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Outline

Title
Abstract
FAQs
Figures
Objectives
Multi-Objective Analysis
Performance Criteria and Objective Function
Final Objective Function
Application and Results
Fuzzy Objective Functions
Stochastic Analysis (1) Markov Chain
Conclusion
References
All Topics
Engineering
Petroleum Engineering

Multi-objective storage reservoir operation under uncertainty

Profile image of Paulo Sergio Dias ChavesPaulo Sergio Dias Chaves

2003, 京都大学防災研究所年報. B= Disaster Prevention …

Abstract

Water quantity and quality are considered to be the main driving forces the reservoir operation. Barra Bonita reservoir, located in the southeast region of Brazil, is chosen as the case study for the application of the proposed methodology. Herein, optimization and artificial intelligence (AI) techniques are applied in the simulation and operation of the reservoir. A fuzzy stochastic dynamic programming model (FSDP) is developed for calculating the optimal operation procedures. Optimization is applied to achieve multiple fuzzy objectives. Markov chain technique is applied to handle the stochastic characteristics of river flow. Water quality analysis is carried out using an artificial neural network model. Organic matter and nutrient loads are modeled as a function of river discharge through the application of a fuzzy regression model based on fuzzy performance functions. The obtained results show that the proposed methodology provides an effective and useful tool for reservoir operation.

FAQs

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AI

What novel methods are used for reservoir operation under uncertainty?add

The study employs fuzzy set theory and dynamic programming to optimize multi-objective reservoir operations, improving management under uncertainty. Traditional techniques like goal programming are compared to fuzzy methods for greater flexibility in handling imprecise objectives.

How does water quality impact reservoir management decisions?add

The paper highlights that water quality variables significantly affect decisions on water allocation for multiple uses, such as irrigation and recreation. Poor water quality can lead to eutrophication, posing further challenges in resource optimization.

What challenges are associated with water quality data in reservoir studies?add

Limited and infrequent observational data restrict the ability to model complex water quality phenomena effectively, resulting in potential inaccuracies. For example, only 24 observations collected over several years were available for analyzing the Barra Bonita reservoir.

How does the application of artificial neural networks enhance water quality modeling?add

The ANN model, preferred over physical models, simplifies the prediction of water quality by reducing computational demands associated with complex parameter estimation. It utilizes genetic algorithms for training, allowing for a more robust simulation despite limited data availability.

What are the implications of multi-objective optimization in reservoir operations?add

Integrating multiple objectives, such as environmental quality and economic efficiency, complicates reservoir management but leads to more sustainable outcomes. The proposed fuzzy multi-objective fuzzy regression approach provides a structured means to evaluate conflicting objectives in real-time decision-making.

Figures (31)
Fig. 1.1 Storage and water quality single outlet, and quality within the reservoir.
Fig. 1.1 Storage and water quality single outlet, and quality within the reservoir.
research are given in the following sections. Fig. 2.1 Proposed methodology for the water quantity
research are given in the following sections. Fig. 2.1 Proposed methodology for the water quantity
Fig. 1.2 Basic components of the Barra Bonita inflow and release act as key factors on the system behavior.
Fig. 1.2 Basic components of the Barra Bonita inflow and release act as key factors on the system behavior.
Fig. 2.2 Modeling the Barra Bonita system
Fig. 2.2 Modeling the Barra Bonita system
Fig. 3.2: Location of Barra Bonita reservoir the Tieté River can be seen in Fig. 3.2.
Fig. 3.2: Location of Barra Bonita reservoir the Tieté River can be seen in Fig. 3.2.
Fig. 3.1 Schematic profile of the Barra Bonita dam storage levels and elevations is presented in Fig 3.1.
Fig. 3.1 Schematic profile of the Barra Bonita dam storage levels and elevations is presented in Fig 3.1.
Fig. 4.1 Basic structure of the ANN water quality
Fig. 4.1 Basic structure of the ANN water quality
ANN model with GA training is shown in Fig. 4.2. Fig. 4.2 Flowchart for the overall model construction
ANN model with GA training is shown in Fig. 4.2. Fig. 4.2 Flowchart for the overall model construction
Fig. 4.3c Results for DO after training Fig. 4.3b Results for chlorophyll after training
Fig. 4.3c Results for DO after training Fig. 4.3b Results for chlorophyll after training
Fig. 4.3a Results for phosphorous after training
Fig. 4.3a Results for phosphorous after training
used in the training process.
used in the training process.
Fig. 5.2 Membership for the fuzzy number A(a, b, c)
Fig. 5.2 Membership for the fuzzy number A(a, b, c)
Fig. 5.5a BOD load into Tiete River Fig. 5.4b TN concentration of Piracicaba River
Fig. 5.5a BOD load into Tiete River Fig. 5.4b TN concentration of Piracicaba River
Fig. 5.3a BOD load into Piracicaba river
Fig. 5.3a BOD load into Piracicaba river
Fig. 5.3b TN loads into Piracicaba river
Fig. 5.3b TN loads into Piracicaba river
Fig. 5.4a BOD concentration of Piracicaba River
Fig. 5.4a BOD concentration of Piracicaba River
Fig. 5.6a BOD concentrations of Tiete River Fig. 5.5b TN load into Tiete River
Fig. 5.6a BOD concentrations of Tiete River Fig. 5.5b TN load into Tiete River
Fig. 5.6b TN concentrations of Tiete River
Fig. 5.6b TN concentrations of Tiete River
Fig. 6.1 Fuzzy membership functions for flow of release and produced energy, respectively It is very important, particularly in the case of
Fig. 6.1 Fuzzy membership functions for flow of release and produced energy, respectively It is very important, particularly in the case of
Fig. 6.2 Fuzzy membership functions for water can be found in Bollman and Marques (2000). quality evaluation within the reservoir
Fig. 6.2 Fuzzy membership functions for water can be found in Bollman and Marques (2000). quality evaluation within the reservoir
Fig. 6.4 Guidecurves for the month of November for the month of November is showed in Fig. 6.4.
Fig. 6.4 Guidecurves for the month of November for the month of November is showed in Fig. 6.4.
Fig. 7.5a Optimized storage for low load situation large variation of release values.
Fig. 7.5a Optimized storage for low load situation large variation of release values.
Fig. 7.5b Optimized storage for high load situation
Fig. 7.5b Optimized storage for high load situation
Fig. 7.7a Results of flow stabilization fuzzy data collection process. hydropower generation, respectively.
Fig. 7.7a Results of flow stabilization fuzzy data collection process. hydropower generation, respectively.
Fig. 7.6a Results of TP for high load situation
Fig. 7.6a Results of TP for high load situation
Fig. 7.6b Results of TP for high load situation
Fig. 7.6b Results of TP for high load situation
Fig. 7.7b Results for the hydropower fuzzy evaluation
Fig. 7.7b Results for the hydropower fuzzy evaluation

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