Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets Assisted by CPS

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy assisted by a cyber-physical system for supporting management decisions to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a stochastic linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modelled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

The Branches of Large Electricity Companies in Portugal: From Trade to the Transfer and Adaptation of Technology (20th Century)

In the last decade of the 19th and first decades of the 20th century there was a movement of capital and engineers from the central and northern Europe to the countries of southern Europe and other continents. Large companies sought to obtain concessions and establish branches in Portugal, favouring the circulation of technical knowledge and transfer of technology for Portuguese industry. Among the various examples of the representatives of foreign companies in Portugal we find Jayme da Costa Ltd. established in 1916 in Lisbon, which was a branch of the Swedish company ASEA, as well as STAAL, ATLAS DIESEL (Sweden), Landis & GYR (Switzerland), Electro Helios, etc.. Another example is EFACEC a company founded in 1948 in Porto, that was a partnership between the Portuguese company CUF – Companhia União Fabril, and ACEC – Ateliers de Constructions Électriques de Charleroi and a small entreprise Electro-Moderna Ldª. This enterprise started the industrial production of electric motors and transformers, and later on acquired a substantial share of the national production of electrical equipment. Using Estatística das Instalações Elétricas em Portugal (Statistics on Electrical Installations in Portugal) from 1928 until 1950 we can identify the foreign enterprises acting in the Portuguese market: Siemens, B.B.C, ASEA, Oerlikon, etc. We can also establish a relationship between the development of the electric network and the growth of production and consumption of electricity in the principal urban centres. Finally we see how foreign firms were a stimulus to the creation of national enterprises, especially those of small scale, in Portugal.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Design of an Efficient Hybrid System for Electricity Production with Supervisory Control

This chapter aims to develop a new method for the economical evaluation of Hybrid Systems for electricity production. The different types of renewable sources are specifically evaluated in the economical performance of the overall equipment. The presented methodology was applied to evaluate the design of a photovoltaic-wind-diesel hybrid system to produce electricity for a community in the neighbourhood of Luanda, Angola. Once the hybrid generator is selected, it is proposed to provide the system with a supervisory control strategy to maximize its operating efficiency.

SKMA - A Key Management Architecture for SCADA Systems

Supervisory Control And Data Acquisition (SCADA) systems are widely used in the management of critical infrastructure such as electricity and water distrubution systems. Currently there is little understanding of how to best protect SCADA systems from malicious attacks. We review the constraints and requirements for SCADA security and propose a suitable architecture (SKMA) for secure SCADA communications. The architecture includes a proposed key management protocol (SKMP). We compare the architecture with a previous proposal from Sandia Labs.

Towards ubiquitous city : concept, planning, and experiences in the Republic of Korea

Information and communication technologies (ICTs) had occupied their position on knowledge management and are now evolving towards the era of self-intelligence (Klosterman, 2001). In the 21st century ICTs for urban development and planning are imperative to improve the quality of life and place. This includes the management of traffic, waste, electricity, sewerage and water quality, monitoring fire and crime, conserving renewable resources, and coordinating urban policies and programs for urban planners, civil engineers, and government officers and administrators. The handling of tasks in the field of urban management often requires complex, interdisciplinary knowledge as well as profound technical information. Most of the information has been compiled during the last few years in the form of manuals, reports, databases, and programs. However frequently, the existence of these information and services are either not known or they are not readily available to the people who need them. To provide urban administrators and the public with comprehensive information and services, various ICTs are being developed. In early 1990s Mark Weiser (1993) proposed Ubiquitous Computing project at the Xerox Palo Alto Research Centre in the US. He provides a vision of a built environment which digital networks link individual residents not only to other people but also to goods and services whenever and wherever they need (Mitchell, 1999). Since then the Republic of Korea (ROK) has been continuously developed national strategies for knowledge based urban development (KBUD) through the agenda of Cyber Korea, E-Korea and U-Korea. Among abovementioned agendas particularly the U-Korea agenda aims the convergence of ICTs and urban space for a prosperous urban and economic development. U-Korea strategies create a series of U-cities based on ubiquitous computing and ICTs by a means of providing ubiquitous city (U-city) infrastructure and services in urban space. The goals of U-city development is not only boosting the national economy but also creating value in knowledge based communities. It provides opportunity for both the central and local governments collaborate to U-city project, optimize information utilization, and minimize regional disparities. This chapter introduces the Korean-led U-city concept, planning, design schemes and management policies and discusses the implications of U-city concept in planning for KBUD.

Sustainable subdivisions – energy efficient design

Australia’s current pattern of residential development is resulting in urban sprawl and highlights the necessity for development to be more sustainable to avoid unnecessary demand on natural resources and to prevent environmental degradation and to safeguard the environment for future generations. This report summarises the results from a series of cases studies that examined the link between sub-divisional layout and dwelling energy efficiency, the possibility for a lot-rating tool and the potential for on site electricity generation.

New insights into rebound effects : theory and empirical evidence

The main objective of the thesis is to seek insights into the theory, and provide empirical evidence of rebound effects. Rebound effects reduce the environmental benefits of environmental policies and household behaviour changes. In particular, win-win demand side measures, in the form of energy efficiency and household consumption pattern changes, are seen as ways for households and businesses to save money and the environment. However, these savings have environmental impacts when spent, which are known as rebound effects. This is an area that has been widely neglected by policy makers. This work extends the rebound effect literature in three important ways, (1) it incorporates the potential for variation of rebound effects with household income level, (2) it enables the isolation of direct and indirect effects for cases of energy efficient technology adoption, and examines the relationship between these two component effects, and (3) it expands the scope of rebound effect analysis to include government taxes and subsidies. MACROBUTTON HTMLDirect Using a case study approach it is found that the rebound effect from household consumption pattern changes targeted at electricity is between 5 and 10%. For consumption pattern changes with reduced vehicle fuel use, the rebound effect is in the order of 20 to 30%. Higher income households in general are found to have a lower total rebound effect; however the indirect effect becomes relatively more significant at higher household income levels. In the win-lose case of domestic photovoltaic electricity generation, it is demonstrated that negative rebound effects can occur, which can potentially amplify the environmental benefits of this action. The rebound effect from a carbon tax, which occurs due to the re-spending of raised revenues, was found to be in the range of 11-32%. Taxes and transfers between households of different income levels also have environmental implications. For example, a more progressive tax structure, with increased low income welfare payments is likely to increase greenhouse gas emissions. Subsidies aimed at encouraging environmentally friendly consumption habits are also subject to rebound effects, as they constitute a substitution of government expenditure for household expenditure. For policy makers, these findings point to the need to incorporate rebound effects in the environmental policy evaluation process.’

A comparative study : regulatory requirements on subdivisions for increasing passive cooling

A major project in the Sustainable Built Assets core area is the Sustainable Sub-divisions – Ventilation Project that is the second stage of a planned series of research projects focusing on sustainable sub-divisions. The initial project, Sustainable Sub-divisions: Energy focused on energy efficiency and examined the link between dwelling energy efficiency and sub-divisional layout. In addition, the potential for on site electricity generation, especially in medium and high-density developments, was also examined. That project recommended that an existing lot-rating methodology be adapted for use in SEQ through the inclusion of sub divisional appropriate ventilation data. Acquiring that data is the object of this project. The Sustainable Sub-divisions; Ventilation Project will produce a series of reports. The first report (Report 2002-077-B-01) summarised the results from an industry workshop and interviews that were conducted to ascertain the current attitudes and methodologies used in contemporary sub-division design in South East Queensland. The second report (Report 2002-077-B-02) described how the project is being delivered as outlined in the Project Agreement. It included the selection of the case study dwellings and monitoring equipment and data management process. This third report (Report 2002-077-B-03) provides an analysis and review of the approaches recommended by leading experts, government bodies and professional organizations throughout Australia that aim to increase the potential for passive cooling and heating at the subdivision stage. This data will inform issues discussed on the development of the enhanced lot-rating methodology in other reports of this series. The final report, due in June 2007, will detail the analysis of data for winter 2006 and summer 2007, leading to the development and delivery of the enhanced lot-rating methodology.

Corona discharges on the surfaces of high voltage composite insulators

The degradation of high voltage electrical insulation is a prime factor that can significantly influence the reliability performance and the costs of maintaining high voltage electricity networks. Little information is known about the system of localized degradation from corona discharges on the relatively new silicone rubber sheathed composite insulators that are now being widely used in high voltage applications. This current work focuses on the fundamental principles of electrical corona discharge phenomena to provide further insights to where damaging surface discharges may localize and examines how these discharges may degrade the silicone rubber material. Although water drop corona has been identified by many authors as a major cause of deterioration of silicone rubber high voltage insulation until now no thorough studies have been made of this phenomenon. Results from systematic measurements taken using modern digital instrumentation to simultaneously record the discharge current pulses and visible images associated with corona discharges from between metal electrodes, metal electrodes and water drops, and between waters drops on the surface of silicone rubber insulation, using a range of 50 Hz voltages are inter compared. Visual images of wet electrodes show how water drops can play a part in encouraging flashover, and the first reproducible visual images of water drop corona at the triple junction of water air and silicone rubber insulation are presented. A study of the atomic emission spectra of the corona produced by the discharge from its onset up to and including spark-over, using a high resolution digital spectrometer with a fiber optic probe, provides further understanding of the roles of the active species of atoms and molecules produced by the discharge that may be responsible for not only for chemical changes of insulator surfaces, but may also contribute to the degradation of the metal fittings that support the high voltage insulators. Examples of real insulators and further work specific to the electrical power industry are discussed. A new design concept to prevent/reduce the damaging effects of water drop corona is also presented.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.