Network constraint based grid adjustable resource regulation capability evaluation method and related system

Abstract

The application belongs to power system automation, and discloses a network constraint-based power grid adjustable resource regulation capacity evaluation method and related system. The application can more accurately reflect the actual operation of the power grid, improve the accuracy and reliability of the evaluation by considering the adjustable load data and energy storage data under the new energy sending section, as well as the section constraint condition and the peak regulation constraint condition. The correction of the up-regulation limit value and the down-regulation limit value according to the data of each single adjustable resource can better optimize the resource allocation, improve the overall regulation capacity of the power grid, and ensure the stability and safety of the power grid operation. The application can provide more accurate reference data for power grid regulation by correcting the active constraint curve of the operation feasible region of the single adjustable resource data, and improve the efficiency and effect of the power grid regulation.

Description

Technical Field

[0001] This invention belongs to the field of power system automation, specifically relating to a method and related systems for assessing the regulation capacity of adjustable resources in a power grid based on network constraints. Background Technology

[0002] Currently, flexible resources such as temperature-controlled loads, electric vehicles, and energy storage devices are a good remedy for mitigating the volatility and uncertainty of new energy sources. Facing the diverse regulation needs of the power grid, timely and accurate understanding of the adjustability potential of flexible resources is crucial for the safe and reliable dispatch of the power grid. However, flexible resources are affected by the inherent operating parameters of the resources themselves, user habits, and external environmental factors such as temperature and humidity. Their adjustability is in a continuous dynamic process, and their regulation capability is also constrained by the network topology. Therefore, it is urgent to study methods for predicting the adjustability of flexible resources under security constraints to improve the accurate perception of flexible resources. Furthermore, flexible resources are numerous and widely distributed. To fully utilize demand-side flexible resources and reduce the complexity of regulation calculations, it is necessary to aggregate flexible resources. Regarding resource aggregation, currently, flexible resources in Hebei Province participate in the North China peak-shaving ancillary service market by aggregating them according to the total amount in the region, without considering the network topology accessed by flexible resources, which cannot support peak-shaving within limited sections and equipment overload scenarios.

[0003] For the aggregation of flexible resources, an aggregator is typically introduced as an intermediate control layer between the user and the power grid. Load aggregation methods include Monte Carlo aggregation, parameter identification aggregation, Markov chain aggregation, and Ford-Planck theorem aggregation. Currently, there is a lot of research on the aggregation of similar resources, but little research on the aggregation of multiple types of resources. For different types of resources connected to the same location in the distribution network, the uncertainties and interactions between them are not considered during resource aggregation. Currently, some research has been conducted both domestically and internationally on predicting the regulation capacity of individual load resources, such as evaluating and analyzing the charging demand and charging / discharging regulation potential of electric vehicles. However, research on the prediction technology of overall aggregated resource regulation capacity is limited, and current studies do not consider the impact of network constraints on regulation capacity. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and related system for evaluating the regulation capacity of adjustable resources in a power grid based on network constraints. It constructs feasible regions for different types of resources, considers cross-sectional constraints and peak-shaving constraints to modify the feasible regions of individual resources and aggregates them to form the total regulation capacity of adjustable resources, thus providing a basis for adjustable resources to participate in auxiliary decision-making for power grid regulation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for evaluating the regulation capability of power grid adjustable resources based on network constraints, comprising the following steps:

[0007] Acquire adjustable resource data in the power grid, which includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load and the grid connection path of energy storage data;

[0008] Based on the adjustable resource data, construct the operational feasible domain of individual adjustable resource data;

[0009] Based on the adjustable load data and energy storage data of the new energy transmission section, calculate the section constraint conditions and peak-shaving constraint conditions.

[0010] Based on the cross-sectional constraints and peak-shaving constraints, calculate the total adjustable amount of load data and energy storage data in the adjustable resource data;

[0011] Based on the total adjustable amount of load data and energy storage data in the adjustable resource data, the upper and lower adjustment limits of each individual load data and energy storage data in the adjustable resource data are obtained;

[0012] Based on the upward and downward adjustment limits of the individual unit load data and energy storage data in the adjustable resource data, the active power constraint curve of the operational feasible domain of the individual adjustable resource data is corrected to complete the evaluation.

[0013] A further improvement of the present invention is that the data for the adjustable load includes: the upper limit of load power L. up and load power lower limit L dn ;

[0014] Energy storage data includes: charging rate C in Discharge rate C out Charging capacity L in Discharge capacity L out And the energy loss rate k of the energy storage.

[0015] A further improvement of this invention lies in the following method for constructing the operational feasible domain of individual adjustable resource data based on adjustable resource data:

[0016] When the adjustable resource data is adjustable load data, the adjustable load data is divided into adjustable characteristic data of preset levels;

[0017] Based on the adjustable characteristic data of the preset level, the hourly response is used as the standard to construct a load hourly response model;

[0018] Based on the hourly load response model, constraint curves for adjustable load data are plotted. The graph enclosed by the load active power constraint curves at adjacent times is used as the operational feasible region for individual adjustable resource data.

[0019] A further improvement of this invention lies in the following method for constructing the operational feasible domain of individual adjustable resource data based on adjustable resource data:

[0020] When the adjustable resource data is energy storage data, a constraint curve is constructed based on the energy storage data. The graph enclosed by the load active power constraint curves at adjacent times is used as the operational feasible region of the individual adjustable resource data.

[0021] A further improvement of this invention is that the cross-sectional constraint conditions include a total adjustment upper limit P for the cross-sectional constraint conditions. s,adjmax The lower limit of the total adjustment P of the cross-sectional constraint conditions s,adjmin The calculation method is as follows:

[0022] P s,adjmax =P s,tmax +P s,new -P sec -P s,ld

[0023] P s,adjmin =P s,tmin +P s,new -P sec -P s,ld

[0024] Among them, P s,new To contribute to the theory of new energy sources within the cross-section, P sec For cross-sectional stability margin, P s,ld The predicted total load within the cross section and P s,tmin P is the total minimum output of the thermal power units within the cross section. s,tmax This represents the total maximum output of the thermal power units within the cross-section.

[0025] Peak shaving constraints include the total adjustment limit P of peak shaving constraints. t,adjmax And the total lower limit of peak-shaving constraint P t,adjmin The calculation method is as follows:

[0026] P t,adjmax =P t,tmax +P t,new -P line -P t,ld

[0027] P t,adjmin =P t,tmin +P t,new -P line -P t,ld

[0028] Among them, P t,new To contribute to the theory of new energy across the entire network, P line For the power planning of interconnection lines, P t,ld P represents the predicted total load of the entire network. t,tmin P is the minimum total output of all thermal power units in the entire network. t,tmax For maximum output, P t,adjmax P is the upper limit of total adjustment under peak-shaving constraints. t,adjmin This represents the lower limit of total regulation under peak-shaving constraints.

[0029] A further improvement of this invention lies in the step of calculating the total adjustable amount of load data and energy storage data in the adjustable resource data based on cross-sectional constraints and peak-shaving constraints. The specific method is as follows:

[0030] Obtain the total regulation limit of the new energy transmission section and the total regulation limit of the whole network resources. Combine the section constraints and peak-shaving constraints to obtain the upward adjustment limit of the adjustable load and the upward adjustment limit of the energy storage under the new energy transmission section.

[0031] Based on the total regulation lower limit of the renewable energy transmission section and the total regulation lower limit of the entire network resources, combined with the section constraints and peak-shaving constraints, the downward adjustment limit of the adjustable load and the downward adjustment limit of the energy storage regulation amount under the renewable energy transmission section are obtained.

[0032] A further improvement of this invention lies in the step of obtaining the upward and downward adjustment limits of individual load data and energy storage data in the adjustable resource data based on the total adjustable amount of load data and energy storage data in the adjustable resource data. The specific method is as follows:

[0033] By combining the upward and downward adjustment limits of the adjustable load under the new energy transmission section and the upward and downward adjustment limits of the energy storage adjustment amount, the adjustment amounts of each individual load data and energy storage data in the adjustable resource data are obtained.

[0034] A further improvement of this invention lies in that, based on the upward and downward adjustment limits of the individual unit load data and energy storage data in the adjustable resource data, the active power constraint curve of the operational feasible region of the individual adjustable resource data is corrected. The specific method is as follows:

[0035] Based on the operational feasible domain of individual adjustable resource data, the actual active power of adjustable resources within a fixed time period is obtained, and the change data of actual active power is obtained.

[0036] Based on the actual active power changes in the individual adjustable resource data, as well as the adjustment amounts in the load and energy storage data, the operational feasibility domain of the individual adjustable resource data is corrected.

[0037] Secondly, the present invention provides a power grid adjustable resource regulation capability assessment system based on network constraints, comprising:

[0038] The data acquisition module is used to acquire adjustable resource data in the power grid. The adjustable resource data includes adjustable load data and energy storage data, as well as adjustable load data and energy storage under the new energy transmission section, and the grid connection path of adjustable load and the grid connection path of energy storage data.

[0039] The feasible domain construction module is used to construct the operational feasible domain of a single adjustable resource data based on the adjustable resource data;

[0040] The constraint adjustment acquisition module is used to calculate the section constraint conditions and peak-shaving constraint conditions based on the adjustable load data and energy storage data under the new energy transmission section.

[0041] The adjustment quantity acquisition module is used to calculate the total adjustable quantity of load data and energy storage data in the adjustable resource data based on the cross-sectional constraints and peak-shaving constraints.

[0042] The adjustment range acquisition module is used to obtain the upper and lower adjustment limits of the load data and energy storage data of each individual adjustable resource data based on the total adjustable amount of the load data and energy storage data in the adjustable resource data of each individual.

[0043] The correction module is used to correct the active power constraint curve of the operational feasible region of the individual adjustable resource data based on the upward and downward adjustment limits of the individual load data and energy storage data in the adjustable resource data, and to complete the evaluation.

[0044] A further improvement of this invention is that the functionality of the feasible domain construction module is implemented through the following method:

[0045] When the adjustable resource data is adjustable load data, the adjustable load data is divided into adjustable characteristic data of preset levels;

[0046] Based on the adjustable characteristic data of the preset level, the hourly response is used as the standard to construct a load hourly response model;

[0047] Based on the hourly load response model, constraint curves for adjustable load data are plotted, and the graph enclosed by the load active power constraint curves at adjacent times is taken as the operational feasible region of individual adjustable resource data. When the adjustable resource data is energy storage data, constraint curves are constructed based on the energy storage data, and the graph enclosed by the load active power constraint curves at adjacent times is taken as the operational feasible region of individual adjustable resource data.

[0048] A further improvement of this invention is that the function of the adjustment amount acquisition module is implemented through the following method:

[0049] Obtain the total regulation limit of the new energy transmission section and the total regulation limit of the whole network resources. Combine the section constraints and peak-shaving constraints to obtain the upward adjustment limit of the adjustable load and the upward adjustment limit of the energy storage under the new energy transmission section.

[0050] Based on the total regulation lower limit of the renewable energy transmission section and the total regulation lower limit of the entire network resources, combined with the section constraints and peak-shaving constraints, the downward adjustment limit of the adjustable load and the downward adjustment limit of the energy storage regulation amount under the renewable energy transmission section are obtained.

[0051] A further improvement of this invention is that the function of the adjustment range acquisition module is implemented through the following method:

[0052] By combining the upward and downward adjustment limits of the adjustable load under the new energy transmission section and the upward and downward adjustment limits of the energy storage adjustment amount, the adjustment amounts of each individual load data and energy storage data in the adjustable resource data are obtained.

[0053] A further improvement of this invention is that the function of the correction module is implemented through the following method:

[0054] Based on the operational feasible domain of individual adjustable resource data, the actual active power of adjustable resources within a fixed time period is obtained, and the change data of actual active power is obtained.

[0055] Based on the actual active power changes in the individual adjustable resource data, as well as the adjustment amounts in the load and energy storage data, the operational feasibility domain of the individual adjustable resource data is corrected.

[0056] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of a method for evaluating the regulation capability of power grid adjustable resources based on network constraints.

[0057] Fourthly, the present invention provides a storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a method for evaluating the regulation capability of power grid adjustable resources based on network constraints.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] This invention, by considering adjustable load data and energy storage data under new energy transmission sections, as well as section constraints and peak-shaving constraints, can more accurately reflect the actual operation of the power grid, improving the accuracy and reliability of the assessment. Correcting the upward and downward adjustment limits based on the adjustable resource data of each individual unit allows for better optimization of resource allocation, improving the overall regulation capability of the power grid and ensuring the stability and security of power grid operation. By correcting the active power constraint curves of the feasible operating domain of individual adjustable resource data, this invention provides more accurate reference data for power grid regulation, improving the efficiency and effectiveness of power grid regulation. By forming the total regulation capability of adjustable resources, this invention provides a basis for auxiliary decision-making in power grid regulation, helping power grid dispatchers make more scientific and reasonable regulation decisions and improving the level of power grid operation and management. In summary, this invention, by comprehensively considering network constraints and peak-shaving constraints, optimizes resource allocation, improves power grid regulation efficiency and new energy absorption capacity, effectively enhances the overall regulation capability and operational stability of the power grid, and provides strong technical support for power grid regulation. Attached Figure Description

[0060] Figure 1 This is a flowchart of the present invention;

[0061] Figure 2 This is a system diagram of the present invention;

[0062] Figure 3 This is a schematic diagram of the feasible operating domain in the embodiment;

[0063] Figure 4 This is a schematic diagram of the Minkowski summation in the example;

[0064] Figure 5 This is a schematic diagram of the system in Example 4. Detailed Implementation

[0065] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0066] Example 1:

[0067] See Figure 1 A method for assessing the regulation capacity of power grid adjustable resources based on network constraints includes the following steps:

[0068] S1, acquire adjustable resource data in the power grid. Adjustable resource data includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load and energy storage.

[0069] S2, based on the adjustable resource data, construct the operational feasible domain of the individual adjustable resource data.

[0070] S3, based on the adjustable load data and energy storage data of the new energy transmission section, calculate the section constraint conditions and peak-shaving constraint conditions.

[0071] S4. Based on the cross-sectional constraints and peak-shaving constraints, calculate the total adjustable amount of load data and energy storage data in the adjustable resource data.

[0072] S5. Based on the total adjustable amount of load data and energy storage data in the adjustable resource data, obtain the upward and downward adjustment limits of each individual load data and energy storage data in the adjustable resource data.

[0073] S6. Based on the upward and downward adjustment limits of the individual unit load data and energy storage data in the adjustable resource data, correct the active power constraint curve of the operational feasible domain of the individual adjustable resource data to complete the evaluation.

[0074] Example 2:

[0075] See Figure 2 A network-constrained power grid adjustable resource regulation capability assessment system includes:

[0076] The data acquisition module is used to acquire adjustable resource data in the power grid. The adjustable resource data includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load and energy storage.

[0077] The feasible domain construction module is used to construct the operational feasible domain of a single adjustable resource data based on the adjustable resource data;

[0078] The constraint adjustment acquisition module is used to calculate the section constraint conditions and peak-shaving constraint conditions based on the adjustable load data and energy storage data under the new energy transmission section.

[0079] The adjustment quantity acquisition module is used to calculate the total adjustable quantity of load data and energy storage data in the adjustable resource data based on the cross-sectional constraints and peak-shaving constraints.

[0080] The adjustment range acquisition module is used to obtain the upper and lower adjustment limits of the load data and energy storage data of each individual adjustable resource data based on the total adjustable amount of the load data and energy storage data in the adjustable resource data of each individual.

[0081] The correction module is used to correct the active power constraint curve of the operational feasible region of the individual adjustable resource data based on the upward and downward adjustment limits of the individual load data and energy storage data in the adjustable resource data, and to complete the evaluation.

[0082] Example 3:

[0083] A method for assessing the regulation capacity of power grid adjustable resources based on network constraints includes the following steps:

[0084] Step 1: Obtain adjustable resource data in the power grid. Adjustable resource data includes the upper limit of adjustable load power L. up Load power lower limit data L dn The charging rate C of energy storage in Discharge rate C out Charging capacity L in Discharge capacity L out Energy storage self-loss rate k. Obtain adjustable load and energy storage under each new energy transmission section, and obtain the grid connection path of adjustable load and energy storage.

[0085] Step 2: Based on the adjustable resource data, construct the operational feasible domain for individual adjustable resource data.

[0086] 1) Adjustable load data: Obtain the seven-level adjustable characteristic data of the adjustable load (the seven-level adjustable characteristic data is the standard format data specified in the State Grid enterprise standard), including the total upward adjustment, the total downward adjustment, the response time, the upward adjustment amount of the first-level response, the duration of the upward adjustment of the first-level response, the downward adjustment amount of the first-level response, the downward adjustment amount of the first-level response, ..., the upward adjustment amount of the seventh-level response, the duration of the upward adjustment of the seventh-level response, the downward adjustment amount of the seventh-level response, and the downward adjustment amount of the seventh-level response.

[0087] The fifth level of response is the hourly response level. Using the hourly response level as the standard, if data for the fifth level of adjustable load response is available in the system, an hourly feasible region for adjustable load operation is constructed based on this data. If data for the fifth level of adjustable load response is available, but data for other levels is available, the adjustment capacity data for similar levels is approximated as hourly adjustment capacity data. If data for all seven levels of response is unavailable, load parameters are identified by analyzing historical load power data to obtain the hourly load response model, as shown below:

[0088] Hourly load regulation capacity

[0089] Hourly load regulation capacity

[0090] Where T represents the time period for acquiring historical load power, t and t+1 are any time points differing by 1 hour, and P t Let t be the load power at time t.

[0091] The feasible region construction method is as follows: draw the constraint curves of the hourly upward adjustment capacity, downward adjustment capacity, upper limit of load power, and lower limit of load power of the adjustable load. The graph enclosed by the load active power constraint curves at two times t and t+1 is the feasible region.

[0092] With the load power at time t+1 as the y-axis and the load power at time t as the x-axis, the curve expression is as follows:

[0093] Upper adjustment constraint y≤x+C up

[0094] Lower adjustment constraint y≥xC dn

[0095] Power constraint at time t: x ≥ L dn , x≤L up

[0096] Power constraint at time t+1: y ≥ L dn y≤L up

[0097] A schematic diagram of the feasible domain is shown below. Figure 3 As shown.

[0098] 2) Energy storage data: Charging rate C based on energy storage in Discharge rate C out Charging capacity L in Discharge capacity L out The constraint curve is constructed using the energy storage self-loss rate k data. The graph enclosed by the load active power constraint curves at two times t and t+1 is the feasible region.

[0099] With the energy storage charging and discharging power at time t+1 as the y-axis and the energy storage charging and discharging power at time t as the x-axis, the curve expression is as follows:

[0100] Charging constraint y≤kx+C in

[0101] Discharge constraint y≥kx-C out

[0102] Power constraint at time t: x ≥ L out , x≤L in

[0103] Power constraint at time t+1: y ≥ L out y≤L in

[0104] The method for constructing the two-dimensional feasible region is the same as that for adjustable loads.

[0105] Step 3: Calculate the section constraints and peak-shaving constraints based on the adjustable load data and energy storage data under the new energy transmission section.

[0106] 1) Considering the constraints of the new energy transmission section, based on the theoretical power output P of the new energy within the section... s,new Cross-sectional stability margin P sec Total load forecast value P within the cross section s,ld and the total minimum output P of the thermal power units within the cross section s,tmin Maximum output P s,tmax Calculate the total adjustment limit P of adjustable resources (adjustable load + energy storage) within the cross section under cross-sectional constraints. s,adjmax And the lower limit of total regulation P s,adjmin .

[0107] P s,adjmax =P s,tmax +P s,new -P sec -P s,ld

[0108] P s,adjmin =P s,tmin +P s,new -P sec -P s,ld

[0109] 2) Regarding peak-shaving constraints, based on the theoretical power output P of the entire network's new energy sources... t,new Inter-network tie line power plan P line Total network load forecast value P t,ld And the total minimum output P of all thermal power units in the network t,tmin Maximum output P t,tmax Calculate the total adjustment ceiling P of the entire network's adjustable resources (load + energy storage) under peak-shaving constraints. t,adjmax And the lower limit of total regulation P t,adjmin .

[0110] P t,adjmax =P t,tmax +P t,new -P line -P t,ld

[0111] P t,adjmin =P t,tmin +P t,new -P line -P t,ld

[0112] Step 4: Based on the cross-sectional constraints and peak-shaving constraints, obtain the adjustment amounts of load data and energy storage data from the adjustable resource data of each individual unit;

[0113] The goal is to maximize the grid's operational adequacy (lowest sum of branch load rates across the entire grid), constrained by network limitations, power source regulation capabilities, upper and lower limits of individual adjustable load regulation, and energy storage charging and discharging limits, with the upper limit of total resource regulation P at the cross-section. s,adjmax Total adjustment limit of network resources P t,adjmax As an equality constraint, solve for the adjustable load and energy storage adjustment of each individual unit.

[0114] Sum of branch load rates

[0115] Where n line Indicates the number of power grid branches, I i and T i This represents the current and thermal limit of branch i.

[0116]

[0117] U i The voltage amplitude of the busbar; This is the lower limit of the voltage amplitude of the busbar; Upper limit of voltage amplitude of the bus.

[0118] Power regulation capability constraints

[0119] P g0 The initial active power of the generator is ΔP. g This refers to the active power adjustment of the generator. This is the lower limit of the generator's active power. This is the upper limit of the generator's active power output.

[0120] Individual adjustable load constraint L dn,i ≤P ld,i +ΔP i ≤L up,i

[0121] Energy storage charge and discharge constraints L out,i ≤P cn,i +ΔP i ≤L in,i

[0122] Total resource adjustment constraints under cross-section

[0123] I sec For all adjustable resources under the cross section, ΔP i This refers to the adjustable resource adjustment amount.

[0124] Total network resource adjustment constraints

[0125] I all For all adjustable resources across the entire network, ΔPi This refers to the adjustable resource adjustment amount.

[0126] Step 5: Based on the total adjustable amount of load data and energy storage data in the adjustable resource data, obtain the upward and downward adjustment limits of each individual load data and energy storage data in the adjustable resource data.

[0127] The adjustable load and energy storage adjustment amount ΔP of each individual unit are obtained. i As an upward limit for adjustable load and energy storage.

[0128] The lower limit of total resource regulation P below the cross section s,adjmin The lower limit of the total network resource adjustment P t,adjmin As an equality constraint, solve for the adjustable load and energy storage adjustment of each individual unit.

[0129] The adjustable load and energy storage adjustment amount ΔP of each individual unit are obtained. i As a lower limit for adjustable load and energy storage.

[0130] Step 6: Based on the upward and downward adjustment limits of the individual unit load data and energy storage data in the adjustable resource data, correct the active power constraint curve of the operational feasible region of the individual adjustable resource data to complete the evaluation.

[0131] The calculated upper and lower limits for adjustable load and energy storage are used to correct the active power constraint curve of the constructed individual load feasible region and update the individual flexibility resource feasible region.

[0132] Based on the network access paths of adjustable resources, the adjustable resources contained in each power grid partition are determined. The operational feasible region of each power grid partition is solved using the Minkowski summation method for the two-dimensional spatial feasible region.

[0133] See Figure 4 Given two adjustable resources (k, k+1) with power-time coupling characteristics, the aggregated feasible region can be represented as the union of the region swept by the vertex of set 2 moving continuously along the envelope of set 1 and set 1 itself, denoted as...

[0134] Every hour, the actual active power of adjustable resources is read, and a post-assessment of flexible resource regulation is performed. Based on the actual power change data, the seven-level response data of adjustable load and the energy storage charging and discharging capacity data are corrected, and the feasible domain of flexible resources is reconstructed with updated parameters. The above steps are repeated.

[0135] This invention constructs a two-dimensional feasible domain for the operation of individual adjustable resources, thereby achieving a unified expression of the adjustment capabilities and adjustment rates of various types of adjustable resources. Based on the Minkowski summation method, it aggregates the feasible domains of individual resources to realize the assessment of adjustment capabilities considering the interactions of different types of resources.

[0136] Example 4:

[0137] Please see Figure 5 As shown, the present invention also provides an electronic device 100 for a method of evaluating the regulation capability of power grid adjustable resources based on network constraints; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0138] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the power grid adjustable resource regulation capability assessment method based on network constraints described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0139] The at least one processor 102 may 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 processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0140] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for assessing the regulation capability of power grid adjustable resources based on network constraints, and the processor 102 can execute the multiple instructions to achieve the following:

[0141] Acquire adjustable resource data in the power grid, which includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load data and energy storage data.

[0142] Based on the adjustable resource data, construct the operational feasible domain of individual adjustable resource data;

[0143] Based on the adjustable load data and energy storage data of the new energy transmission section, obtain the section constraint conditions and peak-shaving constraint conditions.

[0144] Based on the cross-sectional constraints and peak-shaving constraints, obtain the adjustment amount of load data and energy storage data from the adjustable resource data of each individual unit;

[0145] Based on the adjustment amounts of load data and energy storage data in the adjustable resource data of each individual unit, the upper and lower adjustment limits of load data and energy storage data in the adjustable resource data of each individual unit are obtained.

[0146] Based on the upward and downward adjustment limits of load and energy storage data in each individual adjustable resource data set, the active power constraint curve of the operational feasible region of the individual adjustable resource data is corrected to complete the assessment.

[0147] Example 5:

[0148] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0149] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the regulation capability of power grid adjustable resources based on network constraints, characterized in that, Includes the following steps: Acquire adjustable resource data in the power grid, which includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load and energy storage. Based on the adjustable resource data, construct the operational feasible domain of individual adjustable resource data; Based on the adjustable load data and energy storage data of the new energy transmission section, calculate the section constraint conditions and peak-shaving constraint conditions. Based on the cross-sectional constraints and peak-shaving constraints, calculate the total adjustable amount of load data and energy storage data in the adjustable resource data; Based on the total adjustable amount of load data and energy storage data in the adjustable resource data, the upper and lower adjustment limits of each individual load data and energy storage data in the adjustable resource data are obtained; Based on the upward and downward adjustment limits of the individual unit load data and energy storage data in the adjustable resource data, the active power constraint curve of the operational feasible domain of the individual adjustable resource data is corrected to complete the evaluation.

2. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, Adjustable load data includes: upper limit of load power L up and load power lower limit L dn ; Energy storage data includes: charging rate C in Discharge rate C out Charging capacity L in Discharge capacity L out And the energy loss rate k of the energy storage.

3. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, The specific method for constructing the operational feasible domain of a single adjustable resource based on adjustable resource data is as follows: When the adjustable resource data is adjustable load data, the adjustable load data is divided into adjustable characteristic data of preset levels; Based on the adjustable characteristic data of the preset level, the hourly response is used as the standard to construct a load hourly response model; Based on the hourly load response model, constraint curves for adjustable load data are plotted. The graph enclosed by the load active power constraint curves at adjacent times is used as the operational feasible region for individual adjustable resource data.

4. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, The specific method for constructing the operational feasible domain of a single adjustable resource based on adjustable resource data is as follows: When the adjustable resource data is energy storage data, a constraint curve is constructed based on the energy storage data. The graph enclosed by the load active power constraint curves at adjacent times is used as the operational feasible region of the individual adjustable resource data.

5. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, Section constraints include the total adjustment limit P of section constraints. s，adjmax The lower limit of the total adjustment P of the cross-sectional constraint conditions s，adjmin The calculation method is as follows: P s，adjmax =P s，tmax +P s，new -P sec -P s，ld P s，adjmin =P s，tmin +P s，new -P sec -P s，ld Among them, P s，new To contribute to the theory of new energy sources within the cross-section, P sec For cross-sectional stability margin, P s，ld The predicted total load within the cross section and P s，tmin P is the total minimum output of the thermal power units within the cross section. s，tmax This represents the total maximum output of the thermal power units within the cross-section. Peak shaving constraints include the total adjustment limit P of peak shaving constraints. t，adjmax And the total lower limit of peak-shaving constraint P t，adjmin The calculation method is as follows: P t，adjmax =P t，tmax +P t，new -P line -P t，ld P t，adjmin =P t，tmin +P t，new -P line -P t，ld Among them, P t，new To contribute to the theory of new energy across the entire network, P line For the power planning of interconnection lines, P t，ld P represents the predicted total load of the entire network. t，tmin P is the minimum total output of all thermal power units in the entire network. t，tmax For maximum output, P t，adjmax P is the upper limit of total adjustment under peak-shaving constraints. t，adjmin This represents the lower limit of total regulation under peak-shaving constraints.

6. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, Based on the cross-sectional constraints and peak-shaving constraints, the steps involve calculating the total adjustable capacity of load and energy storage data within the adjustable resource data. The specific method is as follows: Obtain the total regulation limit of the new energy transmission section and the total regulation limit of the whole network resources. Combine the section constraints and peak-shaving constraints to obtain the upward adjustment limit of the adjustable load and the upward adjustment limit of the energy storage under the new energy transmission section. Based on the total regulation lower limit of the renewable energy transmission section and the total regulation lower limit of the entire network resources, combined with the section constraints and peak-shaving constraints, the downward adjustment limit of the adjustable load and the downward adjustment limit of the energy storage regulation amount under the renewable energy transmission section are obtained.

7. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 6, characterized in that, Based on the total adjustable amount of load data and energy storage data in the adjustable resource data, the steps are as follows: The upper and lower limits for the individual load data and energy storage data in the adjustable resource data are obtained. By combining the upward and downward adjustment limits of the adjustable load under the new energy transmission section and the upward and downward adjustment limits of the energy storage adjustment amount, the adjustment amounts of each individual load data and energy storage data in the adjustable resource data are obtained.

8. The method for evaluating the regulation capability of power grid adjustable resources based on network constraints according to claim 1, characterized in that, The steps involve adjusting the active power constraint curves of the operational feasible region of the individual adjustable resource data based on the upward and downward adjustment limits of the load data and energy storage data for each individual unit in the adjustable resource data. The specific method is as follows: Based on the operational feasible domain of individual adjustable resource data, the actual active power of adjustable resources within a fixed time period is obtained, and the change data of actual active power is obtained. Based on the actual active power changes in the individual adjustable resource data, as well as the adjustment amounts in the load and energy storage data, the operational feasibility domain of the individual adjustable resource data is corrected.

9. A power grid adjustable resource regulation capability assessment system based on network constraints, characterized in that, include: The data acquisition module is used to acquire adjustable resource data in the power grid. The adjustable resource data includes adjustable load data and energy storage data, as well as adjustable load data and energy storage data under the new energy transmission section, and the grid connection path of adjustable load and energy storage. The feasible domain construction module is used to construct the operational feasible domain of a single adjustable resource data based on the adjustable resource data; The constraint adjustment acquisition module is used to calculate the section constraint conditions and peak-shaving constraint conditions based on the adjustable load data and energy storage data under the new energy transmission section. The adjustment quantity acquisition module is used to calculate the total adjustable quantity of load data and energy storage data in the adjustable resource data based on the cross-sectional constraints and peak-shaving constraints. The adjustment range acquisition module is used to obtain the upper and lower adjustment limits of the load data and energy storage data of each individual adjustable resource data based on the total adjustable amount of the load data and energy storage data in the adjustable resource data of each individual. The correction module is used to correct the active power constraint curve of the operational feasible region of the individual adjustable resource data based on the upward and downward adjustment limits of the load data and energy storage data of each individual unit in the adjustable resource data, and to complete the evaluation.

10. The power grid adjustable resource regulation capability assessment system based on network constraints according to claim 9, characterized in that, The functionality of the feasible domain construction module is implemented through the following methods: When the adjustable resource data is adjustable load data, the adjustable load data is divided into adjustable characteristic data of preset levels; Based on the adjustable characteristic data of the preset level, the hourly response is used as the standard to construct a load hourly response model; Based on the hourly load response model, constraint curves for adjustable load data are plotted, and the graph enclosed by the load active power constraint curves at adjacent times is taken as the operational feasible region of individual adjustable resource data. When the adjustable resource data is energy storage data, constraint curves are constructed based on the energy storage data, and the graph enclosed by the load active power constraint curves at adjacent times is taken as the operational feasible region of individual adjustable resource data.

11. The power grid adjustable resource regulation capability assessment system based on network constraints according to claim 9, characterized in that, The function of the adjustment amount acquisition module is implemented through the following method: Obtain the total regulation limit of the new energy transmission section and the total regulation limit of the whole network resources. Combine the section constraints and peak-shaving constraints to obtain the upward adjustment limit of the adjustable load and the upward adjustment limit of the energy storage under the new energy transmission section. Based on the total regulation lower limit of the renewable energy transmission section and the total regulation lower limit of the entire network resources, combined with the section constraints and peak-shaving constraints, the downward adjustment limit of the adjustable load and the downward adjustment limit of the energy storage regulation amount under the renewable energy transmission section are obtained.

12. The power grid adjustable resource regulation capability assessment system based on network constraints according to claim 11, characterized in that, The functionality of the adjustment range acquisition module is implemented using the following method: By combining the upward and downward adjustment limits of the adjustable load under the new energy transmission section and the upward and downward adjustment limits of the energy storage adjustment amount, the adjustment amounts of each individual load data and energy storage data in the adjustable resource data are obtained.

13. The power grid adjustable resource regulation capability assessment system based on network constraints according to claim 12, characterized in that, The functionality of the correction module is implemented using the following method: Based on the operational feasible domain of individual adjustable resource data, the actual active power of adjustable resources within a fixed time period is obtained, and the change data of actual active power is obtained. Based on the actual active power changes in the individual adjustable resource data, as well as the adjustment amounts in the load and energy storage data, the operational feasibility domain of the individual adjustable resource data is corrected.

14. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating the regulation capability of power grid adjustable resources based on network constraints, as described in any one of claims 1 to 8.

15. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the regulation capability of power grid adjustable resources based on network constraints as described in any one of claims 1 to 8.

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