Deepti Rani

Bio: Deepti Rani is an academic researcher from University of Évora. The author has contributed to research in topics: Dynamic programming & Metaheuristic. The author has an hindex of 5, co-authored 11 publications receiving 457 citations. Previous affiliations of Deepti Rani include Indian Institute of Technology Roorkee.

Simulation-optimization modeling: a survey and potential application in reservoir systems operation.

Abstract: This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.

Genetic Algorithms and Their Applications to Water Resources Systems

Abstract: Real-life water resource problems such as optimal allocation of scare water, watershed management, conjunctive use of surface and groundwater, reservoir operation, water distribution systems, and management of water quality have many complex features. The variables involved may be discrete, discontinuous, or both; frequently the variables are stochastic and highly nonlinear. These typical problems limit the use of traditional nonlinear algorithms, such as the gradient-based algorithms, since the solution may converge to local optima and the computational requirements may be very high. Evolutionary algorithms, such as genetic algorithms (GAs), do not rely on any mathematical properties of the functions employed in the model. This feature makes them robust and more generally applicable than the other directed search methods. GAs are a particular class of evolutionary algorithm based on mechanics of natural selection and natural genetics, and they use techniques inspired by evolutionary biology such as inheritance, mutation, selection, crossover, and survival of the fittest. Since GAs are heuristic search techniques, the global optimum solution is not guaranteed. Nevertheless, GAs give alternative solutions that are close to the optimum after a reasonable number of evolutions, which is acceptable for most real-life problems. This chapter introduces the art and applications of GAs to water resource optimization problems. An illustrative example of reservoir operation optimization has been included to clarify the concepts.

Dynamic programming integrated particle swarm optimization algorithm for reservoir operation

Abstract: The present study suggests an integrated approach for determining the optimal release policy for the Mula reservoir situated at the Godavari basin, India. The proposed integrated algorithm named DP-PSO is a hybridization of Dynamic Programming (DP) and Particle Swarm Optimization (PSO). The reservoir operation problem is demonstrated in the form of a nonlinear optimization model subject to various constraints. Two case studies are considered. In the first case the efficiency of the proposed algorithm is tested on a small data set of 1 year and in the second case, data set taken is for 10 years. The results obtained are compared in terms of objective function value as well CPU time for performance evaluation of the integrated methods.

Estimation of Optimal Crop Plan Using Nature Inspired Metaheuristics

Abstract: Irrigation management has gained significance due to growing social needs and increasing command for food grains while the available resources have remained limited and scarce. Irrigation management includes optimal allocation of water for irrigation purposes, optimal cropping pattern for a given land area and water availabilities with an objective to maximize economic returns. In the present study we consider an optimization model based on linear programming for determining optimal crop plan for command area of Pamba-Achankovil-Vaippar (PAV) link project, Kerala, India. The crop planning model considers various resource constraints (land area, seeds, manure, fertilizers etc.) availability etc. adaptive to national conditions, with the objective to maximize net irrigation benefits. For crop planning, the extent of quantity available for fertilizers, manure and seeds as inputs were unknown. Estimates for the extent of unknown minimum quantities of these resource inputs available are obtained with the help of crop planning model itself. For optimal releases made from reservoir using a multi-reservoir operation model, optimal crop plans are developed under adequate, normal and limited irrigation water defined by 50 percent, 75 percent and 90 percent water year dependable flows, respectively. The optimization model is solved using four popular Evolutionary Algorithms (EA) viz. Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Evolutionary Programming (EP). EA are compared with each other in terms of average CPU time, average number of generations, standard deviation etc. the algorithms are also compared with LINGO, a popular software used for solving LPP models.

Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm

Abstract: Genetic algorithm (GA) has been used repeatedly in reservoir operation studies during last two decades. GAs require trying different alternatives, different GA parameter values, and select those which perform best for a particular application. Besides this, there are chances of getting trapped into local optima, since GA starts with randomly generated initial population within the entire search space. Therefore, GA’s search process is slow and time-consuming. GA’s process may be speeded up if initial population is generated in a narrowed search space. This process may save time spent on sensitivity analysis of parameters. Discrete dynamic programming (DP) provides global optimal solutions, but discreteness and dimensionality are the main disadvantages in reservoir operation applications. To overcome these deficiencies a hybrid approach combining DP and GA (DP–GA) is proposed to study a single reservoir operation problem. Where DP provides the narrowed search space within which the optimal solution for the problem is expected. GA then search to achieve the possible optimal solution within this space avoiding any discreteness of variables. Proposed DP–GA approach was found to outperform both GA and DP in terms of less computational requirement and quality of the solution, respectively.

Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge

Abstract: . The transboundary Mekong River is facing two ongoing changes that are expected to significantly impact its hydrology and the characteristics of its exceptional flood pulse. The rapid economic development of the riparian countries has led to massive plans for hydropower construction, and projected climate change is expected to alter the monsoon patterns and increase temperature in the basin. The aim of this study is to assess the cumulative impact of these factors on the hydrology of the Mekong within next 20–30 yr. We downscaled the output of five general circulation models (GCMs) that were found to perform well in the Mekong region. For the simulation of reservoir operation, we used an optimisation approach to estimate the operation of multiple reservoirs, including both existing and planned hydropower reservoirs. For the hydrological assessment, we used a distributed hydrological model, VMod, with a grid resolution of 5 km × 5 km. In terms of climate change's impact on hydrology, we found a high variation in the discharge results depending on which of the GCMs is used as input. The simulated change in discharge at Kratie (Cambodia) between the baseline (1982–1992) and projected time period (2032–2042) ranges from −11% to p15% for the wet season and −10% to p13% for the dry season. Our analysis also shows that the changes in discharge due to planned reservoir operations are clearly larger than those simulated due to climate change: 25–160% higher dry season flows and 5–24% lower flood peaks in Kratie. The projected cumulative impacts follow rather closely the reservoir operation impacts, with an envelope around them induced by the different GCMs. Our results thus indicate that within the coming 20–30 yr, the operation of planned hydropower reservoirs is likely to have a larger impact on the Mekong hydrograph than the impacts of climate change, particularly during the dry season. On the other hand, climate change will increase the uncertainty of the estimated reservoir operation impacts: our results indicate that even the direction of the flow-related changes induced by climate change is partly unclear. Consequently, both dam planners and dam operators should pay closer attention to the cumulative impacts of climate change and reservoir operation on aquatic ecosystems, including the multibillion-dollar Mekong fisheries.

Reservoir Optimization in Water Resources: a Review

Abstract: This paper reviews current optimization technique developed to solve reservoir operation problems in water resources. The application of conventional, especially evolutionary computation, combination of simulation-optimization and multi objectives optimization in reservoir operation will be discussed and investigated. Furthermore, new optimization algorithm from other applications will be presented by focusing on Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA) as alternative methods that can be explored by researchers in water resources field. Finally this paper looks into the challenges and issues of climate change in reservoir optimization.

Economics and the modeling of water resources and policies

Abstract: Over the last 25 years, economic water policy models have evolved in concept, theoretical and technical methods, scope and application to address a host of water demand, supply, and management policy questions. There have been a number of theoretical and empirical advances over this period, particularly related to estimation of nonmarket, public good water-related values involving different methods of valuation. We discuss modeling advances including modeling multiple, competing demands, types of incentives and technologies and behavioral responses, incorporation of groundwater and other supply alternatives, integration of institutional factors, and increasing attention to system-wide impacts. The largest changes in hydroeconomic policy models have been the integration of the individual demand and supply components, inclusion of environmental values, incorporation of governance and institutional conditions (laws, regulations and policies), and expansion to river basin and even interposing scales of analysis. Important areas of future hydroeconomic model advances will be the use of genetic and neurological based algorithms for solving dynamic, stochastic problems, reconstruction of hydrological and economic relationships for remotely sensed data, and the expansion of models to understand and address transboundary water resource economic, hydrologic, environmental and institutional policy, and interdependencies.

Downstream Hydrological Impacts of Hydropower Development in the Upper Mekong Basin

Abstract: The Mekong River Basin in Southeast Asia is experiencing extensive hydropower development. Concerns have been raised about the consequences of the development for the ecosystems, livelihoods and food security in the region. The largest planned hydropower dam cascade in the basin, the Lancang-Jiang cascade, is currently under construction and already partly built into the Upper Mekong Basin, China. In this paper we assess the impact of the Lancang-Jiang cascade on downstream hydrology by using a combination of a hydrological model and a reservoir cascade optimization model. The hydrological changes were quantified in detail at the Chiang Saen gauging station in Thailand, the first gauge station downstream from the cascade, and in lesser detail at four other downstream locations in the Mekong mainstream. We found that on average the Lancang-Jiang cascade increased the December–May discharge by 34–155 % and decreased the July–September discharge by 29–36 % at Chiang Saen. Furthermore, the Lancang-Jiang cascade reduced (increased) the range of hydrological variability during the wet season (dry season) months. The dry season hydrological changes were significant also in all downstream gauging stations, even as far as Kratie in Cambodia. Thus the Mekong’s hydrological regime has been significantly altered by the Lancang-Jiang cascade, but what the consequences are for ecosystems and livelihoods, needs further study.