CN112766562B - Cascade reservoir group ecological scheduling optimization method considering historical track knowledge - Google Patents

Cascade reservoir group ecological scheduling optimization method considering historical track knowledge Download PDF

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CN112766562B
CN112766562B CN202110053768.5A CN202110053768A CN112766562B CN 112766562 B CN112766562 B CN 112766562B CN 202110053768 A CN202110053768 A CN 202110053768A CN 112766562 B CN112766562 B CN 112766562B
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邹强
鲁军
王学敏
洪兴骏
李肖男
严凌志
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Abstract

The invention relates to the technical field of cascade reservoir group ecological scheduling, and discloses a cascade reservoir group ecological scheduling optimization method considering historical track knowledge, wherein after an initial scheduling process is given, a discrete differential dynamic programming method (DDDP) is taken as a basic frame, and firstly, before each iteration, variable-scale gravity center reverse learning is carried out on a current track by mining knowledge information carried in the historical track process to obtain an optimized track with a better result; then discrete iteration solution is carried out on the basis of the optimized track; and finally, gradually approaching a global optimal solution through iterative optimization. The cascade reservoir group ecological scheduling optimization method considering the historical track knowledge fully utilizes the historical track knowledge information, and excavates and generates diversified search information through variable-scale gravity center reverse learning, so that the global search capability is improved, and the problem of local convergence of the traditional algorithm is effectively solved.

Description

Cascade reservoir group ecological scheduling optimization method considering historical track knowledge
Technical Field
The invention relates to the technical field of cascade reservoir group ecological scheduling, in particular to a cascade reservoir group ecological scheduling optimization method considering historical track knowledge.
Background
Along with the rapid development of the water conservancy project in China, large-scale cascade reservoir groups in various drainage basins are continuously built for operation, so that the huge social benefits and economic benefits such as flood control, power generation, shipping and the like are brought into play, the original natural runoff hydrological situation is also changed rapidly, and a plurality of adverse effects including river channel atrophy, sediment accumulation and biological diversity reduction are also generated on the structure and engineering of a river ecosystem. In order to slow down the negative influence of reservoir operation on the ecological environment of a river channel, ecological scheduling of a reservoir group is taken as a typical non-engineering measure, and a runoff process with the most appropriate ecological system stability and variety preservation is obtained by adjusting the operation mode of the reservoir group. Therefore, it is necessary to develop ecological scheduling of reservoir groups, and the reservoir groups are widely concerned at home and abroad.
The cascade reservoir group ecological scheduling problem has the characteristics of large scale, high dimensionality, multiple stages, strong constraint, nonlinearity and the like, and when the traditional dynamic programming method (DP) is applied to solving the cascade reservoir group joint optimization scheduling problem, the problem of serious dimensionality disasters facing the ground cannot be avoided.
The discrete differential dynamic programming method (DDDP) is a dynamic programming improvement method taking the successive approximation theory as the core. The basic idea is as follows: firstly, obtaining an initial track meeting various complex constraint conditions according to experience or other methods; then, dispersing the state variables of each power station in different time periods in the neighborhood of the track to form a corridor; and secondly, optimizing among discrete state combinations in each time interval based on a conventional dynamic programming method, finding out a new optimal track to serve as a test track of next iteration, and iterating repeatedly until a convergence condition is met. However, when the problem of large-scale reservoir group optimization scheduling is solved, the defects of dimension disaster, premature convergence, insufficient searching capability and the like still exist in DDDP.
Therefore, effective improvement on the DDDP calculation mechanism is urgently needed, and the calculation efficiency and the solving precision of the reservoir group joint scheduling problem are improved.
The gravity center reverse learning is a new intelligent computing technology proposed by relying on the reverse learning in recent years, and the basic idea is to fully utilize the search information of a group, evaluate the current state and the gravity center reverse state thereof, and preferentially use the current state and the gravity center reverse state, so that the search process is accelerated. Since DDDP is a step-by-step calculation process, there is no concept of a group, and the states of each step are regarded as an individual at this time, so that the states of several different steps can be regarded as a group. In order to effectively utilize historical track information, variable-scale gravity center reverse learning considering different historical tracks is proposed, namely, the number of stages with different scales is selected, then a group is formed, the gravity center reverse state is calculated, diversified search information is mined and generated, and the global search capability is improved.
The invention has the advantages that knowledge information carried by historical tracks is considered, variable-scale gravity center reverse learning is combined with DDDP, and the optimized tracks are searched through the variable-scale gravity center reverse learning, so that the problem of local convergence of DDDP is effectively solved, the solving efficiency and the calculation accuracy of the method are improved, and the method has good support application value on the problem of large-scale reservoir group optimized dispatching.
Disclosure of Invention
The invention aims to provide a cascade reservoir group ecological scheduling optimization method considering historical track knowledge aiming at the defects of the technology, and solves the problem of local convergence in the optimized scheduling solution of a complex hydropower system by DDDP.
In order to achieve the purpose, the cascade reservoir group ecological scheduling optimization method considering the historical track knowledge comprises the following steps:
1) determining initial calculation conditions including an optimization objective function, constraint conditions and decision variables of cascade reservoir group ecological scheduling;
2) setting calculation parameters including maximum iteration times M, maximum scale-variable gravity center reverse learning times K, initial discrete step length of each reservoir, convergence precision epsilon, reservoir number N and scheduling period number T;
3) generating initial tracks omega of each reservoir meeting each constraint condition by adopting a conventional dynamic programming method1And will beIt stores into a historical track library
Figure GDA0003542360530000021
4) Initializing the iteration number m to be 1;
5) when history track library
Figure GDA0003542360530000022
When the number reaches the specified number Y, the historical track library
Figure GDA0003542360530000031
Implementing variable-scale gravity center reverse learning considering historical track knowledge, otherwise, directly turning to the step 6), wherein the variable-scale gravity center reverse learning comprises the following steps:
initializing scale-variable gravity center reversal times k as 1;
randomly generating a positive integer Y between 1 and Y, wherein Y is more than or equal to 1 and less than or equal to Y;
third, from historical track library
Figure GDA0003542360530000032
In the method, Y +1-Y historical tracks { omega } stored in sequence from Y times are selectedy,Ωy+1,Ωy+2,…,ΩYCalculating the state average value of each reservoir in each time interval of all selected historical tracks
Figure GDA0003542360530000033
Wherein the content of the first and second substances,
Figure GDA0003542360530000034
the water level value of the reservoir i in the r-th historical track in the time period j is obtained;
fourthly, calculating the current track according to the principle of reverse gravity center
Figure GDA0003542360530000035
Center of gravity reverse trajectory of
Figure GDA0003542360530000036
Figure GDA0003542360530000037
Fifthly, comparing the reverse track of the gravity center with the current track if
Figure GDA0003542360530000038
Is superior to
Figure GDA0003542360530000039
Replacing the current track with the gravity center reverse track, otherwise not processing;
sixthly, if K is equal to K +1, turning to the step II, and otherwise, turning to the step 6;
6) implementing a conventional DDDP algorithm, forming a search corridor in the feasible range of the current track, seeking the current optimal track in the search corridor by using a conventional dynamic programming method, and storing and expanding the current optimal track into a historical track library according to a progressive sequence
Figure GDA00035423605300000310
7) Calculating the water level difference value of each time interval of the adjacent two optimal tracks if
Figure GDA00035423605300000311
Contracting all reservoir discrete step lengths, and turning to step 8), or turning to step 5);
8) making M equal to M +1, if M is larger than M, turning to step 9), otherwise, turning to step 5);
9) stopping calculation and outputting the final optimal track.
Compared with the prior art, the invention has the following advantages:
1. by taking DDDP as a basic frame, variable-scale gravity center reverse learning is carried out on the current track by mining knowledge information carried in the historical track process, and an optimized track with a better result is obtained;
2. the historical track knowledge information is fully utilized, and the diversified search information is mined and generated through variable-scale gravity center reverse learning, so that the global search capability is improved, and the problem of local convergence of the traditional algorithm is effectively solved.
Drawings
FIG. 1 is a flow chart of the cascade reservoir group ecological dispatching optimization method considering historical track knowledge.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
As shown in fig. 1, as shown in the figure, a method for optimizing the ecological dispatch of a cascade reservoir group by considering historical track knowledge includes the following steps:
1) determining initial calculation conditions including an optimization objective function, constraint conditions and decision variables of cascade reservoir group ecological scheduling,
wherein, the cascade reservoir group ecological scheduling model can be described as: knowing the initial water level, the final water level, the warehousing runoff process and the interval runoff process of each reservoir scheduling period, under the condition of meeting the complex constraints of the corresponding water level, flow and the like of each reservoir, taking the minimum sum of the ecological water overflow and the ecological water shortage of the cascade reservoir group as an optimization target, wherein the mathematical expression of the ecological scheduling objective function of the cascade reservoir group is as follows:
minf＝minVeco＝min(VecoOVER+VecoLACK)
Figure GDA0003542360530000041
Figure GDA0003542360530000042
in the formula: vecoFor the total ecological water overflow and shortage (m) of the cascade reservoir group3)，VecoOVERIs the total ecological water overflow (m) of the cascade reservoir group3)，VecoLACKFor the total ecological water shortage (m) of the cascade reservoir group3)；Oi,jFor the delivery rate (m) of reservoir i in time period j3/s)，
Figure GDA0003542360530000043
Is waterUpper limit value (m) of suitable ecological flow of storehouse i in time period j3/s)，
Figure GDA0003542360530000044
Lower limit value (m) of suitable ecological flow for reservoir i in time period j3And/s), N is the number of reservoirs, i is the reservoir number, and i is 1,2, …, N, T is the number of dispatch period slots, j is the slot number, and j is 1,2, …, T, Δ j is the slot length (h).
The constraint conditions to be met mainly comprise:
(1) and (3) water balance constraint:
Figure GDA0003542360530000051
in the formula, Vi,jThe storage capacity value (m) of the reservoir i at the end of the j period3)，Ii,jIs the warehousing flow (m) of the reservoir i in the period of j3/s)；
(2) And (3) hydraulic connection constraint:
Figure GDA0003542360530000052
in the formula, Ri,jIs interval flow (m) of reservoir i in j time period3/s)，UiNumber of upstream reservoirs of reservoir i, Ou,jFor the discharge flow (m) of the upstream reservoir i in the period j3/s)；
(3) Time interval water level restraint:
Figure GDA0003542360530000053
in the formula, Zi,jIs the dam front water level value (m) of the reservoir i in the period j,
Figure GDA0003542360530000054
and
Figure GDA0003542360530000055
are respectively a reservoir iThe lowest value of the dam front water level and the highest value (m) of the dam front water level in the period j;
(4) and (4) ex-warehouse flow constraint:
Figure GDA0003542360530000056
in the formula (I), the compound is shown in the specification,
Figure GDA0003542360530000057
and
Figure GDA0003542360530000058
respectively the minimum delivery flow (m) of the reservoir i in the period j3S) and maximum delivery flow (m)3/s)；
(5) And (3) primary and final water level restraint:
Zi,0＝Zi,start,Zi,T＝Zi,end
in the formula, Zi,startAnd Zi,endRespectively controlling the water level (m) at the initial dispatching stage and the end dispatching stage of the reservoir i;
(6) reservoir output restraint:
Figure GDA0003542360530000059
in the formula, Pi,jFor the output (kW) of reservoir i during period j,
Figure GDA00035423605300000510
and
Figure GDA00035423605300000511
respectively reservoir i at jtA minimum power take-off (kW) and a maximum power take-off (kW) for the period;
(7) non-negative constraints: each variable is non-negative;
the decision variable is water level;
2) setting calculation parameters including maximum iteration times M, maximum scale-variable gravity center reverse learning times K, initial discrete step length of each reservoir, convergence precision epsilon, reservoir number N and scheduling period number T;
3) generating initial tracks omega of each reservoir meeting each constraint condition by adopting a conventional dynamic programming method1And store it in the historical track library
Figure GDA0003542360530000061
4) Initializing the iteration number m to 1;
5) when history track library
Figure GDA0003542360530000062
When the number reaches the specified number Y, the historical track library
Figure GDA0003542360530000063
Implementing variable-scale gravity center reverse learning considering historical track knowledge, otherwise, directly turning to the step 6), wherein the variable-scale gravity center reverse learning comprises the following steps:
initializing scale-variable gravity center reversal times k as 1;
randomly generating a positive integer Y between 1 and Y, wherein Y is more than or equal to 1 and less than or equal to Y;
slave history track library
Figure GDA0003542360530000064
In the method, Y +1-Y historical tracks { omega } stored in sequence from Y times are selectedy,Ωy+1,Ωy+2,...,ΩYCalculating the state average value of each reservoir in each time interval of all selected historical tracks
Figure GDA0003542360530000065
Wherein the content of the first and second substances,
Figure GDA0003542360530000066
the water level value of the reservoir i in the r-th historical track in the time period j is obtained;
fourthly, calculating the current track according to the principle of reverse gravity center
Figure GDA0003542360530000067
Center of gravity reverse trajectory of
Figure GDA0003542360530000068
Figure GDA0003542360530000069
Fifthly, comparing the reverse track of the gravity center with the current track if
Figure GDA00035423605300000610
Is superior to
Figure GDA00035423605300000611
Replacing the current track with the gravity center reverse track, otherwise not processing;
sixthly, if K is equal to K +1, turning to the step II, and otherwise, turning to the step 6;
6) implementing a conventional DDDP algorithm, forming a search corridor in the feasible range of the current track, seeking the current optimal track in the search corridor by using a conventional dynamic programming method, and storing and expanding the current optimal track into a historical track library according to a progressive sequence
Figure GDA00035423605300000612
7) Calculating the water level difference value of each time interval of the adjacent two optimal tracks if
Figure GDA00035423605300000613
Contracting all reservoir discrete step lengths, and turning to the step 8), otherwise, turning to the step 5);
8) making M equal to M +1, if M is larger than M, turning to step 9), otherwise, turning to step 5);
9) stopping calculation and outputting the final optimal track.
Take the example of Wujiang of China for research. Wujiang is the largest branch flow of the upstream right bank of Yangtze river, and the basin area is 87920km2Total length of main flow 1037km, average flow rate 1690m for years3(s) annual average runoff of 534 hundred million m3. Flood in the stem flow of Wujiang of ChinaThe ecological scheduling problems of the cascade reservoir group of six power stations of home crossing, east wind, cable wind camp, Wujiang river crossing, constructed beach and Pengshui are taken as examples for research.
Three incoming water frequencies (30%, 50% and 70%) are selected as implementation working conditions, and the method and the DDDP are respectively adopted to carry out the step reservoir group ecological scheduling. Table 1 lists the results of the method of the present invention and the calculation of the ecological overflow and water shortage of DDDP.
TABLE 1 comparison of the method of the invention and DDDP calculation results
Figure GDA0003542360530000071
The calculation result shows that compared with DDDP, the method of the invention has the advantages that: under all water incoming conditions, the calculation result of the method is superior to DDDP, the frequency of the three incoming water is respectively improved by about 1.0 percent, and the result is more obvious; secondly, the calculation time of the method is less than that of DDDP, the calculation time can be reduced by about 89% corresponding to three incoming water frequencies, and the calculation performance advantage is more prominent along with the increase of the calculation scale of the reservoir.
Therefore, compared with the existing DDDP, the method considers the historical track knowledge before each iteration, optimizes the search process through variable-scale gravity center reverse learning, has better global search capacity and higher calculation efficiency, is an effective tool for ecological optimization scheduling of the cascade reservoir group, and has better decision support effect on effectively maintaining the ecological health of rivers.
In a word, the cascade reservoir group ecological scheduling is a complex coupling optimal control problem with large scale, high dimensionality, multiple stages, strong constraint and nonlinearity, comprises a plurality of constraint conditions such as water level, reservoir capacity and flow, and faces a larger technical bottleneck for effective and efficient solution.
When the DDDP is applied to the problem optimization solution, the DDDP is easy to fall into local convergence as the number of power stations and the discrete number are increased. The cascade reservoir group ecological scheduling problem and DDDP characteristics are fully analyzed, after an initial scheduling process is given, a discrete differential dynamic programming method (DDDP) is taken as a basic frame, and before each iteration, variable-scale gravity center reverse learning is performed on a current track by mining knowledge information carried in a historical track process to obtain an optimized track with a better result; then discrete iteration solution is carried out on the basis of the optimized track; and finally, gradually approaching a global optimal solution through iterative optimization. The invention fully utilizes the historical track knowledge information, excavates and generates diversified search information by variable-scale gravity center reverse learning, improves the global search capability and effectively solves the local convergence problem of DDDP.

Claims (1)

1. A cascade reservoir group ecological scheduling optimization method considering historical track knowledge is characterized by comprising the following steps: the method comprises the following steps:
1) determining initial calculation conditions including an optimization objective function, constraint conditions and decision variables of cascade reservoir group ecological scheduling;
2) setting calculation parameters including maximum iteration times M, maximum scale-variable gravity center reverse learning times K, initial discrete step length of each reservoir, convergence precision epsilon, reservoir number N and scheduling period number T;
3) generating initial tracks omega of each reservoir meeting each constraint condition by adopting a conventional dynamic programming method1And store it in the historical track library
Figure FDA0003542360520000011
4) Initializing the iteration number m to be 1;
5) when history track library
Figure FDA0003542360520000012
When the number reaches the specified number Y, the historical track library
Figure FDA0003542360520000013
Implementing variable-scale gravity center reverse learning considering historical track knowledge, otherwise, directly turning to the step 6), wherein the variable-scale gravity center reverse learning comprises the following steps:
initializing scale-variable gravity center reversal times k as 1;
randomly generating a positive integer Y between 1 and Y, wherein Y is more than or equal to 1 and less than or equal to Y;
third, from historical track library
Figure FDA0003542360520000014
In the method, Y +1-Y historical tracks { omega ] stored in sequence from Y times are selectedy,Ωy+1,Ωy+2,...,ΩYCalculating the state average value of each reservoir in each time interval of all selected historical tracks
Figure FDA0003542360520000015
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003542360520000016
the water level value of the reservoir i in the r-th historical track in the time period j;
fourthly, calculating the current track according to the principle of reverse gravity center
Figure FDA0003542360520000017
Center of gravity reverse trajectory of
Figure FDA0003542360520000018
Figure FDA0003542360520000019
Fifthly, comparing the reverse track of the gravity center with the current track if
Figure FDA00035423605200000110
Is superior to
Figure FDA00035423605200000111
Replacing the current track with the gravity center reverse track, otherwise not processing;
sixthly, if K is equal to K +1, turning to the step II, and otherwise, turning to the step 6;
6) performing a conventional DDDP calculationThe method comprises forming a search corridor in the feasible range of the current track, searching the current optimal track in the search corridor by using a conventional dynamic programming method, and storing and expanding the current optimal track in a historical track library according to a progressive sequence
Figure FDA0003542360520000021
7) Calculating the water level difference value of each time interval of the adjacent two optimal tracks if
Figure FDA0003542360520000022
Contracting all reservoir discrete step lengths, and turning to step 8), or turning to step 5);
8) if M is equal to M +1, if M is larger than M, the step 9) is executed, otherwise, the step 5) is executed;
9) stopping calculation and outputting the final optimal track.
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CN110334851A (en) * 2019-06-03 2019-10-15 华中科技大学 A kind of mixed connection step reservoir joint Flood Optimal Scheduling method that consideration divides flood storage people Wan to use

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CN110334851A (en) * 2019-06-03 2019-10-15 华中科技大学 A kind of mixed connection step reservoir joint Flood Optimal Scheduling method that consideration divides flood storage people Wan to use

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