Method and equipment for improving bearing capacity of power distribution network based on node load directrix, and medium

Abstract

The invention relates to a power distribution network bearing capacity improvement method and device based on a node load directrix and a medium, and belongs to the field of power distribution network bearing capacity improvement, and the method comprises the steps: obtaining power distribution network data, carrying out the probability modeling, and generating an uncertainty scene set; constructing a node load directrix optimization model, introducing a bearing capacity improvement income item and a directrix smoothing regular item to construct an objective function, constructing a physical constraint condition, and solving to obtain a node load directrix optimal form; and for each random scene in the uncertainty scene set, respectively executing simulation in an independent operation mode and a directrix collaborative mutual aid mode, recording evaluation indexes in the two modes, and according to the evaluation indexes, calculating a bearing capacity improvement effect index to guide a planning decision, and the directrix cooperative mutual aid mode is to coordinate the input original load into a node load directrix optimal form by using the flexible interconnection device. Compared with the prior art, the method has the advantages of high synergy efficiency, intuitive bearing capacity improvement effect and the like.

Description

Technical Field

[0001] This invention relates to the field of distribution network carrying capacity enhancement, and in particular to a method, equipment and medium for enhancing distribution network carrying capacity based on nodal load profiles. Background Technology

[0002] Under conditions of high proportion of distributed power generation and new load access, the operating state of the distribution network changes from the traditional static, unidirectional power supply mode to a complex system with significant random fluctuations, temporal coupling, and spatial correlation. Existing engineering practices show that the superposition of uncertainties in photovoltaic power output and electric vehicle charging loads on both temporal and spatial scales can easily cause voltage overruns or equipment overloads in transformer substations and feeders during local periods, thereby limiting the scale of new load access, i.e., the distribution network carrying capacity problem.

[0003] To address the aforementioned issues, existing technologies primarily focus on "post-event verification" or "boundary calculation." One type of method uses power flow calculation or probabilistic power flow analysis to determine the maximum connectable capacity under constraints such as voltage and current. Another type of method alleviates local constraints during operation by configuring energy storage, flexible interconnection devices (SOPs), or implementing demand response, thereby indirectly improving carrying capacity. Related patents typically aim at "limit elimination," "power balance," or "local optima," regulating power at a single moment or within a limited time period, such as Chinese patent application CN118014438A.

[0004] However, in actual operation, distribution networks do not only face the risk of exceeding limits at individual moments, but rather operate close to the constraint boundaries for extended periods. Existing methods generally lack a clear definition of the "ideal operating state," and control strategies often revolve around "whether limits are exceeded," without providing the target operating trajectory or reference benchmark for nodes or distribution areas over time. In situations with frequent source-load fluctuations, this control method lacking a reference benchmark easily leads to frequent control actions, low resource utilization efficiency, and difficulty in systematically releasing the potential carrying capacity of the distribution network.

[0005] Defects and shortcomings of existing technology: (1) The load-bearing capacity assessment results are mainly based on “limit capacity” and lack executable operational references.

[0006] Existing capacity assessment methods often output "maximum accessible capacity" or "boundary points that satisfy constraints." These results are essentially static or statistical capacity limits, failing to address the question of how the load on each node or the load rate of equipment should be regulated during actual operation. Without a clear reference line, operators struggle to directly translate capacity assessment results into actionable operational control objectives, leading to a significant disconnect between assessment and control.

[0007] (2) Flexible devices such as SOP are mostly used for passive correction and do not fully serve the active improvement of bearing capacity.

[0008] Existing patents and literature on Standard Operating Procedures (SOPs) primarily emphasize their capabilities in power flow regulation and voltage support. Their control strategies are typically triggered upon detecting over-limits or imbalances, constituting a passive control approach. In the absence of clear operating guidelines, SOP power regulation struggles to revolve around an "ideal load distribution state," only eliminating over-limits within a localized area and failing to systematically explore the potential carrying capacity of the distribution network.

[0009] (3) The degree of deviation in the operating status lacks a quantitative benchmark, making it difficult to characterize the effect of the load-bearing capacity improvement.

[0010] Existing technologies often use indicators such as "whether limits are exceeded" and "probability of exceeding limits" to evaluate operational safety, but they lack quantitative descriptions of the degree to which node load or equipment load rate deviates from the ideal operating state. Due to the lack of a benchmark as a reference, the effect of carrying capacity improvement can usually only be explained by comparing the maximum access capacity before and after, making it difficult to reveal the internal mechanism of carrying capacity improvement from the perspective of operational trajectory evolution. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a method, device and medium for improving the carrying capacity of distribution networks based on nodal load baselines. By introducing nodal load baselines, a unified and clear operating reference benchmark is constructed, thereby providing a unified control target for flexible resources such as SOPs, and improving coordination efficiency and distribution network carrying capacity.

[0012] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for improving the carrying capacity of a distribution network based on nodal load profiles is provided, the method comprising the following steps: S1, acquire relevant uncertainty data of the power distribution network and perform probability modeling to generate a set of uncertainty scenarios; S2. Taking the uncertain scenario as input, a node load guideline optimization model is constructed. Based on the energy storage and curtailment costs and the inter-station mutual assistance costs, a carrying capacity improvement benefit term and a guideline smoothing regularization term are introduced to construct the objective function. Physical constraints are also constructed, and the optimal shape of the node load guideline is obtained by solving the model. S3. For each random scenario in the uncertainty scenario set, simulations are performed in independent operation mode and quasi-line coordinated mutual assistance mode respectively. The maximum load capacity, peak load rate and abandoned power are recorded in the two modes. Based on this, the load capacity improvement effect index is calculated to guide planning decisions. The quasi-line coordinated mutual assistance mode uses flexible interconnection devices to coordinate the input original load into the optimal form of the node load quasi-line.

[0013] The probabilistic modeling includes probabilistic modeling of distributed photovoltaic output, probabilistic modeling of basic conventional load, and probabilistic modeling of electric vehicle unordered charging load. The photovoltaic output, basic load, and electric vehicle load of each transformer area at each time are obtained respectively. The sum of the basic load and electric vehicle load of each transformer area at each time is the total electricity load, which, together with the photovoltaic output of each transformer area at each time, is used as the input of the node load guideline optimization model.

[0014] The objective function is expressed as: ; in, For the first Energy storage costs per transformer area For the first The cost of power abandonment in each transformer substation area; This represents the total number of stations in the area. Costs of mutual assistance between Taiwan and mainland China; To increase revenue by carrying capacity coefficient, , , , These are the weighting coefficients for each type of revenue. For equivalent carrying capacity gain, Total duration The set safety threshold for transformer load rate, For the first Each district Real-time optimized load rate For the first The transformer capacity of each distribution area As an indicator of the carrying capacity margin for new energy; The regularization coefficient is . For the first The smoothing penalty value for each station area.

[0015] The physical constraints include: Distribution area power balance constraint: Ensure real-time balance of source, load, storage and mutual assistance power within each distribution area; Transformer capacity constraint: The net power exchanged between each distribution area and the upper-level power grid shall not exceed the rated capacity of the transformer; Power flow constraints on distribution network lines: The power transmission distribution factor is used to map the injected power at nodes to the power flow of specific lines to ensure that the limits are not exceeded; Flexible interconnection device capacity constraints: The capacity of flexible interconnection devices connecting the transformer substations must not exceed the rated capacity range; Energy storage system operation constraints: The energy storage state of charge is maintained within a preset range, and the current state of charge is determined by the previous state of charge, the current charging and discharging power, the charging and discharging efficiency, and the rated capacity of the battery.

[0016] The specific method for obtaining the optimal shape of the nodal load guideline by solving the model is as follows: Using the scenario data generated in step S1, which includes total electricity load and photovoltaic output, as input, and based on the constructed objective function and physical constraints, a mathematical optimization solver is used to solve the problem and obtain the values ​​of key decision variables, including the baselines of each transformer area and the mutual power of flexible interconnection devices, as the optimal form of the node load baseline. The solution process coordinates the node load baselines, energy storage charging and discharging, and the mutual power of SOP between transformer areas to optimize the node load distribution under the condition of meeting safety constraints, thereby reducing the risk of local overload and improving the system's ability to withstand source load fluctuations.

[0017] In the independent operation mode, the system does not activate the flexible interconnection device between distribution stations, and each distribution station operates independently. The operation rules are as follows: each distribution station only relies on the local transformer capacity and energy storage resources to adjust the power; there is no power exchange between distribution stations; if the photovoltaic output exceeds the local absorption capacity, power curtailment will occur; the maximum load, peak load rate and power curtailment in this mode are recorded. In the aforementioned guideline coordination and mutual assistance mode, the system incorporates flexible interconnection devices for regulation and uses the node load guideline optimization model constructed in step S2 for operational coordination; the optimized maximum load capacity, optimized peak load rate, and optimized power curtailment are recorded in this mode.

[0018] The indicators for improving carrying capacity include expected equivalent carrying capacity gain, confidence level of carrying capacity improvement rate, expected new energy carrying capacity margin, and load factor balance improvement index. The method for calculating the expected equivalent carrying capacity gain is as follows: ; in, To achieve the desired equivalent carrying capacity gain, For equivalent carrying capacity gain, This indicates the expectation across all simulation scenarios. The number of scenes simulated for Monte Carlo. The total number of stations. Scenario for stand-alone operation mode Peak load rate, Scenario under the standard line collaborative mutual assistance mode Optimized peak load rate For the first Transformer capacity of each distribution area; The method for calculating the confidence level of the bearing capacity improvement rate is as follows: ; in, Scenario under the standard line collaborative mutual assistance mode The optimized maximum load capacity Scenario for stand-alone operation mode Maximum load capacity For the scene The rate of increase in load-bearing capacity; Define the confidence level of the bearing capacity improvement rate as the probability that the bearing capacity improvement rate exceeds the target value: ; in, This indicates that the load-bearing capacity improvement rate has reached the target value. The probability, express An indicator function that returns 1 if the condition is true, and 0 otherwise; The method for calculating the expected new energy carrying capacity margin is as follows: Calculate the transformer area under the two modes respectively In the scene ,time The new energy absorption margin is used to calculate the expected new energy carrying capacity margin. : ; in, As an indicator of the carrying capacity margin of new energy, Total duration , The transformer substations are respectively in the alignment coordination and mutual assistance mode and the independent operation mode. In the scene ,time The new energy consumption margin is calculated as follows: ; in, Taiwan District In the scene ,time Total electrical load Taiwan District In the scene ,time Photovoltaic power output, To determine the new energy absorption margin, substitute the values ​​under different modes to obtain the corresponding new energy absorption margin. The calculation method for the load balance improvement index is as follows: ; in, The load factor balance improvement index. To determine the load balancing efficiency under the standard line collaborative mutual assistance mode. The load balancing degree in standalone operation mode is calculated as follows: ; in, For the scene Load balancing under these conditions Taiwan District In the scene Peak load rate or optimized peak load rate The load balancing degree under the corresponding mode is calculated by substituting the average peak load rate or optimized peak load rate of all transformer areas into the values ​​of different modes.

[0019] Based on the aforementioned carrying capacity improvement effect index, the carrying capacity improvement level of the distribution network is divided into four levels: When the confidence level of the carrying capacity improvement rate when the target value is a preset first threshold is greater than a preset first percentage and the expected equivalent carrying capacity gain is greater than the average transformer capacity of all distribution areas, it is considered a significant improvement level. When the confidence level of the bearing capacity improvement rate when the target value is a preset second threshold is greater than the preset second percentage and the confidence level of the bearing capacity improvement rate when the target value is a preset first threshold is less than or equal to the preset first percentage, it is considered an effective improvement level. When the confidence level of the load capacity improvement rate is less than or equal to the preset second percentage and the load rate balance improvement index is greater than the preset third percentage when the target value is the preset second threshold, it is an auxiliary improvement level; When the confidence level of the load capacity improvement rate is less than or equal to the preset second percentage and the load balance improvement index is less than or equal to the preset third percentage, the improvement level is limited.

[0020] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0021] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) By introducing the node load baseline, a unified and clear operating reference benchmark is constructed.

[0023] This invention introduces a load baseline at the node or transformer substation level, transforming the operational objective of the distribution network from the traditional "whether limits are exceeded" to "deviation control around the baseline." This baseline clearly depicts the ideal operating state under conditions that meet equipment capacity, voltage, and line constraints, providing a unified reference benchmark for multi-node, multi-time-period operation.

[0024] (2) Provide a unified control target for flexible resources such as SOPs to improve collaborative efficiency.

[0025] This invention establishes a node load baseline as a unified operational reference benchmark, clarifying the target operational state of each node in the time series. It introduces a load capacity enhancement benefit term and a baseline smoothing regularization term into the objective function for regulation. When the load of a node in a certain area is higher than the baseline, the system transfers power to neighboring areas with lower loads and below the baseline through a Standard Operating Procedure (SOP). When the node load is lower than the baseline and there is surplus capacity, power from other areas can be accepted through an SOP, thereby forming a cross-area power coordination and regulation mechanism.

[0026] Through this mechanism, the control logic of SOP (Standard Operating Procedure) is transformed from the traditional "over-limit - trigger - correction" mode to a "guideline - deviation identification - active adjustment" mode. This allows it to proactively optimize the load distribution of each node before over-limit occurs, achieving dynamic power mutual assistance among multiple distribution areas. Simultaneously, other flexible resources such as energy storage systems and demand response systems can also be coordinated and regulated through relevant unified indicators, avoiding inconsistencies in control objectives or redundant adjustments between different resources. This improves overall control efficiency and further releases the potential carrying capacity of the distribution network.

[0027] (3) It can quantify the degree of deviation of the operating status, and the effect of improving the bearing capacity is more intuitive.

[0028] This invention characterizes the degree of deviation of nodal loads from the guideline, transforming the operational status from a simple binary "feasible / infeasible" judgment into a deviation problem with continuous and measurable characteristics. Based on this characteristic, the effect of bearing capacity improvement can be quantitatively described by indicators such as expected equivalent bearing capacity gain, confidence level of bearing capacity improvement rate, expected new energy carrying capacity margin, and load rate balance improvement index. This provides a more intuitive reflection of the bearing capacity improvement mechanism and effect, overcoming the problem of difficulty in quantifying the improvement effect in existing technologies. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0030] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0032] Example 1 This embodiment provides a method for improving the carrying capacity of a distribution network based on nodal load profiles, such as... Figure 1 As shown, the method includes the following steps: S1: Acquire uncertainty data related to the power distribution network and perform probability modeling to generate a set of uncertainty scenarios.

[0033] This step aims to establish a mathematical model of distributed generation and multi-load in the distribution network, and to describe their random distribution characteristics in time and space through a probability density function, so as to provide basic input variables for subsequent carrying capacity analysis.

[0034] S11, Probabilistic modeling of distributed photovoltaic power output.

[0035] Considering that photovoltaic power output is significantly affected by meteorological conditions (sunlight intensity), the Beta distribution is used to describe the stochastic characteristics of sunlight intensity, and then a photovoltaic power output calculation model is established.

[0036] Define time Light intensity ratio Let be a random variable that follows a Beta distribution: ; Its probability density function for: ; in, for The probability density function of the ratio of light intensity at any given time; for Light intensity ratio at any given time, range of values ; The shape parameter of the Beta distribution is obtained by statistical fitting of historical meteorological radiation data, reflecting the skewness of the light distribution at different time periods (such as the difference between noon and morning / evening). This is a gamma function.

[0037] Based on this light intensity ratio, the first Each district Photovoltaic output at all times The theoretical calculation formula is: ; in, For the first Distributed photovoltaic installed capacity of each substation area (unit: kW); The overall conversion efficiency of the photovoltaic inverter and system; For the calculated first Each Taiwan area Photovoltaic power output at all times.

[0038] To describe the uncertainty of photovoltaic output, the Monte Carlo method is used to randomly sample from the Beta distribution to generate random samples of light intensity.

[0039] In the Light intensity ratio samples in a random scene Represented as: ; Corresponding photovoltaic output for: .

[0040] Generate Beta-distributed random variables using the Gamma distribution construction method: (1) Generate Gamma-distributed random variables , : ; ; (2) Construct a Beta random variable: The light intensity ratio sample is calculated as follows: ; Satisfaction Random samples with distribution characteristics.

[0041] (3) Repeat the above random sampling process. Next, the first can be obtained Sample set of light intensity ratios at different times: ; Substituting the samples into the photovoltaic output calculation model yields the corresponding photovoltaic output scenarios: ; The above process generates a large number of random photovoltaic output scenarios, providing input data for subsequent distribution network carrying capacity analysis and optimization calculations.

[0042] S12, Probabilistic modeling of basic conventional load.

[0043] Baseline load (including residential and commercial electricity consumption) exhibits a daily periodic pattern, while also experiencing random fluctuations due to user behavior. A modeling approach combining baseline forecasts and normally distributed random errors is employed.

[0044] No. Each district Base load at any time Represented as: ; in, The first one is obtained based on historical typical daily data. Each Taiwan area Average load forecast at any given time, load fluctuation deviation Follows a normal distribution: ; in, for The standard deviation of the load at time t is used to characterize the uncertainty of the load at that time (e.g., set to 10% of the predicted value).

[0045] S13, Modeling of disordered charging load for electric vehicles (EVs).

[0046] To address the random access characteristics of electric vehicles in time and space, probability distribution models for "initial charging time" and "daily mileage" are established to derive the charging load curve. Assume the... Each area retains The first electric vehicle, for the Electric vehicles: (1) Starting charging time model: the time when it starts charging after being connected to the power grid. Follows a normal distribution: ; in, These represent the expected value (e.g., 18:00) and standard deviation of the user's starting charging time, respectively.

[0047] (2) Charging time calculation: based on daily mileage Calculate the initial state of charge Then calculate the required charging time. : ; ; ; in, For the first The daily mileage of a vehicle follows a log-normal distribution; These are the parameters of the log-normal distribution of daily mileage; Energy consumption per 100 kilometers (kWh / 100km); For vehicle battery capacity; For the first The initial state of charge of the vehicle; The target state of charge (usually taken as 1.0); The rated charging power of the charging pile; For the first The required charging time for the vehicle.

[0048] (3) Aggregated charging load in the transformer area: ; Among them, single vehicle power in, For the first Each Taiwan area Total load of electric vehicles after time-aggregation. For the first The total number of electric vehicles in each district.

[0049] S14, Construction of net load input vector for transformer area.

[0050] Based on the modeling results of S11-S13 above, the net load input variable of the transformer area is defined for subsequent load-bearing capacity calculation.

[0051] ; ; in, For the first Each Taiwan area The total electrical load at any given time (including base load and EV load) will be used as the parameter in the subsequent nodal load guideline optimization model. Input items; For the first Each Taiwan area The photovoltaic output at any given time is used as the basis for the optimization model of the nodal load guideline. Input items; It is a net load indicator used to initially determine whether the transformer area is in a state of "sources exceeding load" (requiring absorption) or "load exceeding sources" (requiring support).

[0052] S2. Taking the uncertain scenario as input, a node load guideline optimization model is constructed. Based on the energy storage and curtailment costs and the inter-station mutual assistance costs, a carrying capacity improvement benefit term and a guideline smoothing regularization term are introduced to construct the objective function. Physical constraints are also constructed, and the optimal shape of the node load guideline is obtained by solving the model.

[0053] S21, Construction of the objective function.

[0054] The objective function aims to balance economy and safety. By introducing a carrying capacity enhancement term, it guides the model to explore the potential regulation capacity of the power grid, and is defined as follows: ; in, This represents the cost of energy storage and curtailment, reflecting the operational costs of resources within each transformer substation. For the first Energy storage costs per transformer area For the first The cost of power abandonment in each transformer substation area; This represents the total number of stations in the Taiwan region.

[0055] ; ; in, Total duration The depreciation cost factor for energy storage charging and discharging. , The first Each district The energy storage charging and discharging power at any given time This is the penalty coefficient for discarded light. , The first Each district The photovoltaic output and actual photovoltaic consumption at any given time.

[0056] Inter-station mutual assistance cost, reflecting the power loss and operating cost through SOP transmission, is defined as: ; in, for From the station area Flowing to the Taiwan area Mutual assistance power; This represents the operating cost coefficient for flexible interconnect devices. , The unit time operation and maintenance cost of SOP equipment (RMB / h). The rated transmission power of SOP (kWh) is obtained from the device's hardware information and historical data statistics. The transmission loss factor is... , The equivalent loss factor for the SOP system is determined by the properties of the system itself. Electricity price (yuan / kWh); This indicates a collection of units in the platform area.

[0057] The revenue items for improving carrying capacity include, To increase revenue by carrying capacity The coefficient is defined as: ; in, , , These are the weighting coefficients for each benefit, reflecting the planner's emphasis on equipment life extension, safe operation, and the integration of new energy sources. This refers to the equivalent carrying capacity gain (i.e., the transformer capacity virtually increased through peak shifting and valley filling). The set safety threshold for transformer load rate (e.g., 0.8). For the first Each district Real-time optimized load rate For the first Transformer capacity (kVA) of each distribution area. This serves as an indicator of the carrying capacity margin for new energy sources.

[0058] The normalization term ensures that the generated load normalization line is smooth, making it easy for users to execute. The regularization coefficient is . For the first The smoothing penalty value for each station area.

[0059] S22, Construction of physical constraints.

[0060] Physical constraints define the physical boundaries of the power grid, including: (1) Power balance constraint of distribution area: ensure the real-time balance of source, load, storage and mutual assistance power within each distribution area.

[0061] ; in, Taiwan District The total adjustable load can be directly obtained from the distribution network capacity parameters; Let be the variable to be solved, representing the th variable. The optimal node load baseline value for each transformer area (normalized 0-1); This is the rigid base load (non-adjustable portion), obtained based on historical load data collected from the park. Indicates the relationship with Taiwan region A set of distribution areas that have a mutual support relationship.

[0062] (2) Transformer capacity constraint: The net power exchanged between each distribution area and the upper-level power grid shall not exceed the rated capacity of the transformer.

[0063] ; in, Taiwan District exist Net injection / outflow power at time: .

[0064] (3) Power flow constraints of distribution network lines: The power transmission distribution factor (PTDF) is used to map the injected power of nodes to the power flow of specific lines to ensure that the limits are not exceeded.

[0065] ; in, For line number indexing; For the line Thermal stability limit (maximum permissible transmission power); Taiwan District Unit power injection at nodes causes line The sensitivity coefficient for power flow changes is derived from network topology parameters, and this constraint is a key criterion for identifying "line congestion bottlenecks".

[0066] (4) Flexible interconnection device (SOP) capacity constraint: The capacity of the flexible interconnection device connecting the substation area shall not exceed the rated capacity range.

[0067] ; in, To connect the substation area and The rated capacity of the SOP converter is a key criterion for identifying "interconnection bottlenecks".

[0068] (5) Energy storage system operation constraints: The energy storage state of charge is maintained within a preset range, and the current state of charge is determined by the previous state of charge, the current charging and discharging power, the charging and discharging efficiency and the rated capacity of the battery.

[0069] ; ; in, For the first Each district The state of charge of the stored energy at any given moment; , These are the preset maximum and minimum values ​​of the state of charge; Rated energy storage capacity (kWh); , These represent the charging and discharging efficiencies, respectively. This constraint is used to analyze whether the energy storage configuration is adequate.

[0070] S23, Model Solving and State Output.

[0071] Using the scenario data generated in step S1, which includes total electricity load and photovoltaic output, as input, a mathematical optimization solver is used to solve the problem based on the constructed objective function and physical constraints. The solution process coordinates the node load profile, energy storage charging and discharging, and inter-station SOP mutual assistance power to optimize the node load distribution while meeting safety constraints, thereby reducing the risk of local overload and improving the system's ability to withstand source-load fluctuations.

[0072] Specifically, the model input is the uncertainty scenario generated in step S1. Data ,in, For the scene Total electrical load For the scene Photovoltaic power output.

[0073] The model output includes: 1. Model Status: Optimal solution: By solving for the optimal solution of the guideline, the transformer area is driven to operate at the guideline load, thereby alleviating the risks of overload in the transformer area and optimizing the load-bearing capacity from the perspective of optimizing operational capacity; No solution: This means that under the current source-load scenario, the system cannot meet all operational constraints through energy storage regulation and distribution area mutual assistance, indicating that the grid's rigid capacity is insufficient; Truncation: This indicates that the solution process terminates due to reaching the maximum computation time or iteration limit, failing to obtain a strictly global optimal solution, but the current best feasible solution can be output.

[0074] 2. Key decision-making variable values, including the baselines of each transformer substation. SOP mutual support power (This variable is the same as the one defined in the previous constraints) For the same decision variable, the optimal solution result is... This indicates that it is the optimal value after optimization. This includes the mutual power of each transformer area's baseline and flexible interconnection devices, which serves as the optimal form of the node load baseline. S3. For each random scenario in the uncertainty scenario set, simulations are performed in independent operation mode and quasi-line coordinated mutual assistance mode respectively. The maximum load capacity, peak load rate and abandoned power are recorded in the two modes. Based on this, the load capacity improvement effect index is calculated to guide planning decisions. The quasi-line coordinated mutual assistance mode uses flexible interconnection devices to coordinate the input original load into the optimal form of the node load quasi-line.

[0075] This step aims to quantitatively evaluate the effectiveness of the proposed multi-regional mutual assistance strategy based on nodal load profiles in improving the distribution network's carrying capacity. Using a Monte Carlo simulation framework, the performance of the strategy's capacity improvement is quantified by comparing the performance of independent operation and profile-coordinated operation modes under massive random scenarios.

[0076] S31, construct a "benchmark-strategy" comparative simulation framework.

[0077] To objectively evaluate the improvement effect, for each generated random scene Two sets of simulation calculations need to be executed in parallel to calculate the incremental benefits.

[0078] Simulation process logic: (1) Scene loading: Extract the first scene from the scene set generated in step S1. Group source load data .

[0079] (2) Independent Operation Mode (Control Group): In this mode, the system does not activate the inter-station flexible interconnection device (SOP), and each station operates independently. The operation rules are as follows: Each distribution area relies solely on local transformer capacity and energy storage resources to regulate power; There is no power exchange between transformer substations; If the output of photovoltaic power exceeds the local absorption capacity, then power curtailment will occur.

[0080] Calculate the maximum load under this mode. Peak load rate and abandoned electricity .

[0081] (3) Guideline Coordination and Mutual Assistance Mode (Experimental Group): In this mode, the system is equipped with a flexible interconnection device (SOP) for regulation and control, and the node load guideline optimization model constructed in step S2 is used for operation coordination.

[0082] Calculate the optimized maximum load under this mode. Optimized peak load rate and optimized power waste .

[0083] S32, Calculation of bearing capacity improvement index.

[0084] In this embodiment, the evaluation is carried out from four dimensions: capacity gain, improvement rate, absorption capacity and balance. The expected equivalent carrying capacity gain, the confidence level of carrying capacity improvement rate, the expected new energy carrying margin and the load rate balance improvement index are calculated as indicators of carrying capacity improvement effect.

[0085] (1) Expected equivalent carrying capacity gain: The quantification strategy, through "peak shifting and valley filling" and "mutual assistance," statistically adds transformer capacity to the system. The calculation method is as follows: ; in, The expected equivalent carrying capacity gain (kVA). For equivalent carrying capacity gain, This indicates the expectation across all simulation scenarios. The number of scenes simulated for Monte Carlo. The total number of stations. Scenario for stand-alone operation mode Peak load rate, Scenario under the standard line collaborative mutual assistance mode Optimized peak load rate For the first The transformer capacity of each distribution area.

[0086] This indicator directly reflects the level of transformer capacity increase saved by the strategy. To avoid the increase in local load rate caused by load transfer between transformer substations offsetting the capacity release effect in other substations, the following approach is adopted: The calculation only counts the equivalent capacity release caused by the decrease in peak load rate, thus more accurately characterizing the contribution of the strategy to the improvement of system carrying capacity.

[0087] (2) Confidence level of bearing capacity improvement rate: This assesses the reliability of achieving the expected target for the system's bearing capacity improvement rate, considering the uncertainty of the source load. The calculation method is as follows: First, calculate the capacity improvement rate for a single scenario: ; in, Scenario under the standard line collaborative mutual assistance mode The optimized maximum load capacity Scenario for stand-alone operation mode Maximum load capacity For the scene The rate of increase in load-bearing capacity.

[0088] Define the confidence level of the bearing capacity improvement rate as the probability that the bearing capacity improvement rate exceeds the target value: ; in, This indicates that the load-bearing capacity improvement rate has reached the target value. The probability, express An indicator function that takes the value 1 if the condition is true, and 0 otherwise. Physical meaning: If... , The calculation result of 95% indicates that the strategy can improve the carrying capacity by at least 15% in 95% of future scenarios, proving the robustness of the strategy.

[0089] (3) Expected margin of renewable energy carrying capacity. This indicator reflects the average additional renewable energy power (kWh) that the strategy can absorb. The calculation method is as follows: Calculate the transformer area under the two modes respectively In the scene ,time The new energy absorption margin is used to calculate the expected new energy carrying capacity margin. : ; in, As an indicator of the carrying capacity margin of new energy, Total duration , The transformer substations are respectively in the alignment coordination and mutual assistance mode and the independent operation mode. In the scene ,time The new energy consumption margin is calculated as follows: ; in, Taiwan District In the scene ,time Total electrical load Taiwan District In the scene ,time Photovoltaic power output, To determine the new energy absorption margin, substitute the values ​​under different modes to obtain the corresponding new energy absorption margin.

[0090] (4) Load Balance Improvement Index: This index is used to evaluate the degree to which the strategy improves the problem of uneven load distribution in the transformer area. The calculation method is as follows: ; in, The load factor balance improvement index. To determine the load balancing efficiency under the standard line collaborative mutual assistance mode. The load balancing degree in standalone operation mode is calculated as follows: ; in, For the scene Load balancing under these conditions Taiwan District In the scene Peak load rate or optimized peak load rate The load balancing degree under the corresponding mode is calculated by substituting the average peak load rate or optimized peak load rate of all transformer areas into the values ​​of different modes.

[0091] This indicator can assess the contribution of strategies to alleviating uneven load distribution in transformer areas and eliminating local hotspots.

[0092] The load balance improvement index is calculated based on the dispersion of peak load rate of each distribution area relative to the system average peak load rate. This formula essentially reflects the dispersion of peak load rate among distribution areas. A positive and larger load balance improvement index indicates that the collaborative and mutual-assistance strategy, compared to the traditional independent operation mode, more effectively achieves a reasonable distribution of load among distribution areas. This allows the load of areas with high overload risk to be shared, while simultaneously improving the equipment utilization level of low-load distribution areas, thereby improving the overall asset utilization efficiency of the distribution network. Therefore, a positive and larger index indicates that the strategy more effectively transfers the pressure from high-load distribution areas to low-load distribution areas, improving asset utilization efficiency.

[0093] Example 2 This embodiment, based on embodiment 1, provides a specific implementation method for guiding planning decisions based on the load-bearing capacity improvement effect index.

[0094] Specifically, in this embodiment, the first threshold is set to 20%, the second threshold to 10%, the first percentage to 90%, the second percentage to 80%, and the third percentage to 5%. Thus, based on the carrying capacity improvement effect index, the carrying capacity improvement level of the distribution network is divided into four levels: When the target value is Confidence level of load-bearing capacity improvement rate And the expected equivalent carrying capacity gain Greater than the average transformer capacity of all distribution areas At this point, it is considered a significant upgrade; this rating indicates that the strategy is extremely effective, equivalent to completely replacing the capacity expansion requirement of a transformer through software algorithms.

[0095] When the target value is Confidence level of load-bearing capacity improvement rate And the target value is Confidence level of load-bearing capacity improvement rate At that time, it is an effective upgrade; this rating represents a strategy that can effectively delay power grid upgrading and transformation, and has good economic benefits.

[0096] When the target value is Confidence level of load-bearing capacity improvement rate and At this time, it is an auxiliary upgrade level; this rating indicates that the improvement effect is mainly reflected in the improvement of balance rather than the overall increase. This rating represents that the strategy is mainly used to optimize the operating status and solve the problem of local overheating.

[0097] When the target value is Confidence level of load-bearing capacity improvement rate and At that time, the rating was limited; the rating believed that the strategy output by the model had a limited effect on improving the carrying capacity of the distribution network under the current network structure and source load conditions.

[0098] Based on the rating results, the effect of the model in step S2 on improving the carrying capacity of the distribution network can be quantified, thereby guiding decision-making.

[0099] Example 3 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0100] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0101] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0102] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0103] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for improving the carrying capacity of a distribution network based on nodal load profiles, characterized in that, The method includes the following steps: S1, acquire relevant uncertainty data of the power distribution network and perform probability modeling to generate a set of uncertainty scenarios; S2. Taking the uncertain scenario as input, a node load guideline optimization model is constructed. Based on the energy storage and curtailment costs and the inter-station mutual assistance costs, a carrying capacity improvement benefit term and a guideline smoothing regularization term are introduced to construct the objective function. Physical constraints are also constructed, and the optimal shape of the node load guideline is obtained by solving the model. S3. For each random scenario in the uncertainty scenario set, simulations are performed in independent operation mode and quasi-line coordinated mutual assistance mode respectively. The maximum load capacity, peak load rate and abandoned power are recorded in the two modes. Based on this, the load capacity improvement effect index is calculated to guide planning decisions. The quasi-line coordinated mutual assistance mode uses flexible interconnection devices to coordinate the input original load into the optimal form of the node load quasi-line.

2. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 1, characterized in that, The probabilistic modeling includes probabilistic modeling of distributed photovoltaic output, probabilistic modeling of basic conventional load, and probabilistic modeling of electric vehicle unordered charging load. The photovoltaic output, basic load, and electric vehicle load of each transformer area at each time are obtained respectively. The sum of the basic load and electric vehicle load of each transformer area at each time is the total electricity load, which, together with the photovoltaic output of each transformer area at each time, is used as the input of the node load guideline optimization model.

3. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 1, characterized in that, The objective function is expressed as: ； in, For the first Energy storage costs per transformer area For the first The cost of power abandonment in each transformer substation area; This represents the total number of stations in the area. Costs of mutual assistance between Taiwan and mainland China; To increase revenue by carrying capacity coefficient, , , , These are the weighting coefficients for each type of revenue. For equivalent carrying capacity gain, Total duration The set safety threshold for transformer load rate, For the first Each district Real-time optimized load rate For the first The transformer capacity of each distribution area As an indicator of the carrying capacity margin for new energy; The regularization coefficient is . For the first The smoothing penalty value for each station area.

4. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 1, characterized in that, The physical constraints include: Distribution area power balance constraint: Ensure real-time balance of source, load, storage and mutual assistance power within each distribution area; Transformer capacity constraint: The net power exchanged between each distribution area and the upper-level power grid shall not exceed the rated capacity of the transformer; Power flow constraints on distribution network lines: The power transmission distribution factor is used to map the injected power at nodes to the power flow of specific lines to ensure that the limits are not exceeded; Flexible interconnection device capacity constraints: The capacity of flexible interconnection devices connecting the transformer substations must not exceed the rated capacity range; Energy storage system operation constraints: The energy storage state of charge is maintained within a preset range, and the current state of charge is determined by the previous state of charge, the current charging and discharging power, the charging and discharging efficiency, and the rated capacity of the battery.

5. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 2, characterized in that, The specific method for obtaining the optimal shape of the nodal load guideline by solving the model is as follows: Using the scenario data generated in step S1, which includes total electricity load and photovoltaic output, as input, and based on the constructed objective function and physical constraints, a mathematical optimization solver is used to solve the problem and obtain the values ​​of key decision variables, including the baselines of each transformer area and the mutual power of flexible interconnection devices, as the optimal form of the node load baseline. The solution process coordinates the node load baselines, energy storage charging and discharging, and the mutual power of SOP between transformer areas to optimize the node load distribution under the condition of meeting safety constraints, thereby reducing the risk of local overload and improving the system's ability to withstand source load fluctuations.

6. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 1, characterized in that, In the independent operation mode, the system does not activate the flexible interconnection device between distribution stations, and each distribution station operates independently. The operation rules are as follows: each distribution station only relies on the local transformer capacity and energy storage resources to adjust the power; there is no power exchange between distribution stations; if the photovoltaic output exceeds the local absorption capacity, power curtailment will occur; the maximum load, peak load rate and power curtailment in this mode are recorded. In the described guideline coordination and mutual assistance mode, the system incorporates a flexible interconnection device for regulation and uses the node load guideline optimization model constructed in step S2 for operational coordination. Record the optimized maximum load, optimized peak load rate, and optimized power curtailment under this mode.

7. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 1, characterized in that, The indicators for improving carrying capacity include expected equivalent carrying capacity gain, confidence level of carrying capacity improvement rate, expected new energy carrying capacity margin, and load factor balance improvement index. The method for calculating the expected equivalent carrying capacity gain is as follows: ； in, To achieve the desired equivalent carrying capacity gain, For equivalent carrying capacity gain, This indicates the expectation across all simulation scenarios. The number of scenes simulated for Monte Carlo. The total number of stations. Scenario for stand-alone operation mode Peak load rate, Scenario under the standard line collaborative mutual assistance mode Optimized peak load rate For the first Transformer capacity of each distribution area; The method for calculating the confidence level of the bearing capacity improvement rate is as follows: ； in, Scenario under the standard line collaborative mutual assistance mode The optimized maximum load capacity Scenario for stand-alone operation mode Maximum load capacity For the scene The rate of increase in load-bearing capacity; Define the confidence level of the bearing capacity improvement rate as the probability that the bearing capacity improvement rate exceeds the target value: ； in, This indicates that the load-bearing capacity improvement rate has reached the target value. The probability, express An indicator function that returns 1 if the condition is true, and 0 otherwise; The method for calculating the expected new energy carrying capacity margin is as follows: Calculate the transformer area under the two modes respectively In the scene ,time The new energy absorption margin is used to calculate the expected new energy carrying capacity margin. : ； in, As an indicator of the carrying capacity margin of new energy, Total duration , The transformer substations are respectively in the alignment coordination and mutual assistance mode and the independent operation mode. In the scene ,time The new energy consumption margin is calculated as follows: ； in, Taiwan District In the scene ,time Total electrical load Taiwan District In the scene ,time Photovoltaic power output, To determine the new energy absorption margin, substitute the values ​​under different modes to obtain the corresponding new energy absorption margin. The calculation method for the load balance improvement index is as follows: ； in, The load factor balance improvement index. To determine the load balancing efficiency under the standard line collaborative mutual assistance mode. The load balancing degree in standalone operation mode is calculated as follows: ； in, For the scene Load balancing under these conditions Taiwan District In the scene Peak load rate or optimized peak load rate The load balancing degree under the corresponding mode is calculated by substituting the average peak load rate or optimized peak load rate of all transformer areas into the values ​​of different modes.

8. The method for improving the carrying capacity of a distribution network based on nodal load profiles according to claim 7, characterized in that, Based on the aforementioned carrying capacity improvement effect index, the carrying capacity improvement level of the distribution network is divided into four levels: When the confidence level of the carrying capacity improvement rate when the target value is a preset first threshold is greater than a preset first percentage and the expected equivalent carrying capacity gain is greater than the average transformer capacity of all distribution areas, it is considered a significant improvement level. When the confidence level of the bearing capacity improvement rate when the target value is a preset second threshold is greater than the preset second percentage and the confidence level of the bearing capacity improvement rate when the target value is a preset first threshold is less than or equal to the preset first percentage, it is considered an effective improvement level. When the confidence level of the load capacity improvement rate is less than or equal to the preset second percentage and the load rate balance improvement index is greater than the preset third percentage when the target value is the preset second threshold, it is an auxiliary improvement level; When the confidence level of the load capacity improvement rate is less than or equal to the preset second percentage and the load balance improvement index is less than or equal to the preset third percentage, the improvement level is limited.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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