A thesis based around saving response time costs as well as respecting electrical costs of a homogenous multi-server system. / In this thesis we consider the impacts of energy costs as they relate to Size Interval Task Assignment Equally--loaded (SITA-E) systems. We find that given systems which have small and large jobs being processed (high variance systems) we could in some cases find savings in terms of energy costs and in terms of lowering the mean response times of the system. How we achieve this is by first working from SITA-E, wherein servers are always on to Electrically Aware SITA-E (EA-SITA-E) by seeing if it is beneficial to make any of our servers rotate between being on and being off as needed. When most beneficial to do so we will turn off some of the servers in question, after this is completed we reallocate some of the jobs that are on the servers that we decide will be cycling to servers that will remain on indefinitely to better use their idle time. This also lowers the mean response time below what we originally saw with SITA-E, by lowering the variance in the sizes of jobs seen by the servers with the longest jobs. These long--job servers are by far the most impacted by the variance of the sizes of the jobs, so it is very desirable to lower this variance. The algorithm contained here can provide benefits in terms of both energy costs and mean response time under some specific conditions. Later we discuss the effect of errors in our assumed knowledge of task sizes. This research contributes methodology that may be used to expand on EA-SITA-E system design and analysis in the future. / Thesis / Master of Science (MSc) / The intention of this research is to be able to improve on existing size interval task-based assignment policies. We try to improve by turning servers off at key times to save energy costs, while not sacrificing too greatly in terms of mean response time of the servers, and in some cases even improving the mean response time through an intelligent re-balancing of the server loads.

Thesis (MBA) --University of Limpopo, 2010 / Management of electricity usage by household plays an important role in the growth of the country’s economy, and the avoidance of load shedding from Eskom. Electricity usage is very important for the growth of the economy and creation of job opportunities. The management of electricity usage by household’s customers will play a very critical role in the growth of the country’s economy and the creation of jobs. The contribution of households in applying the electricity saving techniques will reduce the risk of load shedding from Eskom during summer and winter. The save usage of electricity will give Eskom enough chance to build power stations to keep up with the demands and the growth of the South African economy. For the household customers to contribute positively, Eskom and the municipalities should conduct road shows to educate customers about the save usage of electrical appliances and the saving techniques that can be applied by households. Customer awareness campaigns should be conducted in both rural, urban and semi-urban areas. It will be very important for Eskom to communicate with the municipalities to run the awareness campaigns in the urban areas due to the high demand of electricity by households coming from the urban areas. The majority of households who are using the high consuming appliances of electricity reside in the urban areas and can play a vital role in minimizing the risk of load shedding that affected the country negatively in 2008. The quantitative research method was followed for this research. A questionnaire was used to collect the data from the household’s participants. Forty households from the five areas of Polokwane took part in the research totalling 200 participants. It was discovered that the customer awareness campaigns were conducted by Eskom in the areas where they service customers, although there are some gaps in other areas where the customers are complaining about lack of road shows to teach households about the electricity saving tips. Municipalities in all the five areas of Polokwane where the research was conducted are still lagging behind with the customer’s awareness campaigns. However Eskom customer services and the municipalities can work together and conduct road shows to reach more customers in order to reduce the risk of load shedding and power interruptions.

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D. / In today’s highly competitive and deregulated electricity market, companies in the electricity power generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers’ electricity consumption behavior. This strategic information is vital in forecasting and managing the future electricity demand. This information is also very important in utility company’s long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of electric load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: I) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. II) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for categorizing the load profile data. III) The frequency domain load profile statistics and forecasting models. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately.

Achieving sustainable consumption of energy is an important issue due to the increasing demand for energy and its environmental impact. One of the biggest consumers of the global energy production is the residential sector. Factors determining pattern of energy consumption in this sector are firstly, characteristics of the buildings and equipment and appliances which are used inside them and secondly, people who are using the buildings. The former could be approached by using efficiency strategies; i.e. designing and using materials and utilities which are low energy demanding or reducing consumption of energy. The latter could be reached by adoption of demand side management strategies which could improve pattern of energy consumption by the end users. Combining these strategies bring out energy-smart buildings with energy-smart people as the users. This project aims at introducing potential approaches to strategies of promoting sustainable behavior regarding energy consumption in individuals, with the focus on the students of Linköping University living in the properties of housing company of the city, Studentbostäder. For fulfilling this purpose, literature review has been done for finding influencing factors on and strategies for shaping of pro-environmental behavior. In the next step, two projects with focus on demand side management for changing energy consumption of individuals have been studied. Afterward, a questionnaire based on the results of the literature review was prepared and used to gain an understanding of first: attitude, values, knowledge, and awareness of students of Linköping University regarding environmental issues, and second: point of view of the students toward the strategies for shaping pro-environmental behavior. Results of the above mentioned methods were used for identifying characteristics of a demand side management project based on provision of feedback on energy consumption for the users. It has been proposed that designing and implementing such project has the potential of affecting pattern of energy consumption by people and lead to its reduction, especially among students accommodating at housing company of city of Linköping, Studentbostäder. More studies are needed for finding feasibility of implementing such project.

In the development of society towards renewable energy sources, the target in Sweden is 100% energy supply from renewable energy sources by the year 2040. This requires increased energy production from renewable, but also energy optimization of existing buildings. The housing and service sector which includes households and the public services account for about 40 % of Sweden’s total energy use. It is estimated that preschools and schools have an area of 35 million m² which have an energy savings potentials of 0,7-1 TWh in the electricity consumption and 0,9 TWh in energy use for heating. With regard to energy optimization, it is not only interesting to investigate a buildings total energy use on an annual or monthly basis, but also on shorter time intervals such hourly energy use, because of the uneven energy production of some renewable energy sources such as solar and wind. The purpose of this work is to study the energy usage for some of Västerås preschools and schools, and on the basis of it propose some energy optimization actions. To achieve this a literature study has been carried out to get knowledge about how energy usage is at preschools and schools, as well as to gain insight into what actions are appropriate to perform and how they savings look like. Other than that four objects has been studied, two preschools and two schools, one of each kind were chosen amongst those with the highest energy usage among Västerås city’s preschools and schools and one of each kind amongst those with the lowest usage. The annual energy usage have been calculated and been compared to the actual usage, and the monthly and hourly energy usage for district heating have been studied as well as the electricity usage along the day for different periods. The studied periods and energy usage shows that the energy usage, both for the monthly and hourly,  for the district heating moves with regards to the outdoor temperature with some exceptions. As for the electricity usage it shows that the energy usage is even with some exceptions and there is a difference between different outdoor temperature intervals. Some conclusions could be drawn among others that the objects with higher energy usage where older buildings and the objects with lower energy usage where newer ones. There are some energy optimizations actions for the objects which would lower the energy consumption, both for district heating and electricity usage.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.

The development of Digital Twin Technology, with a focus on addressing environmental concerns, has elevated the priority of Industry 4.0-based solutions. The study aimed to design a simulation model to optimize the electricity consumption of the electric bus fleet at Arlanda Airport as a subproject of the main Digital Twin project. The study found that there was no current model designed to simulate electricity consumption by formal methods, Z notation.  The research is guided by four primary objectives find power management strategies for e-buses, identify critical parameters affecting their energy consumption, create a Z Notation simulation model, and assess this model. Through a thorough review of the literature and methodical application, power management strategies were defined, and significant energy consumption parameters were identified. The model's usefulness in modelling and optimizing electricity usage was demonstrated by its careful construction using Z Notation and evaluation with Spivey's Fuzz Checker. The paper demonstrates the use of Design Science Research in creating a digital twin-based simulation, which has important implications for transportation systems as well as theoretical advances in simulation methodologies. Throughout the developed Z notations, it provide a proper insight into operational efficiency and sustainability in energy consumption.  The study also emphasizes the drawbacks of using Z Notation, such as its steep learning curve and limited community assistance. To improve the accuracy of electricity consumption forecasts, future research should use predictive analytics and fine-tune the model granularity. The thesis demonstrates how design science can be applied for preparing specification of services but not only in software development. This work lays the groundwork for more extensive applications in digital twin technologies and energy optimization, in addition to contributing to our understanding of e-bus power management at Arlanda Airport.