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Modeling real-time reservoir operations and developing optimal rules are formidable tasks considering a number of issues that need to be addressed within optimization and simulation models. The issues range from uncertain system inputs to implementation of operating rules in real-time. This dissertation addresses some of these issues that are relevant at different stages of real-time reservoir operation process. These issues are: (i) information uncertainty; (ii) system representation; and (iii) computational intractability. Realtime operation models are developed in the present research for single and multiple reservoir systems while addressing these issues in that order. Uncertainty generally associated with system variables in a variety of forms is a main hurdle in developing a proaches for optimizing reservoir operations. Explicit and implicit stochastic approaches based on traditional probability theory concepts cannot always handle all the uncertain elements of reservoir operation. Approaches to handle imprecise information are required as much as methodologies to address the issue of lack of information. The former issue described as information uncertainty in this thesis is addressed using fuzzy set theory. Mathematical programming models are developed under fuzzy environment to handle imprecise and uncertain components of reservoir operation problem dominated by an economic objective. The concept of 'compromise operating polices' is proposed and its utility is proved. Representation of physical system in mathematical programming formulations affects the extent to which the physics of the problem is captured and nature of the solutions that can be obtained. Tradeoffs between exhaustive representation and optimal solutions can be identified. Operation of a multiple reservoir system is considered to develop formulations of varying degree of system representation. A Mixed Integer Non-Linear Programming (MINLP) Model with binary variables is developed to a speci.
Reservoir operation is a multi-objective optimization problem, and is traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation, named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO) variants, correspondingly MOnDP, MOnSDP and MOnRL. The idea is to include a nested optimization algorithm into each state transition, which reduces the initial problem dimension and alleviates the curse of dimensionality. These algorithms can solve multi-objective optimization problems, without significantly increasing the algorithm complexity or the computational expenses. It can additionally handle dense and irregular variable discretization. All algorithms are coded in Java and were tested on the case study of the Knezevo reservoir in the Republic of Macedonia. Nested optimization algorithms are embedded in a cloud application platform for water resources modeling and optimization. The platform is available 24/7, accessible from everywhere, scalable, distributed, interoperable, and it creates a real-time multiuser collaboration platform. This thesis contributes with new and more powerful algorithms for an optimal reservoir operation and cloud application platform. All source codes are available for public use and can be used by researchers and practitioners to further advance the mentioned areas.
This volume and its companion volume includes the edited versions of the principal lectures and selected papers presented at the NATO Advanced Study Institute on Optimization and Decision Support Systems in Civil Engineering. The Institute was held in the Department of Civil Engineering at Heriot-Watt University, Edinburgh from June 25th to July 6th 1989 and was attended by eighty participants from Universities and Research Institutes around the world. A number of practising civil and structural engineers also attended. The lectures and papers have been divided into two volumes to reflect the dual themes of the Institute namely Optimization and Decision Support Systems in Civil Engineering. Planning for this ASI commenced in late 1986 when Andrew Templeman and I discussed developments in the use of the systems approach in civil engineering. A little later it became clear that much of this approach could be realised through the use of knowledge-based systems and artificial intelligence techniques. Both Don Grierson and John Gero indicated at an early stage how important it would be to include knowledge-based systems within the scope of the Institute. The title of the Institute could have been: 'Civil Engineering Systems' as this would have reflected the range of systems applications to civil engineering problems considered by the Institute. These volumes therefore reflect the full range of these problems including: structural analysis and design; water resources engineering; geotechnical engineering; transportation and environmental engineering.