC, and charging in each low-voltage feeder and phase. The two optimization layers are coupled through the capacity constraints of the distribution transformers, and an iterative coordinated solution is achieved using the alternating direction multiplier method.

[0028] Example 2, refer to Figure 1 This is one embodiment of the present invention, which provides a method for coordinated optimization of photovoltaic, energy storage, and charging configuration in multi-voltage level distribution networks, including: In this embodiment of the invention, step S100 involves collecting power distribution network data and preprocessing and clustering the collected raw data into scenarios, including the following steps S101-S103: S101. Collect basic data of 10kV medium-voltage distribution network and 380V low-voltage distribution network; Medium-voltage distribution network data includes line length from the beginning of the line to each distribution substation, unit impedance, historical load curve, existing photovoltaic, energy storage and charging capacity and location, distribution transformer capacity and impedance parameters, etc. Low-voltage distribution network data includes the line topology from the low-voltage side of the distribution transformer to each user, three-phase line parameters, historical load curves of each node, existing distributed photovoltaic and charging pile distribution, and three-phase imbalance monitoring data, etc. The data acquisition cycle is once every 15 minutes, continuously collecting data for a full year to capture all the temporal characteristics of optical storage, charging, and load.

[0029] In an embodiment of the present invention, S102, quality verification of the collected raw data includes the following steps A1-A2: A1. Remove obvious outlier data points and define the data anomaly detection index as follows: In the formula, The data collected at time t, This is the average of the historical data for this measuring point. Standard deviation; A2. Data points that meet the data anomaly detection criteria are identified as outliers and repaired using linear interpolation.

[0030] In an optional implementation, the quality verification in S102 can be based on anomaly detection and neighbor mean repair using quantile thresholds. For the historical data sequence of each measurement point, a specific quantile is directly calculated as the upper and lower boundary thresholds of normal data. The current collected data value is compared with these two boundary values. If it falls outside the boundary, it is determined that the data at that moment is abnormal. For the data point determined to be abnormal, the arithmetic mean of the two nearest valid normal data before and after the abnormal moment is taken. However, this method is not sensitive enough to local fluctuations and trend changes in the data sequence. When the data is continuously abnormal or there are steep changes at the boundary, the repair effect is not good.

[0031] In another optional implementation, the quality verification in S102 can also be based on anomaly detection of adjacent data change rates and mean repair, calculating the change rate between the current collected data and the previous valid data, and comparing it with a reasonable change rate threshold preset based on experience. If the calculated change rate exceeds the threshold, the current data point is considered to have a sudden abnormality; for data points determined to be abnormal by the change rate, the arithmetic mean of the previous and next valid normal data points is used; however, this implementation relies heavily on experience in setting the threshold, and is prone to misjudgment in scenarios where the load or power generation itself has reasonable rapid fluctuations.

[0032] In an embodiment of the present invention, the collected raw data is preprocessed, including the following steps B1-B2: B1. Normalize the load data to eliminate the influence of dimensions: In the formula, The load data is normalized. Let i be the actual load at time i. and These are the maximum and minimum annual load values, respectively. B2. The normalized load data is used for subsequent cluster analysis to identify typical operating scenarios.

[0033] In an optional implementation, the preprocessing in S102 can be performed using the Z-score normalization method to eliminate the influence of dimensions and make the data conform to a standard normal distribution.

[0034] In another alternative implementation, the preprocessing in S102 can also employ a quantile normalization method to map the data to the same scale range, thereby reducing the impact of outliers on the scaling process.

[0035] In an embodiment of the present invention, S103, clustering the running data using a clustering algorithm includes the following steps C1-C3: C1. The K-means clustering algorithm is used to cluster 8760 hours of operational data throughout the year to extract representative operational scenarios; the clustering objective function is: In the formula, K is the number of clustering scenarios, and 12 typical scenarios are selected to represent three typical days in each of the four seasons: spring, summer, autumn and winter. Let k be the set of times contained in the k-th scene. The operating state vector at time t includes the load of each node, photovoltaic output, and charging load; This represents the k-th cluster center, which is the typical operating state of this scenario.

[0036] C2. Through cluster analysis, massive historical data is condensed into a limited number of typical scenarios, which greatly reduces the complexity of subsequent optimization calculations. C3. Each typical scenario is assigned a weight coefficient, which is determined by the frequency of the scenario throughout the year. The extracted typical scenario data is then used for modeling.

[0037] In an optional implementation, the scene clustering in S103 can adopt hierarchical clustering. Using preprocessed normalized load data, an operating state vector of 8760 hours throughout the year is constructed. The Euclidean distance between all operating state vectors is calculated to form a distance matrix. Aggregate hierarchical clustering is adopted, starting from each data point as an independent cluster, iteratively merging the two closest clusters until the number of clusters is reduced to a preset 12 typical scenes. According to the merging process, the cluster center of each cluster is determined as a typical operating state, and the time set contained in each cluster is counted. According to the frequency of each cluster throughout the year, each typical scene is assigned a corresponding weight coefficient. However, this implementation has high computational complexity of hierarchical clustering and low efficiency when processing large-scale time series data, which affects the scene generation speed.

[0038] In another optional implementation, scene clustering in S103 can also employ the DBSCAN density clustering method. Using preprocessed normalized load data, an annual operating status vector is constructed. Based on the data distribution characteristics, neighborhood radius and minimum sample number parameters are set to identify core points, boundary points, and noise points. Starting from any core point, all density-connected data points are grouped into the same cluster, iterating until all points are visited, forming several clusters. By adjusting the parameters, the number of clusters is controlled to be close to 12 to correspond to typical days in the four seasons. The center point of each cluster is calculated as a typical operating state, and the time set within the cluster is recorded. Noise points can be regarded as special scenes or merged into neighboring clusters. The weight coefficient is determined based on the frequency of each cluster throughout the year. However, this implementation of DBSCAN is sensitive to parameter settings, which may lead to unstable number of scenes or insufficient typicality when the density of distribution network load data is uneven.

[0039] In this embodiment of the invention, establishing a hierarchical coupling model of the medium-voltage-low-voltage distribution network considering the constraints of distribution transformers in step S200 includes the following steps S201-S202: S201. Establish a hierarchical coupling model of medium-voltage and low-voltage distribution networks that takes into account the constraints of distribution transformers.

[0040] The medium-voltage distribution network adopts a radial topology, starting from the 10kV bus and supplying power to each distribution substation through the main line; For the i-th distribution substation, its nodal power balance equation on the medium-voltage side is: In the formula, Let be the net injected power at the i-th node on the medium-pressure side. This represents the medium-voltage load power at this node. The power is transmitted to the low-voltage side through the distribution transformer. and These represent the medium-voltage side photovoltaic power and energy storage charging and discharging power connected to this node, respectively.

[0041] The power transfer relationship of the distribution transformer is established as follows: In the formula, Let be the set of nodes contained on the low-voltage side of the i-th distribution radio area. These represent the load power, photovoltaic power, energy storage power, and charging pile power of the j-th node on the low-voltage side, respectively. This refers to the power loss of the distribution transformer.

[0042] The relationship between transformer losses and transmission power is as follows: In the formula, This refers to the no-load loss of the distribution transformer. The rated capacity of the transformer. This is short-circuit loss.

[0043] The power transmission relationship of distribution transformers and the relationship between transformer losses and transmitted power establish a power coupling relationship between the medium-voltage side and the low-voltage side; In an embodiment of the present invention, defining the capacity constraint of the distribution transformer includes the following steps D1-D2: D1. Set the capacity constraint of the distribution transformer as follows: In the formula, The power factor is typically taken as 0.9. D2. The capacity constraint of the distribution transformer ensures that the transmitted power does not exceed the transformer capacity limit. This is a coupling constraint that must be satisfied by both the upper and lower optimization layers.

[0044] In an optional implementation, the capacity constraint of the distribution transformer defined in S201 can be based on the direct constraint of the total apparent power on the low-voltage side. The total apparent power generated by the operation of all loads, photovoltaic, energy storage and charging piles on the low-voltage side of the distribution area can be directly calculated. The constraint condition is set that the total apparent power must not exceed the rated capacity of the distribution transformer at any operating time. This constraint is used as a hard boundary and is applied to the solution process of the upper-level (medium-voltage side) optimization model and the lower-level (low-voltage side) optimization model to ensure that the optimization result will not lead to transformer overload. However, this implementation does not consider the impact of power factor differences on the actual load-carrying capacity of the transformer. In scenarios with low load power factor, the transformer capacity is not fully utilized, which is relatively conservative.

[0045] S202. The low-voltage distribution network adopts a three-phase four-wire topology, and a three-phase power flow model needs to be established. For phase a at node j on the low-voltage side, the power balance equation is: In the formula, This represents the net injected power of phase a at node j on the low-voltage side; the superscript a indicates phase a; the power balance equations for phase b and phase c are similar. The three-phase unbalance index is defined as follows: In the formula, , , These are the three-phase currents at node j; The three-phase imbalance must meet the national standard requirements. .

[0046] Power balance and three-phase imbalance constitute the basic constraints of low-voltage distribution networks, and these constraints are used for lower-level optimization. The medium-voltage-low-voltage coupling model connects the two voltage levels, providing a mathematical basis for two-level optimization.

[0047] In this embodiment of the invention, S300 performs optimized configuration of photovoltaic, energy storage, and charging systems in the upper-level medium-voltage distribution network, establishing a multi-objective optimization function to determine the total photovoltaic, energy storage, and charging capacity of each distribution area, including the following steps S301-S302: S301. Upper-level optimization focuses on the 10kV medium-voltage distribution network and optimizes the total photovoltaic, energy storage, and charging capacity configuration of each distribution area. The upper-level optimization objectives include minimizing network loss and optimizing voltage quality, thus establishing a multi-objective optimization function. In the formula, To optimize the objective function of the upper layer, and The weighting coefficients are determined using the analytic hierarchy process (AHP). =0.6, =0.4; For medium voltage lines, and Let be the resistance and current of line l, respectively. This represents the number of medium-voltage nodes. Let be the voltage at node i. Reference voltage (rated voltage); The first term represents the minimum network loss, and the second term represents the minimum voltage deviation.

[0048] The decision variable for upper-level optimization is the total optical, energy storage, and charging capacity configuration of each distribution area: In the formula, For the upper-level decision variable vector, These represent the photovoltaic capacity, energy storage capacity, and charging pile capacity configured on the medium-voltage side of the i-th distribution transformer area. This refers to the number of distribution transformers (stations).

[0049] S302. Upper-level optimization must meet the operational constraints of the medium-voltage distribution network, including power balance constraints (i.e., node power balance equations) and line capacity constraints: In the formula, This represents the maximum allowable current for line l; Voltage constraint: In the formula, and These are the lower and upper limits of the voltage, typically taken as ±7% of the rated voltage; Photovoltaic-storage-charging capacity constraints: In the formula, the upper limit of capacity is determined based on the available site area and investment budget of the transformer area.

[0050] The total photovoltaic, energy storage, and charging capacity of each distribution area determined by the upper-level optimization results As the input boundary condition for the lower-level optimization, it is coupled with the lower-level optimization through the capacity constraint of the distribution transformer.

[0051] In this embodiment of the invention, in S400, the lower-level low-voltage distribution network photovoltaic-storage-charging optimization is performed. Under the constraints of the upper-level optimization, the configuration of photovoltaic-storage-charging at each node and phase of the low-voltage feeder is optimized, and the optimization target is set, including the following steps S401-S402: S401. The lower-level optimization takes the 380V low-voltage distribution network as the object. Under the constraint of the total capacity of photovoltaic, energy storage and charging in each distribution area determined by the upper-level optimization, the specific configuration of photovoltaic, energy storage and charging in each node and phase of the low-voltage feeder is optimized. The lower-level optimization objectives include minimizing three-phase imbalance and optimizing terminal voltage quality: In the formula, To optimize the objective function for the lower level; and The weighting coefficients are determined using an expert scoring method in this invention. , ; This refers to the number of nodes in a low-voltage distribution network. Let be the three-phase imbalance at node j. Let ph be the phase voltage at node j, where ph is a, b, or c.

[0052] The decision variables for lower-level optimization are the optical storage and charging configurations for each phase at each node. In the formula, The photovoltaic, energy storage, and charging pile capacities configured for the ph phase of low-voltage side node j are respectively.

[0053] S402. Lower-level optimization must meet the operational constraints of the low-voltage distribution network, including three-phase power balance constraints and three-phase unbalance constraints: Low-voltage line capacity constraints and voltage constraints: And the consistency constraint between the total capacity and the upper-level results: In the formula, This represents the photovoltaic capacity already configured on the medium-voltage side for the i-th distribution area. The left side represents the total photovoltaic capacity on the low-voltage side, and the right side represents the total photovoltaic capacity allocated to this distribution area by the upper layer minus the photovoltaic capacity already configured on the medium-voltage side. The consistency constraints for energy storage and charging pile capacity are similar.

[0054] Consistency constraints ensure that the configuration results of the lower layer are consistent with the decisions of the upper layer, which is a key coupling constraint for two-layer optimization; The lower-level optimization also needs to meet the capacity constraints of the distribution transformer, which are passed down from the upper-level optimization.

[0055] In this embodiment of the invention, the two-layer coordinated solution in S500 is used to obtain the optimal configuration scheme of photovoltaic, energy storage and charging for medium-voltage and low-voltage distribution networks, including the following steps S501-S502: S501. The alternating direction multiplier method is used to achieve coordinated solution of upper and lower level optimizations; Define the augmented Lagrange function In the formula, L is the augmented Lagrangian function. Let Lagrange multiplier vectors be used. These are coupling constraint functions; This is the penalty parameter, initially set to 10, and adjusted during the iteration process.

[0056] S502, the iterative process of the two-layer coordinated solution is as follows: In the k-th iteration, the lower-level decision variables are fixed. and the vehicle Solve the upper-level optimization problem: In the formula, This is the upper-level optimization solution for the k-th iteration; The upper-level optimization is solved using the particle swarm optimization algorithm, with the number of particles set to 50 and the maximum number of iterations set to 100.

[0057] Then fix the upper-level decision variables. and the vehicle Solve the lower-level optimization problem and obtain the lower-level optimization solution. : The lower-level optimization is also solved using the particle swarm optimization algorithm.

[0058] Last updated Lagrange multipliers: Determine the convergence condition: In the formula, As the convergence threshold, this method takes ; If the convergence condition is met, the iteration terminates and the optimal solution is output. Otherwise, let k = k + 1 and continue iterating. It usually converges after 15 to 25 iterations. The result after convergence is... and This refers to the optimized configuration scheme of photovoltaic, energy storage, and charging for medium-voltage and low-voltage distribution networks.

[0059] In this embodiment of the invention, step S600 evaluates the continuous load-carrying capacity of the optimized configuration scheme for photovoltaic storage and charging to determine the maximum load that the configuration scheme can support, including the following steps S601-S602: S601. Conduct a continuous load capacity assessment on the obtained photovoltaic-storage-charging configuration scheme to verify whether it can continuously supply power to important loads under distribution network fault or maintenance scenarios.

[0060] Suppose that a main line of a medium-voltage distribution network fails and needs to be disconnected, causing downstream distribution substations to lose their upstream power supply. In this case, the substation needs to rely on its local photovoltaic energy storage and charging system to switch to islanded operation mode. The power balance equation in islanded mode is: In the formula, The actual output of photovoltaic power at time t. For energy storage discharge power, For load power, The load of the charging pile is given. The variables in the formula change with time, and time series simulation analysis is required.

[0061] The equation for the dynamic change of the state of charge of the energy storage system is: In the formula, SOC(t) represents the state of charge of the stored energy at time t. The simulation time step is set to 15 minutes. This represents the discharge efficiency, typically 0.95. This represents the total energy capacity of the energy storage system.

[0062] Energy storage systems must meet state of charge constraints: In the formula, and These represent the minimum and maximum states of charge, typically taken as 0.2 and 0.9.

[0063] Voltage stability in islanded mode is assessed through voltage deviation: In the formula, The actual voltage at time t in islanded mode is obtained through power flow calculation. The voltage must meet the following requirements: The continuous load-carrying capacity index is defined as the longest time that a photovoltaic energy storage and charging system can maintain power balance, qualified state of charge, and stable voltage. In the formula, For sustainable power supply duration.

[0064] According to power supply reliability requirements, the continuous power supply duration for critical loads should not be less than 4 hours; if the calculated... If the continuous load capacity is less than 4 hours, the configuration is deemed insufficient and needs to be adjusted by increasing the energy storage capacity or optimizing the photovoltaic configuration until the requirement is met. Safety requirements for each hour.

[0065] S602. In addition to the isolated operation of a single power station area, it is also necessary to assess the ability of multiple power stations area to jointly transfer power.

[0066] When a transformer substation fails, the adjacent substation will transfer power to it via a low-voltage interconnection switch.

[0067] Constraints on the transfer capacity assessment include tie line capacity and voltage drop constraints: In the formula, For the connection line current, For the capacity of the connection lines, This refers to the voltage drop of the power transfer path. By evaluating the power transfer capacity under all fault scenarios, the maximum power transfer load that the configuration scheme can support is determined. This indicator serves as an important evaluation criterion for the safety of the configuration scheme.

[0068] In this embodiment of the invention, a complete photovoltaic-storage-charging optimized configuration scheme is generated in S700, and an ordered charging control strategy is given based on the current scheme, including the following steps S701-S702: S701. Based on the aforementioned calculation results, a complete optimized configuration scheme for photovoltaic storage and charging is generated.

[0069] The plan includes information at the medium-voltage distribution network level: the total capacity configuration of photovoltaic, energy storage and charging in each distribution area, recommended access locations, and the improvement effect on medium-voltage network losses and voltage; Detailed information at the low-voltage distribution network level: photovoltaic, energy storage, and charging capacity of each low-voltage feeder node, phase distribution ratio, improvement effect on three-phase imbalance, and improvement of end-point voltage quality.

[0070] S702, the configuration scheme also includes operational control strategy recommendations. For energy storage systems, it provides day-ahead optimized charge and discharge plans; The optimization target for energy storage charging power is In the formula, Let be the electricity price at time t. Power for charging energy storage.

[0071] The optimization strategy involves charging during off-peak hours and discharging during peak hours, while reserving a certain capacity to meet emergency power supply needs in case of failure.

[0072] For charging piles, based on the electric vehicle charging demand forecast and the power distribution network operation status, an orderly charging control strategy is given to avoid excessive load peaks and voltage overruns caused by disordered charging.

[0073] Example 3 is an embodiment of the present invention, which provides a method for coordinated optimization of photovoltaic, energy storage and charging configuration in multi-voltage level distribution networks. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0074] The experiment of this invention is based on the simulation verification of an actual distribution network in a certain city. The distribution network includes one 110kV substation, eight 10kV medium-voltage feeders, and a total of 32 distribution substations.

[0075] The total length of the medium-voltage lines is 68 kilometers, with a unit impedance of 0.27 ohms / km. The low-voltage distribution network adopts a 380V three-phase four-wire system, with 4 to 8 low-voltage feeders downstream of each transformer substation, and the total length of the low-voltage lines is approximately 240 kilometers.

[0076] The existing distributed photovoltaic capacity in the distribution network service area is 12 MW, there are 150 public charging piles, and 2 energy storage systems (total 4 MWh).

[0077] The power grid has a maximum load of 52 MW and a minimum load of 18 MW. A detailed simulation model of this distribution network was built on the MATLAB / Simulink platform, including modules for calculating the power flow of the medium-voltage distribution network, the three-phase unbalanced power flow of the low-voltage distribution network, the operating characteristics model of photovoltaic, energy storage, and charging, and the loss model of the distribution transformer.

[0078] The simulation uses 8,760 hours of historical operating data per year, including hourly load curves, photovoltaic power output data, and charging pile usage data.

[0079] Twelve typical operating scenarios were extracted through cluster analysis, with each scenario containing 24 hours of data.

[0080] Simulation comparison of three configuration schemes: Option 1 is the traditional single-voltage-level optimization method (optimizing only the medium-voltage side photovoltaic-storage-charging configuration); Option 2 is a two-level optimization method without safety constraints; Option 3 is the multi-voltage level collaborative optimization method proposed in this patent, which includes continuous load capacity assessment.

[0081] The three schemes were simulated over a one-year period to collect statistics on indicators such as voltage qualification rate, network loss, three-phase imbalance, and continuous power supply capability under fault scenarios.

[0082] Validation was performed, as shown in Tables 1 to 4: Table 1. Comparison of voltage quality among different schemes

[0083] The method of this invention improves the voltage qualification rate of medium-voltage and low-voltage distribution networks to 98.5% and 97.2% respectively through cross-voltage level collaborative optimization, which is significantly better than the 94.2% and 88.6% of traditional single-voltage level optimization. Voltage deviation and three-phase imbalance are greatly reduced, and voltage quality is significantly improved.

[0084] Table 2 Comparison of Network Loss and Economic Efficiency

[0085] The method of this invention reduces the annual grid loss by 24.3% by optimizing the location and capacity of photovoltaic, energy storage and charging configurations. Although the total investment increases slightly, the annual operating income increases by 32.2%, and the investment payback period is shortened to 6.8 years, resulting in the best economic benefits throughout the entire life cycle.

[0086] Table 3 Results of Continuous Load Capacity Assessment

[0087] By embedding a continuous load capacity assessment module, the photovoltaic energy storage and charging system configured by the method of this invention can provide an average continuous power supply time of 5.8 hours, meeting the emergency power supply requirements of important loads (≥4 hours); while the traditional solution can only support an average of 2.0 hours, which poses obvious safety hazards; the maximum transfer load is increased by 129%, and the power supply reliability is greatly improved.

[0088] Table 4 Simulation results of typical fault scenarios

[0089] Under four typical fault scenarios, the photovoltaic-storage-charging system configured by the method of this invention reduces the average power loss load by 74.4% through islanded operation and power transfer from adjacent transformer areas, and shortens the power restoration time from an average of 43 minutes to 11 minutes, significantly improving power supply reliability.

[0090] Example 4 is an embodiment of the present invention. The above is an illustrative scheme of a method for coordinated optimization configuration of photovoltaic, energy storage, and charging in a multi-voltage level distribution network. It should be noted that the technical solution of a coordinated optimization configuration system for photovoltaic, energy storage, and charging in a multi-voltage level distribution network belongs to the same concept as the above-described method for coordinated optimization configuration of photovoltaic, energy storage, and charging in a multi-voltage level distribution network. Details not described in detail in the technical solution of the coordinated optimization configuration system for photovoltaic, energy storage, and charging in a multi-voltage level distribution network in this embodiment can be found in the description of the above-described method for coordinated optimization configuration of photovoltaic, energy storage, and charging in a multi-voltage level distribution network.

[0091] This embodiment provides a multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration system, including: a data acquisition and preprocessing module, a medium-voltage-low-voltage coupling modeling module, an upper-level medium-voltage distribution network optimization module, a lower-level low-voltage distribution network optimization module, a two-layer coordination solution module, a continuous load-carrying capacity assessment module, and a configuration scheme output module; The data acquisition and preprocessing module acquires data from the power distribution network and preprocesses the acquired raw data. The medium-voltage-low-voltage coupling modeling module establishes a hierarchical coupling model of the medium-voltage-low-voltage distribution network that takes into account the constraints of the distribution transformer. The upper-level medium-voltage distribution network optimization module optimizes the configuration of photovoltaic, energy storage and charging in the upper-level medium-voltage distribution network and establishes a multi-objective optimization function to determine the total capacity of photovoltaic, energy storage and charging in each distribution area. The lower-level low-voltage distribution network optimization module optimizes the photovoltaic, energy storage, and charging configuration of the lower-level low-voltage distribution network. Under the constraints of the upper-level optimization, it optimizes the configuration of photovoltaic, energy storage, and charging at each node and phase of the low-voltage feeder and sets optimization targets. The dual-layer coordinated solution module performs dual-layer coordinated solutions to obtain optimized configuration schemes for photovoltaic, energy storage, and charging in medium-voltage and low-voltage distribution networks. The continuous load capacity assessment module assesses the continuous load capacity of the photovoltaic-storage-charging optimization configuration scheme and determines the maximum transfer load that the configuration scheme can support. The configuration scheme output module generates a complete optimized configuration scheme for photovoltaic energy storage and charging, and provides an orderly charging control strategy based on the current scheme.

[0092] This embodiment also provides an electronic device applicable to a multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method proposed in the above embodiment.

[0093] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method as proposed in the above embodiments.

[0094] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for coordinated optimization of photovoltaic, energy storage and charging configuration in multi-voltage level distribution networks proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0095] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated and optimized configuration of photovoltaic, energy storage, and charging systems in a multi-voltage-level distribution network, characterized in that: include, Collect data from the power distribution network, and preprocess and cluster the collected raw data into scenarios. Establish a hierarchical coupling model for medium-voltage and low-voltage distribution networks that takes into account the constraints of distribution transformers; Optimize the configuration of photovoltaic, energy storage and charging systems in the upper-level medium-voltage distribution network, and establish a multi-objective optimization function to determine the total capacity of photovoltaic, energy storage and charging systems in each distribution area. Optimize the photovoltaic, energy storage, and charging (PV, energy storage, and charging) configuration of the lower-level low-voltage distribution network under the constraints of the upper-level optimization, and set optimization targets. A two-level coordinated solution is performed to obtain the optimal configuration scheme of photovoltaic, energy storage and charging for medium-voltage and low-voltage distribution networks; A continuous load-carrying capacity assessment was conducted on the optimized configuration scheme of photovoltaic storage and charging to determine the maximum transfer load that the configuration scheme can support. Generate a complete optimized configuration scheme for photovoltaic, energy storage, and charging, and provide an orderly charging control strategy based on the current scheme.

2. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 1, characterized in that: The preprocessing and scenario clustering process includes collecting medium-voltage distribution network data and low-voltage distribution network data. The collected raw data is quality checked and preprocessed. Clustering algorithms are used to cluster the running data into scenarios and extract representative running scenarios.

3. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 2, characterized in that: The hierarchical coupling model of the medium-voltage-low-voltage distribution network includes establishing a hierarchical coupling model of the medium-voltage-low-voltage distribution network that takes into account the constraints of the distribution transformer. The medium-voltage distribution network adopts a radial topology, starting from the bus and supplying power to each distribution substation through the main line. For each distribution substation, the nodal power balance equations on the medium-voltage side, the power transmission relationship of the distribution transformer, the relationship between transformer loss and transmission power are established, and the capacity constraints of the distribution transformer are defined. The low-voltage distribution network adopts a three-phase four-wire topology. A three-phase power flow model is established, the power balance equation on the low-voltage side is established, and the three-phase unbalance index is defined.

4. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 3, characterized in that: The optimization configuration of upper-level medium-voltage distribution network photovoltaic-storage-charging system includes: upper-level optimization takes the medium-voltage distribution network as the object, optimizes the total capacity configuration of photovoltaic-storage-charging system in each distribution area, and the upper-level optimization objectives include minimizing network loss and optimizing voltage quality, and establishing a multi-objective optimization function. The decision variable for upper-level optimization is determined to be the total photovoltaic, energy storage, and charging capacity configuration of each distribution area. Upper-level optimization must meet the operational constraints of the medium-voltage distribution network. The total photovoltaic, energy storage, and charging capacity of each distribution area determined by the upper-level optimization results is used as the input boundary condition for the lower-level optimization, and is coupled with the lower-level optimization through the capacity constraint of the distribution transformer.

5. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 4, characterized in that: The optimization of the lower-level low-voltage distribution network includes optimizing the configuration of photovoltaic, energy storage and charging at low-voltage feeder nodes and each phase under the constraints of the upper-level optimization, with the lower-level optimization taking the low-voltage distribution network as the object. The lower-level optimization objectives include minimizing three-phase imbalance and optimizing terminal voltage quality: In the formula, To optimize the objective function at the lower level, and These are the weighting coefficients. This refers to the number of nodes in a low-voltage distribution network. Let be the three-phase imbalance at node j. Let ph be the phase voltage at node j. Here, j is the reference voltage and j is the variable index. The decision variables for lower-level optimization are the optical-storage-charging configuration of each phase at each node: In the formula, The photovoltaic, energy storage, and charging pile capacities configured for phase ph of low-voltage side node j are respectively. Lower-level optimization needs to meet the operational constraints of the low-voltage distribution network, including three-phase power balance constraints, three-phase unbalance constraints, low-voltage line capacity constraints, voltage constraints, consistency constraints between total capacity and upper-level results, and distribution transformer capacity constraints.

6. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 5, characterized in that: The two-level coordinated solution includes using the alternating direction multiplier method to coordinate the optimization of the upper and lower levels, and defining the augmented Lagrangian function: In the formula, L is the augmented Lagrangian function. Let Lagrange multiplier vectors be used. For coupling constraint functions, For penalty parameters, To optimize the objective function for the upper layer; The iterative process of the two-level coordinated solution is as follows: in the k-th iteration, the lower-level decision variables are fixed. and the vehicle Solve the upper-level optimization problem to obtain the upper-level optimization solution for the k-th iteration. The upper-level optimization is solved using the particle swarm optimization algorithm, with the upper-level decision variables fixed. and the vehicle Solve the lower-level optimization problem, and find the lower-level optimization solution in the k-th iteration. Update the Lagrange multipliers: Determine the convergence condition. In the formula, The threshold is used for convergence. If the convergence condition is met, the iteration terminates and the optimal solution is output; otherwise, let k = k + 1 and continue iterating until convergence. and This refers to the optimized configuration scheme of photovoltaic, energy storage, and charging for medium-voltage and low-voltage distribution networks.

7. The method for coordinated optimization of photovoltaic, energy storage, and charging configuration in a multi-voltage level distribution network as described in claim 6, characterized in that: The continuous load capacity assessment includes assessing the continuous load capacity of the obtained photovoltaic-storage-charging configuration scheme to verify whether it can continuously supply power to important loads under distribution network fault or maintenance scenarios, including assessing the islanded operation mode and assessing the joint power transfer capability of multiple distribution areas. Assuming a fault occurs in any main line of the medium-voltage distribution network and needs to be disconnected, the current transformer area switches to islanded operation mode relying on its local photovoltaic, energy storage, and charging system. Calculate the power balance in islanded mode: In the formula, The actual output of photovoltaic power at time t. For energy storage discharge power, For load power, For the charging pile load; Calculate the dynamic changes in the state of charge of the energy storage system under the condition of satisfying the state of charge constraints: In the formula, SOC(t) represents the state of charge of the stored energy at time t. For the simulation time step, For discharge efficiency, This represents the total energy capacity of the energy storage system. Under the condition of satisfying voltage constraints, through voltage deviation To assess and determine voltage stability in islanded mode, the formula is as follows: This represents the actual voltage at time t in islanded mode; The continuous load-carrying capacity index is defined as the longest time that a photovoltaic-storage-charging system can maintain power balance, qualified state of charge, and stable voltage. According to power supply reliability requirements, the continuous power supply duration for critical loads should not be less than 4 hours. If the calculated... If the load capacity is less than 4 hours, the current configuration is deemed insufficient, and the photovoltaic-storage-charging capacity configuration needs to be readjusted, either by increasing the energy storage capacity or optimizing the photovoltaic placement, until the load capacity is met. Safety requirements for the hour; The assessment of the joint power transfer capacity of multiple transformer substations includes the following: when any transformer substation fails, adjacent substations transfer power to it via a low-voltage tie switch. The constraints of the power transfer capacity assessment include tie line capacity and voltage drop constraints. In the formula, For the connection line current, For the capacity of the connection lines, The voltage drop of the power transfer path is considered; by evaluating the power transfer capacity under all fault scenarios, the maximum power transfer load that the configuration scheme can support is determined, which serves as an important evaluation basis for the safety of the configuration scheme.

8. A multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration system, employing the multi-voltage-level distribution network photovoltaic-storage-charging collaborative optimization configuration method as described in any one of claims 1 to 7, characterized in that, include: The module includes: data acquisition and preprocessing module, medium-voltage-low-voltage coupling modeling module, upper-level medium-voltage distribution network optimization module, lower-level low-voltage distribution network optimization module, two-layer coordinated solution module, continuous load capacity assessment module, and configuration scheme output module. The data acquisition and preprocessing module collects power distribution network data and performs preprocessing and scenario clustering on the collected raw data. The medium-voltage-low-voltage coupling modeling module establishes a hierarchical coupling model of the medium-voltage-low-voltage distribution network that takes into account the constraints of the distribution transformer. The upper medium-voltage distribution network optimization module performs photovoltaic, energy storage and charging optimization configuration of the upper medium-voltage distribution network, and establishes a multi-objective optimization function to determine the total photovoltaic, energy storage and charging capacity of each distribution area. The lower-level low-voltage distribution network optimization module optimizes the photovoltaic, energy storage, and charging configuration of the lower-level low-voltage distribution network. Under the constraints of the upper-level optimization, it optimizes the configuration of photovoltaic, energy storage, and charging at each node and phase of the low-voltage feeder and sets optimization targets. The dual-layer coordinated solution module performs dual-layer coordinated solution to obtain the optimal configuration scheme of photovoltaic, energy storage and charging for medium-voltage and low-voltage distribution networks; The continuous load capacity assessment module assesses the continuous load capacity of the photovoltaic-storage-charging optimized configuration scheme and determines the maximum transfer load that the configuration scheme can support. The configuration scheme output module generates a complete optimized configuration scheme for photovoltaic energy storage and charging, and provides an orderly charging control strategy based on the current scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for coordinated optimization configuration of photovoltaic, energy storage and charging in a multi-voltage level distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for coordinated optimization configuration of photovoltaic, energy storage and charging in a multi-voltage level distribution network as described in any one of claims 1 to 7.

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