Multi-parameter evaluation driven balance unit construction method, system, equipment and medium

Abstract

The invention belongs to the field of multi-element resource cooperative regulation and control, and discloses a multi-parameter evaluation-driven balance unit construction method, system and device and a medium, and the method comprises the steps: building an evaluation index for constructing a balance unit in a power supply area; calculating the comprehensive weight of each supply and demand balance evaluation index and each flexibility evaluation index by using a subjective weighting method and an objective weighting method; obtaining an initial evaluation score of each evaluation index, and multiplying the initial evaluation score by a comprehensive weight corresponding to each evaluation index to obtain an evaluation score of each evaluation index in different balance unit construction schemes; summing the evaluation score of each evaluation index in the balance unit construction scheme to obtain an adaptive comprehensive evaluation score of the balance unit construction scheme; and an optimal balance unit construction scheme is screened out. According to the method, the adaptability of the balance unit construction scheme is evaluated by adopting the subjective and objective combined combination weighting method, and the optimal balance unit construction scheme is screened out.

Description

Technical Field

[0001] The present invention relates to the field of coordinated regulation of multiple resources, and in particular to a method, system, equipment and medium for constructing a balancing unit driven by multi-parameter evaluation. Background Art

[0002] Due to the low proportion of new energy access in regional power grids in the traditional sense and the small number of controllable resources, it is often necessary to rely on large power grids to achieve overall active power and energy balance. In the current active power control system, each control center uses voltage level and regional scope as the basis for division to achieve hierarchical and regional coordinated active power control. Some levels of dispatching have not yet been fully incorporated into the active power control system. In the traditional power system dominated by thermal power, the source follows the load, effectively achieving power and electricity balance. However, the future power system will be dominated by new energy power generation with strong uncertainty and weak output control, which will make the power grid operation more complicated, the proportion of traditional flexible resources available in the system will gradually decrease, and the pressure on the safe supply and balance of the power system will increase dramatically. It is urgent to mobilize various distributed resources such as new energy storage, distributed power sources, microgrids, virtual power plants, and adjustable loads to participate in system interaction and share the responsibility for system balance.

[0003] In order to further decentralize the main grid balance responsibility and incorporate the district / county regulation into the active balance system, it is necessary to build regional power grid balance units, divide the power grid into multiple unequal balance units, and use the balance unit as the basic unit of power grid regulation. By achieving the balance of power generation and consumption within the unit, the balance of power generation and consumption in the power grid can be ensured. The pressure of centralized balance control can be alleviated, and the risk of power imbalance in the power system can be reduced, thereby ensuring the reliable supply of electricity and the consumption of new energy under the new power system.

[0004] Relying on mature electricity markets, some regions have built a large number of virtual power plants or small balancing units at the bottom to undertake grid balancing tasks. At present, the research mainly focuses on the introduction of a country's balancing unit mechanism, and analyzes the support of the development of a country's virtual power plant for the balancing unit, the design of the electricity market, and the advantages and disadvantages of the balancing unit model. How to combine the domestic situation, build balancing units from all aspects and angles, evaluate the applicability of building balancing units in different regions, give full play to the growing potential of regional power grids for diversified and flexible resource regulation, and realize efficient utilization of power grid resources at all levels and mutual assistance of surplus and shortage are issues that need to be solved urgently.

[0005] The balance of power supply and demand is one of the key issues in building a new power system. 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 capacity of the power system. At the same time, with the access of a large number of uncontrollable distributed power sources, the net load curve is more complex and changeable, which aggravates the volatility and uncertainty of the distribution network. Therefore, the balancing unit needs to have sufficient supply and demand balancing ability and flexibility. At present, the comprehensive evaluation methods mainly include weighting methods and evaluation models. There are two types of weighting methods: subjective and objective. However, the subjective weighting method ignores the actual objective situation and relies too much on the subjective opinions of experts, while the objective weighting method has high requirements for objective data and cannot clearly 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 combined weighting method that combines the two. Although the combined weighting method has been widely used in evaluation and decision-making problems in many fields, especially in the comprehensive evaluation of both subjective and objective factors, it has shown unique advantages, but in the selection of evaluation indicators and comprehensive evaluation of balancing unit construction, 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. Therefore, it is particularly important to study the selection of its evaluation indicators and the applicability of the evaluation method. At present, although there are discussions on the construction methods of balancing units, there is no recognized theory and method on how to systematically evaluate the advantages and disadvantages of these construction results from multiple dimensions. Summary of the invention

[0006] The embodiments of the present invention provide a method, system, device and medium for constructing a balancing unit driven by multi-parameter evaluation, so as to solve the problem that there is a lack of an evaluation method for constructing a balancing unit in a power supply area in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for constructing a balancing unit driven by multi-parameter evaluation is provided.

[0009] In one embodiment, a method for constructing a balancing unit driven by multi-parameter evaluation includes:

[0010] Establish evaluation indicators for building balancing units within the power supply area, and the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators;

[0011] The subjective weighting method and the objective weighting method are used, and the weight coefficients of the subjective weighting method and the objective weighting method are combined to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index;

[0012] Obtaining the initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balancing unit construction schemes, and multiplying them with the corresponding comprehensive weights of each evaluation index to obtain the evaluation scores of each evaluation index in different balancing unit construction schemes;

[0013] The evaluation scores of each evaluation indicator in the balance unit construction scheme are added together to obtain the adaptability comprehensive evaluation score of the balance unit construction scheme; based on the adaptability comprehensive evaluation scores of different balance unit construction schemes, the best balance unit construction scheme is screened out.

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

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

[0016] Obtain the net load curve of the power supply area, and establish a power fluctuation indicator 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 turning points of the net load power curve;

[0017] Make daily forecasts for the power supply and demand situation in the power supply area, and establish forecast deviation evaluation indicators for the power supply area based on the deviation between the forecast results and the actual power supply and demand situation.

[0018] In one embodiment, a daily forecast is made for the power supply and demand situation in the power supply area, and a forecast deviation evaluation index for the power supply area is established based on the deviation between the forecast result and the actual power supply and demand situation, including:

[0019] The actual value and 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 using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

[0020] In one embodiment, establishing 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, establish the flexibility demand index of the distribution network in the power supply area;

[0022] Based on the working status, maximum charging and discharging power, and charging and discharging efficiency of the energy storage system, a distribution network flexibility supply index in the power supply area is established, and the distribution network flexibility supply index includes an upward flexibility index and a downward flexibility index;

[0023] By using the ratio of the total surplus of distribution network flexibility resources to the surplus period, as well as the ratio of the total shortage to the shortage period, the average surplus index and the average shortage index of distribution network flexibility in the power supply area are established respectively.

[0024] In one embodiment, establishing a distribution network flexibility supply indicator in a power supply area includes:

[0025] The upward and downward flexibility indicators are adjusted by utilizing the reducible load of the power system.

[0026] In one embodiment, the subjective weighting method and the objective weighting method are used, and the weight coefficients of the subjective weighting method and the objective weighting method are combined to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index, including:

[0027] Based on the analytic hierarchy process, a hierarchical structure model for the problem of building a balancing unit 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 using the hierarchical single sorting algorithm, and the validity of the first weight calculation result is judged using the consistency test method;

[0028] The entropy value of each evaluation index is calculated based on the entropy weight method, and the calculation result of the entropy value is standardized to determine the second weight of each evaluation index;

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

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

[0031] Constructing adaptability evaluation criteria for each supply-demand balance evaluation index and each flexibility evaluation index;

[0032] Obtain the values ​​of each supply-demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation standard to obtain the initial evaluation score of each evaluation index.

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

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

[0035] An evaluation index establishment module is used to establish evaluation indicators for building a balancing unit in a power supply area, and the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators;

[0036] A weight assignment module is used to calculate the comprehensive weights of various supply and demand balance evaluation indicators and various flexibility evaluation indicators 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;

[0037] An 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 balancing unit construction schemes, and multiply them with the corresponding comprehensive weight of each evaluation index to obtain the evaluation score of each evaluation index in different balancing unit construction schemes;

[0038] The balancing unit construction scheme screening module is used to add up the evaluation scores of each evaluation indicator in the balancing unit construction scheme to obtain the adaptability comprehensive evaluation score of the balancing unit construction scheme; based on the adaptability comprehensive evaluation scores of different balancing unit construction schemes, the best balancing unit construction scheme is screened out.

[0039] In one embodiment, establishing the supply-demand balance evaluation index includes:

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

[0041] Obtain the net load curve of the power supply area, and establish a power fluctuation indicator 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 turning points of the net load power curve;

[0042] Make daily forecasts for the power supply and demand situation in the power supply area, and establish forecast deviation evaluation indicators for the power supply area based on the deviation between the forecast results and the actual power supply and demand situation.

[0043] In one embodiment, a daily forecast is made for the power supply and demand situation in the power supply area, and a forecast deviation evaluation index for the power supply area is established based on the deviation between the forecast result and the actual power supply and demand situation, including:

[0044] The actual value and 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 using the Euclidean distance algorithm to obtain the prediction deviation evaluation index of the power supply area.

[0045] In one embodiment, establishing 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, establish the flexibility demand index of the distribution network in the power supply area;

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

[0048] By using the ratio of the total surplus of distribution network flexibility resources to the surplus period, as well as the ratio of the total shortage to the shortage period, the average surplus index and the average shortage index of distribution network flexibility in the power supply area are established respectively.

[0049] In one embodiment, establishing a distribution network flexibility supply indicator in a power supply area includes:

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

[0051] In one embodiment, the subjective weighting method and the objective weighting method are used, and the weight coefficients of the subjective weighting method and the objective weighting method are combined to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index, including:

[0052] Based on the analytic hierarchy process, a hierarchical structure model for the problem of building a balancing unit 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 using the hierarchical single sorting algorithm, and the validity of the first weight calculation result is judged using the consistency test method;

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

[0054] The calculation results of the analytic hierarchy process and the entropy weight method are assigned weight coefficients respectively, and the first weight and the second weight calculated by the analytic hierarchy process and the entropy weight method are weighted summed to obtain the comprehensive weight of each evaluation index.

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

[0056] Constructing adaptability evaluation criteria for each supply-demand balance evaluation index and each flexibility evaluation index;

[0057] Obtain the values ​​of each supply-demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation standard to obtain the initial evaluation score of each evaluation index.

[0058] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0059] In some embodiments, the computer device includes 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 embodiments of the present invention, a computer-readable storage medium is provided.

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

[0062] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0063] (1) The multi-parameter evaluation-driven balancing unit construction method, system, equipment and medium provided by the present invention are based on supply and demand balancing indicators such as source-load matching, power fluctuation, prediction deviation, and flexibility indicators such as the flexibility margin (shortage) of upward (downward adjustment). A combined weighting method combining subjective and objective factors is used to evaluate the adaptability of constructing balancing units in different 220kV regional ranges, and the best balancing unit construction scheme is screened out.

[0064] (2) The present invention incorporates massive distributed flexible adjustment resources into the control range through the construction of balancing units, thereby improving the active balancing ability of the power system. When constructing a balancing unit, too many regional divisions will make the dispatching management of the provincial dispatching center more complicated, and the amount of interactive information and computing workload that need to be processed will also increase sharply; too few regional divisions will increase the control burden inside the balancing unit. Therefore, when designing a balancing unit, the present invention comprehensively considers the above factors and the unified resource scheduling and hierarchical management architecture currently implemented by the power grid, selects the 220kV power supply area as the optimal dispatching range, and then analyzes the power grid operation status in different 220kV areas. From the two aspects of supply and demand balance and flexibility, the subjective and objective combined analysis method is used to intuitively and clearly evaluate the adaptability of constructing balancing units in different 220kV areas, and selects the most effective and appropriate construction plan.

[0065] (3) The present invention realizes a 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 decentralized, and the district / county regulation is included in the active power balancing system to construct a regional power grid balancing unit. Using 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 sources, microgrids, virtual power plants, and adjustable loads to participate in the interaction of the power system and share the responsibility of balancing the power system to cope with the uncertainty and poor controllability caused by the large-scale grid connection of new energy. By achieving a balance between power generation, consumption, and use within the unit, the balance of power generation and power consumption in the power grid is ensured, the pressure of centralized balancing control is alleviated, the risk of power imbalance in the power system is reduced, and the reliable supply of electricity and the consumption of new energy are guaranteed under the new power system.

[0066] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0068] Figure 1 is a flow chart of a method for constructing a balancing unit driven by multi-parameter evaluation according to an exemplary embodiment;

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

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

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

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

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

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

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

[0076] Fig. 9 is a daily variation curve diagram of actual and predicted energy output values ​​for 24 hours according to an exemplary embodiment;

[0077] Fig.10 is a daily variation curve diagram of the actual value and the predicted value of the 24-hour load according to an exemplary embodiment;

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

[0079] Fig.12 is a schematic diagram of energy storage power storage according to an exemplary embodiment;

[0080] Fig.13 The figure is a schematic diagram of an air conditioning cluster of a calculation example system according to an exemplary embodiment. DETAILED DESCRIPTION

[0081] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0082] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this article and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal connection of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0083] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0084] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0085] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0086] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages 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 to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

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

[0088] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0089] Figure 1An embodiment of the multi-parameter evaluation driven balancing unit construction method of the present invention is shown.

[0090] In this optional embodiment, the multi-parameter evaluation driven balancing unit construction method includes:

[0091] S101. Establishing evaluation indicators for building balancing units in the power supply area, wherein the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators;

[0092] S103. Calculate the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index 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;

[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 balancing unit construction schemes, and multiplying them by the corresponding comprehensive weight of each evaluation index to obtain evaluation scores of each evaluation index in different balancing unit construction schemes;

[0094] S107, adding up the evaluation scores of each evaluation indicator in the balancing unit construction scheme to obtain the adaptability comprehensive evaluation score of the balancing unit construction scheme; based on the adaptability comprehensive evaluation scores of different balancing unit construction schemes, screening out the best balancing unit construction scheme.

[0095] Figure 2 An embodiment of the multi-parameter evaluation-driven balancing unit construction system of the present invention is shown.

[0096] In this optional embodiment, the multi-parameter evaluation driven balancing unit construction system includes:

[0097] An evaluation index establishment module 201 is used to establish evaluation indicators for building a balancing unit in a power supply area, and the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators;

[0098] The weight assignment module 203 is used to calculate the comprehensive weights of each supply-demand balance evaluation index and each flexibility evaluation index 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;

[0099] The evaluation score calculation module 205 is used to obtain the initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balancing unit construction schemes, and multiply them with the corresponding comprehensive weights of each evaluation index to obtain the evaluation scores of each evaluation index in different balancing unit construction schemes;

[0100] The balancing unit construction scheme screening module 207 is used to add up the evaluation scores of each evaluation indicator in the balancing unit construction scheme to obtain the adaptability comprehensive evaluation score of the balancing unit construction scheme; based on the adaptability comprehensive evaluation scores of different balancing unit construction schemes, screen out the best balancing unit construction scheme.

[0101] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle. Figure 4 shown.

[0102] 1. Establish evaluation indicators for balanced unit construction:

[0103] (1) Supply and demand balance evaluation indicators

[0104] Maintaining a balance between electricity supply and demand is one of the key challenges in building a new power system. 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 capacity of the power system. In the balancing unit, the power consumption of all end users, the power generation of power generators, and the input and output power must be balanced.

[0105] 1) Source-charge matching

[0106] The present invention mainly evaluates the supply and demand balance situation within different 220kV areas based on the matching degree of wind energy, solar energy and load in the balancing unit. In order to measure the matching degree of source and load, the Euclidean distance discrimination method commonly used in engineering applications is used to express it. When the matching degree is high, the power deficit in the balancing unit is relatively small, and less electricity needs to be transmitted from the regional power grid; on the contrary, when the matching degree is low, the power deficit is large, and more power transmission is required, which will not only increase the operating loss, but also increase the construction cost of the distribution network. Therefore, maximizing the matching degree of wind energy, solar energy and load in the balancing unit can effectively improve the economy and reliability of the system.

[0107] The Euclidean distance discriminant method is an effective method to measure the similarity or difference between different objects in a multidimensional space. The formula used for source-load matching evaluation and analysis is:

[0108]

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

[0110] 2) Power fluctuation

[0111] Under the long-term guidance of energy strategy, the new energy industry has entered a new stage dominated by marketization and replacing traditional energy on a large scale. The access of new energy sources such as wind energy and solar energy presents a trend of centralized and distributed parallel and hierarchical access. Since the output of wind and solar power generation is significantly affected by wind speed and sunshine duration, and these factors have high inconsistency and significant volatility in different times and locations, 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 net load size at each time point is different, showing the volatility of the net load. In order to ensure the real-time power balance within the balancing unit, the output of the controllable power supply must track the changes in the net load curve in real time, so it is necessary to evaluate the power fluctuation of the net load curve in different 220kV areas.

[0112] Power fluctuation in distribution network refers to the phenomenon that power undergoes a series of changes and continuous changes. The power fluctuation of the present invention is represented by 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. The expression of the mean value of power fluctuation is:

[0113]

[0114] Where P NL,max and P NL,min are two adjacent maxima and minima on the net load curve, n turn It is the total number of trend turning points of the net load power curve.

[0115] 3) Forecast bias

[0116] With the large-scale grid connection of new energy sources, the uncertainty of the power system has intensified. At the same time, the rapid growth of new loads such as electric vehicles and electric heating has made the load characteristics more complex, further increasing the uncertainty of the power system. In order to ensure the real-time balance of power in the balancing unit, it is necessary to make daily forecasts of the power supply and demand in the local area. If there is a deviation between the forecast results and the actual situation, it will lead to system imbalance. At this time, the balancing unit needs to introduce backup power. Therefore, it is necessary to evaluate the power forecast deviation in different 220kV power supply areas.

[0117] In order to ensure the real-time balance of power in the balancing unit, it is necessary to predict the supply and demand curve of the local area every day. To measure the similarity between the predicted value and the actual value, the Euclidean distance is used:

[0118]

[0119] In the formula, and are the actual value and predicted value of net load at time t, and They are the actual value and predicted value of the net load at time T, T=1, 2,…, t.

[0120] (2) Flexibility evaluation index

[0121] The connection of a large number of uncontrollable distributed power sources to the grid has made the net load curve more complex and changeable, exacerbating the volatility and uncertainty of the distribution network. The flexibility of the power system refers to the ability of the system to avoid cutting renewable energy and ensure the reliability of power supply to users when facing the fluctuations and uncertainties of the net load on multiple time scales. In order to cope with the variability and uncertainty of the net load curve, the balancing unit needs to have sufficient flexibility.

[0122] 1) Distribution network flexibility demand modeling

[0123] The flexibility demand of distribution network can be divided into the flexibility demand caused by the variability of net load and the flexibility demand caused by uncertainty. There are two types of distribution network flexibility demand situations, such as Figure 5 As shown. In the process of optimizing the operation of the power system, the forecast curve of the net load must be referred to when formulating the daily dispatch plan. 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 call on flexible resources to cope with the fluctuations of the net load on different time scales to meet the flexibility needs caused by the changes in the net load. In the real-time dispatch stage, due to the existence of uncertainty, there will be a deviation between the actual value and the predicted value of the net load. Therefore, it is necessary to make real-time adjustments based on the original dispatch plan to cope with the uncertainty of the net load and meet the flexibility needs caused by the uncertainty of the net load.

[0124] The distribution network flexibility demand based on the above two situations is modeled as follows:

[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 is the overall flexibility demand of the distribution network; P k,t+Δt and P j,t+Δt are the actual value of total load and the actual value of new energy output at time t+Δt; P′ k,t and P′ j,t It is the predicted value of total load and the predicted value of new energy output at time t.

[0127] 2) Distribution network flexibility supply modeling

[0128] ①Energy storage system

[0129] The energy storage system has the ability of two-way regulation, which plays an important role in alleviating the fluctuation of new energy output and providing its absorption capacity. The flexibility supply capacity of the energy storage system is affected by many factors, including its current working state, maximum charge and discharge power, and charge and discharge efficiency. The flexibility supply of the energy storage system, such as Figure 6 As shown. Figure 6 In the example of downward flexibility demand, when the output of other energy sources is greater than P1, the energy storage is in a charging state, and the charging power needs to be increased to provide downward flexibility to meet the downward fluctuation of the net load. When the output of other energy sources is less than P1, the energy storage is in a discharging state, and the discharge power needs to be reduced to provide downward flexibility.

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

[0131]

[0132] In the formula, and are the upward and downward flexibility provided by the energy storage s at time t; P c,max and P g,max are the maximum charging and discharging powers of the energy storage at the time scale τ, respectively; and are the charging and discharging powers of 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 energy storage s at time t respectively; η c and η g are the charging and discharging efficiencies of the energy storage s, respectively; and are state variables respectively. When they are 1, they represent that the energy storage s is in the discharging state and the charging state at time t respectively; τ is the time scale.

[0133] ② Load reduction possible

[0134] The curtailable load is a load that can reduce power, shorten time or interrupt operation to a certain extent. This load has a certain degree of flexibility and can be adjusted according to the needs of the power grid to reduce the burden on the power grid. By properly managing the curtailable load, the operating efficiency and reliability of the power grid can be improved. Similar to energy storage discharge, the curtailable load can provide upward (downward) adjustment flexibility by increasing (decreasing) the load curtailment amount. The formula is:

[0135]

[0136] In the formula, and are the upward and downward flexibility provided by the load q that can be reduced at time t; Pq,max is the maximum power reduction that can be achieved by reducing the load q at time t; P q,t is the power reduction that can be achieved by reducing the load q at time t.

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

[0138] Without considering the network transmission capacity, the balance of distribution network flexibility supply and demand mainly focuses on the abundance of flexibility resources, which can be quantified through flexibility supply and demand modeling. Therefore, the abundance of flexibility resource supply can be defined as A t , the formula is:

[0139]

[0140] In the formula, and are the upward and downward adjustment margins of flexible resource supply at time t; n s and n q are the number of energy storage systems and load that can be reduced, F z,t The overall flexibility demand of the distribution network.

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

[0142] Table 1 Distribution network flexibility supply and demand

[0143] Condition Judgment conditions Judgment results 1 <![CDATA[A t >0]]> Sufficient supply of flexibility 2 <![CDATA[A t ≤0]]> Insufficient flexibility

[0144] 4) Distribution network flexibility evaluation indicators

[0145] In order to reflect the abundance and shortage of flexibility resources in the balancing unit, the average flexibility abundance index E is defined: a and the average flexibility deficiency index E e The physical meanings are the ratio of the total surplus of the distribution network flexibility resources to the surplus period, and the ratio of the total shortage to the shortage period. The formula is:

[0146]

[0147] In the formula, and They are the average margin of upward and downward adjustment flexibility respectively; and They are respectively the periods when there is ample flexibility for supply and demand to adjust upward and downward.

[0148]

[0149] In the formula, and They are the average insufficiency of upward and downward adjustment flexibility respectively; and They are the periods of flexibility when supply and demand increase or decrease and when there is a shortage.

[0150] 2. Use subjective and objective combined weighting method to determine the weight coefficient:

[0151] Comprehensive evaluation methods mainly include weighting methods and evaluation models. Among the weighting methods, subjective weighting methods and objective weighting methods are currently commonly used. Subjective weighting methods mainly include the analytic hierarchy process and its improved methods. The analytic hierarchy process is mainly based on qualitative analysis, which can comprehensively reflect the results of the evaluation system and has low requirements for data. However, due to the interrelationship between some indicators, it may lead to information overlap and interference, and it may ignore objective reality and rely too much on the subjective judgment of experts. Therefore, objective weighting methods are sometimes used for evaluation. Objective weighting methods mainly include the entropy weight method, but this method has high requirements on the objectivity and integrity of the data, and is difficult to implement when the data is insufficient. In addition, it does not fully consider the actual importance of the target and cannot clearly reflect the preferences of decision makers.

[0152] In order to determine the weights of evaluation indicators, a combined weighting method that combines subjective experience and objective data is now widely used. This method effectively avoids the limitations of subjective weighting methods that rely too much on expert subjective opinions and objective weighting methods that require high data. By adopting a combination of subjective and objective weighting techniques, the objectivity and scientificity of indicator weight assignment in the evaluation indicator system can be enhanced.

[0153] In order to improve the objectivity and scientificity of the indicator weight assignment in the evaluation indicator system, the present invention adopts a subjective and objective combined weighting method to determine the weight coefficient.

[0154] (1) Analytical Hierarchy Process (AHP)

[0155] AHP is a method of breaking down a complex problem into an ordered hierarchy to systematically deal with various relevant factors. Based on an assessment of objective conditions, this method is able to quantify the relative importance of each factor in each level. Using mathematical methods, it can express the order of relative importance of all factors in each level, and then obtain a ranking result. This method helps to clearly identify and compare the importance of different factors, providing a scientific basis for decision-making.

[0156] The basic idea of ​​AHP is to first establish an independent hierarchical structure that reflects the function or attribute of the system according to the nature of the problem, give the corresponding proportional scale by comparing the relative importance of each factor, construct a judgment matrix of the upper-level factors on the lower-level related factors, and give the relative importance ranking of the lower-level related factors to the upper-level factors. The core problem of AHP is the sorting problem, which is achieved by using the recursive hierarchical structure, scale and sorting principles.

[0157] The process of using AHP to solve problems can be roughly divided into three steps: establishing a hierarchical structure model of the problem; constructing a pairwise comparison judgment matrix; hierarchical single sorting and consistency test. The specific process is as follows:

[0158] ① Establish a hierarchical model

[0159] According to the attributes of the target problem, the factors are layered to form hierarchical structures of different levels. In this structure, the factors at the same level serve as criteria and have a guiding role for the factors at the next level. At the same time, they are also dominated by the factors at the previous level. This top-down dominance relationship constitutes a progressive hierarchical system. The highest level usually contains only one element, namely the target layer; the middle level involves measures, plans, policies, etc. required to achieve the overall goal, which is called the criterion layer; the lowest level contains various ways and methods to solve specific problems, which is called the solution layer. Based on the basic principles of AHP, the present invention constructs a hierarchical analysis structure model for evaluating the selection of the best balancing unit construction scheme in different 220kV areas, such as Figure 7 The hierarchical model is shown in Figure 2.

[0160] Target layer: For the present invention, the target layer is to select a suitable balancing unit construction area in different 220kV areas.

[0161] Criteria layer. The main factors for evaluating whether the balancing unit construction plan is reasonable are supply and demand balance and flexibility indicators. The flexibility aspect includes the average flexibility sufficiency index E a (divided into upward adjustment of the average flexibility adequacy and downward adjustment of the average flexibility adequacy) and the average flexibility deficiency index E e (upward adjustment of the average flexibility deficit and downward adjustment of the average flexibility deficit), etc.

[0162] Scheme layer: for different 220kV power supply areas.

[0163] ②Construct a pairwise comparison judgment matrix

[0164] Suppose we want to compare n f Factor The impact on the target Z, that is, determine their proportion in Z. Take two factors x each time i and x j , with a ij Represents x i and x j The ratio of the influence degree on Z, thus obtaining the pairwise comparison judgment matrix The elements in A should satisfy the following conditions: ij >0,a ji =1 / a ij (i≠j), a ii=1(i,j=1,2,..,n f ).

[0165] Use numbers 1 to 9 and their reciprocals as a ij The scaling method is shown in Table 2.

[0166] Table 2 Comparison scale a ij The value method of

[0167] <![CDATA[x i / x j ]]> equal Strong powerful Very strong Absolutely strong <![CDATA[a ij ]]> 1 3 5 7 9

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

[0169] ③ Hierarchical single sorting and consistency test

[0170] Hierarchical single sorting, solving the weights of indicators at each level in the hierarchical analysis method: First, solve the maximum eigenvalue λ of the judgment matrix A max , reuse:

[0171] AW=λ max W(11)

[0172] Solve for λ max The corresponding feature vector W, the standardized feature vector is the ranking weight of the relative importance of the corresponding factor at the same level corresponding to a factor in the previous level.

[0173] Consistency test: In order to determine the validity of each layer of the judgment matrix, the hierarchical analysis method needs to perform a consistency test on the weight vector of the judgment matrix A. First, calculate its consistency index C I ,definition:

[0174]

[0175] Where n A is the order of the judgment matrix A. When A has complete consistency, C I =0;λ max -n A The larger the C I The larger it is, the less consistent A is.

[0176] In order to determine whether A has satisfactory consistency, C I The average random consistency index R I For comparison, for judgment matrices of order 1 to 9, R is given I The values ​​are shown in Table 3.

[0177] Table 3 Randomness index R I Value

[0178] <![CDATA[Order n A > 1 2 3 4 5 6 7 8 9 <![CDATA[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 should be readjusted until it has satisfactory consistency. max The corresponding feature vector W can be used as the weight of the hierarchical single sort after normalization, that is, the subjective weight vector W sub .

[0180] (2) Entropy Weight Method

[0181] The entropy weight method is an objective weighting method that determines the weight of each indicator based on the amount of information it contains. In decision-making problems involving multiple indicators, the smaller the entropy value of an indicator, the greater the amount of information it contains, the greater its role in the comprehensive evaluation, and the greater the weight of the indicator. The use of the entropy weight method can effectively reduce the impact of subjective judgment on weight allocation and ensure the objectivity of the obtained weights.

[0182] Considering that different evaluation indicators have different dimensions, the indicator calculation results need to be standardized.

[0183] ① Data normalization processing, calculate the proportion of the index value of the i-th scheme under the j-th index:

[0184]

[0185] In the formula, r ij is the value of the jth indicator in the ith solution, and m is the number of solutions.

[0186] ② The formula for calculating the entropy value of the jth indicator is:

[0187]

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

[0189] ③The formula for calculating the weight coefficient of the jth indicator is:

[0190]

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

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

[0193] (3) Combination weighting method

[0194] In view of the limitations of the subjective and objective weighting method, the present invention adopts a combined weighting method based on the hierarchical analysis method and the entropy weight method to perform weighting, so as to make the evaluation results more objective and accurate. The calculation formula of the comprehensive weight is:

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

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

[0197] 3. Starting from the evaluation indicators of supply and demand balance and flexibility, the combined weighting method is used to evaluate the adaptability of building balancing units in different 220kV areas, and the optimal balancing unit construction plan is obtained.

[0198] The present invention describes in detail the specific implementation mode of the present invention by taking a typical day as an example. The adaptability of the construction of the balancing unit of a certain power supply area is evaluated from two aspects, namely, supply and demand balance (calculated using per-unit value) and flexibility. The example system contains wind power, solar photovoltaic, 1 energy storage device, air conditioning cluster, etc. The maximum charge and discharge power of the energy storage device is 0.4MW, the discharge efficiency is 0.9, the minimum storage capacity is 2MWh, and the maximum storage capacity is 4MWh. The topological structure of the supply area, such as Figure 8 The actual value and predicted value curve of the system's energy output are shown in Fig. 9 The actual value and predicted value of the load are shown in Fig.10 The charging and discharging power and storage capacity curve of energy storage are shown in Fig.11 and Fig.12 The scale of the air conditioning cluster is shown in Fig.13 shown.

[0199] The specific steps of this embodiment are:

[0200] Step 1: Establish the adaptability evaluation index of the balance unit construction. Calculate and count the supply and demand balance and flexibility capacity of the region to obtain the adaptability evaluation criteria shown in Table 4 and the evaluation index shown in Table 5. Then, the initial evaluation score is obtained from Table 4 and Table 5, as shown in Table 6.

[0201] Table 4 Adaptability evaluation criteria

[0202]

[0203]

[0204] Table 5 Adaptability evaluation indicators

[0205]

[0206] Table 6 Initial evaluation scores of adaptability

[0207]

[0208]

[0209] Step 2: According to the basic principles of the hierarchical analysis method, a judgment matrix of the factors at the criterion level for the applicability of building a balancing unit in the supply area can be constructed, as shown in Table 7.

[0210] Table 7 Judgment matrix of each factor in the criterion layer for the overall goal

[0211] Evaluation indicators Supply and demand balance flexibility Supply and demand balance 1 3 / 5 flexibility 5 / 3 1

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

[0213] Table 8 Judgment matrix of indicator layer relative to criterion layer (supply and demand balance)

[0214] Supply and demand balance Supply and demand balance Power Fluctuation Prediction bias Source-Charge Matching 1 5 / 3 5 / 4 Power Fluctuation 3 / 5 1 3 / 4 Prediction bias 4 / 5 4 / 3 1

[0215] Table 9 Judgment matrix of indicator layer relative to criterion layer (flexibility)

[0216]

[0217] Step 3: Since the subjective weighting method is based on subjective judgment and determines the importance and weight of each evaluation indicator based on empirical values, its disadvantage is that it cannot adapt to the actual changes in data. The entropy weight method calculates the indicator weight based on the actual information difference, but this method overemphasizes the data itself, which may make the weight too large or too small in some cases.

[0218] It can be seen that the above two weighting methods have their own advantages and disadvantages, so simply using one method will not be appropriate or reasonable. For this reason, the present invention selects a combined weighting method to determine the final weight. This way of determining the weights can avoid too much subjective judgment and reduce the deviation that may be caused by data weighting. The judgment matrix of Tables 7 to 9 is used to obtain the indicator weights of the hierarchical analysis method. Through the weight calculation of the hierarchical analysis method and the entropy weight method, the evaluation weight values ​​of each indicator shown in Table 10 are obtained. The present invention uses the average value of the weights calculated by the hierarchical analysis method and the entropy weight method as the comprehensive weight:

[0219] ω0=0.5ω1+0.5ω2

[0220] Where: ω0 is the average weight calculated by the comprehensive evaluation method, ω1 is the weight calculated by the hierarchical analysis method, and ω2 is the weight calculated by the entropy weight method.

[0221] Table 10 Evaluation weights of each indicator

[0222]

[0223] A percentage evaluation standard for the adaptability of building a balanced unit in this area was established. The comprehensive weight of each indicator was calculated in Table 10 and multiplied by the initial evaluation score under the evaluation standard to obtain the evaluation score of each indicator for building a balanced unit in this area. The score of each indicator was then added together to finally obtain a comprehensive evaluation score of 85.5134 for the adaptability of building a balanced unit.

[0224] It can be seen from the results that the evaluation score for constructing a balanced unit in this area is high, but there are still some shortcomings.

[0225] The calculation results verify that the method proposed in the present invention can effectively evaluate the adaptability of constructing balancing units in different areas, thereby screening out the best balancing unit construction plan.

[0226] The present invention proposes an evaluation framework for the suitability of building balancing units in different 220kV areas. By analyzing the operating conditions of power grids in different areas, supply and demand balance indicators including source-load matching, power fluctuation, and forecast deviation, as well as flexibility indicators of the adequacy (insufficiency) of upward (downward) flexibility are established. A combination of subjective and objective methods is used to intuitively and clearly evaluate the adaptability of building balancing units in different 220kV areas from the above two aspects, and select the most effective and appropriate construction plan. The responsibility for main grid balancing is further decentralized, and local / county regulation is incorporated into the active power balance system. By achieving a balance between power generation, consumption, and use within the unit, the balance between power generation and power consumption of the power grid is ensured, the pressure of centralized balancing control is alleviated, and the risk of power imbalance in the power system is reduced, thereby ensuring the reliable supply of electricity and the absorption of new energy under the new power system.

[0227] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, 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. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0228] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may 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 invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0230] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0231] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention 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 and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0232] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for constructing a balancing unit driven by multi-parameter evaluation, characterized in that: The method includes: Establish evaluation indicators for building balancing units within the power supply area, and the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators; The subjective weighting method and the objective weighting method are used, and the weight coefficients of the subjective weighting method and the objective weighting method are combined to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index; Obtaining the initial evaluation scores of each supply-demand balance evaluation index and each flexibility evaluation index under the preset adaptability evaluation standard in different balancing unit construction schemes, and multiplying them with the corresponding comprehensive weights of each evaluation index to obtain the evaluation scores of each evaluation index in different balancing unit construction schemes; The evaluation scores of each evaluation indicator in the balance unit construction scheme are added together to obtain the adaptability comprehensive evaluation score of the balance unit construction scheme; based on the adaptability comprehensive evaluation scores of different balance unit construction schemes, the best balance unit construction scheme is screened out.

2. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 1, characterized in that: The establishment of the supply and demand balance evaluation index includes: Based on the Euclidean distance discrimination method, a source-load matching evaluation and analysis formula is constructed. The total energy output and the power system load are substituted into the source-load matching evaluation and analysis formula, and the matching degree between the total energy output and the power system load is output; according to the matching degree between the total energy output and the power system load, the supply and demand balance indicator of the power supply area is established; Obtain the net load curve of the power supply area, and establish a power fluctuation indicator 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 turning points of the net load power curve; Make daily forecasts for the power supply and demand situation in the power supply area, and establish forecast deviation evaluation indicators for the power supply area based on the deviation between the forecast results and the actual power supply and demand situation.

3. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 2, characterized in that: The daily forecast of the power supply and demand situation in the power supply area and the establishment of the forecast deviation evaluation index of the power supply area according to the deviation value between the forecast result and the actual power supply and demand situation include: The actual value and 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 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 demand caused by the variability of the net load of the power system and the flexibility demand caused by uncertainty, establish the flexibility demand index of the distribution network in the power supply area; Based on the working status, maximum charging and discharging power, and charging and discharging efficiency of the energy storage system, a distribution network flexibility supply index in the power supply area is established, and the distribution network flexibility supply index includes an upward flexibility index and a downward flexibility index; By using the ratio of the total surplus of distribution network flexibility resources to the surplus period, as well as the ratio of the total shortage to the shortage period, the average surplus index and the average shortage index of distribution network flexibility in the power supply area are established respectively.

5. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 4, characterized in that: The establishment of distribution network flexibility supply indicators in the power supply area includes: The upward and downward flexibility indicators are adjusted by utilizing the reducible load of the power system.

6. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 1, characterized in that: The above-mentioned use of subjective weighting method and objective weighting method, combined with the weight coefficients of subjective weighting method and objective weighting method, to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index includes: Based on the analytic hierarchy process, a hierarchical structure model for the problem of building a balancing unit 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 using the 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 calculation result of the entropy value is standardized to determine the second weight of each evaluation index; The calculation results of the analytic hierarchy process and the entropy weight method are assigned weight coefficients respectively, and the first weight and the second weight calculated by the analytic hierarchy process and the entropy weight method are weighted summed to obtain the comprehensive weight of each evaluation index.

7. The method for constructing a balancing unit driven by multi-parameter evaluation according to claim 1, characterized in that: In the acquisition of different balancing 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 include: Constructing adaptability evaluation criteria for each supply-demand balance evaluation index and each flexibility evaluation index; Obtain the values ​​of each supply-demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation standard to obtain the initial evaluation score of each evaluation index.

8. A multi-parameter evaluation driven balancing unit construction system, characterized in that: The system includes: An evaluation index establishment module is used to establish evaluation indicators for building a balancing unit in a power supply area, and the evaluation indicators include supply and demand balance evaluation indicators and flexibility evaluation indicators; A weight assignment module is used to calculate the comprehensive weights of various supply and demand balance evaluation indicators and various flexibility evaluation indicators 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; An 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 balancing unit construction schemes, and multiply them with the corresponding comprehensive weight of each evaluation index to obtain the evaluation score of each evaluation index in different balancing unit construction schemes; The balancing unit construction scheme screening module is used to add up the evaluation scores of each evaluation indicator in the balancing unit construction scheme to obtain the adaptability comprehensive evaluation score of the balancing unit construction scheme; based on the adaptability comprehensive evaluation scores of different balancing unit construction schemes, the best balancing unit construction scheme is screened out.

9. The multi-parameter evaluation driven balancing unit construction system according to claim 8, characterized in that: The establishment of the supply and demand balance evaluation index includes: Based on the Euclidean distance discrimination method, a source-load matching evaluation and analysis formula is constructed. The total energy output and the power system load are substituted into the source-load matching evaluation and analysis formula, and the matching degree between the total energy output and the power system load is output; according to the matching degree between the total energy output and the power system load, the supply and demand balance indicator of the power supply area is established; Obtain the net load curve of the power supply area, and establish a power fluctuation indicator 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 turning points of the net load power curve; Make daily forecasts for the power supply and demand situation in the power supply area, and establish forecast deviation evaluation indicators for the power supply area based on the deviation between the forecast results and the actual power supply and demand situation.

10. The multi-parameter evaluation driven balancing unit construction system according to claim 9, characterized in that: The daily forecast of the power supply and demand situation in the power supply area and the establishment of the forecast deviation evaluation index of the power supply area according to the deviation value between the forecast result and the actual power supply and demand situation include: The actual value and 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 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 demand caused by the variability of the net load of the power system and the flexibility demand caused by uncertainty, establish the flexibility demand index of the distribution network in the power supply area; Based on the working status, maximum charging and discharging power, and charging and discharging efficiency of the energy storage system, a distribution network flexibility supply index in the power supply area is established, and the distribution network flexibility supply index includes an upward flexibility index and a downward flexibility index; By using the ratio of the total surplus of distribution network flexibility resources to the surplus period, as well as the ratio of the total shortage to the shortage period, the average surplus index and the average shortage index of distribution network flexibility in the power supply area are established respectively.

12. The multi-parameter evaluation driven balancing unit construction system according to claim 11, characterized in that: The establishment of distribution network flexibility supply indicators in the power supply area includes: The upward and downward flexibility indicators are adjusted by utilizing the reducible load of the power system.

13. The multi-parameter evaluation driven balancing unit construction system according to claim 8, characterized in that: The above-mentioned use of subjective weighting method and objective weighting method, combined with the weight coefficients of subjective weighting method and objective weighting method, to calculate the comprehensive weights of each supply and demand balance evaluation index and each flexibility evaluation index includes: Based on the analytic hierarchy process, a hierarchical structure model for the problem of building a balancing unit 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 using the 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 calculation result of the entropy value is standardized to determine the second weight of each evaluation index; The calculation results of the analytic hierarchy process and the entropy weight method are assigned weight coefficients respectively, and the first weight and the second weight calculated by the analytic hierarchy process and the entropy weight method are weighted 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 the acquisition of different balancing 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 include: Constructing adaptability evaluation criteria for each supply-demand balance evaluation index and each flexibility evaluation index; Obtain the values ​​of each supply-demand balance evaluation index and each flexibility evaluation index, and substitute them into the adaptability evaluation standard to obtain the initial evaluation score of each evaluation index.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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