Method and system for predicting adjustable capacity of drainage basin distributed small hydropower station cluster

Abstract

The invention discloses a drainage basin distributed small hydropower station cluster adjustable capacity prediction method and system, and the method comprises the steps: extracting the surface form information of a to-be-calculated drainage basin through a drainage basin digital elevation model; constructing a Xinanjiang hydrological model based on the to-be-calculated drainage basin surface form information, and outputting drainage basin runoff production data; based on the drainage basin runoff production data and in combination with a pre-constructed drainage basin distributed small hydropower station output prediction model, obtaining the maximum total output of the small hydropower stations in the same drainage basin, and obtaining an adjustable capacity based on the maximum total output of the small hydropower stations in the same drainage basin and the actual total output; wherein the drainage basin distributed small hydropower station output prediction model is constructed by taking the maximum total output of small hydropower stations in the same drainage basin as a target to construct a target function and setting constraint conditions for the target function. The invention provides a watershed small hydropower station adjustable potential quantification model considering hydro meteorology, provides a watershed small hydropower station cluster aggregation method, can provide a basis for small hydropower station / photovoltaic combined power generation, and can provide effective guarantee for economic operation of a regional power grid small hydropower station / photovoltaic power generation system.

Description

Technical Field

[0001] The present invention relates to the field of hydropower energy systems, and in particular to a method and system for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin. Background Art

[0002] Hydropower is affected by geographical location, hydrological meteorology, seasons, etc., and the output and adjustable capacity of small hydropower are difficult to predict, which restricts the difficulty of local power grids to manage small hydropower. It is urgent to study and consider the adjustable capacity of distributed small hydropower in the same basin to ensure the economic operation of small hydropower / photovoltaic power generation systems in county power grids. At present, the decomposition methods used for small hydropower related data are mostly more traditional methods such as wavelet analysis, and the decomposition effect is not very ideal. When constructing a prediction model for the modal components obtained by decomposition, most of them only use a single BP neural network, support vector machine, etc., which requires a large amount of runoff and output historical data. However, most of the areas rich in small hydropower are poor in data, especially the meteorological data related to the power generation capacity of small hydropower is seriously insufficient. Not only is the workload huge, but it is also difficult to give full play to the performance of the prediction model. Summary of the invention

[0003] In order to solve the problem that it is difficult to predict the output and adjustable capacity of small hydropower, which restricts the difficulty of local power grid management of small hydropower, the present invention proposes a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin, including:

[0004] Using the digital elevation model of the watershed, the surface morphology information of the watershed to be calculated is extracted;

[0005] Construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated, and output the watershed runoff data;

[0006] Based on the flow data of the river basin combined with the pre-built river basin distributed small hydropower output prediction model, the maximum total output of small hydropower in the same river basin is obtained, and the adjustable capacity is obtained based on the maximum total output of small hydropower in the same river basin and the actual total output;

[0007] The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

[0008] Optionally, the construction of the basin distributed small hydropower output prediction model includes:

[0009] The objective function is constructed with the goal of maximizing the total output of small hydropower in the same basin;

[0010] Setting constraints for the objective function;

[0011] Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions;

[0012] The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

[0013] Optionally, the objective function is as shown below:

[0014]

[0015] Where, N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t; H it is the net power generation head of the i-th hydropower station in period t; t is the period variable; A i is the power station output coefficient; i is the hydropower station number; M is the total number of hydropower stations.

[0016] Optionally, the extracting the surface morphology information of the watershed to be calculated by using the watershed digital elevation model includes:

[0017] Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river;

[0018] Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction;

[0019] Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount;

[0020] Set multiple different minimum river water area thresholds;

[0021] The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map;

[0022] The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

[0023] Optionally, the accumulated flow is calculated as follows:

[0024]

[0025] Among them, F ai is the cumulative flow in the sample area, expressed in pixels; b is the weight, u j is the water flow direction of the jth cell, j is the cell number, and n is the total number of cells.

[0026] Optionally, the constructing of the Xin'an River hydrological model based on the surface morphological information of the watershed to be calculated includes:

[0027] The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station.

[0028] The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model;

[0029] The full-storage runoff model is used to calculate the basin runoff data.

[0030] Optionally, the full storage flow model is shown as follows:

[0031]

[0032] Among them, f is the runoff area; F is the total basin area; W' is the water demand of a single point in the basin; WMM is the maximum water storage capacity of a single point in the basin; and B is the index of the water storage capacity-area distribution curve.

[0033] Optionally, obtaining the adjustable capacity based on the maximum total output of small hydropower stations in the same basin and the actual total output includes:

[0034] The adjustable capacity is obtained by subtracting the actual total output from the maximum total output of small hydropower stations in the same basin.

[0035] On the other hand, the present application also provides a basin distributed small hydropower cluster adjustable capacity prediction system, including:

[0036] A water system extraction unit is used to extract the surface morphology information of the watershed to be calculated using the watershed digital elevation model;

[0037] The runoff prediction unit is used to construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated and output the watershed runoff data;

[0038] An output prediction unit, used to obtain the maximum total output of small hydropower in the same basin based on the basin runoff data combined with a pre-built basin distributed small hydropower output prediction model, and obtain the adjustable capacity based on the maximum total output of small hydropower in the same basin and the actual total output;

[0039] The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

[0040] Optionally, a model building module is also included for:

[0041] The objective function is constructed with the goal of maximizing the total output of small hydropower in the same basin;

[0042] Setting constraints for the objective function;

[0043] Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions;

[0044] The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

[0045] Optionally, the objective function is as shown below:

[0046]

[0047] Where, N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t; H it is the net power generation head of the i-th hydropower station in period t; t is the period variable; A i is the power station output coefficient; i is the hydropower station number; M is the total number of hydropower stations.

[0048] Optionally, the water system extraction unit is specifically used for:

[0049] Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river;

[0050] Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction;

[0051] Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount;

[0052] Set multiple different minimum river water area thresholds;

[0053] The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map;

[0054] The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

[0055] Optionally, the accumulated flow is calculated as follows:

[0056]

[0057] Among them, F aiis the cumulative flow in the sample area, expressed in pixels; b is the weight, u j is the water flow direction of the jth cell, j is the cell number, and n is the total number of cells.

[0058] Optionally, the runoff prediction unit is specifically used for:

[0059] The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station.

[0060] The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model;

[0061] The full-storage runoff model is used to calculate the basin runoff data.

[0062] Optionally, the full storage flow model is shown as follows:

[0063]

[0064] Among them, f is the runoff area; F is the total basin area; W' is the water demand of a single point in the basin; WMM is the maximum water storage capacity of a single point in the basin; and B is the index of the water storage capacity-area distribution curve.

[0065] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0066] The memory is used to store one or more programs;

[0067] When the one or more programs are executed by the at least one processor, the above-mentioned method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin is implemented.

[0068] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin as described above is implemented.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin, including: using a digital elevation model of the river basin to extract the surface morphological information of the river basin to be calculated; constructing a Xin'anjiang hydrological model based on the surface morphological information of the river basin to be calculated, and outputting the flow data of the river basin; based on the flow data of the river basin combined with a pre-constructed distributed small hydropower output prediction model of the river basin, obtaining the maximum total output of small hydropower in the same river basin, and obtaining the adjustable capacity based on the maximum total output of small hydropower in the same river basin and the actual total output; wherein the distributed small hydropower output prediction model of the river basin is constructed with the maximum total output of small hydropower in the same river basin as the goal to construct an objective function, and setting constraints for the objective function. The present invention proposes a quantitative model of adjustable potential of small hydropower in the river basin that takes into account hydrological meteorology, and proposes a method for aggregating small hydropower clusters in the river basin, which can provide a basis for small hydropower / photovoltaic combined power generation, and can also provide effective guarantees for the economic operation of small hydropower / photovoltaic power generation systems in regional power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A flow chart of a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin according to the present invention;

[0072] Figure 2 It is the flow chart of the Xin'an River hydrological model of the present invention;

[0073] Figure 3 The figure is a schematic diagram of the structure of a computer device of the present invention. DETAILED DESCRIPTION

[0074] As hydropower is affected by geographical location, hydrometeorology, seasons, etc., the output and adjustable capacity of small hydropower are difficult to predict, which restricts the difficulty of local power grid management of small hydropower. Therefore, a prediction model for the runoff and reservoir capacity of small hydropower stations considering multiple factors such as geographical location, hydrometeorology, seasons, etc. is proposed. Advanced hydrometeorological monitoring technology is used to study the regulation characteristics of distributed small hydropower clusters in the basin. Based on the basin hydrological forecast, a quantitative model for the adjustable potential of distributed small hydropower in the basin considering hydrometeorology is proposed. A method for aggregating distributed small hydropower clusters in the basin is proposed, which can provide a basis for small hydropower / photovoltaic combined power generation, and also provide effective guarantee for the economic operation of small hydropower / photovoltaic power generation systems in county power grids.

[0075] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0076] Embodiment 1:

[0077] A method for predicting the adjustable capacity of distributed small hydropower clusters in a river basin, such as Figure 1 As shown, including:

[0078] Step S1: using the digital elevation model of the watershed to extract the surface morphological information of the watershed to be calculated;

[0079] Step S2: construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated, and output the watershed runoff data;

[0080] Step S3: based on the basin runoff data and a pre-built basin distributed small hydropower output prediction model, the maximum total output of small hydropower in the same basin is obtained, and the adjustable capacity is obtained based on the maximum total output of small hydropower in the same basin and the actual total output;

[0081] The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

[0082] A method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin of the present invention comprises the following steps:

[0083] Step S1: Using the watershed digital elevation model, extract the surface morphology information of the watershed to be calculated, including:

[0084] Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river;

[0085] Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction;

[0086] Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount;

[0087] Set multiple different minimum river water area thresholds;

[0088] The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map;

[0089] The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

[0090] Step S1 specifically includes:

[0091] Using the basin DEM data, a slope runoff simulation algorithm based on the unit flow direction information is used to determine the river network. A large amount of surface morphological information is extracted, such as the slope direction and slope of the basin grid cells, as well as the relationship between the cells, to extract the water system in the basin.

[0092] Water system extraction based on DEM data includes:

[0093] When using DEM to extract rivers, depressions are preprocessed to determine the appropriate filling threshold. Depressions are identified and filled only when they are below the critical value, and terrain elevations exceeding the critical value remain unchanged.

[0094] Determine the grid flow direction, use the D8 single flow direction algorithm, in the 3×3 grid window, calculate the distance weight drop P between the central grid c and the eight neighboring grids, and determine the flow direction as the direction of the neighboring grid with the largest Р value.

[0095]

[0096] Among them, h c is the center grid cell elevation, h i is the elevation of adjacent grid cells, and d is the distance between the center points of two grids. If it is horizontal or vertical, the value of d is the grid resolution; if it is in the diagonal direction, the value of d is equal to the grid resolution multiplied by

[0097] The cumulative flow is calculated based on the flow direction data to generate the cumulative flow:

[0098]

[0099] Among them, F ai is the cumulative amount of runoff in the sample area, expressed in the number of pixels; b is the weight, and b is 1 when there is no weight data, u j is the water flow direction of the cell.

[0100] The minimum river catchment area threshold is set, and the grids whose runoff exceeds the catchment area threshold are part of the surface runoff. The network composed of these flow paths is the watershed system map.

[0101] The specific implementation steps include: when extracting the water system, based on the cumulative amount of runoff at each grid, it is necessary to set a minimum river catchment area threshold. The grid with a runoff greater than the set minimum river catchment area threshold is the location where the river network is generated. By setting multiple minimum river catchment area thresholds, different basin water system maps can be obtained. At this time, it is necessary to compare these water system data with the actual river network water system map of the basin, and select the water system data that best matches it as the extraction result.

[0102] Step S2: construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated, and output the watershed runoff data, including:

[0103] The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station.

[0104] The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model;

[0105] The full-storage runoff model is used to calculate the basin runoff data.

[0106] Step S2 specifically includes:

[0107] Based on the extraction of basin water system data, the Xin'an River hydrological model is constructed in areas with no or scarce data, such as Figure 2 As shown in the figure, the missing data in the study area are interpolated by appropriate methods to obtain complete basic hydrological data such as rainfall, evaporation and runoff, among which B represents the basin water storage capacity-area distribution curve index, WM represents the average tension water capacity of the basin, IMP represents the proportion of impervious area to the total basin area, ED represents deep evapotranspiration, EL represents lower evapotranspiration, EU represents upper evapotranspiration, WUM represents upper tension water capacity, WLM represents lower tension water capacity, C represents deep evapotranspiration diffusion coefficient, SM represents free water storage capacity, EX represents free water storage capacity-area distribution curve index, KKSS represents soil midstream recession coefficient, KSS represents daily flow coefficient of free water storage reservoir to soil midstream, KG represents daily flow coefficient of free water storage reservoir to groundwater, KKG represents groundwater recession coefficient, and UH represents river network unit line.

[0108] Based on runoff data, this paper analyzes the impact of factors such as watershed and runoff on the output of small hydropower stations, studies the prediction methods of small hydropower station runoff and reservoir capacity based on hydrology and meteorology, and proposes a small hydropower output prediction model.

[0109] Through the analysis and simulation of hydrological, meteorological and other data of each river basin, combined with the geographical distribution of small hydropower stations in each river basin, based on the reservoir capacity, power generation flow and inflow flow of the hydropower station, taking into account the upstream and downstream hydraulic connection and the upper and lower limits of the net head, the maximum output value of distributed small hydropower in the river basin is calculated, and the adjustable capacity of small hydropower in the same river basin is calculated.

[0110] The runoff prediction process of Xin'anjiang hydrological model is as follows:

[0111] The natural watershed division method is used to divide the calculation basin into N unit basins, and each unit basin has at least one rainfall station.

[0112] The soil layer is divided into three layers according to the unevenness of the vertical distribution of the soil, and the evapotranspiration is calculated using a three-layer evaporation model. The upper layer evaporates according to the evaporation capacity. When the water content of the upper layer is not enough for evaporation, the remaining evaporation capacity is evaporated from the lower layer. The evaporation of the lower layer is proportional to the water storage capacity of the lower layer, and the ratio of the calculated evaporation of the lower layer to the remaining evaporation capacity is required to be no less than the deep evaporation coefficient. Otherwise, the insufficient part is supplemented by the water storage capacity of the lower layer. When the water storage capacity of the lower layer is not enough, the deep water storage capacity is used for supplementation.

[0113] The full-storage runoff model is used to calculate runoff. Runoff occurs when the soil water content in the aeration zone reaches the field water holding capacity. The water storage capacity-area distribution curve is used to consider the problem of uneven distribution of soil water shortage. The water storage capacity-area distribution curve refers to the cumulative frequency curve of the partial runoff area changing with the water storage capacity. Assuming that the impervious area IM = 0, its line type is:

[0114]

[0115] Where f is the runoff area, km 2 ; F is the total basin area, km 2 ; W' is the water demand of a single point in the basin, mm; WMM is the maximum water storage capacity of a single point in the basin, mm; B is the index of the water storage capacity-area distribution curve.

[0116] The Sanshuiyuan Xin'anjiang model adopts the full flow production method. The calculated flow production first enters the free water reservoir, and then the water source is divided by the outflow of the free water reservoir. The free water reservoir has two outlets, which flow out in a linear reservoir outflow manner. The bottom hole outflow forms underground runoff R G , the side hole outflow forms soil flow R SS .

[0117] The Sanshuiyuan Xin'anjiang model divides the basin confluence into two stages: river network confluence and river channel confluence. In the river network confluence stage, the surface runoff, soil runoff and underground runoff are calculated separately, and then the runoff components that have passed through the basin regulation are accumulated, and the total runoff is calculated to the outlet of the unit basin.

[0118] Before step S3, the construction of the basin distributed small hydropower output prediction model involved in this application is further introduced.

[0119] The construction of the basin distributed small hydropower output prediction model includes:

[0120] The objective function is constructed with the goal of maximizing the total output of small hydropower in the same basin;

[0121] Setting constraints for the objective function;

[0122] Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions;

[0123] The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

[0124] Step S3 specifically includes:

[0125] Before introducing the basin distributed small hydropower output prediction model, we first build a small hydropower output prediction model based on runoff data, including:

[0126] The small hydropower output prediction model takes the maximum output as the objective function:

[0127] maxN=AγqH

[0128] Where N is the output of the hydropower station, kW; γ is the efficiency of the power station turbine; q is the water diversion flow rate for power generation, m 3 / s; H is the net water head of the hydropower station, m; A is the output coefficient of the power station, which can be determined according to actual conditions. In this embodiment, A can be set to 9.81.

[0129] The constraints of the small hydropower output prediction model are:

[0130] (1) Water balance constraints:

[0131] V t =V t-1 +(Q t -Q δt )Δt-E t S t -D t

[0132] Among them, V t-1 、V t are the reservoir water storage at the beginning and end of period t, m; Q t is the inflow flow in period t, m 3 / s;Q δt is the discharge flow rate during period t, m 3 / s; E t is the evaporation depth during period t, m; S t is the reservoir area during period t, m 2 ;D t is the amount of water lost through leakage during period t, m 3 ; Δt is the scheduling period variable.

[0133] (2) Water level limit:

[0134] Z t,min ≤Z t ≤Z t,max

[0135] Among them, Z t,min The lowest operating water level allowed in period t. If there is no special requirement, it is the dead water level, m; Z t,max is the maximum operating water level allowed in period t. If there is no special requirement, it is the normal water storage level. During the flood season, the flood season limit water level at the corresponding time is taken, m; Z t is the operating water level during period t.

[0136] (3) Power generation flow limit:

[0137] Q t,min≤Q t ≤Q t,max

[0138] Among them, Q t,min is the minimum water diversion flow guaranteed by the reservoir during period t or the minimum water diversion flow required by the turbine, m 3 / s;Q t is the water diversion flow rate during period t; Q t,max is the maximum allowable water diversion flow rate during period t, m 3 / s.

[0139] (4) Power station output constraints:

[0140] N min ≤Aγq t H t ≤N max

[0141] Among them, N min Indicates the minimum output allowed for the hydropower station unit, kW; N max is the maximum output allowed, generally the installed capacity, kW, q t is the water diversion flow rate for power generation in the hydropower station during period t, H t is the net generating head of the hydropower station in period t, m, and A is the output coefficient of the hydropower station, which can be taken as 9.81

[0142] (5) All parameters are non-negative.

[0143] Furthermore, the adjustable capacity of distributed small hydropower in the basin is calculated, including:

[0144] The output prediction model of distributed small hydropower in the basin takes the maximum output as the objective function:

[0145]

[0146] Where N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t, m 3 / s;H it is the net power generation head of the i-th hydropower station in period t, m; t is the period variable; A i is the output coefficient of the ith hydropower station, which can be determined according to actual conditions. In this embodiment, A can be set to 9.81.

[0147] The constraints of the basin distributed small hydropower output prediction model are:

[0148] (1) Water balance constraints:

[0149] V i,t =Vi,t-1 +(Q i,t -Q δi,t )Δt-E i,t S i,t -D i,t

[0150] Among them, V i,t-1 、V i,t are the reservoir water storage at the beginning and end of the t period of the i-th hydropower station, m; Q i,t is the inflow flow of the i-th hydropower station in period t, m 3 / s;Q δi,t is the discharge flow of the i-th hydropower station in period t, m 3 / s; E i,t is the evaporation depth of the i-th hydropower station in period t, m; S i,t is the reservoir area of ​​the i-th hydropower station in period t, m 2 ;D i,t is the leakage loss of the i-th hydropower station in period t, m 3 .

[0151] (2) Water level limit:

[0152] Z i,min ≤Z i,t ≤Z i,max

[0153] Among them, Z i,min The lowest operating water level allowed in period t. If there is no special requirement, it is the dead water level, m; Z i,max is the maximum operating water level allowed in period t. If there is no special requirement, it is the normal water storage level. During the flood season, the flood season limit water level at the corresponding time is taken, m, Z i,t is the operating water level during period t.

[0154] (3) Power generation flow limit:

[0155] Q t,min ≤Q i,t ≤Q t,max

[0156] Among them, Q t,min is the minimum water diversion flow guaranteed by the reservoir during period t or the minimum water diversion flow required by the turbine, m 3 / s;Q t,max is the maximum allowable water diversion flow rate during period t, m 3 / s,Q i,t is the water diversion flow of the reservoir during period t.

[0157] (4) Power station output constraints:

[0158] N i,min ≤Aq it Hit ≤N i,max

[0159] Among them, N i,min Indicates the minimum output allowed for the hydropower station unit, kW; N i,max is the maximum output allowed, generally the installed capacity, kW, q it is the water diversion flow rate for power generation of the i-th hydropower station in period t, H it is the net generating head of the i-th hydropower station in period t, m.

[0160] (5) Upstream and downstream hydraulic connections

[0161] For cascade reservoirs located in the same main stream, the water discharged from the upstream reservoir will always flow into the downstream reservoir and together with the interval flow constitute the inflow runoff of the downstream reservoir.

[0162] q it =Q i-1,t +qj i-1,i,t

[0163] Among them, qj i-1,i,t is the interval water flow between the i-1th reservoir and the i-th reservoir in the tth period, m 3 / s;Q i-1,t is the water diversion flow of the i-1th reservoir during period t, q it is the inflow runoff of the ith reservoir.

[0164] (6) All parameters are non-negative.

[0165] Step S3: Based on the basin runoff data and the pre-built basin distributed small hydropower output prediction model, the maximum total output of small hydropower in the same basin is obtained, and the adjustable capacity is obtained based on the maximum total output of small hydropower in the same basin and the actual total output, including:

[0166] The basin runoff data is brought into the pre-built basin distributed small hydropower output prediction model to obtain the maximum total output of small hydropower in the same basin;

[0167] The adjustable capacity is obtained by subtracting the actual total output from the maximum total output of small hydropower stations in the same basin.

[0168] Example 2

[0169] Based on the same inventive concept, the adjustable capacity prediction system of a distributed small hydropower cluster in a river basin of the present invention comprises:

[0170] A water system extraction unit is used to extract the surface morphology information of the watershed to be calculated using the watershed digital elevation model;

[0171] The runoff prediction unit is used to construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated and output the watershed runoff data;

[0172] An output prediction unit, used to obtain the maximum total output of small hydropower in the same basin based on the basin runoff data combined with a pre-built basin distributed small hydropower output prediction model, and obtain the adjustable capacity based on the maximum total output of small hydropower in the same basin and the actual total output;

[0173] The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

[0174] The following is a further introduction to each unit:

[0175] Water extraction unit, specifically used for:

[0176] Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river;

[0177] Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction;

[0178] Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount;

[0179] Set multiple different minimum river water area thresholds;

[0180] The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map;

[0181] The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

[0182] The cumulative amount of confluence is calculated as follows:

[0183]

[0184] Among them, F ai is the cumulative amount of runoff in the sample area, expressed in the number of pixels; b is the weight, and b is 1 when there is no weight data, u j is the water flow direction of the cell.

[0185] The water system extraction unit is used to extract a large amount of surface morphological information, such as the slope direction and slope of the watershed grid cells, as well as the relationship between the cells, based on the watershed DEM data and the slope runoff simulation algorithm of the river network determined based on the unit flow direction information, so as to extract the water system in the watershed.

[0186] Runoff prediction unit, specifically used for:

[0187] The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station.

[0188] The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model;

[0189] The full-storage runoff model is used to calculate the basin runoff data.

[0190] Optionally, the full storage flow model is shown as follows:

[0191]

[0192] Where f is the runoff area, km 2 ; F is the total basin area, km 2 ; W' is the water demand of a single point in the basin, mm; WMM is the maximum water storage capacity of a single point in the basin, mm; B is the index of the water storage capacity-area distribution curve.

[0193] The runoff prediction unit is used to construct the Xin'an River hydrological model for areas with no or scarce data based on the basin water system data, and to interpolate the missing data in the study area using appropriate methods to obtain complete basic hydrological data such as rainfall, evaporation, and runoff;

[0194] The output prediction unit is used to calculate the maximum output value of distributed small hydropower in the basin and the adjustable capacity of small hydropower in the same basin based on the reservoir capacity, power generation flow and inflow flow of the hydropower station, taking into account the upstream and downstream hydraulic connections and the upper and lower limits of the net head.

[0195] Also included is a model building unit for:

[0196] Constructing an objective function with the goal of maximizing the total output of small hydropower in the same basin; setting constraints for the objective function;

[0197] Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions;

[0198] The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

[0199] The objective function is shown in the following formula:

[0200]

[0201] Where, N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t; H it is the net power generation head of the i-th hydropower station in period t; t is the period variable; A i is the output coefficient of the i-th hydropower station; i is the hydropower station number; M is the total number of hydropower stations.

[0202] The model building unit is also used to: construct a small hydropower output prediction model, based on runoff data, analyze the impact of factors such as watershed and runoff on the output of small hydropower stations, study the runoff and reservoir capacity prediction methods of small hydropower stations based on hydrology and meteorology, and propose a small hydropower output prediction model.

[0203] The present invention uses the DEM data of the watershed to extract the watershed water system data; on the basis of extracting the watershed water system data, the Xin'anjiang hydrological model for areas without data or scarce data is studied and constructed to obtain complete basic hydrological data such as rainfall, evaporation, and runoff; based on the runoff data, a small hydropower output prediction model is proposed; according to the reservoir capacity, power generation flow, and inflow flow of the hydropower station, the upper and lower limits of the upstream and downstream hydraulic connections and the net head are considered, and the maximum output value of the distributed small hydropower in the watershed is calculated, and the adjustable capacity of the small hydropower is calculated in combination with the output curve given by the power grid. The present invention studies the regulation characteristics of the small hydropower cluster in the watershed, proposes a quantitative model of the adjustable potential of the small hydropower in the watershed considering hydrological meteorology, and proposes a method for aggregating the small hydropower cluster in the watershed, which can provide a basis for the combined power generation of small hydropower / photovoltaic power, and can also provide effective guarantees for the economic operation of the small hydropower / photovoltaic power generation system in the county power grid.

[0204] Example 3

[0205] like Figure 3 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0206] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin in the above-mentioned embodiment.

[0207] Example 4

[0208] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin in the above embodiment.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin, characterized in that: include: Using the digital elevation model of the watershed, the surface morphology information of the watershed to be calculated is extracted; Construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated, and output the watershed runoff data; Based on the flow data of the river basin combined with the pre-built river basin distributed small hydropower output prediction model, the maximum total output of small hydropower in the same river basin is obtained, and the adjustable capacity is obtained based on the maximum total output of small hydropower in the same river basin and the actual total output; The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

2. The method according to claim 1, characterized in that The construction of the basin distributed small hydropower output prediction model includes: The objective function is constructed with the goal of maximizing the total output of small hydropower in the same basin; Setting constraints for the objective function; Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions; The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

3. The method according to claim 2, characterized in that The objective function is shown in the following formula: Where, N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t; H it is the net power generation head of the i-th hydropower station in period t; t is the period variable; A i is the power station output coefficient; i is the hydropower station number; M is the total number of hydropower stations.

4. The method according to claim 1, characterized in that The method of using the watershed digital elevation model to extract the surface morphology information of the watershed to be calculated includes: Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river; Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction; Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount; Set multiple different minimum river water area thresholds; The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map; The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

5. The method according to claim 4, characterized in that The cumulative amount of confluence is calculated as follows: Among them, F ai is the cumulative flow in the sample area, expressed in pixels; b is the weight, u j is the water flow direction of the jth cell, j is the cell number, and n is the total number of cells.

6. The method according to claim 4, characterized in that The Xin'an River hydrological model is constructed based on the surface morphological information of the watershed to be calculated, and the watershed runoff data is output, including: The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station. The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model; The full-storage runoff model is used to calculate the basin runoff data.

7. The method according to claim 6, characterized in that The full storage flow model is shown as follows: Among them, f is the runoff area; F is the total basin area; W' is the water demand of a single point in the basin; WMM is the maximum water storage capacity of a single point in the basin; and B is the index of the water storage capacity-area distribution curve.

8. The method according to claim 1, characterized in that The adjustable capacity is obtained based on the maximum total output of small hydropower stations in the same basin and the actual total output, including: The adjustable capacity is obtained by subtracting the actual total output from the maximum total output of small hydropower stations in the same basin.

9. A basin distributed small hydropower cluster adjustable capacity prediction system, characterized in that: include: A water system extraction unit is used to extract the surface morphology information of the watershed to be calculated using the watershed digital elevation model; The runoff prediction unit is used to construct the Xin'an River hydrological model based on the surface morphology information of the watershed to be calculated and output the watershed runoff data; An output prediction unit, used to obtain the maximum total output of small hydropower in the same basin based on the basin runoff data combined with a pre-built basin distributed small hydropower output prediction model, and obtain the adjustable capacity based on the maximum total output of small hydropower in the same basin and the actual total output; The basin distributed small hydropower output prediction model constructs an objective function with the goal of maximizing the total output of small hydropower in the same basin, and sets constraints for the objective function.

10. The system according to claim 9, characterized in that Also included are model building modules for: The objective function is constructed with the goal of maximizing the total output of small hydropower in the same basin; Setting constraints for the objective function; Constructing a basin distributed small hydropower output prediction model based on the objective function and the constraint conditions; The constraints include: water balance constraint, water level limit constraint, power generation flow limit constraint and power station output constraint.

11. The system according to claim 10, characterized in that The objective function is shown in the following formula: Where, N is the total output of small hydropower in the same basin, kw; γ i is the turbine efficiency of the i-th hydropower station; q it The water diversion flow rate for power generation of the i-th hydropower station in period t; H it is the net power generation head of the i-th hydropower station in period t; t is the period variable; A i is the power station output coefficient; i is the serial number of the hydropower station; M is the total number of hydropower stations.

12. The system of claim 9, wherein: The water system extraction unit is specifically used for: Determine whether the depression in the digital elevation model is lower than a set critical value, if it is higher, retain the terrain elevation, otherwise identify the depression as a river; Using the D8 unidirectional flow algorithm, in a 3×3 grid window, the distance weight drop P between the central grid c and the eight neighboring grids is calculated, and the direction of the neighboring grid with the largest Р value is determined as the grid flow direction; Calculate the cumulative flow amount based on the flow direction data to generate the cumulative flow amount; Set multiple different minimum river water area thresholds; The accumulated flow is compared with each minimum river water area threshold, and the grids with accumulated flow greater than the same minimum river water area threshold are combined into a watershed system map; The watershed drainage map is compared with the actual river network drainage map of the watershed, and the watershed drainage map that best matches the actual river network drainage map of the watershed is selected as the surface morphology information.

13. The system of claim 12, wherein: The cumulative amount of confluence is calculated as follows: Among them, F ai is the cumulative flow in the sample area, expressed in pixels; b is the weight, u j is the water flow direction of the jth cell, j is the cell number, and n is the total number of cells.

14. The system of claim 9, wherein: The runoff prediction unit is specifically used for: The natural watershed division method is used to divide the watershed to be calculated into N unit watersheds, and each unit watershed has at least one rainfall station. The soil layer is divided into three layers according to the unevenness of soil vertical distribution, and the evapotranspiration is calculated using the three-layer evapotranspiration model; The full-storage runoff model is used to calculate the basin runoff data.

15. The system of claim 14, wherein: The full storage flow model is shown as follows: Among them, f is the runoff area; F is the total basin area; W' is the water demand of a single point in the basin; WMM is the maximum water storage capacity of a single point in the basin; and B is the index of the water storage capacity-area distribution curve.

16. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin as described in any one of claims 1 to 8 is implemented.

17. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for predicting the adjustable capacity of a distributed small hydropower cluster in a river basin as described in any one of claims 1 to 8 is implemented.

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