CN109636226A - A kind of reservoir multi-objective Hierarchical Flood Control Dispatch method - Google Patents
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Abstract
The invention discloses a kind of reservoir multi-objective Hierarchical Flood Control Dispatch method for taking into account power generation, shipping, this method specifically includes that the form of design classification Flood Control Dispatch regular (HFOR)；Decision variable is encoded according to classification Flood Control Dispatch rule；Establish the reservoir object and multi object mathematical model for considering flood control, power generation and shipping；Finally object and multi object mathematical model is solved using the multi-objective Evolutionary Algorithm MOEA/D based on decomposition, obtains the optimal solution set for taking into account the reservoir classification Flood Control Dispatch rule of power generation, shipping.The present invention can make full use of middle-size and small-size flood, weighs flood-preventing goal, power generation target and the shipping target of reservoir operation, improves the comprehensive benefit of reservoir operation to the maximum extent under the premise of meeting reservior safety and flood protec- tion, can be widely applied to reservoir actual schedule.
Description
Technical Field
The invention belongs to the field of reservoir dispatching operation, and particularly relates to a multi-target grading flood control dispatching method for a reservoir with power generation and shipping functions.
Background
In reservoir scheduling, the determinacy optimization is a decision process for optimizing the target benefit by using the discharge flow of each time interval in a scheduling period as an optimization variable and adopting an operation and research algorithm. However, deterministic optimal scheduling generally treats the warehousing traffic of each time interval as a deterministic runoff process, which limits the application of the deterministic optimal scheduling in the actual scheduling implementation process. The scheduling rule is obtained based on historical runoff data simulation optimization, only current time interval scheduling information is needed when the scheduling rule is used, uncertainty of future runoff has little influence on the scheduling rule, and the scheduling rule has guiding significance in a real-time scheduling process.
The extraction of the scheduling rule usually adopts a hidden random optimization scheduling mode: firstly, searching an optimal operation mode for known long series runoff sequence data by using deterministic optimization calculation; and (4) mining the data of the deterministic optimization result by adopting methods such as statistics, regression or machine learning and the like to obtain a corresponding scheduling rule function. However, the hidden random optimization scheduling mode has the problem that a multi-target scheduling rule function set cannot be obtained, and has limitation on extraction of the multi-target hierarchical flood control scheduling rule of the reservoir which gives consideration to power generation and shipping.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a multi-target grading flood control dispatching method for a reservoir, so that the problem that a multi-target dispatching rule function set cannot be obtained in a hidden random optimization dispatching mode is solved, and the extraction of the multi-target grading flood control dispatching rule for the reservoir considering both power generation and shipping is limited.
In order to achieve the aim, the invention provides a multi-target grading flood control dispatching method for a reservoir, which comprises the following steps:
(1) grading the water level and the warehousing flow before the dam to obtain a water level interval corresponding to each grade and a warehousing flow interval under each grade, and further determining a grading flood control scheduling rule according to the water level interval corresponding to each grade and the warehousing flow interval under each grade, wherein the grading flood control scheduling rule is used for expressing decision-making discharge flow under the water level interval and the warehousing flow interval of each grade;
(2) according to the grading flood control scheduling rule, taking decision-making let-down flow corresponding to water levels and warehousing flow under different grades as variables to be optimized of the MOEA/D algorithm, and adopting real number coding;
(3) establishing a multi-target grading flood control scheduling rule optimization model according to the targets of the upstream flood control safety target, the downstream flood control safety target, the maximum navigation guarantee rate and the maximum generated energy in the scheduling period;
(4) and solving the multi-target grading flood control scheduling rule optimization model by adopting MOEA/D to obtain the highest water level, the maximum discharge flow, the generated energy in the scheduling period and the navigation rate corresponding to each scheme, and further determining a target scheduling scheme meeting the water level requirement and the discharge flow requirement.
Preferably, step (1) comprises:
(1.1) grading the water level in front of the dam and the warehousing flow: zi∈[Zi l,Zi u]，Ij∈[Ij l,Ij u]，ZiAnd IjRespectively representing a water level interval of the ith level and a warehousing flow interval of the jth level, and l and u respectively representing the upper limit and the lower limit of the interval;
(1.2) by Rij＝f(Zi,Ij) Determining a hierarchical flood control scheduling rule, wherein RijAnd f () represents the flood control dispatching rule function.
Preferably, step (3) comprises:
(3.1) preparation ofAnddetermining a flood control target, wherein T is the number of scheduling period time segments, RtThe discharge quantity Z of the reservoir at the t-th time periodtThe water level of the reservoir in the t-th time period;
(3.2) preparation ofDetermining a power generation target, wherein A is a power station output coefficient, delta t is a scheduling time interval, and HtAnd q istWater head and generated flow, N, respectively, of the t-th period of the reservoirtRepresents the output of the t period;
(3.3) preparation ofDetermining a navigation target, wherein fn() Is a calculation function of the navigation guarantee rate and the let-down flow RtAnd (4) correlating.
Preferably, the multi-objective hierarchical flood control scheduling rule optimization model satisfies the following constraint conditions: operating water level constraint, lower leakage flow constraint, output constraint and water quantity balance equation.
Preferably, step (4) comprises:
(4.1) initializing a weight vector, a neighborhood vector and an initial population, wherein the weight vector represents a vector which is uniformly distributed in a target domain, the neighborhood vector represents a plurality of weight vectors which are closest to the weight vector, and each individual in the population represents a solution;
(4.2) generating a random number r for each weight vector, if r is smaller than a preset threshold, setting the mating population pool and the updating population pool as individuals in a neighborhood vector of the current weight vector, and if r is not smaller than the preset threshold, setting the mating population pool and the updating population pool as the whole population;
(4.3) randomly selecting two individuals from the mating population pool as parents, and performing crossing and mutation operations to obtain a progeny individual;
(4.4) comparing the filial generation individuals with the parent generation in the updated population pool, and selecting the superior individuals as the next generation individuals, wherein one filial generation updates at most one parent generation;
and (4.5) if the current evolution algebra is greater than the preset maximum evolution algebra, ending, outputting a result, if the current evolution algebra is not greater than the preset maximum evolution algebra, adding 1 to the current evolution algebra, and transferring to the step (4.2).
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects: the invention designs a hierarchical flood control scheduling rule form; coding the decision variables according to the grading flood control scheduling rules; establishing a reservoir multi-target mathematical model considering flood control, power generation and shipping; and finally, solving the multi-target mathematical model by adopting a multi-target evolutionary algorithm MOEA/D based on decomposition to obtain an optimal solution set of the reservoir grading flood control dispatching rule giving consideration to power generation and shipping, fully utilizing medium and small-sized flood, balancing a flood control target, a power generation target and a shipping target of reservoir dispatching, improving the comprehensive benefit of reservoir dispatching to the maximum extent on the premise of meeting the reservoir flood control safety, and being widely applied to actual dispatching of reservoirs.
Drawings
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a MOEA/D method according to an embodiment of the present invention;
fig. 3 is a process diagram of the leakage flow rate of the five solutions in the 1954 flood season and the water level, wherein fig. 3(a) is a process diagram of the leakage flow rate of the solution 1 in the 1954 flood season, fig. 3(b) is a process diagram of the leakage flow rate of the solution 30 in the 1954 flood season, fig. 3(c) is a process diagram of the leakage flow rate of the solution 40 in the 1954 flood season, fig. 3(d) is a process diagram of the leakage flow rate of the solution 47 in the 1954 flood season, fig. 3(e) is a process diagram of the leakage flow rate of the solution 60 in the 1954 flood season, and fig. 3(f) is a process diagram of the water level of the five solutions in the 1954 flood season;
fig. 4 is a flow rate and water level process diagram of five solutions in the flood season of 1981, where fig. 4(a) is a flow rate process diagram of the solution 1 in the flood season of 1981, fig. 4(b) is a flow rate process diagram of the solution 30 in the flood season of 1981, fig. 4(c) is a flow rate process diagram of the solution 40 in the flood season of 1981, fig. 4(d) is a flow rate process diagram of the solution 47 in the flood season of 1981, fig. 4(e) is a flow rate process diagram of the solution 60 in the flood season of 1981, and fig. 4(f) is a water level process diagram of the five solutions in the flood season of 1981.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a schematic flow chart of a method provided in an embodiment of the present invention, and the method provided in the embodiment of the present invention specifically includes the following steps:
(1) constructing a hierarchical flood control scheduling rule: grading the water level in front of the dam and the warehousing flow according to the water condition of the reservoir in the perennial flood season, the flood control storage capacity of the reservoir, the flood control limit water level, the downstream flood control standard and the likei∈[Zi l,Zi u]，Ij∈[Ij l,Ij u]，ZiAnd IjRespectively representing the water level interval of the ith grade and the warehousing flow interval of the jth grade, and l and u respectively representing the upper limit and the lower limit of the interval. Thus, the form of the hierarchical flood control scheduling rule can be expressed by the following equation:
Rij＝f(Zi,Ij)
wherein R isijAnd f () represents the flood control dispatching rule function.
The method of the invention is explained in detail by taking the three gorges dam as an object: the flood control limit water level of the three gorges dam is 145m, the normal water storage level is 175m, for the flood in one hundred years and below, the highest water level in the flood season is not higher than 171m, and the downward discharge flow rate is not more than 55000m3S to ensure that the water level in the sand market station is below 44.5 m. According to the characteristics, a three gorges hierarchical flood control scheduling rule table is established as shown in table 1:
TABLE 1 hierarchical flood control and dispatching rules table for three gorges
(2) Coding of decision variables: according toThe grading flood control dispatching rule is that the lower leakage flow R is corresponding to the water level and the warehousing quantity under different gradesijFor decision variables, as variables to be optimized of the MOEA/D algorithm, the coding adopts real number coding, as shown in Table 1, the coding totally comprises 29 variables to be optimized, wherein the boundary range of each variable is [30000,55000 ]]。
(3) Establishing a multi-target mathematical model: and establishing a multi-target hierarchical flood control dispatching rule optimization model by using the upstream flood control safety target, the downstream flood control safety target, the maximum navigation guarantee rate and the maximum annual average generated energy. In addition, the model needs to satisfy the following constraints: water balance equation, water level constraint, lower discharge flow constraint and output constraint. The method comprises the following specific steps:
objective function
1) Flood control objectives: reservoir flood control has two main goals, firstly, alleviate downstream flood disasters, secondly reserve the storage capacity and be used for preventing extreme flood. Thus, two flood control goals are established as follows:
wherein T is the number of scheduling period time segments, RtThe discharge quantity Z of the reservoir at the t-th time periodtIs the water level of the reservoir in the t-th period.
2) And (3) generating target: the maximum total generated energy in the reservoir dispatching period is the power generation target:
wherein A is the power station output coefficient, delta t is the scheduling time interval, HtAnd q istWater head and water head of t time period of reservoirAmount of current, NtRepresenting the force over time t.
3) Navigation target: the highest navigation rate in the reservoir dispatching period is a navigation target:
wherein f isn() Is a calculation function of the navigation guarantee rate and the let-down flow RtAnd (4) correlating.
Constraint conditions
1) Water balance equation:
St＝St-1+It-Rt
wherein S istIs the storage capacity of the reservoir at the t-th time period, St-1The storage capacity of the reservoir at the t-1 th time period, ItThe flow rate of the reservoir in the t-th period is the flow rate of the reservoir in storage.
2) Water level restraint:
wherein Z istIs the water level of the t-th period of the reservoir,the upper and lower limits of the water level of the reservoir at the t-th time interval.
3) And (3) restricting the downward flow:
wherein,the upper and lower limits of the discharge quantity of the reservoir at the t-th time period.
4) Force restraint:
wherein,the upper and lower limits of the output of the reservoir at the moment t.
(4) Solving by adopting MOEA/D: the target is the three gorges dam, and the scale data of the water coming from the historical flood season from 1882 to 2011 for 130 years in the Yichang station is used as the warehousing flow of the three gorges dam. Firstly, setting MOEA/D key operator and parameters: the method comprises the following steps of (1) optimizing the population by using 20 crossed operators, 30 mutation operators, 60 population numbers, 15 neighborhood numbers and 500 preset maximum evolutionary algebras, and then optimizing the population by using the following steps:
s1: initialization: executing initialization operation, initializing a weight vector, a neighborhood vector and an initial population, and setting an evolution algebra g to be 0;
s2: for each weight vector, the following operations are performed:
s2.1: determining a mating population pool and an updating population pool: generating a random number r, and if r is less than 0.9, setting the mating population pool and the updating population pool as individuals in the neighborhood vector of the current vector; otherwise, setting the mating population pool and the updating population pool as the whole population;
s2.2: generating the filial generation individuals: randomly selecting two individuals from the mating population pool as parents, and executing crossover and mutation operations of a genetic algorithm to obtain a progeny individual;
s2.3: updating the parent generation: and comparing the filial generation individuals with the parent generation in the updated population pool, selecting the superior individuals as the next generation individuals, and updating one parent generation at most by one filial generation.
S3: and (5) judging the termination condition. If g is more than 500, finishing the algorithm and outputting a result; otherwise, g is g +1, go to step S2.
FIG. 2 shows an algorithm flow chart of MOEA/D. The obtained non-inferior scheme set of the grading flood control dispatching rule is listed in table 2, and the highest water level, the maximum leakage flow, the annual average power generation amount and the navigation rate of each scheme are listed in the table. The maximum water level value is the average value of the maximum water level every 130 years, and the maximum discharge flow, the power generation capacity and the navigation rate are also the same. For each non-inferior solution, flood protection constraints are met: the maximum water level is lower than 171m, and the maximum downward discharge flow is less than 55000m3And s. As can be seen from Table 2, the mean maximum water level isIn the meantime. Correspondingly, the average maximum downward discharge flow is from 49096m3The/s is reduced to 42319m3And s. Scheme 1 has the best upstream flood protection target value, as well as the design rules. Annual average power production was 533.3 (case 1) to 588.6 (case 60) billion kWh, which means that case 60 can increase annual average power production by 10.37%. The average navigation rate can be optimized to 80.2%, and the average navigation rate of the original design rule is 77.8%. Therefore, the optimized grading flood control dispatching rule can greatly improve the power generation amount in the flood season and improve the navigation rate in the flood season on the premise of not violating the flood control standard.
Table 2 hierarchical flood control scheduling rules non-inferior scheme set
And selecting flood season flood process verification of two typical years from 130 years. 1954 and 1981. Fig. 3 and 4 show the detailed flow process and water level process of 5 typical solutions in the case of triple flood, respectively. Wherein, fig. 3(a) is a process diagram of the leakage flow rate of the solution 1 in the 1954 year flood season, fig. 3(b) is a process diagram of the leakage flow rate of the solution 30 in the 1954 year flood season, fig. 3(c) is a process diagram of the leakage flow rate of the solution 40 in the 1954 year flood season, fig. 3(d) is a process diagram of the leakage flow rate of the solution 47 in the 1954 year flood season, fig. 3(e) is a process diagram of the leakage flow rate of the solution 60 in the 1954 year flood season, and fig. 3(f) is a water level process diagram of the five solutions in the 1954 year flood season; fig. 4(a) is a process diagram of the discharge flow rate of the lower scheme 1 in the flood season of 1981, fig. 4(b) is a process diagram of the discharge flow rate of the lower scheme 30 in the flood season of 1981, fig. 4(c) is a process diagram of the discharge flow rate of the lower scheme 40 in the flood season of 1981, fig. 4(d) is a process diagram of the discharge flow rate of the lower scheme 47 in the flood season of 1981, fig. 4(e) is a process diagram of the discharge flow rate of the lower scheme 60 in the flood season of 1981, and fig. 4(f) is a process diagram of the water levels of the five schemes in the flood season of 1981.
As can be seen from the figure, only in case of the scheme 1 is the incoming water more than 55000m3The lower discharge quantity is reduced to 55000m when/s or the water level is higher than 145m3And s. This maximizes upstream safety, but due to the low water level, the power generation is limited. Scheme 30, scheme 40, scheme 47 and scheme 60 not only prevent the height from being higher than 55000m3Peak flood per second, and prevention of peak discharge below 55000m3Medium and small floods in/s. These solutions will increase the water level to different degrees and reduce the amount of the bleed down. Thus, more flood resources are used for power generation and shipping. All schemes are optimized under the flood control standard, the highest water level is below 171m under all conditions, and the maximum water discharge is not more than 55000m3/s。
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A multi-target classification flood control dispatching method for a reservoir is characterized by comprising the following steps:
(1) grading the water level and the warehousing flow before the dam to obtain a water level interval corresponding to each grade and a warehousing flow interval under each grade, and further determining a grading flood control scheduling rule according to the water level interval corresponding to each grade and the warehousing flow interval under each grade, wherein the grading flood control scheduling rule is used for expressing decision-making discharge flow under the water level interval and the warehousing flow interval of each grade;
(2) according to the grading flood control scheduling rule, taking decision-making let-down flow corresponding to water levels and warehousing flow under different grades as variables to be optimized of the MOEA/D algorithm, and adopting real number coding;
(3) establishing a multi-target grading flood control scheduling rule optimization model according to the targets of the upstream flood control safety target, the downstream flood control safety target, the maximum navigation guarantee rate and the maximum generated energy in the scheduling period;
(4) and solving the multi-target grading flood control scheduling rule optimization model by adopting MOEA/D to obtain the highest water level, the maximum discharge flow, the generated energy in the scheduling period and the navigation rate corresponding to each scheme, and further determining a target scheduling scheme meeting the water level requirement and the discharge flow requirement.
2. The method of claim 1, wherein step (1) comprises:
(1.1) grading the water level in front of the dam and the warehousing flow: zi∈[Zi l,Zi u]，Ij∈[Ij l,Ij u]，ZiAnd IjRespectively representing a water level interval of the ith level and a warehousing flow interval of the jth level, and l and u respectively representing the upper limit and the lower limit of the interval;
(1.2) by Rij＝f(Zi,Ij) Determining a hierarchical flood control scheduling rule, wherein RijAnd f () represents the flood control dispatching rule function.
3. The method of claim 1 or 2, wherein step (3) comprises:
(3.1) preparation ofAnddetermining a flood control target, wherein T is the number of scheduling period time segments, RtFor reservoirs during period tDownward discharge flow rate, ZtThe water level of the reservoir in the t-th time period;
(3.2) preparation ofDetermining a power generation target, wherein A is a power station output coefficient, delta t is a scheduling time interval, and HtAnd q istWater head and generated flow, N, respectively, of the t-th period of the reservoirtRepresents the output of the t period;
(3.3) preparation ofDetermining a navigation target, wherein fn() Is a calculation function of the navigation guarantee rate and the let-down flow RtAnd (4) correlating.
4. The method according to claim 3, wherein the multi-objective hierarchical flood control dispatching rule optimization model satisfies the following constraints: operating water level constraint, lower leakage flow constraint, output constraint and water quantity balance equation.
5. The method of claim 1, wherein step (4) comprises:
(4.1) initializing a weight vector, a neighborhood vector and an initial population, wherein the weight vector represents a vector which is uniformly distributed in a target domain, the neighborhood vector represents a plurality of weight vectors which are closest to the weight vector, and each individual in the population represents a solution;
(4.2) generating a random number r for each weight vector, if r is smaller than a preset threshold, setting the mating population pool and the updating population pool as individuals in a neighborhood vector of the current weight vector, and if r is not smaller than the preset threshold, setting the mating population pool and the updating population pool as the whole population;
(4.3) randomly selecting two individuals from the mating population pool as parents, and performing crossing and mutation operations to obtain a progeny individual;
(4.4) comparing the filial generation individuals with the parent generation in the updated population pool, and selecting the superior individuals as the next generation individuals, wherein one filial generation updates at most one parent generation;
and (4.5) if the current evolution algebra is greater than the preset maximum evolution algebra, ending, outputting a result, if the current evolution algebra is not greater than the preset maximum evolution algebra, adding 1 to the current evolution algebra, and transferring to the step (4.2).
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| CN112950033A (en) * | 2021-03-04 | 2021-06-11 | 吴统明 | Reservoir dispatching decision method and system based on reservoir dispatching rule synthesis |
| CN113935603A (en) * | 2021-09-29 | 2022-01-14 | 中水珠江规划勘测设计有限公司 | Reservoir group multi-target forecast pre-discharge scheduling rule optimization method, system and medium |
| CN115049292A (en) * | 2022-06-28 | 2022-09-13 | 中国水利水电科学研究院 | Intelligent single reservoir flood control scheduling method based on DQN deep reinforcement learning algorithm |
| 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 |
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| CN112950033B (en) * | 2021-03-04 | 2024-03-29 | 吴统明 | Reservoir dispatching decision method and system based on reservoir dispatching rule synthesis |
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| CN115049292A (en) * | 2022-06-28 | 2022-09-13 | 中国水利水电科学研究院 | Intelligent single reservoir flood control scheduling method based on DQN deep reinforcement learning algorithm |
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