FUZZY CLOUD-BASED RISK ASSESSMENT METHOD FOR RESERVOIR OPERATION SCHEMES

A fuzzy cloud-based risk assessment method for reservoir operation schemes, including: collecting basic data of target reservoirs; constructing optimal operation model of conventional reservoirs to obtain model calculation results; acquiring change process of operation characteristics during whole operating period according to model calculation results, and constructing multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to multi-level reservoir multi-dimensional risk assessment index system; obtaining large number of sample data xij of reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct risk assessment; calculating cloud digital features of different indexes by inverse cloud generator; and weighting indexes in index layer and criterion layer by Analytic Hierarchy Process to obtain multi-dimensional risk assessment results. In order to promote sustainable development and utilization of water resources and enhance level of reservoir risk management, scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of China application serial no. 202410105798.X, filed on Jan. 25, 2024. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.

TECHNICAL FIELD

The present invention relates to the field of water resources in the discipline of water conservancy engineering, and more particularly, to a fuzzy cloud-based risk assessment method for reservoir operation schemes.

BACKGROUND

As the main tool for the development and utilization of water resources, reservoirs play the comprehensive utilization benefits of water resources such as flood control, power generation, water supply and ecology by regulating the flow process of rivers. However, in the operation process, it is inevitable that the operation targets cannot reach the expected risk events, such as water level overrun, unstable power generation and insufficient water supply, due to natural conditions, engineering conditions and man-made scheduling operations, which will affect the sustainable development and utilization of water resources and even threaten the people's life and property safety in severe cases. Accurate assessment of reservoir risk under different operating conditions is helpful to realize scientific decision-making of reservoir operation schemes and guarantee the sustainable development and utilization of water resources in the watershed, which is of great significance to regional economic development.

The common framework of reservoir risk assessment is based on the simulation results of reservoir operation in a future period, constructing the corresponding index system to quantify the target risk, and then combining with the specific risk criteria for judgment, so as to obtain the reservoir target risk assessment results under specific working conditions. However, the existing reservoir risk assessment methods have the following deficiencies.

(1) The calculation of reservoir operation scheme is easily affected by the initial population distribution of different algorithms and the iterative randomness of parameters, causing uncertainty to the simulation results. In addition, when integrating multi-target risks, the comprehensive risk is calculated by the direct weighted summation method, and then the risk level is judge artificially by the corresponding standard. Both in the process of weighting and risk rating are influenced by human subjective factors, and there is a large fuzzy uncertainty. At present, the reservoir risk assessment lacks a scientific assessment method which can consider multiple uncertainties.

(2) When quantifying the risk characterization, the probability of risk event occurrence in the simulation results is taken as the risk characterization index, only the probability of risk occurrence is quantified, and the consequence of risk events and the restoration process are not quantified.

SUMMARY

In view of the above problems, the present invention has been developed to provide a fuzzy cloud-based risk assessment method for reservoir operation schemes which overcomes or at least partially solves the above problems.

According to an aspect of the present invention, a fuzzy cloud-based risk assessment method for reservoir operation schemes is provided, the risk assessment method including:

Optionally, the collecting the basic data of the target reservoirs specifically includes:

Optionally, the constructing the optimal operation model of conventional reservoirs to obtain the model calculation results specifically includes:

Optionally, the operation features during the whole operating period specifically include a reservoir discharged volume, a reservoir water level, an output and a water level variation.

Optionally, the constructing the multi-level reservoir multi-dimensional risk assessment index system specifically includes:

Optionally, the dividing different risk level intervals according to the multi-level reservoir multi-dimensional risk assessment index system specifically includes:

In the formula, Ex refers to the expected distribution of cloud droplets in a domain space, which is a most representative point of the qualitative concept; En refers to the dispersion degree of cloud droplets, which is the measurable granular representation of qualitative concept; generally, the larger En is, the more macroscopic the qualitative concept is, that is, the greater the fuzziness of qualitative concept is; He represents the uncertainty of entropy; the greater the He is, the greater the thickness of cloud droplets, and the greater the degree of dispersion is, which indicates that the simulation results are more random; Umin and Umax respectively represent the minimum value and the maximum value of the risk level threshold; and k represents an adjustment system for cloud droplet cohesion, and is usually taken as k=0.1 by default.

Optionally, the calculating cloud digital features of different indexes by the inverse cloud generator specifically includes:

represents a variance of the simulation sample; μPi(x) is a membership value of the index i; and vPi(x) is a non-membership value of the index i.

Optionally, the weighting the indexes in the index layer and the criterion layer by the Analytic Hierarchy Process (AHP), and respectively calculating the Pythagoras fuzzy cloud of each index in the criteria layer and the target layer to obtain the multi-dimensional risk assessment results of the target working condition, including:

The present invention provides a fuzzy cloud-based risk assessment method for reservoir operation schemes, the risk assessment method including: collecting basic data of target reservoirs; constructing an optimal operation model of conventional reservoirs to obtain model calculation results; acquiring the change process of the operation characteristics during the whole operating period according to the model calculation results, and constructing a multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to the multi-level reservoir multi-dimensional risk assessment index system; obtaining a large number of sample data xij of the reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct the risk assessment; calculating cloud digital features of different indexes by the inverse cloud generator; and weighting the indexes in the index layer and the criterion layer by the Analytic Hierarchy Process (AHP), and respectively calculating the Pythagoras fuzzy cloud of each index in the criteria layer and the target layer to obtain the multi-dimensional risk assessment results of the target working condition. In order to promote the sustainable development and utilization of water resources and enhance the level of reservoir risk management, the scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

The above description is merely an overview of the technical aspects of the disclosure, which can be carried out in accordance with the contents of the description in order to make the technical aspects of the disclosure more clearly understood. The detailed description of the disclosure will be described below to make the above and other objects, features and advantages of the disclosure more apparent.

DESCRIPTION OF THE EMBODIMENTS

Exemplary embodiments of the present invention are described in more detail below with reference to the accompanying drawings. While the drawings show exemplary embodiments of the present disclosure, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The terms “comprises” and “having”, and any variation thereof, in the embodiments of the description, the claims and the drawings of the invention are intended to cover a non-exclusive inclusion, such as a list of steps or elements.

As shown in FIG. 1, a fuzzy cloud-based risk assessment method for reservoir operation schemes specifically includes the steps below.

The basic data is collected, such as operation regulation, a relationship curve of water level and reservoir capacity, a relationship curve of leakage flow and tail water, and a discharge capacity curve of each discharge facility of the target reservoirs.

The optimal operation model of conventional reservoirs is constructed, and when considering inflow conditions and engineering conditions that need risk assessment, the same is transformed into constraint conditions or boundary input of the model to generate the optimal operation schemes of reservoirs under corresponding working conditions by solving.

The changing process of reservoir discharge volume, reservoir water level and output during the whole operating period is obtained from the calculation results of the model, and the multi-target performance of the reservoir is quantified. The multi-level reservoir multi-dimensional risk assessment index system is constructed by the risk characterization method of reliability (Formula 1), resilience (Formula 3) and vulnerability (Formula 4), as shown in FIG. 2.

Xt is defined as a performance state in some aspect of an assessment object at a moment t; when the system performance is in a normal state (NS), Xt is assigned to 1; and when the system performance is in an impaired state (FS), Xt is assigned to 0.

The reliability of the system in terms of performance in some aspect can be expressed as:

(1) The basic data is collected, such as operation regulation (including a water level, flow, an output and other requirements and operation principles in different operating periods in the year), a relationship curve of water level and reservoir capacity, a relationship curve of leakage flow and tail water, and a discharge capacity curve of each discharge facility of a reservoir.

(2) The optimal operation model of conventional reservoirs is constructed, and when considering inflow conditions and engineering conditions that need risk assessment, the same is transformed into constraint conditions or boundary input of the model. In this example, the maximum power generation in the operating period is taken as the objective function, the flood year runoff condition is taken as the model boundary input, and the engineering fault scenario is set as that the gate fault at the beginning of flood period leads to ⅓ reduction of discharge capacity. There is a maintenance period of three months. The model is built and described as follows:

V
    
     t
     +
     1
    
   
   -
   
    V
    t
   
  
  =
  
   
    
     (
     
      
       Q
       t
       in
      
      -
      
       Q
       t
       out
      
     
     )
    
    ·
    Δ
   
   ⁢
   t

When the multi-level reservoir multi-dimensional risk assessment index system is constructed, in this example, four targets of reservoir power generation, ecology, shipping and water storage are taken as the multi-target performance, forming a multi-dimensional risk assessment index system as shown in FIG. 3.

We determine different risk levels, assign values to the Pythagoras fuzzy numbers in different level intervals, and calculate the cloud digital features of different levels by Formula 5. In this example, the range [0, 100] is divided into five risk levels: low, low, medium, relatively high and high. The cloud digital features are calculated by the Pythagoras fuzzy numbers of different levels with reference to relevant literatures. The results are shown in Table 1.

Risk level of Pythagoras fuzzy cloud

Pythagoras fuzzy

Risk
Cloud digital feature
number

Risk level
interval
Ex
En
He
μ
ν

The parallel calculations for 10 times are performed on the constructed operation model, and the sample data of risk assessment is obtained from the simulation results.

Based on the risk assessment samples, the risk Pythagoras fuzzy cloud of each index in the index layer of the multi-dimensional risk assessment index system of the reservoir is calculated according to Formula 6. As shown in FIG. 4, the risk level of each index is reflected by the expected value in the cloud digital feature. In this example, the ecological reliability is at a low risk level. The generation reliability, generation vulnerability, ecological vulnerability and water storage reliability are all at a low risk level. The water storage vulnerability is at a medium risk level. The ecological resilience, shipping reliability and shipping vulnerability are at relatively high risk levels. The power generation resilience, the shipping resilience and the water storage resilience are at high risk levels. The randomness of the results is intuitively expressed by the dispersion degree of scatter points in the cloud chart. In the exemplary working condition, the simulation randomness of the shipping vulnerability is the highest, and the simulation randomness of the water storage resilience is the lowest. The fuzzy uncertainty in the results is quantified by the Pythagoras fuzzy number. The fuzziness is low if the membership degree is high. In the exemplary working condition, the indexes with the lowest fuzzy degree are shipping resilience and water storage resilience, and the membership degree is 0.9. The indexes with the highest fuzziness are ecological reliability, and the membership degree is only 0.1.

The indexes in the index layer and the criterion layer are weighted by the Analytic Hierarchy Process (AHP), and the calculation results of the weights are shown in Table 2. According to Formula 7, the Pythagoras fuzzy clouds of each index of the criteria layer and the target layer are respectively calculated, so as to obtain the multi-dimensional risk assessment results of the criteria layer and the target layer under the target working condition, as shown in FIGS. 5 and 6.

Weight values of multi-dimensional risk

assessment index system of reservoirs

Criteria layer
Index layer

Target layer
Index name
Weight
Index name
Weight

dimensional
risk B1

assessment

system
risk B2

of the

constructed

Water
0.356
Water storage
0.022

storage

Water storage
0.017

Water storage
0.961

Advantageous Effects: the risk can be evaluated from the perspective of the probability of risk occurrence, the consequences of risk events and the process of event resilience. The reservoir risk can be evaluated scientifically considering various uncertainties to help the sustainable development and utilization of water resources and further enhance the level of reservoir risk management.

The above detailed description further elaborates the purpose, technical solutions and beneficial effects of the invention. It should be understood that the above are only detailed description of the invention, and are not intended to limit the scope of protection of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the invention shall be included in the protection scope of the invention.