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.

Reservoir Optimization in Water Resources: a Review

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Water Resources Systems Planning And Management

Evaluation of stochastic reservoir operation optimization models

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa ...

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Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

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.

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Economics and the modeling of water resources and policies

This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management.

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

This technical note is divided into two parts. The first one investigates the necessity to explicitly incorporate spills into reservoir optimization models based on linear or nonlinear programming and presents a scheme to accomplish this task. The second part shows how reservoir optimization models may apply hedging strategies during droughts even for systems located in semiarid conditions where high evaporation rates could threat the retention of water in storage.

The Role of Spill and Evaporation in Reservoir Optimization Models

Real-time reservoir operation models carried out under short operating horizons have no information about the seasonal changes in streamflow The overall operation may improve provided that long-term information can be incorporated into the short-term process This paper proposes a procedure that attempts to do such incorporation by means of stochastic and deterministic optimization Long-term (monthly) and short-term (daily) reservoir inflow forecasts are assumed to be uncertain and reliable, respectively The idea is to first solve a monthly operation model by an explicit stochastic programming approach and then use its information to guide the daily operation, which is solved by deterministic optimization The monthly information will decide whether hedging should start being implemented during the daily operation in order to mitigate a potential future shortage This stochastic-deterministic procedure is applied to a case study in Japan and the results show that it provides operating rules relatively 

Integrating Long- and Short-Term Reservoir Operation Models via Stochastic and Deterministic Optimization: Case Study in Japan

This technical note introduces a reservoir operation model based on implicit stochastic optimization (ISO) in which the release policy is guided by the forecast of the mean inflow for a given future horizon rather than by the prediction of the current-month inflow, such as in typical ISO models. The model also does not require the forecast of all inflows for the future horizon and shows to be more efficient in finding less vulnerable release policies when compared to several other explicit and implicit stochastic procedures.

Alcigeimes B. Celeste

Papers

Evaluation of stochastic reservoir operation optimization models

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

The Role of Spill and Evaporation in Reservoir Optimization Models

Integrating Long- and Short-Term Reservoir Operation Models via Stochastic and Deterministic Optimization: Case Study in Japan

Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast