CN112966919A - Water utilization and pollution discharge conflict coordination method based on condition value risk - Google Patents

Water utilization and pollution discharge conflict coordination method based on condition value risk Download PDF

Info

Publication number
CN112966919A
CN112966919A CN202110225598.4A CN202110225598A CN112966919A CN 112966919 A CN112966919 A CN 112966919A CN 202110225598 A CN202110225598 A CN 202110225598A CN 112966919 A CN112966919 A CN 112966919A
Authority
CN
China
Prior art keywords
water
risk
value
formula
amount
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
CN202110225598.4A
Other languages
Chinese (zh)
Other versions
CN112966919B (en
Inventor
付湘
王发强
张翔
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
Priority to CN202110225598.4A priority Critical patent/CN112966919B/en
Publication of CN112966919A publication Critical patent/CN112966919A/en
Application granted granted Critical
Publication of CN112966919B publication Critical patent/CN112966919B/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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • 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
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/90—Financial instruments for climate change mitigation, e.g. environmental taxes, subsidies or financing

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • General Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a water consumption and pollution discharge conflict coordination method based on condition value risk, and relates to an interval two-stage coordination model based on condition value risk (CVaR). The method comprises the steps of firstly, collecting and counting historical data of water resources in a drainage basin to obtain probability distribution of available water quantity samples, and dividing the sample data into different scenes according to different probabilities by using a risk value method (VaR); then determining a risk coefficient alpha, solving the upper and lower bounds of the model, and performing a first-stage decision; and finally, determining the maximum allowable sewage discharge amount p, solving the upper and lower bounds of the model, and performing second-stage decision making. The invention combines conditional value risk (CVaR) and an interval two-stage random planning model (ITSP), solves the balance problem of economic benefit and related risk in water resource allocation and the coordination problem of economic benefit and ecological environment benefit. And scientific support is provided for conflict management of water resource utilization and efficient and continuous utilization of water resources.

Description

Water utilization and pollution discharge conflict coordination method based on condition value risk
Technical Field
The invention belongs to the technical field of hydraulic engineering, relates to a water consumption and pollution discharge conflict coordination method, and particularly relates to a water consumption benefit and pollution discharge interval two-stage coordination model based on condition value risk (CVaR).
Background
Water resources are an important resource foundation for economic sustainable development and an important input factor for economic growth. There have long been various uncertainties in water resource systems, including uncertainty in the amount of available water and various parameters, such that managers must balance economic objectives with associated risks. With the wide public concern about the concept of sustainable development, the ecological environment problem also becomes an important factor considered by managers. The data show that in the urbanization process of China, because of the shortage of urban sewage discharge and centralized treatment facilities, a large amount of domestic sewage directly enters a water body without being treated to cause environmental pollution, and in addition, industrial wastewater is discharged in an overproof industrial development manner, so that great adverse effects are generated on the ecological environment. By looking up the current relevant research situation at home and abroad, the research on the comprehensive utilization of the water resources in the drainage basin at present mostly focuses on the conflict game between the economic benefit and the pollution emission or the coordination balance between the economic benefit and the relevant risks, and how to comprehensively consider the two in a systematic way is becoming a research hotspot problem; in the research on water resource risk control, the assumed situation of a single area is mostly adopted, and the applicability of the model is not high. In recent years, the increase of water resource shortage risk and ecological environment demand aggravates competition and conflict of water resource allocation, so that on the premise of considering economic benefit and ecological benefit, various uncertainties of a water resource system are considered, more effective risk control is carried out, and the method has important significance for reasonably developing and utilizing water resources and sustainable development of social economy.
Disclosure of Invention
In water resource management, the manager responsible for supplying water to multiple users within an area is often faced with the following decision-making problems: users need to plan for production and life in the next year, so that they need to know the available water quantity in the next year, and a water resource manager needs to predict the comprehensive water demand and water supply capacity according to empirical data and provide an optimal first-stage solution. Because of the uncertainty of the water resource system, the decision in the first stage has a certain risk, and if the actual water supply amount does not reach the planned water supply target, the water supply department will have to temporarily increase the water regulation or reduce the production scale, which will result in a certain economic loss. When the available water amount is known, the manager makes a decision in the second stage and takes the water shortage amount as a decision variable to make up for the loss caused by unreasonable decision in the first stage. Meanwhile, the river basin manager needs to take into account the ecological environmental benefits of the river basin.
The invention aims to provide a water consumption and pollution discharge conflict coordination method based on condition value risk, and particularly relates to an interval two-stage coordination model based on condition value risk (CVaR). The model combines conditional value risk (CVaR) with an interval two-stage random planning model (ITSP), and solves the balance problem of economic benefit and related risk in water resource allocation and the coordination problem of economic benefit and ecological environment benefit. And scientific support is provided for conflict management of water resource utilization and efficient and continuous utilization of water resources.
In order to achieve the purpose, the invention constructs a CVaR-based two-stage coordination model of economic benefit and pollution emission interval according to the actual background of the two-stage problem by analyzing uncertain factors and the requirements of the ecological environment of a drainage basin in a water resource system, and adopts the technical scheme comprising the following steps:
step 1, collecting and counting historical data of water resources in a drainage basin to obtain probability distribution of available water quantity samples, and dividing the sample data into different situations according to different probabilities by using a risk value method (VaR).
And 2, determining a risk coefficient alpha, solving the upper and lower boundaries of the model, and performing a first-stage decision.
And 3, determining the maximum allowable sewage discharge amount p, solving the upper and lower boundaries of the model, and performing second-stage decision making.
Preferably, the step 1 comprises the following steps:
the risk value method (VaR) is a risk control method, generally defined as the maximum potential loss over a period of time due to the presence of random factors at a certain confidence level, and here represents the maximum amount of incoming water expected at a certain confidence level, and can be expressed by the formula:
p (q. ltoreq. VaR) ═ beta equation (1)
In the formula (1), the first and second groups,
p: the probability that the asset value loss is less than the upper limit of possible loss, here representing the cumulative probability of the incoming water probability density function;
q: value loss amount, here expressed as the amount of incoming water;
VaR: expected maximum amount of incoming water at a given confidence level β;
beta: a confidence level given for the risk value method (VaR).
And obtaining the probability distribution of the water inflow amount by a data statistical fitting method on the basis of the known basin water resource historical data. By using the VaR method, different confidence levels beta are respectively taken, the water amount sample is divided into different scenes corresponding to the determined probability, such as three low, medium and high water amount scenes with the probability of 0.2,0.6 and 0.2 respectively, and each scene corresponds to one water amount interval.
Preferably, the step 2 comprises the following steps:
CVaR, conditional value risk, is based on the concept of VaR to describe the risk of economic loss, representing an expected average loss greater than or equal to the value of VaR, and the risk factor α, i.e., the confidence level of CVaR. At confidence level (. alpha. epsilon. [0,1)), random variables
Figure BDA0002955803090000022
Can be represented as CVaR
Figure BDA0002955803090000021
In the above formula, E represents the mathematical expectation,
Figure BDA0002955803090000023
the random variable is represented.
Since the available water amount is a random variable and each data and parameter is not always a definite value, the water resource management problem can be described as an interval two-stage stochastic programming model. To further enhance risk management, the objective function introduces a CVaR-based two-stage stochastic programming of the water resource management interval, as shown in formula (3).
Figure BDA0002955803090000031
"+/-" means that the variable or parameter is a scalar in a certain interval, x±Indicates the water supply target (10)8m3)，c±Represents the economic loss of unit water shortage (yuan/m)3)，s(ξ)±Indicates the water shortage (10)8m3)，b±Represents the economic benefit (yuan/m) of unit water3)；
Figure BDA0002955803090000032
Expressing an objective function-economic benefit maximization; on the basis of formula (2) and formula (3), by introducing an auxiliary variable v1(xi) and v2(ξ), the upper and lower models are derived as follows:
Figure BDA0002955803090000033
Figure BDA0002955803090000034
Figure BDA0002955803090000035
Figure BDA0002955803090000036
Figure BDA0002955803090000037
Figure BDA0002955803090000038
Figure BDA0002955803090000039
Figure BDA00029558030900000310
Figure BDA00029558030900000311
Figure BDA00029558030900000312
Figure BDA00029558030900000313
the parameters in the above formula are explained as follows:
i is a corner mark and represents the serial number of a user unit; j is a corner mark and represents the sequence number of the sub-area; t is the number of subregions; m is the number of water use departments; n is the number of scenes;
Figure BDA0002955803090000041
for sub-zone j user i unit water use economic benefit (yuan/m)3)；
Figure BDA0002955803090000042
Is a sub-region jEconomic loss of water shortage in user i unit (yuan/m)3)；
Figure BDA0002955803090000043
Minimum water demand target (10) for sub-zone j user i8m3)；
Figure BDA0002955803090000044
For sub-zone j user i maximum water demand target (10)8m3) (ii) a Xi is a certain scene; p (xi) is the occurrence probability of scene xi; q. q.sj(ξ)±Under scene xi, the natural water amount of the sub-region j (10)8m3)；
Figure BDA0002955803090000045
Under xi, the total amount of water resources in the sub-zone j (10)8m3)；
Figure BDA0002955803090000046
In scene xi, the outflow of sub-zone j is (10)8m3)；
Figure BDA0002955803090000047
Water supply target (10) for sub-zone j user i8m3)；
Figure BDA0002955803090000048
Under scene xi, the water shortage of user i in sub-area j (10)8m3)；
Figure BDA0002955803090000049
Representing an upper model objective function-economic benefit maximization;
Figure BDA00029558030900000410
indicating the target water supply shortage, i.e. the actual water supply amount (10)8m3)，
Figure BDA00029558030900000411
Representing the underlying model objective function, b representing unitsWater economy efficiency (yuan/m)3)，xjiWater supply target (10) representing sub-zone j user i8m3)。
Equations (5) and (10) are available water constraints, i.e. in any case, the water intake of a certain partition must not exceed 40% of the total water resource of the region in the same year. According to the limit of international surface water resource development and utilization, referring to the flow percentage and the grade meeting the requirements of fishes and biological habitats in the Tennant method, when the grade is 'excellent', the proportion of the ecological water is 60-100%.
The formula (6) and the formula (11) are water demand constraints, that is, the minimum water demand of each user is met, and meanwhile, the water amount distributed to each department should not exceed the maximum water demand, so that waste is avoided, and the water utilization efficiency is improved.
Equations (7) and (12) are CVaR technical constraints, with the goal of making a trade-off between economic objectives and associated risks.
The formula (8) and the formula (13) are respectively a water shortage constraint and a non-negative constraint, namely that the water shortage of each department is less than the water amount which is promised in the first decision stage, and all variables are not negative.
Equation (14) is a constraint on the regional water relationship, i.e., a water balance constraint. The water balance constraints for different study cases were also different.
In the research, alpha represents a risk coefficient, and the increase of the alpha value represents that the risk to be considered is higher, namely that the water resource allocation scheme in the first stage tends to be more conservative.
Preferably, the step 3 comprises the following steps:
and when the available water quantity is known, the decision of the second stage is carried out, the water shortage quantity is taken as a decision variable to make up the loss caused by unreasonable decision of the first stage, and the ecological environmental benefit of the basin needs to be considered at the same time. Chemical Oxygen Demand (COD) is an important and relatively fast measurable parameter of organic contamination, often referred to as a contaminant representative for model calculations. The emissions constraints are shown in equation (15).
Figure BDA0002955803090000051
In the formula (15), the first and second groups,
CODmax: the maximum allowable discharge level of COD is selected by a manager according to the actual situation;
wji: sub-zone j actual water consumption by user i, wji＝xji-sji(ξ)；
Figure BDA0002955803090000052
User i wastewater discharge coefficient;
Di: user i pollutant emission concentration;
eta: the removal efficiency of the contaminants.
In the second stage, the amount of available water becomes known. After selecting the reasonable maximum allowable sewage discharge amount, the model is calculated by adopting the formulas (3) to (15), only the objective function is changed from the probability type to the determination type, and finally the optimized water resource allocation scheme is obtained.
The invention has the beneficial effects that:
the invention starts from the requirement of water resource allocation, and aims to improve the overall benefit of water for each department of the drainage basin on the one hand; on one hand, in order to deal with various uncertain factors in a water resource system, risks are avoided as much as possible; and on the other hand, the sewage discharge is controlled, the ecological flow demand is ensured, and the sustainable utilization of the basin water resource is maintained. Through statistical research on historical data of water resources in the drainage basin, available water volume intervals corresponding to different probabilities in the drainage basin are obtained through calculation by a risk value method (VaR), for example, the available water volume is divided into three scenes, namely low, medium and high, and the probabilities are 0.2,0.6 and 0.2 respectively; considering uncertainty and risk of a water resource system, combining conditional value risk (CVaR) and interval two-stage stochastic programming (ITSP), considering ecological environmental benefits, and establishing a CVaR-based economic and pollution discharge interval two-stage coordination model on the basis; the method is applied to the case of the Chinese river basin, the basin has nine water using departments in three sub-areas, the water using relation is more complex, the applicability of the model is improved, and a certain theoretical basis is provided for realizing the unified management and the unified protection of the basin water resources.
The method takes the water resource configuration of the Hanjiang river basin as an example, and researches the balance problem of the economic benefit and the related risk in the river basin and the coordination problem of the economic benefit and the ecological environment benefit. Through analyzing respective indexes such as water use benefit, water shortage loss, pollution discharge and the like of nine water use departments in three sub-areas, setting three situations of ecological flow restriction and available water quantity, namely low, medium and high, and combining a conditional value risk (CVaR) and an interval two-stage random planning model (ITSP), the water use and pollution discharge conflict coordination method based on the conditional value risk is provided.
Drawings
FIG. 1 is a schematic diagram of Hanjiang river basin and its partitions.
Fig. 2 is a probability distribution of incoming water volume sample data corresponding to partition 3 of table 1.
FIG. 3 is a graph comparing the effect of different α on the net benefit of the model.
FIG. 4 is a graph comparing net benefits of different alpha models without pollution discharge constraints.
Fig. 5 is a graph comparing different alpha sewage discharge amounts without sewage discharge restriction.
Fig. 6 is a comparison of net benefits of different alpha models after applying a blowdown restriction.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below with reference to the embodiments of the present invention.
The embodiment of the invention comprises the following steps:
step 1, collecting and counting historical data of water resources in a drainage basin to obtain probability distribution of available water quantity samples, and dividing the sample data into different situations according to different probabilities by using a risk value method (VaR).
The research area is a Hanjiang river basin, and is divided into three sub-areas according to natural conditions and river valley forms (as shown in figure 1): (1) and (3) partitioning 1: upstream of the Danjiang river mouth; (2) and 2, partitioning: tang Bai river basin; (3) and (3) partitioning: downstream of the Dangjiang estuary (without Tang Bai river basin). Water resource data information of 1956-2000 years of cities in China river basin is obtained from water resource bulletin, and the distribution rule is obtained through data analysis and data fitting (the probability distribution of water volume in a subarea 3 is shown in figure 2).
The risk value method (VaR) here represents the maximum amount of water expected at a certain confidence level, and can be expressed by the formula:
p (q. ltoreq. VaR) ═ beta equation (1)
In the formula (1), the first and second groups,
p: the probability that the asset value loss is less than the upper limit of possible loss, here representing the cumulative probability of the incoming water probability density function;
q: value loss amount, here expressed as the amount of incoming water;
VaR: expected maximum amount of incoming water at a given confidence level β;
beta: for a given confidence level in the risk value method (VaR).
And obtaining the probability distribution of the water inflow amount by a data statistical fitting method on the basis of the known basin water resource historical data. By using the VaR method, the confidence levels beta are respectively 0.2, 0.8 and 1, namely, the water quantity sample is divided into three scenes of low, medium and high, the probability is respectively 0.2,0.6 and 0.2, and the interval water quantity under each scene is shown in the table 1.
And 2, determining a risk coefficient alpha, solving the upper and lower boundaries of the model, and performing a first-stage decision.
Defining CVaR as conditional value risk is a concept based on risk value method for describing economic loss risk, representing expected average loss greater than or equal to VaR value, and the risk coefficient α is the confidence level of CVaR; at confidence level (. alpha. epsilon. [0,1)), random variables
Figure BDA00029558030900000714
CVaR of (a) may be expressed as:
Figure BDA0002955803090000071
the symbolic meanings of equation (2) and the following equations are shown in Table 2.
The target function introduces a CVaR-based two-stage stochastic programming of the water resource management interval, as shown in formula (3).
Figure BDA0002955803090000072
In the present study case, the risk coefficient α is 0.75, and then model calculation is performed, where the upper and lower models of the applied model are as follows:
Figure BDA0002955803090000073
Figure BDA0002955803090000074
Figure BDA0002955803090000075
Figure BDA0002955803090000076
Figure BDA0002955803090000077
Figure BDA0002955803090000078
Figure BDA0002955803090000079
Figure BDA00029558030900000710
Figure BDA00029558030900000711
Figure BDA00029558030900000712
Figure BDA00029558030900000713
the constraint meaning is the same as the summary of the invention.
The constraint condition (14) is a constraint of the water relation of the region, namely a water balance constraint. The water balance constraints for different study cases were also different. Taking hanjiang river basin as an example, the position relationship of the three partitions is shown in fig. 1. The water flow from zone 1 and zone 2 will merge into zone 3.
In the present study, α represents a risk coefficient, and an increase in α represents a higher risk to be considered, which means that the water resource allocation scheme in the first stage tends to be more conservative, and the variation of the total profit along with the risk coefficient α in the first stage is shown in fig. 3. In this case, we choose the risk factor α to be 0.75. Table 3 shows the optimal solution for the first stage of the model when the values of α are chosen to be 0 and 0.75, respectively, from which it can be seen that the latter promises less water to the water supply department in the first stage, which also reflects the more conservative character of the solution.
To further clarify the influence of the risk factor α and the pollution discharge constraint p on the final result of the model, the invention selects α as 0, and compares the net benefit and the pollution discharge of the model with α as 0.75 under the situations of low, medium and high available water quantities respectively. The results of the model are shown in Table 4, and are shown in FIG. 4 and FIG. 5.
In general, the risk of economic loss is higher in drought scenarios. It can be seen that when α is selected to be 0.75, both the upper bound (maximum economic benefit) and the lower bound (minimum economic benefit or maximum economic loss) of the model are higher than when α is selected to be 0, which indicates that the risk can be effectively avoided under the low available water volume condition by selecting a higher α value. Meanwhile, under the condition of abundant water resources, the pollutant discharge amount is often higher, and the pollutant discharge amount can be controlled to a certain extent by selecting a higher alpha value.
And 3, determining the maximum allowable sewage discharge amount p, solving the upper and lower boundaries of the model, and performing second-stage decision making.
And when the available water quantity is known, the decision of the second stage is carried out, the water shortage quantity is taken as a decision variable to make up the loss caused by unreasonable decision of the first stage, and the ecological environmental benefit of the basin needs to be considered at the same time. Chemical Oxygen Demand (COD) is an important and relatively fast measurable parameter of organic contamination, often referred to as a contaminant representative for model calculations. The emissions constraint is shown in equation (15), in this case we impose an emissions constraint p <60 (ten thousand tons) on the basis of fig. 5.
Figure BDA0002955803090000081
In the formula (15), the first and second groups,
CODmax: the maximum allowable discharge level of COD, in this case 60 million tons, is selected;
wji: sub-zone j actual water consumption by user i, wji＝xji-sji(ξ)；
Figure BDA0002955803090000082
User i wastewater discharge coefficient;
Di: user i pollutant emission concentration;
eta: the removal efficiency of the contaminants.
In order to illustrate the superiority of the present invention in ensuring economic efficiency and controlling pollution discharge, the present invention also selects alpha as 0, and compares the net benefit of the model with alpha as 0.75 under the situations of low, medium and high available water amount respectively. In the second stage, the amount of available water becomes known. After selecting the reasonable maximum allowable sewage discharge amount, the model is calculated by adopting the formulas (3) to (15), only the objective function is changed from the probability type to the determination type, and finally the optimized water resource allocation scheme is obtained. The results of the model are shown in FIG. 6.
It can be seen that after the pollution discharge restriction is applied, under the situations of low, medium and high available water amounts, the upper and lower boundaries of the model when alpha is selected to be 0.75 are all higher than the upper and lower boundaries of the model when alpha is selected to be 0, namely the former is better, and the invention proves that the invention can ensure economic benefit and avoid risks while controlling pollution discharge.
TABLE 1 incoming Water and its probability under different situations
Figure BDA0002955803090000091
TABLE 2 selection table of economic benefit coefficient and water-withdrawal coefficient for each water department in Hanjiang basin
Figure BDA0002955803090000092
TABLE 3 first-stage optimal solution for different alpha lower model
Figure BDA0002955803090000101
TABLE 4 comparison of economic benefit and discharge capacity of different alpha models without pollution discharge restriction
Figure BDA0002955803090000102
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but other embodiments derived from the technical solutions of the present invention by those skilled in the art are also within the scope of the present invention.

Claims (5)

1. A water utilization and pollution discharge conflict coordination method based on condition value risk is characterized by comprising the following steps:
step 1, collecting and counting historical data of water resources of a drainage basin to obtain probability distribution of available water quantity samples, and dividing the sample data into different scenes according to different probabilities by using a risk value method;
step 2, determining a risk coefficient alpha, solving the upper and lower bounds of the model, and performing a first-stage decision;
and 3, determining the maximum allowable sewage discharge amount p, solving the upper and lower boundaries of the model, and performing second-stage decision making.
2. The method for coordinating water use and pollution discharge conflicts based on conditional value risk as claimed in claim 1, wherein: the step 1 comprises the following specific steps:
the risk value method is a risk control method defined as the maximum potential loss over a period of time due to the presence of random factors at a certain confidence level, here expressed as the maximum amount of incoming water expected at a certain confidence level, and expressed by the formula:
p (q. ltoreq. VaR) ═ beta equation (1)
In the formula (1), the first and second groups,
p: the probability that the asset value loss is less than the upper limit of possible loss, here representing the cumulative probability of the incoming water probability density function;
q: value loss amount, here expressed as the amount of incoming water;
VaR: expected maximum amount of incoming water at a given confidence level β;
beta: a confidence level given in the risk value method;
on the basis of known basin water resource historical data, obtaining the probability distribution of the water inflow through a data statistical fitting method; and (3) respectively taking different confidence levels beta by using a risk value method, dividing the water quantity sample into different scenes corresponding to the determined probability, wherein each scene corresponds to an incoming water quantity interval.
3. The method for coordinating water use and pollution discharge conflicts based on conditional value risk as claimed in claim 2, wherein: the step 2 comprises the following specific steps:
defining CVaR as conditional value risk is a concept based on risk value method for describing economic loss risk, representing expected average loss greater than or equal to VaR value, and the risk coefficient α is the confidence level of CVaR; at confidence level (. alpha. epsilon. [0,1)), random variables
Figure FDA0002955803080000013
Is denoted as CVaR
Figure FDA0002955803080000011
In equation (2), E represents the mathematical expectation,
Figure FDA0002955803080000012
representing the random variable;
because the available water amount is a random variable and each data and parameter are not always a determined value, the water resource management problem can be described as an interval two-stage random planning model; to further enhance risk management, the objective function introduces a CVaR-based two-stage stochastic programming of the water resource management interval, as shown in formula (3).
Figure FDA0002955803080000021
In the formula, ± denotes that the variable or parameter is a scalar quantity within a certain interval, x±Indicates the water supply target, c±Indicating economic loss per unit water shortage, s (xi)±Indicating water shortage, b±Represents the economic benefit of unit water consumption;
Figure FDA0002955803080000022
expressing objective function-maximum economic benefitMelting; on the basis of formula (2) and formula (3), by introducing an auxiliary variable v1(xi) and v2(ξ), the constraints deriving the upper and lower models are as follows:
Figure FDA0002955803080000023
Figure FDA0002955803080000024
Figure FDA0002955803080000025
Figure FDA0002955803080000026
Figure FDA0002955803080000027
Figure FDA0002955803080000028
Figure FDA0002955803080000029
Figure FDA00029558030800000210
Figure FDA00029558030800000211
Figure FDA00029558030800000212
Figure FDA00029558030800000213
the parameters in the above formula are explained as follows:
i is a corner mark and represents the serial number of a user unit; j is a corner mark and represents the sequence number of the sub-area; t is the number of subregions; m is the number of water use departments; n is the number of scenes;
Figure FDA0002955803080000031
for sub-zone j user i unit water use economic benefit (yuan/m)3)；
Figure FDA0002955803080000032
Economic loss (yuan/m) for water shortage of user i unit of sub-area j3)；
Figure FDA0002955803080000033
Minimum water demand target (10) for sub-zone j user i8m3)；
Figure FDA0002955803080000034
For sub-zone j user i maximum water demand target (10)8m3) (ii) a Xi is a certain scene; p (xi) is the occurrence probability of scene xi; q. q.sj(ξ)±Under scene xi, the natural water amount of the sub-region j (10)8m3)；
Figure FDA0002955803080000035
Under xi, the total amount of water resources in the sub-zone j (10)8m3)；
Figure FDA0002955803080000036
For scene xi, the outflow of sub-field jAmount of water (10)8m3)；
Figure FDA0002955803080000037
Water supply target (10) for sub-zone j user i8m3)；
Figure FDA0002955803080000038
Under scene xi, the water shortage of user i in sub-area j (10)8m3)；
Figure FDA0002955803080000039
Representing an upper model objective function-economic benefit maximization;
Figure FDA00029558030800000310
the water supply target shortage amount, i.e. the actual water supply amount,
Figure FDA00029558030800000311
representing the objective function of the lower model, b representing the unit water economy, xjiRepresenting the water supply target of the sub-area j user i;
the formula (5) and the formula (10) are available water quantity constraints, namely, in any case, the water taking quantity of a certain partition is not more than 40% of the total water resource quantity of the region in the year;
the formula (6) and the formula (11) are water demand constraints, namely the minimum water demand of each user is met, and meanwhile, the water amount distributed to each department should not exceed the maximum water demand;
equations (7) and (12) are CVaR technical constraints, with the goal of making a trade-off between economic objectives and associated risks;
the formula (8) and the formula (13) are respectively a water shortage constraint and a non-negative constraint, namely the water shortage of each department is less than the water amount which is promised in the first decision stage, and all variables are not negative;
equation (14) is a constraint of the regional water relationship, namely a water balance constraint, and the water balance constraint is different for different research cases.
4. The method for coordinating water use and pollution discharge conflict based on conditional value risk according to any one of claims 1 to 3, wherein: the step 3 comprises the following specific steps:
when the available water quantity is known, a second stage decision is carried out, the water shortage quantity is used as a decision variable to make up for the loss caused by unreasonable decision of the first stage, and meanwhile, the ecological environmental benefit of the basin needs to be considered; taking Chemical Oxygen Demand (COD) as a pollutant representative to participate in model calculation; the blowdown constraint is shown in equation (15);
Figure FDA00029558030800000312
in the formula (15), the first and second groups,
CODmax: selecting the maximum allowable discharge level of Chemical Oxygen Demand (COD) according to actual conditions;
wji: sub-zone j actual water consumption by user i, wji＝xji-sji(ξ)；
Figure FDA0002955803080000041
User i wastewater discharge coefficient;
Di: user i pollutant emission concentration;
eta: the removal efficiency of the pollutants;
in the second stage, the amount of available water becomes known; after selecting the reasonable maximum allowable discharge capacity, the model is calculated by adopting the formulas (3) to (15), only the objective function is changed from the probability type to the determination type, and finally the optimized water resource allocation scheme is obtained.
5. The method for coordinating water use and pollution discharge conflicts based on conditional value risk as claimed in claim 4, wherein: the risk coefficient alpha is an empirical constant and is 0-1 according to a value range; the value range of the low risk alpha is 0-0.5; the value range of the intermediate risk alpha is 0.5-0.7; the value range of the high risk alpha is 0.7-1.
CN202110225598.4A 2021-03-01 2021-03-01 Condition value risk-based water and pollution discharge conflict coordination method Active CN112966919B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110225598.4A CN112966919B (en) 2021-03-01 2021-03-01 Condition value risk-based water and pollution discharge conflict coordination method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110225598.4A CN112966919B (en) 2021-03-01 2021-03-01 Condition value risk-based water and pollution discharge conflict coordination method

Publications (2)

Publication Number Publication Date
CN112966919A true CN112966919A (en) 2021-06-15
CN112966919B CN112966919B (en) 2023-11-14

Family

ID=76276025

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110225598.4A Active CN112966919B (en) 2021-03-01 2021-03-01 Condition value risk-based water and pollution discharge conflict coordination method

Country Status (1)

Country Link
CN (1) CN112966919B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113516305A (en) * 2021-06-29 2021-10-19 太湖流域管理局水利发展研究中心 Scene-target interaction water network regional water resource scheduling intelligent decision method and system

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050246102A1 (en) * 2004-04-30 2005-11-03 Ch2M Hill, Inc. Method and system for evaluating water usage
KR20100138488A (en) * 2009-06-25 2010-12-31 한화에스앤씨주식회사 System and method of decide making for hydrologic circle in a city
CN104598995A (en) * 2015-01-27 2015-05-06 四川大学 Regional water resource allocation bi-level decision-making optimization method based on water right
CN104899668A (en) * 2015-07-03 2015-09-09 四川大学 Drainage basin pollution load distribution bilevel decision optimization method based on pollution tax
CN109472505A (en) * 2018-11-19 2019-03-15 四川大学 Multiple target water resource equilibrium allocation method based on Conditional Lyapunov ExponentP constraint
CN109598408A (en) * 2018-10-29 2019-04-09 华中科技大学 A kind of year water regulation planning device for taking into account fair exploitation and importance
CN109658287A (en) * 2018-12-27 2019-04-19 中国水利水电科学研究院 A kind of basin water dispatching method evenly distributed based on water resource space-time
CN109784582A (en) * 2019-02-15 2019-05-21 黄河勘测规划设计研究院有限公司 A kind of regional economy department water distribution equalization methods and system
AU2020100269A4 (en) * 2020-02-25 2020-03-26 Bian, Yuan DR The SYSTEM AND PLATFORM OF WATER ECOLOGICAL COMPREHENSIVE COMPENSATION
CN111160430A (en) * 2019-12-19 2020-05-15 广东工业大学 Water resource optimization configuration method based on artificial intelligence algorithm
CN111667145A (en) * 2020-05-06 2020-09-15 武汉大学 Riverway internal and external water conflict negotiation method based on non-cooperative game
CN111709618A (en) * 2020-05-29 2020-09-25 中山大学 Binary water right accounting transaction method and system
CN111915065A (en) * 2020-07-15 2020-11-10 天津大学 River dry season multi-target dynamic water resource optimal configuration system and method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050246102A1 (en) * 2004-04-30 2005-11-03 Ch2M Hill, Inc. Method and system for evaluating water usage
KR20100138488A (en) * 2009-06-25 2010-12-31 한화에스앤씨주식회사 System and method of decide making for hydrologic circle in a city
CN104598995A (en) * 2015-01-27 2015-05-06 四川大学 Regional water resource allocation bi-level decision-making optimization method based on water right
CN104899668A (en) * 2015-07-03 2015-09-09 四川大学 Drainage basin pollution load distribution bilevel decision optimization method based on pollution tax
CN109598408A (en) * 2018-10-29 2019-04-09 华中科技大学 A kind of year water regulation planning device for taking into account fair exploitation and importance
CN109472505A (en) * 2018-11-19 2019-03-15 四川大学 Multiple target water resource equilibrium allocation method based on Conditional Lyapunov ExponentP constraint
CN109658287A (en) * 2018-12-27 2019-04-19 中国水利水电科学研究院 A kind of basin water dispatching method evenly distributed based on water resource space-time
CN109784582A (en) * 2019-02-15 2019-05-21 黄河勘测规划设计研究院有限公司 A kind of regional economy department water distribution equalization methods and system
CN111160430A (en) * 2019-12-19 2020-05-15 广东工业大学 Water resource optimization configuration method based on artificial intelligence algorithm
AU2020100269A4 (en) * 2020-02-25 2020-03-26 Bian, Yuan DR The SYSTEM AND PLATFORM OF WATER ECOLOGICAL COMPREHENSIVE COMPENSATION
CN111667145A (en) * 2020-05-06 2020-09-15 武汉大学 Riverway internal and external water conflict negotiation method based on non-cooperative game
CN111709618A (en) * 2020-05-29 2020-09-25 中山大学 Binary water right accounting transaction method and system
CN111915065A (en) * 2020-07-15 2020-11-10 天津大学 River dry season multi-target dynamic water resource optimal configuration system and method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
W. LI, B. WANG等: "An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty", ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, vol. 22 *
Y. L. XIE; G. H. HUANG: "An optimization model for water resources allocation risk analysis under uncertainty", HYDROINFORMATICS, vol. 16, no. 1 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113516305A (en) * 2021-06-29 2021-10-19 太湖流域管理局水利发展研究中心 Scene-target interaction water network regional water resource scheduling intelligent decision method and system
CN113516305B (en) * 2021-06-29 2022-05-03 太湖流域管理局水利发展研究中心 Scene-target interaction water network regional water resource scheduling intelligent decision method and system

Also Published As

Publication number Publication date
CN112966919B (en) 2023-11-14

Similar Documents

Publication Publication Date Title
CN113011662B (en) System and method for integrated joint optimization scheduling of network and river of regional enterprise plant
CN108764573B (en) Inland arid region-oriented multidimensional equilibrium configuration system for water resources
Simonovic et al. A new modeling approach for water resources policy analysis
Li et al. Interval-parameter two-stage stochastic nonlinear programming for water resources management under uncertainty
Leal Neto et al. A system dynamics model for the environmental management of the Sepetiba Bay watershed, Brazil
CN105297827A (en) Water resource allocation method taking multi-user water demand and multi-source water supply into consideration
Hong et al. A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption
Song et al. Dynamic modeling application for simulating optimal policies on water conservation in Zhangjiakou City, China
Huang et al. An optimization model for water resources allocation in Dongjiang River Basin of Guangdong-Hong Kong-Macao Greater Bay Area under multiple complexities
Liu et al. Two-stage multi-water sources allocation model in regional water resources management under uncertainty
CN116484647B (en) Distributed water resource allocation method and system for overall coordination
van Dijk Three ecological cities, examples of different approaches in Asia and Europe
Ma et al. Water-energy nexus under uncertainty: Development of a hierarchical decision-making model
He et al. Quartet trade-off for regional water resources allocation optimization with multiple water sources: A decentralized bi-level multi-objective model under hybrid uncertainty
CN112966919A (en) Water utilization and pollution discharge conflict coordination method based on condition value risk
CN118278687A (en) Urban water supply intelligent scheduling method and system based on mixed integer programming model
Heydari Kushalshah et al. Hybrid modelling for urban water supply system management based on a bi-objective mathematical model and system dynamics: A case study in Guilan province.
Sun et al. A framework for deriving dispatching rules of integrated urban drainage systems
CN112053256A (en) Water resource simulation method based on water source and water user double sequencing rule
CN113505913B (en) Reservoir optimal scheduling decision method and device for stability of aquatic community system
Chen et al. Regional-scale water-energy nexus management by a mixed Possibilistic-Flexible robust nonlinear programming model
Lei et al. The quantitative analysis of ecological compensation responsibility in watershed
Wang et al. A two-stage Stochastic Water resources Planning Approach with fuzzy boundary interval based on Risk Control and Balanced Development
Zhang et al. An inexact-stochastic dual water supply programming model
Wang et al. A modified CVaR‐based interval coordination model for economic benefit and pollutant discharge

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