Optimization control method and system containing distributed photovoltaic power distribution network

Abstract

The invention discloses an optimal control method and system for a distributed photovoltaic power distribution network. The method comprises the following steps: acquiring data information of the distributed photovoltaic power distribution network; taking the minimum voltage deviation and the minimum line loss as targets, combining the data information, and carrying out normalization processing on the voltage deviation and the line loss to obtain an optimized target function; establishing a target constraint condition, and solving the optimization target function by using a particle swarm optimization algorithm under the condition that the target constraint condition is met so as to determine a control parameter value of the UPFC; and adjusting the UPFC controller in real time according to the control parameter value so as to realize the optimal operation of the distributed photovoltaic power distribution network. According to the method, a unified power flow controller (UPFC) is introduced to control power flow, voltage deviation and line loss are optimized with the help of a particle swarm optimization algorithm, and finally the purposes of voltage stabilization and line loss minimization of the power distribution network are achieved, so that the stability of the power grid is ensured, and meanwhile energy loss caused by current flowing is reduced.

Description

Technical Field

[0001] This invention relates to power distribution network optimization control technology, specifically to an optimization control method and system for a power distribution network including distributed photovoltaic power. Background Technology

[0002] With the large-scale integration of distributed photovoltaic (PV) and other renewable energy sources, voltage fluctuations and line losses in distribution networks are becoming increasingly serious. Due to the reduction in active power output from distributed power sources and the decrease in power transmitted along lines after PV grid connection, the voltage at each load node increases. When the PV capacity is large, especially when PV is connected to the end of the feeder, the voltage at some nodes may exceed permissible values. This often leads to increased voltage deviation in the distribution network, thereby affecting grid stability.

[0003] Furthermore, the output power of distributed photovoltaic (PV) power generation is affected by factors such as solar radiation intensity, climate change, and time, exhibiting volatility and instability. This volatility, especially during periods of low PV power generation, leads to the distribution network needing to draw more power from the main grid, increasing power flow to the lines and resulting in additional line losses and energy waste. Therefore, effectively optimizing distribution network voltage and line losses to improve its stability and operational efficiency has become a crucial issue in power system optimization and dispatch. Summary of the Invention

[0004] (I) Purpose of the Invention

[0005] The purpose of this invention is to provide an optimized control method and system for distribution networks containing distributed photovoltaic power. This method controls power flow by introducing a unified power flow controller (UPFC), optimizes voltage deviation and line loss by using a particle swarm optimization algorithm, and ultimately achieves the goal of stable distribution network voltage and minimized line loss. It is also applicable to the optimized scheduling of distribution networks with distributed energy sources such as photovoltaic power.

[0006] (II) Technical Solution

[0007] To address the aforementioned problems, a first aspect of the present invention provides an optimized control method for a distribution network containing distributed photovoltaic power, wherein the method controls the distribution network through a UPFC power flow controller, comprising:

[0008] Collect data information from distributed photovoltaic power distribution networks;

[0009] With the objectives of minimizing voltage deviation and minimizing line loss, and combining the aforementioned data information, the voltage deviation and line loss are normalized to obtain the optimized objective function;

[0010] Establish target constraints, and solve the optimization objective function using the particle swarm optimization algorithm under the target constraints to determine the control parameter values ​​of the UPFC power flow controller; wherein the target constraints include power flow constraints and UPFC power flow controller constraints.

[0011] The UPFC controller is adjusted in real time based on the control parameter values.

[0012] Furthermore, the calculation formula for the optimization objective function is as follows:

[0013]

[0014] In the formula, f is the normalized single-objective function; f u It represents the voltage deviation after distributed photovoltaic (PV) power is connected to the distribution network; n is the number of nodes in the distributed PV distribution network; f loss This refers to the line losses after distributed photovoltaic power is connected to the distribution network; f loss ' is the line loss before distributed photovoltaic power is connected to the distribution network, d1 and d2 are the first weight factor and the second weight factor, respectively, and d1+d2=1, p and q are the first penalty factor and the second penalty factor, respectively.

[0015] Furthermore, if f u If n≥1, then let p take the larger value between the first penalty factor and the second penalty factor;

[0016] If f u If n < 1, then let p = 1;

[0017] If f loss / f loss If '≥1, then let q take the larger value between the first penalty factor and the second penalty factor;

[0018] If f loss / f loss If '<1, then let q=1.

[0019] Furthermore, the formula for calculating the voltage deviation is as follows:

[0020]

[0021] In the formula, U imin U is the minimum voltage at node i. imax U is the maximum voltage at node i. i Let U be the node voltage at node i. ref This is the node reference voltage.

[0022] Furthermore, the formula for calculating the line loss is as follows:

[0023]

[0024] In the formula: t is the number of branches in the distribution network; I k R is the current value of the k-th branch. k Let be the resistance value of the k-th branch.

[0025] Furthermore, the power flow constraints include: power flow constraints, node voltage constraints, branch current constraints, total installed capacity constraints of distributed photovoltaics and installed capacity constraints of distributed photovoltaic nodes, and the UPFC power flow controller constraints include network power flow constraints, distribution network operating condition constraints and UPFC control parameter value constraints.

[0026] Furthermore, the UPFC control parameter values ​​include V s θ s I q V s θ represents the amplitude of the voltage source on the series side of the UPFC. s For the UPFC series side phase angle, I q This refers to the reactive component of the current source on the parallel side of the UPFC.

[0027] Furthermore, the power flow constraints include active power balance constraints and reactive power balance constraints.

[0028] Furthermore, a second aspect of the present invention provides an optimized control system for a distributed photovoltaic distribution network, wherein the system utilizes a UPFC power flow controller to control the distribution network, comprising:

[0029] The data acquisition module is used to collect data information from the distributed photovoltaic power distribution network.

[0030] An objective function module is constructed to obtain an optimized objective function by normalizing the voltage deviation and line loss, taking the minimum voltage deviation and minimum line loss as the objectives, and combining the data information.

[0031] The solution module is used to establish target constraints and, under the condition that the target constraints are met, to solve the optimization objective function using the particle swarm optimization algorithm to determine the control parameter values ​​of the UPFC power flow controller; wherein the target constraints include power flow constraints and UPFC power flow controller constraints.

[0032] The adjustment module is used to adjust the UPFC controller in real time according to the control parameter values.

[0033] (III) Beneficial Effects

[0034] The above-mentioned technical solution of the present invention has the following beneficial technical effects: The present invention proposes an optimized control method and system for a distributed photovoltaic (PV) distribution network. This method introduces a Unified Power Flow Controller (UPFC) into the distributed PV distribution network, precisely regulates voltage and line losses by adjusting the control parameters of the UPFC, and combines a particle swarm optimization (PSO) algorithm to optimize the voltage deviation and line losses of the power grid. An optimization objective function is constructed, with the optimization objectives including minimizing voltage deviation and minimizing line losses. Under the condition of satisfying the objective constraints, the PSO algorithm is used to solve the optimization objective function to determine the control parameter values ​​of the UPFC power flow controller. The UPFC optimizes the power transmission path of the power grid by monitoring and adjusting the power flow direction in real time, ensuring grid stability while reducing energy losses caused by current flow. By monitoring and adjusting the voltage level of the power grid in real time, it ensures the voltage stability of each node in the distribution network, avoiding excessively high or low voltage conditions. The present invention has strong adaptability, automatically adjusting the control strategy according to the volatility of PV power generation and the dynamic changes in the distribution network load. It can flexibly respond to real-time data and adjust the regulation parameters of the unified power flow controller in a timely manner, ensuring that the power grid always maintains its optimal state under different operating conditions. Attached Figure Description

[0035] Figure 1 This is a flowchart of the optimized control method for a distributed photovoltaic power distribution network according to the present invention;

[0036] Figure 2 This is a schematic diagram of the optimized control system for distributed photovoltaic power distribution networks of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0038] like Figure 1 As shown, a first aspect of the present invention provides an optimized control method for a distribution network containing distributed photovoltaic power, the method controlling the distribution network through a UPFC power flow controller, comprising:

[0039] S1 collects data information from the distributed photovoltaic power distribution network;

[0040] S2, with the objectives of minimizing voltage deviation and minimizing line loss, and combining the aforementioned data information, the voltage deviation and line loss are normalized to obtain the optimization objective function. Where, f uThis refers to the voltage deviation after distributed photovoltaic (PV) power is connected to the distribution network. The corresponding function can measure this voltage deviation. When the minimum voltage deviation is the optimization objective, f... u The calculation formula is as follows:

[0041]

[0042] In equation (1), U imin U is the minimum voltage at node i. imax U is the maximum voltage at node i. i Let U be the node voltage at node i. ref U is the node reference voltage. This formula, by summing the absolute values ​​of the ratios of the difference between the node voltage and the reference voltage to the difference between the node's maximum and minimum allowable voltages, can effectively measure the degree of deviation in node voltage. Where: U ref The value is set to 1.0 pu, which conforms to the requirements of "Power Quality Supply Voltage Deviation (GB / T12325-2008)". imax U imin The voltage level can be set visually, for example, to 0.93pu and 1.07pu respectively.

[0043] f loss This refers to the line loss after distributed photovoltaic (PV) power is connected to the distribution network. Its corresponding function aims to minimize the line loss rate in order to reduce line losses and improve the energy efficiency of the power grid. The calculation formula is as follows:

[0044]

[0045] In equation (2): t is the number of branches in the distribution network, I k R is the current value of the k-th branch. k Let be the resistance value of the k-th branch.

[0046] Multi-objective normalization can enhance the correlation of multi-objective functions, enabling all objective functions to reach relative optimality. Furthermore, f... u and f loss Since the units of measurement are different, a weighting coefficient method will be used for normalization. The expression for the optimized objective function (overall objective function) is as follows:

[0047]

[0048] In equation (3), f is the normalized single-objective function; u It represents the voltage deviation after distributed photovoltaic (PV) power is connected to the distribution network; n is the number of nodes in the distributed PV distribution network; f loss This refers to the line losses after distributed photovoltaic power is connected to the distribution network; f loss' represents the line loss before distributed photovoltaic power is connected to the distribution network. d1 and d2 are the first and second weighting factors, respectively, and d1 + d2 = 1. d1 and d2 can be determined based on the actual requirements of the distribution network for voltage deviation and line loss; for example, d1 = 0.6 and d2 = 0.4. p and q are the first and second penalty factors, respectively. These two penalty factors can delete configurations with large deviations during the optimization process without affecting the optimization result. The rules for the values ​​of p and q in this optimization objective function are as follows:

[0049] A. If f u If n≥1, then let p take the larger value between the first penalty factor and the second penalty factor;

[0050] B. If f u If n < 1, then let p = 1;

[0051] C. If f loss / f loss If '≥1, then let q take the larger value between the first penalty factor and the second penalty factor;

[0052] D. If f loss / f loss If '<1, then let q=1.

[0053] S3. Establish target constraints. Under these constraints, solve the objective function using a particle swarm optimization algorithm to determine the control parameter values ​​of the UPFC power flow controller. The target constraints include power flow constraints and UPFC power flow controller constraints. The power flow constraints include: power flow constraints, node voltage constraints, branch current constraints, total distributed photovoltaic (PV) installed capacity constraints, and distributed PV node installed capacity constraints. The UPFC power flow controller constraints include network power flow constraints, distribution network operating condition constraints, and UPFC control parameter value constraints. The UPFC control parameter values ​​include V... s θ s I q V s θ represents the amplitude of the voltage source on the series side of the UPFC. s For the UPFC series side phase angle, I q For the reactive component of the current source on the parallel side of the UPFC, the constraints are set as follows:

[0054] Power flow constraints include:

[0055] 1. Power Flow Constraints: Power flow constraints in distributed photovoltaic (PV) distribution networks include active power balance constraints and reactive power balance constraints. The calculation formulas for active power balance constraints are as follows:

[0056]

[0057] P i =P Gi +P DGi -P Li (5)

[0058] In formulas (4) and (5): P i P represents the total active power at node i; Gi P represents the active power injected into the generator at node i. DGi P represents the output active power at node i of the distributed photovoltaic system. Li G represents the active power consumed by the load at node i. ij B ij Let θ represent the conductance and susceptance of the branch between node i and node j, respectively; ij U represents the phase difference of the voltage between node i and node j. j Let be the voltage at node j. The reactive power balance constraints are as follows:

[0059]

[0060] Q i =Q Gi +Q DGi -Q Li (7)

[0061] In formulas (6) and (7): Q i Let Q be the total reactive power at node i. Gi Inject reactive power Q into the generator at node i. DGi Q represents the output reactive power at distributed photovoltaic node i. Li This represents the reactive power consumed by the load at node i.

[0062] 2. The node voltage constraint conditions are set as follows:

[0063] U imin ≤U i ≤U imax (8)

[0064] In equation (8): U i U is the node voltage at node i; imin U represents the minimum voltage at node i; imax U is the maximum voltage at node i; according to the "Power Quality Supply Voltage Deviation (GB / T12325-2008)" standard, U imin U imax The visible voltage level can be set to 0.93 pu and 1.07 pu respectively.

[0065] 3. Branch current constraint: Due to the limited cross-sectional area of ​​the line, in order to reduce heat generation and ensure safe power use, it is necessary to limit its current carrying capacity.

[0066] I k ≤I kmax (9)

[0067] In equation (9): I k I represents the current in branch k between node i and node j; kmax This represents the maximum value of the k-branch current between node i and node j.

[0068] 4. The total capacity constraints for distributed photovoltaic installations are set as follows:

[0069] 0≤S PV ≤S PV,max (10)

[0070] In equation (10): S PV The total capacity of distributed photovoltaic power generation connected to the distribution network; S PV，max This refers to the upper limit of the capacity for distributed photovoltaic power to be connected to the distribution network.

[0071] 5. Capacity constraints of distributed photovoltaic nodes

[0072] 0≤S PVi ≤S PVi,max (11)

[0073] In equation (11): S PVi S represents the grid connection capacity of distributed photovoltaic power at node i in the distribution network; PVi,max This represents the maximum capacity of distributed photovoltaic (PV) grid connection at node i in the distribution network.

[0074] The constraints of the UPFC power flow controller include:

[0075] 6. The formula for network power flow constraints is as follows:

[0076]

[0077] In equation (12): m is the node number at both ends of the branch where UPFC is located; ΔP m-upfc ΔQ m-upfc P represents the active and reactive power injected by the UPFC to the two end nodes. Gm P is the active power injected into the generator at node m. Li U represents the active power consumed by the load at node m. m For the voltage at node number m, G mj B mj The conductance and susceptance injected at the two end nodes are Q, respectively. Gm Q represents the reactive power injected into the generator at node m.Lm This represents the reactive power consumed by the load at node m.

[0078] 7. Constraints on distribution network operation conditions (constraints on network security operation conditions):

[0079] V imin ≤V i ≤V imax (13)

[0080]

[0081] In equation (13): V imin V imax These are the upper and lower limits of the voltage at node i, respectively, V i I is the voltage amplitude at node i; in equation (14) lmax I is the upper limit of the current flowing through the line. l For the current flowing through the line, P ij Q represents the active power transmitted between node i and node j. ij This represents the reactive power transmitted between node i and node j.

[0082] 8. UPFC Control Parameter Constraints (UPFC Variable Constraints): The UPFC structure can be divided into a series section, a DC section, and a parallel section. The series section functions like a Static Synchronous Series Compensator (SSSC), and the parallel section can be considered a Static Synchronous Compensator (SVG). They are connected via the DC side, allowing active power to flow freely in either direction at the AC terminals of both the series and parallel sections, and enabling independent generation or absorption of reactive power at their AC output terminals. UPFC control parameters: V s θ s I q V s and θ s The amplitude and phase angle of the series voltage source of the UPFC; I q For the reactive component of the current source on the parallel side, the following constraints are set:

[0083]

[0084] In equation (15): V s and θ s The amplitude and phase angle of the series voltage source of the UPFC; I q This represents the reactive component of the current source on the parallel side.

[0085] Furthermore, in step S3, solving the objective function using the particle swarm optimization algorithm includes:

[0086] S31, Initialize the particle swarm parameters for the particle swarm optimization algorithm;

[0087] S32, Initialize the position and velocity of each particle in the particle swarm optimization algorithm;

[0088] S33, based on whether the position and speed synchronously meet the minimum voltage deviation and minimum line loss, determine whether to update and output the optimal solution of the control parameter values, including:

[0089] S331, if the position and speed synchronization satisfies the minimum voltage deviation and minimum line loss, then calculate and output the optimal solution of the control parameter values ​​of the UPFC power flow controller;

[0090] S332, if the position and speed cannot be synchronized to meet the minimum voltage deviation and minimum line loss, then the following steps are performed:

[0091] (1) Update the velocity and position of each particle and calculate the fitness value of each particle;

[0092] (2) Update the individual historical best fitness value and historical best position of each particle;

[0093] (3) Update the historical best fitness value and historical best position of the particle swarm, and update the remaining preset parameters; repeat this process until the position and velocity of each updated particle can synchronously meet the minimum voltage deviation and minimum line loss.

[0094] S4. Adjust the UPFC controller in real time according to the control parameter values ​​to achieve optimal operation of the distributed photovoltaic power distribution network.

[0095] In addition, such as Figure 2 As shown, a second aspect of the present invention provides an optimized control system for a distributed photovoltaic power distribution network, the system utilizing a UPFC power flow controller to control the power distribution network, comprising:

[0096] Data acquisition module 21 is used to collect data information from distributed photovoltaic power distribution networks;

[0097] The objective function module 22 is used to obtain the optimized objective function by normalizing the voltage deviation and line loss with the minimum voltage deviation and minimum line loss, in combination with the data information.

[0098] Solver module 23 is used to establish target constraints and solve the optimization objective function using particle swarm optimization algorithm under the target constraints to determine the control parameter values ​​of the UPFC power flow controller; wherein the target constraints include power flow constraints and UPFC power flow controller constraints.

[0099] The adjustment module 24 is used to adjust the UPFC controller in real time according to the control parameter values ​​to achieve the optimal operation of the distributed photovoltaic power distribution network.

[0100] The present invention will now be described in detail with reference to specific embodiments, and the steps are as follows:

[0101] I. Data Collection and Preparation: The data collected from the distributed photovoltaic power distribution network includes the following:

[0102] 1.1 Network topology data: Collect structural information of the distribution network, including each node and its connection relationship, the connectivity and length of branches, etc.

[0103] 1.2 Electrical Equipment and Line Parameters: Obtain parameters of various electrical equipment (such as transformers, cables, switches, etc.) in the power distribution network, such as rated power, resistance, reactance and rated voltage.

[0104] 1.3 Unified Power Flow Controller (UPFC) Parameters: Collect basic parameters of the UPFC, including its maximum control capability, voltage regulation range, phase angle regulation range, etc.

[0105] 1.4 Photovoltaic power generation parameters: Obtain the power output characteristics of each photovoltaic power generation unit, including solar irradiance, equipment capacity, inverter characteristics, etc.

[0106] 1.5 Node voltage and branch current data: The voltage value of each node, the current value of the branch, and the power flow direction are obtained through monitoring equipment.

[0107] II. Establishing the Optimization Objective Function and Constraints

[0108] Voltage deviation minimization: One of the goals is to reduce voltage fluctuations by adjusting the UPFC parameters to make the voltage of each node as close as possible to its ideal value.

[0109] Minimize network losses: By adjusting parameters such as phase angle, voltage, and current of the UPFC, power losses in the distribution network can be reduced, thereby improving the efficiency of power utilization.

[0110] Power flow constraints and UPFC control constraints: Power flow calculations need to satisfy power balance and voltage and current limits of each node in the network.

[0111] III. Solving Particle Swarm Optimization (PSO) Algorithm

[0112] To optimize grid voltage and line losses, a particle swarm optimization (PSO) algorithm is used to regulate the power grid system. Specifically, the PSO algorithm solves nonlinear optimization problems by including the following steps:

[0113] The steps of the particle swarm optimization algorithm:

[0114] Step 1. Initialization: Particle swarm size N = 30: This represents the number of particles in the swarm; each particle represents a solution. Particle dimension D = 3: Each particle consists of three parameters, namely V... s θ s I q The maximum number of iterations T = 100 is set for optimization iterations. Inertia weight ω, individual learning factor c1, and swarm learning factor c2 are set; these parameters help the particle swarm search for the optimal solution in the space.

[0115] Step 2. Objective Function: The objective is to minimize voltage deviation and line loss. This is achieved by adjusting the UPFC controller parameters Vs, θs, and Iq using particle swarm optimization. The fitness (objective function value) of each particle is calculated based on the following factors: f U It is the voltage deviation, f loss This refers to line losses. The objective function is calculated using these values ​​and weighting factors d1 and d2. If voltage deviation and line losses exceed thresholds, penalty factors p and q are applied to further optimize the solution.

[0116] Step 3. Update Particle Position and Velocity: Each particle updates its velocity and position in each iteration. The particle's velocity is determined by its current velocity, the individual optimal solution, and the swarm optimal solution. By adjusting the particle's position, the algorithm searches for the optimal UPFC controller parameters in the search space. Each parameter of the particle (i.e., V...) s θ s I q All adjustments will be made within the constraints to ensure that the controller parameters do not exceed physical limits.

[0117] AV s In V min and V max between.

[0118] B.θ s Between [-π, π].

[0119] CI q in I qmin and I qmax between.

[0120] Step 4. Update individual and group optimal solutions: Each particle updates its individual optimal solution P based on its fitness value. best Meanwhile, the group optimal solution G best It will also be updated accordingly. The goal of each particle is to find the solution that minimizes the objective function value.

[0121] Step 5. Final Solution: After all iterations are completed, the population optimal solution G is found. best It includes the optimal controller parameters, namely: V sθ s I q .

[0122] This algorithm can take into account multiple factors such as voltage, current load, and photovoltaic output power at each node of the distribution network, and optimize power flow by globally searching for the optimal solution.

[0123] IV. Output UPFC control parameters

[0124] The optimal solution obtained through the particle swarm optimization algorithm will yield the control parameters of the unified power flow controller: V s θ s I q .

[0125] This invention provides an optimized control method and system for a distribution network containing distributed photovoltaic (PV) power. The method uses a Unified Power Flow Controller (UPFC) to precisely regulate the voltage and power flow of the distribution network, effectively reducing line losses during power transmission. The UPFC optimizes the power transmission path of the grid by monitoring and adjusting the power flow direction in real time, ensuring grid stability while reducing energy losses caused by current flow. It also ensures voltage stability at each node in the distribution network by monitoring and adjusting the grid voltage level in real time, avoiding excessively high or low voltage conditions. This invention has strong adaptability, automatically adjusting the control strategy according to the volatility of PV power generation and the dynamic changes in the distribution network load. It can flexibly respond to real-time data and adjust the regulation parameters of the UPFC in a timely manner, ensuring that the grid remains in optimal condition under different operating conditions. This invention can be applied to urban distribution networks with distributed PV, rural power grid PV integration, and smart grid construction. This invention ensures that the grid can efficiently carry more renewable energy. Through intelligent control and real-time optimization technology, it can effectively coordinate the operation of PV power generation and the grid, providing a feasible path for PV integration into rural power grids, improving the power generation efficiency of PV systems and the overall stability of the grid. This invention optimizes power flow and voltage regulation in the power grid, thereby improving the utilization efficiency of electrical resources and reducing energy loss caused by power transmission. This technology provides an advanced solution for the construction of smart grids, helping to enhance the grid's self-healing capabilities, dispatching capabilities, and energy utilization efficiency.

[0126] It should be understood that the specific embodiments described above are merely illustrative of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, or improvements made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes the flows of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The steps in the methods of the embodiments of the present invention can be adjusted, merged, or deleted according to actual needs. The modules in the system of the embodiments of the present invention can be merged, divided, or deleted according to actual needs.

Claims

1. An optimized control method for a distribution network containing distributed photovoltaic power, wherein the method controls the distribution network through a UPFC power flow controller, characterized in that, include: Collect data information from distributed photovoltaic power distribution networks; With the objectives of minimizing voltage deviation and minimizing line loss, and combining the aforementioned data information, the voltage deviation and line loss are normalized to obtain the optimized objective function; Establish target constraints, and solve the optimization objective function using the particle swarm optimization algorithm under the target constraints to determine the control parameter values ​​of the UPFC power flow controller; wherein the target constraints include power flow constraints and UPFC power flow controller constraints. The UPFC controller is adjusted in real time based on the control parameter values.

2. The optimized control method for a distributed photovoltaic power distribution network according to claim 1, characterized in that, The formula for calculating the objective function is as follows: In the formula, f is the normalized single-objective function; f u It represents the voltage deviation after distributed photovoltaic (PV) power is connected to the distribution network; n is the number of nodes in the distributed PV distribution network; f loss This refers to the line losses after distributed photovoltaic power is connected to the distribution network; f loss ' is the line loss before distributed photovoltaic power is connected to the distribution network, d1 and d2 are the first weight factor and the second weight factor, respectively, and d1+d2=1, p and q are the first penalty factor and the second penalty factor, respectively.

3. The optimized control method for a distributed photovoltaic power distribution network according to claim 2, characterized in that, If f u If n≥1, then let p take the larger value between the first penalty factor and the second penalty factor; If f u If n < 1, then let p = 1; If f loss / f loss If '≥1, then let q take the larger value between the first penalty factor and the second penalty factor; If f loss / f loss If '<1, then let q=1.

4. The optimized control method for a distributed photovoltaic power distribution network according to claim 2, characterized in that, The formula for calculating the voltage deviation is as follows: In the formula, U imin U is the minimum voltage at node i. imax U is the maximum voltage at node i. i Let U be the node voltage at node i. ref This is the node reference voltage.

5. The optimized control method for a distributed photovoltaic power distribution network according to claim 2, characterized in that, The formula for calculating the line loss is as follows: In the formula: t is the number of branches in the distribution network; I k R is the current value of the k-th branch. k Let be the resistance value of the k-th branch.

6. The optimized control method for a distributed photovoltaic power distribution network according to claim 1, characterized in that, The power flow constraints include: power flow constraints, node voltage constraints, branch current constraints, total installed capacity constraints of distributed photovoltaic (PV) systems, and installed capacity constraints of distributed PV nodes.

7. The optimized control method for a distributed photovoltaic power distribution network according to claim 1, characterized in that, The constraints of the UPFC power flow controller include network power flow constraints, distribution network operating condition constraints, and UPFC control parameter value constraints.

8. The optimized control method for a distributed photovoltaic power distribution network according to claim 1, characterized in that, UPFC control parameter values ​​include V s θ s I q V s θ represents the amplitude of the voltage source on the series side of the UPFC. s For the UPFC series side phase angle, I q This refers to the reactive component of the current source on the parallel side of the UPFC.

9. The optimized control method for a distributed photovoltaic power distribution network according to claim 1, characterized in that, The power flow constraints include active power balance constraints and reactive power balance constraints.

10. An optimized control system for a distributed photovoltaic distribution network, wherein the system utilizes a UPFC power flow controller to control the distribution network, characterized in that, include: The data acquisition module is used to collect data information from the distributed photovoltaic power distribution network. An objective function module is constructed to obtain an optimized objective function by normalizing the voltage deviation and line loss, taking the minimum voltage deviation and minimum line loss as the objectives, and combining the data information. The solution module is used to establish target constraints and, under the condition that the target constraints are met, to solve the optimization objective function using the particle swarm optimization algorithm to determine the control parameter values ​​of the UPFC power flow controller; wherein the target constraints include power flow constraints and UPFC power flow controller constraints. The adjustment module is used to adjust the UPFC controller in real time according to the control parameter values.

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