Optimized operation method of power distribution network based on linearized full-pure embedded power flow model

Abstract

The invention relates to a power distribution network optimization operation method based on a linearized full-pure embedded power flow model, and belongs to the technical field of power system operation and control. According to the method, first-order linearization processing is carried out on a power series recursive relation of a fully-pure embedded power flow model, and a linearization approximation model between node voltage and power injection increment is constructed by ignoring a high-order attenuation item, so that nonlinear power flow calculation is converted into efficient linear combination operation. And on the basis of the linearization model, constructing a power distribution network multi-period optimization operation model taking system voltage deviation minimization as a target, and coupling operation constraints of the energy storage system and the static reactive power compensation device. According to the method, on the premise that the calculation precision is guaranteed, the efficiency of multi-period continuous power flow calculation and optimal scheduling is remarkably improved, the problems of voltage out-of-limit and power fluctuation of the power distribution network under high-proportion distributed energy access are effectively solved, and the safety and economical efficiency of system operation are improved.

Description

Technical Field

[0001] This invention belongs to the field of power grid control technology, and in particular to a distribution network optimization operation method based on a linearized fully embedded power flow model. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, energy storage, and controllable loads on the distribution side, the distribution network is evolving from a traditional passive power supply network to an active and interactive operation mode. The intermittency and volatility of high-proportion distributed energy sources lead to problems such as node voltage exceeding limits, power flow reversal, and increased fluctuations in interaction with the upstream grid, posing new challenges to the safe operation and refined scheduling of the distribution network. Traditional power flow methods based on Newton-Raphson or fast decoupling suffer from convergence problems under certain operating conditions, while commonly used convex approximation optimizations (such as second-order cone programming) can guarantee global solvability and computational efficiency to a certain extent, but still face a trade-off between modeling accuracy and scalability in multi-time-period and multi-device coupled scenarios.

[0003] Among power flow solutions, the Fully Embedded Power Flow Method (HELM) has gained increasing attention due to its characteristics of being non-iterative, analytically expandable, and convergently decidable. HELM expresses node voltages as power series with respect to complex parameters, allowing the original nonlinear equations to be solved recursively through power series, thus reliably obtaining feasible solutions when solutions exist and providing signals when no solutions exist. HELM provides a theoretically rigorous and groundbreaking framework for power flow solutions, but its engineering applications still face challenges related to the high-order coupling complexity of power series and the coupling of device constraints.

[0004] In the optimization of distribution network operation, the coordinated scheduling of distributed resources (such as energy storage systems and SVCs) is often used to reduce voltage deviations, smooth power exchange, and improve fluctuation absorption capacity. Among them, convex optimization tools such as SOCP are widely used to build solvable models, but because they usually approximate and simplify the power flow model, they will introduce certain errors in high-precision scenarios.

[0005] It can be seen that existing optimization scheduling methods (including those based on SOCP) involve trade-offs in efficiency and accuracy, especially in terms of multi-period coordination, equipment coupling, and compatibility with distributed energy fluctuations, where there is still room for improvement. Therefore, combining the theoretical advantages of HELM with its practical computability and coupling capabilities, while retaining its analytical stability, to meet the requirements of efficiency and coupling in optimization scheduling, is a highly valuable research direction. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose a distribution network optimization operation method based on a linearized fully embedded power flow model. By performing first-order linearization on the HELM power series recursive relationship, an approximate model that can be quickly calculated by linear superposition of node voltage changes is constructed. On this basis, the operation constraints of energy storage system and static var compensator are coupled into a multi-time period optimization scheduling model, and joint scheduling is carried out with the goal of minimizing voltage deviation.

[0007] The technical problem solved by this invention is achieved through the following technical solution: The distribution network optimization operation method based on the linearized fully embedded power flow model includes the following steps: S1. Obtain the topology parameters, line parameters, and an initial feasible power flow solution of the distribution network as the baseline operating state; S2. Based on the fully embedded power flow theory, construct the power series expansion of the node voltage with respect to the complex embedded parameters, and derive the recursive relationship of its power series coefficients. S3. Perform first-order linearization on the recursive relationship, ignore higher-order power series terms, and establish a linearized fully embedded power flow model. The model represents the node voltage after the system operating state changes as a linear superposition of the initial node voltage and the first-order power series coefficients corresponding to the power injection increment of each node. S4. Based on the linearized fully embedded power flow model, construct a multi-period optimized operation model for the distribution network with the goal of minimizing the total voltage deviation of the system within a preset period. The constraints of the optimized operation model include the linearized fully embedded power flow model, energy storage system operation constraints, static var compensator operation constraints, and node voltage safety constraints. S5. Solve the multi-period optimization operation model of the distribution network to obtain the optimal plan for the charging and discharging power of the energy storage system and the reactive power output of the static var compensator in each period of the future scheduling cycle, and schedule the controllable equipment in the distribution network according to the optimal plan.

[0008] Furthermore, the fully embedded power flow model in S3 is as follows: in, For nodes i voltage, Nodes before changes in system operating state i voltage, For nodes after changes in system operating state i voltage, and These are the constant term and first-order coefficients of the voltage holomorphic function, respectively. k Represents the first in the system k 1 node For the firstk Active power injection increment coefficient of each node For the first k The reactive power injection increment coefficient of each node, Inject incremental baseline values ​​into the active power of nodes. For the first k Increase active power injection at each node At that time, node i The voltage first-order power series coefficients, Inject incremental baseline values ​​for node reactive power. For the first k The reactive power injection at each node has increased. At that time, node i The voltage first-order power series coefficients.

[0009] Furthermore, the objective function in S4 is to minimize the total timing voltage deviation of the system: in, N The number of nodes in the network. T The time of one scheduling cycle, For nodes i exist t Voltage amplitude at a given time.

[0010] Furthermore, the constraints in S4 include: energy storage system operation constraints, mutual exclusion constraints of charge and discharge states, secondary constraints of charge and discharge power and apparent power, dynamic update constraints of state of charge, and upper and lower limits constraints of power and capacity.

[0011] The advantages and positive effects of this invention are: This invention first linearizes the power series recursive relationship of the fully embedded power flow model to first-order linearization. By ignoring higher-order attenuation terms, a linearized approximate model of the relationship between node voltage and power injection increments is constructed, thereby transforming nonlinear power flow calculation into efficient linear combination operations. Based on this linearized model, a multi-period optimized operation model for the distribution network with the goal of minimizing system voltage deviation is constructed, coupled with the operational constraints of energy storage systems and static var compensators. This method significantly improves the efficiency of multi-period continuous power flow calculation and optimized scheduling while ensuring computational accuracy, effectively solving the voltage limit exceedance and power fluctuation problems of distribution networks with a high proportion of distributed energy access, and improving the safety and economy of system operation. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention; Figure 2 This is a network structure diagram of the 91-node power distribution system according to Embodiment 9 of the present invention; Figure 3This is a diagram showing the load fluctuation curves for various types of loads according to the present invention; Figure 4 This is a photovoltaic power output curve diagram of the present invention; Figure 5 This is a graph showing the voltage fluctuation curves of node 36 under different scenarios according to the present invention; Figure 6 This is a graph showing the power interaction fluctuation curves between the distribution network and the upstream power grid under different scenarios according to the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to the accompanying drawings.

[0014] The distribution network optimization operation method based on the linearized fully embedded power flow model includes the following steps: S1. Obtain the topology parameters, line parameters, and an initial feasible power flow solution of the distribution network as the baseline operating state.

[0015] S2. Based on the fully embedded power flow theory, construct the power series expansion of the node voltage with respect to the complex embedded parameters, and derive the recursive relationship of its power series coefficients.

[0016] For an N-node power system, the nodes i voltage V i Embedded complex factor α， By constructing a holomorphic voltage function and substituting it into the power flow equations, a holomorphic embedded power flow model can be obtained, as shown in equations (1)-(4). This model can use any feasible operating state of the system as a benchmark. After the operating state of the system changes, the power flow solution of the system under the new operating state can be obtained by substituting the node injection power increment.

[0017] (1) (2) (3) (4) in, Nodes before changes in the operating status of the power distribution system i The conjugate value of the complex power injection; For nodes i The conjugate value of the complex power injection increment after the change in system operating state compared to before the change; Nodes before changes in the operating status of the power distribution system i Active power injection; For nodes i The increase in active power injection after a change in the system's operating state compared to before the change; PV node or balanced nodei The voltage. When When, equations (1)-(4) are the power flow equations before the change in the operating state of the power distribution system; when When the operating state of the power distribution system changes, equations (1)-(4) are the power flow equations.

[0018] because It is a holomorphic function, therefore it can be expanded into a function with respect to... α The power series of is shown below, where the coefficients are... It is a plural number.

[0019] (5) (6) Substituting equations (5) and (6) into equations (1)-(4), let both sides of the equations be related to... α Since the coefficients of power series of the same order are equal, we can obtain the recursive relation for the coefficients of the power series. Separating the real and imaginary parts of this relation, we can obtain the result from 2... N A system of linear equations consisting of 3 equations, namely: (7) in, A for 2N A square matrix of order, composed of The elements that constitute the nodal admittance matrix; X n 2 N dimensional column vector, by It consists of the real part and the imaginary part; b n for 2N dimensional column vector, when hour, b 1 It consists of the power injection increment of each node, when hour, b n Depend on The elements of the nodal admittance matrix constitute the matrix.

[0020] Since the initial solution of the fully embedded power flow model is the power flow solution before the change of the distribution system's operating state, that is, the system power flow solution under the baseline state, therefore when hour, The initial solution is given. Then, the initial solution is substituted into the recursive relation of equation (7) to solve for the coefficients of higher-order power series. Substituting the coefficients of each order into equation (5), we can obtain the node voltages of the system after the change in operating state.

[0021] To obtain the relationship between the power series coefficients in the node voltage function and derive the linearized holomorphic embedded power flow model constructed from the power series coefficients, this section will analyze a three-node system as an example.

[0022] (1) Assume that the active power injection at node 1 is increased by a baseline value. The recursive relation (7) at this time... b 1 Represented as ,have: (8) (2) Assume that the reactive power injection at node 2 is increased by a baseline value. The recursive relation (7) at this time... b 1 Represented as ,have: (9) (3) Assume that the active power injection at node 1 increases by the baseline value. The reactive power injection at node 2 increases the baseline value. The recursive relation (7) at this time... b 1 Represented as ,have: (10) It can be seen that when When, vector b 1 satisfy: (11) Due to the coefficient matrix A The first-order power series coefficients of the node voltage are obtained from equation (11) because they are only related to the system admittance parameters and the node voltage under the reference state, and are independent of the magnitude of the power injection change. (12) in, Increase active power injection for node 1 At that time, node i First-order voltage power series coefficients; Increase reactive power injection at node 2 At that time, node i First-order voltage power series coefficients; Increase active power injection for node 1 The reactive power injection at node 2 increases At that time, node i The coefficients of the first-order voltage power series.

[0023] When the node power injection increases by a reference value, a linear relationship of the first-order voltage power series coefficients can be derived. Based on this, the relationship of the first-order voltage power series coefficients can be obtained when the node power injection increases by any value.

[0024] (1) Assume that the active power injection at node 1 increases by an arbitrary value. Let this value be the baseline value. of c times, that is . In this case, in equation (7) b 1 Represented as ,have: (13) (2) Assume that the reactive power injection at node 2 increases by an arbitrary value. Let this value be the baseline value. of d times, that is . In this case, in equation (7) b 1 Represented as ,have: (14) (3) Assume that the active power injection at node 1 increases by an arbitrary value. The reactive power injection at node 2 increases by an arbitrary value. . In this case, in equation (7) b 1 Represented as ,have: (15) As can be seen from the above relationship, when Sometimes, , Based on the above coefficient matrix A Properties and vectors b 1 The linear relationship can be obtained as follows: (16) in, Increase active power injection for node 1 The reactive power injection at node 2 increases At that time, node i The coefficients of the first-order voltage power series.

[0025] when n= At time 2, the vector when the injected power of a single node changes. b 2 satisfy: (17) (18) If both nodes experience changes in injected power, then: (19) in, This represents the coupling multiplication part between nodes, without an explicit expression.

[0026] Substituting equations (17) and (18) into equation (19), we get: (20) in, This represents the coupled multiplication of the coefficients of the power series.

[0027] Therefore, for the second-order power series coefficients in the node voltage function, the following holds: (twenty one) in, for The coupled part of the calculated power series coefficients has no explicit expression.

[0028] when n≥3 At that time, the power series coefficients of the node voltages satisfy: (twenty two) S3. Perform first-order linearization on the recursive relationship, ignore higher-order power series terms, and establish a linearized fully embedded power flow model. The model represents the node voltage after the system operating state changes as a linear superposition of the initial node voltage and the first-order power series coefficients corresponding to the power injection increment of each node.

[0029] The above conclusions can be generalized to N Node system. Assume nodes... k The active power injection increment is The reactive power injection increment is Then the power series coefficients of the node voltages can be expressed as: (twenty three) (twenty four) From the mathematical relationships shown in equations (23) and (24), it can be deduced that, under the condition that the initial operating state of the system remains unchanged, when the injected power of multiple nodes changes simultaneously, the first-order power series coefficients of the voltage of each node can be obtained by a linear combination of the first-order power series coefficients when the injected power of a single node increases by a reference value. This characteristic indicates that the first-order power series coefficients have a linear superposition property. However, when calculating higher-order power series coefficients, due to the complex nonlinear coupling relationships between the coefficients of each order, it is difficult to establish explicit analytical expressions for the higher-order coefficients.

[0030] However, it is worth noting that in the process of calculating node voltages, the order of the power series changes. n The increase, The value of exhibits a clear decreasing trend. The order of magnitude of higher-order terms decreases sharply with increasing order. Based on this convergence characteristic, while ensuring computational accuracy, higher-order power series can be ignored, thus yielding an approximate analytical expression for the node voltage: (25) Equation (25) establishes a power flow model for a distribution network based on linearized calculations of power series coefficients. Mathematical analysis of this equation reveals that when higher-order power series coefficients are ignored, the node voltage... This can be expressed as the active power injection increment coefficient. and reactive power injection increment coefficient The linear combination of the nodes. That is, the node voltage after the system operating state changes can be decomposed into two components: one is the node voltage before the operating state changes, and the other is the linear superposition of the first-order power series coefficients corresponding to the increase of the injected power of each node by the reference value.

[0031] For a given power grid, a feasible power flow solution can be selected as the initial operating state. Based on this initial operating state, the incremental active power injection reference value for each node can be calculated. The corresponding voltage first-order power series coefficients and reactive power injection incremental reference value The corresponding voltage first-order power series coefficients This establishes a set of voltage power series coefficients corresponding to the power injection increment reference value. This coefficient set is treated as a known quantity, and the power injection increment coefficients under various operating conditions are then used... , The voltage is calculated using equation (25).

[0032] Assuming the system's operating state changes continuously over time, for any given time... t Power flow calculation can be performed as follows: (1) Determine the change in injected power at each node at the current moment and calculate the injection power increment coefficient: (26) (27) in, , They are nodes k exist t The active and reactive power injected at all times; , They are nodes k The active and reactive power injected during initial operation.

[0033] S4. Based on the linearized fully embedded power flow model, construct a multi-period optimized operation model for the distribution network with the objective of minimizing the total voltage deviation of the system within a preset period. The constraints of the optimized operation model include the linearized fully embedded power flow model, energy storage system operation constraints, static var compensator operation constraints, and node voltage safety constraints.

[0034] The distribution network optimization operation model constructed in this invention aims to reduce voltage deviation. Voltage deviation is an important indicator for measuring the reliability of power system operation, and it varies with changes in system operation mode and load size. Due to the integration of a large number of distributed power sources in the distribution system, the problem of voltage exceeding limits in the distribution network is becoming increasingly prominent. To ensure the safe and reliable operation of the distribution network, it is necessary to reduce the system voltage deviation. The mathematical expression for voltage deviation is as follows: (28) in, For nodes i exist t Voltage amplitude at a given time.

[0035] Energy storage system operation constraints (29) (30) (31) (32) in, for t The charging state variable is 1 when charging and 0 otherwise. for t The discharge state variable at any given time is 1 when discharging and 0 otherwise; For energy storage systems in t Output power at any moment and These are the discharge and charging power of the energy storage system, respectively. For energy storage systems int Battery level at any time and These are the charging and discharging efficiencies of the energy storage system, respectively. This represents the maximum charging and discharging power of the energy storage system. and These represent the minimum and maximum energy capacities of the energy storage system, respectively. Equation (29) specifies the energy capacity of the battery energy storage system during the specified time period. t The charging or discharging behavior within the battery energy storage system. The convex quadratic constraints related to the active and reactive power of the battery energy storage system are represented by equation (30). The energy state of the battery energy storage system is characterized by equation (31). The boundary conditions that the battery energy storage system must comply with during operation are given by equation (32).

[0036] Static var compensator operating constraints (33) in, For static var compensators in t Output power at any moment and The static var compensator is in t The minimum and maximum output power at any given time.

[0037] Node voltage constraints (34) in, Represents a node i The lower limit of the allowable operating voltage. Represents a node i The upper limit of the allowed operating voltage.

[0038] S5. Solve the multi-period optimization operation model of the distribution network to obtain the optimal plan for the charging and discharging power of the energy storage system and the reactive power output of the static var compensator in each period of the future scheduling cycle, and schedule the controllable equipment in the distribution network according to the optimal plan.

[0039] Based on the aforementioned distribution network optimization operation method based on a linearized fully embedded power flow model, this paper analyzes a radial 91-node distribution system in a rural area of ​​Northeast China, including residential daily load and agricultural load, to illustrate the effectiveness of the invention. A typical 24-hour dispatch cycle is used, with a dispatch interval of 1 hour. The total network load is (8485 + 4243) kVA. The network structure diagram is shown below. Figure 2 The specific load distribution is shown in Table 1, and the parameters of each unit are shown in Tables 2 to 4. The system base capacity is selected as 1MVA, the base voltage as 10kV, and the node voltage fluctuation range is [0.95, 1.05]pu.

[0040] Table 1 Load Distribution in Area 1

[0041] Table 2 Distributed Photovoltaic Parameters

[0042] Table 3 Energy Storage Unit Parameters

[0043] Table 4 Static Var Compensator Parameters

[0044] Typical intraday load fluctuation curves for various types of loads over a 24-hour period are as follows: Figure 3 As shown in the figure, the residential load mainly includes various household appliances used in daily life. From 0:00 to 7:00, the overall load level is low, as this is the rest time for most residents; from 8:00 to 9:00, residents begin to get up and use appliances such as lights and induction cookers, and the load gradually increases, then decreases slightly, and gradually stabilizes at noon and in the afternoon; from 17:00 to 20:00, residents gradually return home from get off work, and the load rises rapidly, reaching the peak of electricity consumption at 20:00; from 21:00 to 23:00, it decreases somewhat, but the overall load remains at a high level.

[0045] Agricultural load mainly includes electricity consumption for farmland irrigation, drainage, and greenhouse insulation, with peak electricity consumption concentrated in a relatively short period throughout the day. From 0:00 to 6:00, the load is at its lowest point; after 7:00, as farm work begins, the load rapidly increases to a higher level, reaching a stable peak between 10:00 and 16:00; from 17:00 to 23:00, as agricultural work gradually ceases, the load returns to a lower level.

[0046] A typical power output curve of a photovoltaic system over a 24-hour period is shown below. Figure 4 To illustrate the effectiveness of the proposed distribution network optimization operation model and method, the following two scenarios are set up.

[0047] Scenario 1: No energy storage system or reactive power compensation device is configured; only the original operating status of the power distribution system is reflected.

[0048] Scenario 2: Configure energy storage systems and reactive power compensation devices, and apply the power distribution network optimization operation method proposed in this paper to achieve the optimal solution for the power distribution system.

[0049] The total time-series voltage deviation of the system is selected as an indicator parameter to measure the operation level of the distribution network. The operation results under different scenarios are shown in Table 5. The operation results of the proposed method and the traditional second-order cone method in scenario 2 are compared in Table 6.

[0050] Table 5 Comparison of voltage deviations between the two scenarios

[0051] Table 6 Comparison of the results of the two methods

[0052] As shown in Table 5, the system voltage deviation in Scenario 1 is 48.925 pu, reflecting the voltage instability of the original power distribution system. Through the optimized configuration in Scenario 2, the voltage deviation is significantly improved to 2.78 pu, a reduction of 94.3%. This result demonstrates that the proposed optimization method can effectively improve voltage stability, thereby significantly improving the overall operating efficiency and safety of the power distribution network. The comparison of operating results shown in Table 6 shows that the proposed method achieves similar calculation results to the traditional second-order cone method, but the calculation time is reduced from 18.04 seconds to 6.98 seconds, representing 38.7% of the calculation time of the second-order cone method, indicating a significant improvement in computational efficiency.

[0053] The voltage levels of node 36 under different scenarios were compared. Figure 5 The voltage fluctuation curves of node 36 are presented under two scenarios. In scenario 1, the node voltage exhibits significant fluctuations. At midday, influenced by high distributed photovoltaic output, the node voltage shows a clear tendency to exceed limits. In the evening, due to high residential load, the node voltage drops sharply, affecting the power quality of the distribution network. This indicates that the original system is significantly affected by load changes or distributed power output. In scenario 2, through optimized scheduling of energy storage and reactive power compensation devices, the voltage fluctuation amplitude is significantly reduced, and the problems of voltage exceeding limits and voltage drops are significantly improved, with the curve becoming more stable. This result verifies the effectiveness of the proposed model in reducing voltage deviation and improving power quality, contributing to ensuring the reliability of power supply on the user side.

[0054] Figure 6 The power interaction between the distribution network and the upstream grid was compared in two scenarios. In Scenario 1, the power interaction curve fluctuated wildly, indicating that the original system frequently relied on the upstream grid to balance power deficits or surpluses. In Scenario 2, through the charging and discharging regulation of the energy storage system and the dynamic support of the reactive power compensation device, the power interaction curve was significantly smoothed, the dependence of the distribution network on the upstream grid was reduced, and the autonomous operation capability of the distribution network was significantly improved, which is in line with the development needs of future smart grids.

[0055] It should be emphasized that the embodiments described in this invention are illustrative and not limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementation methods derived by those skilled in the art based on the technical solutions of this invention also fall within the scope of protection of this invention.

Claims

1. A distribution network optimization operation method based on a linearized fully embedded power flow model, characterized in that: Includes the following steps: S1. Obtain the topology parameters, line parameters, and an initial feasible power flow solution of the distribution network as the baseline operating state; S2. Based on the fully embedded power flow theory, construct the power series expansion of the node voltage with respect to the complex embedded parameters, and derive the recursive relationship of its power series coefficients. S3. Perform first-order linearization on the recursive relationship, ignore higher-order power series terms, and establish a linearized fully embedded power flow model. The model represents the node voltage after the system operating state changes as a linear superposition of the initial node voltage and the first-order power series coefficients corresponding to the power injection increment of each node. S4. Based on the linearized fully embedded power flow model, construct a multi-period optimized operation model for the distribution network with the goal of minimizing the total voltage deviation of the system within a preset period. The constraints of the optimized operation model include the linearized fully embedded power flow model, energy storage system operation constraints, static var compensator operation constraints, and node voltage safety constraints. S5. Solve the multi-period optimization operation model of the distribution network to obtain the optimal plan for the charging and discharging power of the energy storage system and the reactive power output of the static var compensator in each period of the future scheduling cycle, and schedule the controllable equipment in the distribution network according to the optimal plan.

2. The distribution network optimization operation method based on a linearized fully embedded power flow model according to claim 1, characterized in that: The fully embedded power flow model in S3 is as follows: ； in, For nodes i voltage, Nodes before changes in system operating state i voltage, For nodes after changes in system operating state i voltage, and These are the constant term and first-order coefficients of the voltage holomorphic function, respectively. k Represents the first in the system k 1 node For the first k Active power injection increment coefficient of each node For the first k The reactive power injection increment coefficient of each node, Inject incremental baseline values ​​into the active power of nodes. For the first k Increase active power injection at each node At that time, node i The voltage first-order power series coefficients, Inject incremental baseline values ​​for node reactive power. For the first k The reactive power injection at each node has increased. At that time, node i The voltage first-order power series coefficients.

3. The distribution network optimization operation method based on a linearized fully embedded power flow model according to claim 1, characterized in that: The objective function in S4 is to minimize the total time-series voltage deviation of the system. ； in, N The number of nodes in the network. T The time of one scheduling cycle, For nodes i exist t Voltage amplitude at a given time.

4. The distribution network optimization operation method based on a linearized fully embedded power flow model according to claim 1, characterized in that: The constraints in S4 include: energy storage system operation constraints, mutual exclusion constraints of charge and discharge states, secondary constraints of charge and discharge power and apparent power, dynamic update constraints of state of charge, and upper and lower limits constraints of power and capacity.

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