Driven by a number of factors, the electric power industry is expected to undergo a paradigm shift with a considerably increased level of variable energy sources. A significant integration of such sources requires heavy transmission investments over geographically wide and large-scale networks. However, the stochastic nature of such sources, along with the sheer size of network systems, results in problems that may become intractable. Thus, the challenge addressed in this work is to design efficient and reasonably accurate models, strategies and tools that can solve large-scale TEP problems under uncertainty. A long-term stochastic network planning tool is developed, considering a multi-stage decision framework and a high level integration of renewables. Such a tool combines the need for short-term decisions with the evaluation of long-term scenarios, which is the practical essence of a real-world planning. Furthermore, in order to significantly reduce the combinatorial solution search space, a specific heuristic solution strategy is devised. This works by decomposing the original problem into successive optimization phases.One of the modeling challenges addressed in this work is to select the right network model for power flow and congestion evaluation: complex enough to capture the relevant features but simple enough to be computationally fast. Another relevant contribution is a domain-driven clustering process of snapshots which is based on a “moments” technique. Finally, the developed models, methods and solution strategies have been tested on standard and real-life systems. This thesis also presents numerical results of an aggregated 1060-node European network system considering multiple RES development scenarios. Generally, test results show the effectiveness of the proposed TEP model, since—as originally intended—it contributes to a significant reduction in computational effort while fairly maintaining optimality of the solutions. / Driven by several techno-economic, environmental and structural factors, the electric energy industry is expected to undergo a paradigm shift with a considerably increased level of renewables (mainly variable energy sources such as wind and solar), gradually replacing conventional power production sources. The scale and the speed of integrating such sources of energy are of paramount importance to effectively address a multitude of global and local concerns such as climate change, sustainability and energy security. In recent years, wind and solar power have been attracting large-scale investments in many countries, especially in Europe. The favorable agreements of states to curb greenhouse gas emissions and mitigate climate change, along with other driving factors, will further accelerate the renewable integration in power systems. Renewable energy sources (RESs), wind and solar in particular, are abundant almost everywhere, although their energy intensities differ very much from one place to another. Because of this, a significant integration of such energy sources requires heavy investments in transmission infrastructures. In other words, transmission expansion planning (TEP) has to be carried out in geographically wide and large-scale networks. This helps to effectively accommodate the RESs and optimally exploit their benefits while minimizing their side effects. However, the uncertain nature of most of the renewable sources, along with the size of the network systems, results in optimization problems that may become intractable in practice or require a huge computational effort. Thus, the challenge addressed in this work is to design models, strategies and tools that may solve large-scale and uncertain TEP problems, being computationally efficient and reasonably accurate. Of course, the specific definition of the term “reasonably accurate” is the key issue of the thesis work, since it requires a deep understanding of the main cost and technical drivers of adequate TEP investment decisions. A new formulation is proposed in this dissertation for a long-term planning of transmission investments under uncertainty, with a multi-stage decision framework and considering a high level of renewable sources integration. This multi-stage strategy combines the need for short-term decisions with the evaluation of long-term scenarios, which is the practical essence of a real-world planning. The TEP problem is defined as a stochastic mixed-integer linear programming (S-MILP) optimization, an exact solution method. This allows the use of effective off-the-shelf solvers to obtain solutions within a reasonable computational time, enhancing overall problem tractability. Furthermore, in order to significantly reduce the combinatorial solution search (CSS) space, a specific heuristic solution strategy is devised. In this global heuristic strategy, the problem is decomposed into successive optimization phases. Each phase uses more complex optimization models than the previous one, and uses the results of the previous phase so that the combinatorial solution search space is reduced after each phase. Moreover, each optimization phase is defined and solved as an independent problem; thus, allowing the use of specific decomposition techniques, or parallel computation when possible. A relevant feature of the solution strategy is that it combines deterministic and stochastic modeling techniques on a multi-stage modeling framework with a rolling-window planning concept. The planning horizon is divided into two sub-horizons: medium- and long-term, both having multiple decision stages. The first sub-horizon is characterized by a set of investments, which are good enough for all scenarios, in each stage while scenario-dependent decisions are made in the second sub-horizon. One of the first modeling challenges of this work is to select the right network model for power flow and congestion evaluation: complex enough to capture the relevant features but simple enough to be computationally fast. The thesis includes extensive analysis of existing and improved network models such as AC, linearized AC, “DC”, hybrid and pipeline models, both for the existing and the candidate lines. Finally, a DC network model is proposed as the most suitable option. This work also analyzes alternative losses models. Some of them are already available and others are proposed as original contributions of the thesis. These models are evaluated in the context of the target problem, i.e., in finding the right balance between accuracy and computational effort in a large-scale TEP problem subject to significant RES integration. It has to be pointed out that, although losses are usually neglected in TEP studies because of computational limitations, they are critical in network expansion decisions. In fact, using inadequate models may lead not only to cost-estimation errors, but also to technical errors such as the so-called “artificial losses”. Another relevant contribution of this work is a domain-driven clustering process to handle operational states. This allows a more compact and efficient representation of uncertainty with little loss of accuracy. This is relevant because, together with electricity demand and other traditional sources of uncertainty, the integration of variable energy sources introduces an additional operational variability and uncertainty. A substantial part of this uncertainty and variability is often handled by a set of operational states, here referred to as “snapshots”, which are generation-demand patterns of power systems that lead to optimal power flow (OPF) patterns in the transmission network. A large set of snapshots, each one with an estimated probability, is then used to evaluate and optimize the network expansion. In a long-term TEP problem of large networks, the number of operational states must be reduced. Hence, from a methodological perspective, this thesis shows how the snapshot reduction can be achieved by means of clustering, without relevant loss of accuracy, provided that a good selection of classification variables is used in the clustering process. The proposed method relies on two ideas. First, the snapshots are characterized by their OPF patterns (the effects) instead of the generation-demand patterns (the causes). This is simply because the network expansion is the target problem, and losses and congestions are the drivers to network investments. Second, the OPF patterns are classified using a “moments” technique, a well-known approach in Optical Pattern Recognition problems. The developed models, methods and solution strategies have been tested on small-, medium- and large-scale network systems. This thesis also presents numerical results of an aggregated 1060-node European network system obtained considering multiple RES development scenarios. Generally, test results show the effectiveness of the proposed TEP model, since—as originally intended—it contributes to a significant reduction in computational effort while fairly maintaining optimality of the solutions. / <p>QC 20160919</p>

Computer Aided Engineering driven analysis is gaining grounds in automotive industry. Prediction of brake noise using CAE techniques has become populardue to its overall low cost as compared to physical testing. However, the presence of several uncertain parameters which affect brake noise and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on CAE analysis. Therefore, the confidence level in CAE techniques has to be increased to ensure reliability and robustness in the CAE solutions which support design work. One such way to achieve reliability in the CAE analysis isinvestigated in this thesis by incorporating the effects of different sources of uncertainty and variability in the analysis and estimating the probability of designfailure (probability of brake noise above a certain threshold). While incorporating the uncertainties in the CAE analysis ensures robustness, it is computationally intensive. This thesis work aims to gain an understanding about a brakenoise - creep groan, and to bring robustness into the CAE analysis along with reduction in computational time. A probabilistic analysis technique called hierarchical multi-fidelity statistical approachis explored in this thesis work, to estimate the probability of design failure or design robustness at a faster rate. It incorporates the stochasticity in the input parameters while running simulations. The method involves application of a hierarchy of approximations to the system response computed with variations in mesh resolution or variations in number of modes or changing solver time step,etc. And finally it uses the probability theory, to relate the information provided by approximate solutions to get the target failure estimation.Through this method, reliable data regarding the probability of design failure was approximated for every simulation and at a reduced computational time.Additionally, it provided information about critical parameters that influenced brake noise which was meritorious for design management. Estimation of probability of design failure by this method has been proved to be reliable in the case of brake noise according to the simulation results and the method can be considered robust. / Computer Aided Engineering (cae) driven analysis is gaining grounds in automotive industry. Brake noise is one such place where cae simulations are gaining more attention. The presence of several uncertain parameters which affect brake noises and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on cae deterministic analyses alone.Therefore, the confidence level in cae analyses has to be increased to ensure cae analysis robustness. One way to achieve this is by incorporating the effects of different sources of uncertainty and variability in the cae analysis and estimating the probability of design failure. Such a reliability measure (i.e. probability of noise event occurrence or exceedance of noise level than a threshold) can provide car manufacturers with an idea about the costs of warranty claims due to brake noise and can be used as a metric to evaluate different design solutions, before the final design goes to the production stage.  On one hand, using the high-fidelity models of brake/chassis system is generally computationally intensive, and thus, often only limited number of simula-tion runs are feasible for uncertainty analysis and design failure risk assessment. On the other hand, analyses on low-fidelity models, typically based on simplified assumptions during the development phase are fast but not always accu-rate. Striking for a good balance between efficiency and accuracy/robustness is an important task, when dealing with uncertainty/risk analysis of such complex dynamical systems To address these issues, a hierarchical multi-fidelity statistical approach has been adopted in this study, in order to estimate the probability of design failure. It employs a hierarchy of approximations to the system response computed with different fidelity by surrogate modelling, coarse spatial/temporal model mesh resolution variation, changing solver time step, etc., using probability theory, to relate information provided by approximate solu-tions to the target failure estimation. Using this approach opens up the possi-bility to use a low-fidelity models to accelerate the uncertainty quantification of complex brake/chassis systems, while granting unbiased estimation of system design failure risk/reliability. It also enables management of design changes, during fast iterations of the design process. This approach is used for studying one of the brake noise issue called creep groan, understand the root cause and providedesign proposals.