Multi-parameter evaluation driven balanced unit construction method, system, device and medium

Abstract

The present application belongs to the field of multi-element resource coordination, and discloses a multi-parameter evaluation driven balance unit construction method, system, equipment and medium, which comprises: establishing evaluation indexes for constructing balance units in a power supply area; using subjective weighting method and objective weighting method to calculate the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index; obtaining the initial evaluation scores of each evaluation index, multiplying them by the corresponding comprehensive weights to obtain the evaluation scores of each evaluation index in different balance unit construction schemes; adding up the evaluation scores of each evaluation index in the balance unit construction scheme to obtain the adaptability comprehensive evaluation score of the balance unit construction scheme; and screening out the best balance unit construction scheme. The adaptability of the balance unit construction scheme is evaluated by using the combination weighting method combining subjective and objective methods, and the best balance unit construction scheme is screened out.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of multi-element resource coordinated regulation, and particularly relates to a multi-parameter evaluation driven balancing unit construction method, system, device and medium. BACKGROUND

[0002] Due to the low proportion of new energy access and less controllable resources in the traditional sense of regional power grid, the overall active and energy balance often needs to rely on the large power grid. In the current active regulation system, each regulation center divides the power grid into different levels and regions for coordinated active regulation. Some levels of dispatch have not been fully integrated into the active regulation system. In the traditional power system dominated by thermal power, the source follows the load, effectively realizing the balance of power and energy. However, in the future power system, new energy generation will be the dominant factor, which is characterized by strong uncertainty and weak controllable output. This leads to more complex power grid operation, and the proportion of traditional flexible resources available in the system gradually decreases. The security of power supply and balance protection of the power system is under great pressure. It is urgent to mobilize various distributed resources such as new energy storage, distributed power, microgrid, virtual power plant, and adjustable load to participate in system interaction and share system balance responsibility.

[0003] In order to further sink the main grid balance responsibility and integrate the prefecture-level city into the active balance system, it is necessary to construct a regional power grid balancing unit. The power grid is divided into multiple balancing units of different sizes. The balancing unit is used as the basic unit of power grid regulation. Through the balance of power generation and consumption within the unit, the balance of power generation and consumption in the power grid is ensured. The pressure of centralized balance control is relieved, and the risk of power imbalance in the power system is reduced, thereby ensuring the reliable supply of electric energy and the consumption of new energy in the new power system.

[0004] Some regions rely on mature power markets and have built a large number of virtual power plants or small balancing units at the bottom to undertake the task of power grid balance. Current research mainly focuses on the introduction of the balancing unit mechanism in a certain country. It analyzes the support of virtual power plants for balancing units, the design of power markets, and the advantages and disadvantages of balancing unit modes. How to combine the domestic situation to construct balancing units from all directions and multiple angles, and evaluate the applicability of constructing balancing units in different regions, is an urgent problem to be solved. It is necessary to fully develop the regulation potential of growing multi-element flexible resources in regional power grids, and realize efficient utilization and mutual aid of resources at all levels of power grid.

[0005] Power supply and demand balance is one of the key problems of building a new power system, and improving the balance autonomy at the distribution network level is conducive to reducing the impact of large-scale distributed generation on the transmission network and improving the overall balance ability of the power system. At the same time, with the access of a large number of uncontrollable distributed power, the net load curve is more complex and variable, which aggravates the volatility and uncertainty of the distribution network. Therefore, the balancing unit needs to have sufficient supply and demand balance ability and flexibility. At present, the comprehensive evaluation methods mainly include weighting methods and evaluation models. The weighting methods have subjective and objective methods, but the subjective weighting method ignores the actual situation and other problems, and relies too much on the subjective opinions of experts, while the objective weighting method has high requirements for objective data and cannot obviously reflect the preferences of decision makers. In view of the limitations of the two methods, more and more research and practice tend to adopt a combination weighting method that combines the two. Although the combination weighting method has been widely used in the evaluation and decision-making problems in many fields, and has shown unique advantages in comprehensive evaluation that takes into account subjective and objective factors, but in the selection of evaluation indexes and comprehensive evaluation of balancing unit construction, the related research is still relatively lacking. As an important analysis and management tool in the new power system, the construction quality of the balancing unit directly affects the stability and efficiency of the system, so it is particularly important to study the selection of its evaluation indexes and the applicability of the evaluation method. At present, although there are discussions on the construction method of the balancing unit, how to systematically evaluate the advantages and disadvantages of these construction results from multiple dimensions has not yet formed a recognized theory and method. SUMMARY

[0006] Embodiments of the present application provide a multi-parameter evaluation driven balancing unit construction method, system, device and medium to solve the problem of lack of evaluation method for constructing balancing unit in power supply area in the prior art.

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not an extensive overview of the application, nor is it intended to identify key / critical elements of the application or to delineate the scope of the embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0008] According to a first aspect of the embodiments of the present application, a multi-parameter evaluation driven balancing unit construction method is provided.

[0009] In one embodiment, the multi-parameter evaluation driven balancing unit construction method comprises:

[0010] establishing evaluation indexes for constructing balancing unit in power supply area, and the evaluation indexes include supply and demand balance evaluation indexes and flexibility evaluation indexes;

[0011] The subjective weighting method and the objective weighting method are used, and the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index are calculated by combining the weight coefficients of the subjective weighting method and the objective weighting method;

[0012] The initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balance unit construction schemes are obtained, and are multiplied by the corresponding comprehensive weights of the evaluation indexes to obtain the evaluation scores of each evaluation index in different balance unit construction schemes.

[0013] The adaptability comprehensive evaluation scores of the balance unit construction schemes are obtained by adding and summing the evaluation scores of each evaluation index in the balance unit construction schemes; and the best balance unit construction scheme is selected based on the adaptability comprehensive evaluation scores of different balance unit construction schemes.

[0014] In one embodiment, the establishment of the supply-demand balance evaluation index includes:

[0015] Based on the Euclidean distance discrimination method, a source-load matching degree evaluation analysis formula is constructed, the total energy output and the power system load are substituted into the source-load matching degree evaluation analysis formula, and the matching degree of the total energy output and the power system load is output; and the supply-demand balance situation index of the power supply area is established according to the matching degree of the total energy output and the power system load.

[0016] The net load curve of the power supply area is obtained, and the power fluctuation situation index of the net load curve of the power supply area is established according to the ratio of the difference between two adjacent extreme values on the net load power curve to the total number of trend turning points of the net load power curve.

[0017] The daily prediction of the power supply and demand situation of the power supply area is performed, and the prediction deviation evaluation index of the power supply area is established according to the deviation value between the prediction result and the actual power supply and demand situation.

[0018] In one embodiment, the daily prediction of the power supply and demand situation of the power supply area is performed, and the prediction deviation evaluation index of the power supply area is established according to the deviation value between the prediction result and the actual power supply and demand situation.

[0019] The actual value and the predicted value of the net load of the power supply and demand situation in the power supply area are obtained, and the deviation between the actual value and the predicted value of the net load is calculated by using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

[0020] In one embodiment, the establishment of the flexibility evaluation index includes:

[0021] Based on the flexibility demand caused by the variability of the net load of the power system and the flexibility demand caused by uncertainty, the distribution network flexibility demand index in the power supply area is established.

[0022] The flexible supply index of the distribution network in the power supply area is established based on the working state, the maximum charging and discharging power and the charging and discharging efficiency of the energy storage system, and the flexible supply index of the distribution network includes an upward flexible index and a downward flexible index.

[0023] The flexible average adequacy index and the flexible average deficiency index of the distribution network in the power supply area are respectively established by using the ratio of the total adequacy amount to the adequacy period and the ratio of the total deficiency amount to the deficiency period of the flexible resource supply of the distribution network.

[0024] In one embodiment, the establishment of the flexible supply index of the distribution network in the power supply area includes:

[0025] The upward flexible index and the downward flexible index are adjusted by using the reducible load of the power system.

[0026] In one embodiment, the comprehensive weight of each supply-demand balance evaluation index and each flexibility evaluation index is calculated by using the subjective weighting method and the objective weighting method and combining the weight coefficients of the subjective weighting method and the objective weighting method, and the comprehensive weight of each evaluation index includes:

[0027] Based on the analytic hierarchy process, a hierarchical structure model of the balance unit construction problem in the power supply area is established, and a pairwise comparison judgment matrix of each evaluation index is constructed; the first weight of each evaluation index is calculated by using the hierarchical single ordering algorithm, and the effectiveness of the first weight calculation result is judged by using the consistency checking method;

[0028] The entropy value of each evaluation index is calculated based on the entropy weight method, and the second weight of each evaluation index is determined after the standardization processing of the entropy value calculation result;

[0029] The weight coefficients are assigned to the calculation results of the analytic hierarchy process and the entropy weight method, and the first weight and the second weight calculated by using the analytic hierarchy process and the entropy weight method are weighted and summed to obtain the comprehensive weight of each evaluation index.

[0030] In one embodiment, the initial evaluation score of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balance unit construction schemes includes:

[0031] The adaptability evaluation standard of each supply-demand balance evaluation index and each flexibility evaluation index is constructed;

[0032] The initial evaluation score of each evaluation index is obtained by substituting each supply-demand balance evaluation index value and each flexibility evaluation index value into the adaptability evaluation standard.

[0033] According to the second aspect of the embodiment of the present application, a balance unit construction system driven by multi-parameter evaluation is provided.

[0034] In one embodiment, the multi-parameter evaluation driven balancing unit construction system comprises:

[0035] An evaluation index establishing module is configured to establish evaluation indexes for constructing balancing units in a power supply area, and the evaluation indexes comprise supply-demand balance evaluation indexes and flexibility evaluation indexes;

[0036] A weight assigning module is configured to calculate comprehensive weights of the supply-demand balance evaluation indexes and the flexibility evaluation indexes by using subjective weighting methods and objective weighting methods, and combining weight coefficients of the subjective weighting methods and the objective weighting methods;

[0037] An evaluation score calculating module is configured to obtain initial evaluation scores of the supply-demand balance evaluation indexes and the flexibility evaluation indexes under preset adaptability evaluation standards in different balancing unit construction schemes, and multiply the initial evaluation scores by corresponding comprehensive weights of the evaluation indexes to obtain evaluation scores of the evaluation indexes in the different balancing unit construction schemes;

[0038] A balancing unit construction scheme screening module is configured to add and sum the evaluation scores of each evaluation index in the balancing unit construction scheme to obtain an adaptability comprehensive evaluation score of the balancing unit construction scheme, and screen out an optimal balancing unit construction scheme based on adaptability comprehensive evaluation scores of different balancing unit construction schemes.

[0039] In one embodiment, the establishment of the supply-demand balance evaluation indexes comprises:

[0040] Based on the Euclidean distance discrimination method, a source-load matching degree evaluation analysis formula is constructed, the total energy output and the power system load are substituted into the source-load matching degree evaluation analysis formula, and the matching degree of the total energy output and the power system load is output; and based on the matching degree of the total energy output and the power system load, a supply-demand balance situation index of the power supply area is established;

[0041] A net load curve of the power supply area is obtained, and based on a ratio of a difference between two adjacent extreme values on the net load power curve to a total number of trend turning points of the net load power curve, a power fluctuation situation index of the net load curve of the power supply area is established;

[0042] The power supply and demand situation of the power supply area is daily predicted, and based on a deviation value of the prediction result and the actual power supply and demand situation, a prediction deviation evaluation index of the power supply area is established.

[0043] In one embodiment, the power supply and demand situation of the power supply area is daily predicted, and based on a deviation value of the prediction result and the actual power supply and demand situation, a prediction deviation evaluation index of the power supply area is established.

[0044] The actual value and the predicted value of the net load of the power supply and demand situation in the power supply area are obtained, and the deviation of the actual value and the predicted value of the net load is calculated by using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

[0045] In one embodiment, the establishment of the flexibility evaluation index includes:

[0046] Based on the flexibility demand caused by the variability of the net load of the power system and the flexibility demand caused by uncertainty, a flexibility demand index of the distribution network in the power supply area is established;

[0047] Based on the working state, the maximum charge and discharge power, and the charge and discharge efficiency of the energy storage system, a flexibility supply index of the distribution network in the power supply area is established, and the flexibility supply index of the distribution network includes an upward flexibility index and a downward flexibility index;

[0048] The ratio of the total surplus amount of the flexibility resource supply of the distribution network to the surplus period, and the ratio of the total shortage amount to the shortage period are used to respectively establish a flexibility average surplus index of the distribution network in the power supply area and a flexibility average deficiency index of the distribution network.

[0049] In one embodiment, the establishment of the flexibility supply index of the distribution network in the power supply area includes:

[0050] The upward flexibility index and the downward flexibility index are adjusted by using the reducible load of the power system.

[0051] In one embodiment, the comprehensive weight of each supply and demand balance evaluation index and each flexibility evaluation index is calculated by using the subjective weighting method and the objective weighting method, and combining the weight coefficients of the subjective weighting method and the objective weighting method, which includes:

[0052] Based on the analytic hierarchy process, a hierarchical structure model of the balance unit problem in the power supply area is established, and a pairwise comparison judgment matrix of each evaluation index is constructed; the first weight of each evaluation index is calculated by using the hierarchical single ordering algorithm, and the effectiveness of the first weight calculation result is judged by using the consistency test method;

[0053] The entropy value of each evaluation index is calculated based on the entropy weight method, and the second weight of each evaluation index is determined after the calculation result of the entropy value is standardized;

[0054] The first weight and the second weight calculated by using the analytic hierarchy process and the entropy weight method are weighted and summed to obtain the comprehensive weight of each evaluation index.

[0055] In one embodiment, the initial evaluation score of each supply and demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balance unit construction schemes includes:

[0056] constructing adaptability evaluation criteria of each supply-demand balance evaluation index and each flexibility evaluation index;

[0057] obtaining each supply-demand balance evaluation index value and each flexibility evaluation index value, and substituting into the adaptability evaluation criteria to obtain initial evaluation scores of each evaluation index.

[0058] According to a third aspect of the embodiments of the present application, a computer device is provided.

[0059] In some embodiments, the computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0060] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided.

[0061] In one embodiment, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0062] The technical solutions provided by the embodiments of the present application can include the following beneficial effects:

[0063] (1) The multi-parameter evaluation driven balance unit construction method, system, device and medium provided by the present application evaluate the adaptability of the balance unit constructed in different 220kV regions by using a combination weighting method combining subjective and objective factors, based on supply-demand balance indexes such as source-load matching degree, power fluctuation, prediction deviation, and flexibility indexes such as up-regulation (down-regulation) flexibility adequacy (insufficiency), and select the best balance unit construction scheme.

[0064] (2) The present application brings massive distributed flexible adjustment resources into the regulation and control range by constructing the balance unit, and improves the active balance capability of the power system. When constructing the balance unit, too many regional divisions will make the dispatching management of the provincial dispatching center more complex, and the amount of interactive information and the amount of calculation work to be processed will also increase sharply; too few regional divisions will increase the regulation and control burden within the balance unit. Therefore, when designing the balance unit, the present application comprehensively considers the above factors and the unified resource dispatching and hierarchical management architecture currently implemented in the power grid, selects the 220kV power supply region as the best dispatching range, and then analyzes the power grid operation status in different 220kV regions, evaluates the adaptability of the balance unit constructed in different 220kV regions from the aspects of supply-demand balance and flexibility by using the subjective and objective combination analysis method, and selects the most effective and most suitable construction scheme.

[0065] (3) The application realizes centralized coordination and decentralized autonomous balancing mode based on balancing units. The power grid is divided into multiple unequal balancing units, and the balancing unit is used as the basic unit of power grid regulation. The main grid balancing responsibility is further sunk, and the county / city is included in the active balancing system to build a regional power grid balancing unit. Taking the balancing unit as the basic unit of power grid regulation is conducive to mobilizing various distributed resources such as new energy storage, distributed power supply, microgrid, virtual power plant, and adjustable load to participate in power system interaction and share power system balancing responsibility to cope with the uncertainty and poor controllability problems brought by large-scale new energy grid connection. By realizing power generation and consumption balance within the unit, the balance of power generation and power consumption of the power grid is ensured, the centralized balancing control pressure is relieved, the power imbalance risk of the power system is reduced, and the reliable supply of electric energy and new energy consumption under the new power system are guaranteed.

[0066] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0067] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the application and, together with the specification, serve to explain the principles of the application.

[0068] Figure 1 is a flowchart of a multi-parameter evaluation driven balancing unit construction method according to an exemplary embodiment;

[0069] Figure 2 is a principle block diagram of a multi-parameter evaluation driven balancing unit construction system according to an exemplary embodiment;

[0070] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0071] Figure 4 is a flowchart in specific application according to an exemplary embodiment;

[0072] Figure 5 is a schematic diagram of two types of power distribution network flexibility demand according to an exemplary embodiment;

[0073] Figure 6 is a schematic diagram of flexibility supply of energy storage system according to an exemplary embodiment;

[0074] Figure 7 is a hierarchical structure model according to an exemplary embodiment;

[0075] Figure 8 is a topological structure diagram of a certain power supply area according to an exemplary embodiment;

[0076] Figure 9 is a daily variation curve of actual and predicted values of 24-hour energy output according to an example embodiment;

[0077] Figure 10 is a daily variation curve of actual and predicted values of 24-hour load according to an example embodiment;

[0078] Figure 11 is a schematic diagram of charging and discharging power of energy storage according to an example embodiment;

[0079] Figure 12 is a schematic diagram of storage power of energy storage according to an example embodiment;

[0080] Figure 13 is a schematic diagram of air conditioning cluster of example system according to an example embodiment. DETAILED DESCRIPTION

[0081] The following description and drawings are illustrative of the specific embodiments herein and are not intended to be limiting. Parts and features of some embodiments can be included or replaced by parts and features of other embodiments. The scope of the embodiments herein includes the whole area of possibilities of the claims and all available equivalents of the claims. In this document, the terms "first", "second", etc. are used only to distinguish one element from another, without requiring or implying any actual relationship or order between the elements. In fact, the first element could be named the second element and vice versa without changing the meaning of the description. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a structure, device, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such structure, device, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the structure, device, or apparatus that includes the element. The various embodiments are described in a progressive manner, each embodiment focusing on the differences with the other embodiments, and the same or similar parts between the various embodiments are referred to each other.

[0082] The terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like as used herein to indicate orientation or positional relationships based on the orientations or positional relationships shown in the drawings, are for purposes of this description only, and are not intended to indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the application. In the description of the description, unless otherwise specified and limited, the terms "mount", "connect", "connection" should be interpreted broadly, for example, can be mechanical connection or electrical connection, can be internal communication of two elements, can be direct connection, or indirect connection through intermediate medium, the specific meaning of the above terms can be understood by the person skilled in the art according to the specific circumstances.

[0083] In this paper, unless otherwise specified, the term "a plurality of" means two or more.

[0084] In this paper, the character " / " represents the relationship between the front and rear objects is "or". For example, A / B means: A or B.

[0085] In this paper, the term "and / or" is a description of the association between objects, which means that there can be three relationships. For example, A and / or B means: A or B, or, A and B, three relationships.

[0086] It should be understood that although each step in the flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified in this paper, the execution of these steps has no strict order restriction, and these steps can be executed in other order. Moreover, at least part of the steps in the figure can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or other steps or stages.

[0087] Each module in the device or system of the present application can be realized by software, hardware and their combination in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above modules by the processor.

[0088] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0089] Figure 1An embodiment of a multi-parameter evaluation driven balanced unit construction method of the application is shown.

[0090] In this alternative embodiment, the multi-parameter evaluation driven balanced unit construction method comprises:

[0091] S101, establishing evaluation indexes for constructing balanced units in a power supply area, and the evaluation indexes include supply-demand balance evaluation indexes and flexibility evaluation indexes;

[0092] S103, calculating the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index by using subjective weighting methods and objective weighting methods, and combining the weight coefficients of the subjective weighting methods and the objective weighting methods;

[0093] S105, obtaining initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under a preset adaptability evaluation standard in different balanced unit construction schemes, and multiplying the initial evaluation scores by the corresponding comprehensive weights of the evaluation indexes to obtain evaluation scores of each evaluation index in the different balanced unit construction schemes;

[0094] S107, adding and summing the evaluation scores of each evaluation index in the balanced unit construction scheme to obtain an adaptability comprehensive evaluation score of the balanced unit construction scheme; and based on the adaptability comprehensive evaluation scores of the different balanced unit construction schemes, screening out the best balanced unit construction scheme.

[0095] Figure 2 An embodiment of a multi-parameter evaluation driven balanced unit construction system of the application is shown.

[0096] In this alternative embodiment, the multi-parameter evaluation driven balanced unit construction system comprises:

[0097] The evaluation index establishing module 201 is configured to establish evaluation indexes for constructing balanced units in a power supply area, and the evaluation indexes include supply-demand balance evaluation indexes and flexibility evaluation indexes;

[0098] The weight assigning module 203 is configured to calculate the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index by using subjective weighting methods and objective weighting methods, and combining the weight coefficients of the subjective weighting methods and the objective weighting methods;

[0099] The evaluation score calculating module 205 is configured to obtain initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under a preset adaptability evaluation standard in different balanced unit construction schemes, and multiply the initial evaluation scores by the corresponding comprehensive weights of the evaluation indexes to obtain evaluation scores of each evaluation index in the different balanced unit construction schemes;

[0100] The balance unit construction scheme screening module 207 is configured to add up the evaluation scores of each evaluation index in the balance unit construction scheme to obtain an adaptability comprehensive evaluation score of the balance unit construction scheme; and based on the adaptability comprehensive evaluation scores of different balance unit construction schemes, the best balance unit construction scheme is screened out.

[0101] In order to facilitate the understanding of the above technical solutions of the present application, the above technical solutions of the present application are further described from the aspects of architecture and principle as follows, specifically as shown in the accompanying drawings. Figure 4

[0102] I. Establishing evaluation indexes for balance unit construction

[0103] (1) Supply-demand balance evaluation index

[0104] Maintaining power supply and demand balance is one of the key challenges in constructing a new power system, and strengthening the balance autonomy of the distribution network is conducive to reducing the impact of large-scale distributed generation on the transmission network and improving the overall balance capability of the power system. In the balance unit, the power consumption of all end users, the power generation of power suppliers, and the input and output power must be balanced.

[0105] 1) Source-load matching degree

[0106] The present application mainly evaluates the supply-demand balance situation in different 220kV regions according to the matching degree of wind energy, solar energy and load in the balance unit. To measure the source-load matching degree, the Euclidean distance discrimination method commonly used in engineering applications is adopted. When the matching degree is high, the power shortage in the balance unit is relatively small, and less power needs to be transmitted from the regional power grid; on the contrary, when the matching degree is low, the power shortage is large, and more power needs to be transmitted, which not only increases the operation loss, but also increases the construction cost of the distribution network. Therefore, maximizing the matching degree of wind energy, solar energy and load in the balance unit can effectively improve the economy and reliability of the system.

[0107] The Euclidean distance discrimination method is an effective method for measuring the similarity or difference between different objects in a multi-dimensional space. The formula used for source-load matching degree evaluation and analysis is:

[0108]

[0109] In the formula, P L,t and P G,t are the total load and total energy output at time t, respectively, t = 1, 2, etc.

[0110] 2) Power fluctuation

[0111] ​Under the long-term guidance of energy strategy, new energy industry has entered a new stage of large-scale replacement of traditional energy dominated by marketization. The access of new energy such as wind energy and solar energy presents a situation of centralized and distributed parallel and hierarchical access. Because the output of wind power and solar power generation is significantly affected by wind speed and sunshine duration, and these factors change highly inconsistently and significantly fluctuate at different times and places, the output of wind power and photovoltaic power generation also shows great instability. By analyzing the net load output at different time points, it can be seen that the size of the net load at each time point is different, showing the volatility of the net load. In order to ensure the real-time power balance inside the balancing unit, the output of the controllable power supply needs to track the changes of the net load curve in real time, so it is necessary to evaluate the power fluctuation of the net load curve in different 220kV regions.

[0112] The power fluctuation of distribution network refers to a series of changes and continuous changes of power. The power fluctuation of the present application is represented by the ratio of the difference between two adjacent extreme values on the net load power curve and the total number of trend turning points of the net load power curve. The expression of the average power fluctuation is:

[0113]

[0114] In the formula, P NL,max and P NL,min are two adjacent maximum and minimum values on the net load curve, n turn is the total number of trend turning points of the net load power curve.

[0115] 3) Prediction deviation

[0116] With the large-scale grid connection of new energy, the uncertainty of power system is intensified. At the same time, the rapid growth of new loads such as electric vehicles and electric heating makes the load characteristics more complex, further increasing the uncertainty of power system. In order to ensure the real-time balance of power inside the balancing unit, the power supply and demand situation in the region must be predicted daily. If there is a deviation between the prediction result and the actual situation, it will lead to system imbalance, at this time, the balancing unit needs to introduce standby power. Therefore, it is necessary to evaluate the power prediction deviation in different 220kV power supply regions.

[0117] In order to ensure the real-time balance of power inside the balancing unit, the supply and demand curve of the region needs to be predicted every day. In order to measure the similarity between the prediction value and the true value, the Euclidean distance is used to represent:

[0118]

[0119] In the formula, and are the actual value and the predicted value of the net load at time t, and Let T represent the actual and predicted net load values ​​at time T, where T = 1, 2, ..., t.

[0120] (2) Flexibility evaluation indicators

[0121] The grid connection of numerous uncontrollable distributed power sources has led to more complex and volatile net load curves, exacerbating the volatility and uncertainty of the distribution network. Power system flexibility refers to the system's ability to avoid cutting back renewable energy and ensure reliable power supply to users when faced with fluctuations and uncertainties in net load across multiple time scales. To cope with the variability and uncertainty of net load curves, balancing units need to possess sufficient flexibility.

[0122] 1) Modeling of distribution network flexibility requirements

[0123] The flexibility requirements of distribution networks can be divided into flexibility requirements caused by variability in net load and flexibility requirements caused by uncertainty. These two types of distribution network flexibility requirements are as follows: Figure 5 As shown, during the optimized operation of the power system, the net load forecast curve must be referenced when formulating daily dispatch plans. Due to the large-scale grid connection of renewable distributed power sources, the volatility of the net load forecast curve has increased. The power system needs to allocate flexibility resources to cope with fluctuations in net load at different time scales to meet the flexibility requirements caused by changes in net load. During the real-time dispatch phase, due to the existence of uncertainties, there will be deviations between the actual and forecast values ​​of net load. Therefore, real-time adjustments need to be made to the original dispatch plan to cope with the uncertainty of net load and meet the flexibility requirements caused by this uncertainty.

[0124] The distribution network flexibility requirements arising from the above two scenarios are modeled using the following formula:

[0125] F z,t =(P k,t+Δt -P′ k,t )-(P j,t+Δt -P′ j,t (4)

[0126] In the formula, F z,t To meet the overall flexibility requirements of the distribution network; P k,t+Δt and P j,t+Δt These represent the actual values ​​of the total load and the actual value of the renewable energy output at time t+Δt, respectively; P′ k,t and P′ j,t The predicted values ​​are the total load and the predicted output of new energy sources at time t.

[0127] 2) Modeling of distribution network flexibility supply

[0128] ① Energy storage system

[0129] The energy storage system has the ability of bidirectional regulation, which plays an important role in relieving new energy output fluctuation and providing its accommodation capacity. The flexibility supply capacity of the energy storage system is affected by various factors, including its current working state, maximum charge and discharge power, and charge and discharge efficiency. The flexibility supply situation of the energy storage system is shown in Figure 6 In Figure 6 , taking the down-regulation flexibility requirement as an example, when the other energy output is greater than P1, the energy storage is in the charging state, and the charging power needs to be increased to provide down-regulation flexibility to meet the downward fluctuation of the net load. When the other energy output is less than P1, the energy storage is in the discharging state, and the discharging power needs to be reduced to provide down-regulation flexibility.

[0130] The flexibility supply model of the energy storage system is expressed as:

[0131]

[0132] In the formula, and are the up-regulation and down-regulation flexibility provided by the energy storage s at time t; P c,max and P g,max are the maximum charging and discharging power of the energy storage in the time scale τ; and are the charging and discharging power of the energy storage s at time t; E s,t , E s,min , and E s,max are the storage capacity, minimum and maximum storage capacity of the energy storage s at time t; η c and η g are the charging and discharging efficiency of the energy storage s; and are state variables, which are 1 when the energy storage s is in the discharging state and charging state at time t, respectively; τ is the time scale.

[0133] ② Load that can be cut

[0134] The load that can be cut is a load that can cut power to some extent, shorten time or interrupt operation. Such load has a certain flexibility and can be adjusted according to the demand of the power grid to reduce the burden of the power grid. Through reasonable management of the load that can be cut, the operation efficiency and reliability of the power grid can be improved. Similar to the discharging of the energy storage, the load that can be cut can provide up (down) regulation flexibility by increasing (decreasing) the load cutting amount, and the formula is:

[0135]

[0136] In the formula, and are the up-regulation and down-regulation flexibility provided by the load that can be cut q at time t; Pq,max Pmaxis the maximum curtailment power of load q at time t; P q,t Pcuris the curtailment power of load q at time t.

[0137] 3) Analysis of flexibility supply-demand balance in distribution network

[0138] Without considering the transmission capability of the network, the flexibility supply-demand balance in distribution network mainly focuses on the adequacy of flexibility resources, which can be quantified by flexibility supply-demand modeling. Therefore, the flexibility resource supply adequacy A t is defined as

[0139]

[0140] where A and A are the up-regulation and down-regulation adequacy of flexibility resource supply at time t, respectively; n s and n q are the number of energy storage systems and curtailable loads, respectively, and F z,t is the overall flexibility demand of the distribution network.

[0141] Based on the above analysis, this paper obtains four cases of flexibility supply-demand in distribution network, as shown in Fig. 1.

[0142] Table 1 Flexibility supply-demand cases in distribution network

[0143] Case Judgment condition Judgment result 1 A t >0]]> Flexible supply is sufficient 2 A t ≤0]]> Flexible supply is insufficient

[0144] 4) Flexibility evaluation index of distribution network

[0145] To reflect the adequacy and deficiency of flexibility resources in the balancing unit, the flexibility average adequacy index E a and the flexibility average deficiency index E e are defined. Their physical meanings are the ratio of the total adequacy of flexibility resource supply to the adequacy period, and the ratio of the total deficiency to the deficiency period, respectively. The formula is

[0146]

[0147] where E and E are the up-regulation and down-regulation flexibility average adequacy, respectively; and E are the up-regulation and down-regulation flexibility supply-demand adequacy period, respectively.

[0148]

[0149] where E and E are the up-regulation and down-regulation flexibility average deficiency, respectively. and respectively are flexible supply and demand up, down the period of shortage.

[0150] II. The subjective and objective combination weighting method is used to determine the weight coefficient:

[0151] The comprehensive evaluation method mainly includes the weighting method and the evaluation model. In the weighting method, the subjective weighting method and the objective weighting method are generally used at present. The subjective weighting method mainly has the analytic hierarchy process and the improved method thereof. The analytic hierarchy process is mainly based on qualitative analysis, can comprehensively reflect the result of the evaluation system, and has lower requirement for data, but the correlation between some indexes can lead to overlapping and interference of information, and it can ignore the objective reality and excessively depend on the subjective judgment of experts, therefore, sometimes the objective weighting method is used for evaluation. The objective weighting method mainly has the entropy weight method, but the method has higher requirement for the objectivity and completeness of data, is difficult to implement when the data is insufficient, and cannot fully consider the actual importance of the target and cannot clearly reflect the preference of the decision maker.

[0152] In order to determine the weight of the evaluation index, the combination weighting method combining the subjective experience and the objective data is widely used at present. The method effectively avoids the limitation that the subjective weighting method excessively depends on the subjective opinion of experts and the objective weighting method has high requirement for data. By using the weighting technology combining the subjectivity and the objectivity, the objectivity and the scientificity of the weighting value of the index in the evaluation index system can be enhanced.

[0153] In order to improve the objectivity and the scientificity of the weighting value of the index in the evaluation index system, the subjective and objective combination weighting method is used to determine the weight coefficient.

[0154] (1) Analytic hierarchy process (abbreviated as AHP)

[0155] AHP is a method of decomposing a complex problem into an ordered hierarchical structure to systematically handle various related factors. Through the evaluation based on the objective situation, the method can quantify the relative importance of each factor in each level. By using mathematical methods, it can express the order of the relative importance of all factors in each level, and then obtain a ranking result. The method helps to clearly identify and compare the importance of different factors, and provides a scientific basis for decision making.

[0156] The basic idea of AHP is to first establish an independent and interconnecting hierarchical structure reflecting the functions or properties of the system according to the nature of the problem, to give the corresponding proportional scale by comparing the relative importance between factors, to construct the judgment matrix of the upper layer factors to the lower layer related factors, and to give the relative importance order of the lower layer related factors to the upper layer factors. The core problem of AHP is the ranking problem, which uses the hierarchical structure, scale and ranking principle to realize it.

[0157] The process of solving problems by using AHP can be divided into three steps: establishing the hierarchical structure model of the problem, constructing the two-by-two comparison matrix, and single ordering of the hierarchy and consistency test. The specific process is as follows:

[0158] ①Establishing the hierarchical structure model

[0159] According to the attributes of the target problem, the factors in it are processed in layers to form a hierarchical structure of different levels. In this structure, the factors in the same layer serve as criteria and have a guiding effect on the factors in the next layer, while they are also subject to the factors in the previous layer. This top-down domination relationship constitutes a progressive hierarchical system. The highest layer usually contains only one element, i.e. the target layer; the middle layer involves measures, schemes, policies, etc. required to achieve the overall goal, which is called the criterion layer; the lowest layer contains various ways and methods to solve specific problems, which is called the scheme layer. According to the basic principles of AHP, a hierarchical analysis structure model for evaluating the selection of the best balanced unit construction scheme in different 220kV regions is constructed, as shown in the hierarchical structure model of Figure 7 .

[0160] Target layer. For the target layer of the present application, it is to select appropriate balanced units to build regions in different 220kV regions.

[0161] Criterion layer. The main factors to evaluate whether the balanced unit construction scheme is reasonable are the supply-demand balance and flexibility indicators, including the average flexibility surplus indicator E a (divided into up-regulation average flexibility surplus and down-regulation average flexibility surplus) and the average flexibility deficiency indicator E e (up-regulation average flexibility deficiency and down-regulation average flexibility deficiency).

[0162] Scheme layer. For different 220kV power supply regions.

[0163] ②Constructing the two-by-two comparison matrix

[0164] Suppose n f factors are compared to the influence of the target Z, i.e. to determine their proportion in Z. Each time, two factors x i and x j are taken, and a ij is used to represent the ratio of the influence degree of x i and x j on Z, thereby obtaining the two-by-two comparison matrix A The elements in A ij should satisfy the following conditions: A ji >0, a ij =1 / a ii (i≠j), a ii= 1 (i, j = 1, 2,.., n f ).

[0165] The scale method of a ij is shown in Table 2.

[0166] Table 2 Comparison of the scale method of a ij

[0167] x i / x j ]]> Equal Stronger Strong Very strong Absolutely strong a ij ]]> 1 3 5 7 9

[0168] Note: 2, 4, 6, 8 represent the intermediate values of the above adjacent judgments.

[0169] 3. Hierarchical single ordering and consistency check

[0170] Hierarchical single ordering, to solve the weight of each layer index in the analytic hierarchy process: first solve the maximum eigenvalue λ max of the judgment matrix A, and then use:

[0171] AW = λ max W (11)

[0172] Solve the characteristic vector W corresponding to λ max , and the normalized characteristic vector is the ordering weight of the corresponding factors in the same layer corresponding to the relative importance of a certain factor in the previous layer.

[0173] Consistency check: in order to determine the effectiveness of each layer judgment matrix, the analytic hierarchy process needs to check the consistency of the weight vector of the judgment matrix A. First calculate its consistency index C I , defined as:

[0174]

[0175] In the formula, n A is the order of the judgment matrix A. When A has complete consistency, C I = 0; λ max -n A , the larger C I is, the worse the consistency of A is.

[0176] In order to determine whether A has satisfactory consistency, C I needs to be compared with the average random consistency index R I . For a 1-9 order judgment matrix, the value of R I is given in Table 3.

[0177] Table 3 Value of the randomness index R I

[0178] order n A ]] 1 2 3 4 5 6 7 8 9 [R I ]]> 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 ​​

[0179] Let C R = C I / R I is the randomness consistency ratio, C R <0.10, A has satisfactory consistency, otherwise, A is adjusted until it has satisfactory consistency. At this time, the calculated λ max The corresponding eigenvector W after standardization can be used as the weight of hierarchical single sorting, that is, the subjective weight vector W sub .

[0180] (2) Entropy weight method

[0181] Entropy weight method is an objective weighting method, which determines the weight of each index according to the amount of information it contains. In a decision-making problem involving multiple indexes, the smaller the entropy value of an index, the more information it contains, the greater its role in comprehensive evaluation, and the greater its weight. Using entropy weight method can effectively reduce the influence of subjective judgment on weight allocation and ensure the objectivity of the obtained weight.

[0182] Considering that different evaluation indexes have different dimensions, the results of the indexes need to be standardized.

[0183] ① Data normalization, calculate the proportion of the index value of the ith scheme under the jth index:

[0184]

[0185] In the formula, r ij is the value of the jth index of the ith scheme, and m is the number of schemes.

[0186] ② The entropy value of the jth index is calculated by the formula:

[0187]

[0188] In the formula, k = 1 / lnm.

[0189] ③ The weight coefficient of the jth index is calculated by the formula:

[0190]

[0191] In the formula, n is the number of indexes. Thus, the objective weight vector of the indexes is:

[0192] W obj = (w1, w2,..., wn) n ) T (16)

[0193] (3) Combined weighting method

[0194] In view of the limitation of subjective and objective weighting method, the combination weighting method based on the combination of analytic hierarchy process and entropy weight method is used to weight, so that the evaluation result is more objective and accurate.

[0195] W=k1W sub +k2W obj (17)

[0196] In the formula, k1 and k2 are weight coefficients of subjective and objective weights respectively, here k1=k2=0.5, W sub is a subjective weight vector.

[0197] Thirdly, starting from the supply-demand balance and flexibility evaluation indexes, the combination weighting method is applied to evaluate the adaptability of constructing a balanced unit in different 220kV regions, and the best balanced unit construction scheme is obtained.

[0198] The application takes a typical day as an example to describe the specific embodiment of the application. The adaptability of balanced unit construction in a certain power supply area is evaluated from the aspects of supply-demand balance (calculated by using a per unit value) and flexibility. The example system contains wind power, solar photovoltaic, one energy storage device, air conditioning cluster and the like. The maximum charge-discharge power of the energy storage device is 0.4MW, the discharge efficiency is 0.9, the minimum storage power is 2MWh, and the maximum storage power is 4MWh. The topology structure of the supply area is shown in Figure 8 , the actual value and the predicted value of the system energy output are shown in Figure 9 , the actual value and the predicted value of the load are shown in Figure 10 , the charge-discharge power and the storage power curve of the energy storage are shown in Figure 11 and Figure 12 , and the size of the air conditioning cluster is shown in Figure 13 .

[0199] The specific steps of the embodiment are as follows:

[0200] Step 1: Establish the adaptability evaluation index of balanced unit construction. The supply-demand balance and flexibility of the region are calculated and counted to obtain the adaptability evaluation standard shown in Table 4 and the evaluation index shown in Table 5, and then the initial evaluation score is obtained from Table 4 and Table 5, as shown in Table 6.

[0201] Table 4 Adaptability evaluation standard

[0202]

[0203]

[0204] Table 5 Adaptability evaluation index

[0205]

[0206] Table 6 adaptive initial evaluation score

[0207]

[0208]

[0209] Step 2: According to the basic principles of analytic hierarchy process, a judgment matrix of each factor of the criterion layer for the applicability of constructing a balanced unit in the supply area can be constructed, as shown in Table 7.

[0210] Table 7 judgment matrix of each factor of the criterion layer for the overall target

[0211] Evaluation index Supply and demand balance Flexibility Supply and demand balance 1 3 / 5 Flexibility 5 / 3 1

[0212] Similarly, the comparison judgment matrix of each index layer relative to the criterion layer (supply-demand balance, flexibility) is shown in Tables 8-9.

[0213] Table 8 judgment matrix of the index layer relative to the criterion layer (supply-demand balance)

[0214] Supply and demand balance Supply and demand balance Power fluctuation Prediction deviation Source load matching degree 1 5 / 3 5 / 4 Power fluctuation 3 / 5 1 3 / 4 Prediction deviation 4 / 5 4 / 3 1

[0215] Table 9 judgment matrix of the index layer relative to the criterion layer (flexibility)

[0216]

[0217] Step 3: Since the subjective weighting method is based on subjective judgment and the importance and weight of each evaluation index are determined by experience value, the disadvantage is that it cannot adapt to the actual changes in data. The entropy weight method calculates the index weight according to the actual information difference, but this method overemphasizes the data itself and may result in too large or too small weight in individual cases.

[0218] As can be seen, the above two weighting methods have advantages and disadvantages, so simply using one method will not be appropriate and reasonable. Therefore, the present application selects a combined weighting method to determine the optimal weight. This way of determining the weight can avoid excessive subjective judgment and reduce the deviation that may be caused by data weighting. The index weight of the analytic hierarchy process is obtained by using the judgment matrix of Tables 7-9, and the evaluation weight value of each index is obtained by calculating the weight of the analytic hierarchy process and the entropy weight method, as shown in Table 10. The present application uses the average value of the weight calculated by the analytic hierarchy process and the entropy weight method as the comprehensive weight:

[0219] ω0=0.5ω1+0.5ω2

[0220] In the formula: ω0 is the average weight calculated by the comprehensive evaluation method, ω1 is the weight calculated by the analytic hierarchy process, and ω2 is the weight calculated by the entropy weight method.

[0221] Table 10 each index evaluation weight value

[0222]

[0223] The percentage evaluation standard for the adaptability of the balanced unit constructed in the region is established, the comprehensive weight of each index is multiplied by the initial evaluation score under the evaluation standard to obtain the evaluation score of each index of the balanced unit constructed in the region, and the scores of each index are added to obtain the adaptability comprehensive evaluation score of the balanced unit construction, which is 85.5134.

[0224] It can be seen from the result that the evaluation score of the balanced unit constructed in the region is high, but there are still deficiencies.

[0225] The example result verifies that the method proposed in the application can effectively adaptively evaluate the construction of the balanced unit in different regions, so as to screen out the best balanced unit construction scheme.

[0226] The application proposes an evaluation architecture for the adaptability of the balanced unit constructed in different 220kV regions. By analyzing the operation state of the power grid in different regions, the supply-demand balance index including the source-load matching degree, power fluctuation and prediction deviation, and the flexibility index of the up-regulation (down-regulation) flexibility adequacy (insufficiency) are established. The adaptability of the balanced unit constructed in different 220kV regions is intuitively and clearly evaluated from the above two aspects by using the subjective and objective combination method, and the most effective and suitable construction scheme is selected. The main grid balance responsibility is further sunk, and the county dispatching is included in the active balance system. The power generation and consumption balance is realized in the unit to ensure the balance of power generation and power consumption, relieve the centralized balance control pressure, reduce the power system power imbalance risk, and thus guarantee the reliable power supply and new energy consumption under the new power system.

[0227] In an embodiment, a computer device can be provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 3 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the steps in the above method embodiments.

[0228] Those skilled in the art can understand that Figure 3The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0229] In addition, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0230] In addition, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.

[0231] Those skilled in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above embodiments. Any reference to memory, storage, database or other medium in the embodiments of the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0232] The present application is not limited to the structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is only limited by the appended claims.

Claims

1. A method for constructing a balanced unit driven by multi-parameter evaluation, characterized in that, The method includes: Evaluation indicators are established for constructing balancing units within the power supply area. These indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators. The supply and demand balance evaluation indicators include the degree of matching between total energy output and power system load, the ratio of the difference between two adjacent extreme values ​​on the net load power curve to the total number of trend inflections on the net load power curve, and the deviation between the power supply and demand forecast results and the actual power supply and demand situation. The flexibility evaluation indicators include the ratio of the total sufficiency of distribution network flexibility resources to the sufficiency period, and the ratio of the total deficit to the deficit period. Using subjective and objective weighting methods, and combining the weighting coefficients of subjective and objective weighting methods, the comprehensive weights of each supply and demand balance evaluation indicator and each flexibility evaluation indicator are calculated. In different balance unit construction schemes, the initial evaluation scores of each supply and demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard are obtained, and multiplied with the corresponding comprehensive weight of each evaluation index to obtain the evaluation scores of each evaluation index in different balance unit construction schemes. The evaluation scores of each evaluation index in the balanced unit construction scheme are summed to obtain the comprehensive adaptability evaluation score of the balanced unit construction scheme; based on the comprehensive adaptability evaluation scores of different balanced unit construction schemes, the best balanced unit construction scheme is selected.

2. The method for constructing a balanced unit driven by multi-parameter evaluation according to claim 1, characterized in that, The establishment of the supply and demand balance evaluation indicators includes: Based on the Euclidean distance discrimination method, a source-load matching degree evaluation and analysis formula is constructed. The total energy output and the power system load are substituted into the source-load matching degree evaluation and analysis formula, and the matching degree between the total energy output and the power system load is output. Based on the matching degree between the total energy output and the power system load, an indicator of the supply and demand balance in the power supply area is established. Obtain the net load curve of the power supply area, and establish a power fluctuation index of the net load curve of the power supply area based on the ratio of the difference between two adjacent extreme values ​​on the net load power curve to the total number of trend inflection points of the net load power curve. Daily forecasts of power supply and demand in the power supply area are made, and a forecast deviation evaluation index for the power supply area is established based on the deviation between the forecast results and the actual power supply and demand.

3. The method for constructing a balance unit driven by multi-parameter evaluation according to claim 2, characterized in that, The daily forecasting of power supply and demand in the power supply area, and the establishment of a forecast deviation evaluation index for the power supply area based on the deviation between the forecast results and the actual power supply and demand, include: The actual and predicted values ​​of net load in the power supply area are obtained, and the deviation between the actual and predicted values ​​of net load is calculated using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

4. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 1, characterized in that, The establishment of the flexibility evaluation index includes: Based on the flexibility requirements caused by the variability and uncertainty of the net load of the power system, a flexibility requirement index for the distribution network in the power supply area is established. Based on the operating status, maximum charging and discharging power, and charging and discharging efficiency of the energy storage system, a distribution network flexibility supply index is established in the power supply area. The distribution network flexibility supply index includes both upward and downward flexibility indices.

5. The method for constructing a balanced unit driven by multi-parameter evaluation according to claim 4, characterized in that, The indicators for establishing the flexibility supply of the power distribution network in the power supply area include: The flexibility index for adjusting upward and downward adjustments can be achieved by utilizing the load reduction capacity of the power system.

6. The method for constructing a balanced unit driven by multi-parameter evaluation according to claim 1, characterized in that, The method of calculating the comprehensive weights of each supply-demand balance evaluation indicator and each flexibility evaluation indicator by using subjective weighting and objective weighting methods, and combining the weighting coefficients of subjective weighting and objective weighting methods, includes: Based on the analytic hierarchy process (AHP), a hierarchical structure model for constructing balancing units within a power supply area is established, and pairwise comparison judgment matrices for each evaluation index are constructed. The first weight of each evaluation index is calculated using a hierarchical single-sorting algorithm, and the validity of the first weight calculation result is judged using the consistency test method. The entropy value of each evaluation index is calculated based on the entropy weight method, and the second weight of each evaluation index is determined after the entropy value calculation results are standardized. The calculation results of the analytic hierarchy process (AHP) and the entropy weight method are assigned weight coefficients respectively. The first weight and the second weight calculated by the AHP and the entropy weight method are then weighted and summed to obtain the comprehensive weight of each evaluation index.

7. The method for constructing a balanced unit driven by multi-parameter evaluation according to claim 1, characterized in that, In obtaining different balancing unit construction schemes, the initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation criteria include: Construct adaptive evaluation standards for each supply and demand balance evaluation indicator and each flexibility evaluation indicator; Obtain the values ​​of each supply and demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation criteria to obtain the initial evaluation scores for each evaluation index.

8. A multi-parameter evaluation-driven equilibrium unit construction system, characterized in that, The system includes: The evaluation index establishment module is used to establish evaluation indicators for constructing balancing units within the power supply area. The evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators. Among them, the supply and demand balance evaluation indicators include the degree of matching between total energy output and power system load, the ratio of the difference between two adjacent extreme values ​​on the net load power curve to the total number of trend inflections on the net load power curve, and the deviation between the power supply and demand forecast results and the actual power supply and demand situation. The flexibility evaluation indicators include the ratio of the total sufficiency of distribution network flexibility resources to the sufficiency period, and the ratio of the total deficit to the deficit period. The weighting module is used to calculate the comprehensive weight of each supply and demand balance evaluation indicator and each flexibility evaluation indicator by using subjective weighting method and objective weighting method, and combining the weight coefficients of subjective weighting method and objective weighting method. The evaluation score calculation module is used to obtain the initial evaluation scores of each supply and demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balance unit construction schemes, and multiply them with the corresponding comprehensive weight of each evaluation index to obtain the evaluation scores of each evaluation index in different balance unit construction schemes. The balanced unit construction scheme screening module is used to sum the evaluation scores of each evaluation index in the balanced unit construction scheme to obtain the comprehensive evaluation score of the adaptability of the balanced unit construction scheme; based on the comprehensive evaluation scores of the adaptability of different balanced unit construction schemes, the best balanced unit construction scheme is selected.

9. The multi-parameter evaluation-driven equilibrium unit construction system according to claim 8, characterized in that, The establishment of the supply and demand balance evaluation indicators includes: Based on the Euclidean distance discrimination method, a source-load matching degree evaluation and analysis formula is constructed. The total energy output and the power system load are substituted into the source-load matching degree evaluation and analysis formula, and the matching degree between the total energy output and the power system load is output. Based on the matching degree between the total energy output and the power system load, an indicator of the supply and demand balance in the power supply area is established. Obtain the net load curve of the power supply area, and establish a power fluctuation index of the net load curve of the power supply area based on the ratio of the difference between two adjacent extreme values ​​on the net load power curve to the total number of trend inflection points of the net load power curve. Daily forecasts of power supply and demand in the power supply area are made, and a forecast deviation evaluation index for the power supply area is established based on the deviation between the forecast results and the actual power supply and demand.

10. The multi-parameter evaluation-driven equilibrium unit construction system according to claim 9, characterized in that, The daily forecasting of power supply and demand in the power supply area, and the establishment of a forecast deviation evaluation index for the power supply area based on the deviation between the forecast results and the actual power supply and demand, include: The actual and predicted values ​​of net load in the power supply area are obtained, and the deviation between the actual and predicted values ​​of net load is calculated using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

11. The multi-parameter evaluation-driven balancing unit construction system according to claim 8, characterized in that, The establishment of the flexibility evaluation index includes: Based on the flexibility requirements caused by the variability and uncertainty of the net load of the power system, a flexibility requirement index for the distribution network in the power supply area is established. Based on the operating status, maximum charging and discharging power, and charging and discharging efficiency of the energy storage system, a distribution network flexibility supply index is established in the power supply area. The distribution network flexibility supply index includes both upward and downward flexibility indices.

12. The multi-parameter evaluation-driven equilibrium unit construction system according to claim 11, characterized in that, The indicators for establishing the flexibility supply of the power distribution network in the power supply area include: The flexibility index for adjusting upward and downward adjustments can be achieved by utilizing the load reduction capacity of the power system.

13. The multi-parameter evaluation-driven equilibrium unit construction system according to claim 8, characterized in that, The method of calculating the comprehensive weights of each supply-demand balance evaluation indicator and each flexibility evaluation indicator by using subjective weighting and objective weighting methods, and combining the weighting coefficients of subjective weighting and objective weighting methods, includes: Based on the analytic hierarchy process (AHP), a hierarchical structure model for constructing balancing units within a power supply area is established, and pairwise comparison judgment matrices for each evaluation index are constructed. The first weight of each evaluation index is calculated using a hierarchical single-sorting algorithm, and the validity of the first weight calculation result is judged using the consistency test method. The entropy value of each evaluation index is calculated based on the entropy weight method, and the second weight of each evaluation index is determined after the entropy value calculation results are standardized. The calculation results of the analytic hierarchy process (AHP) and the entropy weight method are assigned weight coefficients respectively. The first weight and the second weight calculated by the AHP and the entropy weight method are then weighted and summed to obtain the comprehensive weight of each evaluation index.

14. The multi-parameter evaluation-driven balancing unit construction system according to claim 8, characterized in that, In obtaining different balancing unit construction schemes, the initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation criteria include: Construct adaptive evaluation standards for each supply and demand balance evaluation indicator and each flexibility evaluation indicator; Obtain the values ​​of each supply and demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation criteria to obtain the initial evaluation scores for each evaluation index.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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