Cascade hydropower station power generation optimization scheduling method and device and computer equipment

Abstract

The application relates to a cascade hydropower station power generation optimization scheduling method and device and computer equipment. The method comprises the following steps: according to historical wind power generation data, historical solar power generation data, historical water power generation data and power generation related parameters, estimated results of multiple resource parameters are calculated; according to the estimated results of the multiple resource parameters, an unbalanced risk constraint condition is constructed; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case that wind power generation and solar power generation appear continuous shortages; under the constraints of the unbalanced risk constraint condition and system operation constraint conditions, a cascade hydropower station power generation optimization scheduling model is solved with the minimum operation cost as the target, and cascade hydropower station power generation scheduling results are obtained. The method can make up for the defect that the limited fluctuation ability of water power in cooperation with wind power and solar power in the month is ignored in the traditional annual reservoir power generation scheduling plan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power generation, and in particular to a power generation optimization scheduling method and device for cascade hydropower stations, a computer device and a storage medium. BACKGROUND

[0002] Cascade hydropower generation optimization scheduling is to optimize the power generation plan of a hydropower station in a year, in units of months, so as to reasonably arrange the power generation of the hydropower station and fully utilize water energy resources. With the increasing importance of clean energy, more and more power systems use the complementary mode of cascade hydropower stations and wind power generation and photovoltaic power generation, which greatly reduces environmental pollution.

[0003] The traditional cascade hydropower generation optimization scheduling uses monthly average values for calculation to determine the power generation scheduling plan of the cascade hydropower station of the reservoir, such as the monthly end water storage plan and the power generation plan. However, for a power system composed of multiple renewable energy sources such as wind energy, solar energy, and water energy, the power generation of wind energy and solar energy depends on natural weather conditions, and the power generation capacity is almost uncontrollable. When there is a continuous shortage of wind energy and solar energy generation within a month, the reservoir needs to continuously release water to ensure sufficient power generation to meet the load power demand. At this time, the water consumption of water power generation is higher than the estimated result of the monthly average value, resulting in a large deviation between the calculated monthly end water storage and the planned value. In a serious case, when the reservoir has little natural inflow and the reservoir releases to the dead storage capacity, the water power cannot provide sufficient power support, and the entire power system will lose load.

[0004] In the traditional scheme, the monthly end water storage plan is mainly determined by using a long time series simulation method. However, due to the low accuracy or even unavailability of time series prediction sequences in practice, the application of this method is greatly restricted. In addition, in order to control the calculation time within the bearing range, the long time series simulation method cannot achieve global optimization for all time periods in a year. The water storage of the reservoir is a time cumulative quantity, and the optimization of time periods may result in a huge cumulative error of the final scheduling result, leading to inaccurate cascade hydropower station power generation scheduling plan. SUMMARY

[0005] Therefore, it is necessary to provide a power generation optimization scheduling method and device for cascade hydropower stations, a computer device and a storage medium in view of the above technical problems.

[0006] The method comprises: calculating an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters; constructing an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case of continuous shortage of wind energy and solar energy power generation; under the constraint of the unbalanced risk constraint condition and system operation constraint condition, a cascade hydropower station power generation optimization scheduling model is solved with the minimum operation cost as the target to obtain a cascade hydropower station power generation scheduling result; wherein the cascade hydropower station power generation scheduling result comprises at least one of a monthly end water storage plan, a wind energy power generation plan, a solar energy power generation plan, and a water energy power generation plan of the cascade hydropower station.

[0007] In one of the embodiments, the plurality of resource parameters comprises a first quantile parameter, a second quantile parameter, an upper bound parameter, and a fourth quantile parameter; the calculating an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters comprises: sequentially estimating the first quantile parameter of the distribution of the wind-solar power generation shortage maintenance time in a statistical period according to the historical power generation power sequence of wind energy and the historical power generation power sequence of solar energy; sequentially estimating the second quantile parameter of the distribution of the solar energy power generation shortage power in a statistical period according to the historical power generation power sequence of solar energy; sequentially estimating the third quantile parameter of the distribution of the wind energy power generation shortage power in a statistical period according to the historical power generation power sequence of wind energy; determining the upper bound parameter of the wind-solar power generation shortage power distribution according to the second quantile parameter and the third quantile parameter; sequentially estimating the fourth quantile parameter of the distribution of the inflow runoff of each cascade hydropower station according to the monthly minimum inflow runoff and the monthly average inflow runoff of the cascade hydropower station.

[0008] In one of the embodiments, the constructing the unbalanced risk constraint condition according to the estimation results of the plurality of resource parameters comprises: determining a maximum upward power support capability of the cascade hydropower station under a reservoir dispatching limit condition and constructing a power generation flow constraint according to the fourth quantile parameter, the first quantile parameter, and an upstream and downstream reservoir flow relationship; and constructing a power generation shortage constraint according to the first quantile parameter, the upper limit parameter, and a maximum upward support power of the cascade hydropower station in a continuous shortage period of wind energy and solar energy in the month; wherein the upstream and downstream reservoir flow relationship represents a relationship between a power generation flow of a direct upstream of a current cascade hydropower station and an inflow flow of the current cascade hydropower station from the direct upstream; the power generation flow constraint represents a maximum power generation flow that can be provided by each reservoir in a corresponding period of continuous shortage of wind energy and solar power generation in the month; and the power generation shortage constraint represents a support power that should be provided by the cascade hydropower station when the natural inflow flow of the cascade hydropower station is at a monthly minimum value in a probabilistic sense and the power generation of wind energy and solar energy is in a shortage in a probabilistic sense, the support power being used to make up for the power generation shortage of wind energy and solar energy.

[0009] In one of the embodiments, the unbalanced risk constraint condition comprises at least one of a reservoir storage capacity, a discharge flow, a power generation flow, an upstream water level, a tailwater level, and a net water head.

[0010] In one of the embodiments, the constructing process of the cascade hydropower station power generation optimization dispatching model comprises: calculating a difference between a theoretical value of an average power generation of wind energy and solar energy in a month and an optimized value of the average power generation of wind energy and solar energy in the corresponding month; and constructing the cascade hydropower station power generation optimization dispatching model according to the difference and a sum of monthly power generation flows of each hydropower station.

[0011] In one of the embodiments, the system operation constraint condition comprises at least one of a reservoir storage capacity related constraint, a run-of-river related constraint, a common constraint of the reservoir storage capacity and the run-of-river, an upper limit constraint of wind and solar power generation, and a supply and demand balance constraint of a power system.

[0012] In one of the embodiments, the unbalanced risk constraint condition comprises a measurement manner of continuous shortage of wind energy and solar power generation, which comprises: determining a monthly average power generation of wind energy and solar energy in a corresponding month according to a planned power generation of the wind energy and a planned power generation of the solar energy in the month; and determining that the wind energy and the solar energy have a continuous shortage if a sum of actual power generations of the wind energy and the solar energy in the corresponding month is less than a sum of the monthly average power generations of the wind energy and the solar energy.

[0013] The device comprises a processing module configured to calculate an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters; the processing module is further configured to construct an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case of continuous shortage of wind energy and solar energy power generation; and a calculation module configured to solve a cascade hydropower station power generation optimization scheduling model under the constraint of the unbalanced risk constraint condition and system operation constraint condition, to obtain a cascade hydropower station power generation scheduling result, with the minimum operation cost as the target; wherein the cascade hydropower station power generation scheduling result comprises at least one of a monthly end water storage plan, a wind energy power generation plan, a solar energy power generation plan, and a water energy power generation plan of the cascade hydropower station.

[0014] The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: calculating an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters; constructing an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case of continuous shortage of wind energy and solar energy power generation; solving a cascade hydropower station power generation optimization scheduling model under the constraint of the unbalanced risk constraint condition and system operation constraint condition, to obtain a cascade hydropower station power generation scheduling result, with the minimum operation cost as the target; wherein the cascade hydropower station power generation scheduling result comprises at least one of a monthly end water storage plan, a wind energy power generation plan, a solar energy power generation plan, and a water energy power generation plan of the cascade hydropower station.

[0015] A computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement the following steps: calculating an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters; constructing an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of a cascade hydropower station in the case of continuous lack of wind energy and solar energy power generation; under the constraint of the unbalanced risk constraint condition and system operation constraint condition, solving a cascade hydropower station power generation optimization scheduling model with the goal of minimizing operation cost to obtain a cascade hydropower station power generation scheduling result; wherein the cascade hydropower station power generation scheduling result includes at least one of a monthly end water storage plan of the cascade hydropower station, a wind energy power generation plan, a solar energy power generation plan, and a water energy power generation plan.

[0016] The cascade hydropower station power generation optimization scheduling method, device, computer equipment and storage medium described above, according to historical power generation data of wind energy and solar energy, and historical power generation data of the cascade hydropower station, respectively calculate an estimation result of resource parameters, so that the unbalanced constraint condition can be constructed therefrom to determine the cascade hydropower station power generation scheduling result considering the possibility of power generation power lack of wind energy and solar energy, considering the possibility of power generation power lack of wind energy and solar energy, and taking it as one of the constraints of cascade hydropower generation scheduling optimization, making up for the defect of ignoring the limited ability of water power to cooperate with wind and solar power generation fluctuation in the traditional annual reservoir power generation scheduling plan, and the cascade hydropower generation optimization scheduling result is more accurate and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the cascade hydropower station power generation optimization scheduling method in one embodiment;

[0018] Figure 2 A flowchart of the step of calculating the estimation result of the plurality of resource parameters in one embodiment;

[0019] Figure 3 A schematic diagram of the geographical location of a 5-stage cascade hydropower station in one embodiment;

[0020] Figure 4 A data statistical diagram of the estimation result of the upper 5% quantile point of the average lack of power of solar power generation and wind power generation in one embodiment;

[0021] Figure 5 A data statistical diagram of the estimation result of the upper 5% quantile point of the monthly minimum value distribution of natural runoff of the cascade hydropower station in one embodiment;

[0022] Figure 6 Fig. 2 is a schematic diagram of data statistics of monthly water storage capacity planning of reservoirs with respective water storage capacities in an embodiment;

[0023] Figure 7 Fig. 3 is a structural block diagram of a power generation optimization scheduling device of cascade hydropower stations in an embodiment;

[0024] Figure 8 Fig. 4 is an internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0025] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0026] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. Figure 1 In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0027] The present embodiment takes the method applied to a terminal as an example for illustration. It should be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction of the terminal and the server.

[0028] The method in the present embodiment includes the following steps:

[0029] In step S102, the estimated results of the plurality of resource parameters are calculated according to the historical power generation data of wind energy, the historical power generation data of solar energy, the historical power generation data of water energy, and the power generation related parameters.

[0030] Historical power generation data for wind, solar, and hydropower refers to the historical power generation sequence of these energy sources. This historical power generation sequence is, for example, a sequence consisting of power generation recorded every 15 minutes within a statistical period, measured in megawatts (MW). The statistical period refers to a manually selected duration used for calculations or statistics, such as a day, a month, a year, or ten years. For more accurate resource parameter estimations, a longer statistical period for the acquired historical power generation data is preferable. Power generation-related parameters include one or more of the basic design parameters and upper / lower regulation limits for each cascade hydropower station, such as reservoir capacity, maximum upper / lower flow limits, regulation type (e.g., monthly or annual regulation), flood discharge, power generation flow, and cost parameters.

[0031] The resource parameters include the upper quantile of the distribution of wind and solar power deficit duration within the statistical period, the upper quantile of the distribution of solar power deficit within the statistical period, the quantile parameter of the distribution of wind power deficit within the statistical period, the upper bound parameter of the distribution of wind and solar power deficit, and the lower quantile of the inflow distribution of each cascade hydropower station. For ease of description, these resource parameters are referred to as the first quantile parameter, the second quantile parameter, the upper bound parameter, and the fourth quantile parameter, respectively.

[0032] Specifically, the terminal estimates the first quantile parameter, the second quantile parameter, the upper bound parameter, and the fourth quantile parameter based on the historical power generation sequence of wind power / solar power / hydro power and related power generation parameters, and obtains the estimation results of each resource parameter.

[0033] In some embodiments, such as Figure 2 As shown, the terminal calculates estimation results for multiple resource parameters based on historical power generation data from wind power, solar power, and hydropower, as well as other power generation-related parameters. These estimates include:

[0034] Step S202: Based on the historical power generation sequence of wind energy and the historical power generation sequence of solar energy, estimate the first quantile parameter of the distribution of the wind and solar power generation deficit duration within the statistical period.

[0035] Specifically, based on empirical distributions and historical power generation sequences for wind and solar energy, the terminal can estimate the quantile value of the duration distribution of a single monthly wind and solar power deficit process; that is, the upper δ quantile of the empirical distribution to which the duration of the wind and solar power deficit follows. For example, the terminal can calculate the wind and solar power deficit power sequence based on the historical power generation sequences for wind and solar energy:

[0036]

[0037] in, denotes the wind-solar historical generation shortage power sequence, denotes the month m to which the time t belongs t denotes the monthly average generation power of solar energy, denotes the solar energy generation power at time t, denotes the month m to which the time t belongs t denotes the monthly average power of wind energy, denotes the wind energy generation power at time t, denotes the starting time of the m t month, denotes the ending time of the m t month.

[0038] Based on the wind-solar historical generation shortage power sequence, the terminal can count the duration of each continuous power shortage period. When the following condition is met , the terminal can obtain the duration of the kth time that wind-solar generation appears continuous power shortage:

[0039]

[0040] wherein τ k denotes the duration of the kth time that wind-solar generation appears continuous power shortage, denotes the starting time of the kth time that wind-solar generation appears continuous power shortage, denotes the ending time of the kth time that wind-solar generation appears continuous power shortage.

[0041] Therefore, the terminal can obtain the empirical distribution F τ (τ) of the duration of historical wind-solar generation appearing continuous power shortage, and the δ-quantile estimation value thereon is: wherein denotes the upper δ-quantile, denotes the upper δ-quantile estimation value of the empirical distribution of the duration of wind-solar generation appearing continuous power shortage.

[0042] Therefore, the terminal estimates the upper δ-quantile of the empirical distribution to which the wind-solar generation shortage maintenance time conforms.

[0043] In step S204, according to the historical generation power sequence of solar energy, the terminal estimates the second quantile parameter of the distribution of solar energy generation shortage power in the statistical period in sequence.

[0044] Specifically, based on the uniform distribution, according to the historical generation power sequence of solar energy, the terminal can estimate the quantile value of the average power distribution of the single energy shortage process of solar energy generation, that is, the upper δ-quantile estimation value of the uniform distribution to which the solar energy generation shortage power conforms.

[0045] For example, a solar power deficit sequence can be calculated based on historical solar power generation sequences.

[0046]

[0047] Using the same method as for calculating the duration of the solar power deficit period, the terminal can obtain the duration τ of the solar power deficit period. k solar This allows us to statistically analyze the average power deficit corresponding to each continuous power deficit period:

[0048]

[0049] in, This represents the average power deficit during the k-th continuous power deficit process of solar power generation.

[0050] Furthermore, the terminal can use a quadratic function and a least squares algorithm to fit the average power deficit during a continuous power deficit process in solar power generation. Regarding its month m k Monthly average power generation Upper and lower envelope functions:

[0051]

[0052] in, These are the parameters of the quadratic, linear, and constant terms of the quadratic function fitted to the upper envelope, respectively. These are the parameters of the quadratic, linear, and constant terms of the quadratic function fitted by the lower envelope.

[0053] Therefore, based on the average predicted power output of solar power generation for each month of the coming year, the terminal can obtain the upper and lower bounds of the solar power generation deficit for each month. The average predicted power output for each month of the coming year is a known parameter and can be predicted in advance by the terminal using a prediction model.

[0054] Typically, the solar power deficit can be assumed to follow a uniform distribution between the obtained upper and lower bounds, thus yielding the upper δ-quantile estimate of the final solar power deficit.

[0055]

[0056] Thus, the terminal estimates obtain the second quantile parameter of the distribution of the solar power deficit within the statistical period, that is, the upper δ quantile estimate of the uniform distribution followed by the solar power deficit.

[0057] Step S206, according to the historical wind power generation sequence, the third quantile parameter of the distribution of the wind power generation shortage power in the statistical period is estimated in turn.

[0058] Specifically, the terminal estimates the quantile value of the average power distribution of the wind power generation single energy shortage process according to the historical wind power generation sequence based on the empirical distribution of the periodic condition, that is, the upper δ quantile estimation value of the empirical distribution of the wind power generation shortage power.

[0059] Exemplarily, the terminal can calculate the wind power generation shortage power sequence based on the historical wind power generation sequence

[0060]

[0061] The terminal can obtain the wind power generation shortage period duration τ by using the same method as the duration statistics of the wind-solar power generation shortage period. k wind And then the average shortage power corresponding to each continuous power shortage period can be statistically obtained:

[0062]

[0063] Wherein, represents the average shortage power of the kth wind power generation continuous power shortage process.

[0064] Based on the average shortage power sequence of the wind power generation continuous power shortage process, the terminal can perform Morlet complex wavelet transform, and according to the positive and negative of the real part of the wavelet transform coefficient, the size of the wind power generation power shortage can be identified. In the embodiment of the application, Morlet complex wavelet is selected as the base wavelet, and the obtained wavelet transform coefficient is a complex number. When the real part of the wavelet transform coefficient is positive, it means that the wind power generation appears large power shortage; and when it is negative, it means that the wind power generation appears small power shortage. Therefore, based on the wavelet transform coefficient, the terminal can determine the time scale of the dominant period by calculating the wavelet variance. The square of the wavelet transform coefficient can be divided into 2 N categories according to the number N (≥1) of dominant periods. Taking two dominant periods as an example, four categories can be divided, which correspond to the combinations of "large power shortage-large power shortage", "large power shortage-small power shortage", "small power shortage-large power shortage", and "small power shortage-small power shortage" of the wind power generation power shortage in the two dominant periods. Other cases are similar. Thus, the wind power generation average shortage power samples can be divided into different subsets according to the categories, and the empirical distribution of the wind power generation average shortage power is obtained based on the samples of each subset The upper δ quantile estimation value is:

[0065]

[0066] Thus, the terminal estimates the third quantile parameter of the distribution of the wind power generation deficiency power in the statistical period, i.e., the upper δ quantile estimate of the empirical distribution of the wind power generation deficiency power.

[0067] In step S208, the upper limit parameter of the wind-solar power generation deficiency power distribution is determined according to the second quantile parameter and the third quantile parameter.

[0068] Specifically, after obtaining the upper δ quantile estimate of the solar power generation deficiency power and the upper δ quantile estimate of the wind power generation deficiency power respectively, the terminal can determine the upper limit of the upper δ quantile of the wind-solar power generation deficiency power distribution according to the upper δ quantile estimate of the uniform distribution of the solar power generation deficiency power and the upper δ quantile estimate of the empirical distribution of the wind power generation deficiency power,

[0069] Exemplarily, the terminal can obtain the estimate of the upper limit of the upper δ quantile of the wind-solar power generation deficiency power distribution by weighting the installed capacity wherein C solar and C wind respectively represent the installed capacity of the solar power generation and the wind power generation. The installed capacity is the sum of the rated power of all hydroelectric generating sets installed in the hydropower station.

[0070] In step S210, the fourth quantile parameter of the monthly inflow runoff distribution of each cascade hydropower station is estimated in turn according to the monthly minimum inflow runoff and the monthly average inflow runoff of the cascade hydropower station.

[0071] Since the monthly minimum inflow runoff varies in direct proportion to the monthly average inflow runoff, the terminal can estimate and calculate the parameters of the corresponding generalized extreme value distribution F w based on the maximum likelihood estimation method according to the monthly minimum inflow runoff and the monthly average inflow runoff of each subset of the monthly inflow runoff.

[0072] Specifically, the terminal estimates the quantile value of the monthly minimum natural inflow runoff distribution of each cascade hydropower station based on the generalized extreme value distribution according to the monthly minimum inflow runoff and the monthly average inflow runoff of the cascade hydropower station, i.e., the lower δ quantile estimate of the monthly minimum inflow runoff distribution of each cascade hydropower station. Thus, the lower δ quantile estimate of the monthly minimum inflow runoff distribution of each cascade hydropower station is

[0073]

[0074] In the above embodiment, the terminal calculates the estimation result of the resource parameter according to the historical power generation sequence of the wind energy and the solar energy, and the historical storage runoff data of the cascade hydropower station, so as to construct the imbalance constraint condition according to the estimation result, determine the power generation scheduling result of the cascade hydropower station in the case of considering the possible power generation shortage of the wind energy and the solar energy, and avoid the problem of inaccurate power generation scheduling result caused by ignoring the power generation shortage of the wind energy and the solar energy.

[0075] In step S104, the imbalance risk constraint condition is constructed according to the estimation result of the plurality of resource parameters.

[0076] The imbalance constraint condition is used to represent the constraint condition that needs to be met by the monthly reservoir regulation of the cascade hydropower station in the case of continuous power generation shortage of the wind energy and the solar energy. That is, the imbalance constraint condition represents the condition that needs to be met by the monthly reservoir regulation in order to control the probability of the event that the wind-solar power generation shortage cannot be made up due to the limitation of the reservoir regulation capacity to be lower than a given threshold. In some embodiments, the measurement index of the monthly reservoir regulation in the imbalance risk constraint condition includes at least one of the reservoir storage capacity, the discharge flow, the power generation flow, the upstream water level, the tail water level, and the net water head.

[0077] In some embodiments, the measurement manner of the continuous power generation shortage of the wind energy and the solar energy in the imbalance risk constraint condition includes: determining the monthly average power generation of the wind energy and the solar energy in the corresponding month according to the planned power generation of the monthly wind energy and the planned power generation of the monthly solar energy; and determining that the wind energy and the solar energy have continuous power generation shortage if the sum of the actual power generation of the wind energy and the actual power generation of the solar energy in the corresponding month is less than the sum of the monthly average power generation of the wind energy and the monthly average power generation of the solar energy.

[0078] Specifically, the terminal can determine the monthly average power generation of the wind energy in each month based on the planned power generation of the wind energy in each month At the same time, the terminal determines the monthly average power generation of the solar energy in each month based on the planned power generation of the solar energy in each month According to the actual power generation P1 of the wind energy and the actual power generation P2 of the solar energy actually collected, the terminal calculates the sum (P1+P2) of the actual power generation of the wind energy and the actual power generation of the solar energy in a certain month, and compares the sum with the sum of the monthly average power generation of the wind energy and the monthly average power generation of the solar energy If the sum of the actual power generation of the wind energy and the actual power generation of the solar energy in the month is less than the sum of the monthly average power generation of the wind energy and the monthly average power generation of the solar energy, that is, it is indicated that the wind energy and the solar energy have continuous power generation shortage.

[0079] The unbalanced constraints can be synthetically represented by multiple constraints, including a maximum upward support power constraint of hydropower in a period of continuous large power shortage of wind and solar power in a month (referred to as "maximum upward support power"), a maximum power generation flow constraint of each reservoir in a period of continuous large power shortage of wind and solar power in a month (referred to as "power generation flow constraint"), an upstream and downstream reservoir flow relationship in a period of continuous large power shortage of wind and solar power in a month (referred to as "upstream and downstream reservoir flow relationship"), and a hydropower balance wind and solar power generation shortage constraint (referred to as "power generation shortage constraint").

[0080] wherein the maximum upward support power represents the maximum power generation of hydropower in a period of continuous large power shortage of wind and solar power in a month, which can be provided by multiple water release compared with the monthly average power generation optimization result of hydropower:

[0081]

[0082] wherein the superscript'~'is used to indicate that the physical quantity is for a period of continuous large power shortage of wind and solar power in a month defined as, represents the maximum upward support power of hydropower in the period, and P H represents the monthly average power generation optimization value of hydropower, represents the reservoir power generation flow in a period of continuous large power shortage of wind and solar power in a month. The net water head for calculating the maximum upward support power of hydropower is calculated according to the monthly average optimization value. This is because the changes of various physical quantities from the beginning of the month to the period of continuous large power shortage of wind and solar power are unknown. Since the extreme scenario of wind and solar power in a month is modeled, it is reasonable to consider that the net water head will not be significantly lower than the monthly average due to continuous water release, and the monthly average can be used instead.

[0083] The power generation flow constraint represents the maximum power generation flow of each reservoir in a period of continuous large power shortage of wind and solar power in a month; that is, the power generation flow constraint represents the maximum power generation flow that can be utilized under the limit condition of reservoir dispatching, which determines the maximum upward power support capability of hydropower, and no water is abandoned at this time. The upstream and downstream reservoir flow relationship represents the relationship between the power generation flow of the direct upstream of the current cascade hydropower station and the inflow flow from the direct upstream of the current cascade hydropower station, that is, the power generation flow of the direct upstream reservoir is the inflow flow from the upstream of the hydropower: The power generation shortage constraint represents the support power that should be provided by the cascade hydropower station when the natural inflow flow of the cascade hydropower station is at the monthly minimum value in a probabilistic sense, and the power generation of wind and solar energy occurs in a probabilistic sense. The support power is used to make up for the power generation shortage of wind and solar energy.

[0084] Specifically, the terminal constructs an unbalanced risk constraint condition by using the estimation result of the resource parameter obtained according to the foregoing embodiments. In some embodiments, the terminal determines the maximum upward power support capability of the cascade hydropower station under the condition of reservoir dispatching limits according to the fourth quantile parameter, the first quantile parameter, and the upstream and downstream reservoir flow relationship, and constructs a power generation flow constraint. Specifically, the power generation flow constraint can be represented by the following formula:

[0085]

[0086] wherein, represents the direct upstream hydropower discharge flow in the period of continuous large-power shortage of wind and solar energy in the month.

[0087] In some embodiments, the terminal constructs a power generation shortage constraint according to the first quantile parameter, the upper bound parameter, and the maximum upward support power of the cascade hydropower station in the period of continuous shortage of wind energy and solar energy in the month. Specifically, the power generation shortage constraint constitutes a sufficient condition for the occurrence probability of the event that the hydropower cannot balance the wind and solar power generation shortage to be less than δ, and can be represented by the following formula:

[0088]

[0089] In the foregoing embodiments, the terminal constructs an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters, considers the situation that wind energy and solar energy may appear power generation shortage, and takes it as one of the constraints of the cascade hydropower generation scheduling optimization, which makes up for the defect that the limitation of the ability of the hydropower to cooperate with the wind and solar power generation fluctuation in the month is ignored in the traditional annual reservoir power generation scheduling plan, and provides a new technical support scheme for the reliable operation of the wind-solar-hydropower clean power system.

[0090] In step S106, under the constraints of the unbalanced risk constraint condition and the system operation constraint condition, a cascade hydropower station generation optimization scheduling model is solved to obtain a cascade hydropower station generation scheduling result.

[0091] Wherein, the cascade hydropower generation scheduling optimization is essentially to construct a target function (i.e., a cascade hydropower station generation optimization scheduling model), and solve the target function under the premise of meeting a series of related constraint conditions, so that the cascade hydropower generation scheduling reaches an optimal value.

[0092] Specifically, the terminal solves the cascade hydropower station generation optimization scheduling model under the constraints of the unbalanced risk constraint condition and the system operation constraint condition, with the objective of minimizing the operation cost, and the obtained solution is the cascade hydropower station generation scheduling result. Exemplarily, the terminal can use a solver CPLEX to solve the cascade hydropower station generation optimization scheduling model.

[0093] The generation scheduling result of the cascade hydropower station includes at least one of a monthly end water storage plan, a wind power generation plan, a solar power generation plan, and a water power generation plan of the cascade hydropower station. The monthly end water storage plan refers to a water storage amount that needs to be reached at each monthly end of the future cascade hydropower station.

[0094] The known parameters of the cascade hydropower annual generation scheduling plan optimization model considering the unbalanced risk constraint include, for example, a water consumption cost coefficient (yuan / m3), a wind-solar power generation abandoned power cost parameter (yuan / MWh), a residual water storage amount of each reservoir capacity hydropower station at the end of the last year (m3), an upper and lower limit of the reservoir water storage amount of each reservoir capacity hydropower station (m3), a function relationship of the reservoir upstream water level of each reservoir capacity hydropower station with respect to the reservoir water storage amount, a function relationship of the tail water level of each reservoir capacity hydropower station with respect to the total discharge flow, a function relationship of the water level loss of each reservoir capacity hydropower station with respect to the power generation flow, an upper and lower limit of the power generation flow of each hydropower station (m3 / s), an upper and lower limit of the total discharge flow of each hydropower station (m3 / s), a installed capacity of each hydropower station (MW), a power generation characteristic curve of each hydropower generator set, that is, a function relationship of the power generation power with respect to the net water head and the power generation flow, a sequence of average power generation power prediction values (MW) of the wind-solar power generation in each month of the future year converted from the theoretical power generation amount, a standby coefficient (%), an average load power (MW) of each month of the future year, a maximum allowed occurrence probability (%) of the wind-solar power generation energy shortage that cannot be made up by the hydropower in the month, and the like.

[0095] In some embodiments, the construction process of the cascade hydropower station generation optimization scheduling model includes: calculating the difference between the theoretical value of the monthly wind-solar average power generation and the optimized value of the corresponding monthly wind-solar average power generation; and constructing the cascade hydropower station generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of each hydropower station. Specifically, the cascade hydropower station generation optimization scheduling model can be represented by the following formula:

[0096]

[0097] wherein ζ represents the water consumption cost coefficient of the hydropower generation, ξ represents the wind-solar abandoned power cost coefficient, ΔT m represents the length of the mth month, q m represents the power generation flow of the mth month, represents the optimized result value of the wind-solar average power generation of the mth month, represents the theoretical average power generation of the mth month, which is converted by dividing the theoretical power generation amount by the total length. The subscript i represents the number of the cascade hydropower station, and the smaller the value is, the closer to the upstream. N H represents the number of the cascade hydropower station.

[0098] In some embodiments, the system operation constraints include at least one of a reservoir-based hydroelectricity related constraint, a run-of-river hydroelectricity related constraint, a common constraint for reservoir-based and run-of-river hydroelectricity, an upper limit constraint for wind and solar power generation, and a supply-demand balance constraint for the power system.

[0099] The reservoir-based hydroelectricity related constraint includes at least one of a water volume balance constraint, a water head constraint, a power generation characteristic constraint, an initial reservoir storage constraint, a reservoir storage upper and lower limit constraint, a reservoir net water head upper and lower limit constraint, and a power generation upper and lower limit constraint.

[0100] The water volume balance constraint represents a relationship between a current reservoir inflow, an outflow, and a storage volume: V i,m = V i,m-1 + (W i,m + u i,m - Q i,m ) ΔT m m>2. Wherein V m represents a reservoir storage volume at the end of the mth month, u m represents a flow from a directly upstream reservoir in the mth month, Q m represents a total outflow in the mth month, and these three are optimization variables; W m represents a natural inflow in the mth month, which is an input known quantity.

[0101] The water head constraint represents a relationship between a water level, a water head and outflow, and storage:

[0102]

[0103] Wherein, represents an upstream water level in the mth month, represents a tail water level in the mth month, represents a water head loss in the mth month, represents a net water head in the mth month, and these variables are optimization variables. f f represents a function of the upstream water level with respect to the reservoir storage volume, f tail represents a function of the tail water level with respect to the total outflow, and f loss represents a function of the water head loss with respect to the power generation outflow, and these function relationships are input known quantities and are processed by piecewise linearization.

[0104] The power generation characteristic constraint represents a relationship between a power generation of a hydroelectricity unit and a reservoir net water head and a power generation outflow:

[0105]

[0106] Wherein, represents a power generation of a reservoir-based hydroelectricity station in the mth month, which is an optimization variable; f DHP (h, Q) represents the power output of the hydropower unit as a function of the net water head and the power generation flow rate, is a known quantity for input, and has been processed by piecewise linearization.

[0107] The initial reservoir storage constraint represents the remaining reservoir storage at the end of the previous year as a boundary condition for the optimization of the next year: wherein, represents the initial reservoir storage, which is a known quantity.

[0108] The upper and lower limits of the reservoir storage constraint represent that the change of the reservoir storage cannot exceed the allowed range:

[0109] V i,min ≤V i,m ≤V i,max

[0110] wherein, V min and V max represent the adjustable lower and upper limits of the reservoir storage, respectively.

[0111] The upper and lower limits of the reservoir net water head constraint represent that the change of the reservoir net water head cannot exceed the allowed range:

[0112]

[0113] wherein, h min and h max represent the adjustable lower and upper limits of the reservoir net water head, respectively.

[0114] The upper and lower limits of the power generation constraint represent that the change of the power generation cannot exceed the allowed range:

[0115]

[0116] wherein, represents the maximum monthly average power generation of the reservoir storage hydropower, which is generally the rated installed capacity.

[0117] The run-of-river hydropower related constraints include at least one of the water balance constraint, the power generation characteristic constraint, and the upper and lower limits of the power generation constraint.

[0118] wherein, the water balance constraint represents the relationship between the inflow and outflow of the current reservoir, and compared with the reservoir storage hydropower, there is no reservoir storage related term: w i,m +u i,m =Q i,m .

[0119] The power generation characteristic constraint represents the relationship between the power generation and the power generation flow rate of the hydropower unit compared with the reservoir storage hydropower:

[0120] The upper and lower limits of the power generation constraint represent that the change of the power generation cannot exceed the allowed range:

[0121]

[0122] wherein, represents the maximum monthly average power generation of the run-of-river hydropower, generally the rated installed capacity.

[0123] The common constraints of the reservoir hydropower and the run-of-river hydropower include at least one of the following: a discharge flow constraint, a reservoir power generation flow upper and lower limit constraint, a reservoir total discharge flow upper and lower limit constraint, and a reservoir upstream and downstream relationship constraint.

[0124] wherein, the discharge flow constraint represents that the total discharge flow includes two parts of the power generation flow and the abandoned water flow: Q i,m = s i,m + q i,m . Wherein, s m is the power generation flow of the mth month.

[0125] The reservoir power generation flow upper and lower limit constraint represents that the change of the reservoir power generation flow cannot exceed the allowed range: q i,min ≤ q i,m ≤ q i,max . Wherein, q min and q max represent the adjustable lower and upper limits of the reservoir power generation flow, respectively.

[0126] The reservoir total discharge flow upper and lower limit constraint represents that the change of the reservoir total discharge flow cannot exceed the allowed range: Q i,mim ≤ Q i,m ≤ Q i,max . Wherein, Q min and Q max represent the adjustable lower and upper limits of the reservoir power generation flow, respectively.

[0127] The reservoir upstream and downstream relationship constraint represents that for the directly connected upstream and downstream hydropower stations, the total discharge flow of the upstream hydropower station is the inflow of the downstream hydropower station: u i,m = Q i-1,m , i ≥ 2.

[0128] The upper limit constraint of the wind and solar power generation represents the upper limit of the wind and solar power generation capacity under the constraint of natural resources:

[0129]

[0130] The supply and demand balance constraint of the power system represents the relationship formula of the power generation balance load demand expressed based on the monthly average power:

[0131]

[0132] wherein, D mwherein, Pm represents the average load power of the mth month, and K represents the system reserve coefficient.

[0133] Therefore, the terminal determines the value range of each variable involved in the solving process of the cascade hydropower station generation optimization scheduling model according to the unbalance risk constraint condition and the system operation constraint condition, and solves the cascade hydropower station generation optimization scheduling model with the minimum operation cost as the target, so as to obtain the cascade hydropower station generation scheduling result.

[0134] The above-mentioned cascade hydropower station generation optimization scheduling method can calculate the estimation result of the resource parameter according to the historical generation power sequence of the wind energy and the solar energy and the historical reservoir inflow data of the cascade hydropower station, so as to construct the unbalance constraint condition, determine the cascade hydropower station generation scheduling result considering the possible generation power shortage of the wind energy and the solar energy, consider the possible generation power shortage of the wind energy and the solar energy as one of the constraints of the cascade hydropower generation scheduling optimization, make up for the defect of the traditional annual reservoir generation scheduling plan which ignores the limited ability of the monthly hydropower to cooperate with the wind and solar power fluctuation, and make the scheduling optimization result more accurate and reliable.

[0135] In a specific embodiment, a five-stage cascade hydropower station and a complementary power system of wind power and solar power are taken as examples. The five-stage cascade hydropower station system includes three reservoir hydropower stations (DH1-DH3) and two run-of-river hydropower stations (ROR1-ROR2), and their geographical positions are distributed as shown in FIG. 1. Figure 3 The detailed parameters of each hydropower station are shown in Table 1 and Table 2.

[0136]

[0137] Table 1

[0138]

[0139]

[0140] Table 2

[0141] wherein, V max and V min represent the maximum storage capacity and the minimum storage capacity respectively, and the unit is Mm 3 ; and represent the maximum net water head and the minimum net water head respectively, and the unit is m; q max represents the maximum generation flow, and the unit is m 3 / s; Q max represents the maximum discharge flow, and the unit is m 3 / s; This indicates the maximum generating capacity of a hydroelectric power station with reservoir capacity, measured in MW. This indicates the maximum generating capacity of a run-of-river hydroelectric power station, measured in MW.

[0142] Taking a statistical period of 1958-2012 as an example, historical hydropower generation data includes natural water inflow data for each hydropower station from 1958 to 2012, with a time resolution of daily. Assuming a planned wind power capacity of 7100MW and a planned solar power capacity of 4200MW, their power generation curves are obtained by converting historical wind speed and irradiance curves. The cut-in wind speed for wind-to-power conversion is 2m / s, the rated wind speed is 7m / s, and the cut-out wind speed is 15m / s. The irradiance-to-power conversion assumes that solar power generation is proportional to irradiance. Load data is based on a scaled-up version of the actual load curve recorded in 2019, with a maximum load set at 9GW. Weighting coefficients ξ and ζ are set to 5000 yuan / MWh and 0.1 yuan / m³, respectively. 3 Based on the above data, the terminal solves the cascade hydropower station power generation optimization scheduling model, thereby obtaining the monthly reservoir scheduling results and the power generation plan for each generator unit for the next 12 months. Based on the above data, the terminal calculates the estimated results of resource parameters as follows: the duration of wind and solar power generation deficit obtained using the upper 5% quantile in parameter estimation; the upper 5% quantile estimation results of the average deficit power of solar and wind power generation; and the upper 5% quantile estimation results of the monthly minimum distribution of natural runoff for DH1, DH2, DH3, and ROR2. For example... Figure 4 As shown in Figures (a) and (b), the left vertical axis represents monthly power generation in pu, the horizontal axis represents time in months, and the right vertical axis represents the power deficit in pu. Figure 4 Figures (a) and (b) show the estimated power deficit, the actual power deficit, and the monthly power generation, respectively. Figure 5 As shown in Figures (a), (b), (c), and (d), the vertical axis represents runoff, and the unit is m. 3 / s, the horizontal axis represents time, and the unit is months. Figures (a), (b), (c), and (d) show the estimated minimum, the actual minimum, and the monthly average of the runoff, respectively. Since ROR1 is adjacent to DH1, its natural runoff is considered to be zero.

[0143] Therefore, the terminal uses the solver CPLEX to obtain the planned monthly reservoir storage for each hydropower station with reservoir capacity (i.e., DH1, DH2, and DH3), for example... Figure 6 The results are shown. Figure 6 As shown, the vertical axis represents the reservoir's water storage capacity, in units of 10. 9 m 3 The horizontal axis represents time, with the unit being months. Figure 6The reservoir storage capacity of each month of each reservoir of the cascade hydropower station is obtained by solving the power generation optimization scheduling model of the cascade hydropower station by using the solver CPLEX.

[0144] In the above embodiment, the actual 5-stage cascade hydropower station is taken as an example for illustration. The power generation optimization scheduling method of the cascade hydropower station in the embodiment is used to estimate the resource parameters and construct the unbalanced risk constraint condition. Under the condition of the constraint and the system operation constraint, the MATLAB is used for modeling and simulation to obtain the reservoir storage capacity plan of each month of each reservoir of the cascade hydropower station. Compared with the traditional power generation optimization scheduling result, the result is more accurate and reliable, and the defect of ignoring the limited ability of the monthly water power cooperation with wind and light power generation fluctuation is avoided.

[0145] It should be understood that, although Figures 1-2 the steps in the flowchart are shown in sequential order, such that each step necessarily occurs after another, the steps are not necessarily performed in the order illustrated by the arrows. Unless specifically stated in this document, execution of the steps is not necessarily limited to the order illustrated by the arrows, and the steps can be executed in other orders. Moreover, Figures 1-2 at least a portion of the steps in the flowchart can include multiple steps or multiple stages, which are not necessarily performed at the same time, but can be performed at different times, and the order of execution of the steps or stages is not necessarily sequential, but can be performed in rotation or alternation with at least a portion of other steps or stages.

[0146] In one embodiment, as shown in Figure 7 , a power generation optimization scheduling device 700 of a cascade hydropower station is provided, comprising a processing module 710 and a calculation module 720, wherein:

[0147] The processing module 710 is configured to calculate an estimation result of a plurality of resource parameters according to historical power generation data of wind energy, historical power generation data of solar energy, historical power generation data of water energy, and power generation related parameters.

[0148] The processing module 710 is further configured to construct an unbalanced risk constraint condition according to the estimation result of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case of continuous lack of wind energy and solar energy power generation.

[0149] The computing module 720 is configured to solve the cascade hydropower station generation optimization scheduling model under the constraints of the imbalance risk constraint condition and the system operation constraint condition, and obtain a cascade hydropower station generation scheduling result, with the objective of minimizing the operation cost. The cascade hydropower station generation scheduling result includes at least one of a monthly end water storage plan, a wind power generation plan, a solar power generation plan, and a water power generation plan of the cascade hydropower station.

[0150] In one embodiment, the processing module is further configured to sequentially estimate first quantile parameters of a distribution of the wind-solar power generation shortage maintenance time in a statistical period according to the historical wind power generation power sequence and the historical solar power generation power sequence; sequentially estimate second quantile parameters of a distribution of the solar power generation shortage power in the statistical period according to the historical solar power generation power sequence; sequentially estimate third quantile parameters of a distribution of the wind power generation shortage power in the statistical period according to the historical wind power generation power sequence; determine an upper bound parameter of the wind-solar power generation shortage power distribution according to the second quantile parameters and the third quantile parameters; and sequentially estimate fourth quantile parameters of a distribution of the monthly inflow runoff of each cascade hydropower station according to the monthly minimum inflow runoff and the monthly average inflow runoff of the cascade hydropower station.

[0151] In one embodiment, the processing module is further configured to determine a maximum upward power support capability of the cascade hydropower station under the reservoir scheduling limit condition and construct a generation flow constraint according to the fourth quantile parameters, the first quantile parameters, and a flow relationship between upstream and downstream reservoirs; and construct a generation shortage constraint according to the first quantile parameters, the upper bound parameter, and the maximum upward support power of the cascade hydropower station in a period of continuous shortage of wind power and solar power in the current month; wherein the flow relationship between upstream and downstream reservoirs represents a relationship between the generation flow of the direct upstream of the current cascade hydropower station and the inflow flow from the direct upstream of the current cascade hydropower station; the generation flow constraint represents the maximum generation flow that can be provided by each reservoir in a period of continuous shortage of wind power and solar power generation in the current month; and the generation shortage constraint represents the support power that should be provided by the cascade hydropower station when the natural inflow flow of the cascade hydropower station is at the monthly minimum value in a probabilistic sense and the generation amount of wind power and solar power has a shortage in a probabilistic sense, which is used to make up for the generation amount shortage of wind power and solar power.

[0152] In one embodiment, the measurement index of the monthly reservoir regulation in the imbalance risk constraint condition includes at least one of a reservoir storage amount, a discharge flow, a generation flow, an upstream water level, a tail water level, and a net water head.

[0153] In one embodiment, the processing module is further configured to construct an optimized scheduling model for the power generation of cascade hydropower stations, including: calculating the theoretical values ​​of the monthly average power generation of wind and solar energy, and the difference between these values ​​and the optimized values ​​of the monthly average power generation of wind and solar energy; and constructing the optimized scheduling model for the power generation of cascade hydropower stations based on the difference and the sum of the monthly power generation flow of each hydropower station.

[0154] In one embodiment, the system operation constraints include at least one of the following: reservoir capacity hydropower-related constraints, runoff hydropower-related constraints, common constraints of reservoir capacity hydropower and runoff hydropower, upper limit constraints of wind and solar power generation, and power system supply and demand balance constraints.

[0155] In one embodiment, the method for measuring the continuous deficit in wind and solar power generation under the imbalance risk constraint includes: determining the monthly average power generation of wind and solar power for the corresponding month based on the planned power generation of wind power and the planned power generation of solar power; if the sum of the actual power generation of wind and solar power in the corresponding month is less than the sum of the monthly average power generation of wind and solar power, then it is determined that there is a continuous deficit in wind and solar power generation.

[0156] Specific limitations regarding the power generation optimization scheduling device for cascade hydropower stations can be found in the limitations on the power generation optimization scheduling method for cascade hydropower stations mentioned above, and will not be repeated here. Each module in the aforementioned power generation optimization scheduling device for cascade hydropower stations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0157] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores XXX data. The network interface communicates with external terminals via a network connection. When the processor executes the computer program, it implements a method for optimizing the power generation scheduling of a cascade hydropower station.

[0158] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0159] In an embodiment, a computer device is also provided, which can be specifically a terminal or a server. The computer device includes a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program. The above computer device calculates the estimation results of the resource parameters respectively according to the historical power generation sequences of wind energy and solar energy and the historical storage runoff data of the cascade hydropower station, so as to enable the unbalanced constraint condition to be constructed accordingly, to determine the power generation scheduling result of the cascade hydropower station in consideration of the possible power generation power shortage of wind energy and solar energy. The possible power generation power shortage of wind energy and solar energy is considered, and is taken as one of the constraints of the cascade hydropower generation scheduling optimization, which makes up for the defect that the limited ability of the water power to cooperate with the wind and solar power fluctuation in a month is ignored in the traditional annual reservoir power generation scheduling plan.

[0160] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments. The above computer readable storage medium calculates the estimation results of the resource parameters respectively according to the historical power generation sequences of wind energy and solar energy and the historical storage runoff data of the cascade hydropower station, so as to enable the unbalanced constraint condition to be constructed accordingly, to determine the power generation scheduling result of the cascade hydropower station in consideration of the possible power generation power shortage of wind energy and solar energy. The possible power generation power shortage of wind energy and solar energy is considered, and is taken as one of the constraints of the cascade hydropower generation scheduling optimization, which makes up for the defect that the limited ability of the water power to cooperate with the wind and solar power fluctuation in a month is ignored in the traditional annual reservoir power generation scheduling plan.

[0161] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, the RAM can be in a variety of forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0162] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0163] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for optimal generation scheduling of a cascade hydropower station, characterized in that, The method comprises: According to the historical power generation data of wind energy, the historical power generation data of solar energy, the historical power generation data of water energy, and the power generation related parameters, the estimation results of the plurality of resource parameters are calculated; According to the estimation results of the plurality of resource parameters, an unbalanced risk constraint condition is constructed; the unbalanced risk constraint condition represents the constraint condition required to be met by the monthly reservoir regulation of the cascade hydropower station in the case of continuous lack of wind energy and solar energy power generation, wherein the unbalanced risk constraint condition comprises a power generation flow constraint and a power generation lack constraint; Under the constraints of the unbalanced risk constraint condition and the system operation constraint condition, a cascade hydropower station power generation optimization scheduling model is solved with the objective of minimizing the operation cost to obtain a cascade hydropower station power generation scheduling result; wherein the cascade hydropower station power generation scheduling result comprises at least one of the monthly end water storage plan, the wind energy power generation plan, the solar energy power generation plan, and the water energy power generation plan of the cascade hydropower station; The cascade hydropower station power generation optimization scheduling model can be represented by the following formula: wherein ζ represents the water consumption cost coefficient of hydropower generation, ξ represents the abandoned energy cost coefficient of wind-solar power generation, ΔT m represents the duration of the mth month, q m represents the power generation flow of the mth month, represents the optimization result value of the average wind-solar power generation of the mth month; represents the average wind-solar power generation of the mth month, which is obtained by dividing the theoretical power generation by the total duration; the subscript i represents the number of the cascade hydropower station, and the smaller the value is, the closer to the upstream; N H represents the number of the cascade hydropower station; The plurality of resource parameters comprises a first quantile parameter, a second quantile parameter, an upper bound parameter, and a fourth quantile parameter; the estimation results of the plurality of resource parameters are calculated according to the historical power generation data of wind energy, the historical power generation data of solar energy, the historical power generation data of water energy, and the power generation related parameters, comprising: According to the historical power generation power sequence of wind energy and the historical power generation power sequence of solar energy, the first quantile parameter of the distribution of the wind-solar power generation lack maintenance time in the statistical period is estimated in turn; According to the historical power generation power sequence of solar energy, the second quantile parameter of the distribution of the solar energy power generation lack power in the statistical period is estimated in turn; According to the historical power generation power sequence of wind energy, the third quantile parameter of the distribution of the wind energy power generation lack power in the statistical period is estimated in turn; According to the second quantile parameter and the third quantile parameter, the upper bound parameter of the wind-solar power generation lack power distribution is determined; According to the monthly minimum value of the inflow runoff of the cascade hydropower station and the monthly average value of the inflow runoff, the fourth quantile parameter of the inflow runoff distribution of each cascade hydropower station is estimated in turn. According to the estimation results of the plurality of resource parameters, the unbalanced risk constraint condition is constructed, comprising: According to the fourth quantile parameter, the first quantile parameter, and the upstream and downstream reservoir flow relationship, the maximum upward power support capacity of the cascade hydropower station under the reservoir scheduling limit condition is determined, and the power generation flow constraint is constructed; According to the first quantile parameter, the upper bound parameter, and the maximum upward support power of the cascade hydropower station in the period of continuous lack of wind energy and solar energy in the month, the power generation lack constraint is constructed; The upstream and downstream reservoir flow relationship represents the relationship between the power generation flow of the direct upstream of the current cascade hydropower station and the inflow flow of the current cascade hydropower station from the direct upstream; The power generation flow constraint represents the maximum power generation flow that each reservoir can provide in the period corresponding to the continuous lack of wind energy and solar energy power generation in the month. The power generation shortage constraint represents the support power that the cascade hydropower station should provide when the natural inflow of the cascade hydropower station is at a monthly minimum level in a probabilistic sense and the power generation of the wind energy and the solar energy has a shortage in a probabilistic sense, and the support power is used to make up for the power generation shortage of the wind energy and the solar energy.

2. The method of claim 1, wherein, In the imbalance risk constraint condition, the measurement indexes of the monthly reservoir regulation include at least one of the reservoir storage capacity, the discharge flow, the power generation flow, the upstream water level, the tail water level, and the net water head.

3. The method of claim 1, wherein, The construction process of the cascade hydropower station power generation optimization scheduling model includes: calculating the difference between the theoretical value of the monthly average power generation of the wind energy and the solar energy and the optimized value of the monthly average power generation of the wind energy and the solar energy corresponding to the month; constructing the cascade hydropower station power generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of the hydropower stations.

4. The method of claim 1, wherein, The system operation constraint condition includes at least one of the reservoir storage capacity related constraint, the run-of-river hydropower related constraint, the common constraint of the reservoir storage capacity hydropower and the run-of-river hydropower, the upper limit constraint of the wind and solar power generation, and the supply and demand balance constraint of the power system.

5. The method according to any one of claims 1 to 4, characterized in that, In the imbalance risk constraint condition, the measurement manner of the continuous shortage of the wind energy and the solar energy power generation includes: determining the monthly average power generation of the wind energy and the solar energy corresponding to the month according to the planned power generation of the monthly wind energy and the planned power generation of the solar energy; if the sum of the actual power generation of the wind energy and the solar energy corresponding to the month is less than the sum of the monthly average power generation of the wind energy and the solar energy, it is determined that the wind energy and the solar energy power generation has a continuous shortage.

6. A device for optimal generation scheduling of a cascade hydropower station, characterized in that, The device includes: a processing module configured to calculate an estimation result of a plurality of resource parameters according to historical power generation data of the wind energy, historical power generation data of the solar energy, historical power generation data of the water energy, and power generation related parameters; the processing module is further configured to construct an imbalance risk constraint condition according to the estimation result of the plurality of resource parameters; the imbalance risk constraint condition represents a constraint condition required to be met by the monthly reservoir regulation of the cascade hydropower station in the case of continuous shortage of the wind energy and the solar energy power generation, and the imbalance risk constraint condition includes a power generation flow constraint and a power generation shortage constraint; a calculation module configured to solve the cascade hydropower station power generation optimization scheduling model under the constraints of the imbalance risk constraint condition and a system operation constraint condition, so as to obtain a cascade hydropower station power generation scheduling result, with the goal of minimizing the operation cost; wherein the cascade hydropower station power generation scheduling result includes at least one of the monthly end reservoir storage plan of the cascade hydropower station, the wind energy power generation plan, the solar energy power generation plan, and the water energy power generation plan. The cascade hydropower station power generation optimization scheduling model can be represented by the following formula: wherein ζ represents a water consumption cost coefficient of hydropower generation, ξ represents a curtailed energy cost coefficient of wind-solar power generation, ΔT m represents a time length of the mth month, q m represents a power generation flow of the mth month, represents an optimized result value of the mth month wind-solar average power generation; represents the mth month wind-solar theoretical average power generation, which is converted by dividing the theoretical power generation by the total time length; the subscript i represents the number of cascade hydropower stations, and the smaller the value is, the closer to the upstream; N H represents the number of cascade hydropower stations; The plurality of resource parameters include a first quantile parameter, a second quantile parameter, an upper limit parameter, and a fourth quantile parameter; and the calculation of the estimation result of the plurality of resource parameters according to the historical power generation data of the wind energy, the historical power generation data of the solar energy, the historical power generation data of the water energy, and the power generation related parameters includes: According to the historical power generation sequence of wind energy and the historical power generation sequence of solar energy, the first quantile parameter of the distribution of the wind-solar power generation shortage maintenance time in the statistical period is estimated in turn; According to the historical power generation sequence of solar energy, the second quantile parameter of the distribution of the solar power generation shortage power in the statistical period is estimated in turn; According to the historical power generation sequence of wind energy, the third quantile parameter of the distribution of the wind power generation shortage power in the statistical period is estimated in turn; According to the second quantile parameter and the third quantile parameter, an upper limit parameter of the wind-solar power generation shortage power distribution is determined; According to the monthly minimum value of the monthly inflow runoff of the cascade hydropower stations and the monthly average value of the monthly inflow runoff, the fourth quantile parameter of the inflow runoff distribution of each cascade hydropower station is estimated in turn. The construction of the unbalanced risk constraint condition according to the estimation results of the plurality of resource parameters comprises: According to the fourth quantile parameter, the first quantile parameter, and the upstream and downstream reservoir flow relationship, the maximum upward power support capability of the cascade hydropower stations under the reservoir scheduling limit condition is determined, and a power generation flow constraint is constructed; According to the first quantile parameter, the upper limit parameter, and the maximum upward support power of the cascade hydropower stations in the period of continuous shortage of wind energy and solar energy in the current month, a power generation shortage constraint is constructed; The upstream and downstream reservoir flow relationship represents the relationship between the power generation flow of the direct upstream of the current cascade hydropower station and the inflow flow from the direct upstream of the current cascade hydropower station. The power generation flow constraint represents the maximum power generation flow that can be provided by each reservoir in the period of continuous shortage of wind energy and solar energy in the current month. The power generation shortage constraint represents the support power that should be provided by the cascade hydropower stations when the natural inflow flow of the cascade hydropower stations is at the monthly minimum value in the probabilistic sense and the power generation of wind energy and solar energy is in the shortage in the probabilistic sense, and the support power is used to make up the power generation shortage of wind energy and solar energy. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 5.

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