Mainframes has been used for well over 50 years and are built for processing demanding workloads fast, with the latest models using IBM’s z/Architecture processors. In the time of writing, the mainframes are a central unit of the world’s largest corporations in banking, finance and health care. Performing, for example, heavy loads of transaction processing. When IBM bought RedHat and acquired the container orchestration platform OpenShift, the IBM lab in Poughkeepsie figured that a new opportunity for the mainframe might have opened. A media streaming server built with OpenShift, running on a mainframe. This is interesting because a media streaming solution built with OpenShift might perform better on a mainframe than on a traditional server. The initial question they proposed was ’Is it worth running streaming solutions on OpenShift on a Mainframe?’. First, the solution has to be built and tested on a mainframe to confirm that such a solution actually works. Later, IBM will perform a benchmark to see if the solution is viable to sell. The authors method includes finding the best suitable streaming software according to some criterias that has to be met. Nginx was the winner, being the only tested software that was open-source, scalable, runnable in a container and supported adaptive streaming. With the software selected, configuration with Nginx, Docker and OpenShift resulted in a fully functional proof-of-concept. Unfortunately, due to the Covid-19 pandemic, the authors never got access to a mainframe, as promised, to test the solution, however, OpenShift is platform agnostic and should, theoretically, run on a mainframe. The authors built a base solution that can easily be expanded with functionality, the functionality left to be built by IBM engineers is included in the future works section, it includes for example, live streaming, and mainframe benchmarking. / Stordatorer har använts i över 50 år och är byggda för att snabbt kunna bearbeta krävande arbetsbelastningar, med de senaste modellerna som använder IBMs z/Architecture processorer. I skrivande stund är stordatorerna en central enhet i världens största företag inom bank, finans och hälsovård. De utför, till exempel, väldigt stora mängder transaktionsbehandling. När IBM köpte RedHat och förvärvade container-hanteringsplattformen OpenShift, tänkte laboratoriet i Poughkeepsie att en ny möjlighet för stordatorn kanske hade öppnats. En mediaströmningsserver byggd med OpenShift, som körs på en stordator. Detta är intressant eftersom en mediaströmningslösning byggd med OpenShift kan fungera bättre på en stordator än på en traditionell server. Den initiala frågan som ställdes var ’Är det värt att köra strömningslösningar på Openshift på en Mainframe?’. Först måste lösningen byggas och testas på en stordator för att bekräfta att en sådan lösning faktiskt fungerar. Senare kommer IBM att utföra ett riktmärke för att se om lösningen är lämplig att sälja.    Författarnas metod inkluderar att hitta den bästa strömningsprogramvaran enligt vissa kriterier som måste uppfyllas. Nginx var vinnaren samt den enda testade programvaran som var öppen källkod, skalbar, körbar i en container och stödde adaptiv strömning. Med den valda programvaran resulterade konfigurationen av Nginx, Docker och OpenShift i en fullt funktionell konceptlösning. På grund av Covid-19-pandemin, fick författarna aldrig tillgång till en stordator, som utlovat, för att testa lösningen. OpenShift är dock plattformsagnostisk och ska teoretiskt sett kunna köras på en stordator. Det som författarna lämnade åt framtida ingenjörer att utforska är en studie som inkluderar fler mjukvaror, även betalversioner, eftersom den här studien endast innehåller öppen källkod. Samt en utvidgning av den befintliga lösningens funktionensuppsättning.

No description available.

Public transport is an efficient way to transport large volumes of travellers. However, there are systemic issues that make it hard for conventional public transport to provide efficient service on finer levels, like first- and last-mile problems or low-demand areas. One of the potential solutions that has been getting a lot of attention recently in research and real practice is Demand-Responsive Transport(DRT). The main difference between demand-responsive services and conventional public transport is the need for explicit requests for a trip from the travellers. The service then adapts the routes of the vehicles to satisfy the requests as efficiently as possible. One of the aims of such transport services is to combine the flexibility and accessibility of travel modes like taxis and private cars with the efficiency of buses achieved through ride-sharing.DRT has the potential to improve public transport in, for example, low population density areas or for people with mobility limitations who could request a trip directly to a home door. Historically DRT has been extensively used for special transportation while the recent trend in research and practice explores the possibility of using this service type for the general population.The history of DRT shows a large degree of discontinued trials and services together with low utilisation of vehicles and limited efficiency levels. In practice, this leads to measures restricting the trip destination, times when service is available, or eligibility to use the service at all in case of special transport DRT. Due to the limited use of DRT services, there is little data collected on the efficiency of the service and transport agencies exploring the possibility of introducing this new service type face difficulties in estimating its potential.The main goal of this thesis is to contribute towards developing a decisionsupport method for transport analysts, planners, or decision-makers who want to evaluate the systemic effect of a DRT service such as costs, emissions and effecton society. Decision-makers should be able to evaluate and compare a large variety of DRT design choices like booking time restrictions, vehicle fleet type, target trip quality level, or stop allocation pattern. Using a design science, we develop a simulation approach which is evaluated with two simulation experiments. The simulation experiments themselves provide valuable insight into the potential of DRT services, explore the niche where DRT could provide the most benefits and advocate taking into account the sustainability perspective for a comprehensive comparison of transport modes. The findings from the simulation experiments indicate that DRT, even in its extreme forms like fully autonomous shared taxis, does not show the level of efficiency that could result in a revolution in transportation — it is hard to compete inefficiency with conventional public transport in urban zones. However, in scenarios with lower demand levels, it could be more efficient to replace conventional buses with a DRT service when considering costs and emissions. We also show that, when integrated with conventional public transport, DRT could help alleviate the last-mile problem by improving accessibility to long-distance lines. Additionally, if car users are attracted to public transport with the help of DRT, there is a potential to significantly reduce the total level of emissions. The simulation results indicate that the proposed simulation method can be applied for the evaluation of DRT. The implementation of trip planning combining DRT and conventional public transport is a major contribution of this thesis. We show that the integration between services may be important for the efficiency of the service, especially when considering the sustainability aspects. Finally, this thesis indicates the direction for further research. The proposed simulation approach is suitable for the estimation of the potential of DRT but lacks the ability to make a prediction of the demand for DRT. Integration of a realistic mode choice model and day-to-day simulations are important for making predictions. We also note the complexity of the DRT routing for large-scale problems which prohibits a realistic estimation with simulation and the efficient operation of the service.

Electrical demand flexibility is a key component to enabling a low cost, low carbon grid. In this study, residential electricity demand and flexibility is explored from the lens of a virtual power plant operator. Individual and aggregate asset consumption is analysed using a pool of >10,000 household assets over 6 years. Key safety, comfort and availability limitations are identified per asset type. Pool flexibility is analysed using a combination of past data and principled calculations, with flexibility quantified for different products and methods of control. A machine learning model is built for a small pool of 200 assets, predicting consumption 24 hours in advance. Calculated flexibility and asset limitations are then used within an optimisation model, leveraging flexibility and combining the value of self consumption and day ahead price optimisation for a residential home. / Flexibilitet i efterfrågan av elektricitet är essentiellt för att möjliggöra ett elnät med låga kostnader och utsläpp. I denna studie undersöks elanvändning av en bostad samt flexibilitet i perspektiv från en virtuell kraftverksoperatör. Individuell och sammanlagd konsumtion analyseras genom tillgång av data från >10 000 bostäder över 6 år. Begränsningar av säkerhet, komfort och tillgänglighet identifieras per tillgångstyp. Sammanlagda flexibiliteten analyseras genom en kombination av tidigare data och principiella beräkningar, med flexibilitet kvantifierad för diverse produkter och kontrollmetoder. En modell för maskininlärning utvecklades för 200 bostäder och förutser konsumtion 24 timmar i förväg. Den beräknade flexibiliteten och tillgångsbegränsningar används sedan i en optimeringsmodell som utnyttjar flexibilitet och kombinerar värdet av självkonsumtion och optimerat pris för nästkommande dag för ett bostadshus.

The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

yes / This work describes how the design and operation parameters of the Multi-Stage Flash (MSF) desalination process are optimised when the process is subject to variation in seawater temperature, fouling and freshwater demand throughout the day. A simple polynomial based dynamic seawater temperature and variable freshwater demand correlations are developed based on actual data which are incorporated in the MSF mathematical model using gPROMS models builder 3.0.3. In addition, a fouling model based on stage temperature is considered. The fouling and the effect of noncondensable gases are incorporated into the calculation of overall heat transfer co-efficient for condensers. Finally, an optimisation problem is developed where the total daily operating cost of the MSF process is minimised by optimising the design (no of stages) and the operating (seawater rejected flowrate and brine recycle flowrate) parameters.

Utility sponsored conservation programs encourage participants to consume less energy. One of the most popular methods used to achieve this is to offer monetary rebates to purchasers of high-efficiency appliances. The costs of these conservation programs are then passed-on to all customers as increased energy prices. Economic theory predicts that the income and substitution affects should decrease the consumption of non-participants in the programs and may increase the consumption of participants. Recent claims in the literature argue that the standard net benefit tests used to evaluate these programs fail to incorporate the full impact of the income and substitution affects. Relying on these theoretical arguments, new evaluation techniques, referred to as Net Economic Benefits (NEB) tests, are being introduced as solutions to this problem. Using the actual experience of a natural gas utility, this thesis analyzed the need for NEB evaluations. The results show that the price of gas is not a significant factor in determining household gas consumption. Therefore, empirical evidence cannot support the NEB claims. The evidence does show that, on an average annual basis, participants are consuming less than non-participants. / Master of Arts

Oregon's nursery and greenhouse industry has ranked the first in the State's agricultural for 18 years. The majority of nursery sales from the Pacific Northwest come from Oregon. Due to data limitations, empirical study of the Oregon nursery industry is rare. The present dissertation consists of three essays that analyze the demand and supply of inputs and outputs and the relationship between producers and retailers in the Oregon nursery industry. Chapter 2 identifies the major factors affecting farm labor supply and demand and evaluates their relative importance in the Oregon nursery industry from 1991 to 2008. Empirical results show that border control effort doesn't have an influential role in labor supply, while the Oregon and Mexican minimum wage do. It is because of the substantial gap between the U.S. and Mexican economies, reflected for an example in the minimum wage gap, which attracts a continual flow of immigrants. Risk of border apprehension is not great enough to prevent the flow. Increases in Oregon minimum wage is more effective than border apprehension policies in boosting the average wage and in reducing the number of hours that illegal immigrants work in the nursery sector. Chapter 3 investigates producers' and retailers' choices of, and reactions to, various contract types in the Oregon nursery industry from 2005 to 2010. As new and fast-growing retailers in the industry, big-box stores are less likely than independent retailers to make pre-order contracts with the producer. However, once a pre-order contract is chosen, big-box stores demand more days of pre-order interval than independent retailers do. Transactions with independent retailers exhibit – on average over the sample range – scale economies and scope diseconomies. Boosting per-transaction revenue scale and the number of species sold to big-box stores enhances transaction efficiency. Chapter 4 examines the interaction between supply and demand in Oregon nursery products. The result indicates that the production and transaction costs are major drivers on the supply side, while transportation costs and consumer demand for nursery products play important roles on the demand side. At the genus level, the supply elasticities of coniferous plants are larger than those of deciduous plants, which in turn are higher than those of flowering plants. The demand elasticities are the lowest in coniferous trees followed by deciduous plants, then flowering plants. Price discounts on plants with high demand elasticities would significantly boost sales and enlarge the market, while those on plants with low demand elasticities would have less sales impact. Empirically, patenting seems to bring no direct signs of greater profitability. The wholesale nursery may wish to reconsider the pricing and marketing policies of its patented plants to differentiate them more effectively from its non-patented plants. / Graduation date: 2013

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ) as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.