Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies

Momtahen, Sh.; Dariane, Alireza B.

doi:10.1061/(asce)0733-9496(2007)133:3(202)

Cited by 90 publications

(34 citation statements)

References 19 publications

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“…The problem can be solved with one of the many algorithms for non-linear optimization (see, for instance [31,47,48]). In the following case study, we selected a genetic approach, since genetic algorithms (GA):…”

Section: Deterministic Open-loop Problemmentioning

confidence: 99%

NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management

Sangiorgio

Guariso

2018

Water

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Multi-reservoir systems management is complex because of the uncertainty on future events and the variety of purposes, usually conflicting, of the involved actors. An efficient management of these systems can help improving resource allocation, preventing political crisis and reducing the conflicts between the stakeholders. Bellman stochastic dynamic programming (SDP) is the most famous among the many proposed approaches to solve this optimal control problem. Unfortunately, SDP is affected by the curse of dimensionality: computational effort increases exponentially with the complexity of the considered system (i.e., number of reservoirs), and the problem rapidly becomes intractable. This paper proposes an implicit stochastic optimization approach for the solution of the reservoir management problem. The core idea is using extremely flexible functions, such as artificial neural networks (NN), for designing release rules which approximate the optimal policies obtained by an open-loop approach. These trained NNs can then be used to take decisions in real time. The approach thus requires a sufficiently long series of historical or synthetic inflows, and the definition of a compromise solution to be approximated. This work analyzes with particular emphasis the importance of the information which represents the input of the control laws, investigating the effects of different degrees of completeness. The methodology is applied to the Nile River basin considering the main management objectives (minimization of the irrigation water deficit and maximization of the hydropower production), but can be easily adopted also in other cases.

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Section: Deterministic Open-loop Problemmentioning

confidence: 99%

NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management

Sangiorgio

Guariso

2018

Water

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“…In this way, most stochastic aspects of the problem, including spatial and temporal correlations of unregulated inflows, are implicitly included, and reservoir rule curves could be derived directly with genetic algorithms and other direct search methods (Koutsoyiannis and Economou, 2003;Labadie, 2004). Because PSO reduces the curse of dimensionality problem in ISO and ESO, it is widely used in reservoir operation optimization (Chen, 2003;Chang et al, 2005;Momtahen and Dariane, 2007). In this study, the PSO-based approach is used to solve the ROS problem.…”

Section: J Chu Et Al: Improving Multi-objective Reservoir Operationmentioning

confidence: 99%

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Chu

Zhang

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivityinformed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

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“…For step 3, ISO models may consider various predefined forms of policies, such as linear and nonlinear polynomials, artificial neural networks, fuzzy rules, etc. (Momtahen & Dariane, 2007). The present study applies multiple nonlinear regression together with a strategy based on two-dimensional interpolation, which are described in sections 2.5 and 2.6, respectively.…”

Section: Implicit Stochastic Optimizationmentioning

confidence: 99%

Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil

Celeste

Curi

2009

Pesqui. Oper.

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This paper deals with the application of Implicit Stochastic Optimization (ISO) to determine monthly operating rules for a reservoir system located in the semiarid Northeast of Brazil. ISO employs a deterministic optimization model to find optimal reservoir allocations under several possible inflow scenarios and later constructs the rules by analyzing the ensemble of these optimal releases. The operating policies provide the monthly reservoir release conditioned on the storage at the beginning of the month and the inflow predicted for the month. In addition to the classical regression analysis, this study establishes the rules by a two-dimensional interpolation strategy. After the rules are identified, they are applied to operate the system under new inflow realizations and show ability to produce policies similar to those obtained by deterministic optimization taking the same inflows as perfect forecasts.Keywords: reservoir operation; implicit stochastic optimization; semiarid regions. ResumoEste artigo investiga a aplicação de Otimização Estocástica Implícita (OEI) para determinar regras de operações mensais em um sistema de reservatórios localizado no nordeste semi-árido brasileiro. OEI emprega um modelo de otimização determinística para encontrar alocações ótimas do reservatório sob vários cenários possíveis de afluências e posteriormente constrói as regras a partir da análise deste conjunto de liberações ótimas. As políticas operacionais fornecem a alocação mensal do reservatório condicionada ao armazenamento no início do mês e a afluência prevista para o mês. Além da clássica análise de regressão, este estudo estabelece as regras por meio de uma estratégia de interpolação bidimensional. Após a sua identificação, as regras são aplicadas para operar o sistema sob novas realizações de afluências e mostram habilidade para produzir políticas semelhantes às obtidas a partir de otimização determinística tendo estas mesmas afluências como previsão perfeita.Palavras-chave: operação de reservatórios; otimização estocástica implícita; semi-árido.Celeste, Curi & Curi -Implicit stochastic optimization for deriving reservoir operating rules in semiarid Brazil

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Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies

Cited by 90 publications

References 19 publications

NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management

NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil

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