The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.

The power utility of South Africa, Eskom, expected a supply shortfall of approximately 400MW between February and August 2006 in the Western Cape. The peak of the crisis was in mid-winter (June to August). This shortfall was firstly caused when Eskom experienced a breakdown on the one of the two nuclear supply units. Secondly the remaining of the Koeberg units was due for refuelling which necessitated the shut-down of the reactor. No electricity was therefore generated by both units. It was clear that if electricity demand was not effectively curbed, extensive power outages would be experienced; which was the case. Various demand side management (DSM) programmes were rolled-out to address lighting, switching from electricity to gas for cooking, compensating customers that could generate own electricity, energy efficiency and load curtailment in the education, commercial, and industrial sectors, as well as an extensive energy efficiency campaign. It is shown in this study that the most constrained periods were expected during the evening peak and was a consequence of electricity consumption in the residential sector. The residential evening peak is very prominent and primarily caused by water heating, cooking, space heating, lighting, and appliances. None of the mentioned programmes focused on the residential evening peak. Traditional residential DSM technologies were almost impossible to implement in the short timeframe because there are more than 625,000 residences in the Western Cape. A solution was looked for that could be implemented in a relatively short period to address the residential evening peak. This study focuses on the development, implementation, and performance evaluation of Power Alert – An innovative residential load management system. The need for such a system was identified and the expected impact was determined through a feasibility study. Power Alert was designed to be a link between Eskom and the public through the national television broadcaster. It was operational during the whole Western Cape winter. A methodology to determine the impact of Power Alert was also developed to demonstrate the actual load reductions. The methodology was applied and Power Alert demonstrated that it was a valuable residential load management tool that could be designed and implemented in a much shorter time than conventional residential DSM measures. / Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2010.

Demand Side Management (DSM) is one of the most viable and sustainable short term methods to address the shortfall in electricity generation in South Africa. This is because DSM projects can be implemented relatively quickly and inexpensively when compared with alternative generation options. This specifically applies to the mining industry. South African mines presently consume 15% of Eskom-generated electricity. Mine compressed air systems are some of the biggest users, consuming approximately 21% of mine electricity consumption. Electricity savings on compressed air systems are therefore important. With this study, various Energy Efficiency methods on compressed air systems were investigated. These methods include variable speed drives on compressor motors, temperature control of compressor discharge, minimising pressure drops in the air distribution systems, eliminating compressed air leaks, and optimising compressor selection and control. The most efficient strategies were identified, taking into account factors such as financial viability, sustainability, and ease of implementation. The best strategies were found to be the optimised control and selection of compressors, minimising compressed air leaks, and the optimal control of system pressure. These strategies were implemented and tested on large compressed air systems in gold and platinum mines. Savings of between 10% and 35% on the maximum demand of the systems were achieved. In present monetary terms this translates to as much as R108 million savings for the mines per year at the end of 2009 tariffs. If total mine compressed air electricity consumption can reduce by 30%, it will result in nearly a 1% reduction in total Eskom demand. This shows that mine compressed air savings can make a significant contribution to the drive for Energy Efficiency in South Africa. / Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2010.

Emerging environmental designs have reacted toward a new environmental scope and agenda to provide a better understanding of functionality and sustainability. Biomimicry is a recent environmental approach that mimics nature for improving processes and structures design. This has important consequences for urban development and planning. The most relevant for us is that these new structures help us to breakdown the false dichotomy between landscape and architecture and therefore are ideal to integrate urban planning and to rethink environmental standards within ongoing development. The main objective of this study is to determine the feasibility of building a peripheral district in Manaus using biomimicry designed lodges. Our main interest is to identify the types of customers that could be involved in this urban development project. This study analyzes the willingness of customers to go for a biomimicry lodge. The analyzed information will come from a survey of a non-randomized population. Our target group are mature postmodern societies -sorted out by country of residence and willingness to travel-. The sample tested different attitudes, values and beliefs on environment which will help us to break down patterns of consumption using the literature review as reference. The study uses ordinary least squares (OLS) to determine significant predictors for different types of customers. This survey might help environmental activists, local authorities and social entrepreneurs with data for successfully developing alternative environmental designs. Our direct clients are real estate developers, social entrepreneurs and the business community in general. However, these results have also an indirect effect on the actual state of environmental design trends and on policy making. Local authorities might consider these results for agenda setting and as well for mobilizing civil society for better ecological practices.

Despite the extensive deployment of smart-meters (SMs) at the low-voltage (LV) level, which are either fully operational or will be in the near future, distribution network operators (DNOs) are still relying on a limited number of permanently installed monitoring devices at primary and secondary medium-voltage (MV) substations, for purposes of network operation and control, as well as to inform and facilitate trading interactions between generators, distributors and suppliers. Accordingly, improved and sufficiently developed models for the analysis of aggregate demands at the MV-level are required for the correct assessment of load variability, composition and time-dependent evolution, necessary for: addressing issues of robustness, security and reliability; accomplishing higher penetration levels from renewable/distributed generation; implementing demand-side-management (DSM) schemes and incorporating new technologies; decreasing environmental and economic costs and aiding towards the realisation of automated and proactive ''smart-grid'' networks. The analysis of MV-demand measurements provides an independent source of information that can capture network characteristics that do not manifest in the data collected at the LV-level, or when such data is restricted or altogether unavailable. This information describes the supply/demand interactions at the mid-level between high-voltage (HV) transmission and LV end-user consumption and opens possibilities for validation of existing bottom-up aggregation approaches, while addressing issues of reliance on survey-based data for technical and economic power system studies. This thesis presents improved and novel methodologies for the analysis of aggregate demands, measured at MV-substations, aimed at more accurate and detailed load profiling, temporal decomposition and identification of the drivers of demand variability, classification of grid-supply- points (GSPs) according to consumption patterns, disaggregation with respect to customer-classes and load-types and load forecasting. The developed models are based on a number of traditional and modern analytical and statistical techniques, including: data mining, correlational and regression analysis, Fourier analysis, clustering and pattern recognition, etc. The approaches are demonstrated on demand datasets from UK and European based DNOs, thus providing specific information for the demand characteristics, the dependencies to external parameters and to socio-behavioural factors and the most likely load composition at the corresponding geographical locations, while the approaches are also intendent to be easily adaptable for studies at equivalent voltage and demand aggregation levels.

Thesis (MTech (Industrial Design))--Cape Peninsula University of Technology, 2009 / Demand Side Management (DSM) within a South African context requires a transdisciplinary approach to comprehend electricity consumption. Current research suggests a technical determinism, whereby design teams fail to acknowledged social factors and cultural influences when conceptualising DSM artefacts. The result of which, is that artefacts fail to be adopted by the market, and consumer behaviour and electricity consumption remains unchanged. The thesis aims to demonstrate the hypothesis, that semiotics and ethnology may affect sustainable residential electricity management in South Africa. The ubiquitous literature on electricity management is administered by means of the theoretical lens, Sociotechnical Theory. Mixed method instrument obtain fieldwork data from three of the eleven official South African languages: Afrikaans, English and IsiXhosa.

abstract: The State of California has made great strides in reducing greenhouse gas (GHG) emissions through mandated, rate-payer funded Investor Owned Utility (IOU) electricity Demand Side Management (DSM) programs. This study quantifies the amount of reduced GHG emissions in Arizona that result from DSM in that state, as well as the DSM reductions within Southern California Edison (SCE), Pacific Gas and Electric (PG&E;), and San Diego Gas and Electric (SDG&E;) during the 2010 through 2012 California Public Utilities Commission (CPUC) DSM program cycle. To accomplish this quantification, it develops a model to allocated GHG emissions based on "operating margin" resources requirements specific to each utility in order to effectively track, monitor, and quantify avoided emissions from grid-based utility resources. The developed model estimates that during the 2010-2012 program cycle, 5,327.12 metric tons (MT) of carbon dioxide equivalents (CO2e) in GHG reductions (or 1.8 percent of total reductions) can be attributed to reduced demand from Arizona--based resources by California IOUs. By focusing on the spatial context of GHG emission reductions, this study models and quantifies the spill-over effect of California's regulatory environment into neighboring states. / Dissertation/Thesis / Ph.D. Geography 2013

This thesis investigates methodologies for valuing the flexibility of demand-side response (DSR) in its ability to respond to future uncertainties. The ability to quantify this flexibility is especially important for energy systems investments given their large and irreversible capital costs. The consideration of uncertainty in electricity markets and energy networks requires solutions that allow decision makers to quickly respond to unexpected events, such as extreme short-term electricity price variations in an operational setting, or incorrect long-term demand projections in planning. This uncertainty, coupled with the irreversibility of energy network investments, results in the need for viable 'wait-and-see' investment strategies that can help hedge electicity price risk in the short-term while hedging planning risk in the long-term, until at least some, if not all, uncertainty is resolved. In both cases, this leads to an added value in the case of temporary flexible investment options like DSR, which may otherwise be considered unattractive under a deterministic analysis setting. A number of significant contributions to power systems research are offered in this work, focusing on valuation methods for quantifying the flexibility value of DSR under both short-term and long-term uncertainty. The first outcome of this research is an extensive review of current real options (RO) methods that clarifies the assumptions and utilization of RO for decision-making in engineering applications. It suggests that many of the assumptions used contribute to a misuse of the models when applied to physical systems. A framework for investing under uncertainty is proposed, where the methodologies, steps, inputs, assumptions, limitations and advantages of different RO models are described so as to offer a practical guide to decision makers for selecting the most appropriate RO model for their valuation purposes. The second outcome is the design of a probabilistic RO framework and operational model for DSR that quantifies its benefits as an energy service for hedging different market price risks. A mathematical formulation for applying “real options thinking” is presented that provides decision makers with a means of quantifying the value of DSR when both operational and planning decisions are subject to uncertainty. In particular, DSR contracts can have tremendous value as an arbitrage or portfolio-balancing tool, helping hedge almost entirely electricity price risk in day-ahead and real-time markets, especially when prices are highly volatile. This value is quantified using a novel RO framework that frees the decision maker from the assumptions needed in financial option models. A new load forecasting and price simulation model is also developed to forecast load profiles and simulate new price series with different average values, higher volatilities and extreme price spikes to represent potential future market scenarios and to determine under which conditions DSR has the most value. The valuation of a DSR investment is then presented to show how the physical characteristics of a system, in this case the physical load recovery effect of loads after a DSR activation, can tremendously affect the profitability of an investment when uncertainty is taken into account. The third outcome of this work is the development of a complete, general and practical tool for making long-term multi-staged investment decisions in future power networks under multiple uncertainties. It is argued throughout this work that many of the current methods are either unsuitable for long-term investment valuation or are too complex for practical application and implementation at the industry level. A strategic spreadsheet-based tool for making long-term investment decisions under uncertainty is therefore created and tested in collaboration with industry for solving real network planning problems.

Demand-side resources are taking an increasingly prominent role in providing essential grid services once provided by thermal power plants. This thesis considers the economic feasibility and environmental effects of integrating demand-side resources into the electric grid with consideration given to the diversity of market and environmental conditions that can affect their behavior. Chapter 2 explores the private economics and system-level carbon dioxide reduction when using demand response for spinning reserve. Steady end uses like lighting are more than twice as profitable as seasonal end uses because spinning reserve is needed year-round. Avoided carbon emission damages from using demand response instead of fossil fuel generation for spinning reserve are sufficient to justify incentives for demand response resources. Chapter 3 quantifies the system-level net emissions rate and private economics of behind-the-meter energy storage. Net emission rates are lower than marginal emission rates for power plants and in-line with estimates of net emission rates from grid-level storage. The economics are favorable for many buildings in regions with high demand charges like California and New York, even without subsidies. Future penetration into regions with average charges like Pennsylvania will depend greatly on installation cost reductions and wholesale prices for ancillary services. Chapter 4 outlines a novel econometric model to quantify potential revenues from energy storage that reduces demand charges. The model is based on a novel predictive metric that is derived from the building’s load profile. Normalized revenue estimates are independent of the power capacity of the battery holding other performance characteristics equal, which can be used to calculate the profit-maximizing storage size. Chapter 5 analyzes the economic feasibility of flow batteries in the commercial and industrial market. Flow batteries at a 4-hour duration must be less expensive on a dollar per installed kWh basis, often by 20-30%, to break even with shorter duration li-ion or lead-acid despite allowing for deeper depth of discharge and superior cycle life. These results are robust to assumptions of tariff rates, battery round-trip efficiencies, amount of solar generation and whether the battery can participate in the wholesale energy and ancillary services markets.

The thesis presents RTB as a new way of buying media. The theoretical part defines the display advertising market, compares different ways of buying media and deeper describes functions of individual entities within the RTB ecosystem. In the application part are used methods such as the deep interviewing, the primary data analysis from the Adform DSP platform and the content analysis, to characterize the Czech market which is also compared with selected markets of Central and Eastern Europe. A future development of RTB on the Czech market is outlined by means of expert interviews. Shortcomings of the current solution are identified based on comparison and analysis of results of retargeting campaigns for the selected subject. Furthermore, an appropriate modification of a retargeting strategy is proposed with support of experts.