CN111079066A - Reservoir group power generation ecological two-target competition relationship analysis method - Google Patents

Reservoir group power generation ecological two-target competition relationship analysis method Download PDF

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CN111079066A
CN111079066A CN201911146524.0A CN201911146524A CN111079066A CN 111079066 A CN111079066 A CN 111079066A CN 201911146524 A CN201911146524 A CN 201911146524A CN 111079066 A CN111079066 A CN 111079066A
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董增川
贾文豪
倪效宽
陈牧风
姚弘祎
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 method for analyzing two-target competition relationship of reservoir group power generation ecology, which comprises the following specific steps: s1: reservoir group system generalization; s2: power generation and ecological target identification; s3: water determination typically in the year; s4: constructing a power generation-ecological two-target optimization scheduling model; s5: solving the model of the component by using an intelligent optimization algorithm NSGA-II; s6: and analyzing the power generation-ecological two-target competition relationship of the replacement rate. According to the invention, a reservoir optimization scheduling model is constructed, and an intelligent optimization algorithm is used for solving, so that a non-inferior front edge containing a power generation and ecological two-target competitive relationship rule is obtained; the concept of the replacement rate is introduced, the replacement rate between two targets is calculated by using a programming and curve fitting software tool, and the competitive relationship between power generation and ecology is further quantitatively analyzed; the method provides reference and basis for optimal scheduling of the reservoir with both power generation and ecological benefits, and has strong practicability and wide applicability.

Description

Reservoir group power generation ecological two-target competition relationship analysis method
Technical Field
The invention relates to a multi-target analysis method for a reservoir, in particular to a two-target competition relationship analysis method for power generation ecology of a reservoir group.
Background
With the continuous establishment of reservoir groups, more and more attention and attention are paid to the influence of natural conditions and resource utilization of riverways, and how to correctly schedule reservoirs to achieve the maximization of benefits on the basis of meeting a series of constraint limiting conditions is a relatively hot and urgent problem and concern at present. The multiple tasks of flood control, power generation, ecology, shipping and the like borne by the reservoir make the competition problem caused by the common demand of limited water resources and different water resource utilization modes between all targets be considered in the process of dispatching the reservoir.
In the existing research, the development of an optimization algorithm and the simple qualitative analysis of the calculation result of the optimization algorithm are mainly focused, the competitive relationship of reservoirs in a research area in the process of realizing multi-target benefits is explained to a certain extent, and theoretical support is provided for the multi-target optimization scheduling decision of the reservoirs in a conclusive mode. However, at present, the expression of the competition relationship among multiple targets is relatively shallow, and is mostly conceptual and qualitative summary, but a reasonable mathematical method is not used, and the description is performed from the perspective of quantitative relationship, so that on one hand, it is difficult to prove the rationality of the qualitatively described competition relationship, and on the other hand, the characteristics of the competition relationship among the targets, such as the strength change rule of the competition relationship, cannot be more finely expressed.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above problems, the present invention aims to provide a method for analyzing the competition relationship between two ecological targets and reservoir power generation, which analyzes the competition relationship between two ecological targets and reservoir power generation from a quantitative perspective.
The technical scheme is as follows: the invention provides a method for analyzing two-target competition relationship of reservoir group power generation ecology, which comprises the following specific steps of:
s1: reservoir group system generalization: in a plurality of explicit or implicit relations of each component element of a water resource system, extracting key elements by analyzing the water resource acquisition, supply, use, consumption, discharge process and water resource management condition characteristics of the system, and establishing a mapping relation from the actual condition of the system to mathematical expression by taking three elements of a water withdrawal node, a computing unit water transmission system and a drainage basin unit water transmission system according to the development and utilization characteristics of regional water resources, the positions of main water intakes, water resource partitions, administrative divisions and the layout conditions of water conservancy projects;
the main elements of the reservoir group system include point elements, line elements and their corresponding entities, which are specifically shown in table 1 below:
TABLE 1 points, line elements and their corresponding entities in the reservoir group system
Figure BDA0002282360320000021
S2: power generation and ecological target identification:
for the power generation target, the maximum annual total power generation is used as a target function, namely:
Figure BDA0002282360320000022
wherein E is the total power generation of the steps,
Figure BDA0002282360320000023
the output of the ith hydropower station in the T-th time period is represented by M, the number of the cascade hydropower stations is represented by T, the total time period length is represented by T, and the time delta T represents a certain time period length;
for the reservoir with the ecological target, the ecological target is that the reservoir meets the water demand as far as possible in each time period by adjusting the operation mode, but as the coming water is not uniform in the year and the year, the sum of squares of the water quantity suitable for ecological water shortage is selected as a target function, namely:
Figure BDA0002282360320000024
wherein L is the total number of the ecological control sections,
Figure BDA0002282360320000025
suitable ecological flow rate of section l time period t, RltThe actual flow of the section l in a time period t;
s3: water determination typically in the year:
firstly, collecting long series actual measurement inflow data of a reservoir group, and because the long series actual measurement runoff data of many years cannot accurately reflect the original hydrological process of a drainage basin, then carrying out mutation point analysis on flow data to determine a time period when runoff is less affected by human activities, and selecting natural incoming water under different frequencies in the time period by combining the results of hydrological analysis and calculation;
then carrying out mutation analysis on the collected long series runoff data of a first reservoir in the reservoir group by adopting a Mann-Kendall inspection method, determining a mutation point of the main and branch runoff sequence by combining the time of building each reservoir and the major change data of the related planning or operating conditions of the reservoirs, and selecting a year with the incoming water frequency of less than 25% as a full-blown year by frequency analysis in a time period before the mutation point; the year with the water coming frequency of 25-75 percent is taken as the horizontal year; the year with the incoming water frequency of more than 75% is taken as the dry year;
s4: constructing a power generation-ecological two-target optimization scheduling model: taking a power generation target and an ecological target as target functions, taking water in a typical year as input, taking the ten-day average water level of the reservoir as a decision variable, and considering the following constraints:
(1) and (3) water balance constraint:
Vit-Vi,t-1＝(Iit-Qit-Eit)*Δt (3)
wherein: vit、Vi,t-1The storage capacity of the last and the first reservoir at the t-th period of the i-reservoir is in unit of cubic meter m3；IitAverage warehousing flow in the t time period of the i warehouse, and the unit is m3/s；QitAverage flow rate of the i library in the t time period, and the unit is m3/s；EitFor the t time period of the i bank, the loss flow is in m3S; Δ t is the calculation period length;
(2) reservoir outflow restraint: the reservoir flow of leaving warehouse should satisfy the requirement of the restriction of maximum, minimum discharge flow, and the discharge flow is earlier generated electricity through the hydraulic turbine down, produces when being greater than the maximum flow capacity of hydraulic turbine and abandons water, promptly:
Qit,min≤Qit≤Qit,max(4)
QEit≤QEit,max(5)
QSit＝Qit-QEit(6)
wherein Q isit,minThe minimum allowed flow of the warehouse-out in the t period of the i warehouse is expressed in m3/s；Qit,maxThe maximum allowed ex-warehouse flow in the t period of the i warehouse is expressed in m3/s；QEitThe lower discharge flow of the hydraulic turbine set at t time of the i-bank is expressed in the unit of m3/s；QEit,maxThe maximum flow of the water turbine set in the unit of m in t time period of the i reservoir3/s；QSitRepresenting the flow of reject water without passing through the turbine in the t period of the i library, and the unit is m3/s；
(3) Reservoir water level constraint: the operation of each time interval of each reservoir should satisfy the water level restriction, promptly:
Zit,min≤Zit≤Zit,max(7)
in the formula, Zit,maxThe maximum allowable water level at the end of the t time period of the reservoir i is represented, the maximum allowable water level is a flood control high water level in a flood season, and the maximum allowable water level is a normal water storage level in a non-flood season, wherein the unit is m; zit,minThe minimum allowable water level at the end of the t time period of the i library is represented as a dead water level, and the unit is m;
(4) reservoir output restraint: the actual output of each time interval of the reservoir is less than or equal to the installed capacity of the reservoir, namely:
Nit,min≤Nit≤Nit,max(8)
in the formula, Nit，minRepresenting i library t periodThe minimum output is ten thousand kW; n is a radical ofit，maxThe maximum output of the i library at the t time period is expressed in unit of ten thousand kW;
(5) decision variable xiNon-negative constraints, namely: x is the number ofi≥0；
S5: solving the model of the component by using an intelligent optimization algorithm NSGA-II: by constructing a non-dominating set and enabling the non-dominating set to continuously approach a Pareto optimal solution set, the optimal solution is finally achieved, and the solution of an algorithm to a model is realized by adopting Matlab programming;
s6: analyzing the power generation-ecological two-target competition relationship of the replacement rate: for a non-bad front edge, the bias conversion rate between targets indicates that at a certain point on a local non-bad surface, when the value of the jth target function is increased or decreased by one unit under the condition that the values of other target functions are all fixed, the value of the ith target function must be decreased or increased by TijThe unit compensation, namely the degree of influence between targets is reflected by the displacement amount of the targets, and the calculation method comprises the following steps:
Figure BDA0002282360320000041
in the formula (f)i、fjFor the objective function involved in the analysis of the rate of substitution, lambdaijThe method is characterized in that the displacement rate between two objective functions is the displacement rate, the difference between the concept of the displacement rate and the marginal substitution rate is expressed on the condition that the displacement rate is not inferior, the measurement mode of the total benefit is more complex, when the displacement rate is calculated, the non-inferior solution obtained by a multi-objective algorithm is subjected to plane projection aiming at two targets to be analyzed, the projection result is fitted into a function form, the first derivative of the projection result is solved, and the obtained corresponding mathematical expression is the displacement rate function.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
1. by constructing a reservoir optimization scheduling model and solving by using an intelligent optimization algorithm, a non-inferior front edge containing a competitive relationship rule of two targets of power generation and ecology is obtained;
2. the concept of the replacement rate is introduced, the replacement rate between two targets is calculated by using a programming and curve fitting software tool, and the competitive relationship between power generation and ecology is further quantitatively analyzed;
3. the method provides reference and basis for optimal scheduling of the reservoir with both power generation and ecological benefits, and has strong practicability and wide applicability.
Drawings
FIG. 1 is a schematic diagram of four pools in a drainage basin according to an embodiment of the present invention;
FIG. 2 is a power generation-ecological Pareto frontier curve in an embodiment of the present invention;
FIG. 3 is a power generation-ecological displacement relationship curve in the embodiment of the present invention.
Detailed Description
Example (b): the method for analyzing the two-target competition relationship of the power generation ecology of the reservoir group consisting of four reservoirs comprises the following specific steps:
s1: firstly, a reservoir group system is generalized, and a generalized diagram of the reservoir system is shown in fig. 1, wherein the generalized diagram comprises reservoir engineering nodes, control nodes of a hydrological station and an ecological control station, and a river channel.
S2: power generation and ecological target identification: the development tasks undertaken by the four banks include power generation and ecology,
1) and (3) generating target: the four storehouses mainly generate electricity in the flood season to improve the annual total generation amount, the minimum load demand of the power system is met by ensuring power generation in the dry season according to the guarantee, the output demand of the dry season is improved along with the fact that the proportion of water and electricity in the power system is larger and larger, however, the increase of the output in the dry season inevitably causes the reduction of the water level of the storehouses, so that the annual total generation amount is reduced, and the annual total generation amount can be adopted as a target function to the power generation target:
Figure BDA0002282360320000051
wherein E is the total power generation of the steps,
Figure BDA0002282360320000052
for the ith hydropower station during the t-th periodAnd (3) output, wherein M represents the number of the cascade power stations, T represents the total time interval length, and delta T represents a certain time interval length.
2) Ecological goals: the ecological target is that the reservoir meets the water demand as much as possible in each time period by adjusting the operation mode, but as the coming water is not uniform in the year and the year, the sum of squares of the water quantity of the suitable ecological water shortage is selected as a target function:
Figure BDA0002282360320000053
wherein L is the total number of the ecological control sections,
Figure BDA0002282360320000054
suitable ecological flow rate of section l time period t, RltThe actual flow of the section l in a time period t;
s3: water determination typically in the year: actual inflow data of the Jinshajiang river downstream reservoir group 1957 and 2012 year old series are collected, then mutation point analysis needs to be carried out on the flow data, and natural inflow water under different frequencies is selected according to the results of hydrological analysis and calculation.
And carrying out mutation analysis on the runoff data of the leading reservoirs in the reservoir group by adopting a Mann-Kendall inspection method according to the collected 56-year runoff data, and determining the mutation point of the main and branch runoff sequence to be 1989 by combining the time of building each reservoir and the major change data of the related planning or operating conditions of the reservoirs. Selecting a year with the incoming water frequency of < 25% as a full water year through frequency analysis in the time period before the mutation point and in the time period of 1957-1989, and particularly selecting 1964; the year with the water frequency of 25-75 percent is taken as the horizontal year, and the 1966 year can be selected specifically; the year with the water inflow frequency of more than 75 percent is taken as the dry year, and the year 1959 can be selected specifically;
s4: constructing a power generation-ecological two-target optimization scheduling model: taking a power generation target and an ecological target as target functions, taking water in a typical year as input, taking the ten-day average water level of the reservoir as a decision variable, and considering the following constraints:
(1) and (3) water balance constraint:
Vit-Vi,t-1＝(Iit-Qit-Eit)*Δt (3)
wherein: vit、Vi,t-1The storage capacity of the last and the first reservoir at the t-th period of the i-reservoir is in unit of cubic meter m3；IitAverage warehousing flow in the t time period of the i warehouse, and the unit is m3/s；QitAverage flow rate of the i library in the t time period, and the unit is m3/s；EitFor the t time period of the i bank, the loss flow is in m3S; Δ t is the calculation period length;
(2) reservoir outflow restraint: the flow of the reservoir out of the reservoir should meet the requirements of the maximum and minimum discharge flow limitation, the discharge flow is generated by the water turbine firstly, and the water is abandoned when the discharge flow is larger than the maximum flow capacity of the water turbine:
Qit,min≤Qit≤Qit,max(4)
QEit≤QEit,max(5)
QSit＝Qit-QEit(6)
wherein Q isit,minThe minimum allowed flow of the warehouse-out in the t period of the i warehouse is expressed in m3/s；Qit,maxThe maximum allowed ex-warehouse flow in the t period of the i warehouse is expressed in m3/s；QEitThe lower discharge flow of the hydraulic turbine set at t time of the i-bank is expressed in the unit of m3/s；QEit,maxThe maximum flow of the water turbine set in the unit of m in t time period of the i reservoir3/s；QSitRepresenting the flow of reject water without passing through the turbine in the t period of the i library, and the unit is m3/s；
(3) Reservoir water level constraint: the operation of each reservoir in each time interval should meet the water level limit:
Zit,min≤Zit≤Zit,max(7)
in the formula, Zit,maxThe maximum allowable water level at the end of the t time period of the reservoir i is represented, the maximum allowable water level is a flood control high water level in a flood season, and the maximum allowable water level is a normal water storage level in a non-flood season, wherein the unit is m; zit,minThe minimum allowable water level at the end of the t time period of the i library is represented as a dead water level, and the unit is m;
(4) reservoir output restraint: the actual output of each time interval of the reservoir is less than or equal to the installed capacity of the reservoir
Nit,min≤Nit≤Nit,max(8)
In the formula, Nit，minThe minimum output at the t time interval of the i library is expressed, and the unit is ten thousand kW; n is a radical ofit，maxThe maximum output of the i library at the t time period is expressed in unit of ten thousand kW;
(5) decision variable xiNon-negative constraints, namely: x is the number ofi≥0；
The specific parameters of the four reservoirs in this example are as follows in table 2:
TABLE 2 concrete parameters of each reservoir
Water reservoir Unit of 1 2 3 4
Regulating capacity Regulating year Regulating year Regulating year Season regulation
Total storage capacity Hundred million (um)3 74.08 206.27 126.70 51.63
Flood control storage container Hundred million (um)3 24.40 75.00 46.50 9.03
Dam height m 988 834 610 384
Normal high water level m 975 825 600 380
Flood limiting water level m 952 785 560 370
Dead water level m 945 765 540 370
Installed capacity Billions of 8.7 14 13.86 6.4
Annual average power generation Hundred million kilowatt hours 387 602 571 307
S5: solving the model of the component by using an intelligent optimization algorithm NSGA-II: by constructing a non-dominating set and enabling the non-dominating set to continuously approach a Pareto optimal solution set, the optimal solution is finally achieved, and the solution of an algorithm to a model is realized by adopting Matlab programming;
the average water level of the reservoir in ten days is taken as a decision variable, and the initial water level and the final water level of the reservoir are fixed, so that the number of variables needing to be optimized is 35 × 4 to 140. And taking 11 ten days of the month as the optimized starting time, the water level of the reservoir is the normal high water level at the moment, taking 10 ten days of the month as the optimized finishing time, the reservoir is fully stored again at the moment, and when the reservoir is in the flood season, the upper limit water level of the reservoir is limited to the flood limiting water level so as to meet the requirement of flood control. The NSGA-II algorithm has the main parameters including population number and algebra, the population number is 100, the evolution algebra is 500, Matlab programming is adopted to realize the solution of the algorithm to the model, and the non-inferior leading edge of the power generation and ecology is obtained, as shown in FIG. 2, Pareto leading edge curves of the rich water year, the open water year and the dry water year are sequentially represented from left to right;
s6: analyzing the power generation-ecological two-target competition relationship of the replacement rate: for non-bad leading edgeIn other words, the bias conversion rate between targets indicates that when the value of the jth objective function is increased or decreased by one unit, the value of the ith objective function must be decreased or increased by T when the values of the jth objective function are all fixed and unchanged at a certain point on the local non-inferior surfaceijThe unit compensation, namely the degree of influence between targets is reflected by the displacement amount of the targets, and the calculation method comprises the following steps:
Figure BDA0002282360320000071
in the formula (f)i、fjFor the objective function involved in the analysis of the rate of substitution, lambdaijFor the replacement rate between two objective functions, the difference between the concept of the replacement rate and the marginal replacement rate is expressed on the condition that the replacement rate is not bad, and the measurement mode of the total benefit is more complex;
when calculating the replacement rate, performing plane projection on two targets to be analyzed by using a non-inferior solution obtained by a multi-objective algorithm, fitting the projection result into a functional form, solving the first derivative of the fitting result to obtain a corresponding mathematical expression which is a replacement rate function, and understanding the replacement rate as the change rate of the ith target when the jth target is a certain value by the calculation process so as to reflect the quantitative relationship between the two targets. In fig. 3, (a), (b), and (c) show the displacement relationship curves of the full, open, and dry years in sequence: in the full-water year, the total generating capacity and the replacement rate of the cascade reservoir group are similar to an exponential relationship, which shows that the replacement rate is accelerated to increase along with the increase of the generating capacity, namely the competition relationship between the generating ecological targets is rapidly intensified; in open water and dry water, the generated energy and the replacement rate are more similar to a linear relationship, and the situation that the competitive relationship of the full water is rapidly enhanced can not occur.

Claims (6)

1. A method for analyzing two-target competition relationship of reservoir group power generation ecology is characterized by comprising the following specific steps:
s1: reservoir group system generalization: establishing a mapping relation from the actual condition of the reservoir group system to mathematical expression;
s2: power generation and ecological target identification: for the power generation target, the maximum annual total power generation is used as a target function: selecting the minimum sum of squares of ecological water shortage and overflow amount as a target function for a reservoir with an ecological target;
s3: water determination typically in the year;
s4: constructing a power generation-ecological two-target optimization scheduling model: taking a power generation target and an ecological target as target functions, taking water in a typical year as input, taking the ten-day average water level of the reservoir as a decision variable to establish a model, and considering constraints;
s5: solving the model of the component by using an intelligent optimization algorithm NSGA-II;
s6: and analyzing the power generation-ecological two-target competition relationship of the replacement rate.
2. The method for analyzing the ecological two-objective competition relationship of power generation of the reservoir group according to claim 1, wherein the power generation objective function in step S2 is as follows:
Figure FDA0002282360310000011
wherein E is the total power generation of the steps,
Figure FDA0002282360310000012
and M represents the number of the cascade hydropower stations, T represents the total time interval length, and Delta T represents a certain time interval length for the output of the ith hydropower station in the T-th time interval.
3. The method for analyzing ecological competition relationship between two targets in power generation of reservoir group according to claim 1, wherein the ecological objective function in step S2 is:
Figure FDA0002282360310000013
wherein L is the total number of the ecological control sections,
Figure FDA0002282360310000014
suitable ecological flow rate of section l time period t, RltIs the actual flow of the section l over the period t.
4. The method for analyzing the ecological two-objective competition relationship of power generation of the reservoir group according to claim 1, wherein the specific determination method in S3 is as follows: collecting long series actual measurement inflow data of a reservoir group, carrying out mutation analysis on the collected long series runoff data by adopting a Mann-Kendall test method, determining a mutation point of a main-branch runoff sequence, and determining three typical years of abundance, average and depletion by frequency analysis in a time period before the mutation point.
5. The method for analyzing the ecological two-objective competition relationship of power generation of a reservoir group according to claim 1, wherein constraints in step S4 are as follows: water balance constraint, reservoir outflow constraint, reservoir water level constraint, reservoir output constraint and decision variable non-negative constraint.
6. The method for analyzing the ecological two-objective competition relationship of power generation of the reservoir group according to claim 1, wherein the S6 analysis method comprises the following steps:
for a non-bad front edge, the bias conversion rate between targets indicates that at a certain point on a local non-bad surface, when the value of the jth target function is increased or decreased by one unit under the condition that the values of other target functions are all fixed, the value of the ith target function must be decreased or increased by TijThe unit compensation, namely the degree of influence between targets is reflected by the displacement amount of the targets, and the calculation method comprises the following steps:
Figure FDA0002282360310000021
wherein，fi、fjFor the objective function involved in the analysis of the rate of substitution, lambdaijIs the rate of substitution between the two objective functions.
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