Hydropower station maintenance space determination method considering peak regulation demand of receiving-end power grid

Abstract

The invention belongs to the field of hydroelectric power generation dispatching, and relates to a hydropower station maintenance space determination method considering peak regulation requirements of a receiving-end power grid. The method specifically comprises the steps of extracting a typical load process of a receiving-end power grid in a flood-out period by using a K-means clustering algorithm according to actual load data of each receiving-end power grid of cascade hydropower, so as to accurately reflect a peak regulation demand of the receiving-end power grid; carrying out normalization processing on the typical load to obtain intra-day change trend information of the load, and carrying out stepped processing on the normalized typical daily load to respond to an extra-high voltage direct current transmission stability demand; the stepped load is reconstructed to be matched with the future predicted total installation amount of the water-wind-light complementary system; coupling a wind-light output process to obtain an intra-day typical output process of water and electricity in a flood-out period, and finally realizing extraction of a conversion coefficient of an intra-day output average value and a daily maximum output of the hydropower station so as to determine the maintenance space of the hydropower station in the water-wind-light complementary system.

Description

Technical Field

[0001] The invention belongs to the field of hydropower generation dispatching, and in particular relates to a method for determining maintenance space of a hydropower station taking into account peak load regulation demand of a receiving-end power grid. Background Art

[0002] Regular and planned preventive and restorative maintenance of hydropower station equipment is an important guarantee for the safe and stable operation of hydropower stations. The maintenance plan is not only an important part of the production management of hydropower stations, but also an important influencing factor and boundary condition for the normal participation of hydropower stations in multi-energy joint scheduling and coordinated operation. Therefore, in this case, it is very important to finely estimate the available maintenance space of hydropower stations during the future maintenance period to ensure the steady implementation and reasonable arrangement of their maintenance plans.

[0003] Optimizing the long-term dispatching results of hydropower stations is the main method to extract the available maintenance space distribution of hydropower units, and it is also an important way to reflect the future changes in hydropower operation and improve the reliability of maintenance arrangements. At present, the traditional long-term dispatching of hydropower stations is usually simulated on a monthly or ten-day scale, and the resources and output levels in the unit time period are assumed to be constant. At the same time, in order to take into account the efficiency of simulation calculations, even if the model is refined, it will be adjusted to the daily scale optimization in most cases. In this case, the traditional long-term dispatching cannot take into account the complex peak-shaving needs of the receiving power grid, and it is even more impossible to finely simulate the daily operation trend of hydropower. However, hydropower units should be in a state of stopping work during the maintenance period, and the maximum output of hydropower during the day is the key indicator for judging the number and duration of unit shutdowns. Therefore, the traditional long-term dispatching ignores the peak-shaving needs of the receiving power grid, which will result in the inability to accurately grasp the maximum output level of hydropower during the day, which will lead to the inaccuracy of the evaluation results of the available maintenance space distribution, and the results are difficult to use. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for determining the maintenance space of a hydropower station taking into account the peak-shaving demand of the receiving-end power grid, and to improve the accuracy of the maintenance space of the hydropower station by extracting the conversion relationship between the average daily output of hydropower and the maximum daily output according to the peak-shaving demand of the receiving-end. The method is applied and tested using an engineering example consisting of the lower reaches of the Jinsha River and the maximum wind and solar capacity configuration that it can carry. The results show that the present invention can effectively extract the conversion relationship between the average daily output of hydropower and the maximum daily output taking into account the peak-shaving demand of the receiving-end power grid, and use the conversion relationship to more accurately describe the maintenance space situation of the hydropower station in the water-wind-solar complementary system, thereby improving the reliability of the maintenance arrangement.

[0005] Technical solution of the present invention:

[0006] A method for determining maintenance space of a hydropower station considering peak load regulation demand of a receiving power grid comprises the following steps:

[0007] Step (1) Based on the actual load data of each receiving power grid of the cascade hydropower station, the K-means clustering algorithm is used to extract the typical load process of the receiving power grid during the flood and dry seasons to accurately reflect the peak load demand of the receiving power grid. The specific steps are as follows:

[0008] (1.1) Initialization: Select K initial cluster centers.

[0009] In order to reflect the load demand of the receiving power grid during the flood and dry seasons, the cluster center K is required to be 2.

[0010] (1.2) Assign each data point to the nearest cluster center.

[0011] The cluster centers of daily load demand and flood and dry season load are defined as a point in high-dimensional space, and the Euclidean distance between daily load demand and each cluster center is calculated. d ,c i ) and assigns it to the nearest cluster center. The calculation formula is as follows:

[0012]

[0013] In the formula, x d is the daily load demand, d is the index of the date; c i is the cluster center of the flood and dry season load, i.e., the typical load, and i is the cluster center index; c ij It is c i The value in the jth dimension, t is the dimension of the data, indicating the number of intraday periods;

[0014] (1.3) Update the cluster centers during flood and dry seasons.

[0015] For both flood and dry seasons, the cluster center is recalculated. The new cluster center is the average value of all daily loads during the flood or dry season, and the calculation formula is as follows:

[0016]

[0017] In the formula, S i is the collection of daily loads during the flood season or dry season, |S i | is the number of days in the flood season or dry season.

[0018] Step (2) normalizes the typical load to obtain the daily load change trend information. The normalization calculation formula is as follows:

[0019]

[0020] In the formula, c i ′ is the normalized typical daily load, It is the maximum value of the typical daily load during the flood season or dry season.

[0021] Step (3) performs stepwise processing on the normalized typical daily load to respond to the stability requirements of UHV DC transmission, that is, performing piecewise linear fitting on the typical daily load, each section lasting no less than 2 hours, and each section of the load remains unchanged.

[0022] Step (4) Load volume matching. The original load is the load demand of each receiving power grid in the province, and a receiving power grid is often supported by power generation from multiple river basins. Therefore, considering the above reasons, the stepped load is reconstructed to match the total installed capacity of the hydro-wind-solar complementary system in the future.

[0023]

[0024] In the formula, p g,t ′ is the load demand of the receiving grid g at time period t after volume matching; p g,t is the load demand at the receiving end power grid at time period t; For hydropower station h installed capacity; The maximum wind power station that the hydropower station can support is n w installed capacity; The maximum photovoltaic power station that the hydropower station can carry is n pv installed capacity.

[0025] Step (5) uses the empirical formula for wind and solar power generation, inputs the hydrological and meteorological variables at the power station, and obtains the change process of wind and solar power output rate. The specific steps are as follows:

[0026] (5.1) Empirical formula for wind power generation:

[0027]

[0028] Where: is the wind speed at the wind turbine height z meters, in m / s; are the cut-in wind speed and cut-out wind speed of the wind power station turbine, in m / s; is the near-surface wind speed at the location of the wind power station, in m / s; z0 is the surface roughness length; For wind power station w Output during period t; is the output rate of the wind power station (i.e. the ratio of output to installed capacity); is the coefficient of the power generation function; Δt is the length of the unit time period;

[0029] (5.2) Photovoltaic power generation empirical formula:

[0030]

[0031] Where: For photovoltaic power stationpv Output rate; is the solar panel performance ratio; For photovoltaic power station pv Surface radiation, unit: W·m 2 ;rsds STC is the surface radiation under standard conditions; γ is the coefficient calculated by the empirical formula; is the temperature of the solar cell, which is affected by temperature, radiation, and wind speed; Tas STC is the ambient air temperature under standard conditions (Tas STC =25°C).

[0032]

[0033] Where: is the power generation function coefficient; For photovoltaic power station pv The ambient temperature at the place, in °C; For photovoltaic power station pv The surface wind speed at the location, in m / s.

[0034] Step (6) Normalizing the typical daily output process of hydropower. The typical daily load of the flood and dry season after volume matching is coupled with the wind and solar output process of the corresponding date to obtain the typical daily output process of hydropower in the flood and dry season, and then the typical daily output process of hydropower is normalized according to the method of step (2);

[0035]

[0036] In the formula, For hydropower station h Typical output during period t, For wind power station w The output during period t, For photovoltaic power station w Output during period t.

[0037] Step (7) extracts the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output. Through the typical daily output process of hydropower obtained in step (6), the ratio of the maximum output to the average output of the hydropower day can be extracted. This ratio is the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output. The calculation expression of the conversion coefficient K is as follows:

[0038]

[0039] Where: is the maximum output of the hydropower station on the dth typical day, is the average output of the hydropower station on the dth typical day.

[0040] By using the conversion coefficient K, the maximum daily output of the hydropower station can be determined, and the maintenance space of the hydropower station can be accurately extracted. The calculation expression for obtaining the maintenance space of the hydropower station is as follows:

[0041]

[0042] Where: Provides available maintenance space for the hydropower station on D-day.

[0043] Beneficial effects of the present invention:

[0044] Compared with ordinary long-term dispatching, the method of determining the maintenance space of a hydropower station that considers the conversion relationship between the daily average output and the daily maximum output can effectively reduce the inaccuracy of the available maintenance space of hydropower, and associate the relationship between the daily average output of hydropower and the daily maximum output, showing good reliability and practicality and reducing the risk of changing the maintenance plan. The conversion coefficient K between the daily average output and the daily maximum output of the hydropower station essentially refers to a further refined analysis of the long-term dispatching results of each hydropower station. The daily maximum output of hydropower is determined by the conversion coefficient K to obtain the change trend of the available maintenance space of the hydropower station in detail. It avoids the irrationality caused by overestimating the available maintenance space of the hydropower station by only considering the daily average output of hydropower. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the overall solution framework diagram of the method of the present invention;

[0046] Figure 2 It is the typical daily load of the receiving power grid during the flood and dry season;

[0047] Figure 3 It is the result of normalized and stepped load of typical day in flood and dry season of receiving power grid;

[0048] Figure 4 It is the matching result of the typical daily load volume of the receiving power grid during the flood and dry season;

[0049] Figure 5 It is the relationship between the daily available maintenance space and the output process of the hydropower station;

[0050] Figure 6 It is the relationship diagram between the average daily output and the maximum daily output of the hydropower station;

[0051] Figure 7 It is a distribution diagram of the available maintenance space and daily maximum output during the annual maintenance period of a hydropower station. DETAILED DESCRIPTION

[0052] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0053] The overall process of the method for determining the maintenance space of a hydropower station considering the peak load demand of the receiving-end power grid of the present invention is as follows: Figure 1 The method of the present invention is now tested by taking the cascade hydropower stations in the lower reaches of the Jinsha River as an actual engineering background.

[0054] (1) According to the actual load data of the power grid at each receiving end of the cascade hydropower station, such as Figure 2 As shown in the figure, the K-means clustering algorithm is used to extract the typical load process of the receiving power grid A1 during the flood and dry seasons to accurately reflect the peak load demand of the receiving power grid A1.

[0055] (2) The typical load is normalized to obtain the daily load change trend information. The normalization calculation formula is as follows:

[0056]

[0057] In the formula, c i ′ is the normalized typical daily load, It is the maximum value of the typical daily load during the flood season or dry season.

[0058] (3) The normalized typical daily load is processed in a step-by-step manner to respond to the stability requirements of UHV DC transmission. That is, the typical daily load is fitted in segments, and each segment lasts no less than 2 hours. The results are as follows: Figure 3 As shown, the typical load of the receiving power grid A1 during the flood and dry season is normalized and converted into a stepped standard form.

[0059] (4) Load volume matching. The original load is the load demand of each province, and a province is often supported by power generation from multiple river basins. Therefore, considering the above reasons, the stepped load is reconstructed to match the total installed capacity of the water-wind-solar complementary system in the future. Figure 4 This is the result of typical daily load volume matching of receiving power grid A1 during flood and dry season.

[0060]

[0061] In the formula, p g,t ′ is the load demand of the receiving grid g at time period t after volume matching; p g,t is the load demand at the receiving end power grid at time period t; For hydropower station h installed capacity; The maximum wind power station that the hydropower station can support is n w installed capacity; The maximum photovoltaic power station that the hydropower station can carry is n pv installed capacity.

[0062] (5) The 10m wind speed is selected to predict the near-surface wind speed at the power station, and the surface shortwave radiation and surface air temperature are used to predict the radiation received by the power station and the ambient temperature. Using the empirical formula for wind and solar power generation, the hydrological and meteorological variables at the power station are input to obtain the change process of wind and solar output rate. The specific steps are as follows:

[0063] Step 1 Wind power generation empirical formula:

[0064]

[0065] Where: is the wind speed at the wind turbine height z meters, m / s; are the cut-in wind speed and cut-out wind speed of the wind power station turbine, m / s; is the near-surface wind speed at the location of the wind power station, m / s; z0 is the surface roughness length, 0.0002m; For wind power station w Output during period t; is the output rate of the wind power station (i.e. the ratio of output to installed capacity); is the coefficient of the power generation function; Δt is the length of the unit time period;

[0066] Step 2. Photovoltaic power generation empirical formula:

[0067]

[0068] Where: For photovoltaic power station pv Output rate; is the solar panel performance ratio; For photovoltaic power station pv Surface radiation, W·m 2 ;rsds STC is the surface radiation under standard conditions (rsds STC =1000W*m 2 );γ takes -0.005℃ -1 , is the coefficient calculated by the empirical formula; is the solar cell temperature, which is affected by temperature, radiation, and wind speed. STC is the ambient air temperature under standard conditions (Tas STC =25°C).

[0069]

[0070] Where: is the power generation function coefficient, the coefficient value For photovoltaic power station pvAmbient temperature, °C; For photovoltaic power station pv Surface wind speed at , m / s.

[0071] (6) Normalization of the typical daily output process of hydropower. The typical daily load of the flood and dry season after volume matching is coupled with the wind and solar output process of the corresponding date to obtain the typical daily output process of hydropower in the flood and dry season. Then, the typical daily output process of hydropower is normalized according to the method in step (2). Figure 5 It is a specific relationship diagram between the daily available maintenance space and the output process of a hydropower station.

[0072]

[0073] In the formula, For hydropower station h Typical output during period t, For wind power station w The output during period t, For photovoltaic power station w Output during period t.

[0074] Step (7) extracts the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output. Through the typical daily output process of hydropower obtained in step (6), the ratio of the maximum output to the average output of the hydropower day can be extracted. This ratio is the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output. The calculation expression of the conversion coefficient K is as follows:

[0075]

[0076] Where: is the maximum output of the hydropower station on the dth typical day, is the average output of the hydropower station on the dth typical day.

[0077] The results of the traditional daily-scale dispatching model can only obtain the daily average output of the hydropower station, but the conversion coefficient K obtained by this method can determine the daily maximum output of the hydropower station, and finally realize the accurate extraction of the maintenance space of the hydropower station. The calculation expression for obtaining the maintenance space of the hydropower station is as follows:

[0078]

[0079] Where: Provides available maintenance space for the hydropower station on D-day.

[0080] Figure 6 It is the relationship between the average daily output and the maximum daily output of the hydropower station, that is, the maximum daily output is K times the average daily output.

[0081] The optimization of maintenance space of Jinsha River cascade hydropower station was used as an example to verify the method of the present invention. Table 1 shows the conversion coefficient results of each specific month. Figure 7 It is the distribution of available maintenance space and daily maximum output during the annual maintenance period of the hydropower station. It can be seen that the overall maintenance space is relatively reasonable, and considering that the hydropower units are in a shutdown state during maintenance, the available maintenance space for hydropower should be an integer multiple of the capacity of a single unit. In actual application, the maintenance space range should be the available range of the installed capacity of the hydropower station minus the daily maximum output.

[0082] Table 1 Conversion coefficients of average daily output and maximum daily output of hydropower stations

[0083]

Claims

1. A method for determining maintenance space of a hydropower station considering the peak load demand of the receiving power grid, characterized in that: The steps include: Step (1) Based on the actual load data of each receiving power grid of the cascade hydropower station, the K-means clustering algorithm is used to extract the typical load process of the receiving power grid during the flood and dry seasons to accurately reflect the peak load demand of the receiving power grid. The specific steps are as follows: (1.1) Initialization: Select K initial cluster centers; In order to reflect the load demand of the receiving power grid during the flood and dry seasons, the cluster center K is required to be 2; (1.2) Assign each data point to the nearest cluster center; The cluster centers of daily load demand and flood and dry season load are defined as a point in high-dimensional space, and the Euclidean distance between daily load demand and each cluster center is calculated. d ,c i ) and assigns it to the nearest cluster center; the calculation formula is as follows: In the formula, x d is the daily load demand, d is the index of the date; c i is the cluster center of the flood and dry season load, i.e., the typical load, and i is the cluster center index; c ij It is c i The value in the jth dimension, t is the dimension of the data, indicating the number of intraday periods; (1.3) Update the cluster center during flood and dry seasons; For both flood and dry seasons, the cluster center is recalculated; the new cluster center is the average value of all daily loads during the flood or dry season, and the calculation formula is as follows: In the formula, S i is the collection of daily loads during the flood season or dry season, |S i | is the number of days in the flood season or dry season; Step (2) normalizes the typical load to obtain the daily load change trend information. The normalization calculation formula is as follows: In the formula, c i ′ is the normalized typical daily load, It is the maximum value of the typical daily load during the flood season or dry season; Step (3) performs step-wise processing on the normalized typical daily load to respond to the stability requirements of UHV DC transmission; that is, a piecewise linear fitting is performed on the typical daily load, each section lasting no less than 2 hours, and each section of the load remains unchanged; Step (4) load volume matching: reconstructing the stepped load to match the total installed capacity of the hydro-wind-solar complementary system expected in the future; In the formula, p g,t ′ is the load demand of the receiving grid g at time period t after volume matching; p g,t is the load demand at the receiving end power grid at time period t; For hydropower station h installed capacity; The maximum wind power station that the hydropower station can support is n w installed capacity; The maximum photovoltaic power station that the hydropower station can carry is n pv installed capacity; Step (5) uses the empirical formula for wind and solar power generation, inputs the hydrological and meteorological variables at the power station, and obtains the change process of wind and solar power output rate; the specific steps are as follows: (5.1) Empirical formula for wind power generation: Where: is the wind speed at the wind turbine height z meters, in m / s; are the cut-in wind speed and cut-out wind speed of the wind power station turbine, in m / s; is the near-surface wind speed at the location of the wind power station, in m / s; z0 is the surface roughness length; For wind power station w Output in period t; is the output rate of the wind power station; is the coefficient of the power generation function; Δt is the length of the unit time period; (5.2) Photovoltaic power generation empirical formula: Where: For photovoltaic power station pv Output rate; is the solar panel performance ratio; For photovoltaic power station pv Surface radiation, unit: W·m 2 ;rsds STC is the surface radiation under standard conditions; γ is the coefficient calculated by the empirical formula; is the temperature of the solar cell, which is affected by temperature, radiation and wind speed; Tas STC is the ambient air temperature under standard conditions (Tas STC =25°C); Where: is the power generation function coefficient; For photovoltaic power station pv The ambient temperature at the place, in °C; For photovoltaic power station pv Surface wind speed at the location, in m / s; Step (6) normalizing the typical daily output process of hydropower; coupling the typical daily load of the flood and dry season after volume matching with the wind and solar output process of the corresponding date, the typical daily output process of hydropower in the flood and dry season can be obtained, and then the typical daily output process of hydropower is normalized according to the method of step (2); In the formula, For hydropower station h Typical output during period t, For wind power station w The output during period t is For photovoltaic power station w Output in period t; Step (7) extracts the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output; through the typical daily output process of the hydropower obtained in step (6), extracts the ratio of the maximum output to the average output of the hydropower day, and this ratio is the conversion coefficient K between the average daily output of the hydropower station and the maximum daily output; the calculation expression of the conversion coefficient K is as follows: Where: is the maximum output of the hydropower station on the dth typical day, is the average output of the hydropower station on the dth typical day; Through the conversion coefficient K, the maximum daily output of the hydropower station can be determined, and finally the accurate extraction of the maintenance space of the hydropower station can be realized; the calculation expression for obtaining the maintenance space of the hydropower station is as follows: Where: Provides available maintenance space for the hydropower station on D-day.

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