A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system

2020 ◽  
Vol 590 ◽  
pp. 125264
Author(s):  
Juan Chen ◽  
Ping-an Zhong ◽  
Weifeng Liu ◽  
Xin-Yu Wan ◽  
William W.-G. Yeh
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Flood Control ◽  
Optimal Operation ◽  
Management Model ◽  
Multi Objective ◽  
Reservoir System ◽  
Objective Risk

scholarly journals Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2542 ◽  
Author(s):  
Mufeng Chen ◽  
Zengchuan Dong ◽  
Wenhao Jia ◽  
Xiaokuan Ni ◽  
Hongyi Yao
Keyword(s):  
Power Generation ◽  
Water Supply ◽  
Yangtze River ◽  
Water Resource Management ◽  
Flood Control ◽  
Optimal Operation ◽  
Total Power ◽  
The Yangtze River ◽  
Multi Objective ◽  
Reservoir System

The multi-objective optimal operation and the joint scheduling of giant-scale reservoir systems are of great significance for water resource management; the interactions and mechanisms between the objectives are the key points. Taking the reservoir system composed of 30 reservoirs in the upper reaches of the Yangtze River as the research object, this paper constructs a multi-objective optimal operation model integrating four objectives of power generation, ecology, water supply, and shipping under the constraints of flood control to analyze the inside interaction mechanisms among the objectives. The results are as follows. (1) Compared with single power generation optimization, multi-objective optimization improves the benefits of the system. The total power generation is reduced by only 4.09% at most, but the water supply, ecology, and shipping targets are increased by 98.52%, 35.09%, and 100% at most under different inflow conditions, respectively. (2) The competition between power generation and the other targets is the most obvious; the relationship between water supply and ecology depends on the magnitude of flow required by the control section for both targets, and the restriction effect of the shipping target is limited. (3) Joint operation has greatly increased the overall benefits. Compared with the separate operation of each basin, the benefits of power generation, water supply, ecology, and shipping increased by 5.50%, 45.99%, 98.49%, and 100.00% respectively in the equilibrium scheme. This study provides a widely used method to analyze the multi-objective relationship mechanism, and can be used to guide the actual scheduling rules.

Decomposition–coordination model of reservoir group and flood storage basin for real-time flood control operation

2013 ◽  
Vol 46 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Benyou Jia ◽  
Ping'an Zhong ◽  
Xinyu Wan ◽  
Bin Xu ◽  
Juan Chen
Keyword(s):  
Real Time ◽  
Flood Control ◽  
Control Operation ◽  
Multi Objective ◽  
Coordination Model ◽  
Multi Objective Programming ◽  
Storage Basin ◽  
Flood Control Operation ◽  
Objective Programming ◽  
Flood Storage

The research of joint optimization operation of complex flood control systems is still in the process of development. This paper introduces a decomposition–coordination model for solving the multi-objective optimization problem for real-time flood control operation in reservoir group and flood storage basin. The multi-objective programming is established for maximum safety of the reservoir group and minimum losses of flood storage basin, according to the real-time flood control requirements. Then, a third-order hierarchical optimization decomposition–coordination model is proposed for solving the multi-objective programming problem, based on the decomposition–coordination principle of large scale system theory. It takes advantage of an objective coordination method and model coordination method to accomplish global optimization and combines progressive optimality algorithm to solve the subsystem local optimization. Finally, the model is applied for simulating the storm flood in July 2007 in the middle reaches of the Huaihe River Basin in China. Results show that the proposed decomposition–coordination model can efficiently calculate the reservoir group optima release strategy and flood storage basin diversion process, and meet the safety discharge at the downstream control section.

scholarly journals Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1898 ◽  
Author(s):  
Nay Myo Lin ◽  
Xin Tian ◽  
Martine Rutten ◽  
Edo Abraham ◽  
José M. Maestre ◽  
...  
Keyword(s):  
Decision Making ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reservoir System ◽  
Time Operation ◽  
Real Time Operation ◽  
Objective Model

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

scholarly journals Application of Marginal Rate of Transformation in Decision Making of Multi-Objective Reservoir Optimal Operation Scheme

2021 ◽  
Vol 13 (3) ◽  
pp. 1488
Author(s):  
Yueqiu Wu ◽  
Liping Wang ◽  
Yanke Zhang ◽  
Jiajie Wu ◽  
Qiumei Ma ◽  
...  
Keyword(s):  
Power Generation ◽  
Decision Making ◽  
Water Level ◽  
Level Scheme ◽  
Flood Control ◽  
Forecast Error ◽  
Optimal Operation ◽  
Total Power ◽  
Marginal Rate ◽  
Multi Objective

For reservoirs with combined storage capacity for flood control and beneficial purposes, there tends to be potential benefit loss when the flood control limited water level is used in medium and small floods. How to find the optimal water level scheme for profit-making and pursue the optimization of comprehensive benefits has always been a difficult problem in multi-objective reservoir optimal operation. Based on the principle of the maximum benefit obtained by the product conversion curve and the isorevenue line in microeconomics, taking flood control and power generation as two products of a reservoir, a multi-objective optimal operation scheme decision-making model is established to seek the highest water level scheme that can produce the maximum comprehensive benefits of flood control and power generation. A case study of the Three Gorges reservoir in the early flood season of a dry year shows that on the one hand, under the condition of deterministic inflow, the model can work out the optimal water level and the corresponding best equilibrium point for both flood control and power generation, and it can increase the total power output by 4.48% without reducing the flood control benefits; on the other hand, it can also obtain the dynamic control area of the maximum allowable water level for power generation considering inflow forecast error, which provides a theoretical reference for determining the starting water level in medium and small floods and utilizing flood resources.

A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System

2016 ◽  
Vol 30 (10) ◽  
pp. 3363-3387 ◽  
Author(s):  
Benyou Jia ◽  
Slobodan P. Simonovic ◽  
Pingan Zhong ◽  
Zhongbo Yu
Keyword(s):  
Real Time ◽  
Decision Model ◽  
Flood Mitigation ◽  
Multi Objective ◽  
Reservoir System

scholarly journals Risk Analysis for Reservoir Real-Time Optimal Operation Using the Scenario Tree-Based Stochastic Optimization Method

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 606 ◽  
Author(s):  
Yimeng Sun ◽  
Feilin Zhu ◽  
Juan Chen ◽  
Jinshu Li
Keyword(s):  
Risk Analysis ◽  
Stochastic Optimization ◽  
Real Time ◽  
Flood Control ◽  
Forecast Accuracy ◽  
Optimization Method ◽  
Optimal Operation ◽  
Scenario Tree ◽  
Analysis Model ◽  
Time Optimal

The inherent uncertainty of inflow forecasts hinders the reservoir real-time optimal operation. This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method. We quantify the probability distribution of inflow forecast uncertainty by developing the relationship between two forecast accuracy metrics and the standard deviation of relative forecast error. An inflow scenario tree is generated via Monte Carlo simulation to represent the uncertain inflow forecasts. We establish a scenario tree-based stochastic optimization model to explicitly incorporate inflow forecast uncertainty into the stochastic optimization process. We develop a risk analysis model based on the principle of maximum entropy (POME) to evaluate the uncertainty propagation process from flood forecasts to optimal operation. We apply the proposed methodology to a flood control system in the Daduhe River Basin, China. In addition, numerical experiments are carried out to investigate the effect of two different forecast accuracy metrics and different forecast accuracy levels on reservoir optimal flood control operation as well as risk analysis. The results indicate that the proposed methods can provide decision-makers with valuable risk information for guiding reservoir real-time optimal operation and enable risk-informed decisions to be made with higher reliabilities.

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