CN111005346A - Reservoir group multi-objective action mechanism and optimization scheduling scheme analysis method - Google Patents

Reservoir group multi-objective action mechanism and optimization scheduling scheme analysis method Download PDF

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CN111005346A
CN111005346A CN201911240272.8A CN201911240272A CN111005346A CN 111005346 A CN111005346 A CN 111005346A CN 201911240272 A CN201911240272 A CN 201911240272A CN 111005346 A CN111005346 A CN 111005346A
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CN111005346B (en
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董增川
陈牧风
姚弘祎
贾文豪
倪效宽
吴振天
陈雨菲
钟加星
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Hohai University HHU
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Abstract

The invention discloses a reservoir group multi-target action mechanism and an optimized scheduling scheme analysis method, which comprises the steps of firstly constructing a reservoir group power generation-water supply-ecology-shipping-flood control five-target optimized scheduling model on the basis of a generalized research area reservoir group system, determining a target function and a constraint condition used by the model, converting a flood control requirement into a flood stage water level constraint condition, taking actual flood data or a designed flood process as model input, solving the model by using an intelligent optimization algorithm, carrying out two-pair target two-dimensional plane mapping on a solving result, qualifying an action relation between targets under the water coming condition according to a relation presented by the two-dimensional plane mapping, analyzing the strength of the action relation between the targets under the water coming condition by combining a selected representative point corresponding target function value, and carrying out judgment on the action relation between the targets and acquisition of a reservoir scheduling mode. The invention simultaneously considers the power generation, water supply, ecology and shipping benefits and provides reference and basis for the optimized dispatching of the reservoir group, thereby having strong practicability and wide applicability.

Description

Reservoir group multi-objective action mechanism and optimization scheduling scheme analysis method
Technical Field
The invention relates to a water resource evaluation management and reservoir scheduling scheme guidance method, in particular to a reservoir group multi-target optimization interaction mechanism and an optimization scheduling scheme analysis method.
Background
With the continuous development of hydropower systems in China, the water energy resources are paid more attention and attention. Meanwhile, with the development of regional economy, the task undertaken by the reservoir is no longer limited to providing electric power support. The operation of the reservoir not only ensures the flood control safety of the river channel, but also plays various roles of generating electricity, supplying water, maintaining ecological environment, keeping navigation state and the like. In an electric power system, a hydropower station is used for peak shaving and frequency modulation due to cleanness and flexible switching scheduling, but the generating capacity of the hydropower station is often greatly limited due to the influence of multi-party benefits borne by the hydropower station at present. Similarly, as the requirements of the generating and ecological runoff, navigation requirements and other hydropower station benefit targets on the water head and the flow of the hydropower station are different, the mutual influence and the mutual dependence between the generating and ecological runoff and the navigation requirements are embodied. Therefore, how to utilize the influence relationship among a plurality of good benefit targets of reservoir scheduling processing and maximizing the benefit of limited water resources in the region is the key point and the difficult problem of the current research. At present, the requirement on the comprehensive development of the reservoir is higher and higher, but the contradiction research faced by the reservoir when the reservoir fulfills the aims is less, and a more intuitive grasp and the analysis on the internal contradiction mechanism are lacked, so that the reservoir and the reservoir group system generate a large amount of energy resource waste in the operation process, and the method is contrary to the development aim of the comprehensive development of resources in China. Therefore, the patent provides an analysis method for researching the interaction relation among multiple targets of the reservoir aiming at the existing problems and aims at providing certain guidance for actual scheduling of the reservoir.
Disclosure of Invention
The purpose of the invention is as follows: a reservoir group multi-target action mechanism and an optimized scheduling scheme analysis method aim at providing certain guidance for actual scheduling of a reservoir.
The technical scheme is as follows: the invention relates to a reservoir group multi-target action mechanism and optimization scheduling scheme analysis method, which comprises the following steps:
(1) reservoir group system generalization: carrying out generalized analysis on main processes and influence factors of the reservoir group system to realize system simulation and establish a mapping relation from the actual condition of the system to abstract mathematical expression;
(2) identifying reservoir benefit functions, determining a target function expression form of a benefit target and reservoir scheduling constraint conditions, and constructing a reservoir group power generation-water supply-ecology-shipping-flood control five-target optimized scheduling model, wherein the flood control target is converted into the constraint conditions to be considered based on the priority and the mandatory of the reservoir scheduling requirements, and a model target function and the constraint conditions are determined;
(3) model input data determination and model calculation: the model input data is considered according to the calculation time interval and the calculation target requirement, actual measurement flood data, design flow data and historical water inflow process data are adopted, and days, ten days and months are taken as calculation step lengths;
(4) solving the model based on an intelligent optimization algorithm: based on the reservoir group system generalization in the step (1), taking the four benefit targets identified in the step (2) as an objective function, and the determined constraint conditions as constraints, and taking the incoming water data determined in the step (3) as input, and taking the incoming water data as a modeling result and calculation preparation;
(5) visualization of solution sets and multi-objective relational analysis based on correlation calculation and replacement rate:
(6) multi-objective relationship analysis based on comparisons between schemes: based on a fuzzy optimization method, obtaining a balance scheme in a scheme set by taking the same optimization degree of each target as a standard, and investigating the change conditions of other targets under the optimal scheme of each benefit target on the basis of each target value in the scheme;
(7) judging the action relationship between the targets and acquiring a reservoir dispatching mode:
and (5) obtaining action relations among the four considered targets based on the results of the step (5) and the step (6): if one target benefit is increased and the other target benefit is decreased according to the replacement rate among the targets with strong correlation in the step (5), the two targets are considered to have strong competitive relationship; the other target benefit is increased, and the two have strong cooperative relationship; judging the targets which do not show strong correlation in the step (5) through the step (6), and comparing with the benefit values of all targets in the balancing scheme, if the benefits of the two targets are reduced in the other target scheme or the benefit of one target is reduced and the benefit of the other target is not changed, determining that the two targets have weak competition; if the two targets increase one by one in the scheme of deviating from the other target, the two targets have weak competition or weak cooperative relationship; if the benefit values of the two targets are increased in a scheme deviating from the other target, or the benefit of one target is increased and the benefit of the other target is not changed, the two targets have a weak synergistic relationship; and obtaining the optimal scheme of each target and four reservoir group scheduling schemes when the benefits are balanced based on the optimization result.
Further, the reservoir group system in the step (1) is generalized to be: the three systems of social economy, ecological environment and water resource are generalized into three elements of a water withdrawal node, a calculation unit water quantity transmission system and a drainage basin unit water quantity transmission system.
Further, the objective function in step (2) is:
the maximum annual total power generation of the reservoir group is taken as a target function:
Figure BDA0002306012270000021
wherein E is the total power generation amount of the cascade reservoir group,
Figure BDA0002306012270000031
the output of the mth hydropower station in the t period is kW; m represents the total number of the cascade power stations, T represents the total time interval length and year; Δ t represents a unit calculation time step, month;
the minimum water demand and water shortage is taken as a water supply target:
Figure BDA0002306012270000032
in the formula, K is the number of socioeconomic water-requiring areas; qktThe economic water demand of the general society of the water demand area k of the time period t is correspondingly calculated to obtain time period flow m3/s；
The minimum ecological water shortage and overflow amount is suitable as a target:
Figure BDA0002306012270000033
in the formula, L is the total number of the ecological control sections of the river reach;
Figure BDA0002306012270000034
is the proper ecological flow of the first section in the time period t, m3/s；RltIs the actual flow of the first section in time t, m3/s；
The minimum water shortage and overflow amount in navigation is an objective function:
Figure BDA0002306012270000035
Figure BDA0002306012270000036
in the formula, R is the total number of the channels; qstFor each calculation period, the absolute value of the difference between the interval of the leakage flow and the interval of the suitable shipping flow, m3/s；SUapp、SLappRespectively representing the upper and lower bounds, m, of the shipping comfort flow interval3/s。
Further, the constraint condition of step (2):
and (3) water balance constraint:
Vm,t-Vm,t-1＝(Im,t-Qm.t)×Δt
Im,t＝Qm-1,t+Inm-1,t-Em,t
in the formula: vm,t、Vm,t-1The storage capacity of the last and the first reservoir at the t-th period of the m reservoir, m3；Im,tAverage warehousing flow m within the t-th time period of the m warehouses3/s；Inm,tFor interval inflow between m banks and m +1 banks during the t-th time period, m3/s；Qm,tAverage flow rate of delivery m of m banks in the t-th period3/s；Em,tThe flow loss in the t-th time period of the m banks mainly comprises the water loss caused by evaporation and infiltration in the process of water transmission between the two banks, and m3S; Δ t represents a unit calculation time step, month;
and (3) restricting the downward flow:
Qmt,min≤Qmt≤Qmt,max
in the formula, Qmt,minRepresents the minimum allowed outbound flow of m banks in t period3/s；Qmt,maxThe maximum allowable delivery flow of the m storehouses in the t time period is shown, the maximum flow capacity of the hydraulic turbine set and the requirements of downstream flood control in the flood season on the flow are considered, and m3/s；
Reservoir water level constraint:
Zmt,min≤Zmt≤Zmt,max
in the formula, ZmtRepresenting the water level at the end of the time period t of the m storehouses, m; zmt,maxThe maximum allowable water level at the end of the t time period of the m storehouses is represented, the flood season is set as a flood limit water level so as to consider the requirement of a flood control target in the target, and the flood season is a normal water storage level m; zmt,minRepresenting the minimum allowable water level at the end of the t time period of the m reservoir, namely the dead water level m;
reservoir output restraint:
Nmt,min≤Nmt≤Nmt,max
in the formula, NmtThe output of the m-base at the t time period is expressed in ten thousand kW; n is a radical ofmt,minThe minimum output in the period t of the m-library is expressed, ten thousand kW; n is a radical ofmt,maxAnd the maximum output in the period t of the m libraries, namely the loading capacity, is expressed in ten thousand kW.
Further, the multi-objective relationship analysis in step (5) is as follows:
calculating the correlation coefficient between two objective functions by using the Pearson correlation coefficient, and judging whether there is correlation coefficient between the objective functions according to the correlation coefficientSignificant correlation; for two targets with significant correlation, the concept of introducing a replacement rate is quantitatively analyzed, for two targets with non-bad leading edges, the replacement rate between the targets is compared with the Euclidean equation, under the condition that other targets keep constant values, when the mth target value changes by one unit, the nth target value increases or decreases by TmnThe unit is used for compensating the influence of the change of m on the overall benefit, and the calculation method is as follows:
Figure BDA0002306012270000041
in the formula (f)m，fnFor the objective function involved in the analysis of the rate of substitution, lambdamnIs the rate of substitution between the two objective functions; during calculation, function fitting is carried out on two-dimensional projection results of the two targets, and then the first derivative of the two-dimensional projection results is obtained, so that a replacement rate function between the two targets can be obtained.
Has the advantages that: compared with the prior art, the invention has the following remarkable progress: 1. by generalizing the complex reservoir group system structure, a reservoir group optimization scheduling model is constructed, and an intelligent optimization algorithm is used for solving, so that the non-inferior frontier reflecting the interaction relation of four targets of power generation, water supply, ecology and shipping is obtained on the premise of meeting the flood control requirement, and the strength of the interaction relation between the targets is reflected more intuitively; 2. the concepts of the correlation coefficient and the replacement rate are introduced, the strength degree of the interaction relationship between the targets is determined, the replacement rate between every two targets is calculated by using tools such as programming and fitting software, the interaction relationship between the targets is quantitatively analyzed, the subjectivity in analysis is avoided, and meanwhile, the concept of the replacement rate has guiding significance on benefit distribution in actual operation; 3. the interaction relation between every two targets is obtained, the interaction between every two targets is considered, meanwhile, the targets are also subjected to the restraining effect of other targets, the interaction relation between the considered targets is more complicated, various factors needing to be considered in actual dispatching of the reservoir are better met, 4, reference and basis are provided for optimal dispatching of the reservoir group with power generation, water supply, ecological and shipping benefits considered, and the method has strong practicability and wide applicability.
Drawings
FIG. 1 is a schematic diagram of a 20 reservoir in an upstream basin of Yangtze river;
FIG. 2 is a Pareto front chart;
FIG. 3 is a two-dimensional map of a target relationship;
FIG. 4 is a graph of power generation versus ecological replacement ratio;
FIG. 5 is a graph showing a power generation-water supply replacement ratio;
FIG. 6 is a water supply-shipping replacement rate graph;
FIG. 7 is a diagram of a first reservoir operating water level process;
FIG. 8 is a diagram of a second reservoir operating water level process;
FIG. 9 is a diagram of a third reservoir operating water level process;
FIG. 10 is a diagram of a fourth reservoir operating water level process;
FIG. 11 is a diagram of a fifth reservoir operating water level process;
FIG. 12 is a diagram of a sixth reservoir operating water level process;
FIG. 13 is a diagram of a seventh reservoir operating water level process;
FIG. 14 is a graph illustrating an operational water level profile of the eighth reservoir;
FIG. 15 is a diagram of a ninth reservoir operating water level process;
FIG. 16 is a diagram of a tenth reservoir operating level process;
FIG. 17 is a diagram of an eleventh reservoir operating water level process;
FIG. 18 is a twelfth reservoir operating water level process diagram;
FIG. 19 is a line graph illustrating the operational water level of the thirteenth reservoir;
FIG. 20 is a graph illustrating a fourteenth operational water level profile of the reservoir;
FIG. 21 is a line graph illustrating a fifteenth reservoir operating water level;
FIG. 22 is a sixteenth chart showing the operational water level of the reservoir;
FIG. 23 is a line graph illustrating a seventeenth water level process of reservoir operation;
FIG. 24 is a diagram of an eighteenth operational water level profile of a reservoir;
FIG. 25 is a diagram of a nineteenth reservoir operating water level process;
fig. 26 is a diagram showing a twenty-second reservoir operation water level process.
Detailed Description
In order to make the purpose and technical solution of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention.
Step 1, reservoir group system generalization.
The reservoir group system comprises three systems of social economy, ecological environment and water resources which are generalized into three elements of a water withdrawal node, a calculation unit water quantity transmission system and a watershed unit water quantity transmission system through simplification, abstraction and integration according to characteristics of research area water resource management and development, administrative regions, water resource management region partitions, main water taking positions of a riverway, various water conservancy building layouts and the like, so that the mapping relation from the actual to the mathematical expression is established, and the generalized reservoir group system realizes the comprehensive reservoir group system, and the main elements of the reservoir group system comprise ① points, engineering nodes (introduction engineering), water sinks (water collection nodes, the final outflow watershed positions of the water source of the system), control nodes (water quality or cross section water quantity required by the water quality or the water flow direction of the riverway) and related river channel nodes (②).
Taking a reservoir group consisting of 20 reservoirs in the upstream basin of the Yangtze river as an example, the large-scale reservoir group system is generalized, and the generalized system diagram is shown in figure 1.
And 2, identifying a benefit target function and determining constraint conditions.
Identifying reservoir benefit functions, determining a target function expression form of a benefit target and reservoir scheduling constraint conditions, and constructing a reservoir group power generation-water supply-ecology-shipping-flood control five-target optimization scheduling model, wherein the flood control target is converted into the constraint conditions to be considered based on the priority and the mandatory of the reservoir scheduling requirements, and the model target function and the constraint conditions are determined. The 20 storehouses in the Yangtze river upstream flow domain undertake four tasks including power generation, water supply, ecology and shipping.
With the more and more important role of water and electricity in the electric power system, the main benefit aims at improving the annual power generation amount so as to ensure the maximum utilization of abundant incoming water in the flood season and the reasonable allocation of the relation between the water level and the drainage flow in the dry season. Therefore, for the power generation target, the maximum annual total power generation of the reservoir group is taken as a target function:
Figure BDA0002306012270000071
wherein E is the total power generation amount of the cascade reservoir group,
Figure BDA0002306012270000072
the output of the mth hydropower station in the t period is kW; m represents the total number of the cascade power stations, T represents the total time interval length and year; Δ t represents a unit calculation time step, month.
The important function of reservoir water storage is to ensure that the natural incoming water can be compensated by the water storage in the reservoir, thereby meeting the water supply requirement of the downstream area of the lower reservoir. The requirement of supplying water to the downstream of the general flood season can be met, but the influence of water shortage on the downstream city is reduced in the non-flood season, and the requirement of meeting the water demand of the downstream area as far as possible is an important task of the reservoir. Therefore, the minimum water shortage in water demand is adopted as a water supply target:
Figure BDA0002306012270000073
in the formula, K is the number of socioeconomic water-requiring areas; qktComputing time interval corresponding to economic water demand of general society of water demand area k of time interval tFlow rate, m3/s。
For the reservoir with ecological requirements, the lower discharge flow of the reservoir can meet the ecological suitable flow demand of the downstream ecological control section as far as possible by adjusting the operation mode of the reservoir at each time interval, so that the downstream ecological environment is ensured, and the ecological diversity is maintained. Considering that the water supply is not uniform in the year and the requirements of the appropriate ecological flow in each calculation time are different, the minimum appropriate ecological water shortage and overflow amount is adopted as the target:
Figure BDA0002306012270000074
in the formula, L is the total number of the ecological control sections of the river reach;
Figure BDA0002306012270000081
is the proper ecological flow of the first section in the time period t, m3/s；RltIs the actual flow of the first section in time t, m3/s。
For the river channel with the navigation function, the reservoir is required to discharge a certain flow in the navigation time period so as to ensure the channel size requirement of the downstream channel, and meanwhile, the discharge flow of the reservoir is required not to be too large, so that the navigation risk caused by too large change of the hydraulic condition of the river channel is prevented. Therefore, according to the channel grades and the reservoir building amount, the navigation flow is conversed, the lower leakage flow range of the reservoir related to navigation is regulated, and the minimum navigation water shortage and overflow amount is adopted as a target function:
Figure BDA0002306012270000082
Figure BDA0002306012270000083
in the formula, R is the total number of the channels; qstFor each calculation period, the absolute value of the difference between the interval of the leakage flow and the interval of the suitable shipping flow, m3/s；SUapp、SLappRespectively representing the upper and lower bounds, m, of the shipping comfort flow interval3/s。
Based on the reservoir group system generalization in the step 1, the identified power generation target, water supply target, ecological target and shipping target are used as target functions, the monthly mean water level of the reservoir is used as a decision variable, and the following constraints are considered:
and (3) water balance constraint:
Vm,t-Vm,t-1＝(Im,t-Qm.t)×Δt
Im,t＝Qm-1,t+Inm-1,t-Em,t
in the formula: vm,t、Vm,t-1The storage capacity of the last and the first reservoir at the t-th period of the m reservoir, m3；Im,tAverage warehousing flow m within the t-th time period of the m warehouses3/s；Inm,tFor interval inflow between m banks and m +1 banks during the t-th time period, m3/s；Qm,tAverage flow rate of delivery m of m banks in the t-th period3/s；Em,tThe flow loss in the t-th time period of the m banks mainly comprises the water loss caused by evaporation and infiltration in the process of water transmission between the two banks, and m3S; Δ t represents a unit calculation time step, month.
And (3) restricting the downward flow:
Qmt,min≤Qmt≤Qmt,max
in the formula, Qmt,minRepresents the minimum allowed outbound flow of m banks in t period3/s；Qmt,maxThe maximum allowable delivery flow of the m storehouses in the t time period is shown, the maximum flow capacity of the hydraulic turbine set and the requirements of downstream flood control in the flood season on the flow are considered, and m3/s。
Reservoir water level constraint:
Zmt,min≤Zmt≤Zmt,max
in the formula, ZmtRepresenting the water level at the end of the time period t of the m storehouses, m; zmt,maxThe maximum allowable water level at the end of the t time period of the m storehouses is represented, the flood season is set as a flood limit water level so as to consider the requirement of a flood control target in the target, and the flood season is a normal water storage level m; zmt,minAnd the minimum allowable water level at the end of the t time period of the m-bank is represented as a dead water level m.
Reservoir output restraint:
Nmt,min≤Nmt≤Nmt,max
in the formula, NmtThe output of the m-base at the t time period is expressed in ten thousand kW; n is a radical ofmt,minThe minimum output in the period t of the m-library is expressed, ten thousand kW; n is a radical ofmt,maxAnd the maximum output in the period t of the m libraries, namely the loading capacity, is expressed in ten thousand kW.
And 3, determining model input data and calculating a model.
The model input data is considered according to the calculation time interval and the calculation purpose requirement, actual measurement flood data, design flow data and historical water inflow process data are adopted, and days, ten days and months are used as calculation step lengths. Determining water inflow data, collecting actually measured inflow data of the basin long series, performing Mann-Kendall inspection and combining with reservoir construction and other data, and determining that the time of the basin runoff mutation point is 2002. Before the mutation point, the year with 25-75% of incoming water is selected as the horizontal year by frequency analysis, and particularly 1988 can be selected.
Step 4, solving the model by an intelligent optimization algorithm (NSGA-III):
based on the reservoir group system generalization in the step 1, the four benefit targets identified in the step 2 are taken as target functions, the determined constraint conditions are taken as constraints, and the incoming water data determined in the step 3 are taken as input to be used as modeling results and calculation preparation. Considering that the four-target optimization scheduling problem of power generation, water supply, ecology and shipping is a multi-target optimization problem, an intelligent optimization algorithm (NSGA-III) is a very effective tool for solving the problem, and the intelligent optimization algorithm is an intelligent algorithm for non-dominated sorting and selection based on a reference point, so that a non-dominated solution set continuously approaches to a Pareto optimal solution set, and finally the optimal solution is achieved. And solving the model by adopting Matlab programming. In the example, the average monthly water level of the reservoir is taken as a decision variable, the initial water level of the reservoir is fixed, and the total optimization variable number is 12 × 20 — 240. And setting the water level at the moment as a normal high water level by taking november as an optimization starting time. And finishing the optimization by november in the next year, and returning the reservoir to the normal high water level. And the water level of the reservoir is limited in the flood season to meet the flood control requirement. The main parameters of the NSGA-III algorithm include population number and algebra, wherein the population number is 120 and the algebra of evolution is 500 in the embodiment. The embodiment adopts Matlab programming to realize the solution of the algorithm to the model, and obtains a four-target optimization non-inferior front edge, as shown in FIG. 2.
And 5, visualizing the solution set, and carrying out multi-target relation analysis based on correlation calculation and replacement rate.
For multi-target non-bad fronts, solution sets are scattered points distributed in space, and analysis and application are not facilitated. Therefore, for the four-target problem, three targets are taken as coordinate axes to draw a three-dimensional graph, and the size of the passing point of the other target represents the value size and the distribution condition of the passing point of the other target. Meanwhile, six groups of pairwise relations among the four targets are displayed on a two-dimensional plane by using a matrix scatter diagram, so that a solution result is visualized, more straight and white, and is convenient to analyze, as shown in fig. 3. And for the existence of the interaction relation and the strength of the correlation between the targets, judging whether the targets have an excessively subjective judgment standard or not by only depending on the two-dimensional scatter diagram, so that the correlation coefficient between the two target functions is calculated by utilizing the Pearson correlation coefficient, and whether the significant correlation exists between the target functions is judged according to the judgment result. The correlation between two targets in the four targets is calculated, and the calculation result is shown in table 1:
TABLE 1 correlation between two of the four targets
Figure BDA0002306012270000101
According to the correlation judging indexes, strong correlation between power generation and ecology can be obtained, and correlation exists between power generation, water supply and water supply shipping. The correlation between the target and the target is weak, and further analysis is needed by other methods.
For two targets with significant correlation, the concept of substitution rate was introduced for quantitative analysis. Non-bad leading edge for both targets. The target-to-target replacement ratio Euclidean equation is such that, when the m-th target value is changed by one unit, the n-th target value is increased or decreased by TmnUnit to compensate for the impact on overall benefit due to m changes. The calculation method comprises the following steps:
Figure BDA0002306012270000102
in the formula (f)m，fnFor the objective function involved in the analysis of the rate of substitution, lambdamnIs the rate of substitution between the two objective functions. During calculation, function fitting is carried out on two-dimensional projection results of the two targets, and then the first derivative of the two-dimensional projection results is obtained, so that a replacement rate function between the two targets can be obtained. This function also reflects the rate of change of the n targets when the m targets take a certain value, which is the concept of analyzing the interaction relationship and degree of interaction between the quantitative targets from a quantitative point of view.
Fig. 4, 5 and 6 show the displacement relationship among power generation ecology, power generation and water supply shipping. Between power generation and ecology, along with the increase of the generated energy, the replacement rate is positive and tends to increase, namely, the destruction degree of the ecology is increased, but the rate is slowed down, and the competitive relationship between the two is increased but the increase rate is slowed down; between power generation and water supply, along with the increase of power generation, the replacement rate is positive and rises linearly, namely, the water supply and water shortage rate is increased, and the two rates have obvious competitive relationship; between water supply and shipping, the replacement rate is positive but the whole rate is in a descending trend along with the increase of the water shortage rate of water supply, namely the damage degree of shipping conditions is increased along with the increase of the water shortage rate of water supply, but the increase is reduced, the two are in a synergistic relationship, but the conversion rate is reduced.
And 6, analyzing the multi-target relation based on comparison among the schemes.
For the objects with unobvious correlation, the interaction relationship is weak, but is not negligible in reservoir scheduling. The multi-objective optimization unlocking represents a scheme set, the overall benefit of each scheme is the same, and if one goal is optimized, the benefit of other goals is reduced. Therefore, by comparing the change conditions of other targets under the optimal scheme of each benefit target, whether the target has a competitive relationship with other targets can be analyzed. During comparison, if the optimal schemes of the targets are directly compared, an index for measuring the change degree of the benefit of each target is lacked, so that a fuzzy optimal method is introduced, the optimal degrees of the targets are the same (the weights are the same) to obtain a balanced scheme with a scheme set as a standard, and the change conditions of other targets under the optimal schemes of the benefits are examined on the basis of the target values in the scheme. The comparison results are shown in table 2, where the generated water shortage and ecological condition destruction degree are expressed in percentage, the shipping condition destruction degree is expressed in specific value, and the positive and negative indicate that the target is increased or decreased compared with the equilibrium solution:
TABLE 2 Change in remaining goals under benefit goal optimization
Figure BDA0002306012270000111
And 7, judging the action relation between the targets and acquiring a reservoir dispatching mode.
And (5) obtaining action relations among the four considered targets based on the results of the step (5) and the step (6): if one target benefit is increased and the other target benefit is decreased according to the replacement rate among the targets with strong correlation in the step (5), the two targets are considered to have strong competitive relationship; and the other target benefit is increased, the two have strong synergistic relationship. The object which does not exhibit strong correlation in step (5) is judged by step (6). Compared with each target benefit value in the balancing scheme, if the two targets are both reduced in benefit in the scheme deviating from the other target or one target benefit is reduced and the other target benefit is unchanged, the two targets have weak competition relation; if the two targets increase one by one in the scheme of deviating from the other target, the two targets have weak competition or weak cooperative relationship; if the benefit values of the two targets are increased in a scheme deviating from the other target, or one target benefit is increased and the other target benefit is unchanged, the two targets have a weak synergistic relationship. The comparison result obtained in the step (6) shows that in the water process in 1988, the condition that the reservoir meets the flood control requirement in the flood season is necessary constraint, a strong competitive relationship exists between the two targets of power generation, ecology and water supply, a weak competitive relationship exists between the power generation and shipping, a strong synergistic relationship exists between the shipping and water supply, the relationship between the ecology and the water supply, the ecology and the shipping is complex, the competitive relationship is presented under a certain scheduling rule, and the win-win of the two benefits can be realized by regulating and controlling the water quantity between the reservoirs in the system.
Under the condition of water in the year, if the reservoir in the dry period is ensured to run at a high water head to meet the power generation requirement, the lower leakage flow is not enough to meet the requirement of downstream water supply, so that the competition relationship between power generation and water supply is caused, the suitable flow for shipping in some periods can not be met, but as the shipping river section in the river basin is mostly positioned at the downstream of the sub-basin, the lower leakage flow of the reservoir with higher downstream regulating capacity and more tasks can meet the lower leakage requirement of various benefits on the reservoir as far as possible by regulating and storing the upstream reservoir, the competition relationship between shipping and power generation of the reservoir is relatively weaker, the relationship between ecology and water supply is relatively complex, and for the reservoir simultaneously bearing the requirements of ecological water supply and regulation of the downstream reservoir, ① is called as the condition that the water supply and regulation of the downstream water supply are carried out simultaneously
Figure BDA0002306012270000121
When, if
Figure BDA0002306012270000122
The damage degree of the two is small; if q ist<QktThe damage degree of both is larger; if it is
Figure BDA0002306012270000123
The ecological damage level is increased but the damage is not due to the reservoir needing to meet the downstream water supply demand, so that the two are in a synergistic relationship ②
Figure BDA0002306012270000124
If q ist>QktThe water supply requirements will be met and the ecological conditions will be destroyed; if it is
Figure BDA0002306012270000125
Ecological conditions are met, water supply requirements are destroyed, and the ecological conditions and the water supply requirements are in a competitive relationship; when in
Figure BDA0002306012270000126
When the two conditions are satisfied, both conditions are destroyed and a synergistic relationship is presented. In conclusion, when the reservoir is scheduled, if the discharge flow can be controlled within a certain range, the situation that the two goals win together can be achieved. According to the above analysis, when the ecologically suitable flow rate is smaller than the water supply demand in a certain calculation period, the value range of the downward discharge flow rate needs to be more strictly controlled. The functional relationship between ecology and shipping is similar in principle to that between ecology and water supply targets. For water supply and shipping, in the watershed, the value of the water supply target is often less than or equal to the lower limit of the shipping target requirement, so that the water supply condition is necessarily met if the shipping capacity is met, and the shipping requirement is also destroyed if the water supply condition is destroyed, which show a synergistic relationship.
And finally, obtaining an optimal scheme of each target and four reservoir group scheduling schemes when the benefits are balanced based on the optimization result. Based on the corresponding reservoir water level process line of the selected point, the scheduling scheme of the reservoir when different targets are selected and the benefits are balanced is biased, as shown in the attached figures 7 to 26, reference and guidance can be provided for the actual reservoir scheduling rules similar to the water level in the year.

Claims (5)

1. A reservoir group multi-objective action mechanism and optimization scheduling scheme analysis method is characterized by comprising the following steps:
(1) reservoir group system generalization: carrying out generalized analysis on main processes and influence factors of the reservoir group system to realize system simulation and establish a mapping relation from the actual condition of the system to abstract mathematical expression;
(2) identifying reservoir benefit functions, determining a target function expression form of a benefit target and reservoir scheduling constraint conditions, and constructing a reservoir group power generation-water supply-ecology-shipping-flood control five-target optimized scheduling model, wherein the flood control target is converted into the constraint conditions to be considered based on the priority and the mandatory of the reservoir scheduling requirements, and a model target function and the constraint conditions are determined;
(3) model input data determination and model calculation: the model input data is considered according to the calculation time interval and the calculation target requirement, actual measurement flood data, design flow data and historical water inflow process data are adopted, and days, ten days and months are taken as calculation step lengths;
(4) solving the model based on an intelligent optimization algorithm: based on the reservoir group system generalization in the step (1), taking the four benefit targets identified in the step (2) as an objective function, and the determined constraint conditions as constraints, and taking the incoming water data determined in the step (3) as input, and taking the incoming water data as a modeling result and calculation preparation;
(5) visualization of solution sets and multi-objective relational analysis based on correlation calculation and replacement rate:
(6) multi-objective relationship analysis based on comparisons between schemes: based on a fuzzy optimization method, obtaining a balance scheme in a scheme set by taking the same optimization degree of each target as a standard, and investigating the change conditions of other targets under the optimal scheme of each benefit target on the basis of each target value in the scheme;
(7) judging the action relationship between the targets and acquiring a reservoir dispatching mode:
and (5) obtaining action relations among the four considered targets based on the results of the step (5) and the step (6): if one target benefit is increased and the other target benefit is decreased according to the replacement rate among the targets with strong correlation in the step (5), the two targets are considered to have strong competitive relationship; the other target benefit is increased, and the two have strong cooperative relationship; judging the targets which do not show strong correlation in the step (5) through the step (6), and comparing with the benefit values of all targets in the balancing scheme, if the benefits of the two targets are reduced in the other target scheme or the benefit of one target is reduced and the benefit of the other target is not changed, determining that the two targets have weak competition; if the two targets increase one by one in the scheme of deviating from the other target, the two targets have weak competition or weak cooperative relationship; if the benefit values of the two targets are increased in a scheme deviating from the other target, or the benefit of one target is increased and the benefit of the other target is not changed, the two targets have a weak synergistic relationship; and obtaining the optimal scheme of each target and four reservoir group scheduling schemes when the benefits are balanced based on the optimization result.
2. The method for analyzing the multi-objective action mechanism and the optimized dispatching scheme of the reservoir group as claimed in claim 1, wherein the reservoir group system in step (1) is generalized as follows: the three systems of social economy, ecological environment and water resource are generalized into three elements of a water withdrawal node, a calculation unit water quantity transmission system and a drainage basin unit water quantity transmission system.
3. The method for analyzing the multi-objective action mechanism and the optimized dispatching scheme of the reservoir group as claimed in claim 1, wherein the objective function in step (2) is as follows:
the maximum annual total power generation of the reservoir group is taken as a target function:
Figure FDA0002306012260000021
wherein E is the total power generation amount of the cascade reservoir group,
Figure FDA0002306012260000022
the output of the mth hydropower station in the t period is kW; m represents the total number of the cascade power stations, T represents the total time interval length and year; Δ t represents a unit calculation time step, month;
the minimum water demand and water shortage is taken as a water supply target:
Figure FDA0002306012260000023
in the formula, K is the number of socioeconomic water-requiring areas; qktThe economic water demand of the general society of the water demand area k of the time period t is correspondingly calculated to obtain time period flow m3/s；
The minimum ecological water shortage and overflow amount is suitable as a target:
Figure FDA0002306012260000024
in the formula, L is the total number of the ecological control sections of the river reach;
Figure FDA0002306012260000025
is the proper ecological flow of the first section in the time period t, m3/s；RltIs the actual flow of the first section in time t, m3/s；
The minimum water shortage and overflow amount in navigation is an objective function:
Figure FDA0002306012260000026
Figure FDA0002306012260000027
in the formula, R is the total number of the channels; qstFor each calculation period, the absolute value of the difference between the interval of the leakage flow and the interval of the suitable shipping flow, m3/s；SUapp、SLappRespectively representing the upper and lower bounds, m, of the shipping comfort flow interval3/s。
4. The method for analyzing the multi-objective action mechanism and the optimized scheduling scheme of the reservoir group according to claim 1, wherein the constraint conditions in the step (2):
and (3) water balance constraint:
Vm,t-Vm,t-1＝(Im,t-Qm.t)×Δt
Im,t＝Qm-1,t+Inm-1,t-Em,t
in the formula: vm,t、Vm,t-1The storage capacity of the last and the first reservoir at the t-th period of the m reservoir, m3；Im,tAverage warehousing flow m within the t-th time period of the m warehouses3/s；Inm,tFor interval inflow between m banks and m +1 banks during the t-th time period, m3/s；Qm,tAverage flow rate of delivery m of m banks in the t-th period3/s；Em,tThe flow loss in the t-th time period of the m banks mainly comprises the evaporation of water in the process of transferring water between the two banksWater loss due to hair and infiltration, m3S; Δ t represents a unit calculation time step, month;
and (3) restricting the downward flow:
Qmt,min≤Qmt≤Qmt,max
in the formula, Qmt,minRepresents the minimum allowed outbound flow of m banks in t period3/s；Qmt,maxThe maximum allowable delivery flow of the m storehouses in the t time period is shown, the maximum flow capacity of the hydraulic turbine set and the requirements of downstream flood control in the flood season on the flow are considered, and m3/s；
Reservoir water level constraint:
Zmt,min≤Zmt≤Zmt,max
in the formula, ZmtRepresenting the water level at the end of the time period t of the m storehouses, m; zmt,maxThe maximum allowable water level at the end of the t time period of the m storehouses is represented, the flood season is set as a flood limit water level so as to consider the requirement of a flood control target in the target, and the flood season is a normal water storage level m; zmt,minRepresenting the minimum allowable water level at the end of the t time period of the m reservoir, namely the dead water level m;
reservoir output restraint:
Nmt,min≤Nmt≤Nmt,max
in the formula, NmtThe output of the m-base at the t time period is expressed in ten thousand kW; n is a radical ofmt,minThe minimum output in the period t of the m-library is expressed, ten thousand kW; n is a radical ofmt,maxAnd the maximum output in the period t of the m libraries, namely the loading capacity, is expressed in ten thousand kW.
5. The method for analyzing the multi-objective action mechanism and the optimized dispatching scheme of the reservoir group as claimed in claim 1, wherein the multi-objective relationship analysis in step (5) is as follows:
calculating a correlation coefficient between the two objective functions by utilizing the Pearson correlation coefficient, and judging whether the objective functions have obvious correlation or not according to the correlation coefficient; for two targets with obvious correlation, the concept of introducing a replacement rate is quantitatively analyzed, and for two targets with non-bad leading edges, the ratio between the target replacement rates is Euclidean under the condition that other targets keep the same value, and when the m-th target value is changed by one unit, the n-th target value is subjected to quantitative analysisIncrease or decrease T of target valuemnThe unit is used for compensating the influence of the change of m on the overall benefit, and the calculation method is as follows:
Figure FDA0002306012260000041
in the formula (f)m，fnFor the objective function involved in the analysis of the rate of substitution, lambdamnIs the rate of substitution between the two objective functions; during calculation, function fitting is carried out on two-dimensional projection results of the two targets, and then the first derivative of the two-dimensional projection results is obtained, so that a replacement rate function between the two targets can be obtained.
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967157A (en) * 2020-08-14 2020-11-20 中国水利水电科学研究院 Reservoir group water and sand optimization scheduling method and system considering staged multi-objective function
CN113312747A (en) * 2021-04-19 2021-08-27 广西大学 Hydropower dynamic storage capacity economic dispatching method considering uncertainty
CN113379152A (en) * 2021-06-28 2021-09-10 武汉大学 Adjustable capacity-based low-influence medium-long term joint scheduling method for gate dam group
CN114281113A (en) * 2021-11-18 2022-04-05 南方电网调峰调频发电有限公司 Method and system for constructing intelligent operation information model of hydroelectric equipment
CN114707913A (en) * 2022-06-02 2022-07-05 长江水利委员会长江科学院 Method and device for determining water supply scheme of reservoir
CN115719041A (en) * 2022-11-24 2023-02-28 国家电投集团广西长洲水电开发有限公司 Reservoir gate group multi-target flood control optimal scheduling method and system
CN115809718A (en) * 2022-10-09 2023-03-17 当涂南科院科研试验有限公司 Multi-objective competition relationship quantification-based cascade power station power generation and ecological collaborative optimization method and system
CN117910711A (en) * 2024-03-20 2024-04-19 长江水利委员会长江科学院 Construction method of flood period water level intelligent dynamic control model for balancing risks and benefits
WO2024113353A1 (en) * 2022-11-30 2024-06-06 中国科学院深圳先进技术研究院 Multi-objective real-time scheduling method based on prediction mechanism and energy storage mechanism
CN118627858A (en) * 2024-08-09 2024-09-10 浙江中控信息产业股份有限公司 Multi-water source cooperative scheduling system of water supply system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106951985A (en) * 2017-03-06 2017-07-14 河海大学 A kind of step reservoir Multiobjective Optimal Operation method based on improvement artificial bee colony algorithm
CN107341567A (en) * 2017-06-19 2017-11-10 武汉大学 The storage capacity displacement computational methods of Cascade Reservoirs
CN109670650A (en) * 2018-12-27 2019-04-23 华中科技大学 The method for solving of Cascade Reservoirs scheduling model based on multi-objective optimization algorithm
US10516265B2 (en) * 2001-05-18 2019-12-24 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10516265B2 (en) * 2001-05-18 2019-12-24 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption
CN106951985A (en) * 2017-03-06 2017-07-14 河海大学 A kind of step reservoir Multiobjective Optimal Operation method based on improvement artificial bee colony algorithm
CN107341567A (en) * 2017-06-19 2017-11-10 武汉大学 The storage capacity displacement computational methods of Cascade Reservoirs
CN109670650A (en) * 2018-12-27 2019-04-23 华中科技大学 The method for solving of Cascade Reservoirs scheduling model based on multi-objective optimization algorithm

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967157A (en) * 2020-08-14 2020-11-20 中国水利水电科学研究院 Reservoir group water and sand optimization scheduling method and system considering staged multi-objective function
CN111967157B (en) * 2020-08-14 2023-10-20 中国水利水电科学研究院 Reservoir group water and sand optimal scheduling method and system considering staged multiple objective functions
CN113312747B (en) * 2021-04-19 2023-07-18 广西大学 Hydropower dynamic reservoir capacity economic dispatching method considering uncertainty
CN113312747A (en) * 2021-04-19 2021-08-27 广西大学 Hydropower dynamic storage capacity economic dispatching method considering uncertainty
CN113379152A (en) * 2021-06-28 2021-09-10 武汉大学 Adjustable capacity-based low-influence medium-long term joint scheduling method for gate dam group
CN113379152B (en) * 2021-06-28 2022-07-05 武汉大学 Adjustable capacity-based low-influence medium-long term joint scheduling method for gate dam group
CN114281113B (en) * 2021-11-18 2023-12-19 南方电网调峰调频发电有限公司 Construction method and system of intelligent operation information model of hydroelectric equipment
CN114281113A (en) * 2021-11-18 2022-04-05 南方电网调峰调频发电有限公司 Method and system for constructing intelligent operation information model of hydroelectric equipment
CN114707913B (en) * 2022-06-02 2022-09-13 长江水利委员会长江科学院 Method and device for determining water supply scheme of reservoir
CN114707913A (en) * 2022-06-02 2022-07-05 长江水利委员会长江科学院 Method and device for determining water supply scheme of reservoir
CN115809718A (en) * 2022-10-09 2023-03-17 当涂南科院科研试验有限公司 Multi-objective competition relationship quantification-based cascade power station power generation and ecological collaborative optimization method and system
CN115809718B (en) * 2022-10-09 2024-03-15 当涂南科院科研试验有限公司 Cascade power station power generation and ecological cooperative optimization method and system based on multi-objective competition relationship quantification
CN115719041A (en) * 2022-11-24 2023-02-28 国家电投集团广西长洲水电开发有限公司 Reservoir gate group multi-target flood control optimal scheduling method and system
CN115719041B (en) * 2022-11-24 2023-07-21 国家电投集团广西长洲水电开发有限公司 Multi-target flood control optimal dispatching method and system for reservoir gate group
WO2024113353A1 (en) * 2022-11-30 2024-06-06 中国科学院深圳先进技术研究院 Multi-objective real-time scheduling method based on prediction mechanism and energy storage mechanism
CN117910711A (en) * 2024-03-20 2024-04-19 长江水利委员会长江科学院 Construction method of flood period water level intelligent dynamic control model for balancing risks and benefits
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