CN113506185A - Power generation optimization scheduling method and device for cascade hydropower station and computer equipment - Google Patents

Power generation optimization scheduling method and device for cascade hydropower station and computer equipment Download PDF

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CN113506185A
CN113506185A CN202110680410.5A CN202110680410A CN113506185A CN 113506185 A CN113506185 A CN 113506185A CN 202110680410 A CN202110680410 A CN 202110680410A CN 113506185 A CN113506185 A CN 113506185A
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hydropower station
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鲁宗相
乔颖
林弋莎
马丽亚
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State Grid Corp of China SGCC
State Grid Xinjiang Electric Power Co Ltd
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Abstract

The application relates to a method and a device for optimal scheduling of power generation of a cascade hydropower station, computer equipment and a storage medium. The method comprises the following steps: calculating to obtain estimation results 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 results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by lunar reservoir adjustment of the cascade hydropower station under the condition that continuous shortage occurs in wind energy and solar energy power generation; and under the constraints of the unbalanced risk constraint condition and the system operation constraint condition, solving the cascade hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target to obtain a cascade hydropower station power generation scheduling result. By adopting the method, the defect that the limitation of the power generation capacity of the water-electricity-wind power generation in the month is neglected in the traditional annual reservoir power generation scheduling plan can be overcome.

Description

Power generation optimization scheduling method and device for cascade hydropower station and computer equipment
Technical Field
The application relates to the technical field of power generation, in particular to a power generation optimization scheduling method and device for a cascade hydropower station, computer equipment and a storage medium.
Background
The optimized dispatching of the cascade hydroelectric generation is to optimize the generating plan of the hydropower station by taking the year as a cycle and taking the month as a unit, thereby reasonably arranging the hydropower station to generate electricity and fully utilizing the water energy resource. With the increasing expression of the importance of clean energy, more and more electric power systems adopt a way of complementing cascade hydropower stations with wind power generation and photovoltaic power generation, thereby greatly reducing the environmental pollution.
The traditional cascade hydroelectric power generation optimization scheduling adopts monthly average value to calculate, so as to determine a cascade hydroelectric station power generation scheduling plan of a reservoir, such as a moon end water storage amount plan and a power generation plan. However, for a power system composed of various renewable energy sources such as wind energy, solar energy, and water energy, the power generation amount of the wind energy and the solar energy depends on natural meteorological conditions, and the power generation capacity is hardly controllable. When continuous shortage of wind energy and solar energy power generation occurs in a month, the reservoir needs to continuously discharge water to ensure enough generated energy to support so as to meet the demand of load electric quantity. At this time, the water consumption of the water energy power generation is higher than the estimation result of the monthly average value, which causes a large deviation between the calculated water storage capacity at the end of the month and the planned value. In severe cases, when the amount of water naturally supplied to the reservoir is small and the reservoir is discharged to a dead storage capacity, the water and electricity cannot provide enough electricity to support, and the whole power system loses load.
In the traditional scheme, a long-time sequence simulation mode is mainly used for determining the water accumulation amount plan at the end of a month, but the application of the mode is greatly restricted due to the fact that the time sequence prediction sequence is low in precision or even unavailable in practice. In addition, in order to control the computation time within the tolerance range, the long-time sequence simulation mode cannot achieve global optimization on all time periods of the year. The water storage amount of the reservoir is the accumulated time, and the optimization in different periods may cause a great accumulated error in the finally obtained scheduling result, so that the power generation scheduling plan of the cascade hydropower station is not accurate enough.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for optimal scheduling of power generation of a cascade hydropower station.
A method for optimized scheduling of power generation for a cascade hydropower station, the method comprising: calculating to obtain estimation results 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 results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation; under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, with the operation minimization cost as a target, solving a cascade hydropower station power generation optimization scheduling model to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
In one embodiment, the plurality of resource parameters includes a first quantile parameter, a second quantile parameter, an upper bound parameter, and a fourth quantile parameter; the method for obtaining the estimation results of a plurality of resource parameters by calculation 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 comprises the following steps: according to the historical generating power sequence of wind energy and the historical generating power sequence of solar energy, sequentially estimating distributed first location parameters of wind-solar generation vacancy maintenance time in a statistical period; according to the historical power generation power sequence of the solar energy, sequentially estimating a second grading parameter of the distribution of the solar power generation shortage power in a statistical period; according to the historical power generation power sequence of the wind energy, sequentially estimating a third grading parameter of the distribution of the wind energy generation shortage power in a statistical period; determining an upper bound parameter of wind-solar power generation shortage power distribution according to the second location division parameter and the third location division parameter; and sequentially estimating fourth quantile parameters of the distribution of the warehousing runoff of each step hydropower station according to the minimum value of the monthly warehousing runoff of the step hydropower stations and the average value of the monthly warehousing runoff.
In one embodiment, the constructing an imbalance risk constraint condition according to the estimation result of the plurality of resource parameters includes: determining the maximum upward power supporting capacity of the cascade hydropower station under the reservoir dispatching limit condition according to the fourth position parameters, the first position parameters and the upstream and downstream reservoir flow relation, and constructing power generation flow constraint; constructing a power generation shortage constraint according to the first positioning parameter, the upper bound parameter and the maximum upward supporting power of the cascade hydropower station in the continuous shortage period of the wind energy and the solar energy in the month; the upstream reservoir flow relation and the downstream reservoir flow relation represent the relationship between the generating flow of the direct upstream of the current cascade hydropower station and the warehousing flow of the current cascade hydropower station from the direct upstream; the power generation flow constraint represents the maximum power generation flow which can be provided by each reservoir in the time period corresponding to continuous shortage of wind energy and solar power generation in the month; and the power generation shortage constraint represents the support electric quantity which is required to be provided by the cascade hydropower station when the natural warehousing flow of the cascade hydropower station is at the monthly minimum level in the probability meaning and the shortage of the wind energy and solar energy in the probability meaning is generated, and the support electric quantity is used for complementing the shortage of the wind energy and solar energy in the power generation quantity.
In one embodiment, the imbalance risk constraint includes at least one of reservoir storage capacity, discharge capacity, power generation capacity, upstream water level, tail water level, and net head.
In one embodiment, the building process of the cascade hydropower station power generation optimization scheduling model comprises the following steps: calculating the difference between the theoretical value of the monthly wind energy and the solar average generating power and the optimized value of the corresponding monthly wind energy and solar average generating power; and constructing a cascade hydropower station power generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of all hydropower stations.
In one embodiment, the system operation constraints include at least one of reservoir capacity hydropower related constraints, radial flow hydropower related constraints, common constraints of reservoir capacity hydropower and radial flow hydropower, upper limit constraints of wind-solar power generation, and supply-demand balance constraints of the power system.
In one embodiment, the unbalance risk constraint condition is a measure of continuous shortage of the wind power generation and the solar power generation, and the measure comprises: respectively determining monthly average generating power of corresponding months of wind energy and solar energy according to the planned generating capacity of the monthly wind energy and the planned generating capacity of the solar energy; and if the sum of the actual generated power of the wind energy and the solar energy in the corresponding month is smaller than the sum of the monthly average generated power of the wind energy and the solar energy, determining that continuous shortage of the wind energy and the solar energy is generated.
A power generation optimized dispatch device for a cascade hydropower station, the device comprising: the processing module is used for calculating and obtaining estimation results of a plurality of resource parameters according to historical wind power generation data, historical solar power generation data, historical water power generation data and power generation related parameters; the processing module is further configured to construct an imbalance risk constraint condition according to the estimation results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation; the calculation module is used for solving the cascade hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target under the constraint of the unbalanced risk constraint condition and the system operation constraint condition to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program: calculating to obtain estimation results 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 results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation; under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, with the operation minimization cost as a target, solving a cascade hydropower station power generation optimization scheduling model to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of: calculating to obtain estimation results 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 results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation; under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, with the operation minimization cost as a target, solving a cascade hydropower station power generation optimization scheduling model to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
According to the method, the device, the computer equipment and the storage medium for optimizing and scheduling power generation of the cascade hydropower station, estimation results of resource parameters are respectively calculated according to historical power generation data of wind energy and solar energy and historical power generation data of the cascade hydropower station, so that an unbalanced constraint condition can be constructed according to the estimation results, the power generation scheduling result of the cascade hydropower station is determined under the condition that the power generation shortage of the wind energy and the solar energy is possibly considered, the condition that the power generation shortage of the wind energy and the solar energy is possibly considered is considered, the unbalanced constraint condition is used as one of constraints of cascade hydropower scheduling optimization, the defect that the limitation of the power generation capacity of water-electricity-wind-electricity combined-wind-electricity generation within a month is ignored in a power generation scheduling plan of a traditional annual reservoir is overcome, and the cascade hydropower optimization scheduling result is more accurate and reliable.
Drawings
FIG. 1 is a schematic flow chart of a method for optimal scheduling of power generation for a cascade hydropower station in one embodiment;
FIG. 2 is a flowchart illustrating the steps of calculating estimates of multiple resource parameters according to one embodiment;
FIG. 3 is a schematic diagram of the geographical location of a 5-step hydroelectric power plant in one embodiment;
FIG. 4 is a data statistical representation of the 5% quantile estimation on average deficit power for solar power generation and wind power generation in one embodiment;
FIG. 5 is a data statistical representation of the upper 5% quantile estimation of the monthly minimum distribution of natural incoming water runoff for a cascade hydroelectric power plant in one embodiment;
FIG. 6 is a graphical representation of data statistics for a monthly end storage capacity plan for reservoirs with individual storage capacities;
FIG. 7 is a block diagram of an embodiment of an optimized scheduling device for power generation of a cascade hydropower station;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In order to solve the problem that the reserved water storage capacity of a cascade hydropower station reservoir affects the capability of the cascade hydropower station reservoir to cooperate with the fluctuation of wind energy and solar energy power generation and further affects the power generation reliability of a power system, in one embodiment, as shown in fig. 1, a power generation optimization scheduling method of the cascade hydropower station is provided, and the method is mainly used for optimizing the annual reservoir power generation scheduling plan of a full-clean power system including wind energy power generation, solar energy power generation and the cascade hydropower station, so that the defect that the limitation of the capability of hydropower station to cooperate with wind-solar power generation fluctuation in the month is often ignored in the traditional annual reservoir power generation scheduling plan, and a new solution is provided for the reliable operation of the wind-light-water full-clean power system.
The embodiment is illustrated by applying the method to a terminal, and it can be understood that the method can also be applied to a server, and can also be applied to a system comprising the terminal and the server, and is implemented by interaction between the terminal and the server.
In this embodiment, the method includes the steps of:
and step S102, calculating to obtain estimation results of a plurality of resource parameters according to historical wind energy generation data, historical solar energy generation data, historical water energy generation data and power generation related parameters.
Wherein, the historical power generation data of wind energy/solar energy/water energy refers to the historical power generation power sequence of wind energy/solar energy/water energy. The historical power generation power sequence is, for example, a sequence including a power generation power composition recorded every 15 minutes within a statistical period in Megawatts (MW). The statistical period refers to a time period artificially selected for calculation or statistics, and may be, for example, one day, one month, one year, ten years, or the like. In order to make the estimation result of the resource parameter more accurate, the longer the statistical period of the acquired historical power generation data is, the better. The power generation related parameters comprise basic design parameters and adjustment upper and lower limit parameters of each cascade hydropower station, such as one or more of storage capacity, maximum upper/lower limit flow, adjustment type (such as monthly adjustment or annual adjustment), flood and water discharge flow, power generation flow, cost parameters and the like.
The resource parameters comprise an upper quantile of the distribution of the wind-solar generation vacancy maintaining time in the statistical period, an upper quantile of the distribution of the solar generation vacancy power in the statistical period, a quantile of the distribution of the wind-energy generation vacancy power in the statistical period, an upper bound parameter of the distribution of the wind-solar generation vacancy power and a lower quantile of the distribution of the warehousing runoff of each cascade hydropower station. For convenience of description, each resource parameter is referred to as a first partition parameter, a second partition parameter, an upper bound parameter, and a fourth partition parameter, respectively.
Specifically, the terminal respectively estimates and calculates a first positioning parameter, a second positioning parameter, an upper bound parameter and a fourth positioning parameter according to the historical power generation power sequence and the power generation related parameters of wind energy, solar energy and water energy to obtain the estimation result of each resource parameter.
In some embodiments, as shown in fig. 2, the terminal calculates 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, and the estimation result comprises:
step S202, according to the historical generating power sequence of wind energy and the historical generating power sequence of solar energy, the distributed first positioning parameters of the wind-solar generating vacancy maintaining time in the statistical period are sequentially estimated.
Specifically, the terminal can estimate a quantile value of duration distribution of the monthly-based wind-solar power generation single energy shortage process, namely an upper delta quantile of the empirical distribution to which the wind-solar power generation shortage maintaining time obeys, according to the historical wind power generation power sequence and the historical solar power generation power sequence based on the empirical distribution. Illustratively, the terminal may calculate the wind-solar generation shortage power sequence based on the historical generation power sequence of wind energy and light energy:
Figure BDA0003122300080000071
Figure BDA0003122300080000072
Figure BDA0003122300080000073
wherein the content of the first and second substances,
Figure BDA0003122300080000074
representing the historical wind-solar power generation shortage power sequence,
Figure BDA0003122300080000075
represents the month m to which the time t belongstThe monthly average generated power of the solar power generation,
Figure BDA0003122300080000076
represents the solar generated power at time t,
Figure BDA0003122300080000077
represents the month m to which the time t belongstThe monthly average power of the wind power generation,
Figure BDA0003122300080000078
representing the wind power generation power at time t,
Figure BDA0003122300080000079
denotes the m-thtThe time of the start of the month is,
Figure BDA00031223000800000710
denotes the m-thtThe end of the month.
Based on the historical wind and light power generation shortage power sequence, the terminal can count the duration of each continuous power shortage period. When it is satisfied with
Figure BDA00031223000800000711
Then, the terminal can obtainDuration of occurrence of continuous power shortage of kth wind-solar generation:
Figure BDA00031223000800000712
wherein, taukIndicating the duration of the occurrence of the continuous power deficit from the kth wind-solar generation,
Figure BDA00031223000800000713
indicating the starting moment of continuous power shortage of the kth wind-solar generation,
Figure BDA00031223000800000714
indicating the end time of continuous power shortage of the kth wind-solar generation.
Thus, the terminal may obtain an empirical distribution of the duration of the continuous power deficit of historical wind and solar power generation
Figure BDA00031223000800000715
The upper delta quantile estimated value is:
Figure BDA0003122300080000081
wherein the content of the first and second substances,
Figure BDA0003122300080000082
the number of upper delta quantiles is expressed,
Figure BDA0003122300080000083
an upper delta quantile estimate representing an empirical distribution of the duration of occurrence of a continuous power deficit from wind-solar power generation.
Thus, the terminal estimates the upper delta quantile of the empirical distribution to which the wind-solar generation shortage holding time is obeyed.
And step S204, sequentially estimating second grading parameters of the distribution of the solar power generation shortage power in the statistical period according to the historical solar power generation power sequence.
Specifically, the terminal can estimate the quantile value of the average power distribution of the solar power generation single energy shortage process according to the historical power generation power sequence of the solar power based on the uniform distribution, namely, the upper delta quantile estimation value of the uniform distribution to which the solar power generation shortage power obeys.
For example, a solar power generation excess power sequence may be calculated based on a solar historical power generation power sequence
Figure BDA0003122300080000084
Figure BDA0003122300080000085
The terminal can obtain the duration tau of the solar power generation shortage period by using the method the same as the duration statistics of the wind and light power generation shortage periodk solarFurther, the average power shortage corresponding to each continuous power shortage period can be counted:
Figure BDA0003122300080000086
wherein the content of the first and second substances,
Figure BDA0003122300080000087
represents the average power deficit of the kth solar power generation during the continuous power deficit.
Further, the terminal can respectively fit the average shortage power of the continuous power shortage process in the solar power generation based on a quadratic function and by adopting a least square algorithm
Figure BDA0003122300080000088
About the month m to which it belongskMonthly average generated power
Figure BDA0003122300080000089
Upper and lower envelope functions of (c):
Figure BDA00031223000800000810
Figure BDA00031223000800000811
wherein the content of the first and second substances,
Figure BDA00031223000800000812
parameters of a quadratic term, a primary term and a constant term of the upper envelope fitting quadratic function are respectively;
Figure BDA0003122300080000091
the parameters of the quadratic term, the primary term and the constant term of the lower envelope fitting quadratic function are respectively.
Therefore, the terminal can obtain the upper and lower bounds of the solar power generation shortage power in each month in the future according to the average predicted power of the solar power generation in each month in the future. The average predicted power of each month of solar power generation in each month of the next year is a known parameter, and can be obtained by predicting by using a prediction model in advance through the terminal.
Generally, the solar power generation shortage power can be considered to be uniformly distributed between an upper boundary and a lower boundary obtained by solving, so that the upper delta quantile estimated value of the solar power generation shortage power obtained by the terminal is as follows
Figure BDA0003122300080000092
Therefore, the terminal estimates a second quantile parameter of the distribution of the solar power generation shortage power in the statistical period, namely an upper delta quantile estimated value of uniform distribution obeyed by the solar power generation shortage power.
And step S206, sequentially estimating the distribution third grading parameter of the wind power generation shortage power in the statistical period according to the historical generation power sequence of the wind energy.
Specifically, the terminal can estimate the quantile value of the average power distribution of the single energy shortage process of wind power generation according to the historical generation power sequence of wind energy based on the empirical distribution of the periodic condition, namely, the upper delta quantile estimation value of the empirical distribution to which the wind power generation shortage power obeys.
For example, the terminal can calculate the wind power generation shortage power sequence based on the wind energy historical generation power sequence
Figure BDA0003122300080000093
Figure BDA0003122300080000094
The terminal can obtain the duration tau of the wind power generation shortage period by using the method the same as the duration statistics of the wind power generation shortage periodk windFurther, the average power shortage corresponding to each continuous power shortage period can be counted:
Figure BDA0003122300080000095
wherein the content of the first and second substances,
Figure BDA0003122300080000096
the average power deficit representing the occurrence of a continuous power deficit process for the kth wind energy generation.
Based on the average deficit power sequence of the continuous power deficit process of wind power generation, the terminal can perform Morlet complex wavelet transformation, and the magnitude degree of the power deficit of the wind power generation can be identified according to the positive and negative of the real part of the wavelet transformation coefficient. In the embodiment of the present application, a 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 transformation coefficient is positive, the high-power shortage of wind power generation is shown; and when the voltage is negative, the low-power shortage of the wind power generation is shown. Thus, the terminal can determine the time scale of the dominant period by calculating the wavelet variance based on the wavelet transform coefficients. The square of the wave transform coefficient is determined by the number of dominant cycles N (not less than 1), i.e. it can be divided into 2NAnd (4) a genus. Taking two dominant periods as an example, four categories can be divided at the moment and respectively correspond to wind energy power generationThe power deficit exhibits a combination of "high power deficit-high power deficit", "high power deficit-low power deficit", "low power deficit-high power deficit", "low power deficit-low power deficit" over the two dominant periods. And so on for other cases. Thus, the average power shortage of wind power generation can be reduced
Figure BDA0003122300080000101
The samples are divided into different subsets according to the categories, and the empirical distribution of the average shortage power of the wind power generation is obtained based on the samples of each subset
Figure BDA0003122300080000102
The upper delta quantile estimated value is:
Figure BDA0003122300080000103
therefore, the terminal estimates and obtains a third quantile parameter of the distribution of the wind power generation shortage power in the statistical period, namely an upper delta quantile estimated value of the empirical distribution obeyed by the wind power generation shortage power.
And S208, determining an upper bound parameter of wind-solar power generation shortage power distribution according to the second division parameter and the third division parameter.
Specifically, after the upper delta quantile estimated values of the average solar power generation and wind power generation shortage power are obtained respectively, the terminal can obtain the upper delta quantile estimated values of the even distribution obeyed by the solar power generation shortage power and the upper delta quantile estimated values of the empirical distribution obeyed by the wind power generation shortage power,
for example, the terminal can obtain an estimation value of an upper bound of a delta quantile on the wind-solar power generation shortage power distribution according to the weighting of the installed capacity
Figure BDA0003122300080000104
Wherein, CsolarAnd CwindRespectively showing the installed capacities of solar power generation and wind power generation. Installed capacity is all hydroelectric generators installed in hydropower stationThe sum of the group rated powers.
And step S210, sequentially estimating fourth quantile parameters of the distribution of the warehousing runoff of each step hydropower station according to the minimum value of the monthly warehousing runoff of the step hydropower station and the average value of the monthly warehousing runoff.
Because the minimum value of monthly warehousing runoff is changed in proportion to monthly average warehousing runoff, a plurality of subsets are divided according to the monthly average warehousing runoff size, and the terminal can estimate and calculate corresponding generalized extreme value distribution F based on a maximum likelihood estimation method for the minimum value sample of the monthly warehousing runoff in each subsetwThe parameter (c) of (c).
Specifically, the terminal estimates the quantile numerical value of the minimum value distribution of the natural warehousing runoff of each cascade hydropower station month by month according to the minimum value of the monthly warehousing runoff of the cascade hydropower stations and the average value of the monthly warehousing runoff, namely the lower delta quantile estimated value of the minimum value distribution of the monthly warehousing runoff of each cascade hydropower station. Therefore, the delta quantile estimated value under the distribution of the minimum value of the monthly warehousing runoff of each cascade hydropower station is
Figure BDA0003122300080000111
In the embodiment, the terminal respectively calculates the estimation results of the resource parameters according to the historical generating power sequence of the wind energy and the solar energy and the historical warehousing runoff data of the cascade hydropower station, so that an unbalanced constraint condition can be constructed according to the estimation results, the generating scheduling result of the cascade hydropower station is determined under the condition that the possible generating power shortage of the wind energy and the solar energy is considered, and the problem that the generating scheduling result is inaccurate due to the fact that the generating power shortage of the wind energy and the solar energy is ignored is avoided.
And step S104, constructing an unbalanced risk constraint condition according to the estimation results of the plurality of resource parameters.
The unbalance constraint condition is used for representing the constraint condition which needs to be met by adjusting the lunar reservoir of the cascade hydropower station under the condition that continuous shortage occurs in wind energy and solar energy power generation. That is, the imbalance constraint condition represents a condition that the lunar reservoir regulation amount needs to be met when the probability of the event that the wind-solar power generation shortage energy cannot be complemented is lower than a given threshold value in order to control the hydropower and is limited by the reservoir regulation capacity. In some embodiments, the measures of lunar reservoir regulation in the unbalanced risk constraint include at least one of reservoir hold, letdown, power generation, upstream, tailwater, and net head.
In some embodiments, the measure of the continuous shortage of wind and solar power generation in the imbalance risk constraint includes: respectively determining monthly average generating power of corresponding months of wind energy and solar energy according to the planned generating capacity of the monthly wind energy and the planned generating capacity of the solar energy; and if the sum of the actual generated power of the wind energy and the solar energy in the corresponding month is smaller than the sum of the monthly average generated power of the wind energy and the solar energy, determining that continuous shortage of the wind energy and the solar energy is generated.
Specifically, the terminal may determine the monthly average generated power of the monthly wind energy based on the planned generation of the monthly wind energy
Figure BDA0003122300080000112
Meanwhile, the terminal determines monthly average generating power of the solar energy in each month based on the planned generating capacity of the solar energy in each month
Figure BDA0003122300080000113
According to the actually collected actual generated power P1 of wind energy and the actually collected actual generated power P2 of solar energy, the terminal calculates the sum (P1+ P2) of the actual generated power of wind energy and solar energy in a month and sums the sum and the monthly average generated power of the wind energy and the solar energy
Figure BDA0003122300080000121
Comparing, if the sum of the actual generating power of the monthly wind energy and the solar energy is less than the sum of the monthly average generating power of the wind energy and the solar energy, namely
Figure BDA0003122300080000122
The generation of wind energy and solar energy is illustratedContinuous shortage.
The imbalance constraint may be represented by a plurality of constraints in combination: the method comprises the steps of limiting the maximum upward supporting power of hydropower in a continuous high-power shortage period (referred to as 'maximum upward supporting power') during the wind power in a month, limiting the maximum generating flow of each reservoir in a continuous high-power shortage period (referred to as 'generating flow limitation') during the wind power in a month, limiting the flow of upstream and downstream reservoirs in a continuous high-power shortage period (referred to as 'upstream and downstream reservoir flow relation') during the wind power in a month, and limiting the hydropower balance wind-light generating shortage (referred to as 'generating shortage limitation').
The maximum upward supporting power represents the optimized result of the hydropower station compared with the monthly average generated power, and the generated power can be provided more through the multi-water discharge in the continuous high-power shortage period of the wind power in the month:
Figure BDA0003122300080000123
wherein, the superscript' is used for indicating that the physical quantity is aiming at the continuous high-power shortage period of the wind power in the month
Figure BDA0003122300080000124
In the definition of the formula (I),
Figure BDA0003122300080000125
representing the maximum upward supporting power, P, that the water can provide during that periodHRepresents the mean generating power optimization value of the water and electricity monthly,
Figure BDA0003122300080000126
and the power generation flow of the reservoir in the continuous high-power shortage period of the wind power in the month is shown. And calculating the water purification head for calculating the maximum upward supporting power of the hydropower according to the monthly average optimized value. This is to consider that the change of each physical quantity from the beginning of the month to the time period of wind-light continuous large power shortage is unknown, because the extreme scene of wind-light continuous large power shortage in the month is modeled, so that the water purifying head is not considered to be significantly lower than the water purifying head due to continuous water discharge before the extreme sceneThe monthly mean is reasonable and can be replaced by the monthly mean.
The power generation flow constraint represents the maximum power generation flow which can be provided by each reservoir in the time period corresponding to continuous shortage of wind energy and solar power generation in the month; namely, the power generation flow constraint represents the maximum power generation flow available under the reservoir scheduling limit condition, the maximum upward power supporting capacity of the hydropower is determined, and water abandon does not occur at the moment. The flow relation of the upstream reservoir and the downstream reservoir represents the power generation flow of the direct upstream of the current cascade hydropower station and the relation between the warehousing flow of the current cascade hydropower station from the direct upstream, namely, the power generation flow of the direct upstream reservoir is the warehousing flow of the hydropower station from the upstream:
Figure BDA0003122300080000127
the power generation shortage constraint representation means that when the natural warehousing flow of the cascade hydropower station is at the monthly minimum value level in the probability meaning and the shortage of the power generation amount of the wind energy and the solar energy is in the probability meaning, the cascade hydropower station provides supporting electric quantity which is used for complementing the power generation amount shortage of the wind energy and the solar energy.
Specifically, the terminal constructs the imbalance risk constraint condition by using the resource parameter according to the estimation result of the resource parameter obtained in the foregoing embodiment. In some embodiments, the terminal determines the maximum upward power supporting capacity of the cascade hydropower station under the reservoir dispatching limit condition according to the fourth quantile parameter, the first quantile parameter and the upstream and downstream reservoir flow relation, and constructs the power generation flow constraint. Specifically, the power generation flow constraint may be represented by the following formula:
Figure BDA0003122300080000131
Figure BDA0003122300080000132
wherein the content of the first and second substances,
Figure BDA0003122300080000133
the method represents the downward drainage flow of the direct upstream hydropower in the continuous high-power shortage period of the wind power in the month.
In some embodiments, the terminal constructs a power generation deficit constraint based on the first location parameter, the upper bound parameter, and a maximum upward support power of the cascade hydropower station during successive periods of wind and solar deficit during the month. Specifically, the power generation shortage constraint constitutes a sufficient condition that the probability of occurrence of the wind-solar power generation shortage event is less than δ, and can be represented by the following formula:
Figure BDA0003122300080000134
in the embodiment, the terminal establishes the unbalanced risk constraint condition according to the estimation results of the multiple resource parameters, and compared with the traditional annual reservoir power generation scheduling, the terminal considers the situation that the wind energy and the solar energy possibly have generating power shortage and takes the condition as one of the constraints of the cascade hydroelectric power generation scheduling optimization, so that the defect that the limitation of monthly hydroelectric power and wind power generation scheduling planning is neglected is overcome, and a new technical support scheme is provided for the reliable operation of a wind-light-water full-cleaning power system.
And S106, under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, solving the cascade hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target to obtain a cascade hydropower station power generation scheduling result.
The cascade hydroelectric power generation dispatching optimization is characterized in that an objective function (namely a cascade hydroelectric power station power generation optimizing dispatching model) is constructed substantially, and the objective function is solved on the premise that a series of related constraint conditions are met, so that the cascade hydroelectric power generation dispatching achieves an optimal value.
Specifically, under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, the terminal solves the cascade hydropower station power generation optimization scheduling model by taking the operation minimization cost as a target, and the obtained solution is the cascade hydropower station power generation scheduling result. Illustratively, the terminal can utilize a solver CPLEX to solve the cascade hydropower station power generation optimization scheduling model.
The step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station. The water storage plan at the end of a month refers to the water storage amount required to be achieved at the end of each month of the future cascade hydropower station.
Known parameters of the cascade hydropower annual power generation dispatching plan optimization model considering the unbalanced risk constraint comprise the following parameters: a water consumption cost coefficient (element/m 3), a wind-solar power generation electricity-abandoning cost parameter (element/MWh), the residual water storage capacity of the last year and the last tail reservoir with each reservoir capacity water electricity (m3), the upper and lower limits of the water storage capacity of the reservoir with each reservoir capacity water electricity (m3), the function relation of the upstream water level of the reservoir with each reservoir capacity water electricity on the water storage capacity of the reservoir, the function relation of the tail water level of the reservoir with each reservoir capacity water electricity on the total downward discharge flow, the function relation of the water level loss of each reservoir capacity water electricity on the power generation flow, the upper and lower limits of the power generation flow of each hydropower station (m3/s), the upper and lower limits of the total downward discharge flow of each hydropower station (m3/s), the installed capacity (MW) of each hydropower station, the power generation characteristic curve of each hydropower station, namely the function relation of the water purification head and the power generation flow, and the average power generation predicted value sequence (MW) obtained by theoretical conversion of each month in the future of wind-solar power generation, The spare coefficient (%), the average load power (MW) of each month in the next year, the maximum allowable occurrence probability (%) that the hydroelectric power cannot complement the wind-solar energy shortage in the month, and the like.
In some embodiments, the building process of the cascade hydropower station power generation optimization scheduling model comprises the following steps: calculating the difference between the theoretical value of the monthly wind energy and the solar average generating power and the optimized value of the corresponding monthly wind energy and solar average generating power; and constructing a cascade hydropower station power generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of all hydropower stations. Specifically, the cascade hydropower station power generation optimization scheduling model can be represented by the following formula:
Figure BDA0003122300080000141
wherein zeta represents the water consumption cost coefficient of hydroelectric power generation, zeta represents the energy abandoning cost coefficient of wind-solar power generation, and Delta TmDenotes the duration of the m-th month, qmIndicates the generated power flow rate in the m-th month,
Figure BDA0003122300080000142
represents the optimized result value of the wind-light average generating power in the mth month,
Figure BDA0003122300080000143
and the theoretical average wind-solar power generation power in the mth month is expressed and is obtained by converting the theoretical power generation amount divided by the total time length. The index i indicates the number of the cascade hydropower station, and a smaller value indicates a closer upstream. N is a radical ofHIndicating the number of cascade hydro-power stations.
In some embodiments, the system operating constraints include at least one of reservoir capacity hydropower related constraints, runoff hydropower related constraints, common constraints for reservoir capacity hydropower and runoff hydropower, upper limit constraints for wind-solar power generation, and supply-demand balance constraints for the power system.
The reservoir capacity hydropower related constraints comprise at least one of water quantity balance constraints, water head constraints, power generation characteristic constraints, initial reservoir water storage quantity constraints, upper and lower reservoir water storage quantity limits constraints, upper and lower reservoir water purification head limits constraints and upper and lower power generation power limits constraints.
The water balance constraint represents the relation between the current reservoir warehousing flow, the current reservoir ex-warehouse flow and the water storage amount: vi,m＝Vi,m-1+(Wi,m+ui,m-Qi,m)ΔTm m>2. Wherein, VmRepresents the amount of water stored in the moon's last reservoir in the m-th monthmRepresenting the discharge flow rate, Q, of the water discharged from the immediate upstream reservoir in the m-th monthmThe total leakage flow in the mth month is represented, and the three are all optimization variables; wmThe natural warehousing flow of the mth month is shown and is the known quantity of input.
Head constraint characterizes the relationship between water level, head and flow letdown, and storage:
Figure BDA0003122300080000151
Figure BDA0003122300080000152
Figure BDA0003122300080000153
Figure BDA0003122300080000154
wherein the content of the first and second substances,
Figure BDA0003122300080000155
indicating the upstream water level of the m-th month,
Figure BDA0003122300080000156
indicating the end water level of the m-th month,
Figure BDA0003122300080000157
representing the head loss for the m-th month,
Figure BDA0003122300080000158
representing the clear head of month m, these variables are all optimization variables. f. offA function representing the upstream water level with respect to the reservoir capacity, ftailFunction representing the tail water level in relation to the total discharge of the reservoir, flossAnd the functions represent functions of head loss on the power generation flow, and the functional relations are known quantities of input and are subjected to piecewise linearization processing.
The power generation characteristic constraint represents the relation between the generating power of the hydroelectric generating set and the water purifying head and the generating flow of the reservoir:
Figure BDA0003122300080000159
wherein the content of the first and second substances,
Figure BDA00031223000800001510
representing the generating power of the hydropower station with the storage capacity in the mth month as a substitute quantity; f. ofDHThe function of the generating power of the hydropower station unit related to the clear water head and the generating flow is a known quantity of input, and is subjected to segmented linear processing.
And (3) the initial reservoir storage capacity constraint represents the residual storage capacity of the last year and the last reservoir as the boundary condition of the next year optimization:
Figure BDA0003122300080000161
wherein the content of the first and second substances,
Figure BDA0003122300080000162
indicating the initial reservoir water storage capacity as a known quantity.
The upper and lower limits of the reservoir water storage capacity are restricted to represent that the change of the reservoir water storage capacity cannot exceed the allowable range:
Vi,min≤Vi,m≤Vi,max
wherein, VminAnd VmaxRespectively showing the adjustable lower bound and the upper bound of the reservoir water storage capacity.
The upper and lower limits of the reservoir water purification head are restricted to represent that the change of the reservoir water purification head cannot exceed the allowable range:
Figure BDA0003122300080000163
wherein h isminAnd hmaxRespectively showing the adjustable lower and upper boundaries of the reservoir water purification head.
The upper and lower limits of the generated power are restricted to represent that the variation of the generated power cannot exceed the allowable range:
Figure BDA0003122300080000164
wherein the content of the first and second substances,
Figure BDA0003122300080000165
the maximum monthly average power generation of the stored hydropower is generally the rated installed capacity.
The runoff hydropower related constraint comprises at least one of a water balance constraint, a power generation characteristic constraint and a power generation upper and lower limit constraint.
The water balance constraint represents the relation between the current reservoir warehousing flow and the current reservoir ex-warehouse flow, and compared with water and electricity with reservoir capacity, the method has no related items of reservoir water storage capacity: w is ai,m+ui,m＝Qi,m。
Compared with the hydropower with storage capacity, the power generation characteristic constraint representation has the following relationship between the generating power and the generating flow of the hydroelectric generating set:
Figure BDA0003122300080000166
the upper and lower limits of the generated power are restricted to represent that the variation of the generated power cannot exceed the allowable range:
Figure BDA0003122300080000167
wherein the content of the first and second substances,
Figure BDA0003122300080000168
the maximum monthly average generating power of the radial flow type hydroelectric power is shown, and is generally rated installed capacity.
The common constraints of reservoir capacity hydropower and radial flow hydropower comprise at least one of a lower discharge flow constraint, a reservoir power generation flow upper and lower limit constraint, a reservoir total lower discharge flow upper and lower limit constraint and a reservoir upstream and downstream relation constraint.
Wherein, let down the flow constraint and characterize total let down the flow and include power generation flow and abandon discharge two parts: qi,m＝si,m+qi,m. Wherein s ismReject flow in month m.
The upper and lower limits of the reservoir generating flow restrict the variation of the reservoir generating flow not to exceed the allowable range: q. q.si,min≤qi,m≤qi,max. Wherein q isminAnd q ismaxRespectively representing the adjustable lower bound and the upper bound of the generating flow of the reservoir.
The restriction of the upper and lower limits of the total lower discharge of the reservoir represents that the change of the total lower discharge of the reservoir cannot exceed the allowable range: qi,mim≤Qi,m≤Qi,max. Wherein Q isminAnd QmaxRespectively representing the adjustable lower bound and the upper bound of the generating flow of the reservoir.
The reservoir upstream and downstream relation constraint representation is directly connected with upstream and downstream hydropower stations, and the total downstream discharge flow of the upstream hydropower station is the warehousing flow of the downstream hydropower station: u. ofi,m＝Qi-1,m,i≥2。
The upper limit constraint of the wind-solar power generation represents the upper limit of the wind-solar power generation capacity under the constraint of natural resources:
Figure BDA0003122300080000171
the supply and demand balance constraint of the power system represents a relation formula of power generation balance load demand expressed based on monthly average power:
Figure BDA0003122300080000172
wherein D ismRepresents the average load power of the m-th month, and K represents the system standby coefficient.
Therefore, the terminal determines the value range of each variable involved in the step hydropower station power generation optimization scheduling model solving process according to the unbalanced risk constraint condition and the system operation constraint condition, and solves the step hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target, so that the obtained step hydropower station power generation scheduling result is obtained.
According to the power generation optimization scheduling method of the cascade hydropower station, the estimation results of the resource parameters are respectively calculated according to the historical power generation power sequence of wind energy and solar energy and the historical warehousing runoff data of the cascade hydropower station, so that an unbalanced constraint condition can be constructed according to the estimation results, the power generation scheduling result of the cascade hydropower station is determined under the condition that the power generation shortage of the wind energy and the solar energy is possibly considered, the condition that the power generation shortage of the wind energy and the solar energy is possibly considered and is taken as one of the constraints of the cascade hydropower power generation scheduling optimization, the defect that the limitation of the monthly hydropower and wind power generation capacity is omitted in the traditional annual reservoir power generation scheduling plan is overcome, and the scheduling optimization result is more accurate and reliable.
In a specific embodiment, taking five-stage built cascade hydropower station and wind power and solar power generation complementary power system as an example, the five-stage cascade hydropower station system comprises 3 hydropower stations (DH) with storage capacity1-DH3) And 2 radial-flow hydroelectric power stations (ROR)1-ROR2) Their geographical location distribution is shown in fig. 3. The detailed parameters of each hydropower station are shown in table 1 and table 2.
Figure BDA0003122300080000181
TABLE 1
Figure BDA0003122300080000182
TABLE 2
Wherein, VmaxAnd VminRespectively represents the maximum water storage capacity and the minimum water storage capacity, and the unit is Mm3；
Figure BDA0003122300080000183
And
Figure BDA0003122300080000184
respectively representing a maximum clear head and a minimum clear head, and the unit is m; q. q.smaxRepresents the maximum generated flow rate in m3/s；QmaxRepresents the maximum let-down flow in m3/s；
Figure BDA0003122300080000185
To representThe maximum generating power of the hydropower station with the storage capacity is MW;
Figure BDA0003122300080000186
represents the maximum generated power of a radial hydropower station in MW.
Taking the statistics period of 1958-2012 as an example, the historical power generation data of the water energy is, for example, the natural runoff data of each hydropower station from 1958-2012, and the time resolution is day. Assuming that the planned capacity of wind power generation is 7100MW and the planned capacity of solar power generation is 4200MW, the power generation curves of the wind power generation and the solar power generation are converted into curves through historical wind speed and irradiance curves. The wind-electricity conversion cut-in wind speed is 2m/s, the rated wind speed is 7m/s, the cut-out wind speed is 15m/s, and the irradiance-electricity conversion assumes that the solar power generation power is in direct proportion to the irradiance. The load data is amplified in an equal ratio based on the actual recorded load curve in 2019, and the maximum load is set to be 9 GW. The weighting coefficients xi and zeta are set to 5000 yuan/MWh and 0.1 yuan/m, respectively3. Based on the data, the terminal solves the optimal scheduling model for cascade hydropower station power generation, so that the monthly scheduling result of the reservoir and the electric quantity plan of each generator set in the future 12 months can be obtained. Based on the data, the terminal calculates the estimation results of the resource parameters as follows: duration of wind-solar power generation shortage obtained by using 5% quantile in parameter estimation, estimation result of 5% quantile on average shortage power of solar power generation and wind power generation, and DH1、DH2、DH3And ROR2The upper 5% quantile estimation of the distribution of monthly minimum values for natural influent runoff. As shown in fig. 4 (a) and (b), the left vertical axis represents monthly power generation amount in p.u., the horizontal axis represents time in months, and the right vertical axis represents power shortage in p.u. The map (a) and the map (b) in fig. 4 show the estimated value of the deficit power, the actual value of the deficit power, and the monthly power generation amount, respectively. As shown in FIGS. 5 (a), (b), (c) and (d), the vertical axis is the radial flow, and the unit is m3The horizontal axis is time in months, and the graphs (a), (b), (c) and (d) show the estimated minimum value, the actual minimum value, and the monthly average value of runoff, respectively. Wherein, due to ROR1Immediately adjacent to DH1Its natural runoff water is considered to be 0.
Therefore, the terminal obtains hydropower stations (namely DH) with storage capacities by utilizing the solver CPLEX1、DH2And DH3) The result of the reservoir monthly end storage amount planning of (2) is, for example, the result shown in fig. 6. As shown in FIG. 6, the vertical axis represents the reservoir capacity in 109m3The horizontal axis is time in months. Fig. 6 shows that after solving the power generation optimization scheduling model of the cascade hydropower station by using the solver CPLEX, the water storage capacity of the reservoir in each month corresponding to each hydropower station with the storage capacity is obtained.
In the above embodiment, an actual 5-stage cascade hydropower station is taken as an example for explanation, the generation optimization scheduling method of the cascade hydropower station in the embodiment of the present application is used to estimate resource parameters and construct an unbalanced risk constraint condition, and under the constraint and system operation constraint conditions, the MATLAB is used to perform modeling and simulation to obtain a water storage capacity plan at the end of a month of a reservoir with each reservoir hydropower station.
It should be understood that although the various steps in the flow charts of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 7, there is provided a power generation optimization scheduling device 700 of a cascade hydropower station, including: a processing module 710 and a computing module 720, wherein:
and the processing module 710 is configured to calculate estimation results of a plurality of resource parameters according to historical wind power generation data, historical solar power generation data, historical water power generation data, and power generation related parameters.
The processing module 710 is further configured to construct an imbalance risk constraint condition according to the estimation results of the plurality of resource parameters; the unbalanced risk constraint conditions represent the constraint conditions that need to be met by lunar reservoir regulation of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation.
The calculation module 720 is used for solving the cascade hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target under the constraint of the unbalanced risk constraint condition and the system operation constraint condition to obtain a cascade hydropower station power generation scheduling result; the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
In one embodiment, the processing module is further configured to sequentially estimate a first location parameter of the distribution of the wind-solar generation vacancy holding time within the statistical period according to the historical generation power sequence of the wind energy and the historical generation power sequence of the solar energy; according to the historical power generation power sequence of the solar energy, sequentially estimating a second grading parameter of the distribution of the solar power generation shortage power in a statistical period; according to the historical power generation power sequence of the wind energy, sequentially estimating a third grading parameter of the distribution of the wind energy generation shortage power in a statistical period; determining an upper bound parameter of wind-solar power generation shortage power distribution according to the second location division parameter and the third location division parameter; and sequentially estimating fourth quantile parameters of the distribution of the warehousing runoff of each step hydropower station according to the minimum value of the monthly warehousing runoff of the step hydropower stations and the average value of the monthly warehousing runoff.
In one embodiment, the processing module is further configured to determine a maximum upward power supporting capacity of the cascade hydropower station under the reservoir dispatching limit condition according to the fourth zoning parameter, the first zoning parameter and the upstream and downstream reservoir flow relation, and construct a power generation flow constraint; constructing a power generation shortage constraint according to the first positioning parameter, the upper bound parameter and the maximum upward supporting power of the cascade hydropower station in the continuous shortage time period of the wind energy and the solar energy in the month; the upstream and downstream reservoir flow relation represents the relationship between the generating flow of the direct upstream of the current cascade hydropower station and the warehousing flow of the current cascade hydropower station from the direct upstream; the method comprises the following steps of (1) generating flow restriction, representing the maximum generating flow which can be provided by each reservoir in a time period corresponding to continuous shortage of wind energy and solar energy generation in the current month; and power generation shortage constraint, which represents the support electric quantity provided by the cascade hydropower station when the natural warehousing flow of the cascade hydropower station is at the monthly minimum level in the probability meaning and the shortage of the wind energy and solar energy in the probability meaning occurs, and the support electric quantity is used for complementing the shortage of the wind energy and solar energy in the power generation quantity.
In one embodiment, the measures of regulation of the lunar reservoir in the unbalanced risk constraint include at least one of reservoir capacity, letdown flow, power generation flow, upstream water level, tailwater level, and net head.
In one embodiment, the processing module is further configured to construct a cascade hydropower station power generation optimization scheduling model, including: calculating the difference between the theoretical value of the monthly wind energy and the solar average generating power and the optimized value of the corresponding monthly wind energy and solar average generating power; and constructing a cascade hydropower station power generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of all hydropower stations.
In one embodiment, the system operating constraints include at least one of reservoir capacity hydropower related constraints, runoff hydropower related constraints, common constraints for reservoir capacity hydropower and runoff hydropower, upper limit constraints for wind-solar power generation, and supply-demand balance constraints for the power system.
In one embodiment, the imbalance risk constraint is a measure of the continuous shortage of wind and solar power generation, including: respectively determining monthly average generating power of corresponding months of wind energy and solar energy according to the planned generating capacity of the monthly wind energy and the planned generating capacity of the solar energy; and if the sum of the actual generated power of the wind energy and the solar energy in the corresponding month is smaller than the sum of the monthly average generated power of the wind energy and the solar energy, determining that continuous shortage of the wind energy and the solar energy is generated.
For specific limitations of the power generation optimized dispatching device of the cascade hydropower station, reference may be made to the above limitations of the power generation optimized dispatching method of the cascade hydropower station, and details thereof are not repeated herein. All or part of each module in the power generation optimization scheduling device of the cascade hydropower station can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store XXX data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for optimal scheduling of power generation for a cascade hydropower station.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is also provided, and the computer device may specifically be a terminal or a server. The computer device comprises a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the above-mentioned method embodiments. According to the computer equipment, estimation results of resource parameters are respectively calculated according to historical generating power sequences of wind energy and solar energy and historical warehousing runoff data of the cascade hydropower station, so that an unbalanced constraint condition can be constructed, the generating scheduling result of the cascade hydropower station is determined under the condition that the possible generating power shortage of the wind energy and the solar energy is considered, the generating scheduling result is used as one of constraints of cascade hydropower scheduling optimization, and the defect that the limitation of the generating capacity of hydropower combined with wind power generation within a month is ignored in a traditional annual reservoir generating scheduling plan is overcome.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments. According to the computer-readable storage medium, estimation results of resource parameters are respectively calculated according to historical power generation power sequences of wind energy and solar energy and historical warehousing runoff data of the cascade hydropower station, so that an unbalanced constraint condition can be constructed, the power generation scheduling result of the cascade hydropower station under the condition that the power generation shortage of the wind energy and the solar energy is considered, the condition that the power generation shortage of the wind energy and the solar energy is considered, the power generation scheduling result is used as one of constraints of cascade hydropower scheduling optimization, and the defect that the limitation of power generation capacity of hydropower combined with wind power generation within a month is omitted in a traditional annual reservoir power generation scheduling plan is overcome.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for optimized scheduling of power generation for a cascade hydropower station, the method comprising:
calculating to obtain estimation results 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 results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation;
under the constraint of the unbalanced risk constraint condition and the system operation constraint condition, with the operation minimization cost as a target, solving a cascade hydropower station power generation optimization scheduling model to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
2. The method of claim 1, wherein the plurality of resource parameters comprises a first quantile parameter, a second quantile parameter, an upper bound parameter, and a fourth quantile parameter; the method for obtaining the estimation results of a plurality of resource parameters by calculation 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 comprises the following steps:
according to the historical generating power sequence of wind energy and the historical generating power sequence of solar energy, sequentially estimating distributed first location parameters of wind-solar generation vacancy maintenance time in a statistical period;
according to the historical power generation power sequence of the solar energy, sequentially estimating a second grading parameter of the distribution of the solar power generation shortage power in a statistical period;
according to the historical power generation power sequence of the wind energy, sequentially estimating a third grading parameter of the distribution of the wind energy generation shortage power in a statistical period;
determining an upper bound parameter of wind-solar power generation shortage power distribution according to the second location division parameter and the third location division parameter;
and sequentially estimating fourth quantile parameters of the distribution of the warehousing runoff of each step hydropower station according to the minimum value of the monthly warehousing runoff of the step hydropower stations and the average value of the monthly warehousing runoff.
3. The method according to claim 2, wherein said constructing an imbalance risk constraint based on the estimation of the plurality of resource parameters comprises:
determining the maximum upward power supporting capacity of the cascade hydropower station under the reservoir dispatching limit condition according to the fourth position parameters, the first position parameters and the upstream and downstream reservoir flow relation, and constructing power generation flow constraint;
constructing a power generation shortage constraint according to the first positioning parameter, the upper bound parameter and the maximum upward supporting power of the cascade hydropower station in the continuous shortage period of the wind energy and the solar energy in the month;
the upstream reservoir flow relation and the downstream reservoir flow relation represent the relationship between the generating flow of the direct upstream of the current cascade hydropower station and the warehousing flow of the current cascade hydropower station from the direct upstream;
the power generation flow constraint represents the maximum power generation flow which can be provided by each reservoir in the time period corresponding to continuous shortage of wind energy and solar power generation in the month;
and the power generation shortage constraint represents the support electric quantity which is required to be provided by the cascade hydropower station when the natural warehousing flow of the cascade hydropower station is at the monthly minimum level in the probability meaning and the shortage of the wind energy and solar energy in the probability meaning is generated, and the support electric quantity is used for complementing the shortage of the wind energy and solar energy in the power generation quantity.
4. The method of claim 1, wherein the imbalance risk constraints include measures of lunar reservoir regulation including at least one of reservoir hold, letdown, power generation, upstream, tailwater, and net head.
5. The method according to claim 1, wherein the building process of the cascade hydropower station power generation optimization scheduling model comprises:
calculating the difference between the theoretical value of the monthly wind energy and the solar average generating power and the optimized value of the corresponding monthly wind energy and solar average generating power;
and constructing a cascade hydropower station power generation optimization scheduling model according to the difference and the sum of the monthly power generation flows of all hydropower stations.
6. The method of claim 1, wherein the system operating constraints comprise at least one of reservoir capacity hydropower related constraints, runoff hydropower related constraints, common constraints for reservoir capacity hydropower and runoff hydropower, upper limit constraints for wind-solar power generation, and supply-demand balance constraints for the power system.
7. The method of any one of claims 1 to 6, wherein the imbalance risk constraint wherein the measure of the occurrence of successive shortages in wind and solar power generation comprises:
respectively determining monthly average generating power of corresponding months of wind energy and solar energy according to the planned generating capacity of the monthly wind energy and the planned generating capacity of the solar energy;
and if the sum of the actual generated power of the wind energy and the solar energy in the corresponding month is smaller than the sum of the monthly average generated power of the wind energy and the solar energy, determining that continuous shortage of the wind energy and the solar energy is generated.
8. An optimal scheduling device for power generation of a cascade hydropower station, the device comprising:
the processing module is used for calculating and obtaining estimation results of a plurality of resource parameters according to historical wind power generation data, historical solar power generation data, historical water power generation data and power generation related parameters;
the processing module is further configured to construct an imbalance risk constraint condition according to the estimation results of the plurality of resource parameters; the unbalanced risk constraint condition represents a constraint condition which needs to be met by regulation of a lunar reservoir of the cascade hydropower station under the condition of continuous shortage of wind energy and solar energy power generation;
the calculation module is used for solving the cascade hydropower station power generation optimization scheduling model by taking the operation minimized cost as a target under the constraint of the unbalanced risk constraint condition and the system operation constraint condition to obtain a cascade hydropower station power generation scheduling result; and the step hydropower station power generation scheduling result comprises at least one of a month end water storage plan, a wind energy power generation plan, a solar power generation plan and a water energy power generation plan of the step hydropower station.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN202110680410.5A 2021-06-18 2021-06-18 Power generation optimization scheduling method and device for cascade hydropower station and computer equipment Pending CN113506185A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099468A (en) * 2022-06-06 2022-09-23 中国长江电力股份有限公司 Calculation method for optimal distribution of flood control storage capacity of series reservoir group

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099468A (en) * 2022-06-06 2022-09-23 中国长江电力股份有限公司 Calculation method for optimal distribution of flood control storage capacity of series reservoir group
CN115099468B (en) * 2022-06-06 2024-02-13 中国长江电力股份有限公司 Calculation method for flood control reservoir capacity optimal allocation of serial reservoir group

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