Climate change has already had major impacts on our planet. Loss of sea ice, accelerated sea level rise and longer, more intense heat waves, are a few of these. Many scientists believe that a continued climate change will have even more severe impact on our planet. To tackle the climate change, a fast transition towards renewable energy sources is necessary. One of the most promising sources of renewable energy is solar energy. To achieve the goal of making the world more reliable on solar energy, various actors try to improve the technology and the financial basis regarding this way of extracting energy. In this transition, calculations have to be as accurate as possible, in order to benefit from them when installing solar panels. This bachelor thesis intends to create an investment model for solar plants and an optimization of the plant’s size based on net present value. The model is built in Microsoft Excel, and factors such as electricity prices, electricity production/consumption and several others are taken into account. Based on the comparison with a case study and the calculations made by the model, the results suggest a reliable model. On behalf of Herrljunga Elektriska AB, the model is created to help them generate reliable and fast investment calculations, which will hopefully bring value to their business.

Please read the abstract in the front matter this document / Dissertation (M Eng (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying travelers’ behavior by providing rewards to enable shifts to more energy-efficient modes, (e.g., from auto to either bus or bicycles). Optimization conditions are formulated for the problem to understand solution properties, and numerical tests are carried out to study the effects of system parameters (e.g., token budget and coefficient of tokens) on the optimal solutions (i.e., energy savings). The multinomial logit model is used to formulate the full problem, comprised of an objective function and constraint of a token budget ranging from $5,000-$10,000. Comparably, the full problem is computationally reduced by various parameterization strategies, in that the number of tokens assigned to all travelers’ is parameterized and proportional to the expected energy savings. An optimization solution algorithm is applied with a global and local solver to solve a lagrangian sub-problem and a duo of heuristic solution algorithms of the original problem. These were determined necessary to attain an optimal and feasible solution. Input data necessary for this analysis is obtained for the Town of Amherst, MA from the Pioneer Valley Planning Commission (PVPC). The results demonstrated strong evidence to conclude a positive correlation between the system’s energy savings and the aforementioned system parameters. The local and global solvers solution algorithm reduced the average energy consumption by 11.48% - 19.91% and12.79% – 21.09% consecutively for the identified token budget range from a base case scenario with no tokens assigned. The duo of lagrangian heuristic algorithms improved the full problems solution i.e., higher energy savings, when optimized over a local solver, while the parameterized problem formulations resulted in higher energy savings when compared to the full problems’ formulation solution over local solver, but higher energy savings compared over the global solver. The Computational run-time for the global and local solvers solution algorithm for the full problem formulation required 43 hours and 24 minutes consecutively, while the local solver for the lagrangian heuristics and parameterized problem solution algorithm took 13 minutes and 7 minutes consecutively. Future research on this paper will be comprised of a bi-level optimization problem formulation where a high level optimization aims at maximizing system-wide energy savings, while a low-level consumer surplus maximization problem is solved for each system user.

Philosophiae Doctor - PhD / Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These three models are complemented by a load-balancing model structured to balance energy consumption on the network of cluster heads that forms the backbone for any cluster-based hierarchical sensor network. We present two novel approaches for clustering the nodes of a hierarchical sensor network: a) a distance-aware clustering where nodes are clustered based on their distance and the residual energy and b) a service-aware clustering where the nodes of a sensor network are clustered according to their service offered to the network and their residual energy. These approaches are implemented into a family of routing protocols referred to as EOCIT (Energy Optimization using Clustering Techniques) which combines sensor node energy location and service awareness to achieve good network performance. Finally, building upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed as a novel multipath routing protocol that finds energy efficient routing paths for sensor readings dissemination from the cluster heads to the sink/base station of a hierarchical sensor network. Our simulation results reveal the relative efficiency of the newly proposed approaches compared to selected related routing protocols in terms of sensor network lifetime maximization.

This diploma thesis describes possibilities of thermal insulation of a detached house and choice optimization of insulating materials especially based on economic and ecologic factors. The thesis describes basic physical effects that are associated with heat transfer and that should be respected in insulation design. These effects influence the choice of suitable insulating materials based on their properties.

The diploma thesis with research of efficiency of renewable and low-potential energy sources of buildings. It is available on numerical simulations for sharing office and heating and cooling system buildings in DesignBuilder. There are various energy sources and ways of controlling heating and cooling. The results are evaluated in terms of time, after the expected compromises in the building, in terms of energy consumption and its price.

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.

The research part of the diploma thesis is focused on an issue of energy consumption in sludge treatment at wastewater treatment plants divided by the number of population equivalent. The aim of the practical part is to set indicators and propose evaluative criteria concerning energy performance of urban wastewater treatment plants. The verification of proposed criteria and energetic analysis result from data of the wastewater treatment plant in Novy Jicin.

The main aim of this thesis is design air-conditioning system and optimization of indoor climate by solar shading. In theoretical part I focused on dividing and description off different types of solar shading. In the next part I designed suitable air-conditioning system for the open space office. The last part is first concentrating on the simulation of the indoor climate in software BSim2014. And then on evaulation of the appropriate solar shading system, whick I can use in the building.

Die verschiedenen Maßnahmen zur Senkung des Kraftstoffverbrauchs von dieselbetriebenen Schienenfahrzeugen sind Gegenstand der Forschung und werden in den kommenden Jahren, bedingt durch weltweit anwachsendes Transportvolumen, begrenzte Ressourcen und steigendes Umweltbewusstsein, weiter an Bedeutung zunehmen. Die Nutzung der Abgasenthalpie des Dieselmotors stellt dabei eine Möglichkeit zur Reduktion des Kraftstoffverbrauchs dar. Im Vergleich zu anderen Methoden ist der absolute Einfluss auf den Verbrauch zwar geringer, eine Kombination ist jedoch möglich und führt zu einer Verbesserung der Energieeffizienz. In dieser Arbeit wird die Möglichkeit der Abgasenthalpienutzung mittels thermoelektrischer Generatoren (TEG) betrachtet. Diese hat den Vorteil einer hohen Leistungsdichte, eines geringen Wartungsaufwands sowie einer einfachen Integration in die Topologie der exemplarisch untersuchten Lokomotive. Bei der Umsetzung sind wesentliche Randbedingungen wie die herrschenden Abgastemperaturen und -massenströme das verfügbare Einbauvolumen und der maximal zulässige Abgasgegendruck, zu beachten. Weiterhin stellt sich die thermomechanische Festigkeit der thermoelektrischen Module (TEM) als Herausforderung dar. Dies macht eine Optimierung auf Systemebene unabdingbar, welche mit numerischen Methoden effizient und wirtschaftlich durchgeführt werden kann. Aufgrund der Systemkomplexität und unterschiedlichen Skalierungsebenen der Systemkomponenten wird dazu ein Multi-Layer-Multi-Scale Optimierungsansatz vorgestellt, welcher eine realitätsnahe Konzeption und Bewertung des Systems ermöglicht. Wesentliche Fragestellungen der thermomechanischen Festigkeit des Moduls, der technischen Realisierbarkeit bis hin zur Gestaltung einer systemleistungsorientierten Betriebsstrategie werden im Laufe des implementierten Prozesses mit Hinblick auf die maßgebende maximale Systemleistung adressiert und beantwortet.