Optimization Of Water Resources Systems Research Articles

Joint optimization of water allocation and water quality based on multi-objective control in Nanning, China

Abstract Studying the change mechanism of water quantity and quality is the basis for joint optimization of water resources systems, which is a significant means for modern regional water resources management. A water quantity and quality joint optimization model is built based on multiple control objectives, which include water demand, observed flow rate, and observed pollutant concentration. A coupled water quantity and water quality model was developed for Nanning, China. The natural water cycle and social water cycle in Nanning City and the associated pollutant transport transformation process are simulated. The results indicate that simulation error of water resources allocation is below 5%, the Nash–Sutcliffe efficiency coefficients of the three hydrological stations are 0.85, 0.88, and 0.85 respectively, and the relative errors of the simulated results of three water quality monitoring stations are all within 1.83%, all of which indicates that the model performs well and the simulation results can reproduce the water use process and pollutant transport transformation process of Nanning in time and space. This study can provide effective support for water resources management in Nanning City.

Market-based stochastic optimization of water resources systems for improving drought resilience and economic efficiency in arid regions

Efficient reallocation and conjunctive operation of existing water supplies have been receiving increasing attention as the competition among water users are intensified in worldwide. In this paper, a market-based stochastic optimization model is developed to address inherent uncertainties and complexities in water resources planning in arid regions. This model can be used not only to explore the water conservation potential via three strategies as cropping pattern optimization, irrigation infrastructure improvement and water trading, but also to help generate related water policy that promote the local welfare and facilitate local cleaner production.A real case study of the pumping irrigation system located in Yellow River basin is conducted to demonstrate applicability of the proposed model. Our findings indicate that the adjusted cropping pattern significantly increases the irrigator's income by growing economic crops such as herb and wolfberry; meanwhile, it does not sacrifice the food production. With the efficient infrastructural development, substantial water resources are saved and traded to other industries, thereby stimulating the local economy. Optimum subsidization policy is identified to increase the local welfare by allowing seasonal water trading while lessening the financial burden of the local government.

Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

Abstract Optimal operation of reservoir systems is the most important issue in water resources management. It presents a large variety of multi-objective problems that require powerful optimization tools in order to fully characterize the existing trade-offs. Many optimization methods have been applied based on mathematical programming and evolutionary computation (especially heuristic methods) with various degrees of success more recently. This paper presents an implementation and comparison of multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II) for the optimal operation of two reservoirs constructed on Ozan River catchment in order to maximize income from power generation and flood control capacity using MATLAB software. The alternative solutions were based on Pareto dominance. The results demonstrated superior capacity of the NSGA-II to optimize the operation of the reservoir system, and it provides better coverage of the true Pareto front than MOPSO.

Participatory Framework for Assessment and Improvement of Tools (ParFAIT): Increasing the impact and relevance of water management decision support research

This paper proposes the Participatory Framework for Assessment and Improvement of Tools (ParFAIT) as a way to address low uptake of Water Resources Systems Optimization (WRSO) tools. ParFAIT is a transdisciplinary process conducted in five stages, two of which are participatory modeling (PM) exercises. Herein we describe the framework, introduce our candidate tool- Multiobjective Evolutionary Algorithm (MOEA)-assisted optimization, and present the results of our first PM workshop. MOEA-assisted optimization has been put forth as a planning and decision making aid for utilities facing a large number of decisions and highly uncertain futures. The PM workshop, designed to solicit input on a tool testbed, was held in February 2015 with representatives from six Front Range, Colorado, water utilities. Our results include an expanded characterization of the decision making landscape, feedback on water utility decisions and performance goals commonly employed in WRSO studies, and new questions that warrant future investigation by researchers.

CVaR-based factorial stochastic optimization of water resources systems with correlated uncertainties

In this paper, a conditional value-at-risk based factorial stochastic programming approach is proposed to address random uncertainties and their interactions in a systematic manner. Random variables can be addressed through a risk-averse method within the two-stage stochastic programming framework. Interactions between random variables are examined through conducting a multi-level factorial analysis. The proposed approach is applied to a case study of water resources management to demonstrate its validity and applicability. A number of decision alternatives are obtained under different risk coefficients, which are useful for decision-makers to make sound water management plan and to perform an in-depth analysis of trade-offs between economic objectives and associated risks. Results obtained from the factorial experiment uncover the multi-level interactions between uncertain parameters and their contributions to the variability of net benefits. The performance of the proposed approach is compared with a factorial two-stage stochastic programming method.

A fractional-factorial probabilistic-possibilistic optimization framework for planning water resources management systems with multi-level parametric interactions

In this study, a multi-level factorial-vertex fuzzy-stochastic programming (MFFP) approach is developed for optimization of water resources systems under probabilistic and possibilistic uncertainties. MFFP is capable of tackling fuzzy parameters at various combinations of α-cut levels, reflecting distinct attitudes of decision makers towards fuzzy parameters in the fuzzy discretization process based on the α-cut concept. The potential interactions among fuzzy parameters can be explored through a multi-level factorial analysis. A water resources management problem with fuzzy and random features is used to demonstrate the applicability of the proposed methodology. The results indicate that useful solutions can be obtained for the optimal allocation of water resources under fuzziness and randomness. They can help decision makers to identify desired water allocation schemes with maximized total net benefits. A variety of decision alternatives can also be generated under different scenarios of water management policies. The findings from the factorial experiment reveal the interactions among design factors (fuzzy parameters) and their curvature effects on the total net benefit, which are helpful in uncovering the valuable information hidden beneath the parameter interactions affecting system performance. A comparison between MFFP and the vertex method is also conducted to demonstrate the merits of the proposed methodology.

Water resources management under uncertainty: factorial multi-stage stochastic program with chance constraints

Due to rapid growth of population and development of economy, water resources allocation problems have aroused wide concern. Therefore, optimization of water resources systems is complex and uncertain, which is a severe challenge faced by water managers. In this paper, a factorial multi-stage stochastic programming with chance constraints approach is developed to deal with the issues of water-resources allocation under uncertainty and risk as well as their interactions. It can deal with uncertainties described as both interval numbers and probability distributions, and can also support the risk assessment within a multistage context. The solutions associated with different risk levels of constraint violation can be obtained, which can help characterize the relationship between the economic objective and the system risk. The inherent interactions between factors at different levels and their effects on total net benefits can be revealed through the analysis of multi-parameter interactions.

Multiperiod Hypergraph Models for Water Systems Optimization

Mathematical models describing water resources systems optimization are characterized by a large number of variables and constraints, which are needed to describe the physical components of the system, its functional ties, and its operating modalities. Some special algebraic structures allow its representation as a pure hypergraph flow problem. The hypergraph structure is derived from a multiperiod network describing the spatial and temporal structure of the physical system. As a crucial feature in modeling functional relations and in describing scenario changes in the system, with the hypergraph approach it is possible to reach a close correspondence between the components of the physical problem and those of the mathematical model. In this paper a water resources management model including a reservoir design problem is studied, and the methodology is tested by the hypergraph simplex algorithm on a real water resources system on the island of Sardinia (Italy) in a extended time horizon. The results obtained show that this technique is very competitive in this kind of problem.

Multiobjective Differential Evolution with Application to Reservoir System Optimization

Many water resources systems are characterized by multiple objectives. For multiobjective optimization, typically there can be no single optimal solution which can simultaneously satisfy all the goals, but rather a set of technologically efficient noninferior or Pareto optimal solutions exists. Generating those Pareto optimal solutions is a challenging task and often difficulties arise in using the conventional methods. In the optimization of reservoir systems, most of the times there is interdependence among one or more decision variables. Recently, it is emphasized that the evolutionary operators used in differential evolution algorithms are very much suitable for problems having interdependence among the decision variables. This paper utilizes this aspect and presents an efficient and effective approach for multiobjective optimization, namely multiobjective differential evolution (MODE) algorithm with an application to a case study in reservoir system optimization. The developed MODE algorithm is first tested on a few benchmark test problems and validated with standard performance measures by comparing them with the nondominated sorting genetic algorithm-II. On achieving satisfactory performance for test problems, it is applied to generate Pareto optimal solutions to a multiobjective reservoir operation problem. It is found that MODE provides many alternative Pareto optimal solutions with uniform coverage and convergence to true Pareto optimal fronts. The results obtained show that the proposed MODE can be a viable alternative for generating optimal trade-offs in multiobjective optimization of water resources systems.

Stochastic modeling and optimization of water resources systems—II: The Poteau River basin

The modeling and optimization of a complex river basin is studied based on the Markov process and nonlinear programming by the use of actual data. The monthly flow data are first correlated into probabilistic transition matrices. These matrices together with actual costs and benefits data are then used to obtain the optimal initial capacity and optimal operating conditions. The results show that the proposed techniques are fairly effective approaches and only approx. 15 iterations are needed to obtain a reasonably good accuracy.

Stochastic modelling and optimization of water resources systems

This work involves the simultaneous optimization of the initial design and operating policy over the life of multipurpose multireservoir water resources systems receiving stochastic inflows. The approach is based on the division of the reservoir into two imaginary water storage pools, namely, the conservation and flood pools. Based on this treatment, the optimization problem is stated using the concepts of Lagrange multipliers and parameter optimization. Two nonlinear programming techniques, namely, the generalized reduced gradient technique and the gradient projection technique, combined independently with Markovian decision are proposed to solve such a problem. To illustrate the use of the proposed techniques, the Walnut River Basin in southeastern Kansas, is employed in this work.

Multilevel incremental dynamic programing

An approach to the solution of multidimensional dynamic programs is presented which differs from most decomposition methods currently used in the optimization of water resources systems. The basic concept is directly dependent on certain characteristics of incremental dynamic programing (IDP). The method, referred to as multilevel incremental dynamic programing, results in a marked reduction in computing time and thus increases the power of IDP as a tool in the optimization of multidimensional deterministic systems.

Discrete Differential Dynamic Programing Approach to Water Resources Systems Optimization

The optimization of operating policies of multiple unit and multiple purpose water resources systems by traditional dynamic programing with the use of high speed digital computers encounters two major difficulties: memory requirements and computer time requirements. This paper presents an iterative method that can ease the above difficulties considerably. The method starts with a trial trajectory satisfying a specific set of initial and final conditions and applies Bellman's recursive equation in the neighborhood of this trajectory. At the end of each iteration step a locally improved trajectory is obtained and used as the trial trajectory in the next step. The method has proved particularly effective in the case of so‐called ‘invertible’ systems. The merits of the proposed approach are demonstrated through its application to a four‐unit, two‐purpose water resources system. To save computer time the example is restricted to deterministic inflows.