In computer security, cryptographic algorithms and protocols are required to ensure security of data and applications. This research investigates techniques to reduce the energy consumed by cryptographic hash functions. The specific hash functions considered are Message Digest-2 (MD2), Message Digest-5 (MD5), Secure Hash Algorithm-1 (SHA-1) and Secure Hash Algorithm-2 (SHA-2). The discussion around energy conservation in handheld devices like laptops and mobile devices is gaining momentum. Research has been done at the hardware and operating system levels to reduce the energy consumed by these devices. However, research on conserving energy at the application level is a new approach. This research is motivated by the energy consumed by anti-virus applications which use computationally intensive hash functions to ensure security. To reduce energy consumption by existing hash algorithms, the generic energy complexity model, designed by Roy et al. [Roy13], has been applied and tested. This model works by logically mapping the input across the eight available memory banks in the DDR3 architecture and accessing the data in parallel. In order to reduce the energy consumed, the data access pattern of the hash functions has been studied and the energy complexity model has been applied to hash functions to redesign the existing algorithms. These experiments have shown a reduction in the total energy consumed by hash functions with different degrees of parallelism of the input message, as the energy model predicted, thereby supporting the applicability of the energy model on the different hash functions chosen for the study. The study also compared the energy consumption by the hash functions to identify the hash function suitable for use based on required security level. Finally, statistical analysis was performed to verify the difference in energy consumption between MD5 and SHA2.

Les piles à combustibles sont considérées comme une énergie d’avenir, notamment grâce à leur caractère non polluant à l’usage. Cependant, le déploiement de ces solutions à grande échelle est encore conditionné par l’amélioration de leurs performances et surtout de leur durabilité afin de garantir une industrialisation à faible coût. L’application de la pile à combustible au domaine des transports impose en plus un fonctionnement à puissance variable, ce qui complique l’amélioration des performances et de la durabilité. L’approche retenue pour ces travaux consiste en la conception d’une loi de gestion du système qui génère les conditions opératoires optimales à appliquer au stack (pressions, température, courant, stoechiométries) en fonction de la demande en puissance, de l’état de santé de la pile (perte de surface active) et du taux d’humidité actuel. L’optimalité est entendue au sens de l’augmentation du rendement système et de la diminution des dégradations du platine et de la membrane. Cette loi se base sur des modèles de dégradations et de performances d’un système pile à combustible. Cette loi de gestion requiert pour fonctionner les données de l’état de santé de la pile et du taux d’humidité. L’évaluation de l’état de santé de la pile fait déjà l’objet de nombreux travaux de diagnostic. En revanche, le taux d’humidité doit être estimé par un observateur d’état car les capteurs d’humidité ne sont pas fiables pour une application transport. Pour cela, un observateur d’état a été développé pour estimer les humidités relatives dans les canaux du stack et aussi le chargement en eau de la membrane, la quantité d’hydrogène à l’anode ainsi que la saturation d’azote à l’anode. Cette dernière donnée permet de proposer une stratégie de purge pour une architecture dead-end basée sur la saturation d’azote, qui limite les pertes en hydrogène et réduit les dégradations liées à cette architecture. / Fuel cells are considered as a promising source of energy for the future, thanks to their non-polluting aspect. However, the deployment of these solutions on a large scale is still conditioned by the improvement of their performance and especially of their durability in order to guarantee a low cost industrialization. The transport application also imposes a variable power demand, which complicates the improvement of performance and durability. The approach adopted for this work consists of the design of a system management law that generates the optimal operating conditions to be applied to the stack (pressures, temperature, current, stoichiometries) as a function of the power demand, the state of health (active surface loss) and current humidity. Optimality is understood in the sense of increasing system efficiency and decreasing the degradation of the membrane and the platinum dissolution. This law is based on degradation and performance models of a fuel cell system. This management law requires in real time the data of the state of health of the fuel cell and the humidity rate. The assessment of the state of health is already the subject of many diagnostic work. On the other hand, the humidity rate must be estimated by a state observer because the humidity sensors are not reliable for a transport application. Therefore, a state observer was developed to estimate the relative humidities in the stack channels and also the membrane water content, the hydrogen at the anode as well as the nitrogen saturation at the anode. This last data makes it possible to propose a purge strategy for a dead-end architecture, based on nitrogen saturation, which limits the losses in hydrogen and reduces the damage associated with this architecture.

The thesis deals with the environmental assessment of buildings, concrete objects Letní lázně at Karlova Studánka. The first part describes the issue and legislation. There is also described a method of experimental measurement and methods of moisture measurement. The second part deals with the analysis of the specified object, description of selected renewable sources and description of possible modifications of the object. The third part is devoted to modeling and simulation of the specified object in DesignBuilder. Using the model were created various simulations with various modifications in order to reduce energy intensity of the object. Results were then evaluated and compared.

This thesis deals with electric train optimal control problem with a focus on time and energy optimization. The problem is extended by considering specific speed contraints. We are able to design the sequence of control settings by the use of the optimal control theory. Optimization tools in Matlab environment are used to determine the numerical solution of the considered task.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

Contain of master´s thesis is a energy assessment of multifunctional building in Komňátka. The theoretical part deals with the analysis of legislativ documents, current technical solution and practical application to the specified building. In the practical part thesis focus on experimental determination of the heat transfer coefficient using thermography.

The main aim of this thesis is optimization of energy demands in a new administrative high-rise building Vienna Point II located in Brno. Experimental part of the thesis deals with determination of real energy consumption during winter season and preparation of a set of climate data for theoretical simulations. Theoretical part is based on data obtained by experimental methods and is aimed on optimization of energy demands. The effect of suggested solution is analysed by software solutions which simulate building operation. Resulting energy demands obtained by simulations are compared with real energy consumption.

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>

Detta arbete har undersökt möjligheten att via styrsystemet för en avloppspump-station optimera energiförbrukningen av pumpar och med styrfunktioner förenkla driftunderhållet av stationen.För att undersöka energiförbrukningen har en teoretisk modell av en pumpstation gjorts där energitester för tre olika reglermetoder simulerats. Två av metoderna var start/stopp-styrning och en P-regulator som vanligen används i pumpstationer. Den tredje metoden var en Fuzzy-regulator som med hänsyn till nivån och nivåför-ändring reglerar varvtalet på pumpen.Resultatet från energitesterna visade att start/stopp-styrning förbrukade mest energi och att Fuzzy-regulatorn påvisade bäst egenskaper för att optimera energi-förbrukningen. Fuzzy-regulatorns energiförbrukning var mindre vid merparten av testerna jämfört med P-regulatorn. Fuzzy-regulatorn påvisade bäst egenskaper då inflödet var lågt relativt pumpens kapacitet och då bottenarean på sumpen var mindre.Vid undersökning av nya styrfunktioner har intervjuer av driftchefer utförts för att få en bild av problematiken som kan uppstå i en pumpstation. De styrfunktioner som utvecklats hade syftet att minska antalet förstoppade pumpar då detta var största problematiken. / This paper has investigated the possibility of optimizing the power consumption of pumps through the control system for a wastewater pumping stations. Further-more, the possibilities of developing new control functions has been investigated, with the purpose to simplify the operation maintenance of the station. To investigate energy consumption, a theoretical model of a pumping station has been made where energy tests for three different control methods have been per-formed. Two of the methods studied were on/off control and a P controller which are commonly used in pump stations. The third method was a Fuzzy controller which, with regard to the level and level change, regulates the speed of the pump.The results from the energy tests showed that on/off control consumed most en-ergy and the Fuzzy controller demonstrated best features to optimize energy con-sumption. The Fuzzy controller’s energy consumption was less in most of the tests compared with the P controller. The Fuzzy controller showed the best properties when the inflow was low relative to the pump’s capacity and the bottom area of the sump was smaller.When investigating new control functions, interviews with management executives has been made to get a picture of the problems that may occur in a pump station. The control functions developed had the purpose of reducing the number of clogged pumps as this was the biggest problem.

<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)