Voltage sensitivity embedded DC link locating and sizing method and system

Abstract

The invention discloses a voltage sensitivity embedded DC link locating and sizing method and system, and the method comprises the steps: obtaining a first target parameter, and carrying out the first preprocessing of the first target parameter, and obtaining a second target parameter; acquiring a third target parameter, and determining a first target access point in combination with the second target parameter and a first improved sensitivity algorithm; and establishing a first optimization model, and solving a target locating and sizing result according to the first target access point and the first optimization model. Through establishment of the optimization model, the annual comprehensive total cost can be effectively reduced, the consumption proportion of new energy is improved, and the stability of the system is enhanced. Theoretical support can be provided for transformation of a power system, and transformation of a power distribution network to an alternating-current and direct-current hybrid power distribution form is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage sensitivity embedded DC link site selection and capacity determination, and particularly relates to a method and system for voltage sensitivity embedded DC link site selection and capacity determination. Background Art

[0002] In the process of the transformation of the power system, the large-scale access of new energy to the power grid has led to a series of problems such as voltage fluctuations and voltage over-limit. How to utilize the flexible adjustment characteristics of new power electronic devices to improve the voltage quality of the AC distribution network and optimize the dynamic load-bearing mode of the network, source, and load is of great significance for enhancing system stability and increasing the new energy consumption ratio.

[0003] Currently, the mainstream technical means is to connect a back-to-back converter or a multi-port converter at the end of the feeder to replace the mechanical tie switch. However, this connection method has disadvantages. It often results in poor voltage support of the flexible interconnection device for the feeder, and the converter capacity required for fully transferring electric energy between feeders is large, thus causing high construction costs and insufficient flexibility, and it is difficult to break through the capacity limit bottleneck of distributed power sources on the AC side of the distribution network. In view of this, on the basis of fully relying on the existing power supply pattern of the AC distribution network, embedding a DC link skillfully to gradually transform the distribution network from a simple AC networking form into a distribution form of a hybrid AC-DC network is an effective countermeasure. Relevant research shows that this hybrid distribution form has extremely significant advantages in optimizing power flow distribution, improving voltage quality, etc. Therefore, it is urgent to deeply carry out research on the access planning of the embedded DC link (EDCL) to lay a theoretical foundation for the high-quality development of the high-proportion new energy distribution network and promote a qualitative leap in the new energy bearing capacity and voltage coordinated control of the distribution network. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, the abstract of the specification, and the title of the invention to avoid obscuring the purpose of this part, the abstract of the specification, and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

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

[0006] Therefore, the present invention provides a method and system for voltage sensitivity embedded DC link site selection and capacity determination, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the present invention provides a method for site selection and capacity determination of a voltage sensitivity embedded DC link, including:

[0009] Obtain a first target parameter, and perform a first preprocessing on the first target parameter to obtain a second target parameter;

[0010] Obtain a third target parameter, and determine a first target access point by combining the second target parameter and a first improved sensitivity algorithm;

[0011] Establish a first optimization model, and solve the target site selection and capacity determination result according to the first target access point and the first optimization model.

[0012] As a preferred solution of the method for site selection and capacity determination of the voltage sensitivity embedded DC link according to the present invention, wherein: the first optimization model includes:

[0013] The first optimization model includes a first objective function and a first constraint condition;

[0014] The first objective function is any function for solving the minimum annual comprehensive total cost;

[0015] The first constraint condition at least includes constraints of the AC distribution network part, constraints of the DC distribution network part, and exchange constraints between the AC distribution network and the DC distribution network.

[0016] As a preferred solution of the method for site selection and capacity determination of the voltage sensitivity embedded DC link according to the present invention, wherein: the first improved sensitivity algorithm includes:

[0017] The first improved sensitivity algorithm is any algorithm for obtaining a first target access point by combining the second target parameter and the third target parameter;

[0018] The first improved sensitivity algorithm at least includes using the target voltage violation rate as a sensitivity weight factor.

[0019] As a preferred solution of the method for site selection and capacity determination of the voltage sensitivity embedded DC link according to the present invention, wherein: the step of obtaining the third target parameter, combining the second target parameter and the first improved sensitivity algorithm to determine the first target access point includes:

[0020] Perform a first sorting on the calculation results of the first improved sensitivity algorithm;

[0021] Determine the first target access point according to the result of the first sorting.

[0022] As a preferred solution of the method for site selection and capacity determination of the voltage sensitivity embedded DC link according to the present invention, wherein: the first objective function at least includes the investment cost of the embedded DC link, the operation and maintenance cost, and the annual loss cost.

[0023] As a preferred solution of the method for site selection and capacitance determination of the voltage sensitivity embedded DC link described in the present invention, wherein: the first constraint condition at least includes the power exchange constraint between the DC and AC ports, the cooperative control mode between each end, and the power constraint of the DC network part.

[0024] As a preferred solution of the method for site selection and capacitance determination of the voltage sensitivity embedded DC link described in the present invention, wherein: the first preprocessing includes:

[0025] Generating random scenarios for the first target parameter;

[0026] Calculating the Euclidean distance between the random scenarios;

[0027] Obtaining the minimum probability distance scenario and performing reduction to obtain the second target parameter.

[0028] In a second aspect, the present invention provides a system for site selection and capacitance determination of a voltage sensitivity embedded DC link, including:

[0029] A data processing module, configured to obtain a first target parameter and perform first preprocessing on the first target parameter to obtain a second target parameter;

[0030] An access point determination module, configured to obtain a third target parameter, and determine a first target access point in combination with the second target parameter and a first improved sensitivity algorithm;

[0031] A solution module, configured to establish a first optimization model, and solve the target site selection and capacitance determination result according to the first target access point and the first optimization model.

[0032] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method and system for voltage sensitivity embedded DC link site selection and capacity determination, obtains the first target parameter, and performs the first preprocessing on the first target parameter to obtain the second target parameter; obtains the third target parameter, combines the second target parameter and the first improved sensitivity algorithm to determine the first target access point; establishes the first optimization model, and according to the first target access point and the first optimization model, solves the target site selection and capacity determination result. By establishing the optimization model, the present invention can effectively reduce the annual comprehensive total cost, improve the consumption ratio of new energy, and enhance the stability of the system. The improved sensitivity algorithm of the present invention can more accurately determine the target access point, thereby optimizing the power flow distribution and improving the voltage quality. The system and method of the present invention have good flexibility and scalability in practical applications and can adapt to different scales and types of distribution networks. The embodiments of the present invention can provide theoretical support for the transformation of the power system and promote the transformation of the distribution network to the AC-DC hybrid distribution form. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0036] Figure 1 It is a method flow chart of a method and system for voltage sensitivity embedded DC link site selection and capacity determination provided by an embodiment of the present invention;

[0037] Figure 2 It is a comparison diagram of mechanical switches, SOP and EDCL structures of a method and system for voltage sensitivity embedded DC link site selection and capacity determination provided by an embodiment of the present invention;

[0038] Figure 3 It is a strategy flow chart of a method and system for voltage sensitivity embedded DC link site selection and capacity determination provided by an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the active power flow direction of a method and system for voltage sensitivity embedded DC link site selection and capacity determination provided by an embodiment of the present invention;

[0040] Figure 5 It is an algorithm implementation EDCL site selection and capacity determination flow chart of a method and system for voltage sensitivity embedded DC link site selection and capacity determination provided by an embodiment of the present invention;

[0041] Figure 6Internal structure diagram of a computer device for a method and system for locating and sizing an embedded DC link based on voltage sensitivity provided by an embodiment of the present invention. Detailed implementation manners

[0042] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe in detail the specific implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1

[0044] Referring to Figures 1-6 , which is the first embodiment of the present invention. This embodiment provides a method and system for locating and sizing an embedded DC link based on voltage sensitivity, including:

[0045] In the existing related technologies, there are some problems. For example, when traditional methods for locating and sizing deal with the voltage sensitivity problem of the DC link, they often cannot accurately reflect the voltage fluctuations in the actual power grid operation, resulting in inaccurate results for locating and sizing. In addition, due to the characteristics of the DC link, the traditional methods for locating and sizing in the AC power grid are not fully applicable, and the algorithm needs to be improved to adapt to the particularity of the DC link.

[0046] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement the method for locating and sizing an embedded DC link based on voltage sensitivity;

[0047] Figure 1 shows a flowchart of a method for a method and system for locating and sizing an embedded DC link based on voltage sensitivity, including:

[0048] S101, obtain a first target parameter, and perform a first preprocessing on the first target parameter to obtain a second target parameter;

[0049] In an optional embodiment, the first target parameter may include information such as photovoltaic power, wind turbine power, load power, and DC bus voltage.

[0050] In an optional embodiment, the first preprocessing may be to remove noise through a filtering algorithm to ensure the accuracy of the data. The second target parameter is then used to construct a voltage sensitivity model, which can simulate the voltage response of the DC link under different operating conditions. In this way, the voltage stability of the DC link can be evaluated more accurately, providing a more reliable basis for locating and sizing.

[0051] In an alternative embodiment, the first preprocessing may further include normalizing the first target parameter to eliminate the influence brought by different dimensions and orders of magnitude. After normalization, the data will be transformed to a unified scale, facilitating subsequent analysis and model construction. In addition, data smoothing techniques, such as the moving average method, can be applied to reduce the volatility of the data, improving the stability of the model and the accuracy of prediction.

[0052] It should be noted that through these preprocessing steps, the data quality input into the voltage sensitivity model can be ensured, laying a solid foundation for accurately evaluating the voltage stability of the DC link.

[0053] In the embodiment of the present application, the first preprocessing includes:

[0054] Generating random scenarios for the first target parameter;

[0055] Calculating the Euclidean distance between the random scenarios;

[0056] Obtaining the minimum probability distance scenario and performing reduction to obtain the second target parameter.

[0057] In an alternative embodiment, generating random scenarios for the first target parameter can be achieved by the Monte Carlo simulation method. The Monte Carlo simulation is a computational method based on random sampling. It constructs a probability model and uses the statistical characteristics of random variables to simulate the random process of actual problems. In this embodiment, the Monte Carlo simulation can be used to generate a large number of random scenarios, which represent the possible values of the first target parameter under different conditions. In this way, the voltage stability performance of the DC link under the influence of various uncertain factors can be simulated, providing a more comprehensive and detailed analysis basis for site selection and capacity determination.

[0058] In an alternative embodiment, generating random scenarios for the first target parameter can also be achieved by the Latin hypercube sampling technique. The Latin hypercube sampling is an efficient sampling method. It generates random samples through a set of points evenly distributed in the parameter space, thereby improving the accuracy and efficiency of the simulation. This technique is particularly suitable for high-dimensional parameter spaces and can ensure that most regions of the parameter space are covered within a limited number of simulation times, thus obtaining more reliable random scenarios. The random scenarios generated by the Latin hypercube sampling can further improve the prediction accuracy of the voltage sensitivity model, providing more accurate data support for site selection and capacity determination of the DC link.

[0059] In an alternative embodiment, generating random scenarios for the first target parameter can also be achieved by combining Monte Carlo simulation with Latin hypercube sampling techniques. This combined method first utilizes the randomness of Monte Carlo simulation to generate preliminary random scenarios, and then optimizes these scenarios through Latin hypercube sampling techniques to ensure uniform distribution of sample points in each parameter dimension. In this way, the extensive coverage of Monte Carlo simulation and the high efficiency of Latin hypercube sampling can be taken into account, thereby improving the computational efficiency while ensuring the simulation accuracy. Ultimately, this combined method can provide more accurate and efficient analysis results for the site selection and capacity determination of the DC link.

[0060] In the embodiments of the present application, the Monte Carlo method is selected to generate a random scenario distribution that satisfies the requirements, and the fast backward reduction method is combined for scenario reduction.

[0061] Exemplarily, after randomly processing the source-load data, first determine the voltage weak node as the optimal location of access point 1 through improved voltage sensitivity; based on the selection result of access point 1, determine access point 2 and its capacity of the dual-terminal EDCL through the site selection and capacity determination optimization model, thereby improving the search efficiency and enhancing the overall system performance.

[0062] In an alternative embodiment, for source-load scenario generation and reduction, first use Monte Carlo simulation to generate a random scenario distribution, and combine the fast backward reduction method for scenario reduction, and then calculate the probability distance using the Euclidean distance. The main steps are as follows:

[0063] (1) Generate random scenarios. Assume that the actual values of wind power, photovoltaic power, and load fluctuate around the predicted values, and the error follows a normal distribution. Use the Monte Carlo method to generate n equally probable random scenarios m ij .

[0064] (2) Calculate the Euclidean distance. Use the Euclidean distance to calculate the probability distance k between each scenario ij That is:

[0065]

[0066] m t, i and m t,j respectively represent the wind power, photovoltaic power, and load power at time t of the i-th scenario and the j-th scenario

[0067] (3) Find the scenario with the minimum probability distance and perform reduction. Loop through the reduction operation until only the specified number of scenarios remain.

[0068] It should be noted that obtaining the first target parameter and performing the first preprocessing on the first target parameter to obtain the second target parameter can significantly improve the accuracy and reliability of the voltage sensitivity model. Through the preprocessing step, the data quality input into the model can be ensured, thus providing a more accurate basis for subsequent voltage stability assessment and site selection and capacity determination. In addition, by reducing the volatility and noise of the data, the stability of the model and the accuracy of prediction can be improved, which is crucial for ensuring the stable operation of the DC link. In practical applications, this method can effectively reduce the annual comprehensive total cost, increase the consumption ratio of new energy, and enhance the stability of the system. At the same time, the improved sensitivity algorithm of the present invention can more accurately determine the target access point, optimize the power flow distribution, improve the voltage quality, provide theoretical support for the transformation of the power system, and promote the transformation of the distribution network to the AC-DC hybrid distribution form.

[0069] S102. Obtain a third target parameter, and determine a first target access point by combining the second target parameter and a first improved sensitivity algorithm;

[0070] In the embodiment of the present application, the first improved sensitivity algorithm includes:

[0071] The first improved sensitivity algorithm is any algorithm for obtaining the first target access point by combining the second target parameter and the third target parameter;

[0072] The first improved sensitivity algorithm at least includes using the target voltage violation rate as a sensitivity weight factor.

[0073] In an optional embodiment, the first improved sensitivity algorithm may be an algorithm based on fuzzy logic. This algorithm performs fuzzy processing on the voltage violation rate, converts it into a fuzzy set, and defines the corresponding membership function. Then, according to the membership function, the fuzzy sensitivity weight factor of each target access point is calculated. Finally, through the fuzzy inference and defuzzification process, the optimal target access point is determined to optimize the power flow distribution and improve the voltage quality.

[0074] In an optional embodiment, the first improved sensitivity algorithm may also be an algorithm based on machine learning. This algorithm uses historical data to train a prediction model. Through this model, the impact of different access points on the voltage quality can be predicted, and thus the optimal access point can be selected. The machine learning model can be a support vector machine (SVM), random forest, neural network, etc., and a suitable algorithm is selected according to the complexity of the actual power grid and the availability of data.

[0075] It should be noted that in this way, the algorithm can not only consider the voltage violation rate, but also comprehensively consider other factors, such as load fluctuations, fault history, etc., to achieve more comprehensive optimization.

[0076] In an optional embodiment, the first improved sensitivity algorithm may also be a deep learning-based algorithm, which learns the complex relationship between voltage and access points by constructing a deep neural network model. This model can process large-scale power grid data and automatically extract and learn the key features affecting voltage quality. Through training, the deep learning model can predict the long-term impact of different access point configurations on voltage stability, thus providing more accurate location and capacity determination suggestions for decision-makers. In addition, the algorithm can also combine real-time monitoring data to dynamically adjust the model parameters to adapt to the changes in the power grid operation state and ensure the continuous optimization of voltage quality.

[0077] In the embodiment of the present application, the obtaining of the third target parameter, and determining the first target access point by combining the second target parameter and the first improved sensitivity algorithm includes:

[0078] Performing a first sorting on the calculation result of the first improved sensitivity algorithm;

[0079] Determining the first target access point according to the result of the first sorting.

[0080] In the embodiment of the present application, the third target parameter at least includes traditional voltage sensitivity, voltage offset weight factor, voltage over-limit rate, and DG timing.

[0081] Exemplarily, the specific operation of obtaining the third target parameter, and determining the first target access point by combining the second target parameter and the first improved sensitivity algorithm may be as follows:

[0082] In the distribution network, the active power and reactive power balance equations of the nodes are as follows:

[0083]

[0084] Where: ΔP i and ΔQ i are the active and reactive unbalanced powers of node i respectively; P i and Q i are the injected active and reactive powers of node i respectively; G ij and B ij are the conductance and susceptance in the node admittance matrix of the distribution network respectively; V i is the voltage amplitude of node i, and θ ij = θ i - θ j is the voltage phase angle difference between node i and j.

[0085] By performing Taylor expansion on the above formula and ignoring the high-order terms, the Jacobian matrix J can be obtained, and by taking the inverse of J, the influence of reactive power and active power on voltage, that is, the voltage sensitivity matrix, can be obtained.

[0086]

[0087] Wherein: and are the sensitivity matrices of the active power change of node i to the voltage phase angle and voltage amplitude of node j; and are the sensitivity matrices of the reactive power change of node i to the voltage of node j.

[0088] In an optional embodiment, a weighted voltage sensitivity is calculated by introducing a weight factor based on the active and reactive voltage sensitivities:

[0089]

[0090] Wherein: S ij (t) is the weighted voltage sensitivity matrix of node i to node j at time t; is the sensitivity matrix of the active power change of node i to the voltage of node j at time t; is the sensitivity matrix of the reactive power change of node i to the voltage of node j at time t; ω 1 and ω 2 are weight factors, ω 1 +ω 2 = 1.

[0092] In an optional embodiment, the voltage of the distributed power generation access node is prone to deviation. Therefore, the voltage deviation weight factor of each node in each time period is added as a weight to the sensitivity calculation:

[0093]

[0094] Wherein: v j,t is the voltage deviation weight factor of node j at time t, V j,t is the voltage of node j at time t, V 0,j,t is the expected voltage of node j at time t.

[0095] In an optional embodiment, since the access of distributed power generation is likely to cause voltage over-limit, the voltage over-limit rate of the node during the operation period is considered as the weight factor of the voltage sensitivity:

[0096]

[0097] Wherein: OVRj is the voltage over-limit rate of node j; NN is the number of nodes; Ci is the number of over-limit times. The voltage non-over-limit range is set to 0.95 - 1.05. To avoid the voltage sensitivity being 0, the number of over-limit times is increased by 1 for smoothing processing.

[0098] In an optional embodiment, considering the randomness and volatility of the power source and load, the weighted combination of the voltage sensitivity of each scenario and the corresponding probability of each scenario is performed to obtain the final improved voltage sensitivity S ij (t)″′:

[0099]

[0100] In the formula: C represents the total number of scenarios; p c is the corresponding probability of scenario c; S ij (t)″ c is the cumulative voltage sensitivity of each time period for scenario c.

[0101] It should be noted that by introducing a weight factor on the basis of the active and reactive voltage sensitivities to calculate the weighted voltage sensitivity, considering that the connection nodes of distributed power sources are likely to cause voltage deviation, a voltage deviation weight factor for each time period is introduced into the sensitivity calculation. At the same time, considering voltage over-limit, the voltage over-limit rate is introduced as the voltage sensitivity weight factor.

[0102] It should also be noted that obtaining the third target parameter and combining the second target parameter and the first improved sensitivity algorithm to determine the first target access point can more accurately evaluate the impact of distributed power source access on the grid voltage stability. In this way, the voltage over-limit problem can be effectively avoided, and the grid can be ensured to operate within a safe and stable voltage range. In addition, this method can also provide a scientific basis for grid planning, guide the reasonable layout of distributed power sources, thereby improving the operation efficiency and reliability of the entire grid system.

[0103] S103. Establish a first optimization model, and solve the target site selection and capacity determination result according to the first target access point and the first optimization model.

[0104] In the embodiment of the present application, the first optimization model includes:

[0105] The first optimization model includes a first objective function and a first constraint condition;

[0106] The first objective function is any function for solving the minimum annual comprehensive total cost;

[0107] The first constraint condition at least includes the constraints of the AC distribution network part, the constraints of the DC distribution network part, and the exchange constraints between the AC distribution network and the DC distribution network.

[0108] In an optional embodiment, the first objective function can balance the influence of different cost factors by introducing a weight coefficient, such as equipment investment cost, operation and maintenance cost, and possible power outage loss cost. By adjusting these weight coefficients, optimization can be carried out for different grid operation strategies and economic objectives.

[0109] In an alternative embodiment, the first objective function can also introduce environmental impact factors to minimize the negative impact on the environment. This includes considering environmental indicators such as carbon emissions, noise pollution, and electromagnetic interference. In this way, the optimization model not only focuses on economic benefits but also takes into account the needs of sustainable development and environmental protection. Additionally, specific environmental objective functions can be set to ensure that while meeting the grid operation efficiency and reliability, the predetermined environmental standards can also be achieved.

[0110] In an alternative embodiment, environmental impact limitations such as carbon emission limitations can also be added to the first constraint condition to ensure that while pursuing economic benefits, environmental protection requirements can also be met. Through such an optimization model, precise site selection and capacity determination of distributed power access points can be achieved, thereby promoting the sustainable development of energy while ensuring the stable operation of the power grid.

[0111] In the embodiment of the present application, the first objective function at least includes the investment cost of the embedded DC link, the operation and maintenance cost, and the annual loss cost.

[0112] In the embodiment of the present application, the first constraint condition at least includes the power exchange constraint between the DC and AC ports, the cooperative control mode between each end, and the power constraint of the DC network part.

[0113] In the embodiment of the present application, an EDCL site selection optimization model is designed as the first optimization model. For the EDCL site selection optimization model, the model objective function is set to minimize the annual comprehensive total cost, which includes the investment cost of the embedded DC link, the operation and maintenance cost, and the annual loss cost of the entire system.

[0114] f = f 1 + f 2 + f 3

[0115] Where: f is the annual comprehensive total cost of the system; f 1 is the investment cost of the EDCL; f 2 is the annual operation and maintenance cost of the EDCL; f 3 is the annual loss cost of the entire system.

[0116] The expressions of each part in the objective function are mainly as follows:

[0117] ① Expression of the EDCL investment cost:

[0118]

[0119] Where: r is the discount rate of the EDCL; y is the economic service life of the EDCL; Ω b is the set of all candidate branches of the EDCL; cEDCL is the investment cost per unit capacity; is the capacity of the EDCL installed on the candidate branch (i, j).

[0120] ② Expression for the operation and maintenance cost of the EDCL:

[0121]

[0122] In the formula: β is the annual operation and maintenance cost coefficient.

[0123] ③ System loss cost

[0124] In the distribution network with an embedded DC link, the system loss cost includes the AC network loss cost and the embedded DC link loss cost, and the embedded DC link loss cost includes the converter loss cost at both ends and the DC line loss cost.

[0125] f 3 = 365λf L

[0126]

[0127] In the formula: λ is the unit electricity price, f L is the loss of the system in one day, R ij is the resistance of the AC branch (i, j), I t,ij is the current of the branch (i, j) at time t, N N is the total number of nodes, is the power loss of the embedded DC link connected to node j at time t; Ω dc is the set of all branches in the DC distribution network; is the current flowing through the DC line in the t period; R dc is the DC line resistance.

[0128] In the embodiments of the present application, the main constraint conditions for the objective function specifically include:

[0129] ① The power flow constraint of the AC distribution network part, that is, the power balance constraint, adopts the distflow power flow model, and the expression is:

[0130]

[0131] In the formula: P t,ij and Q t,ij are the active power and reactive power flowing from node i to node j on the branch (i, j) at time t respectively, X ij is the reactance of the branch (i, j), P t,j and Q t,j are the active power and reactive power injected into node j at time t, Ut,i and U t,j are the voltages of node i and node j at time t, and are the active power and reactive power injected by the distributed power source at node j at time t, respectively, and are the active power and reactive power injected by the EDCL at node j at time t, respectively, and are the active power and reactive power consumed by the load at node j at time t, respectively.

[0132] ② The operation constraints of the EDCL port include power balance constraint, reactive power constraint and capacity constraint, as follows:

[0133]

[0134] In the formula: and are the active power losses of the EDCL at nodes i and j during time period t, respectively; and are the loss coefficients of the EDCL at nodes i and j, respectively; and are the lower limit and upper limit of the reactive power injected by the EDCL at nodes i and j during time period t, respectively.

[0135] ③ Assume that the embedded DC link is connected between nodes i and j, and the DC distribution network is represented by a DC branch (a, b), and the distributed power source and DC load are connected to node b. As Figure 4 is the schematic diagram of the active power flow of the distribution network connected to the embedded DC link. The reactive power transmission is not considered in the DC part. The Distflow power flow model of the DC distribution network is shown as follows:

[0136]

[0137] In the formula: and are the active powers flowing into nodes a and b during time period t, respectively; R dc is the DC line resistance; and are the voltages of nodes a and b during time period t, respectively.

[0138] ④ The connection of the active power between the AC distribution network and the DC distribution network, from Figure 3 it can be seen that the relationship between the active power of the DC distribution network and the active power injected by the EDCL port is as follows:

[0139]

[0140] In the formula: is the active power output of the distributed power source connected to node b during period t; is the DC load of node b during period t.

[0141] ⑤ System security constraints. To ensure the safe operation of the system, it is necessary to meet the security constraints of voltage and current. The security constraints of the AC distribution network and the DC distribution network are as follows:

[0142]

[0143] In the formula: U and are the upper and lower limits of the operating voltage restricted by the AC distribution network respectively; is the upper limit of the allowable current of the AC distribution network; V dc and are the upper and lower limits of the operating voltage restricted by the DC distribution network respectively; is the upper limit of the allowable current of the DC distribution network.

[0144] ⑥ EDCL planning constraints

[0145]

[0146] It should be noted that using the above first objective function and the first constraint condition can ensure achieving the optimal economy while meeting the safety and reliability of the power grid operation. Through accurate mathematical models and algorithms, the installation location and capacity configuration of EDCL can be effectively balanced, thus reducing investment and operation and maintenance costs while ensuring the stable operation of the power grid and power quality. In addition, this method can also adapt to power grid systems of different scales and types, and has good versatility and flexibility.

[0147] In an alternative embodiment, different methods can be used to solve the target location and capacity determination results. For example, intelligent optimization techniques such as the particle swarm optimization algorithm (PSO) or the genetic algorithm (GA) can be adopted. These algorithms can handle complex non-linear problems and find the global optimal solution or approximate optimal solution in the multi-dimensional search space. In practical applications, according to the specific situation and requirements of the power grid, appropriate algorithm parameters and constraint conditions are selected to ensure the accuracy and practicality of the calculation results. Through these methods, the optimal location and capacity of EDCL can be effectively determined, thus realizing the efficient operation and cost control of the power grid.

[0148] In an alternative embodiment, the solution of the target site selection and capacity determination results can also use the Simulated Annealing (SA) algorithm. This algorithm gradually reduces the randomness of the system by simulating the temperature drop in the physical annealing process, so as to find the global optimal solution of the system. When solving the EDCL site selection and capacity determination problem, the simulated annealing algorithm can effectively avoid falling into local optimal solutions and improve the quality and stability of the solutions. The basic steps of the algorithm include initializing system parameters, randomly selecting an initial solution, searching in the neighborhood of the current solution and accepting new solutions, and gradually reducing the "temperature" of the system according to the cooling schedule until the stopping condition is met. In this way, the simulated annealing algorithm can find the EDCL configuration plan with the lowest cost on the premise of ensuring the stable operation of the power grid.

[0149] In an alternative embodiment, the solution of the target site selection and capacity determination results can also be obtained by performing a transformation operation through a second-order cone model;

[0150] In the embodiment of the present application, the solution of the target site selection and capacity determination results is obtained by performing a transformation operation through a second-order cone model. Specifically:

[0151] Linearize and convexly relax certain conditions, and then transform the non-linear programming into a second-order cone programming model. The specific method is actually to use and in the design formula replaced by v t,i and l t,ij instead, and use and instead:

[0152]

[0153] After linearization, if there is still non-linearity, it is transformed into a second-order cone constraint:

[0154]

[0155]

[0156] It should be noted that in the EDCL planning constraints, the stability and reliability of the system must be considered. Therefore, during planning, it is necessary to ensure that all EDCL devices can meet the voltage stability requirements under various operating conditions. In addition, environmental factors such as temperature and humidity also need to be considered to ensure the normal operation of EDCL devices in different environments. At the same time, the planning should also consider future possible expandability to facilitate system upgrade and maintenance.

[0157] In summary, the present invention proposes a method for voltage sensitivity embedded DC link site selection and capacity determination, which obtains the first target parameter and performs a first preprocessing on the first target parameter to obtain the second target parameter; obtains the third target parameter, combines the second target parameter and the first improved sensitivity algorithm to determine the first target access point; establishes a first optimization model, and solves the target site selection and capacity determination result according to the first target access point and the first optimization model. By establishing the optimization model, the present invention can effectively reduce the annual comprehensive total cost, improve the accommodation ratio of new energy, and enhance the stability of the system. The improved sensitivity algorithm of the present invention can more accurately determine the target access point, thereby optimizing the power flow distribution and improving the voltage quality. The system and method of the present invention have good flexibility and scalability in practical applications and can adapt to different scales and types of distribution networks. The embodiments of the present invention can provide theoretical support for the transformation of the power system and promote the transformation of the distribution network to an AC-DC hybrid distribution form.

[0158] Embodiment 2

[0159] In a preferred embodiment, the following specific operations are designed according to the above method:

[0160] Figure 3 This embodiment of the present invention is a flow chart of an optimization strategy for the site selection of capacitors in an embedded DC link. Figure 5 This embodiment of the present invention provides a specific algorithm implementation flow chart for EDCL site selection and capacity determination, as Figure 3 and Figure 5 shown.

[0161] By selecting the highest point of voltage sensitivity as the EDCL access point 1 and taking the annual comprehensive cost as the target, an EDCL site selection and capacity determination model is established.

[0162] By generating a random scenario distribution that meets the requirements through the Monte Carlo method and combining with the fast backward reduction method for scenario reduction.

[0163] By introducing a weight factor on the basis of active and reactive voltage sensitivities to calculate the weighted voltage sensitivity. Since the access nodes of distributed power sources are likely to cause voltage offset, a voltage offset weight factor for each time period is introduced into the sensitivity calculation. At the same time, considering voltage over-limit, the voltage over-limit rate is introduced as the voltage sensitivity weight factor.

[0164] By establishing a minimization of the annual comprehensive cost as the objective function, the model is solved.

[0165] By setting up constraint conditions such as AC distribution network power flow constraints, port operation constraints, and DC distribution network power flow constraints, a complete mathematical model is obtained.

[0166] Through second-order cone model transformation; linearize and convexly relax certain conditions, and then transform the non-linear programming into a second-order cone programming model.

[0167] It should be noted that the processing of source-load randomness; the improved voltage sensitivity calculation method considering source-load uncertainty; the selection of inclusion points based on the improved voltage sensitivity; the site selection and capacity determination optimization model of the embedded DC link; the site selection and capacity determination optimization method of the EDCL with the minimum comprehensive cost. The embedded DC link (EDCL) is an extension of the intelligent soft open point (SOP) technology. Currently, the existing SOP often uses scenario analysis technology to construct an optimization model. In the SOP site selection and capacity determination model, common objective functions include minimizing power loss and minimizing cost. The constraint conditions usually cover the constraints of the two-end converters and the AC distribution network constraints. However, the SOP site selection and capacity determination model only considers replacing the fixed tie switch branch as the candidate location of the SOP, and is still limited to the positions of the original feeder branch switches and tie switches.

[0168] The present invention is first different from the SOP in both the site selection and capacity determination optimization model and the candidate access locations. The present invention focuses on optimizing the operation performance of the entire power grid by introducing a DC link. The form of the DC link is diverse, which can be a back-to-back SOP, a multi-port SOP, or a two-terminal DC network or a multi-terminal DC network.

[0169] In terms of constraint conditions: in addition to considering the power exchange between the DC and AC ports, the present invention also needs to fully consider the cooperative control mode between each end and the power constraints of the DC network part.

[0170] In terms of access locations: different from the SOP accessing fixed candidate locations, the EDCL can use all electrical nodes in the feeder or feeder group as candidate access nodes, providing a very flexible energy routing channel and a voltage support method with multi-access point cooperation for a high proportion of new energy in the distribution network.

[0171] In terms of optimizing the operation performance of the power grid, considering using an embedded DC link to improve the anti-interference ability, the embedded DC link has the following three high advantages: First, high scalability: its internal DC network has a wide coverage range, facilitating the access of a large number of distributed power sources, loads, and energy storage devices; Second, high flexibility: without changing the existing feeder topology structure and the existing power supply form, the embedded DC link transforms the distribution network at the physical level, greatly enhancing the structural flexibility of the distribution system; Finally, high reliability: when the EDCL is operating normally, it can continuously adjust the active power, provide reactive power support, improve the voltage quality, and relieve network congestion.

[0172] In terms of the randomness of power sources and loads: The improved voltage sensitivity embedded DC link site selection and capacity determination method of the present invention considering the randomness of power sources and loads starts from the preliminary planning, selects the access points for EDCL and optimizes the installation capacity. The site selection and capacity determination strategy considered simplifies the 528 combinations of site selection schemes to 32 combinations, reducing the calculation amount and complexity. At the same time, while ensuring the voltage level, this strategy can effectively reduce costs and improve the distributed power source carrying capacity of the distribution network.

[0173] Embodiment 3

[0174] This embodiment also provides a voltage sensitivity embedded DC link site selection and capacity determination system, including:

[0175] A data processing module, configured to obtain a first target parameter and perform a first preprocessing on the first target parameter to obtain a second target parameter;

[0176] An access point determination module, configured to obtain a third target parameter, and determine a first target access point in combination with the second target parameter and a first improved sensitivity algorithm;

[0177] A solution module, configured to establish a first optimization model, and solve the target site selection and capacity determination result according to the first target access point and the first optimization model.

[0178] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0179] This embodiment also provides a computer device. This computer device can be a terminal, and its internal structure diagram can be as Figure 6 shown. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (near field communication), or other technologies. When the computer program is executed by the processor, it realizes a voltage sensitivity embedded DC link site selection and capacity determination method. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0180] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0181] Obtain a first target parameter, and perform a first preprocessing on the first target parameter to obtain a second target parameter;

[0182] Obtain a third target parameter, and determine a first target access point by combining the second target parameter and a first improved sensitivity algorithm;

[0183] Establish a first optimization model, and solve the target site selection and capacity determination result according to the first target access point and the first optimization model.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0185] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0187] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the function specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0189] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0190] It is apparent that those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A voltage-sensitive embedded DC link location and capacity determination method, characterized in that: include: Acquire a first target parameter, and perform a first preprocessing on the first target parameter to obtain a second target parameter; Acquire a third target parameter, and determine a first target access point in combination with the second target parameter and a first improved sensitivity algorithm; A first optimization model is established, and a target site selection and capacity determination result is solved according to the first target access point and the first optimization model.

2. The voltage sensitivity embedded DC link location and capacity determination method according to claim 1, characterized in that: The first optimization model comprises: The first optimization model includes a first objective function and a first constraint condition; The first objective function is an arbitrary function that minimizes the annual comprehensive total cost; The first constraint condition includes at least an AC distribution network partial constraint, a DC distribution network partial constraint, and an exchange constraint between the AC distribution network and the DC distribution network.

3. The voltage sensitivity embedded DC link location and capacity determination method according to claim 2, characterized in that: The first improved sensitivity algorithm comprises: The first improved sensitivity algorithm is any algorithm that combines the second target parameter and the third target parameter to obtain the first target access point; The first improved sensitivity algorithm at least includes taking the target voltage over-limit rate as a sensitivity weight factor.

4. The voltage sensitivity embedded DC link location and capacity determination method according to claim 3, characterized in that: The acquiring of the third target parameter and determining the first target access point in combination with the second target parameter and the first improved sensitivity algorithm comprises: performing a first sorting on the calculation results of the first improved sensitivity algorithm; A first target access point is determined according to a result of the first sorting.

5. The voltage sensitivity embedded DC link location and capacity determination method according to claim 4, characterized in that: The first objective function at least includes the embedded DC link investment cost, operation and maintenance cost, and annual loss cost.

6. The voltage sensitivity embedded DC link location and capacity determination method according to claim 5, characterized in that: The first constraint condition at least includes a power exchange constraint between the DC and AC ports, a coordinated control mode between the terminals, and a power constraint of the DC network part.

7. The voltage sensitivity embedded DC link location and capacity determination method according to claim 6, characterized in that: The first preprocessing comprises: generating random scenarios about the first target parameter; Calculating the Euclidean distance between the random scenes; The minimum probability distance scene is obtained and reduced to obtain the second target parameter.

8. A voltage-sensitive embedded DC link site selection and capacity determination system, characterized in that: include: A data processing module, used for acquiring a first target parameter, and performing a first preprocessing on the first target parameter to obtain a second target parameter; An access point determination module, configured to obtain a third target parameter, and determine a first target access point in combination with the second target parameter and a first improved sensitivity algorithm; The solution module is used to establish a first optimization model and solve the target site selection and capacity determination result according to the first target access point and the first optimization model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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