Parameter optimization method and system for flexible interconnection phase shift device

Abstract

The invention discloses a parameter optimization method and system for a flexible interconnection phase shifter, and is applied to the technical field of flexible interconnection phase shifters, and the method comprises the steps: obtaining the power data of a current medium-voltage power distribution network; establishing a multi-objective double-layer optimization model by taking internet feeder power flow balance and network loss as optimization objectives; obtaining a target result output by the multi-target double-layer optimization model, wherein the upper-layer optimization model and the lower-layer optimization model are both designed to process randomly generated initial parameters through a probability agent model and a TPE strategy in sequence; and processing the target result based on the Pareto frontier solution set to obtain a parameter optimization decision result. According to the parameter optimization method and system of the flexible interconnection phase-shifting device, a multi-objective optimization objective function considering improvement of medium-voltage power distribution network feeder power flow balance and network loss is proposed, and a multi-objective optimization objective function considering parameters of the flexible interconnection phase-shifting device is constructed based on the Bayesian optimization algorithm in combination with multi-objective optimization. The invention relates to a Bayesian nested double-layer optimization solution algorithm oriented to installation position optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible interconnected phase shifters, and in particular to a method and system for parameter optimization of a flexible interconnected phase shifting device. Background Art

[0002] As the power grid plays an increasingly important role as a power energy carrier platform, the characteristics of active distribution network and source-load interaction on the distribution network side have become increasingly obvious. The rapid development of distributed generation (DG) and electric vehicles (EV) has made the distribution network show the characteristics of high volatility of both source and load. The high penetration of DG and source-load fluctuations in local areas have caused or exacerbated a series of problems such as difficult DG accommodation, voltage over-limit, and reduced operation efficiency in the distribution network.

[0003] The traditional medium and low voltage AC distribution network with "closed-loop design and open-loop operation" can no longer meet the development needs of the distribution network source and load under the new situation. The flexible interconnected phase shifting device based on power electronic power devices can realize the flexible interconnection of the distribution network, and then effectively improve the regulation ability of the node voltage and branch power of the distribution network. With a small amount of investment and less on-site transformation, it can fully release the power supply capacity of the existing distribution network, efficiently utilize the redundant capacity of the distribution network, and realize functions such as power mutual assistance between distribution networks in different areas and different substations, accurate power flow control, line loss reduction, and power quality improvement.

[0004] However, the existing parameter regulation method for flexible interconnected phase shifters analyzes all parameters one by one under the condition of a single optimization target, with insufficient algorithm efficiency and poor dynamic adaptability, seriously affecting the reasonable regulation of the medium and low voltage AC distribution network. Summary of the Invention

[0005] The present invention provides a method and system for parameter optimization of a flexible interconnected phase shifting device, which designs a two-layer model based on a dual optimization target. The upper layer model and the lower layer model optimize and feedback each other, and reasonably select parameters for subsequent power flow calculation, thus solving the problem of low algorithm efficiency caused by calculating all parameters, and being beneficial to the reasonable regulation of the medium and low voltage AC distribution network introduced with a flexible interconnected phase shifting device.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for parameter optimization of a flexible interconnected phase shifting device, including: Obtain the power data of the current medium voltage distribution network; According to the power data, construct a multi-objective two-layer optimization model with mutual feedback between the upper layer optimization model and the lower layer optimization model, with the balance of feeder power flow and network loss of the interconnected network as the optimization target. The upper layer optimization model is configured to use the position parameters and control parameters of the flexible interconnected phase shifting device as decision variables, and the lower layer optimization model is configured to optimize the control parameters with the selected position parameters of the flexible interconnected phase shifting device. Obtain the target result output by the multi-objective two-layer optimization model, where both the upper-layer optimization model and the lower-layer optimization model are designed to sequentially process randomly generated initial parameters through a probabilistic surrogate model and the TPE strategy, and perform power flow optimization calculations with the processing results. The initial parameters include the position parameters and control parameters of the flexible interconnected phase-shifting device; Process the target result based on the Pareto front solution set to obtain the parameter optimization decision result of the flexible interconnected phase-shifting device in the current medium-voltage distribution network.

[0007] As one preferred solution, the power data at least includes balance node information data, active node information data, reactive node information data, and network element information data.

[0008] As one preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to construct the probabilistic surrogate model by the Gaussian process of Bayesian optimization to process the randomly generated initial parameters.

[0009] As one preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to obtain the objective function corresponding to the current network state through power flow calculation, and use the objective function as the target result. The process of the power flow calculation includes: Under the condition of the currently randomly generated initial parameters, process the topology structure information, component parameter information, power generation information, and load parameter information of the obtained current medium-voltage distribution network based on the selected power flow calculation method to obtain the steady-state operation state parameters of the current medium-voltage distribution network.

[0010] As one preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to construct a sampling function by the TPE strategy, and at least obtain the maximum improvement observation value based on the sampling function.

[0011] Another embodiment of the present invention provides a parameter optimization system for a flexible interconnected phase-shifting device, including: An acquisition module, configured to acquire the power data of the current medium-voltage distribution network; A construction module, configured to construct a multi-objective two-layer optimization model with mutual feedback between the upper-layer optimization model and the lower-layer optimization model with the power flow balance of the interconnected network feeder and network loss as the optimization objectives. The upper-layer optimization model is configured to use the position parameters and control parameters of the flexible interconnected phase-shifting device as decision variables, and the lower-layer optimization model is configured to optimize the control parameters with the selected position parameters of the flexible interconnected phase-shifting device; An output module, configured to obtain a target result output by the multi-objective two-layer optimization model, wherein both the upper-layer optimization model and the lower-layer optimization model are designed to sequentially process randomly generated initial parameters through a probabilistic surrogate model and a TPE strategy, and perform power flow optimization calculations with the processing results, and the initial parameters include the position parameters and control parameters of the flexible interconnected phase-shifting device; A decision-making module, configured to process the target result based on the Pareto front solution set to obtain a parameter optimization decision result of the flexible interconnected phase-shifting device in the current medium-voltage distribution network.

[0012] As a preferred solution, the power data at least includes balance node information data, active node information data, reactive node information data, and network element information data.

[0013] As a preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to construct the probabilistic surrogate model by using a Gaussian process of Bayesian optimization to process the randomly generated initial parameters.

[0014] As a preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to obtain an objective function corresponding to the current network state through power flow calculation, and use the objective function as the target result, wherein the process of the power flow calculation includes: Under the condition of the currently randomly generated initial parameters, process the obtained topological structure information, component parameter information, power generation information, and load parameter information of the current medium-voltage distribution network based on the selected power flow calculation method to obtain the steady-state operation state parameters of the current medium-voltage distribution network.

[0015] As a preferred solution, both the upper-layer optimization model and the lower-layer optimization model are used to construct a sampling function by using the TPE strategy, and at least obtain a maximum improvement observation value based on the sampling function.

[0016] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: The present invention proposes a multi-objective optimization objective function considering improving the power flow balance and network loss of medium-voltage distribution network feeders. Based on the Bayesian optimization algorithm and combined with multi-objective optimization, a Bayesian nested two-layer optimization solution algorithm for optimizing the installation location considering the parameters of flexible phase-shifting devices is constructed. Among them, it is not necessary to perform power flow calculations for all parameters. On the one hand, the embodiments of the present invention use a simplified surrogate model to help predict the feeder balance degree and network loss under different control parameters, so as to find the optimal control parameter set faster without the need to perform cumbersome power flow calculations every time. On the other hand, the embodiments of the present invention adopt the TPE strategy (Tree-structured Parzen Estimator strategy) to collect the observed values and their control parameters when the feeder balance degree and network loss are improved the most, so as to help find the optimal solution faster, and then perform power flow calculations to verify the effect of this set of parameters. The flexible interconnected distribution network obtained by the entire solution through the optimization algorithm can realize the joint consumption of distributed photovoltaics by two feeders, improve the active power balance degree on the source side of the feeder, and greatly improve the negative impact on the power grid caused by the load fluctuations of distributed photovoltaics and electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic flow chart of the parameter optimization method of the flexible interconnected phase-shifting device in one embodiment of the present invention; Figure 2 is a flow chart of the optimization configuration algorithm of the flexible interconnected device in one embodiment of the present invention (upper-layer optimization model); Figure 3 is a flow chart of the optimization configuration algorithm of the flexible interconnected device in one embodiment of the present invention (lower-layer optimization model); Figure 4 is an electrical topology diagram of the 10kV distribution network in one embodiment of the present invention; Figure 5 is a typical daily active load curve diagram in one embodiment of the present invention; Figure 6 is a typical daily photovoltaic power generation curve diagram in one embodiment of the present invention; Figure 7 is a multi-objective two-layer optimization pareto solution in one embodiment of the present invention; Figure 8 is the 10kV distribution network after flexible interconnection in one embodiment of the present invention; Figure 9 is a curve diagram for optimizing the transformation ratio of the flexible interconnected device in one embodiment of the present invention; Figure 10It is an example in one of the embodiments of the present invention - the phase-shift angle optimization curve of the flexible mutual connection device; Figure 11 It is an example in one of the embodiments of the present invention - the curve of the change in the power supply power of the feeder after interconnection; Figure 12 It is an example in one of the embodiments of the present invention - the curve of the change in network loss after interconnection; Figure 13 It is the structural block diagram of the parameter optimization system of the flexible interconnection phase-shift device in one of the embodiments of the present invention. Specific embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0019] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0020] In the description of the present application, it should be noted that, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0021] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which this technical field belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0022] An embodiment of the present invention provides a method for optimizing the parameters of a flexible interconnected phase-shifting device. Specifically, please refer to Figure 1 , Figure 1 which is shown as a schematic flow chart of the method for optimizing the parameters of the flexible interconnected phase-shifting device in one of the embodiments of the present invention, and it includes steps S1 to S4: S1. Obtain the power data of the current medium-voltage distribution network; S2. According to the power data, with the feeder power flow balance and network loss of the interconnected network as the optimization objectives, construct a multi-objective two-layer optimization model with mutual feedback between the upper-layer optimization model and the lower-layer optimization model. The upper-layer optimization model is configured to use the position parameters and control parameters of the flexible interconnected phase-shifting device as decision variables, and the lower-layer optimization model is configured to optimize the control parameters with the selected position parameters of the flexible interconnected phase-shifting device; S3. Obtain the target results output by the multi-objective two-layer optimization model. Among them, both the upper-layer optimization model and the lower-layer optimization model are designed to process the randomly generated initial parameters through the probability surrogate model and the TPE strategy in sequence, and perform power flow optimization calculations with the processing results. The initial parameters include the position parameters and control parameters of the flexible interconnected phase-shifting device; S4. Process the target results based on the Pareto front solution set to obtain the parameter optimization decision results of the flexible interconnected phase-shifting device in the current medium-voltage distribution network.

[0023] A flexible interconnected phase shifter is a device used to control the phase difference between voltage and current in an AC circuit. It can change the relative time relationship between current and voltage in the circuit, thereby adjusting the ratio of active power and reactive power to meet the load demand and stabilize the power system. In the embodiment of the present invention, a multi-objective two-layer optimization model is used for processing. In the medium-voltage active distribution network, in order to give full play to the role of the flexible interconnected device in regulating the power flow in the interconnected network, with the feeder power flow balance and network loss of the interconnected network as the optimization objectives, and the interconnected position and device parameters of the flexible interconnected phase-shifting device as decision variables, a multi-objective two-layer optimization model for the location selection of the flexible interconnected device is constructed. The two-layer optimization model for the location selection of the flexible interconnected device is as follows: In the double - layer optimization model, in the upper - layer optimization, both the node positions where the flexible phase - shifting interconnection devices are installed and the device control parameters are decision variables. However, in the lower - layer optimization, the node positions where the flexible phase - shifting interconnection devices are installed are parameters, and the device control parameters are decision variables. The double - layer optimization model will be described in detail below.

[0024] Optionally, for the upper - layer optimization model, it includes the following steps. Please refer to Figure 2 , Figure 2 which shows the flow chart of the flexible phase - shifting interconnection optimization configuration algorithm (upper - layer optimization model) in one embodiment of the present invention: Step 1: Read the power data of the current medium - voltage distribution network, mainly including node information, such as slack nodes, active nodes, and reactive nodes. Among them, an active node is a node where active power flows towards it from both sides, and a reactive node is a node where reactive power flows towards it from both sides. In addition, it can also include network element information data (such as series lines, transformers, phase shifters, shunt compensators, etc.), connection relationships and their parameter information, and connection relationships and parameters (active and reactive) of power generation and loads. These information are the basis for subsequent steps.

[0025] Step 2: Set the upper - layer and lower - layer variable information, including setting the upper - layer variable information, that is: the starting and ending nodes and their upper and lower limits in the decision variables of the flexible phase - shifting interconnection device. Set the lower - layer variable information, that is: the turns ratio and phase - shifting angle information.

[0026] The control parameter constraints of the flexible phase - shifting device are as follows: Where: tk min , tk max are the upper and lower limits of the turns ratio, usually taken as 0.9 and 1.1, Angle min and Angle max are the upper and lower limits of the phase - shifting angle respectively.

[0027] Step 3: Solve the objective function of the current medium - voltage distribution network and its optimal solution of control parameters, specifically including calling the lower - layer optimization module, passing the installation position parameters, and solving the optimal solution of the objective function of the distribution network and its control parameters. and The expressions of are as follows: It should be noted that P loss and P feeder are functions of x and y, but they do not have explicit function expressions. This model uses numerical solutions rather than analytical solutions. x refers to the position vector formed by two connected nodes, and y refers to the vector formed by tk and angle.

[0028] Among them, To obtain the network loss, To calculate the active power balance degree on the source side of the medium - voltage interconnected network. P loss is the loss of the active power delivered by the i - th branch element in the network, unit: MW; n is the total number of branch elements in the network; is the active power delivered by the source side of the interconnected feeder 1, unit: MW; is the active power delivered by the source side of the interconnected feeder 2, unit: MW; x is a two - dimensional integer vector representing the installation position of the interconnected device, x(1) is the vector of the head node, x(2) is the vector of the end node; y is a two - dimensional real variable representing the control parameters of the interconnected device, y(1) is the vector of the transformation ratio of the flexible phase - shifting device, y(2) is the vector of the angle of the flexible phase - shifting device.

[0029] In the embodiment of the present invention, optionally, the constraints of the medium - voltage distribution network are as follows: where, P ij is the power of branch ij, V i is the voltage of node i, V j is the voltage of node j, G ij is the real part of the admittance of branch ij, B ij is the imaginary part of the admittance of branch ij, θ ij is the voltage phase angle difference between nodes i and j.

[0030] Voltage constraint: where, V i is the voltage of node i, V mini , V maxi are respectively the lower limit and the upper limit of the voltage constraint of node i.

[0031] Branch power constraint: where, P ij is the power of branch ij, P minij , P maxij are respectively the lower limit and the upper limit of the power constraint of branch ij.

[0032] Step 4: Construct the upper - layer optimization observation space and the probabilistic surrogate function. In this step, the data obtained previously is used to construct a model that can predict the values of the objective function at different node positions. In this way, the predicted values of the objective function can be obtained quickly without actual simulation.

[0033] Specifically, in the embodiment of the present invention, the Gaussian process (Gaussian Process, GP) commonly used in Bayesian optimization is adopted as the probabilistic surrogate model. For the input X = [x1 , x 2 ,.., x n T , the output of the objective function y = [y 1 , y 2 ,.., y n T , which satisfies the following formula through the joint probability distribution: where: is the mean function, , and are covariance functions, is the predicted new input, is the corresponding output value.

[0034] Step 5: Use the surrogate model to predict the objective function value and uncertainty of the new parameter point. In the embodiment of the present invention, through the joint probability distribution calculation, the mean and variance of the predicted value can be obtained, and the next sampling point can be determined accordingly. After obtaining the sampling, based on the new observation space, the parameters of the Gaussian process model are updated.

[0035] Step 6: Use the Tree-Structure Parzen Estimator (TPE) sampling strategy to construct a sampling function, and collect the information of the maximum improvement observation value of the objective function and its installation position nodes. TPE is an optimization strategy that can guide the subsequent search direction according to the previous optimization results. In this step, TPE will collect the probability information of the optimization objective to find the optimal search direction. According to the guidance of the TPE strategy, the node positions that are most likely to improve the objective function value will be found and the model will be updated.

[0036] In the model, the TPE sampling strategy used is based on a certain threshold y*, and the objective function values are divided into two categories. The relatively good part is classified into Y L , and the corresponding parameter set is denoted as X L ; the other category is denoted as Y R and X R . The probability density functions are constructed respectively (please refer to the following formula), and the estimator is used to generate new sampling points to realize the effective exploration of the unknown area and the reasonable utilization of the better area, and achieve a better balance in the process of finding the optimal solution.

[0037] where l(x) is in the region X L ​​The probability density function in, g(x) is in the region X R The probability density function in, x i is a sample point, K(x, x i ) is the kernel function. N L and N R are respectively the sample numbers corresponding to X L and X R .

[0038] Step 7: Call the lower-layer optimization module again, transfer the installation location parameters, and solve the objective function of the distribution network and its optimal solution of the control parameters.

[0039] Step 8: Determine whether the termination condition is satisfied. That is, after updating the node positions, the control parameters need to be adjusted again to obtain a new optimal solution of the objective function. By adjusting the control parameters and updating the node positions multiple times, one or more optimal objective solutions can be obtained. These solutions may correspond to different network configurations and performance performances. After obtaining the optimal objective solutions, the equipment needs to be installed according to the actual situation. This may involve adding new equipment in space, or adjusting the positions and parameters of the existing equipment. The process of installing the equipment will continue until certain termination conditions are met, such as achieving the expected network performance or installing a sufficient number of equipment.

[0040] Preferably, the termination condition is usually: reaching the preset number of iterations or the improvement of the objective function value is less than the given threshold. When the condition is satisfied, go to step 11 of this layer, otherwise go to step 9 of this layer.

[0041] Step 9: Add new installation location information and its corresponding objective function value in the observation space.

[0042] Step 10: Update the parameters of the upper-layer optimization probability surrogate model, and transfer to step 5 of this layer optimization.

[0043] Step 11: Obtain the Pareto front solution set of the objective function and make an optimal solution decision. Make a decision between multiple optimal objective solutions and select the optimal solution set. This usually requires considering multiple factors, such as cost, performance, reliability, etc.

[0044] Optionally, for the lower-layer optimization model, it includes the following steps. Please refer to Figure 3 , Figure 3 which is shown as the flowchart of the flexible phase-shifting interconnection installation optimization configuration algorithm (lower-layer optimization model) in one embodiment of the present invention: Step 1: Set the installation node information of the interconnection device according to the variable information transferred by the upper-layer optimization.

[0045] Step 2: Randomly generate the tap ratio and phase-shifting angle parameter sets of the phase-shifting device.

[0046] Step 3: Connect the flexible phase-shifting interconnection device to the distribution network. Through power flow calculation, obtain the network state, and calculate the objective function (see the above double-objective function expression). In the embodiment of the present invention, the topological structure, component parameters, generation, and load parameters of the distribution network after connecting the flexible interconnection device are given. The steady-state operation state parameters of the distribution network can be calculated by conventional power flow calculation methods. Conventional power flow calculation algorithms include, but are not limited to, the Gauss-Seidel method (G-S method), Newton-Raphson method (N-R method), fast decoupled method (PQ decomposition method), etc.

[0047] Step 4: Construct the lower-layer optimization observation space (including the objective function and control parameters) and the probabilistic surrogate function.

[0048] Step 5: Use the surrogate model to predict the objective function value and uncertainty of the new parameter points.

[0049] Step 6: Use the TPE sampling strategy to construct a sampling function, and collect the maximum improvement observation value of the objective function and the node information of its installation location.

[0050] Step 7: Perform power flow calculation and obtain the network state, and calculate the objective function (see the above flexible phase-shifting device control parameter constraint formula).

[0051] Step 8: Determine whether the termination condition is satisfied.

[0052] The termination condition is usually: reaching the preset number of iterations or the improvement of the objective function value is less than the given threshold. When the condition is satisfied, go to step 11 of this layer; otherwise, go to step 9 of this layer.

[0053] Step 9: Append new tap ratio and phase-shifting angle information and their corresponding objective function values in the observation space.

[0054] Step 10: Update the upper-layer optimization probabilistic surrogate model parameters, and transfer to step 5 of this layer for optimization.

[0055] Step 11: Obtain the Pareto front solution set of the objective function and perform optimization decision-making.

[0056] The upper-layer optimization model and the lower-layer optimization model are described above. In the double-layer optimization model, for the design of the installation node positions of the flexible phase-shifting interconnection devices and the device control parameters, the upper-layer optimization and the lower-layer optimization have different focuses. The upper-layer optimization is responsible for determining the installation node positions of the FPSID (flexible phase-shifting interconnection device), which is a global decision and is restricted by various practical constraints such as the network structure, device capacity, and safe operation conditions. The lower-layer optimization focuses on optimizing the device control parameters on the premise of the given FPSID installation node positions. These control parameters include the phase-shifting angle, transmission power, etc., which directly affect the power flow regulation ability and network loss of the FPSID. The results of the lower-layer optimization can be used as feedback information for the upper-layer optimization. Through continuous iteration and optimization, a closed-loop feedback mechanism can be formed between the upper layer and the lower layer, thereby continuously improving the efficiency and accuracy of the entire optimization process, achieving an optimal balance between the global strategy and the local performance, improving the optimization efficiency, and enhancing the practicality of the optimization results.

[0057] To further illustrate the beneficial effects of the embodiments of the present invention, a specific example is described in detail below.

[0058] The network structure and characteristics proposed by the present invention are described in detail below by taking the 10 kV distribution network in a certain area as an example: As Figure 4 shown, the topological structure of the 10 kV distribution network is as follows: Feeder 1 (starting with the number 1 in the figure) and Feeder 2 (starting with the number 1 in the figure) are interconnected through a phase shifter. Each of Feeder 1 and Feeder 2 contains 5 loads and 1 photovoltaic power station. Among them, Node 11 in Feeder 1 is the 10 kV bus of the substation, and the load nodes 12 - 17 and 100 are photovoltaic nodes; Node 21 in Feeder 2 is the 10 kV bus of the substation, and the load nodes 22 - 27 and 200 are photovoltaic nodes. The details of the in-network power supply load level, characteristics, and access node conditions are shown in Table 1, and the characteristic curves of residential and commercial loads are shown in Figure 5 .

[0059] Table 1: Power supply load access, load level, and load characteristics The details of the in-network photovoltaic access situation, installed capacity, and its typical daily active power characteristics are shown in Table 2 and Figure 6 .

[0060] Table 2: Photovoltaic access and power generation level It can be seen from the typical daily electricity load characteristics and photovoltaic power generation curve data. During the peak photovoltaic period during the day (12:00 - 14:00), since the power supply load of the feeder 1 network is mainly residential, the electricity load at noon is small, less than 1 MW, the photovoltaic power generation level is close to 10 MW, the active power flow of feeder 1 is fed back, and the value is large, at a heavy load level. While the power supply load of the feeder 2 network is mainly administrative office with a heavy load (0.8 - 1.0 MW), and the installed photovoltaic capacity is small (1.0 MW), the active power flow of feeder 2 is large, at a heavy load state; during the period when photovoltaic power generation stops at night, the charging load demand of feeder 1 is large and the load is heavy, while the power supply load of feeder 2 is light.

[0061] (2)Flexible phase - shifting interconnection optimization results and analysis Combined with the network topology information, source - load composition and typical daily operation characteristics, aiming at two objectives of the active power deviation and network loss of feeder 1 and feeder 2, the Pareto - front solution set is obtained by applying the Bayesian nested bi - level optimization solution (see details in Figure 7 , Figure 7 which is an example in one of the embodiments of the present invention - the multi - objective bi - level optimization pareto solution). Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the Pareto - front solution set, it is obtained that a flexible phase - shifting interconnection device is installed between node 14 and node 25 (see details in Figure 8 , Figure 8 which is an example in one of the embodiments of the present invention - the 10 kV distribution network after flexible phase - shifting interconnection).

[0062] The typical daily optimization results of the distribution network after flexible phase - shifting interconnection (see details in Figures 9 - 12 , where Figure 9 is an example in one of the embodiments of the present invention - the transformer ratio optimization curve of the flexible phase - shifting interconnection device, Figure 10 is an example in one of the embodiments of the present invention - the phase - shifting angle optimization curve of the flexible phase - shifting interconnection device, Figure 11 is an example in one of the embodiments of the present invention - the change curve of the supply power variation of the feeder after interconnection, Figure 12 is an example in one of the embodiments of the present invention - the change curve of the network loss after interconnection) shows that by controlling the transformer ratio (0.983 - 1.014) and phase - shifting angle parameters (4.2 degrees - 5.6 degrees) of the flexible phase - shifting device, the active power deviation of the power supply of feeder 1 and feeder 2 is between 0.02 and 0.084, and the network loss is between 0.02 and 0.23. The results of the numerical example show that the flexible phase - shifting interconnection distribution network obtained by the optimization algorithm can realize the joint accommodation of distributed photovoltaics by two feeders, improve the balance degree of the active power on the source side of the feeder, and greatly improve the negative impact on the power grid caused by the load fluctuations of distributed photovoltaics and electric vehicles.

[0063] Another embodiment of the present invention provides a parameter optimization system for a flexible interconnection phase - shifting device. Specifically, please refer toFigure 13 , Figure 13 It shows a structural block diagram of a parameter optimization system for a flexible interconnected phase-shifting device in one embodiment of the present invention, which includes: An acquisition module A, configured to acquire power data of the current medium-voltage distribution network; A construction module B, configured to construct a multi-objective two-layer optimization model with mutual feedback between an upper-layer optimization model and a lower-layer optimization model by taking the power flow balance of the interconnected network feeder and network loss as optimization objectives according to the power data. The upper-layer optimization model is configured to use the position parameters and control parameters of the flexible interconnected phase-shifting device as decision variables, and the lower-layer optimization model is configured to optimize the control parameters with the selected position parameters of the flexible interconnected phase-shifting device; An output module C, configured to acquire the target result output by the multi-objective two-layer optimization model. Among them, both the upper-layer optimization model and the lower-layer optimization model are designed to sequentially process the randomly generated initial parameters through a probabilistic surrogate model and a TPE strategy, and perform power flow optimization calculations with the processing results. The initial parameters include the position parameters and control parameters of the flexible interconnected phase-shifting device; A decision-making module D, configured to process the target result based on the Pareto front solution set to obtain the parameter optimization decision result of the flexible interconnected phase-shifting device in the current medium-voltage distribution network.

[0064] Further, in the above embodiment, the power data at least includes balance node information data, active node information data, reactive node information data, and network element information data.

[0065] Further, in the above embodiment, both the upper-layer optimization model and the lower-layer optimization model are used to construct the probabilistic surrogate model by using the Gaussian process of Bayesian optimization to process the randomly generated initial parameters.

[0066] Further, in the above embodiment, both the upper-layer optimization model and the lower-layer optimization model are used to obtain the objective function corresponding to the current network state through power flow calculation, and use the objective function as the target result. Among them, the process of the power flow calculation includes: Under the condition of the currently randomly generated initial parameters, process the obtained topological structure information, component parameter information, power generation information, and load parameter information of the current medium-voltage distribution network based on the selected power flow calculation method to obtain the steady-state operation state parameters of the current medium-voltage distribution network.

[0067] Further, in the above embodiment, both the upper-layer optimization model and the lower-layer optimization model are used to construct a sampling function by using the TPE strategy, and at least obtain the maximum improvement observation value based on the sampling function.

[0068] The parameter optimization method and system for the flexible interconnected phase-shifting device provided by the embodiment of the present invention have at least one of the following beneficial effects: The present invention proposes a multi-objective optimization objective function considering improving the feeder power flow balance and network loss in the medium-voltage distribution network. Based on the Bayesian optimization algorithm and combined with multi-objective optimization, a Bayesian nested double-layer optimization solution algorithm considering the parameters of the flexible phase-shifting device and optimized for the installation location is constructed. Among them, it is not necessary to perform power flow calculations for all parameters. On the one hand, the embodiment of the present invention uses a simplified surrogate model to help predict the feeder balance degree and network loss under different control parameters, so that the optimal control parameter set can be found faster without the need for cumbersome power flow calculations every time. On the other hand, the embodiment of the present invention adopts the TPE strategy (Tree-structured Parzen Estimator strategy) to collect the observed values and their control parameters when the feeder balance degree and network loss are improved the most, to help find the optimal solution faster, and then perform power flow calculations to verify the effect of this set of parameters. The flexible interconnected distribution network obtained by the entire solution through the optimization algorithm can realize the joint accommodation of distributed photovoltaics by two feeders, improve the active power balance degree on the source side of the feeder, and greatly improve the negative impact on the power grid caused by the load fluctuations of distributed photovoltaics and electric vehicles.

[0069] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A parameter optimization method for a flexible interconnect phase shifting device, characterized in that: include: Obtain the current power data of the medium voltage distribution network; According to the power data, taking interconnected network feeder power flow balance and network loss as optimization objectives, a multi-objective two-layer optimization model with mutual feedback between an upper optimization model and a lower optimization model is constructed, wherein the upper optimization model is configured to take position parameters and control parameters of a flexible interconnected phase shifting device as decision variables, and the lower optimization model is configured to optimize the control parameters with the position parameters of the selected flexible interconnected phase shifting device; Obtaining a target result output by the multi-objective two-layer optimization model, wherein the upper optimization model and the lower optimization model are both designed to process randomly generated initial parameters through a probabilistic proxy model and a TPE strategy in turn, and perform power flow optimization calculations based on the processing results, wherein the initial parameters include position parameters and control parameters of a flexible interconnected phase shifting device; The target result is processed based on the Pareto frontier solution set to obtain a parameter optimization decision result of the flexible interconnected phase shifting device in the current medium-voltage distribution network.

2. The parameter optimization method of the flexible interconnect phase shifting device according to claim 1, characterized in that: The power data at least includes balancing node information data, active node information data, reactive node information data and network element information data.

3. The parameter optimization method of the flexible interconnect phase shifting device according to claim 1, characterized in that: The upper optimization model and the lower optimization model are both used to construct the probabilistic proxy model using a Gaussian process of Bayesian optimization to process the randomly generated initial parameters.

4. The parameter optimization method of the flexible interconnect phase shifting device according to claim 1, characterized in that: The upper optimization model and the lower optimization model are both used to obtain the objective function corresponding to the current network state through power flow calculation, and the objective function is used as the target result, wherein the process of power flow calculation includes: Under the current randomly generated initial parameter conditions, the topological structure information, component parameter information, power generation information and load parameter information of the current medium-voltage distribution network are processed based on the selected power flow calculation method to obtain the steady-state operation status parameters of the current medium-voltage distribution network.

5. The parameter optimization method of the flexible interconnect phase shifting device according to claim 1, characterized in that: The upper optimization model and the lower optimization model are both used to construct a sampling function using the TPE strategy, and to obtain at least the maximum improved observation value based on the sampling function.

6. A parameter optimization system for a flexible interconnected phase shifting device, characterized in that: include: An acquisition module is used to obtain the power data of the current medium voltage distribution network; A construction module is used to construct a multi-objective double-layer optimization model with mutual feedback between an upper optimization model and a lower optimization model based on the power data and taking interconnected network feeder power flow balance and network loss as optimization objectives, wherein the upper optimization model is configured to take position parameters and control parameters of the flexible interconnected phase shifting device as decision variables, and the lower optimization model is configured to optimize the control parameters with the selected position parameters of the flexible interconnected phase shifting device; an output module, used to obtain the target result output by the multi-objective two-layer optimization model, wherein the upper optimization model and the lower optimization model are designed to process the randomly generated initial parameters in turn through the probabilistic proxy model and the TPE strategy, and perform power flow optimization calculation based on the processing results, wherein the initial parameters include the position parameters and control parameters of the flexible interconnected phase shifting device; A decision module is used to process the target result based on the Pareto frontier solution set to obtain a parameter optimization decision result of the flexible interconnected phase shifting device in the current medium-voltage distribution network.

7. The parameter optimization system of the flexible interconnected phase shifting device according to claim 6, characterized in that: The power data at least includes balancing node information data, active node information data, reactive node information data and network element information data.

8. The parameter optimization system of the flexible interconnect phase shifting device according to claim 6, characterized in that: The upper optimization model and the lower optimization model are both used to construct the probabilistic proxy model using a Gaussian process of Bayesian optimization to process the randomly generated initial parameters.

9. The parameter optimization system of the flexible interconnected phase shifting device according to claim 6, characterized in that: The upper optimization model and the lower optimization model are both used to obtain the objective function corresponding to the current network state through power flow calculation, and the objective function is used as the target result, wherein the process of power flow calculation includes: Under the current randomly generated initial parameter conditions, the topological structure information, component parameter information, power generation information and load parameter information of the current medium-voltage distribution network are processed based on the selected power flow calculation method to obtain the steady-state operation status parameters of the current medium-voltage distribution network.

10. The parameter optimization system of the flexible interconnect phase shifting device according to claim 6, characterized in that: The upper optimization model and the lower optimization model are both used to construct a sampling function using the TPE strategy, and to obtain at least the maximum improved observation value based on the sampling function.

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