A peak load flexibility assessment method for systems with a high proportion of renewable energy

Abstract

The present invention provides a method for evaluating the peak-shaving flexibility of a system with a high proportion of renewable energy, obtaining the forecast data of the day-ahead output of energy storage units, adjustable generator units, renewable energy units and loads; performing calculations, sampling and first-order difference processing on the data to obtain the fluctuation time series of the system's flexibility supply and flexibility demand; obtaining the upward and downward adjustment supply and demand flexibility evaluation indicators from the perspective of the system output supply and demand balance, obtaining the flexibility evaluation indicators under the operating condition data from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy consumption capacity, changing meteorological conditions and data such as the capacity of energy storage and renewable energy units, and obtaining the flexible changes of the system under different influencing factors. The present invention can achieve quantitative estimation and has wide applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids with a high proportion of renewable energy, and in particular relates to a method for evaluating the peak load flexibility of a system containing a high proportion of renewable energy. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of renewable energy such as photovoltaics, the environmental pollution and energy consumption caused by traditional thermal power generation have been reduced. However, at the same time, the expansion of the scale of renewable energy has also caused a decrease in the proportion of conventional power sources, reducing the adjustable resources of the power grid. In the process of renewable energy grid connection, on the one hand, since photovoltaics and other power sources have almost no active and flexible adjustment capabilities, the volatility of their output increases the peak-shaving demand of the power grid, resulting in a decrease in the flexibility of power grid operation; on the other hand, renewable energy has the characteristics of random output. During its peak output, due to the limited system absorption capacity, it will also cause waste of electricity such as abandoned light. In order to meet the above challenges, on the one hand, we should further tap the potential of existing resource regulation and give full play to its regulation capacity through flexible optimization of conventional power sources; on the other hand, we should adopt new regulation methods such as energy storage to enrich and expand the grid regulation measures to meet the flexibility needs of the grid. This makes it very necessary to quantitatively evaluate the flexibility of power grids with a high proportion of renewable energy.

[0004] At present, there have been studies on the evaluation of peak-shaving flexibility of power grids with a high proportion of renewable energy. Most common evaluation methods estimate and study the flexibility of the system from a certain perspective, such as unit static parameters, unit adjustment range or line transmission capacity, etc. According to the inventors' understanding, this evaluation method often results in conservative evaluation results and lacks dynamic real-time performance. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a peak-shaving flexibility assessment method for a system containing a high proportion of renewable energy. The present invention starts from the perspective of the real-time supply and demand balance relationship of the system output, and on the basis of studying the insufficient flexibility peak-shaving capacity of the system when the output supply is less than the demand, the present invention innovatively incorporates the expected calculation of insufficient renewable energy consumption of the system when the output supply is greater than the demand into the scope of flexibility assessment, thereby achieving quantitative estimation.

[0006] According to some embodiments, the present invention adopts the following technical solutions:

[0007] A method for evaluating the peak load flexibility of a system containing a high proportion of renewable energy comprises the following steps:

[0008] Obtain the day-ahead output forecast data of energy storage units, adjustable generator units, renewable energy units and loads, pre-process the data, and obtain the fluctuation time series of system flexibility supply and flexibility demand;

[0009] From the perspective of the balance between supply and demand of system output, the supply and demand flexibility evaluation indexes for upward and downward adjustment are obtained. The flexibility evaluation indexes under the corresponding operating conditions are calculated from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy absorption capacity. The peak-shaving flexibility under the operating conditions is determined based on the evaluation indexes.

[0010] Change one of the data in the energy storage unit, adjustable generator unit, renewable energy unit and load in turn, repeat the above steps, determine the change of system flexibility under different influencing factors, and obtain the peak-shaving flexibility evaluation results under different working conditions.

[0011] As an optional implementation method, the specific process of obtaining the day-ahead output forecast data of the energy storage unit, the adjustable generator unit, the renewable energy unit and the load includes:

[0012] The meteorological conditions of the high-proportion renewable energy power grid in the target area, as well as the day-ahead output data of the generator sets, energy storage units, renewable energy units and loads are collected. By performing curve fitting and clustering on the data, the output curves of various types of units and loads under typical operating conditions are screened out.

[0013] As a further limitation, the output of the energy storage unit and the adjustable generator unit is added together to obtain the flexibility supply curve; the load output and the output of the renewable energy unit are subtracted to obtain the net load output curve, which is the flexibility demand curve.

[0014] As an optional implementation, the process of preprocessing the data includes:

[0015] The system output supply curve is sampled at a set length time scale and processed by first-order difference to obtain the upward and downward flexibility supply per unit time of the system;

[0016] The system net load output curve, i.e., the system output demand curve, is sampled and first-order differencing is performed to obtain the fluctuating power time series of the system net load, which is then decomposed into the system upward flexibility demand and downward flexibility demand per unit time according to its power direction.

[0017] As an optional implementation, the calculation process of the supply and demand flexibility evaluation index includes obtaining the fluctuation time series of the system flexibility supply and demand per unit time through first-order difference calculation based on the output time series of the system flexibility supply and demand, and calculating the supply and demand flexibility evaluation index ΔP from the perspective of system processing supply and demand balance:

[0018]

[0019] Where ΔP s,up To increase the flexibility supply per unit time of the system, ΔP d,up The flexibility requirement per unit time is increased, ΔP s,down The flexibility supply per unit time of the system is adjusted downward, ΔP d,down The flexibility requirement per unit time is adjusted downward, ΔP net is the fluctuating power time series of the system net load.

[0020] As an optional implementation, the flexibility assessment indicators include expected renewable energy consumption shortfall, expected peak load shortfall and flexibility deficiency probability.

[0021] As a further limitation, the calculation process of the expected shortfall in renewable energy consumption includes: selecting an output segment where power supply is greater than demand based on the real-time supply and demand relationship of the system output, and calculating the expected shortfall in renewable energy consumption from the perspective of flexible power surplus.

[0022] As a further limitation, the calculation process of the expected peak-shaving deficit includes: according to the output sections where the system peak-shaving flexibility supply is less than the flexibility demand, calculating the power deficit of each output section when the supply and demand flexibility evaluation index is less than zero, reflecting the severity of the system flexibility deficit.

[0023] As a further limitation, the calculation process of the probability of insufficient flexibility includes: according to the output sections where the system peak-shaving flexibility supply is less than the flexibility demand, calculating the proportion of each output section in the total output sections when the supply and demand flexibility evaluation index is less than zero, so as to reflect the stability and dynamic balance of the system operation with the probability of insufficient peak-shaving flexibility.

[0024] A peak load flexibility assessment system for a system with a high proportion of renewable energy, comprising:

[0025] A data acquisition module is configured to acquire the day-ahead output forecast data of the energy storage unit, the adjustable generator unit, the renewable energy unit and the load, and pre-process the data to obtain the fluctuation time series of the system flexibility supply and flexibility demand;

[0026] The evaluation index calculation module is configured to obtain the supply and demand flexibility evaluation index of upward adjustment and downward adjustment from the perspective of the supply and demand balance of the system output, calculate the flexibility evaluation index under the corresponding working condition data from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy consumption capacity, and determine the peak-shaving flexibility under the working condition according to the evaluation index;

[0027] The iterative calculation module is configured to change one of the data in the energy storage unit, the adjustable generator unit, the renewable energy unit and the load in turn, re-trigger the above modules, determine the change of system flexibility under different influencing factors, and obtain the peak-shaving flexibility evaluation results under different working conditions.

[0028] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0029] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps in the above method are completed.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention takes into account the real-time supply-demand balance of the system output, and considers both the problem of insufficient system peak-shaving capacity when supply is less than demand and the waste of abandoned light caused by insufficient renewable energy consumption when supply exceeds demand. This makes the evaluation of system flexibility more effective and comprehensive.

[0032] (2) The method proposed in the present invention can not only study the impact of different meteorological conditions and the proportion of renewable energy units on the flexibility of system supply and demand, but also set constraints based on the results of the system's insufficient flexibility and surplus value estimation to study the type and capacity optimization configuration strategy of the energy storage system.

[0033] (3) The flexibility assessment method proposed in the present invention is based on the calculation and analysis of the measured data of the power grid, which ensures the authenticity and validity of the assessment conclusion;

[0034] (4) The present invention has a wide range of applications. It can be used to study the impact of the proportion of renewable energy units on the flexibility of system supply and demand under different meteorological conditions; it can also be used to set constraints based on the results of system flexibility deficiency and surplus value estimation to study the type and capacity optimization configuration strategy of the energy storage system.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] Figure 1 A method for evaluating the peak-shaving flexibility of a system containing a high proportion of renewable energy is provided in an embodiment of the present invention.

[0038] FIG. 2( a ) is a daily power output curve of a photovoltaic generator set under typical sunny weather conditions provided by an embodiment of the present invention.

[0039] FIG2( b ) is a daily output curve of an adjustable unit including energy storage provided in an embodiment of the present invention.

[0040] FIG. 2( c ) is a load-day output prediction curve provided by an embodiment of the present invention.

[0041] Figure 3 The output comparison curve of system flexibility supply and flexibility demand provided in the embodiment of the present invention.

[0042] FIG. 4( a ) is a time series of fluctuations in system flexibility supply per unit time provided in an embodiment of the present invention.

[0043] FIG4( b ) is a time series of fluctuations in system flexibility demand per unit time provided by an embodiment of the present invention.

[0044] FIG. 5( a ) is a diagram showing an upward adjustment flexibility supply and demand evaluation index ΔP provided in an embodiment of the present invention up .

[0045] FIG. 5( b ) is a downward adjustment flexibility supply and demand evaluation index ΔP provided in an embodiment of the present invention down .

[0046] FIG. 6( a ) shows changes in the flexibility index of the system under different photovoltaic unit data provided by an embodiment of the present invention.

[0047] FIG6( b ) shows the changes in the flexibility index of the system under different energy storage unit data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0051] like Figure 1 As shown, a peak-shaving flexibility assessment method for a system containing a high proportion of renewable energy is provided in this embodiment. The present invention comprehensively assesses and analyzes the peak-shaving flexibility when the system has insufficient output and surplus output from the perspective of power supply and demand balance, thereby overcoming the shortcomings of traditional assessment methods.

[0052] The specific steps include:

[0053] Step 1: Read the day-ahead output forecast data of energy storage units, adjustable generator units, renewable energy units and loads, filter, sample and perform first-order difference processing on the data to obtain the fluctuation time series of system flexibility supply and flexibility demand;

[0054] Step 2: From the perspective of the system output supply and demand balance, the supply and demand flexibility evaluation index ΔP for upward adjustment and downward adjustment is obtained, and then the flexibility evaluation index under the operating condition data is obtained from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy consumption capacity, including: insufficient renewable energy consumption expectation, peak-shaving shortage expectation and flexibility deficiency probability;

[0055] Step 3: Change data such as the capacity of energy storage and renewable energy units, repeat steps 2 and 3, and obtain the changes in system flexibility under different influencing factors.

[0056] The specific process of step 1 is as follows:

[0057] Step 1.1 Through field research, collect the meteorological conditions of a high-proportion renewable energy power grid in a certain area, as well as the output data of the generator set, energy storage unit, renewable energy unit and load on the previous day, and screen out the output curves of various types of units and loads under typical working conditions by curve fitting and clustering the data. The output data selected by the embodiment of the present invention are shown in Figures 2(a), (b), and (c), respectively. Figure 2(a) is the daily output curve of the photovoltaic unit under typical sunny weather provided by the embodiment of the present invention, Figure 2(b) is the daily output curve of the adjustable unit including energy storage provided by the embodiment of the present invention, and Figure 2(c) is the load daily output prediction curve provided by the embodiment of the present invention.

[0058] Step 1.2: Add the output of the energy storage unit and the adjustable generator unit to get the flexibility supply curve; subtract the load output from the output of the renewable energy unit to get the net load output curve, which is the flexibility demand curve. By comparing the system output curves, we can get the real-time correspondence between flexibility supply and demand. For example, Figure 3 The system flexibility supply and flexibility demand output comparison curve provided in the embodiment of the present invention is shown.

[0059] The basic operating principle of the power system is power balance, which means that the power system needs to ensure the balance of power supply and demand in real time. When the system flexibility meets the balance of power supply and demand, the power relationship between the unit and the load can be expressed by the equation:

[0060] K G ·C G +P storage =(1+μ)·P load -P res (1)

[0061] Among them, K G is the technical output coefficient of conventional power sources, usually 1.0 for large conventional thermal power plants; C G is the startup capacity of the conventional power supply; P storage The energy storage system at that moment contributes to the energy storage system; P res The output of renewable energy units at that moment; P load is the system load output at that moment; μ is the reserve factor of the generator set.

[0062] When the adjustable unit operates at the minimum normal operating power state and the energy storage unit is charged at the maximum power, the load still cannot fully absorb the renewable energy output, that is, when the system flexibility cannot meet the output supply and demand balance, the output supply exceeds the demand and the renewable energy consumption problem occurs. The power relationship between the unit and the load can be expressed by the equation:

[0063] K G,min ·C G +P storage,in,max ≥(1+μ)·P load -P res (2)

[0064] Among them, K G,min P is the minimum technical output coefficient of conventional power sources, which is usually 0.5 when large conventional thermal power plants are in minimum operation state; storage,in,max It is the maximum charging power of the energy storage system at that moment.

[0065] Step 1.3 defines the upward (downward) flexibility supply per unit time of the system as: the difference between the upper (lower) limit of the system flexibility resource output at a certain moment and the system flexibility resource output at the previous moment. The system output supply curve is sampled at a 15-minute time scale and first-order difference processing is performed to obtain the upward (downward) flexibility supply per unit time of the system. Specifically, as shown in the fluctuation time series of the system flexibility supply per unit time provided in the embodiment of the present invention in Figure 4 (a), they are respectively recorded as: ΔP s,up and ΔP s,down .

[0066] ΔP s,up = {ΔPs,up,t |ΔP s,up,t =P s,up,max -P s,t-1} (3)

[0067] ΔP s,down = {ΔP s,down,t |ΔP s,down,t =P s,t-1 -P s,down,max} (4)

[0068] Where P s,up,max is the upper bound of the system flexibility resource output at a certain moment, P s,down,max is the lower bound of the system flexibility resource output at a certain moment, P s,t-1 It is the system flexibility resource output value at the previous moment.

[0069] Corresponding to the system unit time increase (decrease) flexibility supply, the system net load output curve, that is, the system output demand curve, is sampled and first-order differentiated to obtain the system net load fluctuation power time series ΔP net , and then decomposed into the upward flexibility demand and downward flexibility demand of the system per unit time according to its power direction. Specifically, as shown in the fluctuation time series of the system flexibility demand per unit time provided in the embodiment of the present invention in FIG4(b), they are respectively denoted as: ΔP d,up and ΔP d,down .

[0070] ΔP d,up = {ΔP d,up,t |ΔP d,up,t =max(ΔP net,t ,0)} (5)

[0071] ΔP d,down = {ΔP d,up,t |ΔP d,up,t =-min(ΔP net,t ,0)} (6)

[0072] The specific process of step 2 is as follows:

[0073] Step 2.1 Since renewable energy has the characteristic of random output, during its peak output period, it will cause waste of electricity such as abandoned light due to the limited system load absorption capacity and charging speed of energy storage units.

[0074] like Figure 3 As shown in the system flexibility supply and flexibility demand output comparison curve provided in the embodiment of the present invention, by subtracting the system's real-time flexibility supply and demand output curves, it is possible to obtain the system power surplus when the output supply is greater than the demand, that is, the insufficient renewable energy consumption.

[0075] Define the expected index \(E\) of insufficient consumption of renewable energy margin , which represents the part of the power supply that cannot be fully consumed by the energy storage units and the load when the adjustable generating units in the system operate at the minimum normal operating power. It can characterize the overall flexibility peak shaving surplus of the system, that is, the situation of insufficient consumption capacity of renewable energy output. The larger this index is, the more renewable energy the system cannot consume, and the more power waste such as light curtailment is caused. The expected value of insufficient consumption of renewable energy in the system can be expressed by the equation:

[0076]

[0077] where \(P\) d,i is the output of the power generation and energy storage units in the \(i\)-th output segment, \(P\) net,i is the net load output of the system in the \(i\)-th output segment, and \(N\) is the number of sampling points on the output curve.

[0078] Step 2.2 Subtract the time series of the fluctuations of the upward (downward) flexibility supply and demand of the system per unit time. From the perspective of the balance between the supply and demand of the system output, the flexibility evaluation index \(\Delta P\) of the system supply and demand is obtained. Specifically, as shown in Fig. 5(a) the upward flexibility supply and demand evaluation index \(\Delta P\) provided in the embodiment of the present invention up and Fig. 5(b) the downward flexibility supply and demand evaluation index \(\Delta P\) provided in the embodiment of the present invention down are shown, and can be expressed by the equation:

[0079]

[0080] where \(\Delta P\) s,up is the upward flexibility supply per unit time of the system, \(\Delta P\) d,up is the upward flexibility demand per unit time, \(\Delta P\) s,down is the downward flexibility supply per unit time of the system, \(\Delta P\) d,down is the downward flexibility demand per unit time, and \(\Delta P\) net is the time series of the fluctuating power of the system net load.

[0081] When \(\Delta P>0\), the flexibility demand is less than the flexibility supply, and the flexibility demand is met. \(\Delta P\) is the surplus amount of flexibility resources in this output segment. When \(\Delta P < 0\), the flexibility demand is greater than the flexibility supply, and the flexibility demand is not fully met. \(\Delta P\) is the shortage amount of flexibility resources in this output segment. The smaller \(\Delta P\) is, the more serious the lack of flexibility of the system is.

[0082] Step 2.3 Define the expected index \(E\) of peak shaving deficit lackThis indicator reflects the severity of the system flexibility shortage by counting the output sections where the system peak-shaving flexibility supply is less than the flexibility demand, and calculating the power shortage of each output section when ΔP<0. The peak-shaving shortage expectation can be expressed by the equation:

[0083]

[0084] Where ΔP lack,j is the system supply and demand flexibility index in the jth insufficient flexibility output segment, n is the number of insufficient flexibility output segments, and N is the number of sampling points on the output curve.

[0085] Define the probability of insufficient flexibility R lack This indicator is calculated by counting the output sections where the system's peak-shaving flexibility supply is less than the flexibility demand, and calculating the proportion of each output section in the total output section when ΔP < 0, so as to reflect the stability and dynamic balance of the system operation with the probability of insufficient peak-shaving flexibility. The probability of insufficient flexibility can be expressed by the equation:

[0086]

[0087] Where n is the number of output sections with insufficient flexibility, and N is the number of sampling points on the output curve.

[0088] The specific process of step 3 is as follows:

[0089] Step 3.1 records the system peak-shaving flexibility evaluation index calculated under the working conditions of Example 1, including: expected insufficient renewable energy consumption, expected peak-shaving shortfall, and probability of insufficient flexibility. While keeping the adjustable units, energy storage units, and loads constant, change the proportion of photovoltaic unit capacity to the total unit capacity in the initial working conditions of Example 1, substitute the data and repeat steps 2 and 3 to obtain the changes in system flexibility evaluation indexes under different proportions of photovoltaic units. Specifically, as shown in Figure 6(a), the changes in system flexibility indicators under different photovoltaic unit data provided in an embodiment of the present invention.

[0090] Based on the changes in the system flexibility index under different photovoltaic unit data provided by the embodiment of the present invention in Figure 6 (a), the influence mechanism of different photovoltaic unit proportions on system flexibility is analyzed. From the perspective of output supply and demand balance, as the proportion of photovoltaic units continues to increase, the probability of insufficient renewable energy consumption, peak load capacity vacancies and insufficient peak load flexibility in the system gradually increases, and the system flexibility is reduced overall, which is in line with the current situation that a high proportion of renewable energy will cause difficulties in system peak load and consumption.

[0091] Step 3.2 records the system peak-shaving flexibility evaluation index calculated under the working conditions of Example 1, including: expected insufficient renewable energy consumption, expected peak-shaving shortfall, and probability of insufficient flexibility. While keeping the adjustable units, photovoltaic units, and loads constant, change the capacity of the energy storage unit in the initial working conditions of Example 1, substitute the data and repeat steps 2 and 3 to obtain the changes in the system flexibility evaluation index under different energy storage unit capacities. Specifically, as shown in Figure 6(b), the changes in the system flexibility index under different energy storage unit data provided by the embodiment of the present invention.

[0092] Based on the changes in the flexibility index of the system under different energy storage unit data provided by the embodiment of the present invention in Figure 6 (b), the influence mechanism of different energy storage unit capacities on system flexibility is analyzed. From the perspective of output supply and demand balance, with the continuous increase in the capacity of energy storage units, the probability of insufficient renewable energy absorption, peak-shaving capacity vacancies and insufficient peak-shaving flexibility in the system gradually decreases, and the flexibility of the system is comprehensively improved, which is in line with the current situation that energy storage systems can solve the problems of power fluctuations and abandoned light absorption in power grids with a high proportion of renewable energy through power regulation and energy storage.

[0093] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for evaluating the peak load flexibility of a system with a high proportion of renewable energy. Its characteristics are: The following steps are involved: Obtain the day-ahead output forecast data of energy storage units, adjustable generator units, renewable energy units and loads, pre-process the data, and obtain the fluctuation time series of system flexibility supply and flexibility demand; From the perspective of the balance between supply and demand of system output, the supply and demand flexibility evaluation indexes for upward and downward adjustment are obtained. The flexibility evaluation indexes under the corresponding operating conditions are calculated from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy absorption capacity. The peak-shaving flexibility under the operating conditions is determined based on the evaluation indexes. Change one of the data in the energy storage unit, adjustable generator unit, renewable energy unit and load in turn, repeat the above steps, determine the change of system flexibility under different influencing factors, and obtain the peak load flexibility evaluation results under different working conditions; The process of preprocessing data includes: The system output supply curve is sampled at a set length time scale and processed by first-order difference to obtain the upward and downward flexibility supply per unit time of the system; The net load output curve of the system, i.e., the output demand curve of the system, is sampled and first-order differentiated to obtain the fluctuating power time series of the net load of the system, which is decomposed into the upward flexibility demand and downward flexibility demand of the system per unit time according to its power direction; The fluctuation time series of the upward and downward flexibility supply of the system per unit time is and : Among them is the upper bound of the output of system flexibility resources at a certain moment, is the lower bound of the output of system flexibility resources at a certain moment, is the output value of system flexibility resources at the previous moment; The fluctuation time series of the upward and downward flexibility demands of the system flexibility per unit time is: and : in is the fluctuating power time series of the system net load; The calculation process of the supply and demand flexibility evaluation index includes obtaining the fluctuation time series of system flexibility supply and demand per unit time through first-order difference calculation based on the output time series of system flexibility supply and demand, and calculating the supply and demand flexibility evaluation index from the perspective of system processing supply and demand balance. : in Increase flexibility supply per unit time for the system, Increase flexibility requirements per unit time, Adjust the flexibility supply to the system unit time downward, Lower flexibility requirements per unit time, is the fluctuating power time series of the system net load; The flexibility evaluation indicators include the expected shortfall in renewable energy consumption, the expected shortfall in peak load regulation and the probability of insufficient flexibility; The calculation process of the expected insufficient consumption of renewable energy includes: selecting the output section where the power supply is greater than the demand according to the real-time supply and demand relationship of the system output, and calculating the expected insufficient consumption of renewable energy from the perspective of flexible power surplus. The expected insufficient consumption of renewable energy is: in is the output of the power generation and energy storage units in the i-th output section, is the net load output of the system in the i-th output section, and N is the number of sampling points on the output curve; The calculation process of the expected peak load shortage includes: according to the output section where the system peak load flexibility supply is less than the flexibility demand, the power shortage of each output section when the supply and demand flexibility evaluation index is less than zero is calculated to reflect the severity of the system flexibility shortage. The expected peak load shortage is: in is the system supply and demand flexibility index in the jth inflexibility-deficient output segment, n is the number of inflexibility-deficient output segments, and N is the number of sampling points on the output curve; The calculation process of the probability of insufficient flexibility includes: according to the output section where the system peak-shaving flexibility supply is less than the flexibility demand, calculating the proportion of each output section in the total output section when the supply and demand flexibility evaluation index is less than zero, and reflecting the stability and dynamic balance of the system operation with the probability of insufficient peak-shaving flexibility. The probability of insufficient flexibility is: Where n is the number of output sections with insufficient flexibility, and N is the number of sampling points on the output curve; The flexibility supply curve is obtained by adding the output of the energy storage unit and the adjustable generator unit; the net load output curve is obtained by subtracting the load output from the output of the renewable energy unit, which is the flexibility demand curve.

2. A method for evaluating the peak load flexibility of a system containing a high proportion of renewable energy as claimed in claim 1, Its characteristics are: The specific process of obtaining the day-ahead output forecast data of energy storage units, adjustable generator units, renewable energy units and loads includes: The meteorological conditions of the high-proportion renewable energy power grid in the target area, as well as the day-ahead output data of the generator sets, energy storage units, renewable energy units and loads are collected. By performing curve fitting and clustering on the data, the output curves of various types of units and loads under typical operating conditions are screened out.

3. A peak load flexibility assessment system for systems with a high proportion of renewable energy, Its characteristics are: include: A data acquisition module is configured to acquire the day-ahead output forecast data of the energy storage unit, the adjustable generator unit, the renewable energy unit and the load, and pre-process the data to obtain the fluctuation time series of the system flexibility supply and flexibility demand; The evaluation index calculation module is configured to obtain the supply and demand flexibility evaluation index of upward adjustment and downward adjustment from the perspective of the supply and demand balance of the system output, calculate the flexibility evaluation index under the corresponding working condition data from the two aspects of insufficient system peak-shaving supply capacity and insufficient renewable energy consumption capacity, and determine the peak-shaving flexibility under the working condition according to the evaluation index; The iterative calculation module is configured to sequentially change one of the data in the energy storage unit, the adjustable generator unit, the renewable energy unit and the load, re-trigger the above modules, determine the change of system flexibility under different influencing factors, and obtain the peak load flexibility evaluation results under different working conditions; The process of preprocessing data includes: The system output supply curve is sampled at a set length time scale and processed by first-order difference to obtain the upward and downward flexibility supply per unit time of the system; The net load output curve of the system, i.e., the output demand curve of the system, is sampled and first-order differentiated to obtain the fluctuating power time series of the net load of the system, which is decomposed into the upward flexibility demand and downward flexibility demand of the system per unit time according to its power direction; The fluctuation time series of the upward and downward flexibility supply of the system per unit time is and : in The upper limit of the system flexibility resource contribution at a certain moment, The lower bound of the system flexibility resource contribution at a certain moment, It is the output value of system flexibility resources at the previous moment; The fluctuation time series of the upward and downward flexibility demands of the system flexibility per unit time is: and : in is the fluctuating power time series of the system net load; The calculation process of the supply and demand flexibility evaluation index includes obtaining the fluctuation time series of system flexibility supply and demand per unit time through first-order difference calculation based on the output time series of system flexibility supply and demand, and calculating the supply and demand flexibility evaluation index from the perspective of system processing supply and demand balance. : in Increase flexibility supply per unit time for the system, Increase flexibility requirements per unit time, Adjust the flexibility supply to the system unit time downward, Lower flexibility requirements per unit time, is the fluctuating power time series of the system net load; The flexibility evaluation indicators include the expected shortfall in renewable energy consumption, the expected shortfall in peak load regulation and the probability of insufficient flexibility; The calculation process of the expected insufficient consumption of renewable energy includes: selecting the output section where the power supply is greater than the demand according to the real-time supply and demand relationship of the system output, and calculating the expected insufficient consumption of renewable energy from the perspective of flexible power surplus. The expected insufficient consumption of renewable energy is: in is the output of the power generation and energy storage units in the i-th output section, is the net load output of the system in the i-th output section, and N is the number of sampling points on the output curve; The calculation process of the expected peak load shortage includes: according to the output section where the system peak load flexibility supply is less than the flexibility demand, the power shortage of each output section when the supply and demand flexibility evaluation index is less than zero is calculated to reflect the severity of the system flexibility shortage. The expected peak load shortage is: in is the system supply and demand flexibility index in the jth inflexibility-deficient output segment, n is the number of inflexibility-deficient output segments, and N is the number of sampling points on the output curve; The calculation process of the probability of insufficient flexibility includes: according to the output section where the system peak-shaving flexibility supply is less than the flexibility demand, the proportion of each output section in the total output section when the supply and demand flexibility evaluation index is less than zero is calculated, and the probability of insufficient peak-shaving flexibility is used to reflect the stability and dynamic balance of system operation. The probability of insufficient flexibility is: Where n is the number of output sections with insufficient flexibility, and N is the number of sampling points on the output curve; The flexibility supply curve is obtained by adding the output of the energy storage unit and the adjustable generator unit; the net load output curve is obtained by subtracting the load output from the output of the renewable energy unit, which is the flexibility demand curve.

4. A peak load flexibility assessment system for a system with a high proportion of renewable energy as claimed in claim 3, Its characteristics are: The specific process of obtaining the day-ahead output forecast data of energy storage units, adjustable generator units, renewable energy units and loads includes: The meteorological conditions of the high-proportion renewable energy power grid in the target area, as well as the day-ahead output data of the generator sets, energy storage units, renewable energy units and loads are collected. By performing curve fitting and clustering on the data, the output curves of various types of units and loads under typical operating conditions are screened out.

5. An electronic device, Its characteristics are: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 2 are completed.

6. A computer-readable storage medium, Its characteristics are: Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 2.

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