Distribution transformer investment benefit multi-dimensional evaluation and investment decision-making method

Abstract

The invention relates to a distribution transformer investment benefit multi-dimensional evaluation and investment decision-making method, which provides a scientific basis for the investment decision-making of distribution transformer equipment and optimizes the resource allocation of a power system by constructing a distribution transformer investment benefit evaluation index system, and specifically comprises the following steps of: 1, data acquisition and preprocessing: collecting related data of the distribution transformer equipment in each city, comprise distribution transformer capacity, monthly electric quantity, reverse electric quantity and the like, and basic data is provided for the evaluation model; step 2, core evaluation index calculation: calculating four core indexes: a unit distribution transformer capacity electricity sale quantity, a maximum load rate, a reverse electric quantity ratio and a monthly electric quantity volatility index, and providing data support for comprehensive evaluation; 3, constructing a comprehensive evaluation model; 4, carrying out investment return analysis and decision support; step 5, calculating according to city investment demands; the method has the advantages that the multi-dimensional evaluation model is constructed, multi-stage dynamic evaluation is achieved, and the annual utilization rate four-two-three evaluation method is adopted.

Description

Technical Field

[0001] This invention belongs to the technical fields of distribution transformers, investment benefits, comprehensive evaluation, and investment decision-making, specifically involving a multi-dimensional evaluation method for the investment benefits of distribution transformers and an investment decision-making method. Background Technology

[0002] Currently, the State Grid Corporation of China is focusing on input-output evaluation to optimize investment direction, scale, structure, and timing, promoting a virtuous cycle where investment drives electricity growth and incremental benefits guarantee investment scale. Distribution transformers are crucial power equipment in the distribution network, with large usage, wide application, long operating time, and significant energy-saving potential. For a long time, distribution transformer investment planning has focused on its phased costs, neglecting the overall and potential costs throughout the equipment's service life. This has resulted in a lack of a holistic perspective in capacity and model selection, easily leading to overly conservative or aggressive planning schemes and significant investment waste. Existing evaluation methods often identify value from a single dimension, such as focusing only on solving existing operational safety issues, lacking a systematic evaluation of the investment benefits of distribution transformers throughout their entire life cycle. Currently, research on distribution transformer investment benefit evaluation and investment strategy optimization mainly focuses on the following directions and existing problems: Cost-benefit-based investment evaluation emphasizes single economic indicators, such as investment... The evaluation of return on investment and operating costs neglects the impact of key technical factors such as power consumption and load fluctuations. This results in evaluations that only reflect the direct economic returns of distribution transformer equipment, failing to fully consider the indirect benefits and potential risks generated during transformer operation, thus affecting the accuracy of decision-making. While system simulation-based evaluation methods can better reflect the technical value of distribution transformers, such as grid stability and power supply reliability, they face significant difficulties in quantifying economic benefits. Although simulation models can accurately simulate grid load fluctuations and the operation of distribution transformer equipment, they cannot directly translate these technical performances into economic benefits, leading to a lack of economic support for investment decisions. The overall evaluation framework of investment decision optimization methods is still imperfect, lacking weight allocation and comprehensive optimization of indicators across different dimensions, making it difficult to provide a comprehensive and accurate basis for investment decisions. Therefore, it is essential to provide a multi-dimensional evaluation and investment decision-making method for distribution transformer investment benefits that constructs a multi-dimensional evaluation model, performs multi-stage dynamic evaluation, and adopts the "4-2-3" evaluation method based on annual utilization rate. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-dimensional evaluation and investment decision-making method for the investment benefits of distribution transformers, which involves constructing a multi-dimensional evaluation model, multi-stage dynamic evaluation, and adopting the "four-two-three" evaluation method of annual utilization rate.

[0004] The objective of this invention is achieved as follows: a multi-dimensional evaluation method for the investment benefits of distribution transformers and an investment decision-making method, which provides a scientific basis for investment decisions on distribution transformer equipment and optimizes the resource allocation of the power system by constructing an evaluation index system for the investment benefits of distribution transformers. The method includes the following steps:

[0005] Step 1: Data Acquisition and Preprocessing: Collect relevant data on distribution transformer equipment in various cities, including transformer capacity, monthly electricity consumption, and reverse electricity consumption, to provide basic data for the evaluation model;

[0006] Step 2: Calculation of core evaluation indicators: Calculate four core indicators: electricity sales per unit distribution transformer capacity, maximum load rate, reverse electricity ratio, and monthly electricity volatility index to provide data support for comprehensive evaluation;

[0007] Step 3: Construction of comprehensive evaluation model: Based on four core indicators, a comprehensive evaluation model is constructed, and the total benefit score of the distribution transformer equipment is obtained by weighted average method;

[0008] Step 4: Return on Investment Analysis and Decision Support: Based on the evaluation results, each distribution transformer is evaluated from multiple dimensions, and the evaluation results determine whether the investment in the distribution transformer needs to be adjusted or optimized, assisting decision-makers in selecting the distribution transformer equipment with the highest return on investment.

[0009] Step 5: Calculate investment demand by city: Combine the needs of guaranteed investment and efficient investment to calculate the investment demand of specific cities to ensure that the investment direction is scientific and reasonable.

[0010] The data collection in step 1 specifically involves: collecting and organizing relevant data on distribution transformers in various cities and prefectures, including:

[0011] Distribution transformer capacity data: rated capacity and actual operating capacity of each distribution transformer;

[0012] Monthly electricity consumption data: Electricity consumption data for the past 12 months, used to calculate the electricity sales per unit distribution transformer capacity;

[0013] Reverse power data: Percentage of power fed back from distributed energy sources;

[0014] Overload transformer data: Total capacity of currently heavily overloaded transformers;

[0015] Future load forecast data: Average annual load growth data helps predict future electricity demand;

[0016] Target setting data: Set the target unit transformer capacity electricity sales volume for a certain period in the future.

[0017] The data preprocessing in step 1 specifically involves cleaning and standardizing the collected data and storing it in a database to ensure data integrity and comparability.

[0018] The calculation process for the core evaluation indicators in step 2 is as follows:

[0019] Step 2.1: Electricity Sales per Unit Distribution Transformer Capacity: Calculate the electricity sold per kilowatt of distribution transformer capacity to reflect the working efficiency of the distribution transformer;

[0020] Step 2.2: Maximum load rate: Calculate the ratio of the maximum load of the distribution transformer to its rated capacity, reflecting the load utilization efficiency;

[0021] Step 2.3: Reverse power ratio: Calculate the proportion of power fed back to the grid from the distribution transformer to the total power, reflecting the synergistic effect with renewable energy;

[0022] Step 2.4: Monthly Electricity Volatility Index (MVI).

[0023] The electricity sales per unit transformer capacity specifically refers to the electricity sold per kilowatt of transformer capacity. Among them, S total C represents the total electricity sold by the distribution transformer. rated This refers to the rated capacity of the distribution transformer.

[0024] The maximum load factor, specifically, refers to the ratio between the maximum load carried by the distribution transformer during operation and its rated capacity. Among them, L max L represents the maximum load factor of the distribution transformer. maxload This represents the maximum load on the distribution transformer.

[0025] The reverse power ratio specifically refers to the proportion of electrical energy fed back to the power grid by the distribution transformer equipment. Among them, E total E represents the total power output of the distribution transformer equipment. reverse Power is fed back to the distribution transformer equipment.

[0026] The monthly power fluctuation index (MVI) is specifically calculated by taking into account the degree of fluctuation in the monthly power consumption of distribution transformers, combined with the coefficient of variation (CV), peak-to-valley difference (D), and standard deviation of load fluctuation rate (σ). r MVI = w1·CV + w2·D + w3·σ r Where CV is the coefficient of variation of monthly electricity consumption; D is the peak-to-valley difference; σ r is the standard deviation of the rate of change of electricity; w1, w2, and w3 are the weighting coefficients of each factor.

[0027] The specific process for calculating investment demand by city / region in step 5 is as follows:

[0028] Step 5.1: Data preparation and target setting: Collect data on existing distribution transformer capacity, electricity sales, and overloaded distribution transformer capacity for each city, and set target electricity sales per unit distribution transformer capacity;

[0029] Step 5.2: Meet the capacity requirements for load growth: Calculate the total electricity sales of distribution transformers over the next five years, and assess the additional capacity required to meet load growth based on the capacity required for current efficiency.

[0030] Step 5.3: Calculation of guaranteed investment demand: Based on the solution for the overloaded transformer capacity, calculate the guaranteed investment demand, including the cost of on-site replacement and new deployment solutions;

[0031] Step 5.4: Capacity changes resulting from efficiency targets: Calculate the capacity required to achieve efficiency targets, assess the impact of efficiency improvements on capacity demand, and further optimize investment strategies;

[0032] Step 5.5: Comprehensive Synthesis of Final Investment Requirements: Combining guaranteed investment and profitable investment, calculate the total investment requirements to provide detailed investment decision support for the power company.

[0033] The beneficial effects of this invention are as follows: This invention provides a multi-dimensional evaluation method for investment benefits and investment decision-making in distribution transformers. In practice, this invention proposes a method for evaluating investment benefits and making investment decisions in distribution transformers based on a multi-dimensional benefit evaluation model. This method constructs a comprehensive evaluation framework that includes technical benefit subsystems and economic benefit subsystems, and combines multi-dimensional indicators such as power consumption, load fluctuation, social benefits, and environmental benefits of the distribution transformer to conduct a comprehensive value quantification analysis. By establishing a dynamic evaluation model, this invention can realistically depict the technical and economic performance of distribution transformer equipment throughout its entire life cycle, accurately assess its investment returns and system contributions under different application scenarios, and provide a scientific basis for investment decisions, operational optimization, and cost recovery mechanism design for distribution transformer projects. This invention has the advantages of constructing a multi-dimensional evaluation model, multi-stage dynamic evaluation, and adopting the "four-two-three" evaluation method for annual utilization rate. Attached Figure Description

[0034] Figure 1 This is a flowchart of the present invention.

[0035] Figure 2 This is a schematic diagram of the "4-2-3" evaluation method for annual utilization rate of the present invention. Detailed Implementation

[0036] Existing methods for evaluating the investment benefits of distribution transformers often focus on a single dimension of economic or technical performance analysis, lacking a unified framework that comprehensively considers benefits across various dimensions. This approach typically fails to adequately account for the complex value chain and dynamic characteristics of distribution transformers under different operating scenarios, resulting in assessments that cannot accurately reflect the overall benefits of the transformers. This limitation prevents investment decisions from comprehensively evaluating multiple dimensions such as the technical value, economic benefits, load fluctuations, power consumption, and environmental benefits of distribution transformers, thus affecting the rationality and accuracy of investment decisions. Therefore, this invention constructs a multi-dimensional evaluation model for the investment benefits of distribution transformers, conducts multi-stage dynamic evaluations of the investment benefits of all distribution transformers, and proposes a distribution transformer investment decision-making method that comprehensively considers factors such as grid security constraints and policy-driven investments, helping to optimize regional investment scale and direction.

[0037] The present invention will be further described below with reference to the embodiments and / or accompanying drawings.

[0038] Example 1

[0039] like Figure 1-2 As shown, a multi-dimensional evaluation method for the investment benefits of distribution transformers and an investment decision-making method are proposed. By constructing an evaluation index system for the investment benefits of distribution transformers, a scientific basis is provided for investment decisions on distribution transformer equipment, thereby optimizing the resource allocation of the power system. The specific process is as follows:

[0040] Step 1: Data Acquisition and Preprocessing: Collect relevant data on distribution transformer equipment in various cities, including transformer capacity, monthly electricity consumption, and reverse electricity consumption, to provide basic data for the evaluation model;

[0041] In this embodiment, the specific steps are as follows: ① Data acquisition: First, it is necessary to collect relevant data on distribution transformers in various cities, including but not limited to the following:

[0042] Distribution transformer capacity data: The existing distribution transformer capacity of each city, including the rated capacity and actual operating capacity of each distribution transformer.

[0043] Monthly electricity consumption data: Electricity consumption data for each distribution transformer over the past 12 months, used to calculate electricity sales per unit transformer capacity and load fluctuation.

[0044] Reverse power data: Records the proportion of power fed back to the grid by distribution transformers and distributed energy sources (such as photovoltaic and wind power).

[0045] Overload transformer data: The current overload capacity of distribution transformers in the distribution equipment, including the number and capacity of overload transformers in each area.

[0046] Future load forecast data: Data on the average annual growth rate of load over the next five years helps predict future electricity demand.

[0047] Target setting data: Set the target unit electricity sales volume of distribution transformer capacity for a future period (e.g., 2025, 2030).

[0048] ② Data preprocessing: Data cleaning and filtering: Removing missing or outlier data to ensure the integrity and accuracy of each data point.

[0049] Data standardization: Standardize various types of data to ensure that all indicators are comparable, such as electricity sales per unit capacity and load rate.

[0050] Data storage: The organized data is stored in a database to ensure efficient retrieval and updates.

[0051] Step 2: Calculation of core evaluation indicators: Calculate four core indicators: electricity sales per unit distribution transformer capacity, maximum load rate, reverse electricity ratio, and monthly electricity volatility index to provide data support for comprehensive evaluation;

[0052] In this embodiment, a comprehensive evaluation system is crucial for improving investment efficiency, ensuring power grid safety and operational efficiency in distribution transformer investment decisions. Based on four core indicators—electricity sales per unit distribution transformer capacity, maximum load rate, reverse electricity ratio, and monthly electricity volatility index—this invention proposes a comprehensive distribution transformer investment benefit evaluation index system and provides a scientific basis for distribution transformer investment decisions through a multi-dimensional evaluation model.

[0053] Definition and calculation method of core indicators: ① Electricity sales per unit transformer capacity (UnitSalesperkW): Definition: Electricity sales per unit transformer capacity refers to the electricity sold per kilowatt of transformer capacity, reflecting the working efficiency of the transformer; the higher the electricity sales per unit transformer capacity, the more electricity the transformer can output per unit capacity, thus improving energy utilization efficiency and return on investment. Among them, S total C represents the total electricity sold by the distribution transformer. rated This refers to the rated capacity of the distribution transformer.

[0054] Function: By calculating the electricity sales per unit capacity of a distribution transformer, the working efficiency and power generation capacity per unit capacity of the transformer can be evaluated, serving as an important basis for the investment benefits of the distribution transformer.

[0055] ② Maximum Load Ratio: Definition: The maximum load ratio is the ratio between the maximum load carried by the distribution transformer during operation and its rated capacity. This indicator reflects the utilization of the distribution transformer during peak load periods. A higher load ratio may lead to equipment overload and increase the risk of failure; a lower load ratio indicates that the distribution transformer resources are not being fully utilized. Calculation formula: Among them, L max L represents the maximum load factor of the distribution transformer. maxloadThis represents the maximum load on the distribution transformer.

[0056] Function: The maximum load rate can help determine the resource utilization efficiency of the distribution transformer, avoid equipment overload or idleness, and optimize investment benefits.

[0057] ③ Reverse Power Ratio: Definition: The reverse power ratio refers to the proportion of electrical energy fed back to the grid by the distribution transformer. It is commonly found in distribution transformers containing distributed generation systems (such as photovoltaic and wind power). This indicator reflects the synergistic effect between the distribution transformer and the renewable energy system. A higher reverse power ratio means that the distribution transformer plays a greater role in supporting new energy sources. Calculation formula: Among them, E total E represents the total power output of the distribution transformer equipment. reverse Power is fed back to the distribution transformer equipment.

[0058] Function: This indicator reflects the complementarity between distribution transformers and distributed generation. A high reverse power ratio indicates that the distribution transformer has a strong ability to support renewable energy, which is of great significance for evaluating the effect of new energy integration.

[0059] ④ Monthly Load Volatility Index (MVI): MVI is a comprehensive indicator used to quantify the volatility of distribution transformer load and reflect the severity of load changes. This indicator combines multiple factors, including monthly load volatility (CV), peak-to-valley difference (D), and the standard deviation of load volatility (σ). r A final volatility score is obtained by weighted averaging. The higher the MVI value, the greater the load volatility of the distribution transformer; the lower the value, the smaller the load volatility. The MVI calculation formula is: MVI = w1·CV + w2·D + w3·σ r Where CV is the coefficient of variation of monthly electricity consumption; D is the peak-to-valley difference; σ r is the standard deviation of the rate of change of electricity; w1, w2, and w3 are the weighting coefficients of each factor (which can be adjusted according to actual needs).

[0060] CV (Coefficient of Variation): The coefficient of variation measures the degree of fluctuation in monthly electricity consumption; calculation formula: Where, σ monthly denoted as the standard deviation of monthly electricity consumption, reflecting the fluctuation range of electricity consumption; μ monthly The average monthly electricity consumption represents the average electricity consumption of the distribution transformer.

[0061] D: Peak-to-valley difference, measuring the difference between the maximum and minimum monthly electricity consumption; Calculation formula: D = E max -Emin E max E represents the maximum electrical output of the transformer over a 12-month period. min This represents the minimum electrical charge of the transformer over a 12-month period.

[0062] σ r The standard deviation of the rate of change in electricity consumption reflects the volatility of electricity consumption; it can be obtained by calculating the standard deviation of the rate of change of each month's electricity consumption compared to the previous month's electricity consumption, using the following formula: Among them, E i E i-1 μ represents the electricity consumption in the i-th month and the (i-1)-th month, respectively; r is the average rate of change in electricity consumption; N is the number of months in a year, 12.

[0063] Explanation of MVI parameters:

[0064] 1. CV is the most basic fluctuation measurement, reflecting the relative degree of power fluctuation. It is the ratio of standard deviation to mean, which can directly measure the stability of the load. The larger the CV value, the stronger the power fluctuation. Calculation significance: CV quantifies the stability of the load, taking into account the total load and its fluctuation. In load management and distribution transformer optimization, CV is a very critical indicator.

[0065] 2. D (Peak-to-Valley Difference) directly reflects the severity of load fluctuations by calculating the difference between the maximum and minimum monthly electricity consumption. Its significance lies in the fact that in actual operation, power systems are often affected by peak and off-peak loads. Distribution transformers with large peak-to-valley differences may lead to power supply instability, increasing the difficulty of system dispatching. By introducing this parameter, MVI can help assess the pressure on the power grid and the difficulty of dispatching.

[0066] 3, σ r This is the standard deviation of the rate of change in electricity consumption. Calculated based on the rate of change in electricity consumption, the standard deviation reflects the volatility of electricity consumption over time, particularly capturing rapidly changing trends. Its significance lies in the fact that high σ... r Significant fluctuations in electricity consumption may indicate unstable load changes, such as seasonal fluctuations, sudden load spikes, or load changes caused by special events; by using σ... r Incorporating MVI allows for more refined load volatility assessments, especially under rapidly changing load scenarios.

[0067] By combining different weights w1, w2, and w3, the impact of each indicator on MVI can be adjusted according to specific power system needs and optimization objectives. For example, in regions with high renewable energy penetration, peak-valley difference may be more important; while in highly volatile regions, CV and σ may need to be given more attention. r .

[0068] Step 3: Construction of comprehensive evaluation model: Based on four core indicators, a comprehensive evaluation model is constructed, and the total benefit score of the distribution transformer equipment is obtained by weighted average method;

[0069] In this embodiment, the weights of each indicator are adjusted according to factors such as the operating environment of the distribution transformer, market demand, and investment objectives. For example, if the power company focuses on the integration of new energy sources, the weight of the reverse power ratio can be appropriately increased; if the focus is on the operating efficiency of the distribution transformer, the weights of the electricity sales per unit distribution transformer capacity and the maximum load rate can be increased.

[0070] By using the evaluation results of the comprehensive scoring model, power companies can quantify and rank the benefits of each distribution transformer, helping decision-makers to prioritize the optimization of distribution transformers with low load rates or overloads, determine investment priorities based on the comprehensive benefit results, and prioritize the optimization or expansion of distribution transformer equipment with high investment benefits.

[0071] Step 4: Return on Investment Analysis and Decision Support: Based on the evaluation results, each distribution transformer is evaluated from multiple dimensions, and the evaluation results determine whether the investment in the distribution transformer needs to be adjusted or optimized, assisting decision-makers in selecting the distribution transformer equipment with the highest return on investment.

[0072] Step 5: Calculate investment demand by city: Combine the needs of guaranteed investment and efficient investment to calculate the investment demand of specific cities to ensure that the investment direction is scientific and reasonable.

[0073] In summary, this invention has the following advantages: ① Multi-dimensional investment benefit evaluation system: This invention proposes a comprehensive evaluation model with unit transformer capacity electricity sales, maximum load rate, reverse power ratio, and monthly power fluctuation index as the core, comprehensively evaluating the economic and technical benefits of transformer equipment and providing a scientific basis for investment decisions; ② "4-2-3" evaluation method for annual utilization rate: By evaluating the transformers based on technical indicators such as annual maximum load rate, annual average load rate, and annual utilization rate, transformers are divided into different levels of quality. Combined with transformer resource optimization and replacement strategies, this provides a clear priority ranking for equipment optimization and investment; ③ Investment decision support and risk control: Through the comprehensive evaluation model, risk assessment, and investment return analysis, this invention provides decision support for both guaranteed and profitable investments, helping power companies optimize their transformer investment portfolios, reduce overload risk, and improve investment returns.

[0074] This invention provides a multi-dimensional evaluation method for investment benefits and investment decision-making in distribution transformers. In practice, this invention proposes a method for evaluating investment benefits and making investment decisions based on a multi-dimensional benefit evaluation model. This method constructs a comprehensive evaluation framework that includes technical and economic benefit subsystems, combining multi-dimensional indicators such as power consumption, load fluctuation, social benefits, and environmental benefits of the distribution transformer to conduct a comprehensive value quantification analysis. By establishing a dynamic evaluation model, this invention can realistically depict the technical and economic performance of distribution transformer equipment throughout its entire life cycle, accurately assessing its return on investment and system contribution under different application scenarios, and providing a scientific basis for investment decisions, operational optimization, and cost recovery mechanism design for distribution transformer projects. This invention has the advantages of constructing a multi-dimensional evaluation model, multi-stage dynamic evaluation, and adopting the "4-2-3" evaluation method for annual utilization rate.

[0075] Example 2

[0076] like Figure 1-2 As shown, a multi-dimensional evaluation method for the investment benefits of distribution transformers and an investment decision-making method are proposed. By constructing an evaluation index system for the investment benefits of distribution transformers, a scientific basis is provided for investment decisions on distribution transformer equipment, thereby optimizing the resource allocation of the power system. The specific process is as follows:

[0077] Step 1: Data Acquisition and Preprocessing: Collect relevant data on distribution transformer equipment in various cities, including transformer capacity, monthly electricity consumption, and reverse electricity consumption, to provide basic data for the evaluation model;

[0078] Step 2: Calculation of core evaluation indicators: Calculate four core indicators: electricity sales per unit distribution transformer capacity, maximum load rate, reverse electricity ratio, and monthly electricity volatility index to provide data support for comprehensive evaluation;

[0079] Step 3: Construction of comprehensive evaluation model: Based on four core indicators, a comprehensive evaluation model is constructed, and the total benefit score of the distribution transformer equipment is obtained by weighted average method;

[0080] Step 4: Return on Investment Analysis and Decision Support: Based on the evaluation results, each distribution transformer is evaluated from multiple dimensions, and the evaluation results determine whether the investment in the distribution transformer needs to be adjusted or optimized, assisting decision-makers in selecting the distribution transformer equipment with the highest return on investment.

[0081] In this embodiment, specifically: ① The goal and framework of investment decision-making: The support system for investment decision-making should combine multi-dimensional indicators such as the investment benefits, utilization rate and risk control of distribution transformers, and use a comprehensive scoring model and annual utilization rate evaluation method to scientifically predict and optimize investment decisions; In distribution transformer investment decision-making, the goal is not only to ensure the operational efficiency and safety of distribution transformers, but also to maximize their economic benefits and system benefits; Therefore, this invention scientifically evaluates the two parts of distribution transformer investment: the guarantee investment and the benefit investment.

[0082] ② The "4-2-3" evaluation method for annual utilization rate: such as Figure 2 As shown, the "4-2-3" evaluation method for annual utilization rate conducts multi-dimensional evaluations of each transformer and determines whether the investment in the transformer needs to be adjusted or optimized based on the evaluation results; the specific evaluation process is as follows:

[0083] Indicator Classification: Based on indicators such as annual maximum load rate and annual utilization rate, the distribution transformers are divided into 4 major regions and 23 grade zones, forming a clear rating system for superiority and inferiority.

[0084] Grade zone division:

[0085] Excellent Zone (A1, A2, A3, A4): The best performing distribution transformers, with all indicators such as load rate and annual utilization rate in the excellent range, suitable for further utilization and optimization.

[0086] Potential zones (B1, B2, B3, C1, C2): Distribution transformers with room for improvement. Although the load rate is low, the annual utilization rate can be increased by adjusting the load.

[0087] Weak areas (C3, C4, D1, D2): Distribution transformers with certain problems have low annual utilization and load rates, limited potential for improvement, and may need to be replaced or adjusted.

[0088] Overload zones (E1, E2, E3, E4): These zones have low annual utilization rates and overload issues, posing a significant risk and requiring priority for resolution.

[0089] These assessment results will serve as the basis for distribution transformer investment decisions, helping decision-makers distinguish which distribution transformers need to be prioritized for upgrading and which distribution transformers have the potential for further investment and improvement.

[0090] ③ Distribution transformer resource optimization and exchange strategy: Based on the results of the "4-2-3" evaluation method, a distribution transformer overall exchange strategy is proposed, which optimizes the allocation and investment of distribution transformer resources through the following steps:

[0091] 1) Standards for replacing distribution transformers: Load rate replacement standard: By analyzing the changes in load rate of distribution transformers before and after replacement, three replacement standards of high, medium and low are set to reduce heavily overloaded distribution transformers and optimize power grid operation.

[0092] Load rate optimization: For distribution transformers with low load rates, improve their operating efficiency through reasonable load adjustment, while avoiding overload problems caused by high load.

[0093] 2) Replacement priority: High priority replacement: Prioritize replacement of transformers in overload areas such as E1 and E2, focusing on resolving safety hazards and overload issues.

[0094] Medium- and low-priority replacement: For distribution transformers in potential zones such as C1, C2, and B1, their load rate and annual utilization rate will be optimized based on the assessment results to improve overall efficiency.

[0095] 3) Replacement scheme design: consistency of transformer type: When replacing transformers, it should be ensured that the type of transformer to be replaced (such as substation, pole-mounted, box-type) is consistent, and the insulation and cooling methods of the equipment (such as dry type, oil-immersed type) are matched, so as to ensure that the replaced transformer meets the operating requirements.

[0096] New capacity deployment: For areas that require new capacity, new distribution transformer equipment can be built through reasonable deployment to avoid redundant investment.

[0097] ④ Risk control and optimization decision-making: 1) Risk assessment: Overload risk: Identify distribution transformers that may be overloaded by interactive assessment of annual maximum load rate and annual utilization rate, and replace or schedule equipment in advance.

[0098] Operational Risk: Assess the operational risk of high-load distribution transformers, and conduct a quantitative risk analysis by combining the annual utilization rate and load rate of the distribution transformers.

[0099] 2) Return on investment analysis: The return on investment for each distribution transformer is evaluated based on indicators such as the electricity sales per unit capacity and the investment payback period.

[0100] For distribution transformers with high returns on investment, it is recommended to make profitable investments to improve their economic efficiency; for distribution transformers with low returns on investment, the focus should be on guaranteed investments to ensure the safe operation of the power grid.

[0101] ⑤ Implementation plan and adjustment: Adjustment standard: Based on the assessment results of the annual utilization rate and load rate of the distribution transformer, different replacement standards are set to ensure that the load rate and annual utilization rate of the distribution transformer after replacement are within a reasonable range.

[0102] Implementation effect analysis: Through subsequent data tracking and evaluation, we will verify whether the annual utilization rate of the replaced transformer has reached the expected target, thereby providing a reference for subsequent investment decisions.

[0103] Step 5: Calculate investment demand by city: Combine the needs of guaranteed investment and efficient investment to calculate the investment demand of specific cities to ensure that the investment direction is scientific and reasonable.

[0104] In this embodiment, ① data preparation and target setting: C total C represents the current total distribution transformer capacity (unit: MVA); sales The current total electricity sold by the distribution transformer (unit: kWh); GR load Projected average annual growth rate of load over the next five years (unit: %).

[0105] ② Calculation of total future electricity sales: In order to meet future load growth, it is necessary to predict the total electricity sales of distribution transformers over the next five years; C sales,future Total future electricity sales (unit: kWh); GR load This represents the projected average annual growth rate of the load (in %).

[0106] ③ Capacity required based on current efficiency: Calculate the required capacity in the future based on the current electricity sales and the existing distribution transformer capacity efficiency; C required,ce Total capacity required for current efficiency (unit: kW or MVA); C unit Electricity sold per unit capacity at the current time (unit: kWh / kW or kWh / MVA).

[0107] ④ Theoretical capacity gap to meet load growth: The theoretical capacity gap is the additional transformer capacity required to meet load growth, assuming the efficiency of existing transformers remains unchanged; C gap,growth =C required,ce -C total C gap,growth To meet the theoretical capacity gap caused by load growth (unit: kW or MVA).

[0108] ⑤ Calculate the demand for guaranteed investment: Analyze the total capacity of the heavily overloaded distribution transformers that need to be addressed, and determine what proportion will adopt the "on-site replacement" scheme and what proportion will adopt the "new deployment" scheme; this proportion will be determined comprehensively based on the regional distribution transformer efficiency and benefit evaluation results; in order to address the existing heavily overloaded distribution transformers, it is necessary to calculate the demand for guaranteed investment. This part includes both the "on-site replacement" and "new deployment" schemes.

[0109] Based on the capacity of the heavily overloaded transformer and the ratio of the two mitigation schemes, calculate the demand for guaranteed investment, I. guarantee =(C overload ×R in-situ ×C replacement,cost )+(C overload ×R new,deployment ×C new,unit,cost ), I guarantee For guaranteed investment needs (unit: yuan); C overload R represents the total capacity of the currently heavily overloaded distribution transformers (unit: kW or MVA); in-situ R represents the percentage of in-situ replacements. new,deployment The percentage of newly added locations (unit: %); C replacement,cost To replace unit cost (unit: yuan / kW or yuan / MVA); C new,unit,cost The unit cost for new construction (unit: yuan / kW or yuan / MVA).

[0110] ⑥ Capacity changes brought about by efficiency targets: Setting efficiency targets will lead to changes in capacity demand, and reducing the need for new capacity can be achieved by improving efficiency; C ideal C represents the ideal total capacity (unit: kW or MVA) under the target efficiency. unit,target Electricity sold per unit capacity (unit: kWh / kW or kWh / MVA); C capacityeffect =C ideal -C required,ce C capacityeffect Capacity changes resulting from efficiency improvements (unit: kW or MVA).

[0111] ⑦ Calculate the final investment demand by combining capacity change demand and guaranteed investment; C total,change =C ideal -C total C total,change Total capacity change demand (unit: MVA); C extra,new =C total,change -(C overload ×R new,deployment ), C extra,new For additional capacity (unit: kW or MVA); I total =I guatantee +(C extra,new ×C new,unit,cost ), I total Total investment demand (unit: yuan); C extra,new For additional capacity (unit: kW or MVA); C new,unit,cost The unit cost for new construction (unit: yuan / kW or yuan / MVA).

[0112] In summary, this invention has the following advantages: ① Comprehensively improves the scientificity and accuracy of investment decisions: By constructing a multi-dimensional evaluation system centered on unit transformer capacity sales volume, maximum load rate, reverse power ratio, and monthly power fluctuation index, this invention can comprehensively evaluate the economic and technical benefits of transformers, providing power companies with scientific and accurate investment decision support and optimizing resource allocation; ② Optimizes the allocation and utilization efficiency of transformer resources: With the help of the "4-2-3" evaluation method for annual utilization rate and the overall replacement strategy for transformer resources, this invention can efficiently identify heavily overloaded and inefficient transformers, and improve the operating efficiency of transformers and reduce investment and maintenance costs through reasonable transformer replacement and optimized scheduling; ③ Reduces investment risk and increases return on investment: Through risk assessment and return on investment analysis, this invention can accurately predict the investment return of transformers, reduce the risks caused by over-investment or inefficient investment, and ensure that transformer investment achieves long-term stable returns while meeting the requirements of grid safety and maximizing benefits.

[0113] This invention provides a multi-dimensional evaluation method for investment benefits and investment decision-making in distribution transformers. In practice, this invention proposes a method for evaluating investment benefits and making investment decisions based on a multi-dimensional benefit evaluation model. This method constructs a comprehensive evaluation framework that includes technical and economic benefit subsystems, combining multi-dimensional indicators such as power consumption, load fluctuation, social benefits, and environmental benefits of the distribution transformer to conduct a comprehensive value quantification analysis. By establishing a dynamic evaluation model, this invention can realistically depict the technical and economic performance of distribution transformer equipment throughout its entire life cycle, accurately assessing its return on investment and system contribution under different application scenarios, and providing a scientific basis for investment decisions, operational optimization, and cost recovery mechanism design for distribution transformer projects. This invention has the advantages of constructing a multi-dimensional evaluation model, multi-stage dynamic evaluation, and adopting the "4-2-3" evaluation method for annual utilization rate.

Claims

1. A multi-dimensional evaluation method for the investment benefits of distribution transformers and a method for investment decision-making, which provides a scientific basis for investment decisions on distribution transformer equipment by constructing an evaluation index system for the investment benefits of distribution transformers, thereby optimizing the resource allocation of the power system, characterized in that: The method includes the following steps: Step 1: Data Acquisition and Preprocessing: Collect relevant data on distribution transformer equipment in various cities, including transformer capacity, monthly electricity consumption, and reverse electricity consumption, to provide basic data for the evaluation model; Step 2: Calculation of core evaluation indicators: Calculate four core indicators: electricity sales per unit distribution transformer capacity, maximum load rate, reverse electricity ratio, and monthly electricity volatility index to provide data support for comprehensive evaluation; Step 3: Construction of comprehensive evaluation model: Based on four core indicators, a comprehensive evaluation model is constructed, and the total benefit score of the distribution transformer equipment is obtained by weighted average method; Step 4: Return on Investment Analysis and Decision Support: Based on the evaluation results, each distribution transformer is evaluated from multiple dimensions, and the evaluation results determine whether the investment in the distribution transformer needs to be adjusted or optimized, assisting decision-makers in selecting the distribution transformer equipment with the highest return on investment. Step 5: Calculate investment demand by city: Combine the needs of guaranteed investment and efficient investment to calculate the investment demand of specific cities to ensure that the investment direction is scientific and reasonable.

2. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 1, characterized in that: The data collection in step 1 specifically involves: collecting and organizing relevant data on distribution transformers in various cities and prefectures, including: Distribution transformer capacity data: rated capacity and actual operating capacity of each distribution transformer; Monthly electricity consumption data: Electricity consumption data for the past 12 months, used to calculate the electricity sales per unit distribution transformer capacity; Reverse power data: Percentage of power fed back from distributed energy sources; Overload transformer data: Total capacity of currently heavily overloaded transformers; Future load forecast data: Average annual load growth data helps predict future electricity demand; Target setting data: Set the target unit transformer capacity electricity sales volume for a certain period in the future.

3. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 2, characterized in that: The data preprocessing in step 1 specifically involves cleaning and standardizing the collected data and storing it in a database to ensure data integrity and comparability.

4. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 1, characterized in that: The calculation process for the core evaluation indicators in step 2 is as follows: Step 2.1: Electricity Sales per Unit Distribution Transformer Capacity: Calculate the electricity sold per kilowatt of distribution transformer capacity to reflect the working efficiency of the distribution transformer; Step 2.2: Maximum load rate: Calculate the ratio of the maximum load of the distribution transformer to its rated capacity, reflecting the load utilization efficiency; Step 2.3: Reverse power ratio: Calculate the proportion of power fed back to the grid from the distribution transformer to the total power, reflecting the synergistic effect with renewable energy; Step 2.4: Monthly Electricity Volatility Index (MVI).

5. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 4, characterized in that: The electricity sales per unit transformer capacity specifically refers to the electricity sold per kilowatt of transformer capacity. Among them, S total C represents the total electricity sold by the distribution transformer. rated This refers to the rated capacity of the distribution transformer.

6. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 4, characterized in that: The maximum load factor, specifically, refers to the ratio between the maximum load carried by the distribution transformer during operation and its rated capacity. Among them, L max L represents the maximum load factor of the distribution transformer. maxload This represents the maximum load on the distribution transformer.

7. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 4, characterized in that: The reverse power ratio specifically refers to the proportion of electrical energy fed back to the power grid by the distribution transformer equipment. Among them, E total E represents the total power output of the distribution transformer equipment. reverse Power is fed back to the distribution transformer equipment.

8. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 4, characterized in that: The monthly power fluctuation index (MVI) is specifically calculated by taking into account the degree of fluctuation in the monthly power consumption of distribution transformers, combined with the coefficient of variation (CV), peak-to-valley difference (D), and standard deviation of load fluctuation rate (σ). r MVI = w1·CV + w2·D + w3·σ r Where CV is the coefficient of variation of monthly electricity consumption; D is the peak-to-valley difference; σ r is the standard deviation of the rate of change of electricity; w1, w2, and w3 are the weighting coefficients of each factor.

9. The method for multi-dimensional evaluation of investment benefits and investment decision-making for distribution transformers as described in claim 1, characterized in that: The specific process for calculating investment demand by city / region in step 5 is as follows: Step 5.1: Data preparation and target setting: Collect data on existing distribution transformer capacity, electricity sales, and overloaded distribution transformer capacity for each city, and set target electricity sales per unit distribution transformer capacity; Step 5.2: Meet the capacity requirements for load growth: Calculate the total electricity sales of distribution transformers over the next five years, and assess the additional capacity required to meet load growth based on the capacity required for current efficiency. Step 5.3: Calculation of guaranteed investment demand: Based on the solution for the overloaded transformer capacity, calculate the guaranteed investment demand, including the cost of on-site replacement and new deployment solutions; Step 5.4: Capacity changes resulting from efficiency targets: Calculate the capacity required to achieve efficiency targets, assess the impact of efficiency improvements on capacity demand, and further optimize investment strategies; Step 5.5: Comprehensive Synthesis of Final Investment Requirements: Combining guaranteed investment and profitable investment, calculate the total investment requirements to provide detailed investment decision support for the power company.

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