Advances in Energy System Optimization

The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme “Bridging the Gap Between Mathematical Modelling and Policy Support” on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart

Advanced Predictive Control Strategies for More Electric Aircraft

Next generation aircraft designs are incorporating increasingly complex electrical power distribution systems to address growing demands for larger and faster electrical power loads. This dissertation develops advanced predictive control strategies for coordinated management of the engine and power subsystems of such aircraft. To achieve greater efficiency, reliability and performance of a More Electric Aircraft (MEA) design static and dynamic interactions between its engine and power subsystems need to be accounted for and carefully handled in the control design. In the pursued approach, models of the subsystems and preview of the power loads are leveraged by predictive feedback controllers to coordinate subsystem operation and achieve improved performance of the MEA system while enforcing state and input constraints. More specifically, this dissertation contains the following key developments and contributions. Firstly, models representing the engine and power subsystems of the MEA, including their interactions, are developed. The engine is a dual-spool turbojet that converts fuel into thrust out of the nozzle and mechanical power at the shafts. Electrical generators extract some of this power and convert it into electricity that is supplied to a High Voltage DC bus to support connected loads, with the aid of a battery pack for smoothing voltage transients. The control objective in this MEA system is to actuate the engine and power subsystem inputs to satisfy demands for thrust and electrical power while enforcing constraints on compressor surge and bus voltage deviations. Secondly, disturbance rejection, power flow coordination, and anticipation of the changes in power loads are considered for effective MEA control. A rate-based formulation of Model Predictive Control (MPC) allowing for offset free tracking is proposed. Centralized control is demonstrated to result in better thrust tracking performance in the presence of compressor surge constraints as compared to decentralized control. Forecast of changes in the power load allows the control to act in advance and reduce bus voltage excursions. Thirdly, distributed MPC strategies are developed which account for subsystem privacy requirements and differences in subsystem controller update rates. This approach ensures coordination between subsystem controllers based on limited information exchange and exploits the Alternating Direction Method of Multipliers. Simulations demonstrate that the proposed approach outperforms the decentralized controller and closely matches the performance of a fully centralized solution. Finally, a stochastic approach to load preview based on a Markov chain representation of a military aircraft mission is proposed. A scenario based MPC is then exploited to minimized expected performance cost while enforce constraints over all scenarios. Simulation based comparisons indicate that this scenario based MPC performs similarly to an idealized controller that exploits exact knowledge of the future and outperforms a controller without preview.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150003/1/wdunham_1.pd

Réduction de modèles de disques aubagés désaccordés avec non-linéarités géométriques et contact

This thesis introduces a new methodology to study the dynamics of mistuned bladed disks with geometric and contact nonlinearities in a numerically efficient way. The methodology is based on a reduction procedure. This allows to overcome the difficulties related to the high computational costs of nonlinear dynamic simulations performed on industrial finite element models. First, a methodology is developed to study the contact interactions of a single rotating blade with geometric nonlinearities. The reduction method is based on the modal derivative approach, which is adapted to retain physical degrees-of-freedom in the reduced space for the implementation of contact. In order to limit the size of the reduced system, a modal derivative selection criterion is proposed. The nonlinear internal forces due to large displacements are evaluated in the reduced space using the stiffness evaluation procedure. Contact is numerically handled using Lagrange multipliers. The methodology is then generalized to full bladed disk structures using component mode synthesis techniques with fixed interfaces. Mistuning can also be included in the reduced space. Through this work, the numerical strategy is applied to an open industrial compressor bladed disk model based on the NASA rotor 37 in order to promote the reproducibility of results. The obtained results and, when applicable, their comparison with full-order model results give confidence in the methodology. In-depth analyses, including clearance consumption computations and frequency analyses with a continuation procedure, allow to understand and characterize the combined influence of contact and geometric nonlinearities on the structure’s dynamics. Besides providing an accurate description of the time dynamics of the structure, the new methodology also allows to extract quantities of interest that are relevant for both researchers and industrial designers such as contact interaction maps, contact wear maps, critical angular speeds or stress fields in the structure. This non-intrusive strategy can be used in combination with any commercial finite element software

Sequential Linear Programming Coordination Strategy for Deterministic and Probabilistic Analytical Target Cascading.

Decision-making under uncertainty is particularly challenging in the case of multidisciplinary, multilevel system optimization problems. Subsystem interactions cause strong couplings, which may be amplified by uncertainty. Thus, effective coordination strategies can be particularly beneficial. Analytical target cascading (ATC) is a deterministic optimization method for multilevel hierarchical systems, which was recently extended to probabilistic design. Solving the optimization problem requires propagation of uncertainty, namely, evaluating or estimating output distributions given random input variables. This uncertainty propagation can be a challenging and computationally expensive task for nonlinear functions, but is relatively easy for linear ones. In order to overcome the difficulty in uncertainty propagation, this dissertation introduces the use of Sequential Linear Programming (SLP) for solving ATC problems, and specifically extends this use for Probabilistic Analytical Target Cascading (PATC) problems. A new coordination strategy is proposed for ATC and PATC, which coordinates linking variables among subproblems using sequential lineralizations. By linearizing and solving a hierarchy of problems successively, the algorithm takes advantage of the simplicity and ease of uncertainty propagation for a linear system. Linearity of subproblems is maintained using an infinite norm to measure deviations between targets and responses. A subproblem suspension strategy is used to temporarily suspend inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. A global convergence proof of the SLP-based coordination strategy is derived. Experiments with test problems show that, relative to standard ATC and PATC coordination, the number of subproblem evaluations is reduced considerably while maintaining accuracy. To demonstrate the applicability of the proposed strategies to problems of practical complexity, a hybrid electric fuel cell vehicle design model, including enterprise, powertrain, fuel cell and battery models, is developed and solved using the new ATC strategy. In addition to engineering uncertainties, the model takes into account unknown behavior by consumers. As a result, expected maximum profit is calculated using probabilistic consumer preferences with engineering constraints satisfied.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58506/1/shipge_1.pd

Degradation modelling in process control applications

Degradation of industrial equipment is often influenced by how a system is operated, with certain operating points likely to accelerate degradation. The ability to mitigate degradation of an industrial system would result in improved performance and decreased costs of operation. The thesis aims to provide ways for managing degradation by adjusting the operating conditions of a system. The thesis provides original insights and a new classification of models of degradation to facilitate the integration of degradation models into process control applications. The thesis also develops an adaptive algorithm for degradation detection and prediction in turbomachinery, which is able to predict the expected future values of a degradation indicator and to quantify the uncertainty of the prediction. The thesis then proposes two frameworks for load-sharing in a compressor station in which the compressors are subject to degradation. One framework considers management of degradation and the other one focuses on power consumption of the whole station. These examples show how modelling of degradation can have an impact on the operation of an industrial system. The approaches have been evaluated with case studies developed in collaboration with industrial partners. As demonstrated in the case studies, the outcomes of the research presented in this thesis provide new ways to take account of degradation in process control applications. The thesis discusses steps and directions for future work to facilitate the technology transfer from academic to industrial implementation.Open Acces

Multidisciplinary Design Optimization (MDO) of Transonic Fan Blade

RÉSUMÉ La conception de pales d’un rotor est une tâche complexe et difficile en raison de l’écoulement transsonique, du large espace de design et de l’implication de plusieurs disciplines de l'ingénierie dans le but d’augmenter les performances de métriques multidisciplinaire tels que l'efficacité, le rapport de pression, le stress. Pour faire face à tous ces défis, une comparaison d’approches pour les optimisations aérodynamiques et multidisciplinaires automatisés (MDO) des pales de soufflante transsonique est présentée. Le processus de conception proposé intègre une méthode de paramétrisation géométrique des pales, une modélisation CAO et des outils d’analyse hautefidélité pour l'aérodynamique, la structure et la dynamique. Une méthode de paramétrisation de pales à multi-niveau a été utilisée pour modifier efficacement la géométrie de la pale avec un faible nombre de variables de conception. Le modèle CAO a été construit dans CATIA afin d'utiliser un modèle commun pour les analyses de structure et dynamiques. Le modèle des équations de Navier-Stokes (RANS) tridimensionnelles moyennées intégré au logiciel commercial CFD ANSYS CFX, a été utilisé pour l'analyse aérodynamique du rotor transsonique tandis qu’un modèle éléments finis (EF) implémenté sur ANSYS a été utilisé pour réaliser les analyses de structure et dynamique. Des algorithmes d'optimisation heuristiques et hybrides sont utilisés pour résoudre le problème d'optimisation de la forme des pales. La vérification des codes et des méthodes a été effectuée en comparant les résultats calculés à des données expérimentales disponibles dans la littérature pour le NASA Rotor 67, un cas test représentatif d'écoulement complexes en trois dimensions. Afin de vérifier la faisabilité du processus automatisé intégré dans l'optimisation, une optimisation aérodynamique visant à maximiser l'efficacité du point de conception tout en maintenant le débit massique et le rapport de pression constant, est élaboré et exécuté pour redessiner le cas de test Rotor 67. En outre, ce cas a aidé à sélectionner l'algorithme d'optimisation adapté à la résolution du problème et explorer l'espace de conception. Cependant, la conception de pale de soufflante transsonique est inévitablement un processus pluridisciplinaire qui nécessite la participation de nombreuses disciplines telles que l'aérodynamique, la structure, la dynamique, etc., au cours des différentes étapes du processus de conception. En outre, les procédures de conception actuelles impliquent une optimisation de la structure et de la dynamique après l’optimisation aérodynamique.----------ABSTRACT The design of current transonic fan blades is a complex and challenging task due to multifaceted transonic flow field, large design space and involvement of many engineering specialists to increase performance on multidisciplinary metrics such as efficiency, pressure ratio, stress. To tackle all these challenges, a comparison of approaches for the automated aerodynamic and multidisciplinary optimizations (MDO) transonic fan blades is developed. The developed design process integrates the fan blades geometrical parameterization method, CAD modeling and highfidelity analysis tools for aerodynamics, structure and dynamics disciplines. A multi-level parameterization method of fan blade was utilized to efficiently modify the blade geometry with a low number of design variables. The CAD model was built in CATIA, to use a common model for structure and dynamic analyses. The three-dimensional Reynolds-Averaged Navier-Stokes (RANS) equations based commercial software ANSYS CFX was used for aerodynamic analysis of transonic rotor; whereas Finite Element (FE) analysis based commercial software ANSYS Mechanical was used to conduct the structure and dynamic analyses. Heuristic and hybrid optimization algorithms are employed to solve the fan design optimization problem. The capability of the codes and methodologies was validated by comparing the computed results to experimental data available in the open literature for NASA Rotor 67, a test case representative of complex three-dimensional flow structures in transonic blade design problems. In order to verify the feasibility of automated integrated optimization working flow, an aerodynamic optimization aiming to maximize the design point efficiency while maintaining the mass flow rate and pressure ratio, is formulated and executed to redesign a test case. It further helped to select the suitable optimization algorithm and explore the design space. However, transonic fan blade design is inevitably a multidisciplinary process which requires involvement of many disciplines such as aerodynamics, structure, dynamics, etc., during different stages of design process. In addition, the current design procedures involved the structure and dynamic disciplines optimization after aerodynamic discipline i.e. a sequential discipline optimization. The main drawback of this procedure is that a good aerodynamic design might not satisfy the structural and dynamic design requirements which make this design procedure an iterativ

Bidding strategy for a virtual power plant for trading energy in the wholesale electricity market

Virtual power plants (VPPs) are an effective way to increase renewable integration. In this PhD research, the concept design and the detailed costs and benefits of implementing a realistic VPP in Western Australia (WA), comprising 67 dwellings, are developed. The VPP is designed to integrate and coordinate an 810kW rooftop solar PV farm, 350kW/700kWh vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and smart appliances through demand management mechanisms. This research develops a robust bidding strategy for the VPP to participate in both load following ancillary service (LFAS) and energy market in the wholesale electricity market in WA considering the uncertainties associated with PV generation and electricity market prices. Using this strategy, the payback period can be improved by 3 years (to a payback period of 6 years) and the internal rate of return (IRR) by 7.5% (to an IRR of 18%) by participating in both markets. The daily average error of the proposed robust method is 2.7% over one year when compared with a robust mathematical method. The computational effort is 0.66 sec for 365 runs for the proposed method compared to 947.10 sec for the robust mathematical method. To engage customers in the demand management schemes by the VPP owner, the gamified approach is adopted to make the exercise enjoyable while not compromising their comfort levels. Seven gamified applications are examined using a developed methodology based on Kim’s model and Fogg’s model, and the most suitable one is determined. The simulation results show that gamification can improve the payback period by 1 to 2 months for the VPP owner. Furthermore, an efficient and fog-based monitoring and control platform is proposed for the VPP to be flexible, scalable, secure, and cost-effective to realise the full capabilities and profitability of the VPP

A Computational Framework for Efficient Reliability Analysis of Complex Networks

With the growing scale and complexity of modern infrastructure networks comes the challenge of developing efficient and dependable methods for analysing their reliability. Special attention must be given to potential network interdependencies as disregarding these can lead to catastrophic failures. Furthermore, it is of paramount importance to properly treat all uncertainties. The survival signature is a recent development built to effectively analyse complex networks that far exceeds standard techniques in several important areas. Its most distinguishing feature is the complete separation of system structure from probabilistic information. Because of this, it is possible to take into account a variety of component failure phenomena such as dependencies, common causes of failure, and imprecise probabilities without reevaluating the network structure. This cumulative dissertation presents several key improvements to the survival signature ecosystem focused on the structural evaluation of the system as well as the modelling of component failures. A new method is presented in which (inter)-dependencies between components and networks are modelled using vine copulas. Furthermore, aleatory and epistemic uncertainties are included by applying probability boxes and imprecise copulas. By leveraging the large number of available copula families it is possible to account for varying dependent effects. The graph-based design of vine copulas synergizes well with the typical descriptions of network topologies. The proposed method is tested on a challenging scenario using the IEEE reliability test system, demonstrating its usefulness and emphasizing the ability to represent complicated scenarios with a range of dependent failure modes. The numerical effort required to analytically compute the survival signature is prohibitive for large complex systems. This work presents two methods for the approximation of the survival signature. In the first approach system configurations of low interest are excluded using percolation theory, while the remaining parts of the signature are estimated by Monte Carlo simulation. The method is able to accurately approximate the survival signature with very small errors while drastically reducing computational demand. Several simple test systems, as well as two real-world situations, are used to show the accuracy and performance. However, with increasing network size and complexity this technique also reaches its limits. A second method is presented where the numerical demand is further reduced. Here, instead of approximating the whole survival signature only a few strategically selected values are computed using Monte Carlo simulation and used to build a surrogate model based on normalized radial basis functions. The uncertainty resulting from the approximation of the data points is then propagated through an interval predictor model which estimates bounds for the remaining survival signature values. This imprecise model provides bounds on the survival signature and therefore the network reliability. Because a few data points are sufficient to build the interval predictor model it allows for even larger systems to be analysed. With the rising complexity of not just the system but also the individual components themselves comes the need for the components to be modelled as subsystems in a system-of-systems approach. A study is presented, where a previously developed framework for resilience decision-making is adapted to multidimensional scenarios in which the subsystems are represented as survival signatures. The survival signature of the subsystems can be computed ahead of the resilience analysis due to the inherent separation of structural information. This enables efficient analysis in which the failure rates of subsystems for various resilience-enhancing endowments are calculated directly from the survival function without reevaluating the system structure. In addition to the advancements in the field of survival signature, this work also presents a new framework for uncertainty quantification developed as a package in the Julia programming language called UncertaintyQuantification.jl. Julia is a modern high-level dynamic programming language that is ideal for applications such as data analysis and scientific computing. UncertaintyQuantification.jl was built from the ground up to be generalised and versatile while remaining simple to use. The framework is in constant development and its goal is to become a toolbox encompassing state-of-the-art algorithms from all fields of uncertainty quantification and to serve as a valuable tool for both research and industry. UncertaintyQuantification.jl currently includes simulation-based reliability analysis utilising a wide range of sampling schemes, local and global sensitivity analysis, and surrogate modelling methodologies