South African electricity tariffs were relatively low compared to the rest of the world. The average South African business has for long taken this advantage for granted and is now surprised to realise that electricity is becoming an expensive and scarce commodity. The South African electricity supply industry is far more complex than the average person may think. The infrastructure supporting this industry is extremely costly; takes long to develop and build and requires careful planning and management. There are many sources of energy and many technologies for generating electricity. However, many of these do not appear quite ready to serve the needs of the industry. The manufacturing industry depends heavily on electricity. The recent power outages and tariff increases have served as a cruel reminder of this fact. The automotive sector has lost many days of production and the increasing electricity costs erode the profitability of the affected companies. The automotive suppliers and vehicle manufacturers have expressed their concerns. Indications are that some have reduced the number of employees and may even face bankruptcy. This research aims to gain the perspective of senior managers in the automotive industry regarding the impact of the increased electricity tariffs on their manufacturing costs. Naturally, all electricity consumers will be affected by this. However, this research aims to investigate the significance of the effect on the automotive industry as well as obtain some indication of which factors determine the level of dependency. Each company has to react strategically to the situation and apply those measures which are available to them. This research determines how strongly the industry feels about reacting and which strategic measures they will apply. The outcome is descriptive of the circumstances in the industry and indisputably serves as an indication of the financial impact of electricity tariff increases.

Electric pumps are extensively used in many industrial and commercial applications worldwide and account for about twenty percent of the world’s electrical energy demand. The useful energy consumed by the pumps is less than what they demand due to inefficiency caused by a number of reasons including the mismatch between the rated operating conditions and their actual operating conditions. Some studies including those done by the US Department of Energy, show that as much as 30-50% of energy consumed by pumping systems could be saved through equipment and control changes in the pumping systems.   Zambia, like many countries, faces an electricity shortage. Improving energy efficiency in pumping related operations can help save electricity costs and thus promote sustainable development and ultimately reduce global warming. This document discusses various methods of reducing pumping related electricity costs which can be categorised under either mechanical or electrical methods.  Preliminary energy audits on some pumping infrastructure for three water utility companies in Zambia were carried out and results showed various opportunities for saving electricity costs. Detailed study on selected pumping infrastructure revealed that as much as fifty four (54%) electricity cost savings could result at one pump station by correcting the operating points of pumps such that the Best Efficiency Points (BEP) on the pump characteristic curves matched the pumping system head requirements. / <p>Interactive presentation was done via Skype</p>

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.

Idag är det möjligt att koppla upp utrustningar mot nätet hemma och ha ett Smart Hem. Ett hushåll har två elavtal, elnät och elhandel. Elnät är bunden till området man bor och kan inte bytas ut, men det är annorlunda med elhandel. Hos elhandel vill man ha det lägsta priset på el och det är där timpris spelar roll. Elkostnader kan bli höga och för att minska kostnaderna samt använda grön energi har detta examensprojekt tagit fram ett system som ska kunna informera en användare om spotpriset respektive väder. Projektet har använt sig av Android Studio (front-end), Firebase Realtime database och ett javaprogram (back-end). Med Grönt Väders API och Google Maps Geocoding API har data om användaren, elpriser och väder samlats i databasen. Resultatet av projektet blev en mobilapplikation som visar information om dagens elpris, väder och andra faktorer som hjälper ett hushåll med egenproducerad energi att följa. Dock kunde elpriser inte jämföras med vädret. För vidareutveckling hade back-end kunnat vara på en Raspberry Pi där man kopplar en vitvara som man kan styra i framtiden. / Today it is possible to connect equipment to the network at home and have a Smart Home. A household has two electricity agreements, electricity networks and electricity trading. Electricity network is tied to the area you live and cannot be changed, but electricity trading is a different matter. In electricity trading, what you want is the lowest price and that is where the hourly rate matters. Electricity costs can be high and to reduce costs and use green energy, this degree project has developed a system that will inform a user about the spot price and weather. The project has used Android Studio (front-end), Firebase Realtime database and a Java program (back-end). With the Green Weather API and the Google Maps Geocoding API has information about the user, electricity prices and weather been collected in the database. The results in this project was an app that showed electricity prices of the day, weather and other factors that helps a household with self-produced energy to follow. However, electricity prices could not be compared with weather. Further development would be to have the back-end running on a Raspberry Pi where appliances could be controlled in the future.