Power distribution network flexibility evaluation method, system, equipment and medium

Abstract

The invention discloses a power distribution network flexibility evaluation method, system and device and a medium, and the method comprises the steps: carrying out the modeling of all kinds of flexibility resources through analyzing the operation characteristics of all kinds of flexibility resources in a target power distribution network system; quantifying dynamic index parameters output by various flexible resources obtained according to a modeling result, and determining a margin index according to a quantification result so as to generate a multi-dimensional evaluation index system; weighting the indexes in the multi-dimensional evaluation index system according to a set level in a collaborative subjective and objective manner, and determining a comprehensive score of each level of index according to a weighting result; and inputting the comprehensive score into a set cloud model to output a flexibility level of each flexibility resource, and performing flexibility evaluation on the power distribution network according to the flexibility level, and the reliability of a power grid system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a method, system, device and medium for evaluating the flexibility of a distribution network. Background Art

[0002] Devices in a power system that can provide power regulation capabilities can be collectively referred to as flexibility resources. The improvement of power system flexibility depends to a large extent on the coordinated control of flexibility resources. With the increasing electrical coupling and information exchange among the source-network-load-storage, it has become an inevitable trend for the distribution system to develop into a new type of system that highly integrates new energy and power electronic devices and is full of uncertainties. Especially the integration of a high proportion of new energy and the increase of various new loads have further exacerbated the problem of power supply-demand imbalance, which makes the distribution system face more complex, refined and interactive service requirements, and thus puts forward higher-level requirements for the flexibility evaluation of the system.

[0003] At present, relatively extensive research has been carried out on the flexibility of power systems, but the evaluation indicators for flexibility are often relatively single and rely on subjective experience, resulting in poor control effects and unreasonable control methods for the power grid system, and it is difficult to meet the requirements for the reliable operation of the power grid.

[0004] Therefore, how to comprehensively and accurately evaluate the flexibility of the distribution network and ensure the stable operation of the power grid has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, system, device and medium for site selection and planning of distribution network engineering areas, which solves the problem of how to comprehensively evaluate the flexibility of the power grid by establishing a flexibility evaluation index system for the integrated energy system, so as to optimize resource allocation and achieve reasonable and reliable control of the power grid.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for evaluating the flexibility of a distribution network, including:

[0007] Obtain the operation data corresponding to different types of flexibility resources in the target distribution network system, and perform intelligent analysis on the operation data to obtain the operation characteristic data corresponding to each type of flexibility resource;

[0008] Model various types of flexibility resources according to the operation characteristic data;

[0009] Quantify the dynamic index parameters output by various types of flexibility resources obtained from the modeling results, and determine the margin index according to the quantification results to generate a multi-dimensional evaluation index system;

[0010] Weigh the indicators in the multi-dimensional evaluation index system in a collaborative subjective and objective manner according to the set hierarchy, and determine the comprehensive scores of each level of indicators according to the weighting results;

[0011] Input the comprehensive scores into the set cloud model to output the flexibility levels of each flexibility resource, and the flexibility levels are used to control each flexibility resource in the target distribution network system.

[0012] Furthermore, the process of modeling various flexibility resources according to the operation characteristic data includes:

[0013] Classify the flexibility resources in the target distribution network system into the source side, load side, storage side and energy coupling side according to their attributes, and obtain the operation characteristic data of each side's resources;

[0014] Model the controllable units on the source side, load demand response, energy storage devices and energy coupling devices respectively according to the operation characteristic data.

[0015] Furthermore, the process of generating the multi-dimensional evaluation index system includes:

[0016] Quantify the characteristic indicators reflecting adjustable flexibility output by each flexibility resource in the controllable units on the source side, load demand response, energy storage devices and energy coupling devices within a preset time period according to the modeling results to construct the third-level indicators;

[0017] Calculate the adjustable flexibility margin and adjustable flexibility of each flexibility resource according to the quantification results to construct the second-level indicators;

[0018] Determine the quantification results reflecting the flexibility of the source side, load side, storage side and energy coupling side respectively according to the second-level indicators to construct the first-level indicators;

[0019] Integrate the third-level indicators, the second-level indicators and the first-level indicators to generate a multi-dimensional evaluation index system.

[0020] Furthermore, the construction process of the second-level indicators and the third-level indicators includes:

[0021] According to the modeling results of the controllable units on the source side, quantify the power generation adjustment range and the corresponding ramp rate output by each flexibility resource in the controllable units on the source side within a preset time period respectively to construct the third-level indicators on the source side, and calculate the upward flexibility margin and downward flexibility margin of each flexibility resource in the controllable units on the source side according to the quantification results to obtain the second-level indicators on the source side;

[0022] According to the modeling results of the energy storage device, the three-level indexes on the storage side are constructed by quantifying the electric energy storage capacity, charge-discharge power, and charge-discharge efficiency of each flexibility resource in the energy storage device within a preset time period. According to the quantification results, the upward flexibility and downward flexibility of each flexibility resource in the energy storage device are calculated to obtain the secondary indexes on the storage side;

[0023] According to the modeling results of the load demand response, the three-level indexes on the load side are constructed by quantifying the load change value in the face of demand response of the load within a preset time period. According to the quantification results, the upward flexibility and downward flexibility of the load are calculated to obtain the secondary indexes on the load side;

[0024] According to the modeling results of the energy coupling device, the three-level indexes on the energy coupling side are constructed by quantifying the energy power and the corresponding ramp rate of each flexibility resource in the energy coupling device within a preset time period. According to the quantification results, the upward and downward flexibility margins and the upward and downward flexibility of each flexibility resource in the energy coupling device are calculated to obtain the secondary indexes on the energy coupling side.

[0025] Furthermore, in the process of generating the multi-dimensional evaluation index system, it also includes:

[0026] The three-level indexes on the network side are constructed by quantifying the new energy consumption rate, network loss rate, and line overload rate in the target distribution network system. According to the quantification results, the secondary indexes on the network side of the new energy consumption capacity, network operation efficiency, and distribution network reliability are obtained;

[0027] According to the secondary indexes on the network side, the primary indexes on the network side reflecting the flexibility of the target distribution network system are determined and incorporated into the primary indexes of the multi-dimensional evaluation index system.

[0028] Furthermore, the method of assigning weights to the indexes in the multi-dimensional evaluation index system in a collaborative subjective and objective manner and determining the comprehensive scores of each level of indexes according to the weight assignment results includes:

[0029] Using the subjective weight assignment method to assign weights to the indexes in the multi-dimensional evaluation index system level by level and obtaining the evaluation scores of the flexibility of each level of indexes;

[0030] According to the subjective weight assignment results, the objective weights are assigned to each level of indexes in the multi-dimensional evaluation index system by the entropy weight method and weighted calculation is performed to obtain the comprehensive scores of each level of indexes.

[0031] Furthermore, inputting the comprehensive score into the set cloud model to output the flexibility levels of each flexibility resource includes:

[0032] Dividing the flexibility gradients of each flexibility resource according to the evaluation scores to generate a standard cloud map;

[0033] Calculate the actual cloud digital features of the actual cloud map according to the comprehensive score;

[0034] By matching the actual cloud digital features of the actual cloud with the standard cloud digital features of the standard cloud map, obtain the flexibility levels of various flexibility resources.

[0035] Another embodiment of the present invention provides a distribution network flexibility evaluation system, including:

[0036] A data acquisition module, configured to acquire operation data corresponding to different flexibility resource types in the target distribution network system, perform intelligent analysis on the operation data, and obtain operation characteristic data corresponding to each flexibility resource type;

[0037] A model construction module, configured to model various flexibility resources according to the operation characteristic data;

[0038] An index system construction module, configured to quantify the dynamic index parameters output by various flexibility resources obtained from the modeling results, determine margin indexes according to the quantification results, and generate a multi-dimensional evaluation index system;

[0039] A weight analysis module, configured to assign weights to the indexes in the multi-dimensional evaluation index system in a collaborative subjective and objective manner according to the set hierarchy, and determine the comprehensive scores of each level of indexes according to the weight assignment results;

[0040] A flexibility evaluation and resource control module, configured to input the comprehensive score into a set cloud model to output the flexibility levels of various flexibility resources, and the flexibility levels are used to control various flexibility resources in the target distribution network system.

[0041] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network flexibility evaluation method described above is implemented.

[0042] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the distribution network flexibility evaluation method described above is implemented.

[0043] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0044] In this embodiment, by analyzing and modeling the operating characteristics of the flexibility resources in each link of the integrated energy system, the operating characteristics and regulation capabilities of the resources can be accurately reflected; fully considering the categories and regulation characteristics of the flexibility resources, corresponding flexibility quantification and flexibility margin indicators are proposed, and a multi-dimensional evaluation index system for the flexibility of the integrated energy system is established to ensure the comprehensiveness and accuracy of the evaluation, and the allocation of resources can be optimized; the hierarchical evaluation of flexibility is realized by collaborating the cloud model and the combined weight analysis, making the results more intuitive and operable, so as to achieve reasonable and effective control of the power grid and ensure the reliable operation of the power grid system. Description of the Drawings

[0045] Figure 1 It is a schematic flowchart of the method for evaluating the flexibility of a distribution network in one embodiment of the present invention;

[0046] Figure 2 It is a schematic structural diagram of the system for evaluating the flexibility of a distribution network in one embodiment of the present invention;

[0047] Figure 3 It is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. Detailed Embodiments

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0050] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0051] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0052] An embodiment of the present invention provides a method for evaluating the flexibility of a distribution network. Specifically, please refer to Figure 1 , Figure 1 which is shown as a schematic flowchart of the method for evaluating the flexibility of a distribution network in one of the embodiments of the present invention, and includes the following steps:

[0053] S1 - S2: Obtain the operation data corresponding to different flexibility resource types in the target distribution network system, perform intelligent analysis on the operation data to obtain the operation characteristic data corresponding to each flexibility resource type, and model various flexibility resources according to the operation characteristic data.

[0054] It should be understood that there are multiple categories of flexibility resources in the distribution network system. To fully analyze the indicators covered by the flexibility resources and comprehensively evaluate the flexibility of the distribution network system, in this embodiment, various types of flexibility resources covered in the target distribution network system are classified into the source side, load side, storage side, and energy coupling side according to their physical attributes. The operation data of various types of flexibility resources in the target distribution network are comprehensively collected by using monitoring platforms, sensors, unit control centers, etc., including relevant output values, real-time power, ramping rate, as well as original load data, elasticity coefficient matrix, and other data. The data is preprocessed by data cleaning, normalization, and feature alignment operations to obtain the operation characteristic data of the flexibility resources on each side, so as to model the controllable units on the source side, load demand response, energy storage devices, and energy coupling devices according to these operation characteristic data.

[0055] It can be understood that the controllable units on the source side are the main sources of energy in the integrated energy system and an important link to maintain the safe and stable operation of the system. Such devices can ensure stable energy output and have a large adjustable range, which is suitable for flexibility supply adjustment on a relatively long time scale. In addition, strong controllability enables such devices to reliably respond to dispatch requirements, thus ensuring the balance of flexibility supply and demand within the system.

[0056] In this embodiment, the modeling process of the controllable units on the source side will be described in detail taking thermal power units as an example:

[0057] During the operation of thermal power units, they must meet the constraints of upper and lower output limits and ramping rate, which can be expressed as:

[0058]

[0059] In the formula: P i,t is the power generation power of the i-th thermal power unit at time t; are the upper limit value and lower limit value of the power generation power of the i-th thermal power unit respectively; are the maximum upward ramping rate and maximum downward ramping rate of the power generation power of the i-th thermal power unit respectively.

[0060] The adjustable load on the energy consumption side can release the adjustment potential of other flexibility resources by adjusting the energy consumption demand, thereby improving the overall operation flexibility of the system. The adjustment ability of the load demand is affected by the demand response strategy, and its main types include price-based, incentive-based demand response in a single energy system and substitution-based demand response between multiple energy systems.

[0061] Among them, for the modeling of price-based demand response, the price elasticity coefficient matrix can be used to describe the corresponding relationship between the load response amount and the change amount of the energy consumption price. Taking the price-based demand response of electricity load as an example for illustration, it is expressed as follows:

[0062]

[0063] In the formula: E is the price elasticity coefficient matrix, and ε ij is the price-based demand response elasticity coefficient, which is the self-elasticity coefficient at time period i if i = j, and the cross-elasticity coefficient between time periods i and j if i ≠ j; P L,i , ΔP L,i are respectively the load value at time period i before demand response and the load change at time period i after demand response; q j , Δq j are respectively the electricity price at time period i before demand response and the electricity price change at time period j after demand response; is the electric load value matrix after price-based demand response: T is the total number of scheduling time periods.

[0064] Incentive-based demand response is to determine the adjustable load participating in demand response by formulating demand response contracts in advance, and to guide users to adjust the demand for this part of the load through a price compensation mechanism, which is expressed as follows:

[0065]

[0066] In the formula: is the electric load value at time t after incentive-based demand response; is the electric load change under incentive-based demand response; are respectively the maximum and minimum values of the electric load change at time t determined by the incentive-based demand response strategy.

[0067] Alternative demand response is to supply different energies without changing the energy demand at each moment. In this embodiment, the electricity-gas alternative demand response is taken as an example, which is expressed as follows:

[0068] ΔD C,b,t =-γΔG C,b,t

[0069]

[0070] In the formula: ΔD C,b,t , ΔG C,b,t are respectively the electric load change and gas load change at the corresponding node after alternative demand response; γ is the energy conversion coefficient, η e , η g are respectively the utilization rates of electricity and natural gas; are respectively the upper and lower limit values of the gas load change after alternative demand response; are respectively the upper and lower limit values of the electric load change after alternative demand response.

[0071] Furthermore, in this embodiment, the modeling process of the energy storage device will be described in detail taking the electrical energy storage device as an example:

[0072] The storage balance equation of electrical energy storage is as follows:

[0073]

[0074] In the formula: is the electrical energy storage amount of the e-th electrical energy storage device at time t; are respectively the charging and discharging powers of the e-th electrical energy storage device at time t; are respectively the charging and discharging efficiencies of the e-th electrical energy storage device; are respectively the upper and lower limit values of the energy storage capacity of the e-th electrical energy storage device; is a 0-1 variable, representing the charging and discharging states of the e-th electrical energy storage device at time t respectively; are respectively the maximum and minimum values of the charging power of the e-th electrical energy storage device; are respectively the maximum and minimum values of the discharging power of the e-th electrical energy storage device; are respectively the electrical energy storage amounts of the e-th electrical energy storage device at the final moment and the initial moment within the scheduling period.

[0075] It should be noted that in the integrated energy system, the mathematical models of gas energy storage and thermal energy storage devices are similar to that of electrical energy storage devices, and all need to satisfy the energy storage capacity constraint, the charging and discharging energy constraint, and the charging and discharging mutual exclusion constraint, and the stored energy returns to the initial state at the end of a scheduling period.

[0076] For the modeling process of the energy coupling device, this embodiment takes the CHP unit and the P2G unit as examples. Among them, the CHP unit includes two types: back-pressure type and condensing type. The back-pressure type unit produces electric power and heat at a fixed ratio, and its conversion relationship is as follows:

[0077]

[0078] In the formula: are respectively the electric power, thermal power generated by the c-th CHP unit at time and the gas power consumed; are respectively the fixed electric-thermal power ratio and the gas-electric conversion efficiency of the back-pressure type CHP unit.

[0079] Compared with the back-pressure type unit, the condensing type unit can flexibly allocate the production of electric energy and thermal energy by adjusting the electric-thermal power ratio during the production process, thus having a larger energy regulation space:

[0080]

[0081] Among them, Constraint on the power generation range of the CHP unit, r g is the minimum electric - heat power ratio of the CHP unit operation; Constraint on the gas consumption range of the CHP unit, are the upper limit value and the lower limit value of the gas consumption power of the CHP unit respectively, are the gas - to - electricity conversion efficiency and the gas - to - heat conversion efficiency respectively; Constraint on the heat generation power range of the CHP unit, are the upper limit value and the lower limit value of the heat generation power of the CHP unit respectively; c v is the slope of the upper limit of the operable region of the CHP unit, and its physical meaning is the reduction amount of the power generation power caused by increasing the unit heat supply power under the operation condition of the upper limit of the gas consumption.

[0082] The mathematical model and operation constraints of the P2G unit are expressed as follows:

[0083]

[0084]

[0085] In the formula: are the electric power consumed and the gas power generated by the i - th P2G unit at time t respectively, is the electrical conversion efficiency of the i - th P2G unit; P i p2g,max 、P i p2g,min are the upper limit value and the lower limit value of the power consumption of the i - th P2G unit respectively; are the maximum upward ramp rate and the maximum downward ramp rate of the i - th P2G unit respectively.

[0086] In the embodiment of the present invention, the operation characteristics (such as output range, ramp rate, regulation time scale) of each flexibility resource (such as thermal power unit, energy storage device, demand response, etc.) are clarified through mathematical modeling, thereby laying a foundation for extracting scientific and reasonable quantitative indicators.

[0087] S2. Quantify the dynamic index parameters output by various flexibility resources obtained from the modeling results, and determine the margin index according to the quantization result to generate a multi - dimensional evaluation index system.

[0088] In this embodiment, based on the operation characteristics of the above - mentioned flexibility resources, the characteristic indexes reflecting flexibility are quantified, and the flexibility margin is calculated to realize the quantitative evaluation of the regulation capabilities of different flexibility resources. Based on this, this embodiment will construct an index evaluation system covering multiple levels.

[0089] Specifically, according to the modeling results, this embodiment quantifies the characteristic indexes reflecting the adjustable flexibility output by each flexibility resource in the source-side controllable units, load demand response, energy storage devices, and energy coupling devices within a preset time period. These quantified characteristic indexes will be used to construct the indexes at the third level in the multi-dimensional evaluation index system. Then, these quantified characteristic indexes are further used to calculate the adjustable flexibility margin and adjustable flexibility of each flexibility resource for constructing the second-level indexes. Among them, the adjustable flexibility margin includes the upward adjustment flexibility margin and the downward adjustment flexibility margin.

[0090] It should be noted that in addition to quantitatively analyzing the device-level indexes in the distribution network system such as the source side, load side, energy storage side, and energy coupling side, it is also necessary to hierarchically analyze the system-level indexes, where the system-level indexes refer to the relevant indexes reflecting the overall operation state of the distribution network (such as new energy consumption, network loss, voltage stability).

[0091] Next, the present invention will describe in detail the construction process of the hierarchical index system of the index evaluation system with specific examples:

[0092] Source side:

[0093] According to the modeling results of the source-side controllable units, the power generation power adjustment range and the corresponding ramp rate output by each flexibility resource in the source-side controllable units within a preset time period are respectively quantified to construct the third-level indexes on the source side. And according to the quantification results, the upward adjustment flexibility margin and the downward adjustment flexibility margin of each flexibility resource in the source-side controllable units are calculated to obtain the second-level indexes on the source side.

[0094] Taking a thermal power unit as an example in this embodiment, it should be understood that its flexibility refers to the adjustment range of the thermal power unit within a unit dispatching period at a certain time scale. According to different adjustment directions, it is divided into upward adjustment flexibility and downward adjustment flexibility. The upward adjustment flexibility and downward adjustment flexibility of the thermal power unit in the period from t to t+Δt are related to the unit output limit, ramp constraint, and the output of the unit at time t. Then the calculation method of the quantification index of the thermal power unit is as follows:

[0095]

[0096] In the formula: are respectively the upward adjustment flexibility margin and the downward adjustment flexibility margin of the i-th thermal power unit at time t.

[0097] It can be seen that this flexibility margin is calculated based on the quantification results of the upward and downward adjustment flexibility indexes of the thermal power unit at time t combined with the actual output of the unit in the period from t+Δt. And the flexibility margin can fully reflect the overall upward and downward adjustment flexibility of the thermal power unit.

[0098] Referring to the above-mentioned quantification process and margin calculation process of multiple characteristic indexes of thermal power units, the flexibility margins of various flexibility resources in the controllable units on the source side can be obtained for constructing the secondary indexes on the source side.

[0099] Load side:

[0100] According to the modeling results of the load demand response, the tertiary indexes on the load side are constructed by quantifying the load change values of the load in the preset time period in the face of demand response. According to the quantification results, the upward flexibility and downward flexibility of the load are calculated to obtain the secondary indexes on the load side.

[0101] It can be understood that the flexibility of the load comes from the load demand changes caused by various types of demand response strategies such as price-based, incentive-based, and substitution-based. The change of load demand mainly affects the output levels of other flexible regulating units, thus affecting the power regulation ability of the system at each time period. Since the response speed of the load is slow, and according to the psychology of consumers, users will choose the energy consumption plan with the least cost. In this example, it is assumed that the load participates in the scheduling according to the energy consumption plan after demand response, that is, the system load at each moment is the fixed value after response, and the additional regulation margin on the load side is not considered.

[0102] Then the calculation processes of its upward and downward flexibility are shown as follows:

[0103]

[0104] In the formula: They are the quantification results of the upward flexibility and downward flexibility of the electrical load in the time period from t to t + Δt respectively; P L,t 、P L,t+Δt Are the electrical load values at time t and time t + Δt respectively.

[0105] In this example, the adjustable flexibility of the load demand response is obtained according to the quantification and calculation process of the above characteristic indexes (load) for constructing the secondary indexes on the load side.

[0106] Energy storage side:

[0107] According to the modeling results of energy storage devices, the tertiary indexes on the energy storage side are constructed by quantifying the electric energy storage amount, charge and discharge power, and charge and discharge efficiency of various flexibility resources in the energy storage devices in the preset time period. According to the quantification results, the upward flexibility and downward flexibility of various flexibility resources in the energy storage devices are calculated to obtain the secondary indexes on the energy storage side.

[0108] It can be understood that the flexibility of energy storage devices comes from the charge, discharge and storage of energy. Its upward flexibility is defined as the energy that the energy storage device can release within a unit scheduling period, and its downward flexibility is defined as the energy that the energy storage device can store within a unit scheduling period. Taking electrical energy storage as an example, the calculation of its upward and downward flexibility is as follows:

[0109]

[0110] wherein: They are respectively the quantification results of the upward flexibility and downward flexibility of the e-th electrical energy storage device during the time period from t to t+Δt.

[0111] In this example, the adjustable flexibility of the energy storage device is obtained based on the quantification and calculation process of the above characteristic indicators (electric energy storage capacity, charge and discharge power, and charge and discharge efficiency), and is used to construct the secondary index on the energy storage side.

[0112] Energy coupling side:

[0113] According to the modeling results of the energy coupling device, the tertiary index on the energy coupling side is constructed by quantifying the energy power and the corresponding ramping rate of each flexibility resource in the energy coupling device within a preset time period. According to the quantification results, the upward and downward flexibility margins and upward and downward flexibilities of each flexibility resource in the energy coupling device are calculated to obtain the secondary index on the energy coupling side. It should be understood that the energy power of each flexibility resource in the energy coupling device includes gas consumption power, power consumption, and heat consumption power.

[0114] This embodiment will take the CHP unit and the P2G unit as examples. It should be understood that the CHP unit involves the conversion among three energy types of electricity, gas, and heat, and its coupling relationship is more complex. Therefore, for the calculation of the flexibility of the CHP unit, it is necessary to first determine the adjustment range of natural gas, and further determine the adjustable range of the power generation according to the heating demand. The calculation process is as follows:

[0115]

[0116] wherein: They are respectively the maximum gas consumption power and the minimum gas consumption power of the c-th CHP unit at time t; They are respectively the maximum upward ramping rate and the maximum downward ramping rate corresponding to the gas consumption power of the c-th CHP unit; They are respectively the quantification results of the upward flexibility and downward flexibility indexes of the c-th CHP unit during the time period from t to t+Δt.

[0117] Considering the complexity of the deep coupling of energy in the CHP unit, the calculation of the upward and downward flexibility margins of the CHP unit can first determine the power adjustment range of the CHP unit during the time period from (t-Δt) to t, and then subtract the actual output result of the CHP unit at the moment to obtain, which is expressed as follows:

[0118]

[0119] wherein: They are the upward flexibility margin and downward flexibility margin of the c-th CHP unit at time t, respectively.

[0120] In the natural gas system, the gas source has a fast response speed and a large flexible adjustment space, and can better suppress the energy fluctuations in the natural gas system. Therefore, without considering the insufficient energy regulation ability of the natural gas system, the quantization calculation process of the indicators of P2G units is similar to that of thermal power units. Only the limitations of the unit installed capacity and ramp rate need to be considered. Their upward and downward flexibility and upward and downward flexibility margins are calculated as follows:

[0121]

[0122]

[0123] In the formula: They are the quantization results of the upward flexibility and downward flexibility of the i-th P2G unit during the period from t to t+Δt, respectively; They are the upward flexibility margin and downward flexibility margin of the i-th P2G unit at time t, respectively; is the power consumption of the i-th P2G unit at time t; They are the maximum power consumption and minimum power consumption of the i-th P2G unit, respectively; They are the maximum upward ramp rate and maximum downward ramp rate of the power consumption of the i-th P2G unit, respectively.

[0124] This example obtains the adjustable flexibility of the energy storage device according to the quantization and calculation process of the above multiple characteristic indicators, and is used to construct the secondary indicators on the energy coupling side. <>

[0125] Grid side:

[0126] Quantify the new energy consumption rate, network loss rate and line overload rate in the target distribution network system to construct the tertiary indicators on the grid side, and obtain the secondary indicators of new energy consumption capacity, network operation efficiency and distribution network reliability according to the quantization results.

[0127] Among them, quantify each characteristic indicator that can reflect the consumption capacity and coordinated regulation ability of various new energies. Specifically:

[0128] Consumption capacity: New energy consumption rate capacity λ RC It is expressed as:

[0129]

[0130] In the formula: is the active power actually consumed by the DG connected to the i-th node during the period t; is the active power that the DG of the i-th node can theoretically output during period t; n DG is the total number of nodes accessing the DG; Δt is the unit time interval.

[0131] Coordinated regulation ability: New energy fluctuation support level λ RE It is expressed as:

[0132]

[0133] In the formula: is the actual interactive power of the flexible resources during period t; n FR is the total number of nodes accessing the flexible resources; are respectively the output power of the flexible resources during period t and the output power of the previous period; is the new energy fluctuation power during period t; and are respectively the output power of the new energy during period t and the output power of the previous period.

[0134] Peak-to-valley ratio change rate λ p-v It is expressed as:

[0135]

[0136] In the formula: P p-v 、 are respectively the peak-to-valley differences of the load without flexible resource interaction and with flexible resource interaction.

[0137] Furthermore, the characteristic indicators reflecting the network operation efficiency are quantified. Specifically, the network loss λ loss It is expressed as:

[0138]

[0139] In the formula: is the current flowing through branch k during period t; r k is the resistance of branch k; n B is the total number of branches.

[0140] In some embodiments of the present invention, it will also include characteristic indicators such as the system average voltage deviation and the maximum allowable volatility of the net load. As an example, the system average voltage deviation λ ΔV It is expressed as:

[0141]

[0142] In the formula: n L is the total number of nodes; U i,t is the actual voltage value of node i during period t; U N is the reference voltage value.

[0143] Maximum allowable volatility of net load, λ FR,max It is expressed as:

[0144]

[0145] Where: is the allowable ramp rate of the i-th DG; is the allowable ramp rate of the i-th flexible resource; R S is the allowable ramp rate of the distribution network; P NL (t) is the net load at time period t.

[0146] Furthermore, the characteristic indexes reflecting the reliability of the distribution network are quantified. The indexes include the line overload rate λ LF It is expressed as:

[0147]

[0148] Where: N k is the number of lines; F i,max is the maximum power transfer capacity of line i; F i,t is the power transfer capacity of line i during system operation, and it is assumed that when F i,t / F i,max the ratio exceeds a certain threshold, the line is considered overloaded.

[0149] In some embodiments of the present invention, it will also include the line power flow entropy λ IE , which is expressed as:

[0150]

[0151] Where: is the load rate of line i ∈ (U k , U k+1 is the proportion of the number of lines in the total number of lines; C is the power flow entropy coefficient.

[0152] Based on the quantification and calculation process of the above multiple characteristic indexes reflecting the overall operation of the distribution network, this example constructs three-layer and two-layer indexes on the grid side, and determines the grid-side primary index reflecting the flexibility of the target distribution network system according to the grid-side secondary indexes.

[0153] Finally, this embodiment will respectively determine the quantification results reflecting the source side, load side, storage side, energy coupling side and flexibility according to the above secondary indexes to construct the primary indexes, and at the same time incorporate the grid-side primary index into the primary indexes. Then, by integrating the tertiary indexes, the secondary indexes and the primary indexes, a multi-dimensional evaluation index system is generated.

[0154] It can be understood that the multi-dimensional evaluation index system constructed in this embodiment adopts a three-level hierarchical design, and is considered from both the system and local levels, fully reflecting the flexibility contributions of the source side, load side, storage side, and energy coupling side, as well as the overall flexibility of the integrated energy system. On this basis, it will also intuitively and clearly reflect the flexibility contributions of various flexibility resources on each side.

[0155] S4. Weigh the indicators in the multi-dimensional evaluation index system at the set levels in a collaborative subjective and objective manner, and determine the comprehensive scores of each level of indicators according to the weighting results.

[0156] It should be understood that the subjective weighting method is to collect opinions from relevant experts and unify the expert opinions through a multi-round scoring system to achieve a subjective evaluation of a certain evaluation system. The objective weighting method is to evaluate the operating status of the system through the relevant digital characteristics of the system operating indicators, which can better fit the actual operating conditions of the system. Based on this, this embodiment will weight the constructed multi-dimensional evaluation index system through a combined subjective and objective weighting method, in order to complement the advantages and disadvantages of the subjective-objective evaluation method and make the weights of the evaluation system more in line with engineering practice.

[0157] Specifically, this embodiment will use the subjective weighting method to allocate weights to the indicators in the multi-dimensional evaluation index system level by level, and obtain the evaluation scores of the flexibility of each level of indicators.

[0158] Preferably, in some embodiments of the present invention, the AHP subjective weighting method is preferred. By soliciting the opinions of relevant experts in multiple rounds and combining the questionnaire survey method, the weights are allocated to the multi-dimensional evaluation index system level by level. And the flexibility of each level of indicators is classified as: [ω min , ω max . Then, through index data conversion, normalization processing is performed on the indicators with the larger the better and the smaller the better, and the corresponding index evaluation scores are converted into the flexibility classification range through linear mapping.

[0159] Among them, the normalization of the larger-the-better type indicators is expressed as:

[0160]

[0161] The normalization of the smaller-the-better type indicators is expressed as:

[0162]

[0163] According to the above subjective weighting results, this embodiment preferably assigns objective weights to each level of indicators in the multi-dimensional evaluation index system through the entropy weight method, and performs weighted calculation to obtain the comprehensive scores of each level of indicators.

[0164] Exemplarily, in some embodiments of the present invention, the three-level indicators in the multi-dimensional evaluation index system are weighted first by the AHP-entropy weight method, and the calculation is performed by the following formula:

[0165] η = aη1 + bη2

[0166] In the formula: η1 and η2 respectively represent the weight vectors obtained by calculating through the AHP and the entropy weight method; a and b satisfy a, b > 0, a + b = 1.

[0167] Next, according to the structure of the evaluation index system and the determined weights of the three-level indicators, the weights of the secondary and primary indicators are calculated respectively, and the calculation method is as follows:

[0168]

[0169] In the formula: η u is the weight of the primary indicator; n v,j is the weight of the secondary indicator; η w,j is the weight of the three-level indicator.

[0170] In this embodiment, the multi-dimensional index evaluation system reflecting the flexibility of the distribution system is weighted by the AHP-entropy weight method combining subjective and objective factors, which improves the accuracy and practicability of the evaluation method.

[0171] S5. Input the comprehensive score into the set cloud model to output the flexibility levels of each flexibility resource, and the flexibility levels are used to control each flexibility resource in the target distribution network system.

[0172] It can be understood that, in order to accurately reflect the change of the flexibility state of the distribution network system, this embodiment further introduces a cloud model to realize the hierarchical evaluation of the distribution network flexibility.

[0173] Specifically, the flexibility gradients of each flexibility resource are divided according to the evaluation scores to generate a standard cloud map. As an example, in this embodiment, the flexibility is divided into several level gradients (such as high, medium, and low), and a numerical interval is set for each gradient (for example: the scoring interval corresponding to high flexibility is set to 80 - 100, the medium flexibility corresponds to 60 - 80, and the low flexibility corresponds to 0 - 60). Calculate the standard cloud digital features of each gradient, including the expectation Ex, entropy En, and hyper-entropy He.

[0174] Calculate the actual cloud digital features of the actual cloud map according to the comprehensive score. As an example, a certain scale of samples is formed by collecting the actual operation data of the system multiple times. According to the allowable range of indicators in the actual operation of the distribution network, the collected samples are normalized and linearly mapped so that the scores fall within the interval of [ω min , ω max , and calculate the mean and variance of each three-level indicator. The calculation process is shown as follows:

[0175]

[0176] Where: E xj is the mean of the j-th index, x j,i is the value of the j-th index, is the variance of the j-th index.

[0177] Next, based on the above formula, calculate the actual cloud digital features in the actual cloud map, which are expressed as:

[0178]

[0179] Where, E xj represents the expectation Ex of the actual cloud map, reflecting the mean of the comprehensive score; E nj represents the entropy En of the actual cloud map, reflecting the discreteness of the data distribution; H ej represents the hyperentropy He of the actual cloud map, reflecting the uncertainty of the entropy.

[0180] Through the forward cloud generator, a large number of cloud droplets (i.e., random numbers) are generated based on the parameters (Ex, En, He) of the actual cloud to simulate the distribution of the comprehensive score. Then, by matching the actual cloud digital features of the actual cloud with the standard cloud digital features of the standard cloud map, calculate the overlapping area or the maximum membership degree between the two to obtain the flexibility level of each flexibility resource. Exemplarily, if the overlapping area between the actual cloud and the "high flexibility" standard cloud is the largest, it is determined that the current system flexibility is at a high level.

[0181] The flexibility level output by the cloud model can provide an optimization direction for resource modeling. By combining the output results of the cloud model and the index system for a large amount of data analysis, it can be obtained that when the flexibility level on one side is in the third interval and is "low flexibility", it means that the flexibility of the power grid state is insufficient at this time, and deep peak regulation of the power grid is required to achieve safety control. Example: If the evaluation shows that "the flexibility of energy storage is insufficient and the flexibility level is in the third level interval", it reflects that the power grid system has a tight supply and demand at this time, and standby resources need to be started. The energy storage capacity or charge and discharge strategy model can be adjusted.

[0182] When the flexibility level on one side is in the second interval and is "medium flexibility", it means that the power grid needs to further strengthen resource management. Example: If the evaluation shows that "the flexibility of the load side is medium and the flexibility level is in the second level interval", the load side regulation ability can be improved by enhancing the demand response contract (incentive-based DR), and the corresponding model parameters can be updated to strengthen the demand response management and adjust the load distribution to ensure the stable operation of the power grid.

[0183] When the flexibility level on one side is in the first interval and is "high flexibility", it means that the power grid state is stable, and the output of distributed power sources and the charge and discharge strategies of energy storage devices can be optimized to further improve the economy.

[0184] In this embodiment, the cloud model theory is combined to evaluate the flexibility status of the distribution system, improving the evaluation efficiency and accuracy of the flexibility of the distribution system, so as to realize the reasonable and safe control of the grid flexibility resources.

[0185] In summary, in the embodiment of the present invention, a multi-dimensional evaluation index system reflecting the flexibility of the distribution network system is constructed. This system includes system-level indicators focusing on the overall flexibility system level and local-level indicators of flexibility focusing on specific links of each flexibility resource; the AHP-entropy weight method is used to synergistically assign weights subjectively and objectively to the multi-dimensional evaluation index system; the cloud model theory is introduced to establish a comprehensive evaluation model, and cloud map analysis and comprehensive evaluation are performed on the index weights of the system to ensure the accuracy and reliability of the evaluation results. Through the construction of a systematic evaluation framework, a subjective and objective combined evaluation method, and the application of cloud model technology, this embodiment significantly improves the accuracy and reliability of the comprehensive energy system flexibility evaluation and ensures the stability of the grid operation.

[0186] An embodiment of the present invention provides a distribution network flexibility evaluation system. Specifically, please refer to Figure 2 , Figure 2 which shows the structural block diagram of the distribution network flexibility evaluation system in one of the embodiments of the present invention, including:

[0187] A data acquisition module M1, configured to acquire operation data corresponding to different flexibility resource types in the target distribution network system, perform intelligent analysis on the operation data, and obtain operation characteristic data corresponding to each flexibility resource type;

[0188] A model construction module M2, configured to model various flexibility resources according to the operation characteristic data;

[0189] An index system construction module M3, configured to quantify the dynamic index parameters output by various flexibility resources obtained from the modeling results, determine margin indexes according to the quantification results, and generate a multi-dimensional evaluation index system;

[0190] A weight analysis module M4, configured to assign weights to the indexes in the multi-dimensional evaluation index system in a hierarchical manner in a subjective and objective combined way, and determine the comprehensive scores of each level of indexes according to the weight assignment results;

[0191] A flexibility evaluation and resource control module M5, configured to input the comprehensive scores into a set cloud model to output the flexibility levels of each flexibility resource, and the flexibility levels are used to control each flexibility resource in the target distribution network system.

[0192] As Figure 3 shown, the embodiment of the present invention also provides a computer deviceFigure 3 The block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned distribution network flexibility evaluation method is implemented.

[0193] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2,...). The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0194] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the terminal device and connects various parts of the terminal device through various interfaces and circuits.

[0195] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory can also be other volatile solid-state storage devices.

[0196] It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand, Figure 3The structural block diagram is only an example of the terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine some components, or different components. Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above methods. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0197] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the distribution network flexibility assessment method of the above embodiment, such as Figure 1 the steps S1 to S5 described therein.

[0198] The technical features and technical effects of the distribution network flexibility assessment system proposed in the embodiment of the present invention are the same as those of the distribution network flexibility assessment method proposed in the embodiment of the present invention, and will not be elaborated here.

[0199] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A flexibility assessment method for a distribution network, characterized in that Including: Obtain the operation data corresponding to different flexibility resource types in the target distribution network system, perform intelligent analysis on the operation data, and obtain the operation characteristic data corresponding to each flexibility resource type; Model various flexibility resources according to the operation characteristic data; Quantify the dynamic index parameters output by various flexibility resources obtained from the modeling results, and determine the margin index according to the quantification results to generate a multi-dimensional evaluation index system; Assign weights to the indicators in the multi-dimensional evaluation index system in a collaborative subjective and objective manner according to the set levels, and determine the comprehensive scores of each level of indicators according to the weight assignment results; Input the comprehensive scores into the set cloud model to output the flexibility levels of each flexibility resource, and the flexibility levels are used to control each flexibility resource in the target distribution network system.

2. The flexibility evaluation method of the distribution network according to claim 1, wherein The modeling of various flexibility resources according to the operation characteristic data includes: Divide the flexibility resources in the target distribution network system into the source side, load side, storage side, and energy coupling side according to their attributes, and obtain the operation characteristic data of the resources on each side; Model the controllable units on the source side, load demand response, energy storage devices, and energy coupling devices respectively according to the operation characteristic data.

3. The flexibility evaluation method for a distribution network according to claim 1, wherein The process of generating the multi-dimensional evaluation index system includes: According to the modeling results, quantify the characteristic indicators reflecting the adjustable flexibility output by each flexibility resource in the controllable units on the source side, load demand response, energy storage devices, and energy coupling devices within a preset time period to construct the third-level indicators; Calculate the adjustable flexibility margin and adjustable flexibility of each flexibility resource according to the quantification results to construct the second-level indicators; Respectively determine the quantification results reflecting the flexibility of the source side, load side, storage side, and energy coupling side according to the second-level indicators to construct the first-level indicators; Integrate the third-level indicators, the second-level indicators, and the first-level indicators to generate a multi-dimensional evaluation index system.

4. The flexibility evaluation method of the distribution network according to claim 3, wherein The construction process of the second-level indicators and the third-level indicators includes: According to the modeling results of the controllable units on the source side, quantify the power generation power adjustment range and the corresponding ramp rate output by each flexibility resource in the controllable units on the source side within a preset time period to construct the source side third-level indicators, and calculate the upward flexibility margin and downward flexibility margin of each flexibility resource in the controllable units on the source side according to the quantification results to obtain the source side second-level indicators; According to the modeling results of the energy storage devices, quantify the electric energy storage capacity, charge and discharge power, and charge and discharge efficiency of each flexibility resource in the energy storage devices within a preset time period to construct the storage side third-level indicators, and calculate the upward flexibility and downward flexibility of each flexibility resource in the energy storage devices according to the quantification results to obtain the storage side second-level indicators; According to the modeling results of the load demand response, quantify the load change value in the face of demand response of the load within a preset time period to construct the load side third-level indicators, and calculate the upward flexibility and downward flexibility of the load according to the quantification results to obtain the load side second-level indicators; According to the modeling results of the energy coupling device, the third-level indicators on the energy coupling side are constructed by quantifying the energy power and corresponding ramping rates of each flexibility resource in the energy coupling device within a preset time period. Based on the quantification results, the upward and downward flexibility margins and upward and downward flexibilities of each flexibility resource in the energy coupling device are calculated to obtain the second-level indicators on the energy coupling side.

5. The flexibility evaluation method of the distribution network according to claim 3, characterized in that, The generation process of the multi-dimensional evaluation index system further includes: Quantify the new energy consumption rate, network loss rate, and line overload rate in the target distribution network system to construct the third-level indicators on the network side. Based on the quantification results, obtain the second-level indicators on the network side of new energy consumption capacity, network operation efficiency, and distribution network reliability; Determine the first-level indicators on the network side reflecting the flexibility of the target distribution network system according to the second-level indicators on the network side, and incorporate the first-level indicators on the network side into the first-level indicators of the multi-dimensional evaluation index system.

6. The flexibility evaluation method for a distribution network according to claim 1, wherein, The method of assigning weights to the indicators in the multi-dimensional evaluation index system in a collaborative subjective and objective manner according to the set hierarchy, and determining the comprehensive scores of each level of indicators includes: Use the subjective weighting method to gradually assign weights to the indicators in the multi-dimensional evaluation index system and obtain the evaluation scores of the flexibility of each level of indicators; According to the subjective weighting results, use the entropy weight method to assign objective weights to each level of indicators in the multi-dimensional evaluation index system and perform weighted calculations to obtain the comprehensive scores of each level of indicators.

7. The method for evaluating the flexibility of a distribution network according to claim 6, wherein, The method of inputting the comprehensive scores into a set cloud model to output the flexibility levels of each flexibility resource includes: Divide the flexibility gradients of each flexibility resource according to the evaluation scores to generate a standard cloud map; Calculate the actual cloud digital characteristics of the actual cloud map according to the comprehensive scores; By matching the actual cloud digital characteristics of the actual cloud with the standard cloud digital characteristics of the standard cloud map, obtain the flexibility levels of each flexibility resource.

8. A distribution network flexibility evaluation system, characterized in that It includes: A data acquisition module for acquiring the operation data corresponding to different flexibility resource types in the target distribution network system, performing intelligent analysis on the operation data, and obtaining the operation characteristic data corresponding to each flexibility resource type; A model construction module for modeling various flexibility resources according to the operation characteristic data; An index system construction module for quantifying the dynamic index parameters output by various flexibility resources obtained from the modeling results, determining the margin index according to the quantification results, and generating a multi-dimensional evaluation index system; A weight analysis module for assigning weights to the indicators in the multi-dimensional evaluation index system in a collaborative subjective and objective manner according to the set hierarchy, and determining the comprehensive scores of each level of indicators according to the weighting results; A flexibility evaluation and resource control module for inputting the comprehensive scores into a set cloud model to output the flexibility levels of each flexibility resource, and the flexibility levels are used to control each flexibility resource in the target distribution network system.

9. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distribution network flexibility evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the method for evaluating the flexibility of a distribution network as described in any one of claims 1 to 7 is implemented.

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