Electric energy quality comprehensive evaluation method for effectively reflecting overall condition of long-time scale electric energy quality

Abstract

The invention relates to the technical field of power grid operation and maintenance, in particular to an electric energy quality comprehensive evaluation method for effectively reflecting the overall condition of long-time scale electric energy quality, which comprises the following steps: inputting electric energy quality long-term monitoring data; constructing an electric energy quality evaluation index system, and determining grade division of each electric energy quality evaluation index; constructing a subentry index model considering index relevance; performing optimal weight vector solving on the sub-item index model to obtain a time sequence weight of each index; calculating a relative efficiency value of the subentry index model, and obtaining a weight coefficient of each index; and based on the obtained weight of each index, judging the electric energy quality evaluation grade of the evaluation object under the long-time scale. According to the embodiment of the invention, through a DEA and fuzzy comprehensive evaluation fusion mode, long-term monitoring data are fully utilized, the overall power quality condition and the power quality problem accumulation effect of the power distribution network under a long time scale can be accurately reflected, and a scientific basis is provided for power quality benchmarking management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid operation and maintenance, and in particular to an electric energy quality comprehensive evaluation method capable of effectively reflecting overall electric energy quality in a long time scale. BACKGROUND

[0002] In the prior art, for the electric energy quality comprehensive evaluation reflecting the overall condition of the distribution network electric energy quality, the change process and distribution characteristics of the electric energy quality indicators are less considered, the massive monitoring data accumulated by the electric energy quality monitoring system is not fully utilized, and it is difficult to reflect the overall electric energy quality in a long time scale and the accumulation effect of the electric energy quality problems. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application provides an electric energy quality comprehensive evaluation method capable of effectively reflecting the overall electric energy quality in a long time scale. Through the fusion of DEA and fuzzy comprehensive evaluation, the long-term monitoring data is fully utilized, and the overall electric energy quality in a long time scale and the accumulation effect of the electric energy quality problems can be accurately reflected, thereby providing a scientific basis for the electric energy quality benchmarking management.

[0004] The above application objectives of the present application are achieved by the following technical solutions: An electric energy quality comprehensive evaluation method capable of effectively reflecting the overall electric energy quality in a long time scale, comprising: inputting long-term electric energy quality monitoring data; constructing an electric energy quality evaluation index system and determining the grade division of each electric energy quality evaluation index; constructing a sub-index model considering the correlation of the indexes; solving the optimal weight vector of the sub-index model to obtain the time sequence weight of each index; calculating the relative efficiency value of the sub-index model to obtain the weight coefficient of each index; judging the electric energy quality evaluation grade of the evaluation object in a long time scale based on the obtained weight of each index.

[0005] Optionally, the electric energy quality evaluation indexes include voltage deviation, three-phase imbalance, frequency deviation, voltage harmonic, voltage fluctuation and inter-harmonic, and the grades of the evaluation indexes include excellent grade, good grade, medium grade, qualified grade and unqualified grade.

[0006] Optionally, the sub-index model considering the correlation of the indexes is based on fuzzy comprehensive evaluation and data envelopment analysis.

[0007] Optionally, the method further comprises: calculating the electric energy quality comprehensive deviation degree; Based on the comprehensive deviation degree of power quality, the dynamic characteristics of long-term monitoring data and the trend of the comprehensive evaluation result are judged.

[0008] Optionally, the solving of the optimal weight vector of the sub-index model comprises: A fractional programming model is constructed with the maximum efficiency evaluation index of the sub-index decision unit as the target and the efficiency index of all index decision units as the constraint; After linearization, the optimal weight of the sub-index and the optimal output weight vector are obtained; The optimal output weight vector of the sub-index is normalized to obtain the time sequence weight vector of the improved sub-index.

[0009] Optionally, the obtaining of the weight coefficient of each index comprises: The sub-index to be evaluated is removed from the constraint; An efficiency model is established; Based on the efficiency model, the relative efficiency value is calculated.

[0010] Optionally, the judging of the power quality evaluation grade of the evaluation object under a long time scale comprises: Each index is evaluated by a single factor using a membership function; Based on the obtained time sequence weight vector and monitoring data, the fusion data of each index within a standard time is calculated; Based on the obtained index weight coefficient and fuzzy comprehensive evaluation matrix, the membership vector of the evaluation object is calculated.

[0011] The embodiment of the application also provides a power quality multi-agent collaborative economic evaluation method, based on any one of the aforementioned power quality comprehensive evaluation methods which effectively reflect the overall situation of power quality under a long time scale, comprising: Determining a multi-agent collaborative control operation mechanism economic evaluation method; Evaluating the economic performance of the multi-agent collaborative control operation mechanism based on the quality-based pricing mode and the power quality management auxiliary service mode; Based on the economic evaluation result, the mode adopted by the multi-agent collaborative control operation mechanism is determined.

[0012] Optionally, the multi-agent collaborative control operation comprises a distributed photovoltaic collaborative control mechanism, a distributed energy storage collaborative control mechanism, and a charging pile collaborative control mechanism.

[0013] In summary, the application has the following beneficial technical effects: This application embodiment utilizes a fusion of DEA and fuzzy comprehensive evaluation to fully leverage long-term monitoring data. This approach can accurately reflect the overall power quality situation and the cumulative effect of power quality problems over a long time scale in the distribution network, providing a scientific basis for power quality benchmarking management. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall process of one embodiment of this application; Figure 2 This is a schematic diagram of the evaluation index system of one embodiment of this application; Figure 3 This is a schematic diagram illustrating the classification of multi-entity construction economic evaluation methods according to one embodiment of this application; Figure 4 This is a schematic diagram comparing the economic efficiency of distributed photovoltaic power under two different modes according to one embodiment of this application; Figure 5 This is a schematic diagram comparing the economics of distributed energy storage under two modes according to one embodiment of this application. Detailed Implementation

[0015] The present application will be further described in detail below with reference to the accompanying drawings.

[0016] To better understand the technical solutions presented in the embodiments of this application, a brief introduction to the prior art and background will be given first.

[0017] The widespread application of modern power electronic equipment and high-precision instruments has led to increasingly higher demands from power users for power quality. However, the extensive use of distributed generation and nonlinear loads has exacerbated power quality pollution in the power grid, intensifying the contradiction between power supply and demand. Therefore, how to achieve a reasonable and comprehensive assessment of power quality to reflect its overall condition, providing a theoretical basis and technical support for quantifying losses caused by disturbances, benchmarking power quality management of the power grid, and "quality-based pricing" for multi-stakeholder collaborative participation in power quality governance in the power market environment, has become one of the important directions in current power quality research.

[0018] Currently, to ensure the maintenance of good power quality in power systems, both domestically and internationally, a series of national power quality standards have been established to address various power quality issues and regulate power quality indicators to ensure they remain within reasonable ranges. However, a clear standard for comprehensive power quality assessment, reflecting the overall power quality of the distribution network, has yet to be established. Furthermore, existing methods rarely consider the changing processes and distribution characteristics of power quality indicators, fail to fully utilize the massive amounts of monitoring data accumulated by power quality monitoring systems, and are unable to reflect the overall power quality situation over long timescales or the cumulative effects of power quality problems. Therefore, based on research into multi-stakeholder collaborative control technologies for power quality management, evaluating the economics of management schemes is also crucial. Researching multi-stakeholder collaborative power quality economics evaluation methods is beneficial for intuitively demonstrating the economic merits of different management schemes, thereby directly impacting the final power quality management effectiveness and economic benefits, and thus has significant research value.

[0019] Currently, power quality management in distribution networks typically focuses on three typical power quality problems: voltage deviation, three-phase imbalance, and harmonic distortion. Power quality management usually relies on the configuration of power quality control equipment. For example, on-load tap-changing transformers, switchable capacitors, and static var compensators (SVCs) can be used to address voltage / reactive power issues, while active and passive filters can be used to address harmonic problems. However, with the development of power electronics technology and advancements in related control technologies, increasing research indicates that grid-connected inverters, such as those based on photovoltaic (PV) and energy storage systems, can also compensate for voltage / reactive power and harmonic distortion through proper control. Furthermore, fully utilizing the remaining capacity of these devices to assist in power quality management is a more economical approach. Therefore, collaborative control technologies increasingly consider the participation of PV and energy storage in power quality management. Conducting economic analyses of power quality control involving multiple stakeholders, including PV and energy storage, will also contribute to building a more rational multi-stakeholder collaborative power quality management system. Therefore, an economic evaluation method should be developed for multiple stakeholders involved in collaborative power quality control to achieve an economic evaluation of the collaborative control effect.

[0020] This application addresses the shortcomings of existing technologies, which fail to consider the changing processes and distribution characteristics of power quality indicators, fail to fully utilize the massive monitoring data accumulated by power quality monitoring systems, struggle to reflect the overall power quality situation over long time scales, and are unable to reflect the cumulative effects of three typical power quality problems in distribution networks: voltage deviation, three-phase imbalance, and harmonic distortion. It proposes a comprehensive power quality assessment method that effectively reflects the overall power quality situation over long time scales, comprising the following steps: S101: Input statistical values ​​of long-term power quality monitoring data; S102: Construct a power quality assessment index system and determine the level classification of each power quality assessment index. A comprehensive power quality assessment first requires determining the power quality indicators to be considered and establishing a comprehensive power quality assessment index system. In this application embodiment, the power quality standards mainly include eight items: voltage deviation, voltage fluctuation and flicker, interharmonics, harmonics, three-phase voltage imbalance, frequency deviation, temporary overvoltage and transient overvoltage, voltage sag and short-term interruption. Since transient power quality is an event-type power quality, characterized by randomness and poor operability, transient indicators are usually not considered in the comprehensive assessment process. The power quality assessment index system can be constructed as follows: Figure 2 As shown; The comprehensive power quality assessment determines the power quality levels based on relevant national standards, classifying power quality into five levels: Excellent (Q1), Good (Q2), Moderate (Q3), Satisfactory (Q4), and Unsatisfactory (Q5). The power quality at each level is further defined by considering the power quality limits for different voltage levels, with the level boundaries for each indicator as shown in Table 1. Table 1: Limits of Power Quality Indicator Levels S103: Based on the idea of ​​fuzzy comprehensive evaluation, by introducing DEA (Data Envelopment Analysis) theory and selecting the DEA-CCR model, for the six power quality problems under consideration, the minimum value of the long-term monitoring data of each indicator is used as input and all monitoring data are used as output. A sub-indicator model considering the correlation of indicators is constructed, and six sub-indicator decision units, DMU1 to DMU6, are constructed. Each sub-indicator decision unit has only one input indicator and m output indicators. S104: Solve for the optimal weight vector of the sub-indicator model to obtain the time-series weights of each indicator. Use DEA theory to determine the time-series weights of each indicator. This requires comparing all sub-indicator decision units to determine their relative importance, i.e., determining the relative importance among the indicators. With the objective of maximizing the efficiency evaluation index hk of the k-th decision unit, and using the efficiency indices of all decision units as constraints, construct a fractional programming model. Linearize the model before solving. The optimal weights of DMUk can then be obtained. and Then, the optimal output weight vector is... Normalized quantization yields the time-series weight vector of the k-th indicator. .

[0021] S105: Calculate the relative efficiency values ​​of the sub-indicator models and obtain the weight coefficients of each indicator. To effectively solve the ranking problem of models with an efficiency value θ of 1, the specific DMU to be evaluated needs to be removed from the constraints. An efficiency model can then be established to obtain the relative efficiency values ​​of the decision-making units for each sub-indicator. Then, the relative efficiency value θ is normalized and quantized to obtain the weight vector A of each indicator; S106: Based on the obtained time-series weight vector ξ*k and the actual online monitoring data Xk, the fused data X*k of various indicators within a certain observation period can be calculated. Combined with the obtained indicator weights and the fuzzy comprehensive evaluation matrix, the membership vector M of the evaluation object, representing its overall classification at each level, can be calculated. This is used to determine the overall power quality level of the evaluation object. .

[0022] In summary, the embodiments of this application, by fusing DEA and fuzzy comprehensive evaluation, make full use of long-term monitoring data, which can accurately reflect the overall power quality of the distribution network over a long time scale and the cumulative effect of power quality problems, providing a scientific basis for power quality benchmarking management.

[0023] As a feasible specific implementation of the present application, a sub-index model considering the correlation of indicators is constructed by combining fuzzy comprehensive evaluation and data envelopment analysis. Data Envelopment Analysis (DEA) is a method based on "relative efficiency assessment" that uses linear programming to evaluate the relative efficiency of multi-input and multi-output decision-making units (DMUs). The goal of DEA is to determine the time-series weights and indicator weights of each indicator. In other words, it requires comparing all sub-indicator decision-making units to determine their relative importance, i.e., determining the relative importance among the indicators. Therefore, the CCR (Charnes Cooper Rhodes) model is chosen for DEA. Furthermore, to fully reflect the dynamic characteristics of long-term power quality monitoring data and the impact of indicator correlations on the comprehensive power quality assessment, and to facilitate the solution of time-series weights and indicator weights, thereby improving the reliability of the comprehensive assessment results, the traditional DEA method has been improved. Specifically, for the six power quality problems considered, the minimum value among the long-term monitoring data of each indicator is used as input, and all monitoring data are used as outputs to construct six sub-indicator decision-making units (DMU1-DMU6). Each sub-indicator decision-making unit has only one input indicator and m output indicators.

[0024] As a feasible specific implementation method of this application, the optimal weight vector is solved for the sub-indicator model, and the time-series weights of each indicator are calculated accordingly: Based on the determined sub-indicators of the decision-making unit, the total input Ij and total output Oj of the j-th decision-making unit DMUj are as follows: In the formula: This is the output weight vector of the j-th decision unit. The ratio of the total output Oj to the total input Ij is the efficiency evaluation index hj of the j-th decision unit DMUj, i.e.: Where: output vector It is pending; there always exists a reasonable weight vector that makes the efficiency evaluation index satisfy... ≤1. Therefore, with the objective of maximizing the efficiency evaluation index hk of the k-th decision-making unit, and with the efficiency indices of all decision-making units as constraints, a fractional programming model is constructed, namely: In the formula: Let xk be the output vector of DMUk; and xsk be the input data of DMUk. Since the constructed fractional programming model is difficult to solve, it needs to be linearized before solving. The optimal weights of DMUk can then be obtained after solving the linearized model. and Then, the optimal output weight vector is... Normalized quantization yields the time-series weight vector of the k-th indicator. ,Right now: As a feasible specific implementation of this application, the DEA method considers the correlation between decision-making units and can determine the relative importance of each decision-making unit by comparing efficiency values. Each decision-making unit corresponds to a different power quality indicator; that is, the relative importance of the decision-making unit represents the relative importance of each indicator. To effectively solve the ranking problem of the model with an efficiency value θ of 1, the relative efficiency values ​​of the sub-indicator models are solved, and the indicator weight coefficients are calculated accordingly. The specific DMU to be evaluated needs to be removed from the constraint formula to establish a super-efficiency model, i.e.: In the formula: λj represents the weight coefficient of the j-th DMU; S﹣ represents the input slack variable; S+ represents the output slack variable. By solving the above model, the relative efficiency value of each sub-indicator decision unit can be obtained. Then, the relative efficiency value θ is normalized and quantized to obtain the weight vector A of each indicator, that is: Step 4.2: Based on the obtained time-series weight vector ξ*k and the actual online monitoring data Xk, the fused data X*k of various indicators within a certain observation period can be calculated as follows: Then, the membership function was used to perform a single-factor evaluation of the comprehensive data of each indicator, and the fuzzy comprehensive evaluation matrix is ​​shown below: Finally, by combining the obtained index weights and the fuzzy comprehensive evaluation matrix, the membership vector M of the evaluated object belonging to each level from an overall perspective can be calculated. This is used to determine the overall power quality level of the evaluated object, i.e.: This allows for the output of comprehensive power quality assessment results, reflecting the overall power quality level of the distribution network.

[0025] In this embodiment, DEA theory is introduced to construct a fuzzy comprehensive evaluation and DEA fusion assessment framework. The DEA-CCR model is selected to address six types of power quality issues, including voltage deviation, three-phase imbalance, and harmonics. Using the minimum value of long-term monitoring data for each indicator as input and all monitoring data as output, six sub-indicator decision units are constructed to fully leverage the value of massive monitoring data and reflect the impact of indicator correlation on long-term scale assessments. Simultaneously, a fractional programming model is constructed to determine the time-series weight vector for each indicator, achieving dual-dimensional weight quantification of time-series weights and indicator weights, thus improving the reliability of the assessment results.

[0026] As a feasible specific implementation of this application, it also includes calculating the comprehensive deviation of power quality, and judging the dynamic characteristics of long-term monitoring data and the trend of comprehensive evaluation results based on the comprehensive deviation of power quality.

[0027] The comprehensive deviation of power quality is defined as the degree of deviation between the comprehensive power quality value at each moment and the comprehensive value over a long time scale. To better reflect the dynamic characteristics of long-term monitoring data and uncover trend information in the comprehensive evaluation results, it is necessary to preprocess the power quality indicators. The indicator system established in this paper consists of extremely small indicators, where smaller values ​​are better. Therefore, the data of each indicator are dimensionless transformed according to the extreme value processing method to facilitate the calculation of the comprehensive deviation of power quality, i.e.: Secondly, the index deviation value refers to the difference between the measured data of each index at each time point and the comprehensive data of all indicators. It is the deviation value of the j-th index at time point i relative to the comprehensive data of that index. The expression is: In the formula, This represents the comprehensive data for the j-th index after extreme value processing. When... When , it indicates that the deviation value of the j-th indicator at time i is a positive deviation. When, it means that the deviation value of the j-th indicator at the i-th time is negative.

[0028] In summary, the expression for the overall power quality deviation at time i is: In the formula: , These represent the positive and negative deviation coefficients, respectively. , Let Ki represent the combined positive and negative index deviation values ​​at time i. A larger absolute value of Ki indicates a greater deviation in the overall power quality over a longer time scale at time i. When Ki > 0, it indicates that the overall power quality at time i is worse than the overall power quality over a longer time scale, denoted as a positive deviation. When Ki < 0, it indicates that the overall power quality at time i is better than the overall evaluation result over a longer time scale, denoted as a negative deviation. The determination of the positive and negative deviation coefficients must adhere to the following two principles: Deviation conservation principle: the total value of positive and negative deviations is equal for all time-to-time index deviation values. Index deviation principle: the sum of the deviation coefficients is 1.

[0029] By setting and evaluating the overall power quality deviation, it is beneficial to better reflect the dynamic characteristics of long-term monitoring data, explore the trend information of the comprehensive evaluation results, reflect the quality of the overall power quality at each time, help identify power quality problems and carry out targeted management, and intuitively reflect the degree and trend information of power quality deviation.

[0030] To address the challenges of power quality assessment over long timescales, provide technical support for quality-based pricing in the electricity market and multi-stakeholder collaborative control and governance of the power grid, and improve the power quality assessment and regulation technology system, this application also provides a multi-stakeholder collaborative economic evaluation method for power quality, specifically for decision-making regarding collaborative governance schemes involving distributed photovoltaics, energy storage, and charging piles. Based on the aforementioned comprehensive power quality assessment method that effectively reflects the overall power quality situation over long timescales, this method includes: S201: Construct a multi-entity economic quantifiable benefit and cost evaluation model for each entity participating in governance, and determine the economic evaluation method for the multi-entity collaborative control operation mechanism. The multi-entity collaborative control operation includes a distributed photovoltaic collaborative control mechanism, a distributed energy storage collaborative control mechanism, a charging pile collaborative control mechanism, a power quality governance energy consumption comprehensive control mechanism, and a power quality multi-entity collaborative control mechanism. S202: Economic evaluation of the multi-entity collaborative control operation mechanism based on the quality-based pricing model and the power quality governance auxiliary service model; S203: Determine the operating model of the multi-entity collaborative control mechanism based on the results of economic evaluation.

[0031] The constructed multi-entity economic evaluation model can quantify the benefits and costs of each entity participating in governance, forming a complete multi-entity collaborative economic evaluation system. The economic comparison of the two collaborative operation modes can provide technical support for power market design and multi-entity interest coordination, ensuring the economy and feasibility of governance solutions, and achieving low-cost and high-efficiency power quality governance of the distribution network.

[0032] As a feasible specific implementation of this application, for distributed photovoltaic (PV) users, the economic benefits of participating in power quality collaborative control are reflected on the one hand in the revenue from selling the active power output of distributed PV power generation, and on the other hand in the ancillary service revenue of distributed PV users participating in power quality regulation using surplus capacity. The revenue model of distributed PV users can be described as follows: In the formula: Rpower,pv represents the total revenue of distributed photovoltaic power generation participating in power quality regulation; Cpower(t) represents the unit revenue price of power regulation at time t; Ppv(t) represents the amount of power regulated by distributed photovoltaic power generation at time t; Rpq,pv represents the total revenue of distributed photovoltaic power generation participating in active power quality regulation; Pkpv represents the ancillary service revenue corresponding to each unit of active power regulation capacity / regulation amount of distributed photovoltaic power generation at time t.

[0033] Correspondingly, the operating cost of distributed photovoltaic (PV) power will include two parts: the cost of purchasing active and reactive power from the upstream grid when necessary, and the cost of power quality management. Based on existing research, the cost of purchasing active and reactive power can be assumed to be 10% of the price of active power. The cost of power quality management can be calculated based on the capacity occupied for compensation. Therefore, the costs of distributed PV in these two aspects can be expressed as follows: In the formula: cgrid(t) represents the purchase price of active power at time t; Pgrid(t) represents the amount of active power purchased by distributed photovoltaic power from the upstream grid at time t; Qgrid(t) represents the amount of reactive power purchased by distributed photovoltaic power from the upstream grid at time t; Cpower,pv represents the total revenue from the sale of active power output of distributed photovoltaic power; cpq(t) represents the purchase price of active power at time t; Spq(t) is the active power output of the distributed photovoltaic power generation system at time t.

[0034] In summary, the economic viability of distributed photovoltaic users can be evaluated based on the benefits they receive from participating in power supply and power quality regulation services, as well as the costs incurred in purchasing electricity when necessary and the costs required to achieve power quality regulation.

[0035] As a feasible specific implementation of this application, the economic benefits of distributed energy storage users participating in power quality collaborative control lie in their ability to rationally formulate charging plans and pricing strategies based on the dynamic electricity price of the distribution network, thereby profiting from the price difference during the peak shaving and valley filling process of the power system through charging and discharging. Furthermore, grid connection of energy storage also requires the use of grid-connected inverters with a topology similar to SVG; therefore, distributed energy storage also possesses the ability to conduct power quality regulation using grid-connected inverters. The revenue model of distributed energy storage can be described as follows: In the formula: Rpower,es represents the total revenue of distributed energy storage participating in power quality regulation; Pes(t) represents the amount of power regulated by distributed energy storage at time t; Rpq,es represents the total revenue of distributed energy storage participating in power quality regulation; Pkes represents the revenue of distributed photovoltaic power per unit of regulation capacity / regulation amount at time t.

[0036] Correspondingly, the operating cost of distributed energy storage, besides the electricity purchase cost reflected in the peak-shaving and valley-filling price difference revenue, also includes the impact of the number of charge-discharge cycles and the depth of charge-discharge on its lifespan, which will reflect the operation and maintenance costs during its participation in coordinated control. Therefore, the evaluation of the operating cost of distributed energy storage should fully consider the above two factors. To this end, based on existing research results, the relationship between the depth of charge-discharge and the number of cyclic charge-discharge cycles of distributed energy storage can be represented by the following fourth-order fitting: In the formula, This indicates the cycle life of distributed energy storage at a specific depth of charge and discharge. The depth of charge and discharge for distributed energy storage.

[0037] It is evident that the varying depths of charge and discharge during operation of distributed energy storage objectively determine its maximum lifespan during regulation. Therefore, the number of years a distributed energy storage system reaches the end of its operational lifespan depends on the number of charge-discharge cycles required annually to meet peak shaving and valley filling requirements. Based on the average annual charge-discharge depth of distributed energy storage, the lifespan loss caused by annual operational scheduling can be assessed and described as follows: In the formula, This refers to the lifetime loss of distributed energy storage during the scheduling period T. This represents the number of cycles in the scheduling period T for distributed energy storage. This represents the average depth of charge and discharge of distributed energy storage within the scheduling period T. This represents the total number of cycles required for distributed energy storage to reach the end of its lifespan when used according to a certain average depth of charge and discharge.

[0038] The cost of operational lifespan loss resulting from this loss in distributed energy storage can be expressed as follows: In the formula, This represents the lifetime loss cost coefficient of distributed energy storage.

[0039] In summary, the economic viability of distributed energy storage users can be evaluated based on the price difference revenue they receive from participating in peak shaving and valley filling, the revenue they receive from power quality control services, and the cost of losses incurred by themselves due to the number of annual charge / discharge cycles and the depth of charge / discharge.

[0040] As a feasible specific implementation method of this application, the application of charging piles can mainly utilize V2G technology to rationally coordinate the charging and discharging of idle electric vehicles, thereby mitigating the uncertainty of wind and solar power output to a certain extent, improving the absorption of wind and solar power output, reducing the output of thermal power units, and achieving carbon reduction. Therefore, its economic efficiency is also reflected in the revenue from the electricity price difference obtained during the charging and discharging process. The revenue model can be described as follows: In the formula: Rv represents the total revenue of the charging pile, that is, the total income obtained through arbitrage of charging and discharging electricity price differences; Pv(t) represents the charging and discharging power of the electric vehicle at time t.

[0041] As a feasible specific implementation method of this application, the economic evaluation method of the comprehensive energy consumption control mechanism for power quality management is essentially an economic evaluation method of the impact of power quality management on the energy consumption of the distribution network. Due to the low voltage level, wide line distribution, and numerous electrical equipment, low-voltage distribution networks always suffer from high loss rates. To ensure the high economic efficiency of power quality management involving multiple stakeholders, network loss will be one of the important indicators for evaluating the economic impact of power quality management on the energy consumption of the distribution network. Currently, low-voltage distribution network losses mainly consist of line losses and transformer losses. The main factors affecting the energy losses of these distribution network devices are equipment condition and operating mode. Although existing loss reduction measures take equipment factors into consideration relatively comprehensively, such as increasing conductor cross-section, optimizing distribution transformer capacity, and using low-loss transformers, power quality management involving multiple stakeholders such as distributed photovoltaics and distributed energy storage, including three-phase load imbalance management, voltage deviation management, load distribution imbalance management, harmonic distortion suppression, and power factor optimization, will also have a significant impact on distribution network losses.

[0042] On the one hand, the power generated by distributed generation alters the power flow distribution of the distribution network, directly impacting line and equipment losses. On the other hand, the random fluctuations in the power output of distributed generation introduce power quality issues such as harmonics and voltage fluctuations, which interact with existing power quality disturbances, further complicating the power quality disturbance mechanisms in the distribution network. Therefore, to effectively assess the economics of multi-stakeholder collaborative power quality management, it is necessary to quantify the losses during the power quality management process to analyze its impact on distribution network energy consumption.

[0043] The power loss in a distribution network is the sum of the power losses of all components within the network. To study the power loss generated during power transmission, it is necessary to understand the power consumption characteristics of each component and determine their mathematical models. Losses generated on distribution lines and transformers constitute the main part of the distribution network loss. Other components, such as substation operating power supplies, heating, lighting, and other station loads; primary equipment directly connected to the grid, such as transformers, reactors, and capacitors; and secondary loads of voltage and current transformers, including relay protection devices and energy meters, also generate a small portion of power loss during actual grid operation. Typically, feeder outlets are equipped with ammeters and wattmeters to obtain the hourly current representing a 24-hour period. Therefore, the root mean square current method is the most common theoretical method for calculating line losses in distribution networks.

[0044] When using the root mean square current method to calculate losses, the following assumptions are generally made: the shape of the load curve of each load node is the same as that of the beginning; the power factor of each load node is equal to that of the beginning; the influence of voltage loss along the line on energy consumption is ignored; the load distribution is proportional to the rated capacity of the transformer installed at the load node, that is, the load factor of each transformer is the same, and the load factor is the ratio of the apparent power through the transformer to its rated capacity. Therefore, the network loss of the distribution network can be calculated using the following method: Let the resistance of the power grid element be R, and the current flowing through the element be I. The three-phase active power loss generated when the current flows through the element is: The energy loss of this component over 24 hours is: Current I is a random variable and generally cannot be obtained accurately. However, if the time period within the calculation period is divided into sufficiently small segments, it can achieve complete equivalence. The current value is typically obtained by representing the hourly load over a 24-hour period. Assuming the current value remains constant within each hour, the total loss in the distribution network can be expressed as the sum of the energy losses in the resistance of all components: In the formula, and The root mean square current of the line and the transformer, respectively; and These represent the resistance of the lines and transformers, respectively; n and m represent the total number of lines and the total number of transformers, respectively; t represents the number of hours in the calculation period.

[0045] The root-mean-square current can be calculated using the following formula: Furthermore, when the load represents the three-phase active power, reactive power, and line voltage measured at the top of each hour over a 24-hour period, it can be further expressed as follows: If the measured quantities are active power, reactive power, and voltage, then the root mean square current can be calculated using the following formula: In summary, based on real-time electricity prices, the economic losses caused by energy loss can be calculated as follows: As a feasible specific implementation method of this application, the economic evaluation method of the multi-entity collaborative control mechanism for power quality is essentially an overall economic evaluation method for the participation of multiple entities in the collaborative control of power quality. It can be evaluated based on the comprehensive benefits obtained from the collaborative control of multiple entities. This is because, to better coordinate the control of multiple entities such as distributed photovoltaics, distributed energy storage, and charging piles, they can usually be handed over to the power grid for centralized and unified dispatch through contracts. In this case, the interests of multiple entities can be regarded as a whole, and the economic efficiency can be judged based on the magnitude of the comprehensive benefits. Reasonable collaborative control should maximize the comprehensive benefits obtained. Therefore, the economic evaluation of multi-entity collaboration can be based on comprehensive benefits, as described below: In the formula, R represents the total benefit of multi-entity collaborative power quality governance.

[0046] As a feasible specific implementation of the embodiments of this application, the two modes are the quality-based pricing mode and the power quality management auxiliary service mode. The quality-based pricing model means that different power quality levels result in different electricity prices. The higher the power quality, the higher the governance costs and the higher the price. This mechanism focuses on pricing based on differences in power quality levels in order to incentivize users to provide higher quality power.

[0047] The power quality management ancillary service model, in which power quality management is an ancillary service of the market, mainly provides users with power quality management services of different qualities through the electricity market. This mechanism sets prices based on different power quality problems (such as voltage deviation, harmonic distortion, three-phase imbalance, etc.) and provides power quality services according to different needs.

[0048] To better understand the two modes, let's look at a specific use case.

[0049] Assume the basic operational capacity and conditions for distributed photovoltaic power generation systems and distributed energy storage systems respectively, namely: Distributed photovoltaic system: Assuming the rated capacity of the distributed photovoltaic system is 10kW, its annual power generation is 15000kWh, and the conventional photovoltaic power generation price is 0.5 yuan / kWh.

[0050] Under the quality-based pricing mechanism, power quality is divided into three levels: high, medium, and low. The unit price and governance cost for each level are shown in Table 2. Table 2: Unit Price and Governance Cost of Photovoltaic Power of Different Quality Under the power quality management auxiliary service mechanism, the adjustment services for three typical power quality problems—voltage deviation, harmonic distortion, and three-phase imbalance—have different prices and management costs, as summarized in Table 3: Table 3: Unit Price and Remediation Cost of Different Power Quality Issues in Photovoltaics Distributed energy storage system: Assuming the rated capacity of the distributed energy storage system is 20kW, the price of electricity supplied to the grid is 0.5 yuan / kWh, while the price of electricity purchased from the grid is 0.2 yuan / kWh, the annual average charge and discharge depth is 50%, and the cycle life of the energy storage system decreases by 10% per year.

[0051] Under the quality-based pricing mechanism, power quality is also divided into three levels: high, medium, and low. The unit price and governance cost for each level are shown in Table 4. Table 4: Unit Price and Governance Costs of Energy Storage at Different Quality Levels Under the power quality management auxiliary service mechanism, the differentiated unit prices and management costs for three typical power quality problems—voltage deviation, harmonic distortion, and three-phase imbalance—can be summarized in the table below: surface Unit price and remediation cost of different power quality issues in energy storage Under the quality-based pricing mechanism, since power quality regulation is closely related to power supply, the revenue of distributed photovoltaic (PV) systems is mainly concentrated during the daytime. Because capacity can be freely allocated, distributed PV primarily participates in providing medium-to-high quality power. Assuming the annual power generation of the PV system is 15,000 kWh, of which 8,000 kWh is high-quality power, 5,000 kWh is medium-quality power, and 2,000 kWh is ordinary power, then: The total revenue that can be obtained from providing electricity through photovoltaics is: The governance costs incurred in this process are: The total revenue from distributed photovoltaic power is 11,900 yuan.

[0052] Secondly, for distributed energy storage, since energy storage cannot provide regulation services during the charging period, its revenue comes from the discharging phase. The flexibility and controllability of the discharging phase are relatively low. Therefore, energy storage is mainly used to provide low-to-medium quality electricity. However, because its charging and discharging are not affected by weather conditions, the usable capacity is relatively high. Assuming a capacity of 20,000 kWh, with 12,000 kWh used to provide low-quality electricity, 5,000 kWh used to provide medium-quality electricity, and 3,000 kWh used to provide conventional-quality electricity, then: The total revenue that can be obtained from energy storage to provide electricity is: The costs incurred in this process, including governance costs and electricity purchase costs, are as follows: The total revenue from distributed energy storage is 10,500 yuan.

[0053] Therefore, under the quality-based pricing model, the total revenue from power quality governance involving distributed photovoltaic and energy storage collaboration is 22,400 yuan.

[0054] Under the power quality management ancillary service mechanism, since power supply and power quality management are independent, the revenue generated by power quality management is an additional revenue. Under the same conditions as described above, assuming high-quality power requires simultaneous management of three power quality issues, medium-quality power requires simultaneous management of at least two power quality issues, and low-quality power requires at most one power quality issue, then: The benefits that can be obtained from photovoltaic power generation are: The governance costs incurred in this process are: The total revenue from distributed photovoltaic power generation is 11,340 yuan.

[0055] The total benefit that can be obtained from energy storage to provide electricity is: The governance costs incurred in this process are: At this point, the total revenue from distributed energy storage is 9,530 yuan.

[0056] Therefore, under the power quality management auxiliary service mechanism, the total revenue from power quality management involving distributed photovoltaic and energy storage collaboration is 20,870 yuan.

[0057] like Figure 4 and Figure 5 As shown, under the above circumstances, it is more economical for distributed photovoltaic (PV) and distributed energy storage to participate in power quality management through a quality-based pricing model. This is because the cost of purchasing electricity from the upper-level grid is unavoidable for distributed energy storage. Under the power quality management ancillary service mechanism, since power supply and power quality management are independent, power quality management cannot be bundled with power supply for sale as a whole. Therefore, the low flexibility in energy storage capacity allocation means that the revenue generated from participating in power quality management cannot be guaranteed, leading to a decrease in the total revenue of distributed energy storage. Therefore, economic analysis shows that in this scenario, power quality management through the collaboration of distributed PV and distributed energy storage should adopt a quality-based pricing model to protect the interests of all stakeholders.

[0058] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A comprehensive power quality assessment method that effectively reflects the overall power quality over a long time scale, characterized in that, include: Input long-term power quality monitoring data; Construct a power quality assessment index system and determine the classification of each power quality assessment index level; Construct a sub-indicator model that considers the correlation between indicators; The optimal weight vector is solved for the sub-indicator model to obtain the time-series weights of each indicator; Calculate the relative efficiency value of the sub-indicator model to obtain the weight coefficient of each indicator; Based on the weights of the obtained indicators, the power quality assessment level of the assessed object over a long time scale is determined.

2. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that... The power quality assessment indicators include voltage deviation, three-phase imbalance, frequency deviation, voltage harmonics, voltage fluctuations, and inter-voltage harmonics. The assessment indicator levels include excellent, good, medium, qualified, and unqualified.

3. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that... The sub-indicator model that considers the correlation between indicators is based on fuzzy comprehensive evaluation and data envelopment analysis.

4. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that... Also includes: Calculate the overall power quality deviation. The dynamic characteristics of long-term monitoring data and the trend of comprehensive evaluation results are judged based on the comprehensive deviation of power quality.

5. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that... The optimal weight vector solution for the sub-indicator model includes: With the objective of maximizing the efficiency evaluation index of the decision-making units of each sub-indicator, and constrained by the efficiency indices of all decision-making units, a fractional programming model is constructed. After linearization, the optimal weights of the sub-indicators and the optimal output weight vector are obtained by solving the problem. The most effective output weight vector of each sub-indicator is normalized to obtain the time-series weight vector of that sub-indicator.

6. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that... The weighting coefficients for each indicator include: Remove the required sub-indicators from the restrictive formula; Establish an efficiency model; The relative efficiency value is calculated based on the efficiency model.

7. The comprehensive power quality assessment method according to claim 1, which effectively reflects the overall power quality over a long time scale, is characterized in that, The assessment of the power quality level of the assessment object over a long time scale includes: Membership functions were used to evaluate each indicator using single-factor methods. Based on the acquired time-series weight vector and monitoring data, the fused data of various indicators within the standard time period is calculated; Based on the obtained indicator weight coefficients and fuzzy comprehensive evaluation matrix, the membership vector of the evaluation object is calculated.

8. A multi-subject collaborative economic evaluation method for power quality, characterized in that, A comprehensive power quality assessment method based on any one of claims 1-7, which effectively reflects the overall power quality over a long time scale, includes: Determine the economic evaluation method for multi-entity collaborative control operation mechanisms; An economic evaluation of the multi-entity collaborative control operation mechanism is conducted based on the quality-based pricing model and the power quality governance auxiliary service model. The model adopted for the multi-entity collaborative control operation mechanism is determined based on the results of the economic evaluation.

9. The method for multi-subject collaborative economic evaluation of power quality according to claim 8, characterized in that, The multi-entity collaborative control operation includes a collaborative control mechanism involving distributed photovoltaic power, a collaborative control mechanism involving distributed energy storage, and a collaborative control mechanism involving charging piles.

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