CN113505913A - Reservoir optimal scheduling decision method and device for stability of aquatic community system - Google Patents

Reservoir optimal scheduling decision method and device for stability of aquatic community system Download PDF

Info

Publication number
CN113505913A
CN113505913A CN202110660802.5A CN202110660802A CN113505913A CN 113505913 A CN113505913 A CN 113505913A CN 202110660802 A CN202110660802 A CN 202110660802A CN 113505913 A CN113505913 A CN 113505913A
Authority
CN
China
Prior art keywords
reservoir
population
scheduling
random
aquatic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110660802.5A
Other languages
Chinese (zh)
Other versions
CN113505913B (en
Inventor
王奕博
刘攀
安日辉
谢康
李潇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University WHU filed Critical Wuhan University WHU
Publication of CN113505913A publication Critical patent/CN113505913A/en
Application granted granted Critical
Publication of CN113505913B publication Critical patent/CN113505913B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Educational Administration (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a reservoir optimal scheduling decision method and a device for aquatic community system stability, wherein the method comprises the following steps: step 1, determining a research situation, collecting basic data of a reservoir group system, historical scheduling data, hydrological data of a downstream station and biomass data, and determining influence factors and decision variables of reservoir scheduling behaviors; step 2, considering the interaction among multiple populations, and constructing a random multi-population dynamic model; step 3, calculating the hydrological variation degree, describing the disturbance of the hydrological variation on the aquatic community through the noise intensity, establishing a flow-ecology relation, and analyzing the stability of the aquatic community system under the influence of the hydrological variation; step 4, identifying resilience and resistance of the multi-population system by using two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment respectively; and 5, comprehensively considering the water supply and power generation targets of the reservoir and the ST and CVST ecological targets, and optimizing the operation curve of the reservoir to obtain an optimal scheduling decision of the reservoir.

Description

Reservoir optimal scheduling decision method and device for stability of aquatic community system
Technical Field
The invention belongs to the technical field of reservoir scheduling, and particularly relates to a reservoir optimal scheduling decision method and device for aquatic community system stability.
Technical Field
The reservoir is a widely applied flood storage and dry-up project, and 98822 reservoirs are built in total in China according to the national water conservancy development statistical bulletin of 2018. With the social and economic development at the cost of sacrificing the ecological environmental benefits in the global scope, the sustainable development of wetland ecosystem is more and more emphasized. How to enhance the stability and sustainability of aquatic ecosystems through optimal scheduling decisions of reservoirs has become a research focus, which prompts relevant scholars to focus on hydrological processes and physical and ecological processes beneficial to aquatic communities.
The reservoir has the functions of flood control, water supply, power generation, navigation, travel and the like, can bring huge social and economic benefits, but greatly influences the space-time distribution of natural runoff. The seasonal and annual distribution hydrological characteristics of rivers are greatly changed, which has adverse effects on the functions, structures and dynamics of the downstream ecological system of the reservoir, and the contradiction between social economy and ecological sustainable development is increasingly prominent. Various reservoir dispatching modes are available at home and abroad to ensure multi-target water demand including ecological water demand. Before the 21 st century, the most common way of scheduling was to increase reservoir discharge at specific times to meet downstream ecological water demands. After the 21 st century, relevant studies at home and abroad began to consider changes in ecological flux, water quality, aquatic organisms and habitat. Research has discussed that the stability of river ecosystem depends on the hydrologic situation to a great extent, such as the flow, duration, time, change rate and frequency are often the key driving factors of fish diversity, and the hydrologic situation is influenced by reservoir dispatching at random.
However, the existing reservoir ecological scheduling technical method has the following defects:
(1) the reservoir dispatching ecological target is single, and the integral consideration of an aquatic community system is lacked;
(2) the cooperative evolution process of various groups of the downstream aquatic community system of the reservoir is not clear;
(3) the mechanism of influence of changes in hydrological conditions on aquatic flora systems is not clear.
Disclosure of Invention
The invention is carried out to solve the problems and aims to provide a method and a device for optimizing and scheduling a reservoir for aquatic community system stability, which can quantify the influence of the downstream runoff change of the reservoir on the multi-community dynamics, improve the resilience and resistance of the downstream multi-community system by optimizing the flow of reservoir delivery, determine the key river and lake ecological flow and adapt to different decision-making scenes of reservoir scheduling.
In order to achieve the purpose, the invention adopts the following scheme:
< method >
The invention provides a reservoir optimal scheduling decision method for aquatic community system stability, which is characterized by comprising the following steps of:
step 1, determining a research situation, collecting basic data of a water reservoir group system, historical scheduling data, hydrological data and biomass data of a downstream station of a reservoir, and determining an influence factor and a decision variable of reservoir scheduling behavior;
step 2, on the basis of a logistic single population growth model, considering the interaction relation among multiple populations, including the inter-species relation of competition, predation, dependence and the like, constructing a random multi-population dynamic model, and simulating the collaborative evolution process of a multi-population system;
step 3, calculating the hydrological variation degree, describing the disturbance of the hydrological variation on the aquatic community through the noise intensity in the random multi-community dynamic model, establishing a flow-ecological relation to determine the response of the multi-community system dynamics of the aquatic community on the reservoir outflow, and analyzing the stability of the aquatic community system under the influence of the hydrological variation;
the noise intensity is calculated using the following equation:
Figure BDA0003115194490000021
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000022
the maximum value of the noise intensity acceptable for the population N; daltered flowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dnatural flowThe natural runoff is the comprehensive hydrological variation degree of the natural runoff, wherein the natural runoff can be regarded as reservoir inflow;
the "flow-ecological" relationship is specifically: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1 (namely when the natural flow state is completely changed), the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the hydrological variation degree is between 0 and 1, the noise intensity is determined according to equal proportion;
step 4, based on the stability analysis of the aquatic community system, respectively identifying the resilience and the resistance of the multi-community system by adopting two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment; ST is the recovery time from an unstable state to a stable state of a dynamic population system subjected to random continuous disturbance, ST can change along with the change of the noise intensity of the population system, and the smaller the ST value is, the quicker the response of the population system to external disturbance is; the CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and the smaller the CVST value is, the stronger the ability of the population system at the time of the steady state to resist external interference is;
and 5, comprehensively considering reservoir water supply and power generation targets and ecological targets such as ST, CVST and the like, and optimizing the reservoir operation curve by adopting a multi-objective optimization algorithm to obtain a reservoir optimal scheduling decision.
Preferably, the optimal scheduling decision method for the reservoir for the stability of the aquatic community system, provided by the invention, can also have the following characteristics: in step 1, the influence factors of the reservoir dispatching behaviors comprise all or part of the following variables: time sequence T and warehousing flow Q of reservoir in current time periodin TUpstream water level Z of reservoir in previous periodu TThe downstream water level Z of the reservoir in the previous periodd TLast period of reservoir delivery flow Qout T-1Output N of reservoir in last periodout T-1Rainfall P of reservoir at current time intervalTAnd the evaporation capacity E of the reservoir at the current time intervalT(ii) a The decision variable of the reservoir scheduling behavior is the delivery flow Q of the reservoir in the current time periodout T。
Preferably, the optimal scheduling decision method for the reservoir for the stability of the aquatic community system, provided by the invention, can also have the following characteristics: in step 2, the following random multi-population dynamic models are adopted to simulate the dynamic changes of the population biomass (such as the number and the density) under the external random interference:
Figure BDA0003115194490000031
wherein N (t) is the population density of the population N at time t; r isNIs the intrinsic growth rate of the population N; kN、
Figure BDA0003115194490000032
And
Figure BDA0003115194490000033
is respectively a population N and a population Li(i ═ 1.... multidot.l), a population Mj(j ═ 1.. said., m), population Xp(p ═ 1.. multidot., x) and a population Yq(q 1.. y), where i, j, p, q are counting variablesL, m, x, y are the upper limits of the counting variables i, j, p, q, respectively;
Figure BDA0003115194490000034
and
Figure BDA0003115194490000035
are respectively a population N and a population LiGroups N and MjGroup N and XpAnd populations N and YqA competition coefficient therebetween;
Figure BDA0003115194490000036
is the noise intensity to the population N caused by the change of the hydrological situation; population LiHindering the growth of population N as they compete for limited resources; group XpThe increase of the population N is hindered, and the population N density is directly reduced because the population N is fed; population MjInterdependence with population N, population YqAre food for population N, they promote the growth of population N; therefore, the temperature of the molten metal is controlled,
Figure BDA0003115194490000037
and
Figure BDA0003115194490000038
the two items are preceded by a negative sign,
Figure BDA0003115194490000039
and
Figure BDA00031151944900000310
the two items are preceded by positive signs; w (t) is a random variable, called the wiener process, which can be seen as the integral of white noise over time; w (t) satisfies the following conditions: (1) w (0) ═ 0; (2) w (T) -W (g) obeys normal distribution with the mean value of zero and the variance of T-g in the interval of g being more than or equal to 0 and less than or equal to T; (3) w (T) -W (g) and W (v) -W (u) are independent of each other in the interval 0. ltoreq. g < T < u < v. ltoreq.T.
In addition, in step 2, the random multi-population dynamic model is applied with the following assumptions: (1) all individuals in the same population are equivalent; (2) all individuals in the same population can survive independently of other species; (3) the environmental tolerance and the intrinsic growth rate are constant and are independent of the time and age distribution of the population; (4) the response of the actual growth rate of each individual to population dynamics does not take hysteresis into account; (5) species migration is not considered.
Preferably, the optimal scheduling decision method for the reservoir for the stability of the aquatic community system, provided by the invention, can also have the following characteristics: in step 4, when the population system reaches a steady state, the probability density function of the population density tends to be stable, and ST is calculated by the following formula:
ST＝t，when||PDFt transient-PDFstationary||2≈0，
in the formula, PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryIs a one-dimensional matrix of the steady-state probability density function of the random population system at the last moment of the simulation period.
CVST is calculated using the following formula:
Figure BDA0003115194490000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000042
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure BDA0003115194490000043
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
Preferably, the optimal scheduling decision method for the reservoir for the stability of the aquatic community system, provided by the invention, can also have the following characteristics: in step 5, the multi-objective consideration is as follows:
Figure BDA0003115194490000044
Figure BDA0003115194490000045
min ST＝t，when||PDFt transient-PDFstationary||2≈0，
Figure BDA0003115194490000046
in the formula, S is the total number of reservoir dispatching time periods; wsThe diversion water amount in a scheduling time interval s; WS is the average diversion water volume in all scheduling periods; hsThe generated energy in the scheduling time interval s; HG is the average power generation in all scheduling periods; PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryA one-dimensional matrix of a steady-state probability density function of the random population system at the last moment of a simulation period;
Figure BDA0003115194490000051
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure BDA0003115194490000052
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
Preferably, the optimal scheduling decision method for the reservoir for the stability of the aquatic community system, provided by the invention, can also have the following characteristics: and 5, optimizing the reservoir operation curve to obtain a reservoir optimization scheduling graph meeting the ecological system sustainability and stability development, and further controlling the operation of the reservoir according to the reservoir optimization scheduling graph.
< apparatus >
Further, the present invention provides a reservoir optimal scheduling apparatus based on the above < method >, including:
the system comprises a data acquisition part, a data analysis part and a data analysis part, wherein the data acquisition part is used for determining a research scene, collecting basic data of a water reservoir group system, historical scheduling data, hydrological data and biomass data of downstream stations of a reservoir and determining influence factors and decision variables of reservoir scheduling behaviors;
the multi-population system simulation part is used for constructing a random multi-population dynamic model and simulating a collaborative evolution process of the multi-population system on the basis of a logistic single population growth model, considering the interaction relationship among the multi-population, including the relationships among competition, predation, prey and dependent species;
the computational analysis part is used for calculating the hydrological variation degree, describing the disturbance of the hydrological variation on the aquatic community through the noise intensity in the random multi-community dynamic model, establishing a flow-ecological relation to determine the response of the multi-community system dynamics of the aquatic community on the reservoir outflow, and analyzing the stability of the aquatic community system under the influence of the hydrological variation;
the noise intensity is calculated using the following equation:
Figure BDA0003115194490000053
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000054
the maximum value of the noise intensity acceptable for the population N; daltered flowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dnatural flowThe comprehensive hydrological variation degree of the natural runoff is obtained;
the "flow-ecological" relationship is specifically: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1, the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the hydrological variation degree is between 0 and 1, the noise intensity is determined according to equal proportion;
the identification part is used for identifying resilience and resistance of the multi-population system by adopting two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment based on stability analysis of the aquatic population system; ST is the recovery time from an unstable state to a stable state of a dynamic population system subjected to random continuous disturbance, ST can change along with the change of the noise intensity of the population system, and the smaller the ST value is, the quicker the response of the population system to external disturbance is; the CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and the smaller the CVST value is, the stronger the ability of the population system at the time of the steady state to resist external interference is;
the reservoir operation optimizing part comprehensively considers reservoir water supply and power generation targets and ST and CVST ecological targets, adopts a multi-objective optimization algorithm to optimize a reservoir operation curve, and obtains a reservoir optimization scheduling decision reflecting the mapping relation of each parameter variable of reservoir operation;
the reservoir scheduling part controls the reservoir operation according to the reservoir optimal scheduling decision determined by the reservoir operation optimizing part; and
and the control part is in communication connection with the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part, the reservoir operation optimization part and the reservoir scheduling part and controls the operation of the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part, the reservoir operation optimization part and the reservoir scheduling part.
Preferably, the optimal reservoir scheduling device for aquatic community system stability provided by the invention can also have the following characteristics: and the reservoir dispatching part substitutes the reservoir measured data into a function which reflects variable mapping relations of each parameter of reservoir operation and is used for optimizing dispatching decision of the reservoir, and specific dispatching parameters are obtained through calculation so as to control the reservoir operation.
Preferably, the optimal scheduling device for a reservoir for stability of an aquatic community system provided by the invention further comprises: and the dynamic simulation part is in communication connection with the control part, calculates the hydrological variation degree according to the reservoir outflow calculated by the reservoir scheduling part, converts the hydrological variation degree into the noise intensity of the aquatic community through the flow-ecological relation, and performs dynamic random simulation on the collaborative evolution process of the multi-population system.
Preferably, the optimal scheduling device for a reservoir for stability of an aquatic community system provided by the invention further comprises: the input display part is in communication connection with the control part and is used for allowing a user to input an operation instruction and performing corresponding display; the input display part can display the reservoir operation curve obtained by optimizing the reservoir operation optimizing part according to the operation instruction, can display the specific scheduling parameter obtained by calculating the reservoir scheduling part according to the operation instruction, and can also dynamically display the multi-population system collaborative evolution process simulated by the dynamic simulation part in real time; the specific scheduling parameters comprise the inlet flow and the outlet flow of each gate of the reservoir, the water storage amount of the reservoir, the water consumption for power generation and the like.
Action and Effect of the invention
Compared with the prior art, the invention has the beneficial effects that:
(1) the optimal scheduling decision method for the reservoir facing the stability of the aquatic community system can scientifically and reasonably determine the optimal scheduling strategy and ecological flow beneficial to the ecological system of the downstream river of the reservoir based on the basic data of the reservoir, historical scheduling data and hydrological and biomass data of the downstream site of the reservoir.
(2) The optimal scheduling decision method for the reservoir facing the stability of the aquatic community system, provided by the invention, considers the interaction relationship of multiple communities of the aquatic community system, can effectively simulate the collaborative evolution process of the multiple community system when the multiple community system is disturbed by reservoir discharge, improves the sustainability of the river ecosystem from the aspect of the stability of the aquatic community system, and has the advantages of various ecological targets and comprehensive protected objects compared with the conventional reservoir scheduling mode and water ecological restoration measures.
(3) The reservoir optimal scheduling decision method for the stability of the aquatic community system establishes a 'flow-ecological' relationship, is beneficial to identifying important environmental covariates, promotes implementation of river ecosystem restoration, provides a way for quantifying the influence of reservoir discharge on the aquatic community system, and makes up for the defects of the prior art.
(4) According to the aquatic community system stability-oriented reservoir optimal scheduling decision method, the resilience and the resistance of a multi-community system are identified through the ST ecological index and the CVST ecological index respectively, the provided ecological indexes are suitable for various different reservoir scheduling decision scenes, and the corresponding scheduling measures can be determined according to the ecological indexes to regulate and control the operation of the reservoir, so that reservoir scheduling is greener, and the scheduling measures are more scientific and reasonable.
(5) The optimal reservoir scheduling device for the stability of the aquatic community system can scientifically and reasonably determine optimal scheduling strategies and ecological flows beneficial to the ecological system of the downstream river of the reservoir based on basic reservoir data, historical scheduling data and hydrological and biomass data of downstream sites of the reservoir, and can determine corresponding scheduling measures according to the optimal scheduling strategies to control the operation of the reservoir, so that the reservoir scheduling meets the requirements of water use and power generation capacity and simultaneously considers the requirements of the sustainability and stability development of the ecological system.
Drawings
FIG. 1 is a flow chart of a reservoir optimal scheduling decision method for aquatic community system stability according to an embodiment of the present invention;
fig. 2 is a schematic diagram of steady state transition of a population system according to an embodiment of the present invention, wherein (a) is transition of a random dynamic population system from an unstable state to a stable state; (b) different stable states of the population system described by the steady state probability density function; (c) the transfer of the steady state balance point of the population system is increased along with the noise intensity;
FIG. 3(a) is a graph showing the dynamic evolution of the multi-population system of (a) fish, (b) phytoplankton, (c) phytoplankton, (d) benthonic animals, (e) large plants, under different noise intensities according to the example of the present invention;
FIG. 4 is a phase diagram of multi-population dynamic system and a steady-state balance point transition process diagram under the condition of increasing noise intensity, wherein (a) is a phase diagram of three-population dynamic system of fish, phytoplankton and a steady-state balance point transition process diagram under the condition of increasing noise intensity; (b) the method comprises the following steps of (1) obtaining a steady state balance point transfer process diagram under the conditions of three population dynamic system phase diagrams of fishes, benthonic animals and large-scale plants and increasing noise intensity;
FIG. 5 is a graph of "steady state time" of a fish population system at different noise intensities according to an embodiment of the present invention;
fig. 6 is a graph of "coefficient of variation at steady state time" when the system noise intensities of the fish population according to the embodiment of the present invention are (a)0.02, (b)0.04, (c)0.06, (d)0.08, (e)0.10, (f)0.12, (g)0.14, (h)0.16, (i)0.18, and (j)0.20, respectively;
FIG. 7 is a comparison graph of pareto solutions of multi-objective reservoir optimal scheduling, optimal scheduling results (pareto optimal solutions) of the present invention, and conventional scheduling results of reservoirs according to an embodiment of the present invention;
FIG. 8 is a graph showing the conventional scheduling operation curve and the optimized scheduling operation curve of the Danjiang estuary reservoir (a) according to the embodiment of the present invention;
FIG. 9 is a graph showing the results of random simulation of the population dynamics of (a) fish, (c) phytoplankton, (e) zooplankton, (g) benthonic animals, (i) large plants according to the optimal scheduling strategy for reservoirs according to the embodiment of the present invention; and (b) fish, (d) phytoplankton, (f) zooplankton, (h) benthonic animals, (j) plot of transition probability density function of large plant population dynamics;
fig. 10 is a comparison graph of the monthly average warehousing traffic, the monthly average actually measured ex-warehouse traffic, the monthly average optimized ex-warehouse traffic and the monthly average water level of the Dangjiang reservoir according to the embodiment of the present invention.
Detailed Description
The following describes in detail the decision method and device for optimizing scheduling of a reservoir for stability of an aquatic community system according to the present invention with reference to the accompanying drawings.
< example >
As shown in fig. 1, the method for optimizing and scheduling a reservoir for stability of an aquatic community system provided in this embodiment includes the following steps:
step 1, determining a research situation, collecting reservoir basic data, historical scheduling data, hydrological data and biomass data of a downstream station of a reservoir, and determining influence factors and decision variables of reservoir scheduling behaviors; the influence factors of reservoir dispatching behavior include the following variables: time sequence T and warehousing flow Q of reservoir in current time periodin TUpstream water level Z of reservoir in previous periodu TThe downstream water level Z of the reservoir in the previous periodd TLast period of reservoir delivery flow Qout T-1Output N of reservoir in last periodout T-1Rainfall P of reservoir at current time intervalTAnd the evaporation capacity E of the reservoir at the current time intervalT(ii) a The decision variable of the reservoir scheduling behavior is the delivery flow Q of the reservoir in the current time periodout T；
Step 2, based on a logistic single population growth model and a random differential equation, considering the interaction relationship among the multi-population, such as the interspecific relationship of competition, predation, dependence and the like, constructing a random multi-population dynamic model, and simulating the dynamic change of multi-population systems (such as fishes, phytoplankton, zooplankton, benthonic animals and large plants) under the random interference of the outside; the random multi-population dynamical model is written in the form of differential equations as follows:
Figure BDA0003115194490000081
wherein N (t) is the population density of the population N at time t; r isNIs the intrinsic growth rate of the population N; kN、
Figure BDA0003115194490000082
And
Figure BDA0003115194490000091
is respectively a population N and a population Li(i ═ 1.... multidot.l), a population Mh(j ═ 1.. said., m), population Xp(p ═ 1.. multidot., x) and a population Yq(q 1.. y), where i, j, p, q are counting variables, and l, m, x, y are upper limits of the counting variables i, j, p, q, respectively;
Figure BDA0003115194490000092
and
Figure BDA0003115194490000093
are respectively a population N and a population LiGroups N and MjGroup N and XpAnd populations N and YqA competition coefficient therebetween;
Figure BDA0003115194490000094
is the noise intensity to the population N caused by the change of the hydrological situation; population LiHindering the growth of population N as they compete for limited resources; group XpHinderThe increase of the population N directly reduces the density of the population N as the population N is fed by the population N; population MjInterdependence with population N, population YqThey are food for population N and promote the growth of population N. Therefore, the temperature of the molten metal is controlled,
Figure BDA0003115194490000095
and
Figure BDA0003115194490000096
the two items are preceded by a negative sign,
Figure BDA0003115194490000097
and
Figure BDA0003115194490000098
the two items are preceded by a plus sign. Embodiments of the invention do not take parasitic relationships into account, since it is assumed that all individuals are able to survive independently. W (t) is a random variable, called the wiener process, which can be seen as the integral of white noise over time. W (t) satisfies the following conditions: (1) w (0) ═ 0; (2) w (T) -W (g) obeys normal distribution with the mean value of zero and the variance of T-g in the interval of g being more than or equal to 0 and less than or equal to T; (3) w (T) -W (g) and W (v) -W (u) are independent of each other in the interval 0. ltoreq. g < T < u < v. ltoreq.T.
The application of the random multi-population dynamic model has the following assumptions: (1) all individuals in the same population are equivalent; (2) all individuals in the same population can survive independently of other species; (3) the environmental tolerance and the intrinsic growth rate are constant and are independent of the time and age distribution of the population; (4) the response of the actual growth rate of each individual to population dynamics does not take hysteresis into account; (5) species migration is not considered.
In this example, the multi-population comprises fish, phytoplankton, zooplankton, benthos and macrophytes, and the stochastic population dynamics model is as follows:
Figure BDA0003115194490000099
Figure BDA00031151944900000910
Figure BDA00031151944900000911
Figure BDA0003115194490000101
Figure BDA0003115194490000102
in the formula, the footmarks 1-5 sequentially represent fishes, phytoplankton, zooplankton, benthos and macrophyte; the meanings of the other variables are given in the above formula.
And 3, calculating the hydrological variation degree, describing the disturbance of the hydrological variation on aquatic communities through the noise intensity in the random multi-population dynamic model, wherein the noise intensity is calculated by the following formula:
Figure BDA0003115194490000103
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000104
the maximum value of the noise intensity acceptable for the population N; daltered flowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dnatural flowThe natural runoff is the comprehensive hydrological variation degree of the natural runoff, and the natural runoff can be regarded as reservoir inflow. The comprehensive degree of hydrological variation in this example was calculated using the 32 indexes of hydrological variation proposed by Richter et al in 1996 and the variation range method proposed in 1997.
And establishing a 'flow-ecological' relationship to determine the response of the multi-population system dynamics of the aquatic population to the reservoir outflow, and analyzing the stability of the aquatic population system under the influence of hydrological variation. The "flow-ecological" relationship is expressed as follows: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1 (namely when the natural flow state is completely changed), the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the degree of hydrological variation is between 0 and 1, the noise intensity is determined in equal proportion.
And 4, based on the stability analysis of the aquatic community system, respectively identifying the resilience and the resistance of the multi-community system by adopting two ecological indexes of ST and CVST.
ST is the recovery time from an unstable state to a stable state of a zoological population system subjected to random continuous disturbance, a smaller ST indicates that the response of the population system to external disturbance is quicker, and the ST can change along with the change of the noise intensity of the population system. When the population system reaches a steady state, the probability density function of the population density tends to be stable, therefore, the difference between the transition probability density function and the steady state probability density function is calculated by adopting a two-norm, and when the two-norm tends to be zero, the random system is considered to reach a steady state. ST is calculated using the formula:
ST＝t，when||PDFt transient-PDFstationary||2≈0，，
in the formula, PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryIs a one-dimensional matrix of the steady-state probability density function of the random population system at the last moment of the simulation period.
The CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and a smaller CVST shows that the population system at the time of the steady state has stronger capacity of resisting external interference. CVST is calculated using the following formula:
Figure BDA0003115194490000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000112
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure BDA0003115194490000113
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
And 5, comprehensively considering social and economic targets such as reservoir water supply and power generation and ecological targets such as ST and CVST, and optimizing the reservoir operation curve by adopting a multi-target optimization algorithm (such as a genetic algorithm and a cuckoo search algorithm) to obtain a reservoir optimal scheduling decision.
In this embodiment, the multi-objective considerations are as follows:
Figure BDA0003115194490000114
Figure BDA0003115194490000115
min ST＝t，when||PDFt transient-PDFstationary||2≈0，
Figure BDA0003115194490000116
in the formula, S is the total number of reservoir dispatching time periods; wsThe diversion water amount in a scheduling time interval s; WS is the average diversion water volume in all scheduling periods; hsThe generated energy in the scheduling time interval s; HG is the average power generation in all scheduling periods; PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryA one-dimensional matrix of a steady-state probability density function of the random population system at the last moment of a simulation period;
Figure BDA0003115194490000117
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure BDA0003115194490000118
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
In the present embodiment, the constraint conditions are considered as follows:
Vs+1＝Vs+Is-Ws-Os，
Vs，min≤Vs≤Vs，max，
Os，min≤Os≤Os，max，
Hs≤Hs，max，
in the formula, VsAnd Vs+1Respectively the initial storage capacity and the final storage capacity of the reservoir in the scheduling time interval s; i iss、Ws、OsAnd HsRespectively the warehousing water quantity, the water diversion quantity, the ex-warehouse water quantity and the hydroelectric power generation quantity in the scheduling time interval s; vs，minAnd Vs，maxRespectively corresponding to the dead water level and the flood control limit water level in the scheduling time interval s; o iss，minAnd Os，maxRespectively the minimum and maximum let-down flow in the scheduling time interval s; hs，maxThe maximum generated output within the scheduling time interval s; the evaporation capacity is smaller than the water storage capacity, the water diversion capacity and the water discharge capacity, and is ignored.
In this embodiment, the multi-objective optimization algorithm employs Cuckoo Search (CS), and includes the following specific steps: (1) setting the scale of a solution, the iteration times and the initial position of the solution, and defining a target function; (2) calculating a target value through non-dominated sorting to obtain a pareto solution set; (3) updating the position of the solution, recalculating the target value and updating the current pareto solution set; (4) generating a random number r (r is equal to 0, 1)]) If r > Pa(PaProbability that the position of the solution is updated), then the position of the solution is updated; (5) once the maximum iteration times or the stopping standard is reached, the next step is carried out, otherwise, the step 3 is returned; (6) and outputting the optimized pareto solution.
In the embodiment, the multi-objective performance is improved by optimizing the key points of the reservoir operation curve, and each curve to be optimized is provided with 4 key points on the basis of a large amount of practice; setting the scale of a CS algorithm solution as 100, the iteration times as 1000, the total number of optimized variables as 27, the lower bound of the optimized variables as a dead water level and the upper bound of the optimized variables as a flood control limit water level; and adding a punishment item in the optimized scheduling model to ensure that the optimized reservoir operation curves are not crossed with each other.
In this embodiment, the input items of the optimal scheduling decision method for the reservoir for the stability of the aquatic community system include actually measured reservoir warehousing flow, a conventional reservoir scheduling map or conventional scheduling, actually measured reservoir delivery flow, and actually measured target population biomass downstream of the reservoir, and the output items include an optimal reservoir scheduling map or optimal scheduling, an optimal reservoir delivery flow, an optimal reservoir operating water level, a multi-objective benefit value under an optimal scheduling strategy, and multi-population dynamics under an optimal scheduling strategy.
As can be seen from fig. 2, a population dynamics system disturbed by different noise intensities may have a plurality of stable states defined by different peak values of the steady-state probability density function, and when the population dynamics system is disturbed transiently, the population density changes little, and the state variables may fluctuate near the current stable state; when the population system is continuously disturbed within the upper and lower thresholds, the population system may appear in an alternate stable state; when the noise level increases and exceeds the upper threshold, the population system will be destroyed and one or more populations of aquatic populations may be destroyed, which can severely reduce biodiversity and destroy the integrity of the river ecosystem.
As can be seen from fig. 3, for each population system, the population density of benthonic animals and macrophytes is more sensitive to changes in noise intensity; with the increase of the noise intensity, the population density at the steady state is in a descending trend, when the steady state population density is close to zero, the corresponding noise intensity value is taken as the upper limit of the acceptable noise intensity threshold value of the population system, in the embodiment, the upper limits of the acceptable noise intensity of the population system of the fishes, the phytoplankton, the zooplankton, the benthonic animals and the large plants are respectively 0.2, 0.22, 0.4 and 0.4; excessive noise levels can cause extreme changes in population dynamics and even population extinction, thereby disrupting the normal function and structure of the overall aquatic community system.
As can be seen from fig. 4, as the noise intensity increases, the steady-state balance point of the multi-population system shows a monotonous decreasing trend, and when the noise intensity reaches the maximum value acceptable for the multi-population system, the steady-state balance point approaches the lowest value; the noise intensity is enhanced along with the increase of the variation degree of the reservoir discharge hydrology, and the stable state of the multi-population system is continuously converted to the deterioration state until the stable state is destroyed; the influence of the aggravated degree of the hydrological situation change on the multi-population system is consistent with the influence caused by the increased noise intensity, which shows that the hydrological variation degree and the noise intensity are in positive correlation, and verifies the 'flow-ecology' relationship established by the invention.
As can be seen from FIG. 5, when the noise intensity is low (less than 0.07), the fish population dynamics is restricted by the environmental tolerance, the intrinsic growth rate and the interpopulation relationship, and the ST value is not obviously changed along with the increase of the hydrological variation degree of the reservoir drainage; when the noise intensity is in a medium level (between 0.07-0.15), the fish population dynamics is obviously disturbed by the drainage of the reservoir, and the ST value is increased along with the increase of the hydrological variation degree of the drainage of the reservoir; when the noise intensity is high (between 0.15 and 0.2, 0.2 is the upper limit of the noise intensity acceptable by a fish population system), the influence of the hydrologic situation change on the fish population dynamics is large, and the ST value is kept at a high level; in conclusion, the larger the variation degree of reservoir discharge hydrology, the longer the recovery time of the population system from an unstable state to a stable state, and the poorer the resilience of the system.
As can be seen from fig. 6, the resistance of the population system at the steady-state time is mainly affected by the noise intensity of the population system, and the CVST value decreases as the noise intensity of the population system increases.
As can be seen from fig. 7, the reservoir optimization scheduling result of the embodiment of the invention shows that the annual average water supply amount, annual average power generation amount and ST target benefits under the optimization scheduling strategy are respectively improved by 2.37%, 2.40% and 2.67%, which are superior to those of the conventional reservoir scheduling strategy, and the CVST value is the same as that under the conventional reservoir scheduling strategy.
As can be seen from FIG. 8, the optimal operation curve of the reservoir obtained by the embodiment of the invention is reasonable.
As can be seen from fig. 9, under the reservoir optimization scheduling strategy, the dynamic mean values of various groups in the simulation period tend to be stable, and the transition probability density function also tends to be stable; it can be seen from the transition probability density function that the resistance of the fish, phytoplankton, benthonic animals and large-scale plant population system at the beginning of the collaborative evolution process is stronger than that at the later stage, the resistance of the phytoplankton system is obviously reduced along with the time lapse, the resistance of the zooplankton system is continuously stronger, and the result of the dynamic simulation of the aquatic population system under the reservoir optimization scheduling strategy of the embodiment is reasonable by comparing measured values of various group biomass and related research results.
As can be seen from fig. 10, under the reservoir optimization scheduling strategy, the optimized reservoir discharge is more consistent with the trend and the size of the natural flow; according to practical experience, the optimized reservoir discharge in the spawning period (2-4 months) and the growing period (7-9 months) of the fishes can meet the requirements of the fishes on the flow rate; according to the embodiment, the adverse effect of reservoir discharge on a downstream aquatic community system is reduced through reservoir optimization scheduling decision, and a more beneficial reservoir scheduling operation scheme is provided.
The reservoir optimal scheduling decision method for the stability of the aquatic community system, provided by the embodiment of the invention, considers the interaction relation of multiple communities of the aquatic community system, and can effectively simulate the cooperative evolution process of the multiple community system when the multiple community system is disturbed by reservoir discharge; a 'flow-ecological' relationship is established, which is beneficial to identifying important environmental covariates, promotes the implementation of river ecosystem restoration, provides a way for quantifying the influence of reservoir discharge 1 on an aquatic community system, and makes up the defects of the prior art; in addition, the ecological indexes provided by the embodiment of the invention are suitable for various different reservoir dispatching decision scenes, and can scientifically and reasonably determine the reservoir optimal dispatching strategy beneficial to a river ecological system and determine the formulation of ecological flow, thereby providing technical support for the sustainable reservoir dispatching scientific decision of the ecological system.
Further, the embodiment also provides a reservoir optimization scheduling device capable of automatically implementing the method, and the device comprises a data acquisition part, a multi-population system simulation part, a calculation and analysis part, an identification part, a reservoir operation optimization part, a reservoir scheduling part, a dynamic simulation part, an input display part and a control part.
The data acquisition part can determine research situations, collect basic data of a water reservoir group system, historical scheduling data, hydrological data and biomass data of downstream stations of the reservoir, and determine influence factors and decision variables of reservoir scheduling behaviors.
The multi-population system simulation part can construct a random multi-population dynamic model by considering the interaction relationship among the multi-population based on the logistic single population growth model, including the relationship among competition, predation, predated and dependent species, and simulate the collaborative evolution process of the multi-population system.
The computational analysis part can calculate the hydrological variation degree, describes the disturbance of the hydrological variation on the aquatic community through the noise intensity in the random multi-community dynamic model, establishes a 'flow-ecology' relationship to determine the response of the multi-community system dynamics of the aquatic community on the reservoir outflow, and analyzes the stability of the aquatic community system under the influence of the hydrological variation.
The noise intensity is calculated using the following equation:
Figure BDA0003115194490000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003115194490000142
the maximum value of the noise intensity acceptable for the population N; daltered flowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dnatural flowThe comprehensive hydrological variation degree of the natural runoff is obtained;
the "flow-ecological" relationship is specifically: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1, the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the degree of hydrological variation is between 0 and 1, the noise intensity is determined in equal proportion.
The identification part can respectively identify the resilience and the resistance of the multi-population system by adopting two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment based on the stability analysis of the aquatic population system; ST is the recovery time from an unstable state to a stable state of a dynamic population system subjected to random continuous disturbance, ST can change along with the change of the noise intensity of the population system, and the smaller the ST value is, the quicker the response of the population system to external disturbance is; the CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and the smaller the CVST value is, the stronger the ability of the population system at the time of the steady state to resist external interference is;
the reservoir operation optimizing part comprehensively considers reservoir water supply and power generation targets and ST and CVST ecological targets, and optimizes a reservoir operation curve by adopting a multi-objective optimization algorithm to obtain a reservoir optimization scheduling decision reflecting the variable mapping relation of each parameter of reservoir operation. In this embodiment, the optimal scheduling decision of the reservoir is the optimal scheduling operation curve shown in fig. 8b, which can be used to determine the specific parameters related to reservoir scheduling.
The reservoir operation is controlled by the reservoir scheduling part according to the reservoir optimal scheduling decision determined by the reservoir operation optimizing part; and the reservoir dispatching part substitutes the reservoir measured data into a function which reflects variable mapping relations of each parameter of reservoir operation and is used for optimizing dispatching decision of the reservoir, and specific dispatching parameters are obtained through calculation so as to control the reservoir operation.
Taking the embodiment of the invention as an explanation object, according to the obtained optimized scheduling operation curve, when the reservoir water level is in the region 1, the water intake (water is taken from the reservoir to the outside) 520m in the flood season (21 days in 6 months to 30 days in 9 months)3Water diversion rate 420m in non-flood season3S; when the reservoir water level is in the area 2, the water diversion amount is 450m in the flood season3Water diversion quantity of 350m in non-flood season3(s) when reservoir water level is in region 2, the water diversion amount is 300m3S; when the reservoir water level is in the area 4, the water intake is 260m3S; when reservoir water level is in area 5, the water quantity is 135m3And s. The generated energy can be calculated according to the functions and task requirements of the reservoir. The reservoir operation is then controlled based on these data.
The dynamic simulation part calculates the hydrological variation degree according to the reservoir outflow calculated by the reservoir dispatching part, and then converts the hydrological variation degree into the noise intensity of aquatic communities through the stream-ecological relation, so as to carry out dynamic random simulation on the cooperative evolution process of the multi-population system.
The input display part is used for allowing a user to input an operation instruction and performing corresponding display. Specifically, in the present embodiment, the input display portion may display the following: the pareto solution in the figure 7 can be displayed in a drawing mode after the multi-objective optimization calculation is completed, and is used for observing the value taking condition of multiple objectives; secondly, the optimized reservoir operation curve can be output and displayed as a figure 8b and compared with the conventional reservoir operation curve (figure 8 a); thirdly, taking the optimized reservoir operation curve selected by the user as one of input conditions of reservoir scheduling (other input conditions of reservoir scheduling do not participate in optimization and are consistent with conventional scheduling values), performing reservoir scheduling, and outputting and displaying the optimized reservoir operation water level and the optimized delivery flow; and fourthly, further calculating the hydrological variation degree according to the optimized reservoir outflow, converting the hydrological variation degree into the noise intensity of the aquatic community through the flow-ecological relation, randomly simulating the cooperative evolution process of the multi-population system, and dynamically displaying the noise intensity in real time.
The control part is in communication connection with the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part, the reservoir operation optimization part, the reservoir scheduling part, the dynamic simulation part and the input display part to control the operation of the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part, the reservoir operation optimization part, the reservoir scheduling part, the dynamic simulation part and the input display part.
The above embodiments are merely illustrative of the technical solutions of the present invention. The method and device for optimizing and scheduling the reservoir for stability of the aquatic community system in accordance with the present invention are not limited to the contents described in the above embodiments, but are subject to the scope defined by the claims. Any modification or supplement or equivalent replacement made by a person skilled in the art on the basis of this embodiment is within the scope of the invention as claimed in the claims.

Claims (10)

1. The optimal reservoir scheduling decision method for the stability of the aquatic community system is characterized by comprising the following steps of:
step 1, determining a research situation, collecting basic data of a water reservoir group system, historical scheduling data, hydrological data and biomass data of a downstream station of a reservoir, and determining an influence factor and a decision variable of reservoir scheduling behavior;
step 2, on the basis of a logistic single population growth model, considering the interaction relation among multiple populations, including the relations among competition, predation, predated and dependent species, constructing a random multi-population dynamic model, and simulating the collaborative evolution process of a multi-population system;
step 3, calculating the hydrological variation degree, describing the disturbance of the hydrological variation on the aquatic community through the noise intensity in the random multi-community dynamic model, establishing a flow-ecological relation to determine the response of the multi-community system dynamics of the aquatic community on the reservoir outflow, and analyzing the stability of the aquatic community system under the influence of the hydrological variation;
the noise intensity is calculated using the following equation:
Figure FDA0003115194480000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003115194480000012
the maximum value of the noise intensity acceptable for the population N; daltered flowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dnatural flowThe comprehensive hydrological variation degree of the natural runoff is obtained;
the "flow-ecological" relationship is specifically: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1, the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the hydrological variation degree is between 0 and 1, the noise intensity is determined according to equal proportion;
step 4, based on the stability analysis of the aquatic community system, respectively identifying the resilience and the resistance of the multi-community system by adopting two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment; ST is the recovery time from an unstable state to a stable state of a dynamic population system subjected to random continuous disturbance, ST can change along with the change of the noise intensity of the population system, and the smaller the ST value is, the quicker the response of the population system to external disturbance is; the CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and the smaller the CVST value is, the stronger the ability of the population system at the time of the steady state to resist external interference is;
and 5, comprehensively considering reservoir water supply and power generation targets and ST and CVST ecological targets, and optimizing a reservoir operation curve by adopting a multi-objective optimization algorithm to obtain a reservoir optimal scheduling decision.
2. The optimal scheduling decision method for the reservoir for aquatic community system stability of claim 1, characterized in that:
wherein, in step 1, the influence factors of the reservoir dispatching behaviors comprise all or part of the following variables: time sequence T and warehousing flow Q of reservoir in current time periodin TUpstream water level Z of reservoir in previous periodu TThe downstream water level Z of the reservoir in the previous periodd TLast period of reservoir delivery flow Qout T-1Output N of reservoir in last periodout T-1Rainfall P of reservoir at current time intervalTAnd the evaporation capacity E of the reservoir at the current time intervalT(ii) a The decision variable of the reservoir scheduling behavior is the delivery flow Q of the reservoir in the current time periodout T。
3. The optimal scheduling decision method for the reservoir for aquatic community system stability of claim 1, characterized in that:
in step 2, the following random multi-population dynamic models are adopted to simulate the dynamic changes of the population biomass under the external random interference:
Figure FDA0003115194480000021
wherein N (t) is the population density of the population N at time t; r isNIs the intrinsic growth rate of the population N; kN、
Figure FDA0003115194480000022
Figure FDA0003115194480000023
And
Figure FDA0003115194480000024
is respectively a population N and a population Li(i ═ 1.... multidot.l), a population Mj(j ═ 1.. said., m), population Xp(p ═ 1.. multidot., x) and a population Yq(q 1.. y), where i, j, p, q are counting variables, and l, m, x, y are upper limits of the counting variables i, j, p, q, respectively;
Figure FDA0003115194480000025
and
Figure FDA0003115194480000026
are respectively a population N and a population LiGroups N and MjGroup N and XpAnd populations N and YqA competition coefficient therebetween;
Figure FDA0003115194480000027
is the noise intensity to the population N caused by the change of the hydrological situation; population LiHindering the growth of population N as they compete for limited resources; group XpThe increase of the population N is hindered, and the population N density is directly reduced because the population N is fed; population MjInterdependence with population N, population YqAre food for population N, they promote the growth of population N; w (t) is a random variable and satisfies the following conditions: (1) w (0) ═ 0; (2) w (T) -W (g) obeys normal distribution with the mean value of zero and the variance of T-g in the interval of g being more than or equal to 0 and less than or equal to T; (3) w (T) -W (g) and W (v) -W (u) are independent of each other in the interval 0. ltoreq. g < T < u < v. ltoreq.T.
4. The optimal scheduling decision method for the reservoir for aquatic community system stability of claim 1, characterized in that:
in step 4, when the population system reaches a steady state, the probability density function of the population density tends to be stable, and ST is calculated by the following formula:
ST＝t，when||PDFt transient-PDFstationary||2≈0，
in the formula, PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryA one-dimensional matrix of a steady-state probability density function of the random population system at the last moment of a simulation period;
CVST is calculated using the following formula:
Figure FDA0003115194480000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003115194480000032
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure FDA0003115194480000033
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
5. The optimal scheduling decision method for the reservoir for aquatic community system stability of claim 1, characterized in that:
in step 5, the multi-objective considerations are as follows:
Figure FDA0003115194480000034
Figure FDA0003115194480000035
min sT＝t，when||PDFt transient-PDFstationary||2≈0，
Figure FDA0003115194480000036
in the formula, S is the total number of reservoir dispatching time periods; wsThe diversion water amount in a scheduling time interval s; WS is the average diversion water volume in all scheduling periods; hsThe generated energy in the scheduling time interval s; HG is the average power generation in all scheduling periods; PDFt transientA one-dimensional matrix of a transition probability density function of the random population system at the time t; PDFstationaryA one-dimensional matrix of a steady-state probability density function of the random population system at the last moment of a simulation period;
Figure FDA0003115194480000041
the density of the population N at the steady state moment in the b-th random simulation is obtained;
Figure FDA0003115194480000042
the average density of the population N at the steady state moment is obtained; and B is the random simulation times.
6. The optimal scheduling decision method for the reservoir for aquatic community system stability of claim 1, characterized in that:
in the step 5, the reservoir operation curve is optimized to obtain a reservoir optimization scheduling graph meeting the ecological system sustainability and stability development, and the operation of the reservoir is controlled according to the reservoir optimization scheduling graph.
7. Reservoir optimization scheduling device towards aquatic community system stability, its characterized in that includes:
the system comprises a data acquisition part, a data analysis part and a data analysis part, wherein the data acquisition part is used for determining a research scene, collecting basic data of a water reservoir group system, historical scheduling data, hydrological data and biomass data of downstream stations of a reservoir and determining influence factors and decision variables of reservoir scheduling behaviors;
the multi-population system simulation part is used for constructing a random multi-population dynamic model and simulating a collaborative evolution process of the multi-population system on the basis of a logistic single population growth model, considering the interaction relationship among the multi-population, including the relationships among competition, predation, prey and dependent species;
the computational analysis part is used for calculating the hydrological variation degree, describing the disturbance of the hydrological variation on the aquatic community through the noise intensity in the random multi-community dynamic model, establishing a flow-ecological relation to determine the response of the multi-community system dynamics of the aquatic community on the reservoir outflow, and analyzing the stability of the aquatic community system under the influence of the hydrological variation;
the noise intensity is calculated using the following equation:
Figure FDA0003115194480000043
in the formula (I), the compound is shown in the specification,
Figure FDA0003115194480000044
the maximum value of the noise intensity acceptable for the population N; dalteredflowThe comprehensive hydrological variation degree of the drainage after being regulated and controlled by the reservoir; dalteredflowThe comprehensive hydrological variation degree of the natural runoff is obtained;
the "flow-ecological" relationship is specifically: (1) under the natural river flow state, the noise intensity borne by the population system is zero; (2) when the hydrologic variation degree is 1, the noise intensity borne by the population system is the maximum value of the noise intensity acceptable by the population system; (3) when the hydrological variation degree is between 0 and 1, the noise intensity is determined according to equal proportion;
the identification part is used for identifying resilience and resistance of the multi-population system by adopting two ecological indexes of a steady-state time ST and a variation coefficient CVST at a steady-state moment based on stability analysis of the aquatic population system; ST is the recovery time from an unstable state to a stable state of a dynamic population system subjected to random continuous disturbance, ST can change along with the change of the noise intensity of the population system, and the smaller the ST value is, the quicker the response of the population system to external disturbance is; the CVST is a variation coefficient of all random analog values of the population system at the time of recovering the steady state, and the smaller the CVST value is, the stronger the ability of the population system at the time of the steady state to resist external interference is;
the reservoir operation optimizing part comprehensively considers reservoir water supply and power generation targets and ST and CVST ecological targets, adopts a multi-objective optimization algorithm to optimize a reservoir operation curve, and obtains a reservoir optimization scheduling decision reflecting the mapping relation of each parameter variable of reservoir operation;
the reservoir operation optimizing part determines a reservoir operation optimizing decision according to the reservoir operation information; and
and the control part is in communication connection with the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part, the reservoir operation optimization part and the reservoir scheduling part and controls the operation of the data acquisition part, the multi-population system simulation part, the calculation analysis part, the identification part and the reservoir operation optimization part.
8. The optimal scheduling device of reservoirs for aquatic community system stability of claim 7, wherein:
the reservoir dispatching part substitutes the reservoir measured data into a function which reflects the variable mapping relation of each parameter of reservoir operation and is used for optimizing the dispatching decision of the reservoir, and specific dispatching parameters are obtained through calculation so as to control the reservoir operation.
9. The optimal scheduling apparatus for reservoirs for aquatic community system stability according to claim 8, further comprising:
and the dynamic simulation part is in communication connection with the control part, calculates the hydrological variation degree according to the reservoir outflow calculated by the reservoir scheduling part, converts the hydrological variation degree into the noise intensity of the aquatic community through the flow-ecological relation, and performs dynamic random simulation on the cooperative evolution process of the multi-population system.
10. The optimal scheduling apparatus for reservoirs for aquatic community system stability according to claim 9, further comprising:
the input display part is in communication connection with the control part and is used for allowing a user to input an operation instruction and performing corresponding display;
the input display part can display the reservoir operation curve obtained by optimizing the reservoir operation optimizing part according to an operation instruction, can display specific scheduling parameters obtained by calculating the reservoir scheduling part according to the operation instruction, and can also dynamically display the multi-population system collaborative evolution process simulated by the dynamic simulation part in real time.
CN202110660802.5A 2021-06-03 2021-06-15 Reservoir optimal scheduling decision method and device for stability of aquatic community system Active CN113505913B (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN2021106184579 2021-06-03
CN202110618457 2021-06-03

Publications (2)

Publication Number Publication Date
CN113505913A true CN113505913A (en) 2021-10-15
CN113505913B CN113505913B (en) 2022-07-05

Family

ID=78009768

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110660802.5A Active CN113505913B (en) 2021-06-03 2021-06-15 Reservoir optimal scheduling decision method and device for stability of aquatic community system

Country Status (1)

Country Link
CN (1) CN113505913B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115034425A (en) * 2022-03-08 2022-09-09 武汉大学 Dynamic simulation and scheduling method and device for coupling water quantity-water quality-aquatic organism integrated community

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107862421A (en) * 2017-12-27 2018-03-30 河海大学 A kind of river-like reservoir influence to the construction Pre-Evaluation method
CN109868784A (en) * 2019-01-28 2019-06-11 深圳市碧园环保技术有限公司 A kind of Ecosystem restoration system in moisturizing type river
CN110334876A (en) * 2019-07-10 2019-10-15 中国科学院地理科学与资源研究所 A kind of environment flow process adjustment method based on hydrological regimes, water quality and the more elements of biology
CN110443530A (en) * 2019-09-12 2019-11-12 北京师范大学 A kind of preferential recovery district of aquatic ecosystem and its preferentially restore factor determination method
US20200051183A1 (en) * 2018-06-24 2020-02-13 Cube Hydro Partners, LLC Power generation scheduling optimization
CN110851977A (en) * 2019-11-06 2020-02-28 武汉大学 Water supply-power generation-ecological multi-target scheduling graph optimization method based on ecological flow
US20200315144A1 (en) * 2017-06-05 2020-10-08 Yunnan University A reservoir tail reverse regulation method for native fish protection
CN111783305A (en) * 2020-07-06 2020-10-16 厦门大学 Water area ecosystem regulation and control method based on biological control

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200315144A1 (en) * 2017-06-05 2020-10-08 Yunnan University A reservoir tail reverse regulation method for native fish protection
CN107862421A (en) * 2017-12-27 2018-03-30 河海大学 A kind of river-like reservoir influence to the construction Pre-Evaluation method
US20200051183A1 (en) * 2018-06-24 2020-02-13 Cube Hydro Partners, LLC Power generation scheduling optimization
CN109868784A (en) * 2019-01-28 2019-06-11 深圳市碧园环保技术有限公司 A kind of Ecosystem restoration system in moisturizing type river
CN110334876A (en) * 2019-07-10 2019-10-15 中国科学院地理科学与资源研究所 A kind of environment flow process adjustment method based on hydrological regimes, water quality and the more elements of biology
CN110443530A (en) * 2019-09-12 2019-11-12 北京师范大学 A kind of preferential recovery district of aquatic ecosystem and its preferentially restore factor determination method
CN110851977A (en) * 2019-11-06 2020-02-28 武汉大学 Water supply-power generation-ecological multi-target scheduling graph optimization method based on ecological flow
CN111783305A (en) * 2020-07-06 2020-10-16 厦门大学 Water area ecosystem regulation and control method based on biological control

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ROHAN BENJANKAR: "Dam operations may improve aquatic habitat and offset negative effects of climate change", 《JOURNAL OF ENVIRONMENTAL MANAGEMENT》 *
刘攀: "基于HMA方法的宜昌站生态径流演变规律分析", 《中国农村水利水电》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115034425A (en) * 2022-03-08 2022-09-09 武汉大学 Dynamic simulation and scheduling method and device for coupling water quantity-water quality-aquatic organism integrated community

Also Published As

Publication number Publication date
CN113505913B (en) 2022-07-05

Similar Documents

Publication Publication Date Title
CN113554466B (en) Short-term electricity consumption prediction model construction method, prediction method and device
CN109002942A (en) A kind of short-term load forecasting method based on stochastic neural net
CN110472840B (en) Agricultural water conservancy scheduling method and system based on neural network technology
CN110299008B (en) Traffic flow multi-step prediction method based on reinforcement learning
CN110363349B (en) ASCS-based LSTM neural network hydrological prediction method and system
CN113033917B (en) Sewage treatment plant prediction planning operation management method based on peripheral data
CN109376950A (en) A kind of polynary Load Forecasting based on BP neural network
CN106228235B (en) A kind of land utilization space Optimal Configuration Method for taking pattern Yu PROCESS COUPLING effect into account
Zou et al. Quantitative assessment of adaptive measures on optimal water resources allocation by using reliability, resilience, vulnerability indicators
CN113505913B (en) Reservoir optimal scheduling decision method and device for stability of aquatic community system
Ai et al. Optimization of ecological reservoir operation rules for a northern river in China: Balancing ecological and socio-economic water use
CN116562583A (en) Multidimensional water resource supply and demand prediction method and system
CN117575289B (en) Reservoir optimal scheduling method with water quantity-water quality multi-objective cooperation
CN117744501B (en) Water network system regulation node optimal scheduling and decision-making method considering ecological flow
CN109408896B (en) Multi-element intelligent real-time monitoring method for anaerobic sewage treatment gas production
CN107909478A (en) FOF mutual fund portfolio system and methods based on social network clustering and information gain entropy index
CN117094119B (en) Reservoir dispatching method and computer taking power generation, ecological flow and surface water temperature into consideration
Li et al. Incorporating ecological adaptation in a multi-objective optimization for the Three Gorges Reservoir
CN112053256A (en) Water resource simulation method based on water source and water user double sequencing rule
Kong et al. Ecological multi-objective joint optimized scheduling of cascade hydropower plants based on improved marine predators algorithm
CN115619120A (en) Reservoir scheduling method for recovering natural hydrologic situation
CN114372645A (en) Energy supply system optimization method and system based on multi-agent reinforcement learning
Ban et al. Combined forecasting model of urban water consumption based on adaptive filtering and BP neural network
CN112966919A (en) Water utilization and pollution discharge conflict coordination method based on condition value risk
CN109190830B (en) Energy demand prediction method based on empirical decomposition and combined prediction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant