Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Smart metering residential demand management programs, such as consumption feedback, variable pricing regimes and the remote control of appliances, are being used to respond to the resource management problems of peak electricity demand, climate change and water shortages. Like other demand management programs, these strategies fail to account for (and respond to) the reasons why people consume resources in their homes, namely to carry out everyday practices such as bathing, laundering, heating and cooling. In particular, comfort and cleanliness practices together constitute most of Australia's potable water consumption in urban centres, and represent most of household energy consumption. In addition, new household cooling practices involving air-conditioning appliances are the major contributor to the nation's rising peak electricity demand, which overloads the electricity system on hot days, costing consumers millions of dollars each year. The oversight of comf ort and cleanliness practices in smart metering demand management programs is concerning because these practices are continuing to shift and change, often in more resource-consuming directions, potentially negating the resource savings achieved through demand management programs. This thesis aims to bridge the problematic divide between the policies and strategies of demand managers, and the day-to-day practices which constitute everyday life. Using the empirical 'hook' of smart metering demand management programs and the everyday practices of comfort and cleanliness, this thesis develops a practice-based conceptual framework to study, understand and analyse these practices and the ways in which smart metering demand management programs reconfigure or further entrench them. A series of qualitative methods were employed in studying 65 households across four research groups, focusing specifically on the household practices of heating, cooling, bathing, laundering, toilet flushing and house cleaning. In addition, 27 interviews were conducted with smart metering industry stakeholders involved or implicated in delivering demand management strategies. Together, these lines of inquiry are used to analyse householders' existing and changing comfort and cleanliness practices, the role of several smart metering demand management strategies in reconfiguring these practices, and potential avenues and opportunities for further practice change in less resource-intensive directions. In particular, this thesis highlights the inherent contradictions and problems in accounting for everyday practices within the dominant demand management paradigm, and offers an alternative paradigm termed the co-management of everyday practices. The thesis concludes by briefly identifying the ways in which smart metering could potentially constrain or catalyse a transition towards this new paradigm.

[pt] Projetos de infraestrutura de transporte de massa desempenham um importante papel no planejamento urbano das zonas urbanas, podendo influenciar na geração de crescimento econômico e o aumento do bemestar social. Entre as variadas opções de modos de transporte de massas existentes está o BRT, caracterizado por ser um sistema que requer um curto tempo de implementação, baixo custo de construção, e possui velocidade operacional e nível de capacidade médios. Em comparação com outros modos de transporte de maior capacidade, como trens e metrôs, o BRT é uma opção viável e atraente ao modo de transporte, dado o ritmo crescente de sua implementação em várias cidades ao redor do mundo nos últimos anos. Neste estudo, foi feita uma análise do corredor BRT TransOeste da cidade do Rio de Janeiro, sob a ótica da Teoria das Opções Reais, com o objetivo de avaliar o valor da opção de expandir a capacidade de passageiros transportados atualmente naquele corredor. Os resultados do estudo sugerem que a incorporação de estratégias interativas das Opções Reais, relativas às decisões de expansão da capacidade máxima de passageiros do corredor e de adiamento do exercício desta decisão, contribuíram para aumentar o valor esperado do projeto em 30,63 por cento. Além disso, foi observado que o aporte de recursos realizados pelo poder concedente contribuiu para aumentar o valor da opção real de expansão. / [en] Mass transportation infrastructure projects play an important role in the process of development of urban areas, leading to economic growth and increase of social welfare. Among the various existing types of transit modes is the BRT, which is characterized by being a system that requires a short time of implementation and a low cost of construction, operating in an medium level of operational speed and capacity. In comparison with other transit modes of higher capacity such as trains and subways, the BRT has shown to be a feasible and attractive alternative, as noted by the increasing pace of its implementation in many cities throughout the world in recent years. In this study, an analysis was made of the BRT TransOeste corridor in the city of Rio de Janeiro from the perspective of the Theory of Real Options in order to assess the value of the option of expanding passenger capacity in that corridor. The results suggest that the incorporation of interactive strategies of Real Options related to the decisions of expanding the maximum capacity of passengers and deferring this decision contributed to increase the expected value of the project by 30.63 percent. In addition, the contribution of the granting authority led to an increase in value of the real option.

With the increasing electrification of todays society the electrical grid is experiencing increasing pressure from demand. One factor that affects the stability of the grid are the time intervals at which power demand is at its highest which is referred to as peak demand. This project was conducted in order to reduce the peak demand through a process called peak shaving in order to relieve some of this pressure through the use of batteries and renewable energy. By doing so, the user of such systems could reduce the installation cost of their electrical infrastructure as well as the electrical billing. Peak shaving in this project was implemented using machine learning algorithms that predicted the daily power consumption in school kitchens with help of their food menus, which were then fed to an algorithm to steer a battery according to the results. All of these project findings are compared to another system installed by a company to decide whether the algorithm has the right accuracy and performance. The results of the simulations were promising as the algorithm was able to detect the vast majority of the peaks and perform peak shaving intelligently. Based on the graphs and values presented in this report, it can be concluded that the algorithm is ready to be implemented in the real world with the potential to contribute to a long-term sustainable electrical grid while saving money for the user.

The 2008 Climate Change Act set legally-binding carbon reduction targets. Demand side management (DSM) includes energy use reduction and peak shaving and offers significant potential to reduce the amount of carbon used by the electricity grid. The demand side management (DSM) schemes that have tried to meet this challenge have been dominated by engineering-based approaches and so favour tools like automation (which aims to make shifting invisible) and pricing (which requires customer response) to shift demand. These approaches tend to focus on the tools for change and take little account of people and energy-use practices. This thesis argues that these approaches are limited and therefore unlikely to produce the level of response that will be needed in future. The thesis therefore investigates the potential for time-shifting domestic energy demand but takes a different angle by trying to understand how people use energy in their daily lives, whether this use can be shifted and some of the implications of shifting it. The centrepiece of the work is an empirical study of eleven households energy-use practices. The interdisciplinary methodology involved in-house observations, interviews, photographs, metered energy data and disruptive interventions. The data was collected in two phases. Initially, a twenty-four hour observation was carried out in each household to find out how energy was implicated in everyday practices. Next, a series of three challenges were carried out, aimed at assessing the implications of disrupting practices by time-shifting food preparation, laundry and work/ leisure. A practice theory approach is used to shift the focus of attention from appliances, tools for change, behaviour or even people, to practices. The central finding of this work is that practices were flexible. This finding is nuanced, in the light of the empirical research, by an extended discussion on the nature of practices; in particular, the relationship between practices and agency and the temporal-spatial locatedness of practices. The findings demonstrate that, in this study at least, expanding the range of demand response options was possible. The research suggests numerous possibilities for extending the potential of practices to shift in time and space, shift the energy used in practices or substitute practices for other non-energy-using practices, though there are no simple technological or behavioural fixes . More profoundly, however, the thesis concludes that infrastructures of provision , such as the electricity grid and the companies that run it, underpin and facilitate energy-use practices irrespective of the time of day and year. In this context technology-led demand response schemes may ultimately contribute to the problem they purport to solve. A more fundamental interrogation of demand and the infrastructures that serve it is therefore necessary and is almost entirely absent from the demand response debate.

MSc (Statistics) / Department of Statistics / Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom. / NRF

In the context of decarbonizing the electricity generation of French non-interconnected territories, the knowledge of future electricity demand, in particular annual and peak demand in the long-term, is crucial to design new renewable energy infrastructures. So far, these territories, mainly islands located in the Pacific and Indian ocean, relies mainly on fossil fuels powered facilities. Energy policies envision to widely develop renewable energies to move towards a low-carbon electricity mix by 2028.  This thesis focuses on the long-term forecasting of hourly electricity demand. A methodology is developed to design and select a model able to fit accurately historical data and to forecast future demand in these particular territories. Historical data are first analyzed through a clustering analysis to identify trends and patterns, based on a k-means clustering algorithm. Specific calendar inputs are then designed to consider these first observations. External inputs, such as weather data, economic and demographic variables, are also included.  Forecasting algorithms are selected based on the literature and they are than tested and compared on different input datasets. These input datasets, besides the calendar and external variables mentioned, include different number of lagged values, from zero to three. The combination of model and input dataset which gives the most accurate results on the testing set is selected to forecast future electricity demand. The inclusion of lagged values leads to considerable improvements in accuracy. Although gradient boosting regression features the lowest errors, it is not able to detect peaks of electricity demand correctly. On the contrary, artificial neural network (ANN) demonstrates a great ability to fit historical data and demonstrates a good accuracy on the testing set, as well as for peak demand prediction. Generalized additive model, a relatively new model in the energy forecasting field, gives promising results as its performances are close to the one of ANN and represent an interesting model for future research.  Based on the future values of inputs, the electricity demand in 2028 in Réunion was forecasted using ANN. The electricity demand is expected to reach more than 2.3 GWh and the peak demand about 485 MW. This represents a growth of 12.7% and 14.6% respectively compared to 2019 levels. / I samband med utfasningen av fossila källor för elproduktion i franska icke-sammankopplade territorier är kunskapen om framtida elbehov, särskilt årlig förbrukning och topplast på lång sikt, avgörande för att utforma ny infrastruktur för förnybar energi. Hittills är dessa territorier, främst öar som ligger i Stilla havet och Indiska oceanen, beroende av anläggningar med fossila bränslen. Energipolitiken planerar att på bred front utveckla förnybar energi för att gå mot en koldioxidsnål elmix till 2028.  Denna avhandling fokuserar på den långsiktiga prognosen för elbehov per timme. En metod är utvecklad för att utforma och välja en modell som kan passa korrekt historisk data och för att förutsäga framtida efterfrågan inom dessa specifika områden. Historiska data analyseras först genom en klusteranalys för att identifiera trender och mönster, baserat på en k-means klusteralgoritm. Specifika kalenderinmatningar utformas sedan för att beakta dessa första observationer. Externa inmatningar, såsom väderdata, ekonomiska och demografiska variabler, ingår också.  Prognosalgoritmer väljs utifrån litteraturen och de testas och jämförs på olika inmatade dataset. Dessa inmatade dataset, förutom den nämnda kalenderdatan och externa variabler, innehåller olika antal fördröjda värden, från noll till tre. Kombinationen av modell och inmatat dataset som ger de mest exakta resultaten på testdvärdena väljs för att förutsäga framtida elbehov. Införandet av fördröjda värden leder till betydande förbättringar i exakthet. Även om gradientförstärkande regression har de lägsta felen kan den inte upptäcka toppar av elbehov korrekt. Tvärtom, visar artificiella neurala nätverk (ANN) en stor förmåga att passa historiska data och visar en god noggrannhet på testuppsättningen, liksom för förutsägelse av toppefterfrågan. En generaliserad tillsatsmodell, en relativt ny modell inom energiprognosfältet, ger lovande resultat eftersom dess prestanda ligger nära den för ANN och representerar en intressant modell för framtida forskning.  Baserat på de framtida värdena på indata, prognostiserades elbehovet 2028 i Réunion med ANN. Elbehovet förväntas nå mer än 2,3 GWh och toppbehovet cirka 485 MW. Detta motsvarar en tillväxt på 12,7% respektive 14,6% jämfört med 2019 års nivåer.

[pt] No Brasil, grandes consumidores podem estabelecer seus contratos de energia elétrica em dois ambientes: Ambiente de Contratação Regulado e Ambiente de Contratação Livre. Grandes consumidores são aqueles que possuem carga igual ou superior a 2 MW e podem ser atendidos sob contratos firmados em quaisquer um desses ambientes. Já os consumidores com demanda contratada inferior a 2 MW e superior a 500 kW podem ter seu contrato de energia estabelecido no Ambiente de Contratação Livre proveniente de geração de energia renovável ou no Ambiente de Contratação Regulada através das distribuidoras de energia. A principal vantagem do Ambiente de Contratação Livre é a possibilidade de negociar contratos com diferentes parâmetros, como, por exemplo, preço, quantidade de energia e prazo. Eventuais diferenças entre a energia contratada e a consumida, são liquidadas ao preço de energia de curto prazo, que pode ser bastante volátil.Neste caso o desafio é estabelecer uma estratégia de contratação que minimize os riscos associados a este ambiente. Esta dissertação propõe uma metodologia que envolve a simulação estatística de cenários correlacionados de energia, demanda máxima e preço de curto prazo (também chamado de PLD – Preço de Liquidação das Diferenças) para serem inseridos em um modelo matemático de otimização estocástica, que define os parâmetros ótimos da contratação de energia e demanda. Na parte estatística, um modelo Box e Jenkins é usado para estimar os parâmetros das séries históricas de energia e demanda máxima com o objetivo de simular cenários correlacionados com o PLD. Na parte de otimização, emprega-se uma combinação convexa entre Valor Esperado (VE) e Conditional Value-at-Risk (CVaR) como medidas de risco para encontrar os valores ótimos dos parâmetros contratuais, como a demanda máxima contratada, o volume mensal de energia a ser contratado, além das flexibilidades inferior e superior da energia contratada. Para ilustrar a abordagem proposta, essa metodologia é aplicada a um estudo de caso real para um grande consumidor no Ambiente de Contratação Livre. Os resultados indicaram que a metodologia proposta pode ser uma ferramenta eficiente para consumidores no Ambiente de Contratação Livre e, dado à natureza do modelo, pode ser generalizado para diferentes contratos e mercados de energia. / [en] In Brazil, big consumers can choose their energy contract between two different energy environments: Regulated Contract Environment and Free Contract Environment. Big consumers are characterized by installed load capacity equal or greater than 2 MW and can firm an energy contract under any of these environments. For those consumers with installed load lower than 2 MW and higher than 500 kW, their energy contracts can be firmed in the Free Contract Environment using renewable energy generation or in the Regulated Contract Environment by local distribution companies. The main advantage of the Free Market Environment is the possibility of negotiating contracts with different parameters such as, for example, price, energy quantity and deadlines. Possible differences between contracted energy and consumed energy are settled by the spot price, which can be rather volatile. In this case, the challenge is to establish a contracting strategy that minimize the associated risks with this environment. This thesis proposes a methodology that involves statistical simulation of correlated energy, peak demand and Spot Price scenarios to be used in a stochastic optimization model that defines the optimal energy and demand contract parameters. In the statistical part, a Box and Jenkins model is used to estimate parameters for energy and peak demand in order to simulate scenarios correlated with Spot Price. In the optimization part, a convex combination of Expected Value (EV) and Conditional Value-at-Risk (CVaR) is used as risk measures to find the optimal contract parameters, such as the contracted peak demand, the seasonal energy contracted volumes, in addition to the upper and lower energy contracted bound. To illustrate this approach, this methodology is applied in a real case study for a big consumer with an active Free Market Environment contract. The results indicate that the proposed methodology can be a efficient tool for consumers in the Free Contract Environment and, due to the nature of the model, it can be generalized for different energy contracts and markets.