Design of optimal reservoir operating rules in large water resources systems combining stochastic programming, fuzzy logic and expert criteria.

Abstract

Given the high degree of development of hydraulic infrastructure in the developed countries, and with the increasing opposition to constructing new facilities in developing countries, the focus of water resource system analysis has turned into defining adequate operation strategies. Better management is necessary to cope with the challenge of supplying increasing demands and conflicts on water allocation while facing climate change impacts. To do so, a large set of mathematical simulation and optimization tools have been developed. However, the real application of these techniques is still limited. One of the main lines of research to fix this issue regards to the involvement of experts' knowledge in the definition of mathematical algorithms. To define operating rules in a way in which system operators could rely, their expert knowledge should be fully accounted and merged with the results from mathematical algorithms. This thesis develops a methodological framework and the required tools to improve the operation of large-scale water resource systems. In such systems, decision-making processes are complex and supported, at least partially, by the expert knowledge of decision-makers. This importance of expert judgment in the operation strategies requires mathematical tools able to embed and combine it with optimization algorithms. The methods and tools developed in this thesis rely on stochastic programming, fuzzy logic and the involvement of system operators during the whole rule-defining process. An extended stochastic programming algorithm, able to be used in large-scale water resource systems including stream-aquifer interactions, has been developed (the CSG-SDDP). The methodological framework proposed uses fuzzy logic to capture the expert knowledge in the definition of optimal operating rules. Once the current decision-making process is fairly reproduced using fuzzy logic and expert knowledge, stochastic programming results are introduced and thus the performance of the rules is improved. The framework proposed in this thesis has been applied to the Jucar river system (Eastern Spain), in which scarce resources are allocated following complex decision-making processes. We present two applications. In the first one, the CSG-SDDP algorithm has been used to define economically-optimal conjunctive use strategies for a joint operation of reservoirs andaquifers. In the second one, we implement a collaborative framework to couple historical records with expert knowledge and criteria to define a decision support system (DSS) for the seasonal operation of the reservoirs of the Jucar River system. The co-developed DSS tool explicitly reproduces the decision-making processes and criteria considered by the system operators. Two fuzzy logic systems have been developed and linked with this purpose, as well as with fuzzy regressions to preview future inflows. The DSS developed was validated against historical records. The developed framework offers managers a simple way to define a priori suitable decisions, as well as to explore the consequences of any of them. The resulting representation has been then combined with the CSG-SDDP algorithm in order to improve the rules following the current decision-making process. Results show that reducing pumping from the Mancha Oriental aquifer would lead to higher systemwide benefits due to increased flows by stream-aquifer interaction. The operating rules developed successfully combined fuzzy logic, expert judgment and stochastic programming, increasing water allocations to the demands by changing the way in which Alarcon, Contreras and Tous are balanced. These rules follow the same decision-making processes currently done in the system, so system operators would feel familiar with them. In addition, they can be contrasted with the current operating rules to determine what operation options can be coherent with the current management and, at the same time, achieve an optimal operation