CN111915164A - Fine scheduling control method and system for full ecological elements of cascade reservoir group - Google Patents

Fine scheduling control method and system for full ecological elements of cascade reservoir group Download PDF

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CN111915164A
CN111915164A CN202010682898.0A CN202010682898A CN111915164A CN 111915164 A CN111915164 A CN 111915164A CN 202010682898 A CN202010682898 A CN 202010682898A CN 111915164 A CN111915164 A CN 111915164A
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牛文静
冯仲恺
蒋志强
刘帅
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Abstract

本发明提出一种梯级水库群全生态要素精细调度控制方法与系统，本发明在水库水位值约束下随机初始化种群，得到包含多个个体的初始种群；计算当前种群中个体适应度值，并更新种群全局最优位置和个体历史最优位置；引入个体历史最优位置和精英个体集合中的随机个体位置增加种群多样性，有效避免个体“早熟收敛”；引入精细化搜索策略，有效提升了种群收敛精度；通过迭代计算对种群中所有个体位置进行更新直至达到种群最大迭代次数；输出当前种群全局最优位置作为梯级水电站最终调度过程。本发明与经典智能优化方法相比鲁棒性高，能够有效减少整个梯级水库生态缺水，从而达到保护流域生态的目的；同时，本发明原理简单，求解精度高。

Figure 202010682898

The present invention proposes a fine scheduling control method and system for all ecological elements of a cascade reservoir group. The present invention randomly initializes the population under the constraint of the water level value of the reservoir to obtain an initial population containing multiple individuals; calculates the fitness value of the individuals in the current population, and updates it The global optimal position of the population and the optimal historical position of the individual; the introduction of the historical optimal position of the individual and the random individual position in the elite individual set increases the diversity of the population and effectively avoids the "premature convergence" of the individual; the introduction of a refined search strategy effectively improves the population Convergence accuracy; update the positions of all individuals in the population through iterative calculation until the maximum number of iterations of the population is reached; output the global optimal position of the current population as the final scheduling process of cascade hydropower stations. Compared with the classical intelligent optimization method, the invention has high robustness and can effectively reduce the ecological water shortage of the entire cascade reservoir, thereby achieving the purpose of protecting the ecology of the river basin; meanwhile, the invention has simple principle and high solution precision.

Figure 202010682898

Description

一种梯级水库群全生态要素精细调度控制方法与系统A method and system for fine scheduling control of all ecological elements in cascade reservoirs

技术领域technical field

本发明属于梯级水库群生态调度领域，更具体地，涉及一种梯级水库群全生态要素精细调度控制方法与系统。The invention belongs to the field of ecological regulation of cascade reservoir groups, and more particularly relates to a control method and system for fine regulation of all ecological elements of cascade reservoir groups.

背景技术Background technique

许多已运行的大型梯级水电站群运行结果表明：水电站群的建成与运营在带来巨大社会效益和经济效益的同时，也在改变天然径流水文情势，对流域生态系统健康带来了严重威胁。The operation results of many large-scale cascade hydropower stations in operation show that the construction and operation of hydropower stations not only bring huge social and economic benefits, but also change the hydrological regime of natural runoff, which poses a serious threat to the health of the basin ecosystem.

1973-2019年国内曾先后举办八次全国环境保护会议，国家从统筹环境保护的战略方针到解决环境破坏问题做出了一系列重大决策。近年来，围绕着打好蓝天、碧水、净土三大保卫战，《关于推进实施钢铁行业超低排放的意见》、《长江保护修复攻坚战行动计划》、《“无废城市”建设试点工作方案》等一系列政策推出，开创了全国生态环保新局面。水库修建的主要目的就是对水流进行调节起到拦洪蓄水的作用，但是同时会带来水库泥沙淤积、水温升高、水质变差以及下游河床严重冲刷等不良影响。为改善水库建设和运行对周围生态环境所带来的负面影响，水库管理者开始在水库带来经济效益的同时考虑对环境的影响，但现有文献资料很难给出统一的生态流量过程。为此，国内相关专家对水库的生态调度展开深入研究，主要从改善水库传统运行方式或者逐渐恢复流域周边生态系统的架构和功能来达到环境保护的目的。但传统优化方法(例如线性规划、动态规划等)在求解梯级水库生态调度时存在的“维数灾”等缺陷。From 1973 to 2019, eight national environmental protection conferences were held in China, and the country made a series of major decisions from coordinating the strategic policy of environmental protection to solving the problem of environmental damage. In recent years, focusing on the three major defense battles of blue sky, clear water and pure land, "Opinions on Promoting the Implementation of Ultra-Low Emissions in the Iron and Steel Industry", "The Action Plan for the Protection and Restoration of the Yangtze River", and "The Pilot Work Plan for the Construction of a "Waste-Free City" A series of policies have been introduced, creating a new situation in the country's ecological and environmental protection. The main purpose of reservoir construction is to regulate the flow of water and play the role of flood retention and water storage, but at the same time it will bring adverse effects such as sediment deposition in the reservoir, rising water temperature, poor water quality and serious erosion of the downstream riverbed. In order to improve the negative impact of reservoir construction and operation on the surrounding ecological environment, reservoir managers have begun to consider the impact on the environment while bringing economic benefits to the reservoir, but it is difficult to provide a unified ecological flow process in the existing literature. To this end, relevant domestic experts have conducted in-depth research on the ecological regulation of reservoirs, mainly by improving the traditional operation mode of the reservoir or gradually restoring the structure and function of the ecosystem around the basin to achieve the purpose of environmental protection. However, traditional optimization methods (such as linear programming, dynamic programming, etc.) have defects such as "dimension disaster" when solving the ecological dispatch of cascade reservoirs.

发明内容SUMMARY OF THE INVENTION

针对现有技术的缺陷或需求，本发明旨在提供一种梯级水库群全生态要素精细调度控制方法与系统，由此解决传统优化方法(例如线性规划、动态规划等)在求解梯级水库生态调度时存在的“维数灾”等缺陷。In view of the defects or needs of the prior art, the present invention aims to provide a fine scheduling control method and system for all ecological elements of a cascade reservoir group, thereby solving the problem of traditional optimization methods (such as linear programming, dynamic programming, etc.) in solving the ecological scheduling of cascade reservoirs. The defects such as the "dimension disaster" that existed at the time.

为实现上述目的，作为本发明的一个方面，提供了一种梯级水库群全生态要素精细调度控制方法，包括以下步骤：In order to achieve the above purpose, as an aspect of the present invention, a method for finely dispatching and controlling the whole ecological elements of a cascade reservoir group is provided, comprising the following steps:

(1)将水库群中所有水电站不同时刻的水位值作为个体，设置最大迭代次数为

Figure BDA0002586491610000027
当迭代次数k＝1时，在水库水位值约束下随机初始化种群，得到包含多个个体的初始种群，将初始种群作为当前种群；(1) Take the water level values of all hydropower stations in the reservoir group at different times as individuals, and set the maximum number of iterations as
Figure BDA0002586491610000027
When the number of iterations k=1, the population is randomly initialized under the constraint of the water level of the reservoir, and an initial population containing multiple individuals is obtained, and the initial population is used as the current population;

(2)计算当前种群中每个个体的适应度，将当前种群中每个个体的位置作为历史最优位置，并更新所有个体的历史最优位置和当前种群中全局最优位置；(2) Calculate the fitness of each individual in the current population, take the position of each individual in the current population as the historical optimal position, and update the historical optimal position of all individuals and the global optimal position in the current population;

(3)基于步骤(2)更新种群所有个体位置后得到的临时种群，选取适应度更好的前G个个体建立精英个体集合；对所有临时种群个体，引入个体历史最优位置和精英个体集合中的随机个体位置增加种群多样性得到多样性种群；再通过精细化搜索策略更新多样性种群个体位置，形成下一代种群；(3) Based on the temporary population obtained after updating all the individual positions of the population in step (2), select the first G individuals with better fitness to establish an elite individual set; for all temporary population individuals, introduce the individual historical optimal position and the elite individual set The random individual positions in the group increase the diversity of the population to obtain a diverse population; and then update the individual positions of the diverse population through a refined search strategy to form the next generation of populations;

(4)令k＝k+1，若

Figure BDA0002586491610000021
则将下一代种群作为当前种群，重复执行步骤(2)和步骤(3)；否则停止计算，并将当前种群的全局最优个体作为最佳调度过程输出。(4) Let k=k+1, if
Figure BDA0002586491610000021
Then take the next generation population as the current population, and repeat steps (2) and (3); otherwise, stop the calculation, and output the global optimal individual of the current population as the optimal scheduling process.

进一步地，第k代第i个个体位置表示为：

Figure BDA0002586491610000022
Further, the position of the i-th individual in the k-th generation is expressed as:
Figure BDA0002586491610000022

其中，N表示电站数目，T表示时段数目，且满足1≤i≤m，m表示种群规模；

Figure BDA0002586491610000023
Figure BDA0002586491610000026
为[0,1]区间均匀分布的随机数，
Figure BDA0002586491610000024
为第n个水电站在第t个时段的水位上限，
Figure BDA0002586491610000025
为第n个水电站在第t个时段的水位下限。Among them, N represents the number of power stations, T represents the number of time periods, and satisfies 1≤i≤m, and m represents the population size;
Figure BDA0002586491610000023
Figure BDA0002586491610000026
is a random number uniformly distributed in the interval [0,1],
Figure BDA0002586491610000024
is the upper limit of the water level of the nth hydropower station in the tth period,
Figure BDA0002586491610000025
is the lower limit of the water level of the nth hydropower station in the tth period.

进一步地，步骤(2)中采用惩罚函数法计算当前种群中每个个体的适应度，个体

Figure BDA0002586491610000031
的适应度
Figure BDA0002586491610000032
为：
Figure BDA0002586491610000033
Further, in step (2), the penalty function method is used to calculate the fitness of each individual in the current population, and the individual
Figure BDA0002586491610000031
fitness
Figure BDA0002586491610000032
for:
Figure BDA0002586491610000033

其中，yn,t为第n个电站第t个时段缺水量，Δt为第t个时段的小时数；ab是第b个不等式约束的惩罚系数；θb是第b个不等式约束的违背值，χ为总的不等式约束数目；βl是第l个等式约束的惩罚系数；φl是第l个等式约束的违背值，η为总的等式约束数目。Among them, y n,t is the water shortage of the n-th power station in the t-th period, Δ t is the number of hours in the t-th period; a b is the penalty coefficient of the b-th inequality constraint; θ b is the b-th inequality constraint The violation value of χ is the total number of inequality constraints; β l is the penalty coefficient of the l-th equality constraint; φ l is the violation value of the l-th equality constraint, and η is the total number of equality constraints.

进一步地，步骤(2)中更新所有个体的历史最优位置和当前种群中全局最优位置包括：Further, in step (2), updating the historical optimal position of all individuals and the global optimal position in the current population includes:

由

Figure BDA0002586491610000034
更新所有个体的历史最优位置，由
Figure BDA0002586491610000035
更新当前种群中全局最优位置；Depend on
Figure BDA0002586491610000034
Update the historical optimal positions of all individuals, given by
Figure BDA0002586491610000035
Update the global optimal position in the current population;

其中：

Figure BDA0002586491610000036
表示第k-1代第i个个体的历史最优位置，
Figure BDA0002586491610000037
表示
Figure BDA0002586491610000038
的适应度，
Figure BDA0002586491610000039
表示第k代第i个个体的适应度，gBestk表示第k代种群的全局最优位置。in:
Figure BDA0002586491610000036
represents the historical optimal position of the i-th individual in the k-1 generation,
Figure BDA0002586491610000037
express
Figure BDA0002586491610000038
fitness,
Figure BDA0002586491610000039
Represents the fitness of the i-th individual in the k-th generation, and gBest k represents the global optimal position of the k-th generation population.

进一步地，步骤(3)中，Further, in step (3),

由

Figure BDA00025864916100000310
得到多样性种群；Depend on
Figure BDA00025864916100000310
obtain diverse populations;

其中，

Figure BDA00025864916100000311
为种群中第k代第i个变异个体第j维的位置；
Figure BDA00025864916100000312
为种群中第k代第r1个个体第j维的历史最优位置，r1为种群中随机选择的个体下标；
Figure BDA00025864916100000313
为[0,1]区间均匀分布的随机数；
Figure BDA00025864916100000314
为精英集合中第k代第r2个个体第j维的位置，r2为精英集合中随机选择的个体下标。in,
Figure BDA00025864916100000311
is the position of the j-th dimension of the i-th variant individual of the k-th generation in the population;
Figure BDA00025864916100000312
is the historical optimal position of the jth dimension of the k-th generation r 1 individual in the population, and r 1 is the randomly selected individual subscript in the population;
Figure BDA00025864916100000313
is a random number uniformly distributed in the interval [0,1];
Figure BDA00025864916100000314
is the position of the jth dimension of the k-th generation r 2 individual in the elite set, and r 2 is the index of the randomly selected individual in the elite set.

进一步地，步骤(3)中，Further, in step (3),

由

Figure BDA00025864916100000315
更新多样性种群个体位置，形成下一代种群；Depend on
Figure BDA00025864916100000315
Update the individual positions of diverse populations to form the next generation of populations;

其中，

Figure BDA0002586491610000041
Figure BDA0002586491610000042
δ为中间变量，Gauss(0,1)为正态分布的随机数，
Figure BDA0002586491610000043
为[0,1]区间均匀分布的随机数，
Figure BDA0002586491610000044
为种群中第k代第i个精细化搜索个体第j维的位置；
Figure BDA0002586491610000045
为种群最大迭代次数；
Figure BDA0002586491610000046
为精英集合中第k代第r3个个体第j维的位置，r3为精英集合中随机选择的个体下标。in,
Figure BDA0002586491610000041
Figure BDA0002586491610000042
δ is an intermediate variable, Gauss(0,1) is a normally distributed random number,
Figure BDA0002586491610000043
is a random number uniformly distributed in the interval [0,1],
Figure BDA0002586491610000044
Search for the position of the j-th dimension of the individual for the i-th refinement of the k-th generation in the population;
Figure BDA0002586491610000045
is the maximum number of iterations of the population;
Figure BDA0002586491610000046
is the position of the jth dimension of the k - th generation r3 individual in the elite set, and r3 is the index of the randomly selected individual in the elite set.

作为本发明的另一个方面，提供了一种梯级水库群全生态要素精细调度控制系统，包括：As another aspect of the present invention, a fine scheduling control system for all ecological elements of a cascade reservoir group is provided, including:

初始化模块，用于将水库群中所有水电站不同时刻的水位值作为个体，设置最大迭代次数为

Figure BDA0002586491610000047
当迭代次数k＝1时，在水库水位值约束下随机初始化种群，得到包含多个个体的初始种群，将初始种群作为当前种群；The initialization module is used to take the water level values of all hydropower stations in the reservoir group at different times as individuals, and set the maximum number of iterations as
Figure BDA0002586491610000047
When the number of iterations k=1, the population is randomly initialized under the constraint of the water level of the reservoir, and an initial population containing multiple individuals is obtained, and the initial population is used as the current population;

适应度计算模块，用于计算当前种群中每个个体的适应度，将当前种群中每个个体的位置作为历史最优位置，并更新所有个体的历史最优位置和当前种群中全局最优位置；The fitness calculation module is used to calculate the fitness of each individual in the current population, take the position of each individual in the current population as the historical optimal position, and update the historical optimal position of all individuals and the global optimal position in the current population ;

位置更新模块，用于基于所述适应度计算模块更新种群所有个体位置后得到的临时种群，选取适应度更好的前G个个体建立精英个体集合；对所有临时种群个体，引入个体历史最优位置和精英个体集合中的随机个体位置增加种群多样性得到多样性种群；再通过精细化搜索策略更新多样性种群个体位置，形成下一代种群；The position update module is used to update the temporary population obtained by updating the positions of all individuals in the population based on the fitness calculation module, and select the first G individuals with better fitness to establish an elite individual set; for all temporary population individuals, introduce the optimal individual history The random individual positions in the position and the elite individual set increase the diversity of the population to obtain a diverse population; then update the individual positions of the diverse population through a refined search strategy to form the next generation of populations;

输出模块，用于则将下一代种群作为当前种群，反复执行适应度计算模块至位置更新模块的操作，直至满足预设迭代停止条件，并将当前种群的全局最优个体作为最佳调度过程输出。The output module is used to take the next generation population as the current population, and repeatedly perform the operations from the fitness calculation module to the position update module until the preset iteration stop condition is met, and output the global optimal individual of the current population as the optimal scheduling process .

总体而言，通过本发明所构思的以上技术方案与现有技术相比，能够取得下列有益效果：In general, compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

本发明在水库水位值约束下随机初始化种群，得到包含多个个体的初始种群；计算当前种群中个体适应度值，并更新种群全局最优位置和个体历史最优位置；引入个体历史最优位置和精英个体集合中的随机个体位置增加种群多样性，有效避免个体“早熟收敛”；引入精细化搜索策略，有效提升了种群收敛精度，能够很好完成探索到开发的过渡。综上，本发明与经典智能优化方法相比鲁棒性高，能够有效减少整个梯级水库生态缺水，从而达到保护流域生态的目的。同时，本发明方法求解梯级水库生态调度问题，原理简单，求解精度高。The invention randomly initializes the population under the constraint of the water level value of the reservoir, and obtains the initial population including a plurality of individuals; calculates the individual fitness value in the current population, and updates the global optimal position of the population and the individual historical optimal position; introduces the individual historical optimal position And the random individual position in the elite individual set increases the diversity of the population and effectively avoids the "premature convergence" of individuals; the introduction of a refined search strategy effectively improves the accuracy of population convergence, and can well complete the transition from exploration to development. In conclusion, the present invention has high robustness compared with the classical intelligent optimization method, and can effectively reduce the ecological water shortage of the entire cascade reservoir, thereby achieving the purpose of protecting the ecology of the watershed. At the same time, the method of the invention solves the ecological regulation problem of cascade reservoirs, with simple principle and high solution precision.

附图说明Description of drawings

图1是本发明实施例提供的一种梯级梯级水库群全生态要素精细调度控制方法的流程示意图；1 is a schematic flowchart of a method for finely scheduling and controlling all ecological elements of a cascade reservoir group according to an embodiment of the present invention;

图2(a)是本发明实施例提供的一种最小生态需求75％来水频率下采用本发明方法的箱型图示意图；Figure 2(a) is a schematic diagram of a box diagram using the method of the present invention under a water supply frequency of 75% of the minimum ecological demand provided by the embodiment of the present invention;

图2(b)是本发明实施例提供的一种最小生态需求80％来水频率下采用本发明方法的箱型图示意图；Figure 2(b) is a schematic diagram of a box diagram using the method of the present invention under a water supply frequency of 80% of the minimum ecological demand provided by the embodiment of the present invention;

图2(c)是本发明实施例提供的一种最小生态需求85％来水频率下采用本发明方法的箱型图示意图；Figure 2(c) is a schematic diagram of a box diagram using the method of the present invention under a water supply frequency of 85% of the minimum ecological demand provided by the embodiment of the present invention;

图2(d)是本发明实施例提供的一种最小生态需求90％来水频率下采用本发明方法的箱型图示意图。Fig. 2(d) is a schematic diagram of a box diagram of the method of the present invention under a water supply frequency of 90% of the minimum ecological demand provided by the embodiment of the present invention.

具体实施方式Detailed ways

为了使本发明的目的和方法更加清晰直观，在以下结合附图和实例对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，而不是限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。In order to make the object and method of the present invention more clear and intuitive, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

本发明提出一种梯级水库群全生态要素精细调度控制方法与系统，着力运用群组学习算法的极值搜索策略增加种群多样性、精细化搜索策略提升种群收敛精度，从而有效减少了流域梯级系统生态缺水量，为梯级水库生态调度提供了科学依据。The invention proposes a fine scheduling control method and system for all ecological elements of a cascade reservoir group, which focuses on using the extreme value search strategy of the group learning algorithm to increase the diversity of the population, and the refined search strategy to improve the convergence accuracy of the population, thereby effectively reducing the cascade system of the watershed. The ecological water shortage provides a scientific basis for the ecological regulation of cascade reservoirs.

本发明以流域整个梯级水库系统总缺水量最小作为目标函数，其数学形式为：The present invention takes the minimum total water shortage of the entire cascade reservoir system in the basin as the objective function, and its mathematical form is:

Figure BDA0002586491610000061
Figure BDA0002586491610000061

Figure BDA0002586491610000062
Figure BDA0002586491610000062

式中：N为梯级水库数目；T为调度总时段数目；F为整个梯级水库总缺水量；

Figure BDA0002586491610000063
为第n个电站第t个时段的最大生态流量需求；
Figure BDA0002586491610000064
为第n个电站第t个时段最小生态流量需求；On,t为第n个电站第t个时段总出库流量；yn,t为第n个电站第t个时段缺水量，Δt为第t个时段小时数。In the formula: N is the number of cascade reservoirs; T is the total number of scheduling periods; F is the total water shortage of the entire cascade reservoir;
Figure BDA0002586491610000063
is the maximum ecological flow demand of the nth power station in the tth period;
Figure BDA0002586491610000064
is the minimum ecological flow demand of the n-th power station in the t-th period; On, t is the total outbound flow of the n-th power station in the t-th period; y n,t is the water shortage of the n-th power station in the t-th period, Δ t is the number of hours in the t-th period.

需要满足的约束条件如下：The constraints that need to be satisfied are as follows:

(1)水量平衡约束：

Figure BDA0002586491610000065
其中，Vn,t为第n个水电站在第t个时段的库容；qn,t为第n个水电站在第t个时段的区间流量；In,t为第n个水电站在第t个时段的入库流量；On,t为第n个水电站在第t个时段的出库流量；Qn,t为第n个水电站在第t个时段的发电流量；Sn,t为第n个水电站在第t个时段的弃水流量；Un为直接连接在第n个水电站的上游电站数目。(1) Water balance constraints:
Figure BDA0002586491610000065
Among them, V n,t is the storage capacity of the n-th hydropower station in the t-th period; q n,t is the interval flow of the n-th hydropower station in the t-th period; I n,t is the n-th hydropower station in the t-th period The inflow flow of the period; On ,t is the outflow flow of the nth hydropower station in the tth period; Qn ,t is the power generation flow of the nth hydropower station in the tth period; Sn ,t is the nth period The abandoned water flow of each hydropower station in the t-th period; U n is the number of upstream power stations directly connected to the n-th hydropower station.

(2)始末水库水位约束：

Figure BDA0002586491610000066
其中，
Figure BDA0002586491610000067
为第n个水电站在初始水位；
Figure BDA0002586491610000068
为第n个水电站的期末水位。(2) Reservoir water level constraints at the beginning and end:
Figure BDA0002586491610000066
in,
Figure BDA0002586491610000067
is the initial water level of the nth hydropower station;
Figure BDA0002586491610000068
is the water level at the end of the nth hydropower station.

(3)发电流量约束：

Figure BDA0002586491610000069
其中，
Figure BDA00025864916100000610
为第n个水电站在第t个时段的发电流量下限；
Figure BDA00025864916100000611
为第n个水电站在第t个时段的发电流量上限；(3) Power generation flow constraints:
Figure BDA0002586491610000069
in,
Figure BDA00025864916100000610
is the lower limit of the power generation flow of the nth hydropower station in the tth period;
Figure BDA00025864916100000611
is the upper limit of the power generation flow of the nth hydropower station in the tth period;

(4)水头平衡约束：

Figure BDA0002586491610000071
其中，Hn,t为第n个水电站在第t个时段的水头；Zn,t为第n个水电站在第t个时段的坝前水位；dn,t第n个水电站在第t个时段下游水位。(4) Head balance constraint:
Figure BDA0002586491610000071
Among them, H n,t is the water head of the n-th hydropower station in the t-th period; Z n,t is the water level in front of the dam of the n-th hydropower station in the t-th period; d n,t The n-th hydropower station is in the t-th period The downstream water level of the time period.

(5)水电站出力约束：

Figure BDA0002586491610000072
其中，
Figure BDA0002586491610000073
为第n个水电站在第t个时段的出力上限；
Figure BDA0002586491610000074
为第n个水电站在第t个时段的出力下限。(5) Output constraints of hydropower stations:
Figure BDA0002586491610000072
in,
Figure BDA0002586491610000073
is the output upper limit of the nth hydropower station in the tth period;
Figure BDA0002586491610000074
is the lower output limit of the nth hydropower station in the tth period.

图1为本发明实施例提供的一种梯级水库群全生态要素精细调度控制方法的流程示意图，具体步骤包括：FIG. 1 is a schematic flowchart of a method for finely scheduling and controlling all ecological elements of a cascade reservoir group according to an embodiment of the present invention, and the specific steps include:

(1)选择参与计算的电站，并将每个电站不同时刻水位值作为自变量进行串联编码，其中，种群中任意一个个体代表梯级水库在整个调度时段的水位值。令迭代次数k＝1并在搜索空间中随机生成初始种群，则第k代第i个个体位置表示为：

Figure BDA0002586491610000075
其中，N表示电站数目；T表示时段数目；且满足1≤i≤m，m表示种群规模。在初始种群中，第k代第n个电站第t个时段水位值
Figure BDA0002586491610000076
生成方式为
Figure BDA0002586491610000077
为[0,1]区间均匀分布的随机数。
Figure BDA0002586491610000078
第n个水电站在第t个时段的水位下限；
Figure BDA0002586491610000079
第n个水电站在第t个时段的水位上限。(1) Select the power stations participating in the calculation, and use the water level value of each power station at different times as an independent variable for serial coding, where any individual in the population represents the water level value of the cascade reservoir during the entire dispatch period. Let the number of iterations k = 1 and randomly generate the initial population in the search space, then the position of the i-th individual in the k-th generation is expressed as:
Figure BDA0002586491610000075
Among them, N represents the number of power stations; T represents the number of time periods; and 1≤i≤m is satisfied, and m represents the population size. In the initial population, the water level value of the n-th power station in the k-th generation in the t-th period
Figure BDA0002586491610000076
Generated as
Figure BDA0002586491610000077
It is a random number uniformly distributed in the interval [0,1].
Figure BDA0002586491610000078
The lower limit of the water level of the nth hydropower station in the tth period;
Figure BDA0002586491610000079
The upper limit of the water level of the nth hydropower station in the tth period.

(2)使用惩罚函数法计算种群中所有个体适应度，则第k代第i个个体

Figure BDA00025864916100000710
的适应度
Figure BDA00025864916100000711
计算公式为：
Figure BDA00025864916100000712
式中，Δt为第t个时段的小时数。ab是第b个不等式约束的惩罚系数；θb是第b个不等式约束的违背值，χ为总的不等式约束数目；βl是第l个等式约束的惩罚系数；φl是第l个等式约束的违背值，η为总的等式约束数目。(2) Use the penalty function method to calculate the fitness of all individuals in the population, then the i-th individual of the k-th generation
Figure BDA00025864916100000710
fitness
Figure BDA00025864916100000711
The calculation formula is:
Figure BDA00025864916100000712
In the formula, Δt is the number of hours in the t -th period. a b is the penalty coefficient of the b-th inequality constraint; θ b is the violation value of the b-th inequality constraint, χ is the total number of inequality constraints; β l is the penalty coefficient of the l-th equality constraint; φ l is the l-th inequality constraint The violation value of each equality constraint, η is the total number of equality constraints.

(3)更新所有个体的历史最优位置与种群的全局最优位置，(3) Update the historical optimal position of all individuals and the global optimal position of the population,

Figure BDA00025864916100000713
Figure BDA00025864916100000713

Figure BDA0002586491610000081
Figure BDA0002586491610000081

式中：

Figure BDA0002586491610000082
表示第k-1代第i个个体的历史最优位置；
Figure BDA0002586491610000083
表示
Figure BDA0002586491610000084
的适应度；
Figure BDA0002586491610000085
表示第k代第i个个体适应度，gBestk表示第k代种群的全局最优位置；where:
Figure BDA0002586491610000082
Represents the historical optimal position of the i-th individual in the k-1 generation;
Figure BDA0002586491610000083
express
Figure BDA0002586491610000084
fitness;
Figure BDA0002586491610000085
represents the fitness of the i-th individual in the k-th generation, and gBest k represents the global optimal position of the k-th generation population;

(4)更新种群中的精英个体集合，精英个体集合为种群中前G个具有更好适应度的个体，之后使用群组学习算法的极值搜索策略增加种群多样性，有效避免个体“早熟收敛”：(4) Update the set of elite individuals in the population. The set of elite individuals is the first G individuals with better fitness in the population, and then use the extreme value search strategy of the group learning algorithm to increase the diversity of the population and effectively avoid the “premature convergence of individuals”. ":

Figure BDA0002586491610000086
Figure BDA0002586491610000086

式中：

Figure BDA0002586491610000087
为种群中第k代第i个变异个体第j维的位置；
Figure BDA0002586491610000088
为种群中第k代第r1个个体第j维的位置，r1为种群中随机选择的个体下标；
Figure BDA0002586491610000089
为[0,1]区间均匀分布的随机数；
Figure BDA00025864916100000810
为精英集合中第k代第r2个个体第j维的位置，r2为精英集合中随机选择的个体下标。where:
Figure BDA0002586491610000087
is the position of the j-th dimension of the i-th variant individual of the k-th generation in the population;
Figure BDA0002586491610000088
is the position of the jth dimension of the k-th generation r 1 individual in the population, and r 1 is the index of the randomly selected individual in the population;
Figure BDA0002586491610000089
is a random number uniformly distributed in the interval [0,1];
Figure BDA00025864916100000810
is the position of the jth dimension of the k-th generation r 2 individual in the elite set, and r 2 is the index of the randomly selected individual in the elite set.

(5)使用群组学习算法的精细化搜索策略提升种群收敛精度：(5) Use the refined search strategy of the group learning algorithm to improve the population convergence accuracy:

Figure BDA00025864916100000811
Figure BDA00025864916100000811

Figure BDA00025864916100000812
Figure BDA00025864916100000812

Figure BDA00025864916100000813
Figure BDA00025864916100000813

式中：δ为中间变量；Gauss(0,1)为正态分布的随机数；

Figure BDA00025864916100000814
为[0,1]区间均匀分布的随机数，
Figure BDA00025864916100000815
为种群中第k代第i个精细化搜索个体第j维的位置；
Figure BDA00025864916100000816
为种群最大迭代次数；
Figure BDA00025864916100000817
为精英集合中第k代第r3个个体第j维的位置，r3为精英集合中随机选择的个体下标。In the formula: δ is the intermediate variable; Gauss(0,1) is the random number of normal distribution;
Figure BDA00025864916100000814
is a random number uniformly distributed in the interval [0,1],
Figure BDA00025864916100000815
Search for the position of the j-th dimension of the individual for the i-th refinement of the k-th generation in the population;
Figure BDA00025864916100000816
is the maximum number of iterations of the population;
Figure BDA00025864916100000817
is the position of the jth dimension of the k - th generation r3 individual in the elite set, and r3 is the index of the randomly selected individual in the elite set.

(6)令k＝k+1。若

Figure BDA00025864916100000818
则返回步骤(2)；否则停止计算，并将当前种群的全局最优个体gBestk作为最佳调度过程输出。(6) Let k=k+1. like
Figure BDA00025864916100000818
Then return to step (2); otherwise, stop the calculation, and output the global optimal individual gBest k of the current population as the optimal scheduling process.

下面结合附图和实施例对本发明作进一步的描述。The present invention will be further described below with reference to the accompanying drawings and embodiments.

以乌江干流上的洪家渡、东风、索凤营、乌江渡及构皮滩五座电站为本发明实施对象，相应参数设置为m＝30、

Figure BDA0002586491610000091
各约束破坏惩罚系数均设定为10000。Taking the five power stations of Hongjiadu, Dongfeng, Suofengying, Wujiangdu and Goupitan on the main stream of Wujiang as the implementation objects of the present invention, the corresponding parameters are set as m=30,
Figure BDA0002586491610000091
The penalty coefficient of each constraint destruction is set to 10000.

为验证本发明高效性，将遗传算法(Genetic algorithm，GA)、差分进化算法(Differential Evolution,DE)、布谷鸟算法(Cuckoo Search Algorithm,CS)作为对比方法，所有方法独立运行10次。选择最小生态流量需求和S四种来水频率(75％、80％、85％、90％)作为实施工况，表1列出了最小生态流量需求下四种方法在四种来水频率下的统计结果；表1的统计结果中包含最优值、中位数、平均值、最差值、标准差。由表1可知，在所有情况下，相对于所有统计指标，本发明方法算法都优于其他方法。例如，当每个水库来水的频率设定为90％时，本发明方法可以使最优值相对GA，DE和CS分别提高约89％，58％和84％。证明了本发明方法能够有效减少梯级生态系统生态缺水量。由此可知，本发明方法是一种梯级水库生态调度新颖求解方法，能够为梯级水库调度运行提供科学依据。In order to verify the high efficiency of the present invention, Genetic Algorithm (GA), Differential Evolution (DE) and Cuckoo Search Algorithm (CS) were used as comparison methods, and all methods were run independently for 10 times. Select the minimum ecological flow demand and the four water inflow frequencies (75%, 80%, 85%, 90%) as the implementation conditions. Table 1 lists the four methods under the minimum ecological flow demand under the four water inflow frequencies. The statistical results of Table 1 include the optimal value, median, average value, worst value, and standard deviation. It can be seen from Table 1 that in all cases, with respect to all statistical indicators, the algorithm of the method of the present invention is superior to other methods. For example, when the frequency of incoming water from each reservoir is set to 90%, the method of the present invention can increase the optimal value by about 89%, 58% and 84%, respectively, relative to GA, DE and CS. It is proved that the method of the invention can effectively reduce the ecological water shortage of the cascade ecosystem. It can be seen from this that the method of the present invention is a novel solution method for cascade reservoir ecological dispatch, and can provide scientific basis for cascade reservoir dispatch operation.

表1 (单位：亿立方米)Table 1 (Unit: billion cubic meters)

Figure BDA0002586491610000092
Figure BDA0002586491610000092

图2(a)-图2(d)给出了最小生态需求下不同来水频率下的箱型图。由图2(a)-图2(d)可知，随着来水减少，梯级水电系统缺水量也在增加，通过本发明方法获得的目标函数值较其他方法要更集中且更小，说明了本发明方法是求解梯级水库生态调度的一种有效求解工具。Fig. 2(a)-Fig. 2(d) show the box plots of different inflow frequencies under the minimum ecological demand. It can be seen from Fig. 2(a)-Fig. 2(d) that as the incoming water decreases, the water shortage of the cascade hydropower system is also increasing, and the objective function value obtained by the method of the present invention is more concentrated and smaller than other methods. It is concluded that the method of the present invention is an effective solution tool for solving the ecological regulation of cascade reservoirs.

本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.

Claims (7)

1. A step reservoir group full ecological element fine scheduling control method is characterized by comprising the following steps:
(1) setting the maximum iteration times as follows by taking the water level values of all hydropower stations in the reservoir group at different moments as individuals
Figure FDA0002586491600000017
When the iteration number k is 1, randomly initializing a population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population;
(2) calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all the individuals and the global optimal position in the current population;
(3) based on the temporary population obtained after updating the positions of all individuals in the population in the step (2), selecting the first G individuals with better fitness to establish an elite individual set; introducing individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase population diversity to obtain a diversity population; updating the positions of the individual diversity populations through a refined search strategy to form next generation populations;
(4) let k equal k +1, if
Figure FDA0002586491600000011
Taking the next generation population as the current population, and repeatedly executing the step (2) and the step (3); otherwise, stop the calculation, anAnd outputting the global optimal individual of the current population as an optimal scheduling process.
2. The method of claim 1, wherein the ith generation of individual positions is represented as:
Figure FDA0002586491600000012
wherein N represents the number of power stations, T represents the number of time periods, i is more than or equal to 1 and less than or equal to m, and m represents the population scale;
Figure FDA0002586491600000013
Figure FDA0002586491600000014
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0002586491600000015
for the upper water level limit of the nth hydropower station during the t-th period,
Figure FDA0002586491600000016
the lower limit of the water level of the nth hydropower station in the t-th period.
3. The method according to claim 2, wherein the fitness of each individual in the current population is calculated in step (2) by a penalty function method, and the individual is selected from the group consisting of
Figure FDA0002586491600000021
Is adapted to
Figure FDA0002586491600000022
Comprises the following steps:
Figure FDA0002586491600000023
wherein, yn,tFor the water shortage, Delta, at the t-th time interval of the nth planttIs the t thHours of the session; a isbIs the penalty coefficient of the b-th inequality constraint; thetabIs the violation value of the b-th inequality constraint, and χ is the total number of inequality constraints; beta is alIs the penalty coefficient for the ith equality constraint; phi is alIs the violation of the ith equality constraint and η is the total number of equality constraints.
4. The method of claim 3, wherein updating the historical optimal locations and the global optimal locations in the current population for all individuals in step (2) comprises:
by
Figure FDA0002586491600000024
Updating the historical optimal locations of all individuals by
Figure FDA0002586491600000025
Updating the global optimal position in the current population;
wherein:
Figure FDA0002586491600000026
representing the historical optimal position of the ith individual in the k-1 generation,
Figure FDA0002586491600000027
to represent
Figure FDA0002586491600000028
The degree of fitness of (a) to (b),
Figure FDA0002586491600000029
denotes the fitness of the ith individual of the kth generation, gBestkRepresenting the global optimal position of the population of the kth generation.
5. The method according to claim 4, wherein, in step (3),
by
Figure FDA00025864916000000210
Obtaining a diversity population;
wherein,
Figure FDA00025864916000000211
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure FDA00025864916000000212
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure FDA00025864916000000213
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure FDA00025864916000000214
is the kth generation r in the elite set2Position of individual j dimension, r2Randomly selected individual subscripts in the elite set.
6. The method according to claim 5, wherein, in step (3),
by
Figure FDA00025864916000000215
Updating the individual positions of the diversity population to form a next generation population;
wherein,
Figure FDA0002586491600000031
as an intermediate variable, Gauss (0,1) is a normally distributed random number,
Figure FDA0002586491600000032
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0002586491600000033
for the ith refinement of the kth generation in the populationSearching the position of the j dimension of the individual;
Figure FDA0002586491600000034
the maximum iteration number of the population is obtained;
Figure FDA0002586491600000035
is the kth generation r in the elite set3Position of individual j dimension, r3Randomly selected individual subscripts in the elite set.
7. The utility model provides a meticulous dispatch control system of full ecological factor of step reservoir crowd which characterized in that includes:
the initialization module is used for setting the maximum iteration times as the individual water level values of all hydropower stations in the reservoir group at different moments
Figure FDA0002586491600000036
When the iteration number k is 1, randomly initializing a population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population;
the fitness calculation module is used for calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all the individuals and the global optimal position in the current population;
the position updating module is used for updating the positions of all individuals of the population based on the fitness calculating module to obtain a temporary population, and selecting the first G individuals with better fitness to establish an elite individual set; introducing individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase population diversity to obtain a diversity population; updating the positions of the individual diversity populations through a refined search strategy to form next generation populations;
and the output module is used for repeatedly executing the operation from the fitness calculation module to the position updating module by taking the next generation population as the current population until a preset iteration stop condition is met, and outputting the globally optimal individual of the current population as the optimal scheduling process.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119168300A (en) * 2024-09-05 2024-12-20 华中科技大学 A method and device for ecological regulation of cascade reservoirs based on chaos enhanced HHO

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN106203689A (en) * 2016-07-04 2016-12-07 大连理工大学 A kind of Hydropower Stations cooperation Multiobjective Optimal Operation method
CN108710970A (en) * 2018-05-07 2018-10-26 华中科技大学 A kind of parallel dimension reduction method of Multiobjective Scheduling of huge Hydro Power Systems with Cascaded Reservoirs
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN110222938A (en) * 2019-05-10 2019-09-10 华中科技大学 A kind of Hydropower Stations head relation cooperative optimization method and system
CN110363343A (en) * 2019-07-11 2019-10-22 水利部交通运输部国家能源局南京水利科学研究院 A Hybrid Adaptive Intelligent Optimal Scheduling Method and System for Hydropower Station Groups
CN110598919A (en) * 2019-08-28 2019-12-20 华中科技大学 Method and system for dynamically regulating and controlling cascade hydropower stations
CN110751365A (en) * 2019-09-11 2020-02-04 华中科技大学 Multi-target balanced scheduling method and system for cascade reservoir group
CN110766210A (en) * 2019-10-12 2020-02-07 华中科技大学 A short-term optimal scheduling method and system for cascade reservoir groups

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN106203689A (en) * 2016-07-04 2016-12-07 大连理工大学 A kind of Hydropower Stations cooperation Multiobjective Optimal Operation method
CN108710970A (en) * 2018-05-07 2018-10-26 华中科技大学 A kind of parallel dimension reduction method of Multiobjective Scheduling of huge Hydro Power Systems with Cascaded Reservoirs
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN110222938A (en) * 2019-05-10 2019-09-10 华中科技大学 A kind of Hydropower Stations head relation cooperative optimization method and system
CN110363343A (en) * 2019-07-11 2019-10-22 水利部交通运输部国家能源局南京水利科学研究院 A Hybrid Adaptive Intelligent Optimal Scheduling Method and System for Hydropower Station Groups
CN110598919A (en) * 2019-08-28 2019-12-20 华中科技大学 Method and system for dynamically regulating and controlling cascade hydropower stations
CN110751365A (en) * 2019-09-11 2020-02-04 华中科技大学 Multi-target balanced scheduling method and system for cascade reservoir group
CN110766210A (en) * 2019-10-12 2020-02-07 华中科技大学 A short-term optimal scheduling method and system for cascade reservoir groups

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHONG-KAI FENG: ""Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation"", 《JOURNAL OF HYDROLOGY》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119168300A (en) * 2024-09-05 2024-12-20 华中科技大学 A method and device for ecological regulation of cascade reservoirs based on chaos enhanced HHO
CN119168300B (en) * 2024-09-05 2025-04-25 华中科技大学 A method and device for ecological regulation of cascade reservoirs based on chaos enhanced HHO

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