Optimal Size of Auxiliary Storage Reservoir for Rain Water Harvesting and Better Crop Planning in a Minor Irrigation Project

The scope and feasibility of auxiliary storage reservoir in the outlet command of a flow based minor irrigation project was studied to overcome the inadequate irrigation water availability during the dry season. A multi objective optimization model was developed to determine the optimal size of auxiliary storage reservoir and optimal cropping pattern. Assuming that about 50% main reservoir capacity water will be available for irrigating dry season crops and fixing the first priority level of the objective function as maximization of net seasonal benefit and maximization of cropped area, the optimal surface area for auxiliary storage reservoir as the percentage of the command area was obtained as 17.40% and 10.92%, respectively. The performance of the minor irrigation project significantly increased due to provision of auxiliary storage reservoir. The economic analysis also revealed that the intervention is economically viable.     

Modelling an overdeveloped irrigation system in south India

An optimization model for irrigation planning is developed based on the experience gained from an overdeveloped irrigation system in South India. This model helps the decision maker in choosing the appropriate policy decisions under conditions of shortage of the available water potential to meet the demand of already overgrown irrigation systems. The objective of the model is to maximize the net benefits from crops in the commands of the irrigation projects considered. The constraints of the model include total land limitations of each project, subregional land limitations; reservoir balance, storage capacity, beginning‐year storage constraints for each reservoir; range of possible downstream riparian release policies; sociological constraints regarding essential food crop policy and commercial crop limitations.     

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.     

Optimizing Reservoir Operation Policy Using Chance Constraint Nonlinear Programming for Koga Irrigation Dam,Ethiopia

One of typical problems in water resources system modeling is derivation of optimal operating policy for reservoir to ensure water is used more efficiently. This paper introduces optimization analysis to determine monthly reservoir operating policies for five scenarios of predetermined cropping patterns for Koga irrigation scheme, Ethiopia. The objective function of the model was set to minimize the sum of squared deviation (SSD) from the desired targeted supply. Reservoir operation under different water availability and thresholds of irrigation demands has been analyzed by running a chance constraint nonlinear programming model based on uncertain inflow data. The model was optimized using Microsoft Excel Solver. The lowest SSD and vulnerability, and the highest volumetric reliability were gained at irrigation deficit thresholds of 20 % under scenario I, 30 % under scenario II, III and V, and at 40 % under scenario IV when compensation release is permitted for downstream environment. These thresholds of deficits could be reduced by 10 % for all scenarios if compensation release is not permitted. In conclusion the reservoir water is not sufficient enough to meet 100 % irrigation demand for design command areas of 7,000 ha. The developed model could be used for real time reservoir operation decision making for similar reservoir irrigation systems. In this specific case study system, attempt should be made to evaluate the technical performance of the scheme and introduce a regulated deficit irrigation application.     

Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization.     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization

Multiple studies have developed management models to identify optimal operating policies for reservoirs in the last four decades. In an uncertain environment, in which climatic factors such as stream flow are stochastic, the economic returns from reservoir releases that are based on policy are uncertain. Furthermore, the consequences of reservoir release are not fully realized until it occurs. Rather than explicitly recognizing the full spectrum of consequences that are possible within an uncertain environment, the existing optimization models have focused on addressing these uncertainties by identifying the release policies that optimize the summative metric of the risks that are associated with release decisions. This technique has limitations for representing risks that are associated with release policy decisions. In fact, the approach of these techniques may conflict with the actual attitudes of the decision-makers regarding the risk aspects of release policies. The risk aspects of these decisions affect the design and operation of multi-purpose reservoirs. A method is needed to completely represent and evaluate potential consequences that are associated with release decisions. In this study, these techniques were reviewed from the stochastic model and risk analysis perspectives. Therefore, previously developed optimization models for operating dams and reservoirs were reviewed based on their advantages and disadvantages. Specifically, optimal release decisions that use the stochastic variable impacts and the levels of risk that are associated with decisions were evaluated regarding model performance. In addition, a new approach was introduced to develop an optimization model that is capable of replicating the manner in which reservoir release decision risks are perceived and interpreted. This model is based on the Neural Network (NN) theory and enables a more complete representation of the risk function that occurs from particular reservoir release decisions.     

Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm

A genetic algorithm (GA) and a backward moving stochastic dynamic programming (SDP) model has been developed for derivation of operational policies for a multi-reservoir system in Kodaiyar River Basin, Tamil Nadu, India. The model was developed with the objective of minimizing the annual sum of squared deviation of desired target releases. The total number of population, crossover probability and number of generations of the GA model was optimized using sensitivity analysis, and penalty function method was used to handle the constraints. The policies developed using the SDP model was evaluated using a simulation model with longer length of inflow data generated using monthly time stepped Thomas–Fiering model. The performance of the developed policies were evaluated using the performance criteria namely, the monthly frequency of irrigation deficit (MFID), Monthly average irrigation deficit (MAID), Percentage monthly irrigation deficit (PMID), Annual frequency of irrigation deficit (AFID), Annual average irrigation deficit (AAID), and Percentage annual irrigation deficit (PAID). Based on the performance, it was concluded that the robostic, probabilistic, random search GA resulted in better optimal operating policies for a multi-reservoir system than the SDP models.     

Ant Colony Optimization for Multi-Purpose Reservoir Operation

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation.     

Development of 3-D Optimal Surface for Operation Policies of a Multireservoir in Fuzzy Environment Using Genetic Algorithm for River Basin Development and Management

A Multi objective, Multireservoir operation model for maximization of irrigation releases and maximization of hydropower production is proposed using Genetic Algorithm. These objectives are fuzzified and are simultaneously maximized by defining and then maximizing level of satisfaction (λ). In the present study a multireservoir system in Godavari River sub basin in Maharashtra State, India is considered. Problem is formulated with four reservoirs and a barrage. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFUOPT) model for the present study is developed in ‘C’ Language. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. The entire range of optimal operation policies, for different levels of satisfaction i.e. λ (ranging from 0 to 1), are determined. From the relationships developed amongst irrigation releases, hydropower production and level of satisfaction, a three dimensional (3-D) surface covering the whole range of policies has been developed. This solution surface can be the basis for decision makers for implementing the policies. Considering the future requirements in the command area, both the irrigation and hydropower demands are increased by 10 and 20%. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is also presented for these cases. The 3-D solution surface is also developed in these cases.     

Operating Rules of an Irrigation Purposes Reservoir Using Multi-Objective Optimization

A multi-objective optimization technique for the operation of an irrigation reservoir is presented in this paper. The study deals with two different objective functions (OF): the minimization of reservoir release deficit from the irrigation demand (OF1) and the maximization of net benefit by the demand sector (OF2). In the first step, monthly optimization of each individual objective was performed with a deterministic non-linear programming (NLP) algorithm, that gave the lower and upper bounds for the multi-objective analysis. In the second step, multi-objective optimization was performed through the Constraint method that operates by optimising the objective function OF1, while the other (OF2) was constrained to satisfy release strategies generated by the optimization. Non-dominated set of release strategies is generated by parametrically varying the bounds of the constraints obtained from the individual optimal solutions. In the third step, the interactive analytical Step method was applied to find the best compromise solution, between the two OFs, by minimizing the distance of each non-dominated solution to an ideal solution that represents the utopian optimum for both OF1 and OF2. Furthermore, the interactive approach allows to improve the performance of the reservoir in terms of compromise irrigation releases, by changing the OF values until the satisfaction of predetermined criteria fixed by the planners and decision makers. The proposed water allocation model was applied to the Pozzillo reservoir operation, that supplies the Catania Plain irrigation area (Eastern Sicily).     

Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.     

Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.     

An alternative approach to the operation of multinational reservoir systems: Application to the Amistad &; Falcon system (Lower Rio Grande/Río Bravo)

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses Stochastic Dynamic Programming (SDP) algorithms – both steady-state and real-time – to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation's water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country's water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.     

Simulation-Optimization Modeling for Conjunctive Use Management under Hydrological Uncertainty

The canal water supply, which is the only source of irrigation, in the rice-dominated cropping system of the Hirakud canal command (eastern India) is able to meet only 54 % of the irrigation demand at 90 % probability of exceedance. Hence, considering groundwater as the supplemental source of irrigation, conjunctive use management study by combined simulation-optimization modelling was undertaken in order to predict the maximum permissible groundwater pumpage from the command area. Further, optimal land and water resources allocation model was developed to determine the optimal cropping pattern for maximizing net annual return. The modelling results suggested that 2.0 and 2.3 million m³ of groundwater can be pumped from the bottom aquifer during monsoon and non-monsoon seasons, respectively, at 90 % probability of exceedance of rainfall and canal water availability (PERC). Optimal cropping patterns and pumping strategies can lead to about 51.3–12.5 % increase in net annual return from the area at 10–90 % PERC. The sensitivity analysis of the model indicates that the variation in the market price of crops has very high influence on the optimal solution followed by the cost of cultivation and cultivable area. Finally, different future scenarios of land and water use were formulated for the command area. The adoption of optimal cropping patterns and optimal pumping strategies is strongly recommended for sustainable management of available land and water resources of the canal command under hydrological uncertainties.     

Integrated use of water resources in the Lower Bhavani Project in India

Current irrigation water releases from the Lower Bhavani Project are largely governed by the rainfall and inflow pattern rather than by the periodic water needs of the crops grown. The groundwater potential in the command area is also ignored when water release schedules are planned. In this paper a quantitative analysis is carried out to assess the impact of optimizing water resources use with and without supplementary groundwater use. The results validate the role of groundwater in improving the performance of the irrigation project.     

Conjunctive Water Use Planning in an Irrigation Command Area

In the present study, an integrated soil water balance algorithm was coupled to a non-linear optimization model in order to carry out water allocation planning in complex deficit agricultural water resources systems based on an economic efficiency criterion. The LINGO 10.0, optimization package has been used to evolve at optimal allocation plan of surface and ground water for irrigation of multiple crops. The proposed model was applied for Qazvin Irrigation Command Area, a semi-arid region in Iran. Various scenarios of conjunctive use of surface and ground water along-with current and proposed cropping pattern have been explored. Some deficit irrigation practices were also investigated. The results indicate that conjunctive use practices are feasible and can be easily implemented in the study area, which would enhance the overall benefits from cropping activities. The study provides various possible operational scenarios of the branch canals of the command area in the common and dry condition, which can help managers in decision making for the optimum allocation plans of water resources within the different irrigation districts. The findings demonstrate that for deficit irrigation options, the mining allowance of ground water value of the command area is greatly reduced and ground water withdrawal may be also restricted to the recharge to maintain the river–aquifer equilibrium.     

Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control

Water supply reservoir management is based on long-term management policies which depend on customer demands and seasonal hydrologic changes. However, increasing frequency and intensity of precipitation events is necessitating the short-term management of such reservoirs to reduce downstream flooding. Operational management of reservoirs at hourly/daily timescales is challenging due to the uncertainty associated with the inflow forecasts and the volumes in the reservoir. We present an ensemble-based streamflow prediction and optimization framework consisting of a regional scale hydrologic model forced with ensemble precipitation inputs to obtain probabilistic inflows to the reservoir. A multi-objective dynamic programming model was used to obtain optimized release strategies accounting for the inflow uncertainties. The proposed framework was evaluated at a water supply reservoir in the Hackensack River basin in New Jersey during Hurricanes Irene and Sandy. Hurricane Irene resulted in the overtopping of the dam despite releases made in anticipation of the event and resulted in severe downstream flooding. Hurricane Sandy was characterized by low rainfall, however, raised significant concerns of flooding given the nature of the event. The improvement in NSE for the Hurricane Irene inflows from 0.5 to 0.76 and reduction of the spread of PBIAS with decreasing lead times resulted in improvements in the forecast informed releases. This study provides perspectives on the benefits of the proposed forecasting and optimization framework in reducing the decision making burden on the operator by providing the uncertainties associated with the inflows, releases and the water levels in the reservoir.

     

Integrated irrigation development planning by multi‐objective optimization

A multi‐objective linear‐programming‐based planning model for irrigation development, incorporating the integrated use of surface and groundwater resources, is presented. Applicability of the model is illustrated by a case study of the Bagmati River Basin, Nepal. Alternative plans for irrigation development are identified by analysing trade‐offs between the specified objectives of maximizing total net economic benefits from agriculture (economic efficiency) and total irrigated cropped area (balanced regional development). Evaluation of the alternatives by compromise programming is carried out in order to indicate the optimal scale of development, cropping plans, system design capacities and water allocation policies.     

Mitigating Socio-Economic-Environmental Impacts During Drought Periods by Optimizing the Conjunctive Management of Water Resources

Multi-period optimization of conjunctive water management can utilize reservoirs and aquifer carry-over to alleviate drought impacts. Stakeholders’ socio-economic and environmental indices can be used to minimize the socio-economic and environmental costs associated with water shortages in drought periods. The knowledge gap here is the evaluation and inclusion of the socio-economic and environmental value of conjunctive water management in terms of its drought mitigation capability. In this paper, an integrated water quantity-quality optimization model that considers socio-economic and environmental indices is developed. The model considers and integrates reservoir and aquifer carry-over, river-aquifer interaction and water quality with stakeholders’ socio-economic indices of production, net income and labor force employment to evaluate the socio-economic and environmental value of conjunctive water management. Total dissolved solid (TDS) is used as the water quality index for environmental assessments. The model is formulated as a multi-period nonlinear optimization model, with analysis determining the optimal decisions for reservoir release and withdrawal from the river and aquifer in different months to maximize the socio-economic indices of stakeholders within the environmental constraints. The proposed model is used in Zayandehrood water resource system in Iran, which suffers from water supply and pollution problems. Model analysis results show that conjunctive water use in the Zayandehrood water basin reduces salinity by 50 % in the wetland and keeps water supply reduction during a drought under 10 % of irrigation demand.