A power distribution network capacity configuration scheme evaluation method and related device

Abstract

This invention provides a method and related apparatus for evaluating distribution network capacity configuration schemes. The method first calculates the default operating cost of the current capacity configuration scheme within a preset optimization period, quantifying the degree of default operation into directly calculable economic costs. Then, based on the construction cost, operating cost, and default operating cost of the current scheme, it calculates the reliability economic efficiency to measure the economic value of adding capacity in improving the reliability of the distribution network. Subsequently, the current scheme is evaluated based on the default operation situation and reliability economic efficiency. When the capacity optimization conditions are met, the reliability economic efficiency of all candidate schemes within the preset evaluation period is calculated. The optimal scheme is selected to replace the current scheme and is re-evaluated, forming a dynamic cyclical optimization mechanism. This invention not only achieves the scientific quantification of the economic value of reliability improvement but also enhances the dynamic adaptability of the planning scheme, effectively ensuring the long-term effectiveness and economy of the distribution network capacity configuration scheme.

Description

Technical Field

[0001] This invention belongs to the field of power system distribution network planning technology, specifically relating to a method and related apparatus for evaluating distribution network capacity configuration schemes. Background Technology

[0002] Distribution network capacity configuration, as a key component of power grid planning, aims to rationally determine the capacity of key equipment such as transformers and lines to meet ever-changing electricity demands. With the widespread integration of new elements such as electric vehicles, distributed power sources, and energy storage systems into the distribution network, the load characteristics of the distribution network have changed significantly, exhibiting high elasticity and high uncertainty, such as a sharp increase in the peak-to-valley load difference, frequent short-term impulsive loads, and widespread bidirectional power flow.

[0003] Current methods for optimizing distribution network capacity have shortcomings in terms of quantifying reliability value and dynamically adapting planning schemes. Regarding quantifying reliability value, traditional methods cannot provide a refined analysis and quantification of the economic benefits of reliability improvements, making it difficult to find the optimal balance between economy and reliability in planning schemes. In terms of dynamic adaptability, existing methods are mostly static and offline, unable to effectively adjust to changes in the power grid operating environment, thus reducing the long-term effectiveness and economy of the planning schemes. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and related apparatus for evaluating power distribution network capacity configuration schemes to address the aforementioned technical problems, in order to solve the problems that existing power distribution network capacity planning methods cannot scientifically quantify the economic value of improving reliability and are difficult to adapt to dynamic changes in the power grid operating environment.

[0005] In a first aspect, the present invention provides a method for evaluating power distribution network capacity configuration schemes, comprising the following steps:

[0006] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0007] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0008] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0009] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0010] Furthermore, the default operating costs of the current capacity configuration scheme are calculated, including:

[0011] Several default severity levels are preset, along with a corresponding load rate threshold and unit default cost weight for each level; the load rate threshold and unit default cost weight for each level increase progressively with the default severity level.

[0012] The default indication function value and default degree for each level are determined based on the load rate threshold for each time period. The default indication function value is 1 or 0, where 1 indicates that the corresponding level has defaulted during the time period and 0 indicates that the corresponding level has not defaulted during the time period. The default degree is a quantitative value that quantifies the severity of default for the corresponding level during the time period. The degree of default is characterized by the default indication function value and the default degree.

[0013] The product of the default indicator function value, the default degree, and the unit default cost weight is used as the default cost for the corresponding level. The default costs of each level within a preset period are then accumulated sequentially to obtain the default operating cost.

[0014] Furthermore, the operating costs of default include:

[0015] Short-term default operating costs and total default operating costs;

[0016] If the time range of the preset period is greater than or equal to the preset time period range, the default operating cost is the total default operating cost; if the time range of the preset period is less than the time period range, the default operating cost is the short-term default operating cost.

[0017] The time range of the optimization cycle is shorter than the time period range, while the time range of the evaluation cycle is longer than the time period range.

[0018] Furthermore, the default performance is characterized by the default frequency and average default severity for each default severity level;

[0019] For each level of default severity, the default frequency is the value obtained by summing the default indicator function values ​​at each moment within the optimization period and then taking the average value for the period; the average default degree is the value obtained by summing the default degree at each moment within the optimization period and then taking the average value for the period.

[0020] Furthermore, the current capacity configuration scheme will be evaluated based on its default operation and reliability-economic efficiency, including:

[0021] The frequency and average degree of default under each level of default severity are compared with the preset frequency threshold and degree of default threshold for the corresponding level.

[0022] The reliability-economic efficiency is compared with a preset reliability-economic efficiency threshold.

[0023] If any one of the following conditions is met: the default frequency is greater than the frequency threshold, the average default degree is greater than the default degree threshold, or the reliability economic efficiency is less than the reliability economic efficiency threshold, the evaluation result is deemed to meet the capacity optimization conditions.

[0024] Furthermore, based on the construction and operation costs of the current capacity configuration scheme, as well as the default operation costs, the reliability and economic efficiency of the current capacity configuration scheme are calculated, including:

[0025] Calculate the difference between the construction and operation costs corresponding to the current capacity configuration scheme and the previous capacity configuration scheme to obtain the first cost difference;

[0026] The difference between the default operating costs corresponding to the previous capacity configuration scheme and the current capacity configuration scheme is calculated to obtain the second cost difference.

[0027] The ratio of the second cost difference to the first cost difference is used as the reliability and economic efficiency of the current capacity configuration scheme.

[0028] Furthermore, before calculating the reliability and economic efficiency of all preset candidate capacity configurations within a preset evaluation period, the following steps are also included:

[0029] Obtain historical datasets of the distribution network and new datasets of the distribution network under the current capacity configuration scheme;

[0030] Based on the data distribution characteristics of historical and new datasets, a pre-trained generative adversarial network is used to generate a dataset.

[0031] The similarity of data distribution between the historical dataset and the new dataset is calculated. Combining the similarity of data distribution with the average default rate of the new data, the weight coefficients of the historical dataset, the new dataset, and the generated dataset are dynamically determined. The weight of the historical dataset increases with the stability of the system, the weight of the new dataset is positively correlated with the average default rate, and the weight of the generated dataset increases with the difference in distribution between the historical data and the new data.

[0032] The weight coefficients of the three datasets are normalized and mapped to obtain the final sampling ratio for each dataset. Based on the final sampling ratio, the three datasets are sampled separately and then merged to obtain the merged evaluation dataset.

[0033] The default operating costs of all preset candidate capacity configuration schemes within a preset evaluation period are calculated based on the evaluation dataset.

[0034] Secondly, the present invention provides a power distribution network capacity configuration scheme evaluation device, comprising:

[0035] The default operation cost calculation module is used to calculate the default operation cost of the current capacity configuration scheme within a preset optimization period; the default operation cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0036] The reliability-economic efficiency calculation module is used to calculate the reliability-economic efficiency based on the construction and operation costs and default operation costs of the current capacity configuration scheme. The reliability-economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0037] The scheme evaluation and optimization trigger module is used to evaluate the current capacity configuration scheme based on the default operation status and reliability and economic efficiency of the current capacity configuration scheme. If the evaluation result meets the capacity optimization conditions, it calculates the reliability and economic efficiency of all preset candidate capacity configuration schemes within the preset evaluation period.

[0038] The scheme replacement and cyclic evaluation module is used to select the candidate capacity configuration scheme with the best reliability and economic efficiency as the new current capacity configuration scheme, and to re-evaluate the scheme after it is put into operation.

[0039] Thirdly, the present invention provides a computer device, the device including a processor and a memory:

[0040] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0041] The processor executes the following steps according to the instructions of the computer program:

[0042] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0043] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0044] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0045] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0046] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, the computer program performing the following steps when executed by a processor:

[0047] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0048] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0049] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0050] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0051] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0052] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0053] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0054] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0055] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0056] In summary, this invention provides a method and related apparatus for evaluating distribution network capacity configuration schemes. The method first calculates the default operating cost of the current capacity configuration scheme within a preset optimization period, quantifying the degree of default operation into a directly calculable economic cost. Then, based on the construction cost, operating cost, and default operating cost of the current scheme, it calculates the reliability economic efficiency to measure the economic value of the added capacity in improving the reliability of the distribution network, solving the problem that traditional methods cannot scientifically quantify the economic value of reliability improvement. Subsequently, the current scheme is evaluated based on the default operation situation and reliability economic efficiency. When the capacity optimization conditions are met, the reliability economic efficiency of all candidate schemes within the preset evaluation period is calculated. The optimal scheme is selected to replace the current scheme and is re-evaluated, forming a dynamic cyclical optimization mechanism. This breaks the limitations of the existing static offline mode and achieves adaptive adjustment according to changes in the power grid operating environment. This invention not only achieves the scientific quantification of the economic value of reliability improvement, enabling the planning scheme to find the optimal balance between economy and reliability, but also improves the dynamic adaptability of the planning scheme, effectively ensuring the long-term effectiveness and economy of the distribution network capacity configuration scheme, reducing the long-term operating cost of the distribution network, and improving the reliability and stability of the distribution network operation. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating a method for evaluating power distribution network capacity configuration schemes, provided in an embodiment of the present invention;

[0059] Figure 2 A flowchart of the distribution network capacity optimization method based on reliability and economic efficiency provided by the present invention;

[0060] Figure 3 This is a block diagram of a power distribution network capacity configuration scheme evaluation device provided in an embodiment of the present invention;

[0061] Figure 4 This is a block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0063] The background technology of this invention will be further introduced below.

[0064] The distribution network is a crucial component of the power system, directly providing electricity services to end users. Its planning economy and reliability directly impact social production order and the quality of people's daily lives. Distribution network capacity configuration is a core aspect of power grid planning, with the primary goal of scientifically determining the capacity specifications of power equipment such as transformers and lines to adapt to ever-increasing and constantly changing electricity demand. In actual planning, traditional capacity configuration methods often face a dilemma: over-configuring capacity, while ensuring power supply reliability, leads to significant investment waste and idle power assets; under-configuring capacity, while saving initial investment, easily results in equipment overload and voltage exceeding limits during peak load periods, potentially even causing widespread power outages and substantial economic losses.

[0065] Currently, the optimization of distribution network capacity mainly adopts optimization methods based on prioritizing economic efficiency or meeting basic reliability constraints. These methods can play a certain planning role in traditional power grid environments. However, with the widespread integration of new power elements such as electric vehicles, distributed power sources, and energy storage systems, the load characteristics of distribution networks have undergone fundamental changes, exhibiting high elasticity and high uncertainty, posing a severe challenge to traditional planning methods. Specifically, the peak-to-valley load difference is expanding rapidly, short-term impulsive loads are occurring frequently, and bidirectional power flow is becoming increasingly common. These changes urgently require more refined, intelligent, and dynamic adaptability in distribution network capacity configuration. Traditional planning methods are no longer sufficient to meet the needs of modern smart distribution networks for deep synergistic optimization of reliability and economic efficiency.

[0066] Existing power distribution network capacity planning technologies suffer from two main shortcomings. Firstly, in terms of quantifying reliability value, traditional methods lack the ability to analyze and quantify the economic benefits of reliability improvements. They often treat reliability as a binary "qualified" or "unqualified" standard, using fixed, rigid indicators as constraints, failing to scientifically assess the economic rationality of incremental investments in improving reliability. Specifically, this manifests as a singular optimization objective, overemphasizing investment costs or grid losses while neglecting default losses due to insufficient capacity; a crude risk assessment, failing to distinguish the differentiated economic consequences of overload events of different levels and durations; and insufficient decision-making basis, particularly when configuring redundant capacity to cope with occasional extreme loads, lacking scientific economic evaluation methods. Ultimately, this leads to planning schemes that are either overly conservative, resulting in wasted investment, or overly aggressive, leaving safety hazards.

[0067] On the other hand, regarding the dynamic adaptability of planning schemes, existing capacity planning methods are mostly static and offline, lacking adaptive adjustment mechanisms to changes in the power grid operating environment, thus belonging to a "one-off planning" model. These methods complete planning based on historical operating data and load forecast results. After the scheme is put into operation, it is difficult to effectively modify it according to the actual operating conditions. Specifically, it lacks an effective feedback mechanism and cannot use massive real-time operating data to calibrate and optimize the original planning model; there is a significant lag in response to changes in the operating environment. When the load growth pattern or fluctuation characteristics change significantly, the original scheme will gradually deviate from the optimal state or even lose its applicability, but the adjustment process is often delayed by several years; the planning model is rigid, and when the data distribution drifts, it cannot improve the model's generalization ability and robustness through learning and evolution, resulting in a significant decrease in the long-term effectiveness and economy of the planning scheme.

[0068] In addition, while some solutions attempt to improve the operation of the distribution network by optimizing operating strategies or real-time management, they either have a single optimization goal, focusing only on minimizing costs under normal operating conditions, lacking a quantitative assessment of the value of power supply reliability, failing to solve the trade-off between reliability and economy, and lacking a dynamic adaptation mechanism; or they can only passively correct the current operating state, lacking the ability to fundamentally avoid problems at the planning level, and failing to assess the long-term economic efficiency of capacity configuration optimization.

[0069] To address the shortcomings of existing technologies, such as insufficient quantification of reliability value and poor dynamic adaptability of planning schemes, this invention provides a method and related apparatus for evaluating distribution network capacity configuration schemes that can scientifically quantify the economic value of reliability and possess dynamic adaptive adjustment capabilities. This overcomes the deficiencies of existing technologies and improves the scientific rigor, economic efficiency, and long-term effectiveness of distribution network capacity planning. The following provides a detailed description of various embodiments of this invention.

[0070] Please see Figure 1 This invention provides a method for evaluating power distribution network capacity configuration schemes, comprising the following steps:

[0071] S101: Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0072] Among them, the default operation cost is used to quantify the degree of default operation of the current capacity configuration plan into an economic cost value. More specifically, it is to transform the default operation state caused by failure to meet the preset reliability requirements during the distribution network capacity configuration process into a calculable and comparable economic value. It is a quantitative indicator for measuring the reliability risk of the current capacity configuration plan.

[0073] Optionally, this step can first clarify the representational dimensions of default operation, determine the basic parameters that can be used to quantify the degree of default, and then transform the abstract default operation status into specific economic costs through standardized quantitative methods.

[0074] For example, within a preset optimization period, basic operational data during the operation of the current capacity configuration scheme is collected. Combined with preset reliability constraints, the degree of default operation of the current scheme is initially quantified, and then the corresponding default operation cost is calculated.

[0075] S102: Calculate the reliability economic efficiency based on the construction and operation costs of the current capacity configuration scheme and the failure operation costs; the reliability economic efficiency is an indicator used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0076] Among them, reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network compared to the previous capacity configuration scheme. More specifically, it reflects the cost-effectiveness between incremental capacity investment and reliability improvement benefits. It transforms the abstract reliability improvement into a quantifiable economic efficiency indicator, solving the problem that traditional methods cannot quantify the value of reliability investment.

[0077] Optionally, this step can first clarify the difference in benefits (benefits from improved reliability) and cost differences (incremental investment costs) between the two options, and then obtain an efficiency index reflecting the cost-effectiveness of incremental investment by calculating the ratio of the two.

[0078] For example, first determine the previous capacity configuration scheme as the benchmark scheme, collect relevant basic data such as construction cost, operating cost and default operating cost of the current scheme and the benchmark scheme, calculate the cost difference and reliability benefit difference between the two, and obtain the reliability economic efficiency of the current scheme through the preset ratio calculation logic.

[0079] S103: Evaluate the current capacity configuration scheme based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation result meets the capacity optimization conditions, calculate the reliability-economic efficiency of all preset candidate capacity configuration schemes within the preset evaluation period.

[0080] Among them, the default operation status refers to the overall status of the current capacity configuration plan during the optimization period, including the frequency and severity of defaults; the evaluation result meets the capacity optimization conditions, which means that the operational reliability or economy of the current plan does not meet the preset requirements, and it is necessary to start the comparative optimization of multiple candidate capacity configuration plans.

[0081] Optionally, this step can first preset evaluation thresholds related to reliability and economy, then collect the actual operating indicators of the current solution, compare them one by one with the preset thresholds, and determine whether optimization needs to be initiated based on the comparison results.

[0082] For example, the settings include A set of capacity configuration options for the candidate solutions. Within a preset optimization period, data related to the default operation of the current capacity configuration scheme and the calculated reliability-economic efficiency are collected. The two types of data are compared with preset evaluation criteria to determine whether the current scheme meets the capacity optimization conditions. If it does, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated.

[0083] S104: Select the candidate capacity configuration scheme with the best reliability and economic efficiency as the new current capacity configuration scheme, and re-evaluate it after the scheme is put into operation.

[0084] Among them, the optimal capacity configuration scheme with the best reliability and economic efficiency refers to the scheme with the highest incremental investment cost-effectiveness among all preset alternative schemes, which can achieve the best balance between the economy and reliability of the distribution network; the re-evaluation after the scheme is put into operation refers to taking the new scheme as the current scheme and repeating the evaluation steps S101-S103 of this embodiment to form a closed-loop rolling optimization.

[0085] Optionally, this step can first select the optimal solution with the best core indicators from the candidate solutions and put it into operation. Then, through continuous monitoring and evaluation over a preset period, deviations between the solution and the actual operating status can be detected in a timely manner, thereby realizing dynamic correction of the solution.

[0086] For example, after determining that the current scheme meets the capacity optimization conditions, the reliability and economic efficiency of all preset candidate capacity configuration schemes within a preset evaluation period are calculated, and the scheme with the best index is selected as the new current capacity configuration scheme. After the new scheme is put into operation, the evaluation process of S101-S103 in this embodiment is repeated according to the preset optimization period to achieve closed-loop rolling optimization.

[0087] This embodiment quantifies the operating costs of default and the economic efficiency of reliability to evaluate and dynamically optimize the capacity configuration scheme of the distribution network. It can scientifically assess the incremental investment value of improving power supply reliability, take into account the economy and reliability of capacity configuration decisions, and avoid the problems of insufficient or excessive investment caused by the inability to quantify the reliability value in traditional methods.

[0088] In one exemplary embodiment, calculating the default operating cost of the current capacity configuration scheme includes:

[0089] S201: Preset several levels of default severity, as well as the load rate threshold and unit default cost weight for each level; the load rate threshold and unit default cost weight for each level increase progressively according to the level of default severity.

[0090] Among them, the severity level of default is divided into several levels based on the degree of risk of distribution network operation, which is used to distinguish the economic losses corresponding to different degrees of default; the load rate threshold is the judgment standard for classifying each severity level of default, which refers to the threshold of the ratio of load power to distribution network capacity; the unit default cost weight is used to quantify the economic cost coefficient corresponding to each level of default severity, and its value increases with the severity level of default, reflecting the high economic cost of high-level defaults.

[0091] For example, establish There are several levels of reliability constraints, with the load rate threshold corresponding to level k being... And satisfy That is, the higher the level, the higher the load factor threshold; at the same time, each level is assigned a weight based on the unit default cost. This weight increases sharply as the default level rises. This is used to reflect the high economic costs of serious default events.

[0092] S202: Determine the default indication function value and default degree for each level under each time period based on the load rate threshold; the default indication function value is 1 or 0, where 1 indicates that the corresponding level has defaulted during the time period, and 0 indicates that the corresponding level has not defaulted during the time period; the default degree is a quantitative value that quantifies the severity of default for the corresponding level under the time period; the degree of default operation is characterized by the default indication function value and the default degree.

[0093] The default indicator function value is a binary variable used to determine whether a default has occurred at a certain default level within a certain period. It takes the value 1 or 0, where 1 indicates that the corresponding level has defaulted during the period and 0 indicates that the corresponding level has not defaulted during the period. The default severity is a quantitative value that quantifies the severity of the default at the corresponding level during the period. It is used to characterize the severity of the default when it occurs, rather than simply determining whether a default has occurred. The default operation degree is jointly characterized by the default indicator function value and the default severity, reflecting both whether a default has occurred and the severity of the default.

[0094] For example, define the first Time slot plan of Level Default Indication Function , represented as:

[0095] (1)

[0096] In the formula, Representation scheme The power distribution network capacity used. It is the first The load power during a certain period. k is a positive integer from 1 to K, where K is the total number of preset severity levels of default. When the ratio of the load power to the distribution network capacity during a certain period is greater than the load rate threshold of level k, the default indication function value of level k is 1; otherwise, it is 0. Since the threshold values ​​of each level increase, the default indication function values ​​of levels 1 to k are all 1, and the default indication function values ​​of levels k+1 to K are all 0 (when k=K, the default indication function values ​​of levels 1 to K are all 1).

[0097] definition Level of default Used to characterize the degree of default, expressed as:

[0098] (2)

[0099] S203: The product of the default indicator function value, the default degree, and the unit default cost weight is used as the default cost of the corresponding level, and the default costs of each level within a preset period are accumulated in sequence to obtain the default operating cost.

[0100] Among them, the default cost of the corresponding level is the economic loss corresponding to the severity of default of that level in a certain period of time. It is obtained by multiplying the default indicator function value, the degree of default, and the unit default cost weight. The larger the product result, the greater the economic loss of default of that level in that period of time. Time-series accumulation refers to adding the default costs of each period and each level in the preset period one by one to obtain the total economic loss of default in the entire period, that is, the default operation cost.

[0101] This embodiment achieves accurate calculation of the economic losses from default operations of distribution network capacity configuration schemes by presetting default levels, quantifying default characterization parameters, and accumulating default costs over time.

[0102] In one exemplary embodiment, the default operating costs include:

[0103] Short-term default operating costs and total default operating costs;

[0104] If the time range of the preset period is greater than or equal to the preset time period range, the default operating cost is the total default operating cost; if the time range of the preset period is less than the time period range, the default operating cost is the short-term default operating cost.

[0105] The time range of the optimization cycle is shorter than the time period range, while the time range of the evaluation cycle is longer than the time period range.

[0106] Among them, short-term default operating cost refers to the economic cost of the distribution network capacity configuration scheme due to default operation within a relatively short time period (optimization period); total default operating cost refers to the total economic cost of the distribution network capacity configuration scheme due to default operation within a relatively long time period (evaluation period); preset period refers to the time range set for calculating default operating cost; time period range refers to the pre-set time benchmark used to distinguish between short-term and long-term, and both the optimization period and the evaluation period are defined with reference to this time period range.

[0107] For example, combining the classification and period definition rules of default operating costs, a time period range is first preset. The time range of the assessment period is longer than the time period range. Therefore, the default operating cost calculated within the assessment period is the total default operating cost, which can be calculated by formula (3), that is, throughout the entire assessment period. Within this framework, considering both the degree of default and the duration of default, the proposed solution... Total default operating costs This cost quantifies the economic cost corresponding to the overall reliability risk, and is expressed as:

[0108] (3)

[0109] Since the time range of the optimization period is shorter than the time period range, the default operating cost calculated within the optimization period is the short-term default operating cost, which is used for short-term scheme evaluation. The short-term default operating cost can be calculated by combining equation (4) with the number of time periods in the optimization period, i.e., statistical scheme. Short-term default operating costs Represented as:

[0110] (4)

[0111] In the formula, This represents a relatively short rolling time window; t is the current optimization time, with a value of [value missing]. Positive integers up to T; t' is the summation variable, taking values ​​of Positive integers up to t. When t = When t' takes values ​​from 1 to 1, This corresponds to the first rolling time window; when t=T, t' takes the value of T corresponds to the last rolling time window. The number of possible values ​​for t' is the number of time periods included in the rolling time window.

[0112] This embodiment clarifies the classification of default operating costs and the cost types under different periods, solves the problem of defining the calculation of default costs under different assessment scenarios (short-term monitoring, long-term optimization), and ensures that the cost data used for short-term scheme monitoring and long-term scheme optimization are matched.

[0113] In one exemplary embodiment, default performance is characterized by the frequency of defaults and the average degree of default for each default severity level.

[0114] For each level of default severity, the default frequency is the value obtained by summing the default indicator function values ​​at each moment within the optimization period and then taking the average value for the period; the average default degree is the value obtained by summing the default degree at each moment within the optimization period and then taking the average value for the period.

[0115] The operational status of the default is characterized by the default frequency and average default severity of each default severity level. The combination of the two can comprehensively reflect the frequency and severity of defaults. The default frequency refers to the frequency of defaults at a certain default level within the optimization period, and is a quantitative value. The average default severity refers to the average severity of defaults at a certain default level within the optimization period, and is a quantitative value. The optimization period refers to a short time window that is preset for monitoring and evaluating the current operational status of the scheme.

[0116] For example, with a shorter scrolling time window As an optimization cycle, real-time operational status is monitored. Multi-level reliability failures are statistically analyzed within the time window, including... Level of default frequency and Average default rate , respectively represented as:

[0117] (5)

[0118] (6)

[0119] This embodiment clarifies the specific characterization parameters and calculation methods for the default operation status, transforming the abstract default operation status into quantifiable and comparable specific indicators.

[0120] In one exemplary embodiment, the current capacity configuration scheme is evaluated based on its default operation and reliability-economic efficiency, including:

[0121] S301: Compare the default frequency and average default severity under each default severity level with the preset frequency threshold and default severity threshold for the corresponding level.

[0122] Among them, the frequency threshold for each level of default severity refers to the upper limit of the preset and acceptable frequency of default at that level. Exceeding this threshold indicates that defaults occur too frequently and the operational risk is too high. The default severity threshold refers to the upper limit of the preset and acceptable severity of default at that level. Exceeding this threshold indicates that the severity of default is too high and the potential economic loss is too great. The comparison refers to comparing the actual default frequency and average default severity of the current plan with the corresponding preset thresholds one by one to determine whether they exceed the acceptable range.

[0123] This step proposes a capacity assessment optimization triggering mechanism based on the deterioration of default conditions. Deterioration of default conditions refers to a short-term occurrence where the frequency or severity of high-level defaults exceeds a preset dynamic threshold. or This dynamic threshold is calculated based on the tolerance limits for different levels of default events in the power distribution network operation regulations, combined with historical operating data, and according to a preset safety margin ratio (such as the 99th percentile).

[0124] S302: Compare the reliability-economic efficiency with a preset reliability-economic efficiency threshold.

[0125] The preset reliability-economic efficiency threshold refers to the minimum economic efficiency value of a preset, acceptable capacity configuration scheme. This threshold is set based on the expected short-term reliability-economic efficiency of a scheme with a higher capacity level. If it is lower than this threshold, it means that the incremental investment cost-effectiveness of the current scheme is too low and it is no longer economical.

[0126] This step proposes a capacity assessment optimization triggering mechanism based on economic efficiency reversal. Economic efficiency reversal refers to the current scheme's short-term reliability economic efficiency falling below a preset threshold.

[0127] S303: If any one of the following conditions is met, such as the default frequency being greater than the frequency threshold, the average default degree being greater than the default degree threshold, or the reliability economic efficiency being less than the reliability economic efficiency threshold, the evaluation result is deemed to meet the capacity optimization conditions.

[0128] Among them, the capacity optimization condition refers to the judgment condition that the current capacity configuration scheme can no longer meet the operational safety or economic requirements and that multiple schemes need to be compared and optimized. Meeting any of the conditions means that as long as there is one of the following: the default frequency exceeds the threshold, the average default degree exceeds the threshold, or the reliability and economic efficiency are lower than the threshold, it is determined that optimization is required, which reflects the principle of prioritizing safety and taking into account economy.

[0129] This embodiment proposes a composite triggering mechanism, which initiates the capacity assessment and optimization process when either the default situation worsens or the economic efficiency reverses. This mechanism combines two modes: risk-driven (default worsening) and benefit-driven (efficiency reversal). The former serves as a safety baseline, ensuring the system does not fall into an unacceptable risk state; the latter is a proactive economic optimization that actively seeks better solutions when the system is safe but no longer economical, thereby achieving an intelligent upgrade from passive response to proactive optimization.

[0130] In an exemplary embodiment, the reliability-economic efficiency of the current capacity configuration scheme is calculated based on the construction and operation costs and default operation costs of the current capacity configuration scheme, including:

[0131] S401: Calculate the difference between the construction and operation costs corresponding to the current capacity configuration scheme and the previous capacity configuration scheme to obtain the first cost difference value.

[0132] The construction and operation costs of the current capacity configuration scheme refer to the sum of the initial investment costs (equipment purchase, installation, and engineering management costs) and the regular operating costs (equipment depreciation, network loss, and load loss costs) required to implement the scheme. The previous capacity configuration scheme refers to the capacity configuration scheme before the current scheme was upgraded, serving as the benchmark scheme. The first cost difference is the difference between the construction and operation costs of the current scheme and the benchmark scheme, reflecting the sum of the incremental configuration costs and incremental operating costs required for capacity upgrades.

[0133] For example, for those containing A set of capacity configuration options for the candidate solutions. Considering the initial investment costs required to implement this plan, calculate the capacity configuration scheme. Configuration costs (construction costs) , represented as:

[0134] (7)

[0135] In the formula, , and These represent equipment purchase cost, installation cost, and project management cost, respectively.

[0136] Conventional operating costs primarily refer to costs incurred due to network power losses. Power losses include fixed losses that do not change with load (such as transformer iron losses) and load losses that change with load (such as transformer copper losses). Calculate capacity configuration schemes. Operating costs , represented as:

[0137] (8)

[0138] In the formula, , and These represent equipment depreciation costs, network loss costs, and load loss costs, respectively.

[0139] The first cost difference includes the construction cost difference. Difference between operating costs and Specifically, it is expressed as and ,in, and These are the construction and operating costs of the baseline scheme (i.e., the previous capacity configuration scheme), respectively.

[0140] S402: Calculate the difference between the default operating costs corresponding to the previous capacity configuration scheme and the current capacity configuration scheme to obtain the second cost difference value.

[0141] The second cost difference is the difference between the default operating cost of the previous capacity configuration scheme and the current capacity configuration scheme. Since capacity upgrades reduce default operating costs, this value reflects the default operating cost avoided by capacity upgrades (i.e., the reduction in default costs), which is the reliability benefit brought by capacity upgrades.

[0142] For example, the second cost difference This can be further understood as the operational cost of default avoided after capacity upgrade (i.e., the reduction in default cost), specifically calculated as follows: , The cost of default for the benchmark solution.

[0143] S403: The ratio of the second cost difference to the first cost difference is used as the reliability-economic efficiency of the current capacity configuration scheme.

[0144] Among them, reliability-economic efficiency is the ratio of incremental revenue to incremental cost. This ratio directly reflects the reliability benefits that can be obtained by a unit of incremental investment. The higher the ratio, the higher the cost-effectiveness of the current capacity configuration plan and the greater the value of the incremental investment.

[0145] To address the effectiveness of increasing capacity investment to improve reliability, this embodiment proposes reliability economic efficiency, which measures the economic efficiency of the increased capacity added to improve operational reliability. It defines capacity configuration schemes. Compared to the benchmark scheme Reliability and economic efficiency Represented as:

[0146] (9)

[0147] In the formula, This represents the total default operating cost avoided after upgrading capacity (i.e., the reduction in default cost), specifically calculated as follows: , and These represent the additional construction and operating costs required to satisfy multi-level constraints, respectively, specifically expressed as follows: and ,in, , and These are the default costs, construction costs, and operating costs of the benchmark solution, respectively.

[0148] The numerator of this indicator represents the total operating cost in case of default, while the denominator represents the additional construction and operating costs required. This indicator clearly reveals the reliability benefits that can be gained per unit of incremental investment; the higher the value, the higher the cost-effectiveness of the current capacity configuration. This indicator transforms the abstract improvement in reliability into a concrete rate of return, avoiding the underinvestment or overinvestment problems caused by the inability to quantify reliability value in traditional methods. In particular, it provides a reasonable decision-making basis for capacity margin investments to cope with occasional extreme loads.

[0149] For short-term scenario evaluation, calculate its short-term reliability and economic efficiency relative to the benchmark scenario. , represented as:

[0150] (10)

[0151] In the formula, , The operating costs of default that can be avoided in the short term through capacity upgrades. The short-term default operating costs of the benchmark scheme, For statistical scheme The short-term operating costs of default.

[0152] In one exemplary embodiment, before calculating the reliability and economic efficiency of all preset candidate capacity configurations within a preset evaluation period, the method further includes:

[0153] S501: Obtain the historical dataset of the distribution network and the new dataset of the distribution network under the current capacity configuration scheme.

[0154] Historical datasets refer to the collection of historical data accumulated by the distribution network during its past operation, which is related to capacity configuration, load operation, and default situations. These include historical load power, historical capacity configuration schemes, and historical default records. New datasets refer to the latest set of operational data generated during the operation of the distribution network under the current capacity configuration scheme, including data such as load power within the current optimization cycle.

[0155] For example, once the evaluation optimization is triggered, the historical dataset of records is first retrieved. And new datasets during this monitoring period .

[0156] S502: Generate a dataset by using a pre-trained generative adversarial network based on the data distribution characteristics of historical and new datasets.

[0157] Generative Adversarial Networks (GANs) are deep learning-based generative models consisting of a generator and a discriminator. Through adversarial training between the two, they can generate virtual data that is similar to the distribution of real data. Generated datasets refer to sets of virtual data that are similar to the distribution of two types of real datasets, generated using GAN networks based on the data distribution characteristics of historical and new datasets. These datasets are used to supplement data samples and improve the representativeness of evaluation datasets.

[0158] For example, a pre-trained GAN (Generative Adversarial Network) is used to generate the dataset based on the data distribution characteristics of both. .

[0159] S503: Calculate the data distribution similarity between the historical dataset and the new dataset. Combine the data distribution similarity with the average default rate of the new data to dynamically determine the weight coefficients of the historical dataset, the new dataset, and the generated dataset. The weight of the historical dataset increases with the stability of the system operation. The weight of the new dataset is positively correlated with the average default rate. The weight of the generated dataset increases with the increase of the difference in distribution between the historical data and the new data.

[0160] Among them, data distribution similarity refers to the degree of similarity between the data distribution characteristics of historical datasets and new datasets, which can be measured by the Kullback-Leibler (KL) divergence index. The smaller the KL divergence value, the more similar the distributions are. The weight coefficient refers to the proportion of the three types of datasets in the evaluation dataset, which is used to adjust the sampling ratio of each type of data. The weight of the historical dataset increases with the improvement of the stability of the system operation, the weight of the new dataset is positively correlated with the average default degree, and the weight of the generated dataset increases with the increase of the difference between the distribution of historical data and new data, so as to ensure that the weight adjustment fits the current operating status.

[0161] For example, the Kullback-Leibler (KL) divergence metric is used to measure the historical dataset. With the new dataset Distribution similarity Then, based on the similarity between the new data and historical data and the average default rate of the new data... The weights of the three parts of the evaluation dataset are dynamically determined. , and Specifically, it is expressed as:

[0162] (11)

[0163] (12)

[0164] (13)

[0165] In the formula, , and These are the weighting coefficients for the three datasets, used to adjust the sampling ratio as needed. Historical data weights. This is related to the stability and predictability of the current situation. When the system is running smoothly and the load pattern does not change significantly, historical data should have a higher weight to reduce the training burden. Conversely, when the system experiences anomalies or drastic changes in patterns, the reliance on historical data should be reduced. This invention uses an exponential function to enhance sensitivity to extreme cases. New data weights Positively correlated with the severity of recent defaults, to ensure a rapid response to deteriorating operational conditions, this invention employs an exponential function to amplify focus on high-severity events. Data weighting is generated. Positively correlated with the degree of change in data distribution, this invention uses an exponential function to define its weights, thereby achieving a sensitive amplification effect for lower similarities. When the distribution differences are large and the similarities are low, more reliance should be placed on generated data to explore new loading patterns.

[0166] S504: Normalize and map the weight coefficients of the three datasets to obtain the final sampling ratio for each dataset, and sample and merge the three datasets according to the final sampling ratio to obtain the merged evaluation dataset.

[0167] Normalization mapping refers to mapping the weight coefficients of the three datasets to a unified probability space, ensuring that the sum of the weight coefficients is 1. This can be achieved using the Softmax function, which can convert multiple weight values ​​into probability values, and the sum of all probability values ​​is 1. The final sampling ratio refers to the sampling proportion of the three datasets in the evaluation dataset, which is determined by the normalized weight coefficients. Data fusion refers to sampling the three datasets according to the sampling ratio and integrating them into a unified evaluation dataset.

[0168] For example, based on the weights of the three parts of data , and The sampling proportion is mapped to a uniform probability space using the softmax function to obtain the final sampling proportion. , and Sampling is performed proportionally, and the data is then merged to obtain the evaluation dataset. .

[0169] S505: Calculate the default operating costs of all preset candidate capacity configuration schemes within a preset evaluation period based on the evaluation dataset.

[0170] Among them, the candidate capacity configuration schemes refer to a set of multiple capacity configuration schemes that are preset for comparison and optimization; calculating the default operating cost based on the evaluation dataset means taking the evaluation dataset as input and using the corresponding default operating cost calculation method to calculate the default operating cost of each candidate scheme within the preset evaluation period.

[0171] For example, the merged dataset As new input, the entire evaluation process is executed, including recalculating the costs of each option. And solve for the optimal reliability-economic efficiency. This leads to a new optimal capacity configuration scheme. If the optimal solution is the same as the current solution... If the situation differs, capacity expansion or process adjustment will be initiated, thus forming a complete, adaptive, dynamic optimization closed loop.

[0172] Please see Figure 2 , Figure 2The flowchart of the distribution network capacity optimization method based on reliability-economic efficiency designed based on the above embodiments is shown. It includes a distribution network capacity configuration scheme evaluation method based on reliability-economic efficiency and a distribution network capacity optimization method based on a trade-off between multi-level reliability defaults and reliability-economic efficiency constraints. Specifically, the distribution network capacity configuration scheme evaluation method based on reliability-economic efficiency is as follows:

[0173] Step 1: Calculate the configuration cost and operating cost of each solution.

[0174] The configuration (construction) cost of each candidate scheme is calculated using formula (7), which includes the cost of purchasing, installing and managing the equipment. The routine operating cost of each scheme is calculated using formula (8), which includes the cost of equipment depreciation, network loss and load loss.

[0175] Step 2: Calculate the multi-level reliability constraint indexes for each scheme based on the dataset.

[0176] First, preset multiple levels of default severity and corresponding load rate thresholds and unit default cost weights;

[0177] For each time period, calculate the default indication function value for each level using equation (1) (to determine whether a default of that level has occurred).

[0178] The degree of default at each level is calculated using equation (2) (quantifying the severity of the default).

[0179] During the optimization period, the default frequency of each level is calculated by Equation (5), and the average default degree is calculated by Equation (6), so as to obtain a multi-level reliability constraint index that can be used for evaluation.

[0180] Step 3: Calculate the total default operating cost for each option.

[0181] Within an assessment period T that is longer than the time period, the default indication function values, default degree, and unit default cost weights for each time period and level are multiplied and accumulated over time.

[0182] The total default operating cost of each scheme is calculated using equation (3), which quantifies the economic loss of reliability risk throughout the entire evaluation period.

[0183] Step 4: Calculate the reliability and economic efficiency of each scheme.

[0184] Using the benchmark scheme as a reference, calculate the difference in construction and operation costs between the current scheme and the benchmark scheme (the first cost difference).

[0185] Calculate the difference in total default operating costs between the two (the second cost difference, i.e. the default losses avoided by capacity upgrade);

[0186] The reliability-economic efficiency of each scheme is calculated by formula (9), that is, the reliability benefit corresponding to unit incremental investment, so as to realize the quantitative trade-off between economy and reliability.

[0187] Step 5: Select the optimal solution for commissioning.

[0188] Based on the reliability and economic efficiency indicators of each scheme, the scheme with the highest cost performance is selected as the current capacity configuration scheme and put into actual operation.

[0189] After commissioning, the process proceeds to a distribution network capacity optimization method based on a trade-off between multi-level reliability defaults and reliability-economic efficiency constraints, specifically as follows:

[0190] Step 1: Short-term operational status monitoring and statistics.

[0191] After the solution is put into operation, the optimization period is T, which is shorter than the time period. short Window, continuous monitoring. Calculate the short-term default operating cost of the current plan using Equation (4); continuously count the default frequency and average default degree of each default level using Equations (5) and (6) to characterize the default situation of the current operating status.

[0192] Step 2: Calculate short-term reliability economic efficiency.

[0193] Combining the construction and operation costs of the current scheme with the short-term default operation costs, the short-term reliability economic efficiency of the current scheme is calculated by formula (10), reflecting the cost-effectiveness of incremental investment under the current operating state.

[0194] Step 3: Determine if the optimization trigger conditions are met?

[0195] The dual triggering conditions are "worsening default conditions" and "reversal of economic efficiency":

[0196] If the frequency of defaults or the average degree of defaults exceeds the preset threshold, or the short-term reliability and economic efficiency are lower than the preset threshold, then the optimization conditions are met and the process proceeds to the next step.

[0197] If the conditions are not met, return to "Short-term Operation Status Monitoring and Statistics" and continue rolling monitoring.

[0198] Step 4: Perform adaptive data fusion based on similarity and default rate.

[0199] After triggering optimization, construct a new evaluation dataset:

[0200] Obtain historical operating datasets and new datasets under current operating conditions, and use GAN to generate supplementary datasets;

[0201] KL divergence is used to calculate the distribution similarity between the two types of real data. Combined with the average default of the new data, the dynamic weights of the historical dataset, the new dataset, and the generated dataset are calculated by Equations (11), (12), and (13), respectively.

[0202] After normalization using the Softmax function, the three types of data are sampled and merged according to their weights to construct a new evaluation dataset that is adapted to the current running state.

[0203] Step 5: Use the merged evaluation dataset as new input and re-execute the evaluation process.

[0204] Using the merged dataset as input, the cost accounting, default indicator calculation, default operation cost calculation, and reliability and economic efficiency calculation processes on the left are completely repeated. All candidate solutions are re-traversed and the optimal solution is selected. The current configuration solution is replaced and put into operation. Then, the short-term monitoring steps on the right are returned to form a closed-loop optimization.

[0205] Compared to existing technologies, this invention proposes a method for evaluating distribution network capacity configuration schemes based on reliability and economic efficiency. First, by integrating and quantifying the investment, routine operation, and default costs of multi-level reliability constraints for different capacity configuration schemes, a full-cost evaluation system reflecting the comprehensive economic and risk characteristics of each scheme is constructed, achieving an accurate characterization of the economic benefits and potential risks of a single scheme. Then, focusing on the marginal benefits of capacity increases, and comprehensively considering the default costs avoided by capacity upgrades and the incremental configuration costs incurred, a reliability and economic efficiency evaluation index is constructed to calculate the economic rate of return for upgrading from a low-capacity scheme to a high-capacity scheme. This invention enables a scientific evaluation of the incremental investment value for improving power supply reliability, providing core quantitative basis and decision support for capacity configuration decisions that seek the optimal balance between economy and reliability.

[0206] This invention also proposes a distribution network capacity optimization method based on a trade-off between multi-level reliability defaults and reliability-economic efficiency constraints. First, by conducting short-term rolling monitoring of the operational status of equipment after commissioning, a dual dynamic triggering mechanism integrating default deterioration and efficiency reversal is constructed to achieve accurate identification and timely response to capacity mismatch risks. Then, considering the similarity of the distribution of old and new data and the severity of recent defaults, an adaptive data fusion strategy based on dynamic weights is constructed to dynamically generate the optimal evaluation dataset for the current operational status. This invention enables closed-loop rolling optimization and adaptive correction of distribution network capacity configuration, providing core technical support for ensuring the long-term effectiveness and economy of planning schemes in dynamically changing environments.

[0207] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0208] Based on the same inventive concept, this application also provides a distribution network capacity configuration scheme evaluation device for implementing the above-mentioned distribution network capacity configuration scheme evaluation method. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in the following embodiments of the distribution network capacity configuration scheme evaluation device can be found in the above-described limitations of the distribution network capacity configuration scheme evaluation device, and will not be repeated here.

[0209] Please see Figure 3 This invention also provides a distribution network capacity configuration scheme evaluation device, comprising:

[0210] The default operation cost calculation module is used to calculate the default operation cost of the current capacity configuration scheme within a preset optimization period; the default operation cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0211] The reliability-economic efficiency calculation module is used to calculate the reliability-economic efficiency based on the construction and operation costs and default operation costs of the current capacity configuration scheme. The reliability-economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0212] The scheme evaluation and optimization trigger module is used to evaluate the current capacity configuration scheme based on the default operation status and reliability and economic efficiency of the current capacity configuration scheme. If the evaluation result meets the capacity optimization conditions, it calculates the reliability and economic efficiency of all preset candidate capacity configuration schemes within the preset evaluation period.

[0213] The scheme replacement and cyclic evaluation module is used to select the candidate capacity configuration scheme with the best reliability and economic efficiency as the new current capacity configuration scheme, and to re-evaluate the scheme after it is put into operation.

[0214] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0215] Reference Figure 4 This invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it performs the following steps:

[0216] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0217] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0218] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0219] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0220] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 4The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0221] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0222] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0223] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps:

[0224] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0225] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0226] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0227] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0228] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0229] This invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0230] Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; the default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into economic cost.

[0231] Based on the construction and operation costs of the current capacity configuration scheme and the cost of default operation, calculate the reliability economic efficiency; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme.

[0232] The current capacity configuration scheme is evaluated based on the default operation status and reliability-economic efficiency of the current capacity configuration scheme. If the evaluation results meet the capacity optimization conditions, the reliability-economic efficiency of all preset candidate capacity configuration schemes is calculated within the preset evaluation period.

[0233] The candidate capacity configuration scheme with the best reliability and economic efficiency will be adopted as the new current capacity configuration scheme, and will be re-evaluated after the scheme is put into operation.

[0234] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0235] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0236] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0237] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating power distribution network capacity configuration schemes, characterized in that, Includes the following steps: Within a preset optimization period, calculate the default operating cost of the current capacity configuration scheme; The default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into an economic cost. Based on the construction and operation costs of the current capacity configuration scheme and the default operation costs, the reliability economic efficiency is calculated; the reliability economic efficiency is used to measure the economic efficiency of the new capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme. The current capacity configuration scheme is evaluated based on the default operation status and the reliability and economic efficiency. If the evaluation result meets the capacity optimization conditions, the reliability and economic efficiency of all preset candidate capacity configuration schemes within the preset evaluation period is calculated. The candidate capacity configuration scheme with the best reliability and economic efficiency is selected as the new current capacity configuration scheme, and it is re-evaluated after the scheme is put into operation.

2. The method for evaluating distribution network capacity configuration schemes according to claim 1, characterized in that, Calculate the default operating costs of the current capacity configuration plan, including: Several default severity levels are preset, along with a corresponding load rate threshold and unit default cost weight for each level; the load rate threshold and unit default cost weight for each level increase progressively with the default severity level. Based on the load rate threshold, the default indication function value and default degree for each level are determined for each time period; the default indication function value is 1 or 0, where 1 indicates that the corresponding level has defaulted during the time period, and 0 indicates that the corresponding level has not defaulted during the time period; the default degree is a quantitative value that quantifies the severity of default for the corresponding level during the time period; the degree of default is characterized by the default indication function value and the default degree. The product of the default indication function value, the default degree, and the unit default cost weight is used as the default cost for the corresponding level. The default costs for each level within a preset period are then accumulated sequentially to obtain the default operating cost.

3. The method for evaluating power distribution network capacity configuration schemes according to claim 2, characterized in that, The operating costs of the default include: Short-term default operating costs and total default operating costs; If the time range of the preset period is greater than or equal to the preset time period range, then the default operation cost is the total default operation cost; if the time range of the preset period is less than the time period range, then the default operation cost is the short-term default operation cost. The time range of the optimization period is shorter than the time period range, while the time range of the evaluation period is longer than the time period range.

4. The method for evaluating distribution network capacity configuration schemes according to claim 2, characterized in that, The default operation status is characterized by the default frequency and average default degree for each default severity level; For each of the aforementioned severity levels of default, the default frequency is a value obtained by summing the default indication function values ​​at each moment within the optimization period and then taking the average value for the period; the average default degree is a value obtained by summing the default degrees at each moment within the optimization period and then taking the average value for the period.

5. The method for evaluating distribution network capacity configuration schemes according to claim 4, characterized in that, The current capacity configuration scheme is evaluated based on its default operation and reliability-economic efficiency, including: The frequency and average degree of default under each level of default severity are compared with the preset frequency threshold and degree of default threshold for the corresponding level. The reliability-economic efficiency is compared with a preset reliability-economic efficiency threshold. If any one of the following conditions is met: the default frequency is greater than the frequency threshold, the average default degree is greater than the default degree threshold, and the reliability economic efficiency is less than the reliability economic efficiency threshold, the evaluation result is determined to meet the capacity optimization condition.

6. The method for evaluating distribution network capacity configuration schemes according to claim 1, characterized in that, Based on the construction and operation costs of the current capacity configuration scheme and the default operation costs, the reliability and economic efficiency of the current capacity configuration scheme are calculated, including: Calculate the difference between the construction and operation costs corresponding to the current capacity configuration scheme and the previous capacity configuration scheme to obtain the first cost difference value; The difference between the default operating costs corresponding to the previous capacity configuration scheme and the current capacity configuration scheme is calculated to obtain the second cost difference value; The ratio of the second cost difference to the first cost difference is used as the reliability and economic efficiency of the current capacity configuration scheme.

7. The method for evaluating distribution network capacity configuration schemes according to claim 1, characterized in that, Before calculating the reliability and economic efficiency of all preset candidate capacity configurations within a preset evaluation period, the following steps are also included: Obtain historical datasets of the distribution network and new datasets of the distribution network under the current capacity configuration scheme; Based on the data distribution characteristics of the historical dataset and the new dataset, a generated dataset is obtained using a pre-trained generative adversarial network; Calculate the data distribution similarity between the historical dataset and the new dataset, and dynamically determine the weight coefficients of the historical dataset, the new dataset, and the generated dataset by combining the data distribution similarity and the average default rate of the new data; wherein, the weight of the historical dataset increases with the improvement of system stability, the weight of the new dataset is positively correlated with the average default rate, and the weight of the generated dataset increases with the increase of the distribution difference between the historical data and the new data. The weight coefficients of the three datasets are normalized and mapped to obtain the final sampling ratios for each dataset. The three datasets are then sampled and merged according to the final sampling ratios to obtain the merged evaluation dataset. Based on the evaluation dataset, calculate the default operating costs of all preset candidate capacity configuration schemes within the preset evaluation period.

8. A device for evaluating power distribution network capacity configuration schemes, characterized in that, include: The default operation cost calculation module is used to calculate the default operation cost of the current capacity configuration scheme within a preset optimization period. The default operating cost is a quantitative value used to quantify the degree of default operation of the current capacity configuration scheme into an economic cost. The reliability-economic efficiency calculation module is used to calculate the reliability-economic efficiency based on the construction and operation costs of the current capacity configuration scheme and the default operation cost; the reliability-economic efficiency is used to measure the economic efficiency of the added capacity in improving the reliability of the distribution network operation compared to the previous capacity configuration scheme. The scheme evaluation and optimization triggering module is used to evaluate the current capacity configuration scheme based on the default operation status and the reliability and economic efficiency. If the evaluation result meets the capacity optimization conditions, the module calculates the reliability and economic efficiency of all preset candidate capacity configuration schemes within a preset evaluation period. The scheme replacement and cyclic evaluation module is used to select the candidate capacity configuration scheme with the best reliability and economic efficiency as the new current capacity configuration scheme, and to re-evaluate it after the scheme is put into operation.

9. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes a power distribution network capacity configuration scheme evaluation method as described in any one of claims 1-7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for evaluating power distribution network capacity configuration schemes as described in any one of claims 1-7.

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