An improved encapsulated algorithm-based distributed energy storage configuration optimization method and system for power distribution networks

Abstract

The application belongs to the technical field of optimal configuration in power systems, and particularly relates to a distribution network distributed energy storage configuration optimization method and system based on an improved bagging algorithm, which comprises the following steps: a double-factor electric vehicle load prediction model is established, and double-factor predicted load data is obtained by inputting load data of a public area prediction period to the model; collection position information of the double-factor predicted load data is obtained, corresponding charging piles are selected according to the collection position information, and then a corresponding type of converter model is established according to the types of the charging piles; based on the multi-type converter model, a distributed energy storage multi-objective optimization configuration problem is established, with the minimum voltage deviation ratio and static voltage stability level as the target; the improved bagging algorithm based on a constraint candidate strategy is used to solve the distributed energy storage multi-objective optimization configuration problem, and finally a distribution network distributed energy storage configuration optimization scheme is obtained; and the application can reasonably configure the distributed energy storage.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization configuration technology, and relates to the field of multi-objective optimization configuration technology of distributed energy storage in power systems. Specifically, it relates to a method and system for optimizing the configuration of distributed energy storage in distribution networks based on an improved encapsulation algorithm. Background Technology

[0002] In current power flow calculations for distribution networks, traditional generating units such as thermal power and hydropower have strong inertia. Their stability analysis methods are mainly studied through methods such as Park transformation and equal area rule analysis. However, for weakly inertial systems containing a large number of power electronic devices, considering the operating characteristics and control strategies of different power electronic devices is closer to the actual operation of distribution networks.

[0003] Currently, with the deployment of electric vehicles (EVs), a large number of EVs will be connected to the power distribution network in the future, and their charging and discharging behavior will put enormous pressure on the network. Concentrated charging of EVs during peak hours will widen the peak-to-valley load difference in the power grid, posing a challenge to the normal operation of the distribution network. Furthermore, the flexible nature of EV user charging behavior will further increase the complexity of distribution network control and management. Large-scale EV charging will alter power grid characteristics, especially the load structure and characteristics of the distribution network, making traditional power grid planning methods inadequate for handling large-scale EV integration. EV charging load forecasting serves as the foundation for research on charging facility layout planning.

[0004] As part of the "high-energy-consuming and high-polluting" development trend, electric vehicles (EVs) involve a large number of users utilizing power electronic devices during charging and discharging. These devices suffer from issues such as the lack of damping and inertia inherent in traditional generator sets, and excessively fast response times. This can weaken the overall synchronization mechanism and frequency regulation function of the power system, impacting its power balance. Furthermore, EV chargers are evolving towards higher voltage and higher current. During high-power charging and discharging, interactions with the grid can generate power quality problems. Therefore, it is crucial to pay attention to the power quality issues arising from the charging and discharging behavior of EVs. Thus, considering the power electronic impact of stochastic loads, primarily EVs, on the distribution network is fundamental to optimal configuration.

[0005] The key to solving the optimal allocation of distributed energy storage (OAESS) lies in handling constraints and optimizing the allocation of high-quality distributed energy storage while satisfying these constraints. A typical method for handling constraints is the use of penalty functions. However, as a large-scale network, the distribution network may result in inappropriate penalty coefficients, which could prevent the OAESS problem from achieving zero constraint violations. Therefore, it is necessary to propose a constraint handling method for multi-objective optimization allocation problems.

[0006] Among numerous intelligent algorithms, the Transient Optimization (TSA) algorithm is not widely used for solving multi-objective problems. Its advantages lie in its simplicity, minimal parameter adjustments, and strong ability to escape local optima, making it more suitable for single-objective optimization. However, in real-world applications, the optimization of objectives often involves multiple factors, requiring consideration of several mutually constraining and potentially conflicting objective functions. The TSA algorithm has many practical applications in power systems, offering a simple update process and powerful functionality. Therefore, how to apply the TSA algorithm to multi-objective optimization configurations is a topic worthy of discussion.

[0007] Distributed energy storage configuration optimization can be divided into single-objective problems and multi-objective problems. For multi-objective problems, the traditional approach is to transform each objective into a single objective using a weighted average. This approach is suitable when the decision-maker has a clear bias towards a particular objective. Currently, the feasible optimal set of Pareto optimality search is widely used in multi-objective problem solving to obtain Pareto front allocation schemes without any subjective bias. Compared with the multi-objective processing method of transforming a weighted average into a single objective, the Pareto optimality search method can obtain multiple feasible objective composition schemes. Since the decision-maker's emphasis on each objective cannot be determined, how to determine an optimal compromise scheme from the multiple feasible objective composition schemes as the solution to the multi-objective optimization configuration problem is a very worthwhile research problem. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a method and system for optimizing the configuration of distributed energy storage in distribution networks based on an improved encapsulation algorithm.

[0009] In a first aspect, the present invention provides a method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm, comprising the following steps:

[0010] S1. Establish a two-factor electric vehicle load forecasting model and input the load data of the public area forecast period to obtain the two-factor forecast load data;

[0011] S2. Obtain the collection location information of the two-factor prediction load data, filter out the corresponding charging piles according to the collection location information, and then establish the corresponding type of converter model according to the type of charging pile.

[0012] S3. Based on multiple types of converter models, establish a distributed energy storage multi-objective optimization configuration problem with the goal of minimizing the voltage offset ratio and static voltage stability level; initialize the iteration count T=1, and proceed to step S4;

[0013] S4. An improved encapsulation algorithm based on a constraint candidate strategy is used to solve the multi-objective optimization configuration problem of distributed energy storage, and an optimized configuration scheme for distributed energy storage in the distribution network is obtained; the optimized configuration scheme for distributed energy storage in the distribution network includes the location and output of distributed energy storage.

[0014] S5. Determine whether the maximum number of iterations Tmax has been reached. If yes, proceed to step S6. If not, let T = T + 1, connect the distribution network distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, use the actual constraint set to optimize the distributed energy storage multi-objective optimization configuration problem, and return to step S4.

[0015] S6. Calculate the power electronic relationship based on the distribution network distributed energy storage configuration optimization scheme, and substitute the power electronic relationship into the distribution network power flow calculation; the power electronic relationship is:

[0016] J = A J ×SOC

[0017] D p =B D ×SOC

[0018] Where J represents the inertia coefficient, D p A represents the damping coefficient. J This represents the transformation matrix between charge states and inertia coefficients, where SOC is the stored charge state, and B... D Let be the transformation matrix between charge state and damping coefficient.

[0019] In a second aspect, based on the method proposed in the first aspect, the present invention also provides a distributed energy storage configuration optimization system for distribution networks based on an improved encapsulation algorithm, comprising:

[0020] The energy storage configuration optimization scheme generation system includes a data acquisition and control module, a data processing module, a charging pile management module, a converter model building module, a distributed energy storage multi-objective optimization configuration modeling module, and a solution module, wherein:

[0021] The data acquisition and control module is used to collect load datasets for public areas during predicted periods. The load datasets include the active and reactive power received by each electric vehicle at each charging pile during the predicted period. The module also records the acquisition location information for each load data point.

[0022] The data processing module is used to input the load data of the public area during the forecast period into the two-factor electric vehicle load forecasting model to obtain two-factor forecast load data including time characteristics and electricity price characteristics.

[0023] The charging pile management module is used to statistically manage the charging piles corresponding to the collection location information of each load data in the load dataset, and to record the type of each charging pile.

[0024] The converter model building module is used to build the converter model corresponding to each type of charging pile;

[0025] The distributed energy storage multi-objective optimization configuration modeling module is used to establish a distributed energy storage multi-objective optimization configuration problem with the objectives of minimizing voltage offset ratio and static voltage stability level based on multiple types of converter models.

[0026] The solution module is used to solve the multi-objective optimization configuration problem of distributed energy storage using an improved encapsulation algorithm based on a constraint candidate strategy, and finally obtains the distribution network distributed energy storage configuration optimization scheme; the distribution network distributed energy storage configuration optimization scheme includes the location and output of distributed energy storage;

[0027] The scheme evaluation system includes a scheme effectiveness evaluation module and an actual distribution network simulation module, wherein:

[0028] The scheme validity judgment module is used to determine whether the number of calculations by the solution module has reached the maximum number of iterations. If so, the power electronic relationship is calculated according to the distribution network distributed energy storage configuration optimization scheme; if not, the actual distribution network simulation module is called.

[0029] The actual distribution network simulation module is used to connect the current distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, use the actual constraint set to optimize the constraints in the distributed energy storage multi-objective optimization configuration problem, and then return to the solution module.

[0030] The beneficial effects of this invention are:

[0031] Distributed energy storage connected to the grid has brought new vitality to power supply and provided support for peak power loads. However, the variability of its operating characteristics and the accompanying power electronics effects make the mathematical models more complex, posing challenges to power flow calculations.

[0032] This invention considers the application of new scenarios for electric vehicles connecting to the power distribution network and the power electronic influence inherent in distributed power sources in the optimization configuration problem of distributed energy storage. It predicts the load of electric vehicles in the region by comprehensively considering time and electricity price factors. At the same time, it divides the current region and uses a more accurate model to describe the power electronic influence of electric vehicles connecting to the power distribution network through charging piles and the power electronic influence inherent in energy storage connected to the power distribution network. The solution algorithm is improved, a new constraint handling strategy is proposed, and the dominance relationship of the Pareto solution set in the multi-objective solution process is improved, ensuring that the solution results of the optimization configuration problem of distributed energy storage are reasonable and accurate.

[0033] Optimized configuration of distributed energy storage systems can improve the reliability of power grid supply, especially providing support during peak load periods. Through more accurate models and load forecasting, power demand can be better managed, mitigating the risk of grid overload. Comprehensive forecasting considering time and electricity price factors, as well as the influence of power electronics, helps to achieve smarter power management, making the power system more adaptable to the needs of different scenarios and improving the flexibility and resilience of the grid. Improved algorithms and models can improve the efficiency of the power system, reducing energy waste through more precise power demand management and energy allocation, thereby improving the overall system efficiency. Attached Figure Description

[0034] Figure 1 This is a flowchart of the distribution network distributed energy storage configuration optimization method based on the improved encapsulation algorithm of the present invention;

[0035] Figure 2 This is a structural block diagram of the second type of charger according to an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of a single-stage transformation structure according to an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of the two-stage transformation structure according to an embodiment of the present invention;

[0038] Figure 5 This is a schematic diagram illustrating the static voltage stability index analysis of an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention provides a method for optimizing the configuration of distributed energy storage in distribution networks based on an improved encapsulation algorithm, such as... Figure 1 As shown, it includes the following steps:

[0041] S1. Establish a two-factor electric vehicle load forecasting model and input the load data of the public area forecast period to obtain the two-factor forecast load data.

[0042] Specifically, the process of establishing a two-factor electric vehicle load prediction model includes:

[0043] S11. Obtain load data for historical periods in the public area, wherein the load data is the active power and reactive power received by each electric vehicle by each charging pile each day during the historical period; and simultaneously obtain daily meteorological data and electricity price data during the historical period.

[0044] S12. The K-means algorithm is used to cluster the load data for historical periods to obtain seasonal base load data and demand response-driven load data. Clustering the load data using the K-means algorithm is equivalent to labeling the load data. The labels mainly include two types: seasonal base and demand response-driven. This clarifies the category of each data point in the historical load data, allowing for separate processing. The seasonal base load data is load change data highly correlated with meteorological influences, while the demand response-driven load data is load change data highly correlated with electricity price changes.

[0045] S13. Construct a classification decision tree using meteorological data, and extract similar days from the seasonal basic load data based on the decision tree to obtain similar load data and dissimilar load data.

[0046] S14. Wavelet decomposition is used to obtain the electricity price characteristic trend from the electricity price data, and the electricity price trend load data is extracted from the demand response-dominant load data based on the electricity price characteristic trend.

[0047] S15. Combine similar load data and electricity price characteristic trend load data into training data, and use the training data to train a two-factor electric vehicle load prediction model.

[0048] Specifically, each segment of data from similar load data and electricity price characteristic trend load data is combined according to date information, and the combination obtained on the same day is a set of sample data.

[0049] S16. Obtain the load data for the forecast period within the public area, and input the load data for the forecast period into the trained two-factor electric vehicle load forecasting model to obtain two-factor forecast load data including electricity price factors and time factors; wherein, if the load data and meteorological data of a certain forecast day are similar to the similar load data and meteorological data of a certain historical day in the load data of the forecast period, then the similar load data of that historical day is directly used as the two-factor forecast load data of that forecast day, and the electricity price data corresponding to the similar data is directly subjected to wavelet decomposition to obtain the electricity price features, and the load data of that forecast day does not need to be passed through the two-factor electric vehicle load forecasting model again.

[0050] Specifically, to better utilize load data, this invention analyzes the influencing factors of load data using methods such as data statistics and Pearson correlation coefficient, and proposes the concept of predicted daily similarity, which is expressed as:

[0051]

[0052] Where, r i η represents the correlation between the i-th historical day and a predicted day, also known as the similarity between predicted days; t represents the number of feature types, and this invention uses four features: weather conditions, temperature, wind direction and speed, and precipitation; i (k) represents the feature correlation between the i-th historical day and the predicted day for the k-th feature, expressed as:

[0053]

[0054] Where x0(k) represents the data of the k-th feature on the prediction day, x i (k) represents the data of the k-th feature on the i-th historical day, and ρ represents x0(k) and x... i (k) The Pearson correlation coefficient between the two data points. The general formula for calculating the Pearson correlation coefficient is as follows:

[0055]

[0056] Where, ρ XY This is the Pearson correlation coefficient, where X and Y are two variables. In the specific calculation, the characteristic variables are first dimensionless to eliminate the influence of dimensions in subsequent analysis. Then, the correlation of the same characteristic on different dates is calculated, X... i For the case of feature i on day x, Y i Let represent the case of feature i on day y, and nd be the total number of features. Clearly, the larger the sample size, the more reliable the calculated Pearson correlation coefficient.

[0057] The historical day and forecast day mentioned in the above formula are relative concepts. If relevant data (including load data, meteorological data, electricity price data, etc.) are collected from September 1 to September 30, 2023, then each of these 30 days can be called a historical day or a forecast day. Taking September 5 as an example, if September 5 is taken as the forecast day, then the four days from September 1 to September 4 are all historical days relative to September 5. If September 5 is taken as the historical day, then September 6 to September 30 are all forecast days relative to September 5. The calculation of forecast day similarity here is to extract two days as historical days and forecast days respectively, and calculate the data similarity between them based on the data of these two days.

[0058] Based on the above, step S13 performs similar day extraction processing on the seasonal basic load data according to the decision tree, including:

[0059] Four features from meteorological data—weather conditions, temperature, wind direction and speed, and precipitation—are used as decision variables for the decision tree. The meteorological data corresponding to the seasonal basic load data are divided into different subsets according to feature types, and each subset corresponds to a node in the decision tree. Sub-nodes are extended from each data point in each subset to represent the feature correlation degree between the data and the prediction date based on the feature type of the subset. Then, the correlation degree of each seasonal basic load data point with the four features of the same prediction date is added to obtain the prediction date similarity. Seasonal basic load data with prediction similarity values ​​greater than a threshold are considered similar load data, and the rest are considered dissimilar load data.

[0060] S2. Obtain the collection location information of the two-factor predicted load data, filter out the corresponding charging piles based on the collection location information, and then establish the corresponding type of converter model according to the type of charging pile.

[0061] Specifically, since the connection of electric vehicles to the power grid is accompanied by the impact of a large number of power electronic devices on the power grid, this invention further considers the impact of power electronics on the distribution network and establishes different converter models based on the different considerations of charging piles corresponding to the two-factor predicted load data.

[0062] First, it is necessary to classify the types of charging stations in public areas, and at the same time, classify the types of charging piles within the charging stations. The classification of charging station types and charging pile types includes:

[0063] S201. Based on regional characteristics (i.e., planned construction area, planned demand capacity, and required number of charging piles), three types of charging stations are defined, including bus charging stations, commercial building centralized charging stations, and residential community centralized charging stations; among which:

[0064] Bus charging stations are dedicated charging stations that provide power to electric buses and coaches. These stations are suitable for buses, company shuttles, tourist buses, and commuter buses operating on relatively fixed routes with concentrated high-power charging needs. The charging characteristics of these stations include concentrated charging at night, with fast charging available during the day as needed. The chargers are high-power DC chargers.

[0065] Centralized charging stations in commercial buildings are multi-purpose charging stations that provide users with short-term charging facilities. They are suitable for charging locations such as shopping malls and office buildings with their own public parking spaces. The charging characteristics of these locations are a mixed configuration of DC chargers and AC charging piles. AC slow charging can basically meet the charging needs of most vehicles, while a small number of DC charging terminals are configured for fast charging in emergency situations to meet different user charging needs.

[0066] Centralized charging stations in residential communities provide a relatively stable charging location for residential users. These stations are suitable for locations with high demand for slow charging and a large number of parking spaces, such as residential communities. The charging demand in residential communities is characterized by concentrated charging at night, especially as residents start charging after returning home from get off work and fully charge their vehicles before leaving for work in the morning. The demand for DC charging is relatively low, so the proposed configuration principle is to adopt AC charging for all vehicles.

[0067] S202. Based on the charging current type, charging piles are classified into DC type and AC type; based on the internal structure of the charger, DC type charging piles are further subdivided into DC type charging piles with a first-type charger, DC type charging piles with a second-type charger, and DC type charging piles with a third-type charger; wherein:

[0068] The first type of charger consists of a power frequency transformer, an uncontrolled rectifier, and a chopper. It exhibits low DC-side voltage ripple and good dynamic performance; however, it has poor voltage selectivity and adjustability, cannot perform selective charging, and generates significant harmonics during electric vehicle charging. As can be seen from the current harmonic distortion rate, if harmonics cannot be effectively controlled, the first type of charger poses a significant threat to the power grid.

[0069] The second type of charger consists of a power frequency transformer, a three-phase uncontrolled rectifier, and a high-frequency transformer-isolated DC / DC converter. Its structural block diagram is as follows: Figure 2 As shown, the AC power from the grid is rectified into DC power through a three-phase uncontrolled rectifier circuit, then filtered by an RLC filter, and the stable DC power is sent to the DC / DC power converter. The DC / DC power converter transforms the voltage to meet the needs of the power battery, and then passes through a secondary filter to output a relatively stable DC voltage. The operating characteristics of the second type of charger are: low DC-side voltage ripple; good dynamic performance, high-frequency isolation, and small size; the grid-side current harmonics are relatively large, but the harmonic content is significantly reduced compared to the first type of charger.

[0070] The third type of charger consists of a three-phase PWM rectifier and a high-frequency transformer-isolated DC / DC converter. The presence of the three-phase PWM rectifier can effectively reduce the harmonics of the grid-side current, and at the same time, voltage and current can be effectively controlled through control strategies. Currently, due to technological advancements, PWM rectifier chargers are overcoming issues such as high cost and are becoming the main rectifier equipment in electric vehicle chargers.

[0071] Furthermore, based on the switching power supply topology, AC charging piles are further subdivided into AC charging piles with a single-stage conversion structure and AC charging piles with a two-stage conversion structure, wherein:

[0072] Single-stage transformation structure such as Figure 3As shown, the input AC power is converted into DC power by a single-stage AC-DC rectifier, directly supplying power to the load. Rectifier circuits can be broadly classified into three categories: based on the components, they can be classified as uncontrolled, semi-controlled, and fully controlled; based on the circuit structure, they can be classified as bridge circuits and zero-phase circuits; based on the number of phases of the AC input, they can be classified as single-phase circuits and multi-phase circuits; and based on the direction of the transformer's secondary current, they can be classified as single-phase circuits and double-phase circuits. They have different application ranges based on their advantages and disadvantages. Single-stage isolated converters have a simple structure, require fewer components, and have relatively simple control circuits. However, because there is only one stage of conversion, the converter's output voltage range is narrow, making it unsuitable for situations with a wide output range. Furthermore, achieving high power factor, harmonics, and efficiency is quite difficult.

[0073] Second-level transformation structure as follows Figure 4 As shown, the first-stage converter is an active power factor correction converter, which improves the input power factor and reduces input current harmonics, providing a stable DC power supply to the second-stage converter. The second-stage converter typically provides the load with a wide output voltage range, low ripple, and other high-quality DC power. Adding a DC-DC converter to the AC-DC rectifier structure allows it to operate under a wider range of input and output voltage conditions, providing a more stable DC power supply to the load.

[0074] Specifically, step S2 establishes a corresponding type of converter model based on the type of charging pile, including:

[0075] S211. Establish a general virtual synchronous generator model that considers the influence of power electronics in the distribution network;

[0076] S212. Multiple charging piles are extracted based on the collection location information of the two-factor predicted load data, and the type of charging pile is recorded. In this embodiment, when collecting load data, the collection source is recorded at the same time, that is, the type of charging pile in the type of charging station in the type of area that collected the data.

[0077] S213. Calculate the corresponding model parameters according to the type of charging pile, and configure them into the general virtual synchronous generator model to obtain multiple types of converter models.

[0078] Specifically, when establishing a general virtual synchronous generator model that considers the influence of power electronics in the distribution network, this invention uses the converter model adopted in electric vehicle charging devices as a basis, and considers control strategies to perform grid-connected modeling of a single electric vehicle. This embodiment uses a voltage-type virtual synchronous generator, which mainly includes the output of control frequency and voltage amplitude, corresponding to the active power control loop and reactive power control loop, respectively.

[0079]

[0080] Tm and Te are the mechanical torque and electromagnetic torque of the virtual synchronous generator, respectively, and D p J and D are the damping coefficient and inertia coefficient of the virtual synchronous generator, respectively, and θ is the power angle. It is precisely because J and D... p The existence of this allows the virtual synchronous generator to simulate the inertia and damping in the dynamic process of power and frequency, possessing the ability to dampen power oscillations and suppress excessively rapid response of energy storage. The reactive power generated by the virtual synchronous generator is...

[0081]

[0082] Among them, e q As the q-axis component of the modulation wave, K is reflected in the actual node voltage considering the influence of power electronics in power flow steady-state calculations. a For voltage gain, V set The rated voltage value set for the node, U0 is the actual voltage amplitude of the node, and n q For reactive power gain, Q set Q represents the reactive power of the virtual synchronous generator.

[0083] The parameters of the virtual synchronous generator model are determined according to the different types of charging piles. The different types of charging piles mainly affect the damping coefficient D of the virtual synchronous generator in the control model. p And the inertia coefficient J.

[0084] S3. Based on multiple types of converter models, establish a multi-objective optimization configuration problem for distributed energy storage with the goal of minimizing voltage offset ratio and static voltage stability level.

[0085] Specifically, step S3 establishes a multi-objective optimization configuration problem for distributed energy storage with the objectives of minimizing the voltage offset ratio and the static voltage stability level, expressed as:

[0086] F min =(f1(x,u),f2(x,u)),u=[u1,u2,…,u D ]

[0087] Constraints:

[0088] G i (x,u)≥0,i=1,2

[0089] H i (x,u)=0,i=1,2

[0090] Among them, F minThis represents a multi-objective optimization configuration problem for distributed energy storage. f1(x,u) represents the optimization objective of minimizing the voltage offset ratio, and f2(x,u) represents the optimization objective of minimizing the static voltage stability level. x represents the state variables, which in this embodiment include the voltage of each node in the distribution network, the initial load connected to each node, and the power and impedance of each branch. u represents the control variables, which in this embodiment include the location, connected capacity, and charge state of the distributed energy storage. D is the dimension of the control variables. G i (x,u) represents the set of inequality constraints for the i-th optimization objective, H i (x,u) represents the set of equality constraints for the i-th optimization objective.

[0091] The optimization objective f1(x,u) for minimizing the voltage offset ratio is specifically expressed as follows:

[0092]

[0093] nbus is the number of nodes in the distribution network, V k V represents the actual per-unit voltage value of the k-th node in the distribution network. f This represents the per-unit value of the rated voltage at each node in the distribution network, V. max V represents the maximum actual voltage per unit value among all nodes in the distribution network. min This represents the minimum actual voltage per unit value among all nodes in the distribution network. Generally, the better the voltage quality, the closer the actual voltage is to the rated voltage. Therefore, the smaller the voltage deviation ratio in the distribution network, the closer the node voltage is to the rated voltage, and the better the improvement effect.

[0094] The optimization objective f2(x,u) for minimizing the static voltage stability level is specifically expressed as follows:

[0095]

[0096] Γ represents the set of nodes in the distribution network excluding the slack node, n1 represents the number of nodes in set Γ, and ε VSI,j This represents the static voltage stability index of the j-th node in set Γ. This embodiment uses... Figure 5 To conduct analysis, Figure 5 The active power and reactive power satisfy the following relationship:

[0097]

[0098] in, This represents the voltage at the sending node i. This represents the voltage at the receiving node j. R represents the conjugate of the voltage at the receiving node j; ij and X ij Let P be the resistance and reactance of the branch between nodes i and j, respectively;j and Q j These represent the total active power and reactive power equivalent to node j, respectively.

[0099] From the transformation equation (8), we can obtain:

[0100]

[0101] Among them, U i This represents the voltage at the sending node i. It's U i The vector representation of contains direction, and the other parameters, such as the point at the top of the head, are also vector representations.

[0102] To ensure the existence of a feasible power flow solution for the distribution network, then we have

[0103]

[0104]

[0105] Therefore, the static voltage stability index of node j can be defined as:

[0106]

[0107] A steady-state operating system must meet the following requirements: use The magnitude of the static voltage stability index is used to determine the static voltage stability level of the distribution network. The smaller the index, the lower the static voltage stability level of the distribution network. The node with the smallest static voltage stability index in the distribution network is also the node most prone to voltage collapse.

[0108] Specifically, the set of equality constraints for each optimization objective includes:

[0109]

[0110]

[0111]

[0112] Among them, P k P represents the original active power of the k-th node in the system. k+1 P represents the original active power of the (k+1)th node in the system. L(k+1) P represents the active power of the distributed energy storage connected at the (k+1)th node. v(k+1) R represents the active power at the (k+1)th node considering the impact of charging piles connecting to the distribution network. k,k+1 Q represents the impedance between the k-th node and the (k+1)-th node. k Q represents the reactive power of the original k-th node. v(k+1)X represents the reactive power at the (k+1)th node considering the impact of charging piles connected to the distribution network. k,k+1 V represents the reactance between the k-th node and the (k+1)-th node. k This represents the per-unit voltage value of the k-th node;

[0113] The set of inequality constraints for each optimization objective includes:

[0114] C1:V min,k ≤V k ≤V max.k

[0115] C2:I min,j ≤I j ≤I max.j

[0116]

[0117]

[0118]

[0119] C6:Lo min ≤Lo i ≤Lo max

[0120]

[0121]

[0122] Where C1 represents the actual voltage per-unit constraint of each node in the distribution network, V k This represents the actual per-unit voltage value at node k = 1, 2, ..., nbus in the distribution network, where nbus represents the number of nodes in the distribution network, and V. min,k V represents the minimum per-unit value of the actual voltage at the k-th node in the distribution network. max.k C1 represents the maximum per-unit value of the actual voltage at the k-th node in the distribution network; C2 represents the current constraint of each branch in the distribution network; I j I represents the current in the j = 1, 2, ..., nbus-1 branch of the distribution network. min,j I represents the minimum current in the j-th branch of the distribution network. max.j C1 represents the maximum current value of the j-th branch in the distribution network; C2 represents the energy storage capacity constraint. N represents the active power of energy storage connected at the k-th node of the distribution network. ESS This indicates the total number of energy storage units connected to the network. C4 represents the active power of the load at the k-th node of the distribution network; C4 indicates that the energy storage power at any given time can only be either generating power or absorbing power, and cannot generate power and absorb power simultaneously. i (t) represents the current external power state of the stored energy, P i c (t) represents the power absorbed by the energy storage at this moment, denoted as P. i c (t), P i d (t) represents the power emitted by the stored energy at this moment, denoted as P. i d (t); C5 represents the capacity constraint and maximum power constraint of a single energy storage unit. This indicates the minimum capacity of a single energy storage unit. This indicates the actual capacity of the current energy storage configuration. Indicates the maximum capacity of a single energy storage unit; This indicates the minimum power that the energy storage device can generate or absorb. This indicates the current power status of a single energy storage unit. C6 represents the maximum power generated or absorbed by the energy storage; Lo represents the location constraint of the energy storage connection node to the distribution network. i Indicates the current access location of the energy storage configuration, Lo. min Lo represents the minimum location of the energy storage node connected to the distribution network. max The maximum location of energy storage access to the distribution network node; C7 represents the generator inertia coefficient constraint, J represents the inertia coefficient of the virtual synchronous generator, P max ω represents the maximum power in the virtual synchronous generator; C8 represents the angular frequency of the virtual synchronous generator; D represents the generator damping coefficient constraint. p Δω represents the damping coefficient, and Δω represents the change in angular frequency.

[0123] S4. An improved encapsulation algorithm based on a constraint candidate strategy is used to solve the multi-objective optimization configuration problem of distributed energy storage, ultimately obtaining an optimized configuration scheme for distributed energy storage in the distribution network; the optimized configuration scheme includes the location and output of distributed energy storage. Specifically, step S4 uses an improved encapsulation algorithm based on a constraint candidate strategy to solve the multi-objective optimization configuration problem of distributed energy storage, ultimately obtaining an optimized configuration scheme for distributed energy storage in the distribution network, including:

[0124] S41. Set the maximum number of rounds T1max, and initialize the number of iterations T1 = 1;

[0125] S42. The improved backpack algorithm is used to solve the multi-objective optimization configuration problem of distributed energy storage to obtain a set of multiple solutions. It is determined whether all solutions in the set of multiple solutions satisfy the constraints. If yes, proceed directly to step S44; otherwise, the constraint candidate strategy is used to remove the solutions that do not satisfy the constraints and step S43 is executed.

[0126] Specifically, the Tunicate Swarm Algorithm (TSA) is a metaheuristic algorithm that simulates the jet propulsion and group behavior of tunicates. Jet propulsion primarily utilizes the tunicate's own gravity, deep-sea currents, and inter-individual interactions to avoid search conflicts while also enabling movement towards the optimal individual. Jet propulsion consists of three main parts: avoiding individual search conflicts, maintaining proximity to the optimal individual, and moving towards the optimal individual's location. Group behavior mainly involves updating the optimal position of individual search targets. It determines the position of companions by sensing changes in water flow and observing their bioluminescence, allowing them to collectively gather towards the target food source, thus achieving group foraging. TSA employs both jet propulsion and group behavior strategies to more realistically simulate the group foraging behavior of tunicates in the ocean, achieving simple algorithm implementation, strong ability to escape local optima, and high optimization accuracy. However, traditional tunic algorithms use random initialization to determine the initial position of the population, which may result in uneven position distribution. Therefore, to address this issue and enhance the algorithm's local optimization capability, this invention proposes an improved tunic algorithm.

[0127] This invention proposes an improved Intended Enclosing Algorithm (ITSA) based on a constrained candidate strategy. The core idea of ​​this improvement is that: before searching for food sources, the enclosing population uses a chaotic mapping method to determine the initial position of the population, enhancing the algorithm's ability to escape local optimization; the search process introduces a sine and cosine algorithm, considering both the position change of the search individual towards the food source and the introduction of the best search individual from the previous generation as a guide, jointly guiding the position update of the enclosing population with the food source. The specific process includes:

[0128] S421. Initial Stage: Set relevant parameters such as population size and maximum number of iterations N1, then initialize the population, and use the Tent chaotic map to initialize the initial position of the population, represented as:

[0129]

[0130] Where 'a' is a coefficient value, ranging from (0,1), and in this embodiment, 'a' is taken as 0.8; x t+1 Let t represent the initial position in the (t+1)th round, where t = 1, 2, ..., T, and T represents the maximum number of iterations.

[0131] S422. Iteration Phase: Calculate the fitness value of each individual in the population to obtain the optimal fitness value, and then sequentially execute sine and cosine jet propulsion behavior and group behavior, including:

[0132] Avoid conflicts between search agents:

[0133]

[0134] in, This represents the new individual search position, where c1, c2, and c3 represent random numbers in the range [0,1]. P min P max These represent the initial velocity and the subordinate velocity of individual interactions, respectively;

[0135] Move to the location of the best search agent:

[0136]

[0137] in, This indicates the distance between the food source and the individual being searched. This indicates the location of the food source, and x1 is the current iteration number. Indicates the location of the encapsulated individual;

[0138] Focus on the best search agent:

[0139]

[0140] in, The updated position is represented by the sine and cosine algorithms. To enhance the algorithm's local optimization ability, the positions of the enclosed individuals are updated using these algorithms. After this introduction, the position update of the enclosed individuals needs to consider the positions updated by the search individuals based on the food source's position and the optimal search individual's position from the previous generation. The search individuals then use sine and cosine paths to approach the food source, jointly guiding the position update of the enclosed individuals. This is represented as:

[0141]

[0142]

[0143] r2 is a random number between 0 and 2π, and r3 is a random number between 0 and 2. a′ represents the constant 2, P p(x1) P represents the updated position of the best search individual in the previous generation. p(x2) The random position of the individual selected from the previous generation.

[0144] Group behavior:

[0145]

[0146] S423. Termination Phase: Determine whether the termination condition has been met (i.e., the maximum number of iterations N1 has been reached). If so, output the set of multiple solutions; otherwise, return to step S422.

[0147] Specifically, successful power flow calculation relies on the premise that constraints are satisfied, ensuring that the control variables remain within the set range as much as possible. If any individual violates these constraints, it needs to be handled according to the constraint candidate strategy. Assume:

[0148] Y = (y1, y2, ..., y sum )

[0149] C = (C1, C2, ..., C sum )

[0150] Y represents the set of multiple solutions obtained by the improved backpack algorithm. i Let represent the i-th solution in the multi-solution set Y, where sum is the number of solutions; C is the set of default records corresponding to the multi-solution set Y. i The constraint evaluation function for the i-th solution is calculated as follows:

[0151]

[0152] c i This represents the i-th out-of-bounds value in the i-th solution; nc is the number of inequality constraints in the system; where c i Calculate using the following formula:

[0153]

[0154] In the formula, c max and c min c represents the upper and lower limits of each state variable, respectively. k Let be the actual value of the k-th state variable, and nx be the total number of all state variables.

[0155] For each solution in the multi-solution solution set Y, the constraint evaluation function value is evaluated. If the constraint evaluation function C of the i-th solution is... i If the constraint evaluation function C of the i-th solution is 0, then the solution is saved. i If the value is not equal to 0, then the solution is discarded.

[0156] S43. Determine if the current multi-solution set is empty. If it is, let T1 = T1 + 1 and return to step S41; otherwise, proceed to step S44.

[0157] S44. Determine the constraints between all solutions in the current multi-solution solution set based on Pareto judgment; sort the solutions according to the constraints, and then determine whether the maximum round T1max has been reached. If so, output the optimal solution to obtain the distribution network distributed energy storage configuration optimization scheme; otherwise, let T1 = T1 + 1 and return to step S41.

[0158] Specifically, in the distributed energy storage multi-objective optimization configuration problem, since the various objectives may be mutually constrained and influenced, the Pareto dominance method can effectively determine the superiority of the solutions. For two solutions YP1 and YP2 in the multi-solution solution set YP, there are three correspondences: YP1 is dominant over YP2, YP2 is dominant over YP1, or neither is dominant. If for any solution set number l, the fitness value of YP1 is no greater than the fitness value of YP2, and there exists at least one solution q such that the fitness of YP1 is less than the fitness of YP2, then YP1 is called a non-dominated solution and YP2 is a dominated solution, and YP1 is ranked ahead of YP2.

[0159] S5. Determine if the maximum number of iterations Tmax has been reached. If yes, proceed to step S6; otherwise, let T = T + 1, connect the distribution network distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, optimize the distributed energy storage multi-objective optimization configuration problem using the actual constraint set, and return to step S4. The actual constraint set is the set of equality constraints and inequality constraints mentioned above. Due to the difference between the actual environment and the ideal environment, connecting the distribution network distributed energy storage configuration optimization scheme to the distribution network will change the upper and lower limits of C3, C7, and C8 in the three equality constraints and inequality constraints. Therefore, adjustments are needed to make the scheme closer to the actual environment. Simultaneously, each time the distribution network distributed energy storage configuration optimization scheme is connected to the distribution network, the two-factor predicted load data obtained in step S1 will be input.

[0160] S6. Calculate the power electronic relationship based on the distribution network distributed energy storage configuration optimization scheme, and substitute the power electronic relationship into the distribution network power flow calculation; the power electronic relationship is:

[0161] J = A J ×SOC

[0162] D p =B D ×SOC

[0163] Where J represents the inertia coefficient, D p A represents the damping coefficient. J This represents the transformation matrix between charge states and inertia coefficients, where SOC is the stored charge state, and B... D Let be the transformation matrix between charge state and damping coefficient.

[0164] In one embodiment, the present invention also proposes a distributed energy storage configuration optimization system for distribution networks based on an improved encapsulation algorithm, comprising:

[0165] The energy storage configuration optimization scheme generation system includes a data acquisition and control module, a data processing module, a charging pile management module, a converter model building module, a distributed energy storage multi-objective optimization configuration modeling module, and a solution module, wherein:

[0166] The data acquisition and control module is used to collect load datasets for public areas during predicted periods. The load datasets include the active and reactive power received by each electric vehicle at each charging pile during the predicted period. The module also records the acquisition location information for each load data point.

[0167] The data processing module is used to input the load data of the public area during the forecast period into the two-factor electric vehicle load forecasting model to obtain two-factor forecast load data including time characteristics and electricity price characteristics.

[0168] The charging pile management module is used to statistically manage the charging piles corresponding to the collection location information of each load data in the load dataset, and to record the type of each charging pile.

[0169] The converter model building module is used to build the converter model corresponding to each type of charging pile;

[0170] The distributed energy storage multi-objective optimization configuration modeling module is used to establish a distributed energy storage multi-objective optimization configuration problem with the objectives of minimizing voltage offset ratio and static voltage stability level based on multiple types of converter models.

[0171] The solution module is used to solve the multi-objective optimization configuration problem of distributed energy storage using an improved encapsulation algorithm based on a constraint candidate strategy, and finally obtains the distribution network distributed energy storage configuration optimization scheme; the distribution network distributed energy storage configuration optimization scheme includes the location and output of distributed energy storage;

[0172] The scheme evaluation system includes a scheme effectiveness evaluation module and an actual distribution network simulation module, wherein:

[0173] The scheme validity judgment module is used to determine whether the number of calculations by the solution module has reached the maximum number of iterations. If so, the power electronic relationship is calculated according to the distribution network distributed energy storage configuration optimization scheme; if not, the actual distribution network simulation module is called.

[0174] The actual distribution network simulation module is used to connect the current distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, use the actual constraint set to optimize the constraints in the distributed energy storage multi-objective optimization configuration problem, and then return to the solution module.

[0175] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "rotation," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0176] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the configuration of distributed energy storage in distribution networks based on an improved encapsulation algorithm, characterized in that, Includes the following steps: S1. Establish a two-factor electric vehicle load forecasting model, input load data of the public area forecast period into it to obtain two-factor forecast load data; the two factors include electricity price factor and time factor; S2. Obtain the collection location information of the two-factor prediction load data, filter out the corresponding charging piles according to the collection location information, and then establish the corresponding type of converter model according to the type of charging pile. S3. Based on multiple types of converter models, establish a distributed energy storage multi-objective optimization configuration problem with the goal of minimizing the voltage offset ratio and static voltage stability level; initialize the iteration count T=1, and proceed to step S4; S4. An improved encapsulation algorithm based on a constraint candidate strategy is used to solve the multi-objective optimization configuration problem of distributed energy storage, and an optimized configuration scheme for distributed energy storage in the distribution network is obtained. The optimized configuration scheme for distributed energy storage in the power distribution network includes the location and output of distributed energy storage. The specific process of improving the capsule algorithm includes: S421. Initial stage: Set the population size and maximum number of iterations N1, then initialize the population and use Tent chaotic mapping to initialize the initial position of the population; S422. Iteration phase: Calculate the fitness value of each individual in the population to obtain the optimal fitness value, and then execute the sine and cosine jet propulsion behavior and the group behavior in sequence; among them, the sine and cosine algorithm is introduced to update the position of the engulfed individual, so that the engulfed individual considers the position of the search individual facing the food source and the updated position of the best search individual in the previous generation when updating the position; S423. Termination Phase: Determine whether the maximum number of iterations N1 has been reached. If yes, output the set of multiple solutions; otherwise, return to step S422. S5. Determine whether the maximum number of iterations Tmax has been reached. If yes, proceed to step S6. If not, let T=T+1, connect the distribution network distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, use the actual constraint set to optimize the distributed energy storage multi-objective optimization configuration problem and return to step S4. S6. Calculate the power electronic relationship based on the distribution network distributed energy storage configuration optimization scheme, and substitute the power electronic relationship into the distribution network power flow calculation; the power electronic relationship is: Where J represents the inertia coefficient, Indicates the damping coefficient. This represents the transformation matrix between charge states and inertia coefficients, where SOC is the stored charge state, and B... D Let be the transformation matrix between charge state and damping coefficient.

2. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 1, characterized in that, The process of establishing a two-factor electric vehicle load forecasting model includes: S11. Obtain load data for public areas during historical periods, wherein the load data is the active power and reactive power received by each electric vehicle at each charging pile during the historical period; simultaneously obtain daily meteorological data and electricity price data during the historical period; S12. The K-means algorithm is used to cluster the load data for historical periods to obtain seasonal basic load data and demand response-driven load data; S13. Construct a classification decision tree using meteorological data, and extract similar days from the seasonal basic load data based on the decision tree to obtain similar load data and dissimilar load data; S14. Wavelet decomposition of electricity price data is used to obtain the characteristic trend of electricity price, and the electricity price trend load data is extracted from the demand response-dominant load data based on the characteristic trend of electricity price; S15. Combine similar load data and electricity price characteristic trend load data into training data, and use the training data to train a two-factor electric vehicle load prediction model; S16. Obtain the load data for the forecast period within the public area, and input the load data for the forecast period into the trained two-factor electric vehicle load forecasting model to obtain two-factor forecast load data including electricity price factors and time factors; wherein, if the load data and meteorological data of a certain forecast day are similar to the similar load data and meteorological data of a certain historical day in the load data of the forecast period, then the similar load data of that historical day is directly used as the two-factor forecast load data of that forecast day, and the load data of that forecast day does not need to be processed by the two-factor electric vehicle load forecasting model again.

3. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 1, characterized in that, Step S2 also includes classifying the types of charging stations within the public area, and classifying the types of charging piles within the charging stations, wherein the classification of charging station types and charging pile types includes: S201. Based on regional characteristics, three types of charging stations are classified, including bus charging stations, commercial building centralized charging stations, and residential community centralized charging stations. S202. Based on the charging current type, charging piles are divided into DC type and AC type; based on the internal structure of the charger, DC type charging piles are further subdivided into DC type charging piles with a first type charger, DC type charging piles with a second type charger, and DC type charging piles with a third type charger; at the same time, based on the switching power supply topology, AC type charging piles are subdivided into AC type charging piles with a single-stage conversion structure and AC type charging piles with a two-stage conversion structure.

4. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 3, characterized in that, Step S2 establishes a corresponding converter model based on the type of charging pile, including: S211. Establish a general virtual synchronous generator model that considers the influence of power electronics in the distribution network; S212. Extract multiple charging piles based on the location information of the two-factor prediction load data and record the type of charging pile; S23. Calculate the corresponding model parameters according to the type of charging pile, and configure them into the general virtual synchronous generator model to obtain multiple types of converter models.

5. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 3, characterized in that, The first type of charger consists of a power frequency transformer, an uncontrolled rectifier, and a chopper; the second type of charger consists of a power frequency transformer, a three-phase uncontrolled rectifier, and a high-frequency transformer-isolated DC / DC converter; the third type of charger consists of a three-phase PWM rectifier and a high-frequency transformer-isolated DC / DC converter.

6. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 1, characterized in that, Step S3 establishes a multi-objective optimization configuration problem for distributed energy storage, aiming to minimize the voltage offset ratio and the static voltage stability level, as follows: Constraints: in, This represents a multi-objective optimization configuration problem for distributed energy storage. This represents the optimization objective of minimizing the voltage offset ratio. Let x represent the optimization objective of minimizing the static voltage stability level, u represent the control variables, D represent the dimension of the control variables, and nbus represent the number of nodes in the distribution network. This represents the per-unit value of the actual voltage at the k-th node in the distribution network. This represents the per-unit value of the rated voltage at each node in the distribution network. This represents the maximum actual voltage per unit value across all nodes in the distribution network. This represents the minimum actual voltage per unit value among all nodes in the distribution network; This represents the set of nodes in a distribution network, excluding the slack node. Represents a set The static voltage stability index of the j-th node; Let represent the set of inequality constraints for the i-th optimization objective. Let represent the set of equality constraints for the i-th optimization objective.

7. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 6, characterized in that, The set of equality constraints for each optimization objective includes: in, This represents the original active power of the k-th node in the system. This represents the original active power of the (k+1)th node in the system. This represents the active power of the distributed energy storage connected at the (k+1)th node. This represents the active power at the (k+1)th node, considering the impact of charging piles connecting to the power distribution network. This represents the impedance between the k-th node and the (k+1)-th node. This represents the reactive power of the original k-th node. This represents the reactive power at the (k+1)th node, considering the impact of charging piles connecting to the power distribution network. This represents the reactance between the k-th node and the (k+1)-th node. This represents the per-unit value of the actual voltage at the k-th node; The set of inequality constraints for each optimization objective includes: Where C1 represents the per-unit voltage constraint for each node in the distribution network. This represents the actual per-unit voltage value at node k=1,2,...,nbus in the distribution network, where nbus represents the number of nodes in the distribution network. This represents the minimum per-unit value of the actual voltage at the k-th node in the distribution network. C1 represents the maximum per-unit value of the actual voltage at the k-th node in the distribution network; C2 represents the current constraint of each branch in the distribution network. This represents the current in the j=1,2,…,nbus-1 branch of the distribution network. Let represent the minimum current in the j-th branch of the distribution network. C1 represents the maximum current value of the j-th branch in the distribution network; C2 represents the energy storage capacity constraint. This represents the active power of energy storage connected at the k-th node of the distribution network. This indicates the total number of energy storage units connected to the network. C4 represents the active power of the load at the k-th node of the distribution network; C4 indicates that the energy storage power at any given time can only be either generating power or absorbing power, and cannot generate power and absorb power simultaneously. This indicates the current external power status of the energy storage. If the energy storage absorbs power at this moment, then the absorbed power is denoted as . , If the energy storage device generates power at this moment, then the generated power is denoted as . C5 represents the capacity constraint and maximum power constraint of a single energy storage unit. This indicates the minimum capacity of a single energy storage unit. This indicates the actual capacity of the current energy storage configuration. Indicates the maximum capacity of a single energy storage unit; This indicates the minimum power that the energy storage device can generate or absorb. This indicates the current power status of a single energy storage unit. This indicates the maximum power output or absorption of the energy storage; C6 indicates the location constraint for connecting the energy storage to the distribution network node. Indicates the current access location of the energy storage configuration. This indicates the smallest location at which energy storage is connected to the distribution network node. The maximum location of energy storage access to the distribution network node; C7 represents the generator inertia coefficient constraint. The inertia coefficient of the virtual synchronous generator. This represents the maximum power in the virtual synchronous generator. C1 represents the angular frequency of the virtual synchronous generator; C8 represents the generator damping coefficient constraint. Indicates the damping coefficient. It represents the change in angular frequency.

8. The method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm according to claim 1, characterized in that, Step S4 employs an improved encapsulation algorithm based on constrained candidate strategies to solve the multi-objective optimization configuration problem of distributed energy storage, obtaining an optimized configuration scheme for distributed energy storage in the distribution network, including: S41. Set the maximum number of rounds T1max, and initialize the number of iterations T1=1; S42. The improved backpack algorithm is used to solve the multi-objective optimization configuration problem of distributed energy storage to obtain a set of multiple solutions. It is determined whether all solutions in the set of multiple solutions satisfy the constraints. If yes, proceed directly to step S44; otherwise, the constraint candidate strategy is used to remove the solutions that do not satisfy the constraints and step S43 is executed. S43. Determine if the current multi-solution set is empty. If it is, let T1 = T1 + 1 and return to step S41; otherwise, proceed to step S44. S44. Determine the constraints between all solutions in the current multi-solution solution set based on Pareto judgment; sort the solutions according to the constraints, and then determine whether the maximum round T1max has been reached. If so, output the optimal solution to obtain the distribution network distributed energy storage configuration optimization scheme; otherwise, let T1=T1+1 and return to step S41.

9. A method for optimizing the configuration of distributed energy storage in a distribution network based on an improved encapsulation algorithm, as described in claim 8, is characterized in that... Step S42 uses an improved backpack algorithm to solve the distributed energy storage multi-objective optimization configuration problem to obtain a multi-objective solution, including: S421. Initialization Phase: Set the population size and maximum number of iterations N1, then initialize the population and use the Tent chaotic map to initialize the initial position of the population, represented as: Where a is a coefficient value, ranging from (0,1), and in this embodiment a is 0.8; This represents the initial position in the (t+1)th round, where t = 1, 2, ..., T, and T represents the maximum number of iterations. S422. Iteration Phase: Calculate the fitness value of each individual in the population to obtain the optimal fitness value; then, sequentially execute the sine and cosine jet propulsion behavior and the group behavior, including: Avoid conflicts between search agents: in, This represents the new individual search position, where c1, c2, and c3 represent random numbers in the range [0,1]. min P max These represent the initial velocity and the subordinate velocity of individual interactions, respectively; Move to the location of the best search agent: in, This indicates the distance between the food source and the individual being searched. Indicates the location of the food source. This represents the current iteration number. Indicates the location of the encapsulated individual; Focus on the best search agent: Where r2 is a random number between 0 and 2π, and r3 is a random number between 0 and 2. Represented as a constant 2, This indicates the updated position of the best search individual in the previous generation. The random position of the individual selected from the previous generation; Group behavior: S423. Termination Phase: Determine if the maximum number of iterations N1 has been reached. If so, output the set of multiple solutions; otherwise, return to step S422.

10. A distributed energy storage configuration optimization system for a distribution network based on an improved encapsulation algorithm, implementing the method of any one of claims 1-9, characterized in that, include: The energy storage configuration optimization scheme generation system includes a data acquisition and control module, a data processing module, a charging pile management module, a converter model building module, a distributed energy storage multi-objective optimization configuration modeling module, and a solution module, wherein: The data acquisition and control module is used to collect load datasets for public areas during predicted periods. The load datasets include the active and reactive power received by each electric vehicle at each charging pile during the predicted period. The module also records the acquisition location information for each load data point. The data processing module is used to input the load data of the public area during the forecast period into the two-factor electric vehicle load forecasting model to obtain two-factor forecast load data including time characteristics and electricity price characteristics. The charging pile management module is used to statistically manage the charging piles corresponding to the collection location information of each load data in the load dataset, and to record the type of each charging pile. The converter model building module is used to build the converter model corresponding to each type of charging pile; The distributed energy storage multi-objective optimization configuration modeling module is used to establish a distributed energy storage multi-objective optimization configuration problem with the objectives of minimizing voltage offset ratio and static voltage stability level based on multiple types of converter models. The solution module is used to solve the multi-objective optimization configuration problem of distributed energy storage using an improved encapsulation algorithm based on a constraint candidate strategy, and finally obtains the distribution network distributed energy storage configuration optimization scheme; the distribution network distributed energy storage configuration optimization scheme includes the location and output of distributed energy storage; The scheme evaluation system includes a scheme effectiveness evaluation module and an actual distribution network simulation module, wherein: The scheme validity judgment module is used to determine whether the number of calculations by the solution module has reached the maximum number of iterations. If so, the power electronic relationship is calculated according to the distribution network distributed energy storage configuration optimization scheme; if not, the actual distribution network simulation module is called. The actual distribution network simulation module is used to connect the current distributed energy storage configuration optimization scheme to the distribution network to obtain the actual constraint set, use the actual constraint set to optimize the constraints in the distributed energy storage multi-objective optimization configuration problem, and then return to the solution module.

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