Calculation method considering high-proportion new energy access power distribution network line loss

Abstract

The invention discloses a method for calculating the line loss of a high-proportion new energy access power distribution network, and belongs to the technical field of calculation of the line loss of the high-proportion new energy access power distribution network. The calculation method for the line loss of the high-proportion new energy access power distribution network comprises the following steps: S1, carrying out preliminary data acquisition and preprocessing by the calculation method for the line loss of the high-proportion new energy access power distribution network, and providing basic line parameters for all other modules by a module; according to the method for calculating the line loss of the high-proportion new energy access power distribution network, the change characteristics of the line loss of the power electronic power distribution network accessed by the high-proportion distributed power supply and the optimization mechanism are calculated; comprising the aspects of network loss change characteristics and a line loss optimization mechanism under a deterministic model, an uncertainty processing scheme, a line loss and electric energy quality regulation and control method and the like, and a line loss regulation and control scheme based on an evolutionary algorithm is established aiming at the electric energy quality problem caused by high-proportion distributed power supply and nonlinear load grid connection.

Description

Technical Field

[0001] This invention relates to the field of calculation technology for line losses in distribution networks with a high proportion of renewable energy access, specifically a calculation method for line losses in distribution networks with a high proportion of renewable energy access. Background Technology

[0002] The high proportion of distributed power generation connected to the grid and the diverse nonlinear load access have had a significant impact on the power quality and line loss management of the distribution network. With the development of clean energy, the penetration rate of renewable energy in the distribution network will inevitably increase. Considering the uncertainty of the output of high-proportion distributed power generation and load demand, the optimization schemes proposed based on the deterministic model of the distribution network are not statistically significant and are difficult to guide production practices in real-world situations.

[0003] The process can easily lead to a nonlinear integer programming problem of minimizing line loss. Furthermore, due to the heat generated on the lines and transformers, the losses are caused by the heat and change with the line loss. It is difficult to simultaneously optimize line loss and power quality using a single objective optimization model, and it is also difficult to control line loss. This affects the line loss control and power quality transmission of distribution networks with a high proportion of distributed power sources.

[0004] To address the aforementioned issues, there is an urgent need for innovative design based on the existing calculation methods that take into account the line losses of high proportions of renewable energy connected to the distribution network. Summary of the Invention

[0005] The purpose of this invention is to provide a calculation method for line losses in distribution networks that take into account a high proportion of new energy sources. This method addresses the problems in the background art, such as the nonlinear integer programming problem that easily leads to the minimum line loss, the loss caused by heat generation on lines and transformers, the characteristic of the loss changing with the line loss, the difficulty in simultaneously optimizing line loss and power quality in a single-objective optimization model, and the difficulty in controlling line loss. These issues affect the line loss control and power quality transmission of distribution networks with a high proportion of distributed power sources.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a calculation method for line losses in a distribution network considering a high proportion of renewable energy access, characterized in that the calculation method for line losses in a distribution network considering a high proportion of renewable energy access includes the following steps: S1: For the calculation method for line losses in a distribution network considering a high proportion of renewable energy access, a preliminary data acquisition and preprocessing module provides basic line parameters, load, or renewable energy raw data to all other modules; S2: After data acquisition is completed, the prediction results are transmitted to the power flow calculation module through the renewable energy output prediction module as input for node power injection; S3: Through the calculation method considering a high proportion of renewable energy access... The power flow calculation module, which is set up to calculate the line loss of new energy access to the distribution network, outputs line power or voltage distribution data, which is the core input of the line loss calculation module. The power flow results can also provide basic scenario data for the uncertainty analysis module. S4: The bus loss and branch line loss of the line loss calculation module can be used as sample data for the uncertainty analysis module, and can also be input into the verification and optimization module for verification and parameter adjustment. S5: Through the line loss calculation module and the uncertainty analysis module, the verification and optimization module feed back the deviation analysis results to the data acquisition and preprocessing module, which can form an iterative optimization closed loop with the power flow calculation module.

[0007] Preferably, the data acquisition can first collect static parameters through a GIS system or SCADA system, which includes line topology, resistance or reactance data, as well as dynamic load, new energy output or meteorological data.

[0008] The preprocessing module further processes missing values ​​by performing linear interpolation to complete them, and identifies outliers using the 3σ criterion, replacing them with the average of adjacent time points. It also standardizes the data, and through uniform time granularity, converts all time-series data into equal time intervals, and converts topology data into a node-generated line association table to ensure that the data is standardized and usable.

[0009] Preferably, the new energy output prediction module can predict the time-series output of new energy based on historical data and meteorological information, providing input for power flow calculation, and selecting key factors affecting the output of new energy, including solar irradiance, temperature and historical output for photovoltaic prediction, and wind speed, wind direction and air density for wind power prediction, and normalizing the features and filtering their correlation.

[0010] Preferably, during the initialization of the power flow calculation module, the node hierarchy is defined, the node voltage is set as the fixed rated line voltage, and the net power of the node is calculated, which is the output of new energy minus the load consumption. A positive value indicates that the power is injected into the grid, and a negative value indicates that the power is absorbed. The back-substitution step proceeds from the end to the root node, calculates the line active power loss based on the power and resistance of the child nodes, and accumulates it to the output power of the parent node. The forward step updates the voltage from the root node to the end, calculates the child node voltage through the voltage of the parent node and the line power drop, and repeats the iteration until the adjacent voltage difference is minimized, so as to obtain a stable line power and voltage distribution, which provides a basis for line loss calculation.

[0011] Preferably, the line loss calculation module reasonably determines the grid connection point and capacity of distributed power sources, builds a line loss optimization problem model for the power electronic distribution network, and builds a corresponding algorithm platform. It selects appropriate algorithm inputs as the harmonic spectrum parameters of each nonlinear load and distributed power source. After solving the harmonic power flow, it obtains system parameters such as power flow and line loss, providing underlying data support for the subsequent optimization process. Based on simulation results, it analyzes the impact of different distributed power source grid connection numbers and whether harmonic constraints are considered on line loss optimization. Furthermore, it establishes the line loss control mechanism and optimization scheme for power electronic distribution networks with a high proportion of distributed power sources connected, based on the number of distributed power source grid connection points and harmonic constraints.

[0012] Preferably, the line loss optimization problem model of the power electronic distribution network is designed to model the line loss optimization problem and its solution method in the power electronic distribution network. It mainly starts from two aspects: optimization problem and algorithm platform. It builds a line loss optimization model for power electronic distribution networks with a high proportion of distributed generation access. By reasonably determining the grid connection point and capacity of DG, it solves the nonlinear integer programming of system line loss. The line loss considered in the optimization process mainly refers to theoretical line loss, specifically including the loss caused by heat generation on the lines and transformers. This type of loss can be approximated as the active power loss caused by current flowing through the resistor.

[0013] Preferably, the solution process for the line loss optimization problem model of the power electronic distribution network can be roughly divided into three parts: the power flow solution model of the power electronic distribution network, the line loss optimization model, and the final evaluation of the optimization results. The first layer is mainly responsible for building the power flow calculation model of the power electronic distribution network. Its input is the harmonic spectrum parameters of each nonlinear load and the distributed generation (DG). After solving the harmonic power flow, system parameters such as power flow and line loss are obtained, providing underlying data support for the subsequent optimization process. The second layer is the line loss optimization part, whose main function is to build the line loss optimization model and use relevant algorithms to solve the model to obtain the optimization scheme of DG grid connection. The third layer is to process the optimization results and present the system parameters in a concise way.

[0014] Preferably, the line loss calculation module also includes a distribution network line loss optimization model and calculation method under the large-scale access of distributed power sources. The high proportion of distributed power source grid connection and the large-scale access of nonlinear loads make the operation of the distribution network more complex and variable, with strong randomness and volatility. Under the premise of considering the uncertainty of the output power of distributed power sources and load fluctuations, it is necessary to establish a power electronic distribution network uncertainty model, which can be applied to the line loss control of distribution networks with a high proportion of distributed power source access.

[0015] Preferably, the uncertainty analysis module first fits a probability distribution. For photovoltaic power, the Beta distribution parameters α and β are determined using maximum likelihood estimation based on historical data. For wind power, the shape parameter k and scale parameter λ of the Weibull distribution are fitted. Multiple new energy output scenarios are generated through Monte Carlo simulation. For each scenario, the power flow and line loss modules are called to calculate the line loss. Finally, statistical indicators are calculated: the expected value is the mean of the line loss for all scenarios, the standard deviation reflects the degree of dispersion, and the 95% confidence interval is determined by the mean ± 1.96 times the standard deviation, quantifying the range of line loss fluctuations caused by the randomness of new energy.

[0016] Preferably, the verification and optimization module compares the calculated line loss with the measured value during the verification phase, uses the deviation rate to evaluate the accuracy, and corrects parameters when the deviation exceeds 5%, such as adjusting the line resistance according to the temperature coefficient formula. Adjustment and optimization measures include adding capacitors to compensate for reactive power and reduce reactive power transmission losses in the lines; adjusting tie switches to reconstruct the network, transferring heavy-load line loads to light-load lines, recalculating optimized line losses, assessing the reduction amount, and outputting compensation capacity, switch operation, and other solutions to achieve precise control of line losses.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] 1. This method for calculating line losses in distribution networks with a high proportion of new energy sources is proposed. It calculates the characteristics and optimization mechanisms of network losses in electronic distribution networks with a high proportion of distributed power sources, including network loss variation characteristics and line loss optimization mechanisms under deterministic models, uncertainty handling schemes, and line loss and power quality control methods. It also establishes a line loss control scheme based on evolutionary algorithms to address the power quality problems caused by the grid connection of high proportion of distributed power sources and nonlinear loads.

[0019] 2. The calculation method for distribution network line loss that takes into account a high proportion of new energy access is established. The uncertainty processing module is integrated with the deterministic optimization model, and a distribution network line loss optimization scheme suitable for high proportion of distributed power access is proposed and established.

[0020] 3. This paper proposes a calculation method for line losses in distribution networks with a high proportion of new energy sources. A multi-objective optimization model is established, and based on this model, optimization schemes and strategies for line losses and power quality are constructed. The reliability of the established optimization model and related conclusions is verified by simulation based on case studies. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the calculation method of the present invention that takes into account the line loss of a high proportion of new energy access to the distribution network;

[0022] Figure 2 A schematic diagram illustrating the calculation method of the present invention that takes into account the line loss of a high proportion of new energy access to the distribution network;

[0023] Figure 3 This is a schematic diagram illustrating the working steps of the calculation method for line losses in the distribution network that takes into account a high proportion of new energy sources. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-3 This invention provides a technical solution: a calculation method for line losses considering a high proportion of renewable energy access to the distribution network, the calculation method for line losses considering a high proportion of renewable energy access to the distribution network includes the following steps:

[0026] Step 1: For the calculation method that takes into account the line loss of a high proportion of new energy access to the distribution network, the preliminary data acquisition and preprocessing module provides basic line parameters, load or new energy raw data for all other modules;

[0027] The second step: After the data collection is completed, the prediction results will be transmitted to the power flow calculation module through the new energy output prediction module as the input for node power injection;

[0028] The third step: The power flow calculation module, which is set up by the calculation method that takes into account the line loss of high proportion of new energy access to the distribution network, outputs the line power or voltage distribution data, which is the core input of the line loss calculation module, and the power flow results can also provide basic scenario data for the uncertainty analysis module.

[0029] Step 4: The bus loss and branch line loss of the line loss calculation module can be used as sample data for the uncertainty analysis module, or input into the verification and optimization module for verification and parameter adjustment;

[0030] Step 5: Verify the results through the line loss calculation module and uncertainty analysis module, and feed the deviation analysis results back to the data acquisition and preprocessing module through the optimization module, which can form an iterative optimization closed loop with the power flow calculation module.

[0031] Data acquisition can begin with collecting static parameters through GIS or SCADA systems, including line topology, resistance or reactance data, as well as dynamic load, renewable energy output or meteorological data.

[0032] The preprocessing module further processes missing values ​​by performing linear interpolation to complete them, and identifies outliers using the 3σ criterion, replacing them with the average of adjacent time points. It also standardizes the data, and through uniform time granularity, converts all time-series data into equal time intervals. Finally, it converts topology data into a node-generated line association table to ensure that the data is standardized and usable.

[0033] The new energy output prediction module can predict the time-series output of new energy sources based on historical data and meteorological information, providing input for power flow calculation. It also selects key factors affecting the output of new energy sources, including solar irradiance, temperature and historical output for photovoltaic prediction, and wind speed, wind direction and air density for wind power prediction. The features are normalized and their correlation is filtered.

[0034] During the initialization of the power flow calculation module, the node hierarchy is defined, the node voltage is set as the fixed rated line voltage, and the net power of the node is calculated, which is the output of new energy minus the load consumption. Positive values ​​indicate that the power is injected into the grid, and negative values ​​indicate that it is absorbed. The back-substitution step proceeds from the end to the root node, calculates the line active power loss based on the power and resistance of the child nodes, and adds it to the output power of the parent node. The forward step updates the voltage from the root node to the end, calculates the child node voltage through the voltage of the parent node and the line power drop, and repeats the iteration until the voltage difference between adjacent nodes is minimized, so as to obtain a stable line power and voltage distribution, which provides a basis for line loss calculation.

[0035] The line loss calculation module rationally determines the grid connection points and capacity of distributed generation sources, builds a line loss optimization problem model for power electronic distribution networks, and establishes a corresponding algorithm platform. Appropriate algorithm inputs are selected as the harmonic spectrum parameters of each nonlinear load and distributed generation source. After solving the harmonic power flow, system parameters such as power flow and line loss are obtained, providing underlying data support for subsequent optimization processes. Based on simulation results, the module analyzes the impact of different distributed generation grid connection numbers and whether harmonic constraints are considered on line loss optimization. Finally, it explores the line loss control mechanism and optimization scheme for power electronic distribution networks with a high proportion of distributed generation access based on the number of distributed generation grid connection points and harmonic constraints.

[0036] This paper presents a model for line loss optimization in power electronic distribution networks. It addresses the problem of line loss optimization in power electronic distribution networks and its solution methods. The model primarily focuses on two aspects: the optimization problem itself and the algorithm platform. It establishes a line loss optimization model for power electronic distribution networks with a high proportion of distributed generation (DG) access. By rationally determining the grid connection point and capacity of the DG, the model solves the nonlinear integer programming problem related to system line loss. The line loss considered in the optimization process mainly refers to theoretical line loss, specifically including losses caused by heat generation on lines and transformers. These losses can be approximated as active power losses caused by current flowing through resistors.

[0037] The solution process for the line loss optimization problem in a power electronic distribution network can be roughly divided into three parts: the power flow calculation model, the line loss optimization model, and the final evaluation of the optimization results. The first layer is mainly responsible for building the power flow calculation model of the power electronic distribution network. Its inputs are the harmonic spectrum parameters of each nonlinear load and the distributed generation (DG). After solving the harmonic power flow, system parameters such as power flow and line loss are obtained, providing underlying data support for the subsequent optimization process. The second layer is the line loss optimization part, whose main function is to build the line loss optimization model and use relevant algorithms to solve the model to obtain the optimization scheme for DG grid connection. The third layer is to process the optimization results and present the system parameters in a concise way.

[0038] The line loss calculation module also includes a distribution network line loss optimization model and calculation method under the large-scale access of distributed power sources. The high proportion of distributed power source grid connection and large-scale nonlinear load access make the operation of the distribution network more complex and variable, with strong randomness and volatility. Under the premise of considering the uncertainty of the output power of distributed power sources and load fluctuations, it is necessary to establish a power electronic distribution network uncertainty model, which can be applied to the line loss control of distribution networks with a high proportion of distributed power source access.

[0039] The uncertainty analysis module first fits a probability distribution. For photovoltaics, the parameters α and β of the Beta distribution are determined by maximum likelihood estimation based on historical data. For wind power, the shape parameter k and scale parameter λ of the Weibull distribution are fitted. Multiple new energy output scenarios are generated through Monte Carlo simulation. For each scenario, the power flow and line loss modules are called to calculate the line loss. Finally, statistical indicators are calculated: the expected value is the mean of the line loss for all scenarios, the standard deviation reflects the degree of dispersion, and the 95% confidence interval is determined by the mean ± 1.96 times the standard deviation, quantifying the range of line loss fluctuations caused by the randomness of new energy.

[0040] The verification and optimization module compares the calculated line loss with the measured value during the verification phase, uses the deviation rate to evaluate accuracy, and corrects parameters when the deviation exceeds 5%, such as adjusting the line resistance according to the temperature coefficient formula. Adjustment and optimization measures include adding capacitors to compensate for reactive power and reduce reactive power transmission losses in the lines; adjusting tie switches to reconstruct the network, transferring heavy-load line loads to light-load lines, recalculating optimized line losses, assessing the reduction amount, and outputting compensation capacity, switch operation, and other solutions to achieve precise control of line losses.

[0041] The specific calculations are as follows:

[0042] Power flow calculation method

[0043] Unified Solution Method: The unified solution method for harmonic power flow combines the fundamental wave power flow and harmonic power flow into a single process. Specifically, after each fundamental wave power flow calculation, the result is used as input parameters into the harmonic current balance calculation formula shown below. When this harmonic power flow calculation ends, if the distortion rate is within a specified range, the iteration ends; otherwise, the iteration calculation is performed again. This process is repeated until the result meets the distortion rate requirement.

[0044] I k= Y k U k

[0045] Where k is the harmonic order I k Y k U k and are the harmonic current, harmonic admittance matrix, and harmonic voltage for the corresponding orders, respectively;

[0046] As can be seen from the algorithm description above, the solution process of the unified solution method requires repeated iterations. Although it has high accuracy, it also consumes a lot of time and computing resources. In addition, due to the excessive number of iterations, any small change in the fundamental and harmonic calculation process will affect the results, and may even cause the power flow calculation results to fail to converge. This greatly limits the application of the unified solution method in practice.

[0047] Alternating Iteration and Other Improved Methods: Due to the numerous limitations of the unified solution method, subsequent research simplified the unified solution to obtain the alternating iteration method. Simply put, the alternating iteration method assumes that harmonic power flow does not affect the initial value of the fundamental power flow. It directly substitutes the calculated results of the fundamental power flow into the harmonic power flow equation for calculation, and then substitutes the power and other parameters obtained from the harmonic power flow solution into the fundamental power flow equation, iterating repeatedly until the results meet the accuracy requirements. However, since this method initially assumes that the fundamental power flow is unaffected by harmonic power flow, the calculated results of the fundamental power flow need to be corrected in subsequent iterations. Therefore, the use of this method requires high accuracy in modeling the system's harmonic sources.

[0048] Although the alternating iteration method reduces computation time and computational burden, it is still difficult to apply in large power systems. Therefore, subsequent research has further simplified the relationship between harmonic power flow and fundamental power flow, resulting in decoupling method and direct solution method.

[0049] The decoupling method and the direct solution method further weaken the influence relationship between harmonics and the fundamental frequency. Among them, the direct solution method, also known as the linear analysis method, assumes that the harmonic current is only affected by the fundamental voltage during the solution process. The algorithm first obtains relevant parameters such as the bus voltage by solving the fundamental power flow, and then calculates the harmonic current and harmonic voltage based on the results, which greatly simplifies the solution process. Although the direct solution method is slightly less accurate than the previous methods, it greatly simplifies the solution of harmonic power flow and saves a lot of resources. Therefore, this method is widely used in practice. This project adopted this method when solving the harmonic power flow distribution.

[0050] The solution model for harmonic power flow mainly consists of the following parts:

[0051] Establish the harmonic admittance matrix for each power distribution network;

[0052] Solve for the fundamental power flow distribution to obtain data such as line loss and fundamental voltage;

[0053] The harmonic current injected into the system by each harmonic source is solved based on the established harmonic source model.

[0054] The admittance equation is solved iteratively until the voltage converges, and finally the harmonic voltage and current of each node are obtained; the specific admittance matrix can be expressed as the following calculation formula;

[0055]

[0056] The diagonal element Y ij Y is the self-admittance of the node. ij These are called the mutual admittances between nodes i and j; they can be calculated by the following formulas:

[0057]

[0058]

[0059] Where y ij The branch impedance z between nodes i and j ij The reciprocal of;

[0060] Furthermore, when solving for harmonic power flow, the admittance equation of the nonlinear system obtained by iterative solution is shown below:

[0061]

[0062] in The system voltage vector obtained in the (n+1)th iteration. This is the inverse matrix of the system admittance matrix corresponding to the h-th harmonic. ( This is the vector composed of the input currents of each harmonic source;

[0063] Line loss optimization problem modeling and calculation: The project models the line loss optimization problem of the power electronic distribution network. By reasonably determining the grid connection point and capacity of the distributed generation (DG), it solves the nonlinear integer programming problem of minimizing system line loss. Furthermore, the line loss considered in the optimization process mainly refers to theoretical line loss, specifically including losses caused by heat generation on lines and transformers. This type of loss can be approximated as the active power loss caused by current flowing through resistors. The calculation method is shown in the following formula:

[0064]

[0065] in, For losses on the transformer, I represents the line loss, and I represents the current passing through the line and transformer. and These represent the resistances of the transformer coil and the line, respectively. The project's objective function is shown in the following formula, representing minimizing the sum of line losses and transformer losses in the system:

[0066]

[0067] in, For losses on transformers in electronic power distribution networks, For losses on the line, Let the total line loss of the distribution network be denoted as . The main constraints in solving this optimization problem are:

[0068] Bus voltage constraint: According to relevant standards, in order to ensure the safe and reliable operation of the power system, the maximum voltage deviation and the average absolute value of the voltage deviation at each node in the system must satisfy the following relationship:

[0069]

[0070] in The reference voltage for the system. The voltage at node i;

[0071] System voltage harmonic content constraints: According to the IEEE-519-2014 standard, for power electronic distribution networks, the harmonic magnitudes of each bus in the distribution network system must meet the following conditions:

[0072]

[0073] in Let H be the h-th harmonic voltage corresponding to bus i, where H is the maximum value of the system bus number. This represents the magnitude of the total harmonic distortion (THD) of the voltage on the corresponding i-th bus.

[0074] DG grid-connected capacity constraints: The DG data model built for the project has a power factor of 0.95; the main constraint on DG is that the total grid-connected capacity of DG in the optimized scheme must be greater than 0 and less than the total load. The details are as follows:

[0075]

[0076] Integer constraint for DG grid connection point: To ensure the practical feasibility of the optimization scheme, the DG grid connection point needs to be constrained to an integer, meaning the DG is connected to the bus node rather than the line. Furthermore, since this line loss optimization scheme targets a three-phase unbalanced distribution network, the DG grid connection bus must also be a three-phase connection. Therefore, the DG grid connection bus can only be selected from the set of accessible nodes; the specific formula is as follows:

[0077]

[0078] in, It is a binary state variable (1 indicates that DG is connected at node i, and 0 indicates that DG is not connected at node i), which is the set of three-phase busbars that can be connected, and i is the number of each busbar in the system.

[0079] The uncertainty analysis module examines the impact of the randomness of new energy output on line loss. The steps are as follows:

[0080] Probability distribution modeling: Fitting the probability distribution of new energy output based on historical data: Photovoltaic output follows a Beta distribution (parameters α and β are determined by maximum likelihood estimation), and wind power output follows a Weibull distribution (shape parameter k, scale parameter λ).

[0081] Scene generation: Monte Carlo simulation is used: N new energy output scenarios (e.g., N=1000) are generated by randomly sampling from the above distribution. Each scenario contains P for the entire time period. G (t), Q G (t).

[0082] Line loss statistical characteristic calculation: For each scenario, the power flow calculation and line loss calculation modules are invoked to obtain the scenario line loss ΔE. i (i=1, 2, ..., N).

[0083] The verification and optimization module verifies the calculation accuracy and optimizes line loss. Calculation result verification involves comparing the calculated line loss with measured data (total line loss obtained from the electricity metering system) and calculating the deviation rate. If the deviation rate > 5%, the input parameters need to be corrected (e.g., line resistance may need adjustment due to temperature changes). (where α is the temperature coefficient of resistance).

[0084] Line loss optimization measures include reactive power compensation: installing capacitors at renewable energy access nodes to reduce reactive power transmission on the lines (e.g., calculating the optimal compensation capacity QC to minimize reactive power loss). Network reconfiguration: optimizing line power distribution by adjusting tie switch status (e.g., transferring some load from heavily loaded lines to lightly loaded lines).

[0085] Optimization effect evaluation: Recalculate the optimized line loss and compare the loss reduction before and after optimization (e.g., Output optimization plan reports (such as compensation capacity and switch operation sequence) to guide actual power grid operation.

[0086] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A calculation method for line losses considering a high proportion of new energy sources connected to the distribution network, characterized in that, The calculation method that takes into account the line losses of a high proportion of new energy sources connected to the distribution network includes the following steps: S1: For the calculation method that takes into account the line loss of high proportion of new energy access to the distribution network, the preliminary data acquisition and preprocessing module provides basic line parameters, load or new energy raw data for all other modules; S2: After data acquisition is completed, the prediction results will be transmitted to the power flow calculation module through the new energy output prediction module as input for node power injection; S3: The power flow calculation module, which is set up by the calculation method that takes into account the line loss of high proportion of new energy access to the distribution network, outputs line power or voltage distribution data, which is the core input of the line loss calculation module, and the power flow results can also provide basic scenario data for the uncertainty analysis module; S4: The bus loss and branch line loss of the line loss calculation module can be used as sample data for the uncertainty analysis module, or input into the verification and optimization module for verification and parameter adjustment; S5: The line loss calculation module and uncertainty analysis module are used for verification. The deviation analysis results are fed back to the data acquisition and preprocessing module by the optimization module, which can form an iterative optimization closed loop with the power flow calculation module.

2. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The data acquisition can first be carried out through GIS system and SCADA system to collect static parameters, including line topology, resistance or reactance data, as well as dynamic load, new energy output or meteorological data. The preprocessing module further processes missing values ​​by performing linear interpolation to complete them, and identifies outliers using the 3σ criterion, replacing them with the average of adjacent time points. It also standardizes the data, and through uniform time granularity, converts all time-series data into equal time intervals, and converts topology data into a node-generated line association table to ensure that the data is standardized and usable.

3. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The new energy output prediction module can predict the time-series output of new energy sources based on historical data and meteorological information, providing input for power flow calculation. It also selects key factors affecting the output of new energy sources, including solar irradiance, temperature and historical output for photovoltaic prediction, and wind speed, wind direction and air density for wind power prediction. The features are normalized and their correlation is filtered.

4. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: When the power flow calculation module is initialized, the node hierarchy is defined, the node voltage is set as the fixed rated line voltage, and the net power of the node is calculated, which is the output of new energy minus the load consumption. A positive value indicates that the power is injected into the grid, and a negative value indicates that the power is absorbed. The iteration process proceeds from the end to the root node, and the active power loss of the line is calculated based on the power and resistance of the child node and accumulated to the output power of the parent node. The forward push-through process updates the voltage from the root node to the end node, calculates the voltage of the child node by using the voltage of the parent node and the power drop of the line, and repeats the iteration until the voltage difference between adjacent nodes is minimized, thus obtaining a stable distribution of line power and voltage, which provides a basis for line loss calculation.

5. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The line loss calculation module rationally determines the grid connection points and capacity of distributed power sources, builds a line loss optimization problem model for the power electronic distribution network, and establishes a corresponding algorithm platform. It selects appropriate algorithm inputs as the harmonic spectrum parameters of each nonlinear load and distributed power source. By solving the harmonic power flow, it obtains system parameters such as power flow and line loss, providing underlying data support for the subsequent optimization process. Based on simulation results, it examines the impact of different distributed power source grid connection numbers and whether harmonic constraints are considered on line loss optimization. Furthermore, it proposes a line loss control mechanism and optimization scheme for power electronic distribution networks with a high proportion of distributed power sources connected, based on the number of distributed power source grid connection points and harmonic constraints.

6. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 5, characterized in that: The proposed model for line loss optimization in electronic power distribution networks addresses the problem and its solution. It primarily focuses on two aspects: optimization problem and algorithm platform. A line loss optimization model for electronic power distribution networks with a high proportion of distributed generation (DG) access is established. By rationally determining the grid connection point and capacity of the DG, the model solves the nonlinear integer programming problem related to system line loss. The line loss considered in the optimization process mainly refers to theoretical line loss, specifically including losses caused by heating on lines and transformers. This type of loss can be approximated as the active power loss caused by current flowing through resistors.

7. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 5, characterized in that: The solution process for the line loss optimization problem model of the power electronic distribution network can be roughly divided into three parts: the power flow calculation model of the power electronic distribution network, the line loss optimization model, and the final evaluation of the optimization results. The first layer is mainly responsible for building the power flow calculation model of the power electronic distribution network. Its input is the harmonic spectrum parameters of each nonlinear load and the distributed generation (DG). After solving the harmonic power flow, system parameters such as power flow and line loss are obtained, providing underlying data support for the subsequent optimization process. The second layer is the line loss optimization part. Its main function is to build the line loss optimization model and use relevant algorithms to solve the model to obtain the optimization scheme of DG grid connection. The third layer is to process the optimization results and present the system parameters in a concise way.

8. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The line loss calculation module also includes a distribution network line loss optimization model and calculation method under the large-scale access of distributed power sources. The high proportion of distributed power source grid connection and large-scale nonlinear load access make the operation of the distribution network more complex and variable, with strong randomness and volatility. Under the premise of considering the uncertainty of the output power of distributed power sources and load fluctuations, it is necessary to establish a power electronic distribution network uncertainty model, which can be applied to the line loss control of distribution networks with a high proportion of distributed power source access.

9. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The uncertainty analysis module first fits a probability distribution. For photovoltaic power, the Beta distribution parameters α and β are determined using maximum likelihood estimation based on historical data. For wind power, the shape parameter k and scale parameter λ of the Weibull distribution are fitted. Multiple new energy output scenarios are generated through Monte Carlo simulation. For each scenario, the power flow and line loss modules are called to calculate the line loss. Finally, statistical indicators are calculated: the expected value is the mean of the line loss for all scenarios, the standard deviation reflects the degree of dispersion, and the 95% confidence interval is determined by the mean ± 1.96 times the standard deviation, quantifying the range of line loss fluctuations caused by the randomness of new energy.

10. The calculation method for line losses considering a high proportion of new energy access to the distribution network according to claim 1, characterized in that: The verification and optimization module compares the calculated line loss with the measured value, uses the deviation rate to evaluate accuracy, and corrects parameters when the deviation exceeds 5%, such as adjusting the line resistance according to the temperature coefficient formula. Adjustment and optimization measures include adding capacitors to compensate for reactive power and reduce reactive power transmission losses in the lines; adjusting tie switches to reconstruct the network, transferring heavy-load line loads to light-load lines, recalculating optimized line losses, assessing the reduction amount, and outputting compensation capacity, switch operation, and other solutions to achieve precise control of line losses.

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