Method and system for evaluating and processing flexibility of coal power unit of electric power system

Abstract

The invention discloses a flexibility evaluation processing method and system for a coal power unit of an electric power system, and the method comprises the steps: obtaining a wind-solar output curve and a load curve according to the obtained unit parameter data and historical operation data of a renewable energy unit; determining flexibility demand data of a power supply side of the power system based on a time sequence production simulation technology; determining flexible supply capability data of the coal power unit according to operation configuration data of the coal power unit in the power system; and performing comparative analysis on the flexibility demand data and the flexibility supply capability data, determining a flexibility evaluation result of the coal power unit of the power system based on an analysis result, and generating an optimization processing strategy based on the flexibility evaluation result. According to the flexibility evaluation processing method for the coal power unit of the electric power system, the flexibility evaluation processing of the coal power unit can be realized, the flexible regulation and control potential of the coal power unit can be excavated, the flexibility adequacy degree of the electric power system can be judged, and a theory and a basis are provided for the flexibility transformation decision of the coal power unit.

Description

Technical Field

[0001] The present invention relates to the field of power grid energy technology, and in particular to a method and system for evaluating the flexibility of coal-fired power generation units in a power system. Background Art

[0002] The large-scale development and utilization of renewable energy will help reduce dependence on traditional fossil fuels, such as coal, oil and natural gas. The exploitation and use of these traditional energy sources not only face the crisis of resource depletion, but also bring serious environmental pollution and greenhouse gas emissions. Therefore, the introduction of renewable energy is an important way to promote the transformation from traditional energy to clean energy.

[0003] Nowadays, in addition to traditional coal-fired power, the power system has also introduced a large number of renewable energy units, led by wind power and photovoltaic power generation. However, since wind and solar power and other renewable energy sources are affected by meteorological conditions and geographical factors, their output is significantly intermittent and random, so the safe and stable operation of the power system is challenged. In the existing technology, only the operating conditions of renewable energy are analyzed separately, and there is a lack of analysis of the flexibility of coal-fired power units. It cannot meet the evaluation and processing of today's power grid system that combines new and old energy, which is not conducive to the optimization and control of the power grid system. Summary of the invention

[0004] The present invention provides a method and system for evaluating the flexibility of coal-fired power units in an electric power system. In an electric power system that introduces renewable energy, specific method steps are designed to implement flexibility evaluation and processing of coal-fired power units, which helps to tap the potential for flexible regulation of coal-fired power units, judge the degree of flexibility of the electric power system, and provide a theory and basis for decision-making on flexibility transformation of coal-fired power units.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for evaluating the flexibility of coal-fired power units in a power system, which is applied to a power system containing a high proportion of renewable energy units, wherein the renewable energy units include at least wind turbines and photovoltaic units, including:

[0006] Obtaining a wind and solar output curve and a load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit;

[0007] Based on the time-series production simulation technology, at least the wind and solar power output curve and the load curve are processed to determine the flexibility demand data on the power supply side of the power system; and, according to the operation configuration data of the coal-fired power units in the power system, the flexibility supply capacity data of the coal-fired power units are determined;

[0008] Comparing and analyzing the flexibility demand data and the flexibility supply capacity data, and determining the flexibility evaluation results of the coal-fired power units in the power system based on the analysis results, wherein the flexibility evaluation results at least reflect three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility;

[0009] Based on the flexibility evaluation results, optimization processing strategies matching the power supply side, grid side and demand side of the power system are generated respectively.

[0010] As one of the preferred solutions, the flexibility demand data includes upward flexibility demand data and downward flexibility demand data, which are expressed by the following formula:

[0011]

[0012] Among them, F t NL,upd 、F t NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

[0013] As one of the preferred solutions, the flexibility supply capacity data of the coal-fired power unit is expressed by the following formula:

[0014]

[0015]

[0016]

[0017]

[0018] in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t, P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

[0019] As one of the preferred solutions, obtaining the wind and solar output curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit includes:

[0020] Performing data cleaning and normalization processing on the acquired unit parameter data and the historical operation data to obtain sample data, wherein the historical operation data at least includes wind speed, wind direction, light intensity, temperature and power generation;

[0021] Using the sample data to train a prediction model built based on neural network technology;

[0022] The prediction results output by the prediction model are processed based on time series technology to draw the wind and solar power output curve.

[0023] As one of the preferred solutions, the optimization processing strategy at least includes a coal-fired power unit transformation strategy, and the coal-fired power unit transformation strategy includes:

[0024] Based on artificial intelligence learning algorithms and optimization technologies, the operating data of the coal-fired power unit is learned and predicted to adjust the peak-shaving capacity and operating efficiency of the coal-fired power unit.

[0025] Another embodiment of the present invention provides a power system coal-fired power unit flexibility assessment and processing system, which is applied to a power system containing a high proportion of renewable energy units, wherein the renewable energy units include at least wind turbines and photovoltaic units, including:

[0026] A renewable energy module, used to obtain a wind and solar output curve and a load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit;

[0027] A flexibility data module is used to process at least the wind and solar output curve and the load curve based on the time-series production simulation technology to determine the flexibility demand data on the power supply side of the power system; and, according to the operation configuration data of the coal-fired power units in the power system, determine the flexibility supply capacity data of the coal-fired power units;

[0028] An evaluation module, used for comparing and analyzing the flexibility demand data and the flexibility supply capacity data, and determining the flexibility evaluation result of the coal-fired power unit in the power system based on the analysis result, wherein the flexibility evaluation result at least reflects three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility;

[0029] The optimization processing module is used to generate optimization processing strategies matching the power supply side, grid side and demand side of the power system based on the flexibility evaluation results.

[0030] As one of the preferred solutions, the flexibility demand data includes upward flexibility demand data and downward flexibility demand data, which are expressed by the following formula:

[0031]

[0032] Among them, F t NL,upd 、F t NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

[0033] As one of the preferred solutions, the flexibility supply capacity data of the coal-fired power unit is expressed by the following formula:

[0034]

[0035]

[0036]

[0037]

[0038] in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t, P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

[0039] As one preferred solution, the renewable energy module comprises:

[0040] A processing unit, configured to perform data cleaning and normalization processing on the acquired unit parameter data and the historical operation data to obtain sample data, wherein the historical operation data at least includes wind speed, wind direction, light intensity, temperature and power generation;

[0041] A training unit, used to train a prediction model constructed based on a neural network technology with the sample data;

[0042] A drawing unit is used to process the prediction results output by the prediction model based on time series technology to draw the wind and solar power output curve.

[0043] As one of the preferred solutions, the optimization processing strategy at least includes a coal-fired power unit transformation strategy, and the coal-fired power unit transformation strategy includes:

[0044] Based on artificial intelligence learning algorithms and optimization technologies, the operating data of the coal-fired power unit is learned and predicted to adjust the peak-shaving capacity and operating efficiency of the coal-fired power unit.

[0045] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0046] (1) This scheme analyzes the flexibility demand from the supply and demand perspective, taking into account the wind and solar power and load forecast errors, and considers the regulation characteristics of coal-fired power units to obtain the corresponding flexibility supply capacity, thereby providing accurate data support for subsequent comparative analysis and improving the accuracy and comprehensiveness of system flexibility analysis;

[0047] (2) The flexibility assessment results of this scheme reflect three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility. This can improve the quantitative estimation of flexibility assessment, help tap the potential of flexible regulation of coal-fired power units, judge the degree of flexibility of the power system, and provide a theory and basis for decision-making on flexibility transformation of coal-fired power units. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for evaluating the flexibility of coal-fired power generation units in a power system in one embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of a wind-solar output curve and a load curve in one embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of flexibility demand / supply in one embodiment of the present invention;

[0051] Figure 4 It is a schematic diagram showing that the flexibility of downward peak regulation in one embodiment of the present invention is not as expected;

[0052] Figure 5 It is a schematic diagram of the upward flexibility adjustment factor of a coal-fired power unit in one embodiment of the present invention;

[0053] Figure 6 It is a schematic diagram of the downward flexibility adjustment factor of a coal-fired power unit in one embodiment of the present invention;

[0054] Figure 7 It is a structural block diagram of a flexibility assessment and processing system for coal-fired power generation units in a power system in one embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0057] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0058] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0059] It should be noted in advance that the quantitative evaluation of the flexibility of the power system under the condition of high proportion of renewable energy access plays an important role in exploring the flexibility adjustment ability of coal-fired power units to maintain the balance of supply and demand of system flexibility. The present invention quantifies the flexibility of the renewable energy access system from the perspective of supply and demand based on the time series production simulation, and calculates the flexibility evaluation index of the coal-fired power unit by the time series production simulation. Specifically, an embodiment of the present invention provides a method for evaluating the flexibility of coal-fired power units in a power system. For details, please refer to Figure 1 , Figure 1 The flowchart of the method for evaluating the flexibility of coal-fired power units in a power system in one embodiment of the present invention is shown, which specifically includes steps S1 to S4:

[0060] S1. Obtaining a wind and solar output curve and a load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit;

[0061] S2. Based on the time-series production simulation technology, at least the wind and solar power output curve and the load curve are processed to determine the flexibility demand data on the power supply side of the power system; and, based on the operation configuration data of the coal-fired power units in the power system, the flexibility supply capacity data of the coal-fired power units are determined;

[0062] S3. Compare and analyze the flexibility demand data and the flexibility supply capacity data, and determine the flexibility evaluation results of the coal-fired power units in the power system based on the analysis results, wherein the flexibility evaluation results at least reflect three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility;

[0063] S4. Based on the flexibility evaluation result, generate optimization processing strategies matching the power supply side, grid side and demand side of the power system respectively.

[0064] The renewable energy units in this embodiment refer to wind turbines and photovoltaic units. With the increase in wind and solar penetration rates, when facing the flexibility needs of the system, due to the decrease in the proportion of coal-fired power units, the system's own flexibility supply capacity has gradually weakened. Therefore, it is necessary to tap the flexible adjustment potential of coal-fired power units to cope with the challenge of insufficient flexibility under high penetration rates. In the above embodiment, the effectiveness of the method of this embodiment is verified and analyzed on the basis of an improved IEEE-RTS24 node example system, in which the conventional units are replaced with coal-fired power units according to the capacity ratio, and wind power and photovoltaics are added thereto, where the rated capacities of wind farms and photovoltaic power stations are 600MW and 400MW. Assuming that each coal-fired power unit is an adjustable unit, the parameters of the coal-fired power units are shown in the following table.

[0065] Coal-fired power unit parameters

[0066]

[0067] Furthermore, in the above embodiment, in order to obtain the wind and solar output curve, it is first necessary to pre-process the acquired unit parameter data and historical operation data (such as wind speed, wind direction, light intensity, temperature and power generation), mainly for data cleaning and normalization, and then obtain sample data, and then based on the existing mature neural network model technology, use the sample data to train the prediction model built based on the neural network technology, and finally obtain the prediction data to draw the corresponding wind and solar output curve. For the load curve, preferably, the output curves of the wind farm and the photovoltaic power station can be superimposed with the load curve of the power system to obtain the equivalent load curve. In the superposition process, the wind and solar output needs to be treated as a negative load to reflect its contribution to the power supply of the power system, and then the load curve is obtained.

[0068] For details, see Figure 2 , Figure 2 A schematic diagram of a wind-solar output curve and a load curve in one embodiment of the present invention is shown (in the figure, a is the wind-solar output curve, and b is the load curve). After obtaining the above data, analysis and processing are performed based on the time-series production simulation technology. Time-series production simulation is a technology used to simulate the operation of the power system. It aims to simulate the operation of the power system in different time periods, including load demand, power generation output, grid constraints, etc., in order to evaluate the operation of the power system and optimize the power generation plan. Its purpose is to improve energy production efficiency, reduce energy production costs, and enhance the stability and reliability of the power system. Through time-series production simulation, the flexibility demand data on the power supply side of the power system and the flexibility supply capacity data of the coal-fired power units can be obtained. For details, please refer to Figure 3 , Figure 3 A flexibility demand / supply diagram is shown in one embodiment of the present invention, which sets the average relative error between new energy and load to 10%, calculates the flexibility demand of the system at the current penetration rate, and obtains the system flexibility supply from the flexibility supply of coal-fired power units, and draws the system flexibility supply and demand curve on an hourly time scale.

[0069] Furthermore, in the above embodiment, the flexibility demand data and the flexibility supply capacity data are compared and analyzed, and the flexibility evaluation results of the coal-fired power units in the power system are determined based on the analysis results. The following is a detailed description with reference to the formula.

[0070] The flexibility requirement on the power supply side is expressed by the following formula:

[0071]

[0072] Among them, F t NL,upd 、Ft NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

[0073] Within the time scale ΔT, the total flexibility demand of the system can be divided into three categories. t de Less than At this time, the system generates upward flexibility demand; when P t de Greater than The system generates downward flexibility requirements; P t de exist and When the system is in between, two-way flexibility is required.

[0074] The operational flexibility of coal-fired power units is mainly reflected in the power output range and ramp rate of the units. In the face of the upward / downward flexibility demand brought by wind, solar and load fluctuations, the flexibility supply of coal-fired power units within a given time scale is:

[0075]

[0076]

[0077]

[0078]

[0079] in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t (0-1), P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

[0080] In an embodiment of the present invention, the flexibility supply of the coal-fired power generation group minus the flexibility demand caused by wind power, photovoltaic power and load fluctuations is the flexible adjustment capacity available in the current system. When the upward / downward flexibility supply of the coal-fired power unit is less than the upward / downward flexibility demand of the system, that is, the flexibility supply of the coal-fired power unit is insufficient, it will lead to load shedding and wind and solar power abandonment. In order to measure the supply and demand of flexibility in the system, from the perspective of system operation, the embodiment of the present invention proposes the following flexibility indicators (i.e., flexibility evaluation results): flexibility shortage, flexibility adjustment factor, peak-shaving flexibility shortage expectation, and flexibility shortage probability.

[0081] (1) Flexibility deficit: reflects the shortage of flexibility supply and demand of coal-fired power units.

[0082] F t ins,up =F t NL,upd -F t g,ups

[0083] F t ins,dn =F t NL,dnd -F t g,dns

[0084] In the formula, F t ins,up 、F t ins,dn are the upward and downward flexibility deficits of the system at time t, respectively; F t NL,upd 、F t NL,dnd are the upward and downward flexibility requirements of the system at time t; F t g,ups 、F t g,dns is the upward / downward flexibility that the coal-fired power generation group can provide to the system at time t. The total flexibility deficit of the system can be obtained by summing up the flexibility deficit in the total time period. When the flexibility deficit at time t is less than 0, it means that the upward and downward flexibility at time t+1 are both sufficient, otherwise it means that the system has a flexibility deficit.

[0085] (2) Probability of insufficient flexibility: The probability of insufficient flexibility is defined as the ratio of the peak-shaving inflexibility insufficient time period to the total scheduling period. The probability of insufficient flexibility P LOF It can be calculated by the following formula.

[0086]

[0087] Where, T M M is the time period with insufficient peak load flexibility;t is the number of time periods with insufficient peak-shaving flexibility; T is the entire scheduling cycle.

[0088] (3) Expected peak-shaving flexibility shortage: The difference between flexibility supply and flexibility demand within a unit time scale reflects the system's flexibility adequacy and regulation speed. By counting the peak-shaving flexibility shortage sections within a cycle, the system's expected peak-shaving flexibility power shortage can be calculated, which is obtained by the following formula.

[0089]

[0090]

[0091] Where ΔP t up , ΔP t down are the insufficient flexibility of upward / downward peak regulation per unit time; H t , J t are the number of time periods with insufficient output flexibility for upward / downward peak regulation; T H 、T J They are respectively a collection of flexible insufficient output time periods for upward / downhill climbing.

[0092] ΔP t up If it is greater than 0, it means that the coal-fired power unit cannot meet the flexibility requirements brought by new energy and load fluctuations at the maximum output it can achieve, and load reduction will occur. t dn A value greater than 0 indicates that even at this time, the coal-fired power units are operating at the minimum output that can be achieved, but in the face of flexibility demands, some new energy sources cannot be absorbed.

[0093] (4) Flexibility adjustment factor: To measure the flexibility supply proportion of each coal-fired power unit in the entire system, the flexibility adjustment factor is defined as follows:

[0094]

[0095]

[0096] In the formula, They are respectively for flexibly adjusting the upward / downward flexibility supply of the power supply i; They are the proportion of flexible power source i that meets the upward / downward flexibility demand of the system at time t, reflecting the contribution of each coal-fired power unit to the flexibility balance of the power system.

[0097] When the sum of the flexibility adjustment factors of the coal-fired power generation groups is greater than 1, it means that the flexibility supply of the coal-fired power generation groups can fully meet the flexibility requirements of the system, and the system is a system with sufficient flexibility; when the sum of the flexibility adjustment factors is equal to 1, it means that the coal-fired power generation groups just meet the flexibility requirements of the system, and the system flexibility is neither sufficient nor redundant; if the flexibility adjustment factor is less than 1, it means that the system lacks flexibility.

[0098] Based on the flexibility supply and demand in each period, the above formula can be used to calculate the system's flexibility shortage probability and total flexibility shortage index. After calculation, the total flexibility shortage of the system is 2551.7317MW, and the flexibility shortage probability is 0.3958, which shows that the flexibility adjustment capacity of coal-fired power units is insufficient.

[0099] After calculation, the system peak load flexibility is less than the expected 106.3222MW. For details, please refer to Figure 4 , Figure 4 The figure shows a schematic diagram of insufficient downward peak-shaving flexibility in one of the embodiments of the present invention. When the wind and solar power outputs are large, in order to absorb as much wind and solar power as possible, the coal-fired power units operate at a low output level. The upward flexibility supply of the coal-fired power units at this time is sufficient to meet the upward flexibility demand generated by the system, and there is no shortage of upward peak-shaving flexibility. However, when downward flexibility demand arises, the coal-fired power units are limited by the output of the minimum unit, and there will be a serious shortage of downward peak-shaving flexibility.

[0100] For details, see Figure 5 , Figure 5 The figure shows a schematic diagram of the upward flexibility adjustment factor of a coal-fired power unit in one embodiment of the present invention, wherein the red dotted line indicates that the coal-fired power unit can fully meet the flexibility demand of the system. It can be seen that in all time periods, the flexibility adjustment factor is greater than 1, indicating that the upward flexibility supply capacity of the coal-fired power unit can fully meet the flexibility demand of the system.

[0101] For details, see Figure 6 , Figure 6 The figure shows a schematic diagram of the downward flexibility adjustment factor of the coal-fired power unit in one embodiment of the present invention. The downward flexibility supply capacity of the system in the entire time period is mainly provided by the G8 unit with the largest capacity, and the downward flexibility supply capacity of the coal-fired power unit is seriously insufficient during most of the time. The main reason is that when the output value of new energy is high, most coal-fired power units operate at the lowest output and basically have no downward flexibility supply capacity.

[0102] Furthermore, in the above embodiment, after the evaluation, an optimization processing strategy matching the power supply side, the grid side and the demand side of the power system will be generated. This is because the insufficient flexibility supply capacity of energy units is a complex problem and requires optimization and control from multiple aspects. For example, on the power supply side, a coal-fired power unit transformation strategy can be generated to accelerate the progress of thermal power flexibility transformation and improve the peak-shaving capacity and operating efficiency of coal-fired power units. Among them, advanced artificial intelligence algorithms and optimization technologies can be introduced to learn and predict the operating data of coal-fired power units, which will not be repeated here. For the grid side, the grid structure can be optimized to improve the flexibility and reliability of the grid. For the demand side, a demand response mechanism can be established and improved to guide users to adjust their electricity consumption behavior according to the needs of the power system.

[0103] For details, see Figure 7 , Figure 7 The structure block diagram of a power system coal-fired power unit flexibility assessment processing system in one embodiment of the present invention is shown, which is used in a power system containing a high proportion of renewable energy units, wherein the renewable energy units include at least wind turbines and photovoltaic units, including:

[0104] The renewable energy module 11 is used to obtain the wind and solar output curve and load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit;

[0105] The flexibility data module 12 is used to process at least the wind and solar power output curve and the load curve based on the time-series production simulation technology to determine the flexibility demand data of the power supply side of the power system; and determine the flexibility supply capacity data of the coal-fired power unit according to the operation configuration data of the coal-fired power unit in the power system;

[0106] An evaluation module 13 is used to compare and analyze the flexibility demand data and the flexibility supply capacity data, and determine the flexibility evaluation result of the coal-fired power unit in the power system based on the analysis result, wherein the flexibility evaluation result at least reflects three aspects: flexibility shortage, flexibility shortage probability and peak load flexibility shortage expectation;

[0107] The optimization processing module 14 is used to generate optimization processing strategies matching the power supply side, the grid side and the demand side of the power system respectively based on the flexibility evaluation result.

[0108] Furthermore, in the above embodiment, the flexibility requirement data includes upward flexibility requirement data and downward flexibility requirement data, which are expressed by the following formula:

[0109]

[0110] Among them, Ft NL,upd 、F t NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

[0111] Furthermore, in the above embodiment, the flexibility supply capacity data of the coal-fired power unit is expressed by the following formula:

[0112]

[0113]

[0114]

[0115]

[0116] in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t, P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

[0117] Furthermore, in the above embodiment, the renewable energy module includes:

[0118] A processing unit, configured to perform data cleaning and normalization processing on the acquired unit parameter data and the historical operation data to obtain sample data, wherein the historical operation data at least includes wind speed, wind direction, light intensity, temperature and power generation;

[0119] A training unit, used to train a prediction model constructed based on a neural network technology with the sample data;

[0120] A drawing unit is used to process the prediction results output by the prediction model based on time series technology to draw the wind and solar power output curve.

[0121] Furthermore, in the above embodiment, the optimization processing strategy at least includes a coal-fired power unit transformation strategy, and the coal-fired power unit transformation strategy includes:

[0122] Based on artificial intelligence learning algorithms and optimization technologies, the operating data of the coal-fired power unit is learned and predicted to adjust the peak-shaving capacity and operating efficiency of the coal-fired power unit.

[0123] The embodiment of the present invention provides a method and system for evaluating the flexibility of coal-fired power units in a power system, and the beneficial effects thereof are at least one of the following:

[0124] (1) This scheme analyzes the flexibility demand from the supply and demand perspective, taking into account the wind and solar power and load forecast errors, and considers the regulation characteristics of coal-fired power units to obtain the corresponding flexibility supply capacity, thereby providing accurate data support for subsequent comparative analysis and improving the accuracy and comprehensiveness of system flexibility analysis;

[0125] (2) The flexibility assessment results of this scheme reflect three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility. This can improve the quantitative estimation of flexibility assessment, help tap the potential of flexible regulation of coal-fired power units, judge the degree of flexibility of the power system, and provide a theory and basis for decision-making on flexibility transformation of coal-fired power units.

[0126] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating the flexibility of coal-fired power units in a power system, applied to a power system containing a high proportion of renewable energy units, wherein the renewable energy units include at least wind turbines and photovoltaic units, characterized in that: include: Obtaining a wind and solar output curve and a load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit; Based on the time-series production simulation technology, at least the wind and solar output curve and the load curve are processed to determine the flexibility demand data of the power supply side of the power system; and, determining the flexibility supply capacity data of the coal-fired power generation unit according to the operation configuration data of the coal-fired power generation unit in the power system; Comparing and analyzing the flexibility demand data and the flexibility supply capacity data, and determining the flexibility evaluation results of the coal-fired power units in the power system based on the analysis results, wherein the flexibility evaluation results at least reflect three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility; Based on the flexibility evaluation results, optimization processing strategies matching the power supply side, grid side and demand side of the power system are generated respectively.

2. The method for evaluating the flexibility of coal-fired power units in a power system according to claim 1, characterized in that: The flexibility demand data includes upward flexibility demand data and downward flexibility demand data, which are expressed by the following formula: Among them, F t NL,upd 、F t NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

3. The method for evaluating the flexibility of coal-fired power units in a power system according to claim 1, characterized in that: The flexibility supply capacity data of the coal-fired power unit is expressed by the following formula: in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t, P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

4. The method for evaluating the flexibility of coal-fired power units in a power system according to claim 1, characterized in that: The step of obtaining the wind and solar output curve of the renewable energy generator set according to the acquired generator set parameter data and historical operation data of the renewable energy generator set comprises: Performing data cleaning and normalization processing on the acquired unit parameter data and the historical operation data to obtain sample data, wherein the historical operation data at least includes wind speed, wind direction, light intensity, temperature and power generation; Using the sample data to train a prediction model built based on neural network technology; The prediction results output by the prediction model are processed based on time series technology to draw the wind and solar power output curve.

5. The method for evaluating the flexibility of coal-fired power units in a power system according to claim 1, characterized in that: The optimization treatment strategy at least includes a coal-fired power unit transformation strategy, and the coal-fired power unit transformation strategy includes: Based on artificial intelligence learning algorithms and optimization technologies, the operating data of the coal-fired power unit is learned and predicted to adjust the peak-shaving capacity and operating efficiency of the coal-fired power unit.

6. A power system coal-fired power unit flexibility assessment and processing system, applied to a power system containing a high proportion of renewable energy units, wherein the renewable energy units include at least wind turbines and photovoltaic units, characterized in that: include: A renewable energy module, used to obtain a wind and solar output curve and a load curve of the renewable energy unit according to the acquired unit parameter data and historical operation data of the renewable energy unit; A flexibility data module, used for processing at least the wind and solar output curve and the load curve based on a time-series production simulation technology to determine flexibility demand data on the power supply side of the power system; and, determining the flexibility supply capacity data of the coal-fired power generation units according to the operation configuration data of the coal-fired power generation units in the power system; An evaluation module, used for comparing and analyzing the flexibility demand data and the flexibility supply capacity data, and determining the flexibility evaluation result of the coal-fired power unit in the power system based on the analysis result, wherein the flexibility evaluation result at least reflects three aspects: flexibility shortage, probability of insufficient flexibility, and expectation of insufficient peak-shaving flexibility; The optimization processing module is used to generate optimization processing strategies matching the power supply side, grid side and demand side of the power system based on the flexibility evaluation results.

7. The power system coal-fired power unit flexibility assessment and processing system according to claim 6, characterized in that: The flexibility demand data includes upward flexibility demand data and downward flexibility demand data, which are expressed by the following formula: Among them, F t NL,upd 、F t NL,dnd are the upward flexibility demand data and downward flexibility demand data of the power system at time t, respectively, t de is the power supply side flexibility demand data at time t, They are the minimum and maximum adjustable power supply flexibility demand data at time t+1 respectively.

8. The power system coal-fired power unit flexibility assessment and processing system according to claim 6, characterized in that: The flexibility supply capacity data of the coal-fired power unit is expressed by the following formula: in, are the upward / downward flexibility supply data provided by coal-fired power unit i at time t, G is the number of coal-fired power units, u i,t is the preset variable of coal-fired power unit i at time t, P i max , P i min are the maximum and minimum output of coal-fired power unit i at time t, P i,t The real-time output of coal-fired power unit i at time t, are the upward / downward climbing rates of unit i respectively, and ΔT is the time scale.

9. The power system coal-fired power unit flexibility assessment and processing system according to claim 6, characterized in that: The renewable energy module comprises: A processing unit, configured to perform data cleaning and normalization processing on the acquired unit parameter data and the historical operation data to obtain sample data, wherein the historical operation data at least includes wind speed, wind direction, light intensity, temperature and power generation; A training unit, used to train a prediction model constructed based on a neural network technology with the sample data; A drawing unit is used to process the prediction results output by the prediction model based on time series technology to draw the wind and solar power output curve.

10. The power system coal-fired power unit flexibility assessment and processing system according to claim 6, characterized in that: The optimization treatment strategy at least includes a coal-fired power unit transformation strategy, and the coal-fired power unit transformation strategy includes: Based on artificial intelligence learning algorithms and optimization technologies, the operating data of the coal-fired power unit is learned and predicted to adjust the peak-shaving capacity and operating efficiency of the coal-fired power unit.

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