Reservoir Operation Optimization: A Nonstructural Solution for Control of Seepage from Lar Reservoir in Iran

Abstract

In this paper, the results of utilizing a deterministic dynamic programming model for operation of Lar Reservoir in Iran are discussed. This reservoir has experienced extensive seepage from the start of its operation. The optimization model consists of a three-step cycle, which began with the optimization of reservoir operation for a given set of streamflows. The optimal policies are then analyzed in a regression procedure to obtain a set of operating rules. After the first run, operating rules from the previous run were placed as a new constraint on the water releases with some pre-assigned tolerance and the cycle continues. The model also consisted of mathematical functions for modeling the seepage from Lar Reservoir as a function of storage head in the reservoir. The loss function in the model was also modified in order to incorporate parameters that reduce the seepage. Results of different scenarios showed the significant effect of optimal policies on reduction of seepage and increasing the reliability of water supply to Tehran Metropolitan Area. A pumping station was also proposed to utilize the inactive part of the reservoir, in access of over 100 MCM, in order to reduce the seepage. The effectiveness of different pumping capacities to reduce the seepage was also investigated.     

Spatial Scale Resolution of Prognostic Hydrological Models: Simulation Performance and Application in Climate Change Impact Assessment

Water Resources Management - In this paper, long-term hydrological response of a watershed to climate change was investigated taking into account the spatial scale effect on the performance of...     

PROMETHEE with Precedence Order in the Criteria (PPOC) as a New Group Decision Making Aid: An Application in Urban Water Supply Management

In this paper, a new group Multi-Criteria-Decision-Making (MCDM) method is introduced by combining two ??Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)?? and ??Multi-attribute decision making with dominance in the criteria?? methods. PROMETHEE family of outranking methods is among the recently developed MCDM methods which have received lots of attention in the recent years because of its capacity in ranking finite set of alternative actions based on conflicting criteria. The second method helps the decision makers to consider ambiguity and imprecision of relative importance of each objective (criterion) without allocating importance weights to them. The proposed method of PROMETHEE with Precedence Order in the Criteria (PPOC) not only can address capabilities of PROMETHEE method just with determination of precedence order of criteria, but also can make it possible to have a group decision making environment with conflicting objectives. Operational management of an urban water supply system is a good example of a set of decision making problems with several objectives and Decision Makers (DMs). In this paper, PPOC method has been applied to the case study of Melbourne water supply system, previously analyzed in the literature, to assess a number of operation rules with respect to eight criteria evaluated under single or group decision-making situations. The satisfaction degree of each DM and the overall group ranking results have also been provided in the paper. The proposed method is applicable for different decision making problems in urban water supply management.     

Monitoring and Evaluation Scheme using the Multiple-Criteria-Decision-Making Technique: Application to Irrigation Projects

The application of monitoring and evaluation (M&E) systems to assess agricultural projects has received some attention in recent years. These systems provide valuable information for managers and decision makers by analyzing the implementation process, the progress of trends, and the long-term and short-term effectiveness of these projects. An algorithm is developed to monitor and evaluate drip and pressure irrigation projects in Iran. Different indicators are identified and the framework of an integrated evaluation system is demonstrated using an analytical hierarchy process for multiple-criteria-decision making. There is much subjective information that is quantified and normalized in order to remove any bias in evaluators’ assessment of qualitative measures or sensitivity to linguistic expressions. The application of this system to rank projects in different regions (zones) in Iran is also presented in this paper. The results have shown the significant value of such systems in providing information and input for different decision-making levels.     

Decision Support System for Monthly Operation of Hydropower Reservoirs: A Case Study

This paper presents a decision support system for multipurpose reservoir operation. The mathematical models in the system are formulated for monthly operation of hydropower reservoirs. The key components of the system are four main modules: database management, inflow modeling and forecasting, operation management, and real-time operation. Flexibility is the key feature of the system, providing the users with different decision tools and different indices for measuring the performance of each tool. A cost function is developed based on the present value of the total capital cost and the cost of operation and maintenance of the system. This cost function, which is developed based on “reasonable” estimates of water and energy prices, is used to measure the performance of reservoir operation policies. A utility function based on multicriterion decision making (MCDM) that uses an analytical hierarchy process is also developed. The MCDM utility function enables decision makers to incorporate the priority of different objectives in developing optimal operating policies and can be effectively used when the priority of objectives is not clear and the decision-making process relies mainly on the decision maker’s preferences. Both economic and MCDM utility functions are implemented and coupled with deterministic and stochastic optimization models. The decision support system (DSS) is applied to the largest surface water resources system in Iran, namely, the Dez and Karoon river-reservoir system. The results of the case study have shown that the DSS has been able to significantly increase the long-term power generation of the system while satisfying water demands for different purposes.     

Stochastic Time-Cost-Resource Utilization Optimization Using Nondominated Sorting Genetic Algorithm and Discrete Fuzzy Sets

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers.     

Monthly Water Resources and Irrigation Planning: Case Study of Conjunctive Use of Surface and Groundwater Resources

The Tehran metropolitan area is one of the mega cities of the world and has an annual domestic water consumption close to one billion cubic meters. The sewer system mainly consists of traditional absorption wells. Therefore, the return flow from the domestic consumption has been one of the main sources of groundwater recharge. Some part of this sewage is drained into local rivers and drainage channels and partially contaminates the surface runoff and local flows. These polluted surface waters are used in conjunction with groundwater for irrigation purposes in the southern part of the Tehran. In this paper, a systematic approach to surface and groundwater resources modeling in the study area, with its complex system of water supply, groundwater recharge, and discharge, is discussed. A dynamic programming optimization model is developed for conjunctive use planning. The objective function of this model is developed to supply the agricultural water demands, to reduce pumping costs, and to control groundwater table fluctuations. To develop the response function of the aquifers located in the study area, a mathematical model for simulation of the Tehran aquifer water table fluctuations has been developed and calibrated with the available data. Different scenarios are defined to study the long-term impacts of the development projects on conjunctive use policies and water table fluctuations. Comparison of the results showed how significant is the effects of an integrated approach to the surface and groundwater resources allocation in Tehran metropolitan area. The proposed model is a useful tool for irrigation planning in this region.     

Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks

In this paper, a Bayesian Network (BN) is utilized for developing monthly operating rules for a cascade system of reservoirs which is mainly aimed to control floods and supply irrigation needs. BN is trained and verified using the results of a reservoir operation optimization model, which optimizes monthly releases from cascade reservoirs. The inputs of the BN are monthly inflows, reservoir storages at the beginning of the month, and downstream water demands. The trained BN provides the probability distribution functions of reservoirs' releases for each set of input data. The long-term optimization model in monthly scale is formulated to minimize the expected flood and agricultural water deficit damages. The optimization model is developed using an extended version of the Varying chromosome Length Genetic Algorithm (VLGA-II). To incorporate reservoir preparedness for controlling the probable floods in each month, damages associated with floods with different return periods have been considered in the optimization model. For this purpose, a short-term optimization model which provides the optimal hourly releases during floods is utilized and linked to a flood damage estimation model. Damages due to deficit in supplying agricultural water demands are also calculated based on the functions of crop yield responses to deficit irrigation. The developed models are applied to the cascade system of the Dez and Bakhtiari Reservoirs in Southwest of Iran. The result of the trained BN is compared with the rules developed using classical and fuzzy linear regressions and it is shown that the total damage obtained by the BN-based operating rules is about 60 percent less than the total damage obtained using the fuzzy and classical regression analyses. The average relative error in estimating optimal releases is also reduced about 30 percent by using the BN-based rules.     

Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study

Abstract

Excessive demand for water due to a growing population, agricultural, and industrial development, along with climate change and depletion of nonrenewable resources have intensified the need for integrated water resources management and water pollution control. This paper presents different aspects of a master plan for water pollution control and the results of a case study for developing a master plan for water resources pollution control in Isfahan Province in Iran. Different components of the water resources system and pollution sources in the study area were identified and the effects of each of the pollution sources on surface and groundwater resources contamination were investigated. Two Multiple Criterion Decision Making (MCDM) techniques, namely Simple Additive Weighting (SAW) method and Analytical Hierarchy Structure (AHP) were used in order to determine the share of agricultural, industrial, and domestic sectors in polluting the water resources. In the application of MCDM techniques, engineering judgments and the information gathered from brain storming sessions with engineering experts and the agencies' officials have also been incorporated in order to overcome the data deficiency in this region for this type of analysis. Based on this study, several specific major categories of water pollution reduction projects were defined and in each category, several projects were identified. The total cost of implementation of the projects was also estimated and the projects were prioritized based on their potential impact on water pollution control.