Product innovation and the costs of energy-using product policies

Energy-using Products (EuPs) contribute significantly to the United Kingdom’s CO2 emissions, both in the domestic and non-domestic sectors. Policies that encourage the use of more energy efficient products (such as minimum performance standards, energy labelling, enhanced capital allowances, etc.) can therefore generate significant reductions in overall energy consumption and hence, CO2 emissions. While these policies can impose costs on the producers and consumers of these products in the short run, the process of product innovation may reduce the magnitude of these costs over time. If this is the case, then it is important that the impacts of innovation are taken into account in policy impact assessments. Previous studies have found considerable evidence of experience curve effects for EuP categories (e.g. refrigerators, televisions, etc.), with learning rates of around 20% for both average unit costs and average prices; similar to those found for energy supply technologies. Moreover, the decline in production costs has been accompanied by a significant improvement in the energy efficiency of EuPs. Building on these findings and the results of an empirical analysis of UK sales data for a range of product categories, this paper sets out an analytic framework for assessing the impact of EuP policy interventions on consumers and producers which takes explicit account of the product innovation process. The impact of the product innovation process can be seen in the continuous evolution of the energy class profiles of EuP categories over time; with higher energy classes (e.g. A, A+, etc.) entering the market and increasing their market share, while lower classes (e.g. E, F, etc.) lose share and then leave the market. Furthermore, the average prices of individual energy classes have declined over their respective lives, while new classes have typically entered the market at successively lower “launch prices”. Based on two underlying assumptions regarding the shapes of the “lifecycle profiles” for the relative sales and the relative average mark-ups of individual energy classes, a simple simulation model is developed that can replicate the observed market dynamics in terms of the evolution of market shares and average prices. The model is used to assess the effect of two alternative EuP policy interventions – a minimum energy performance standard and an energy-labelling scheme – on the average unit cost trajectory and the average price trajectory of a typical EuP category, and hence the financial impacts on producers and consumers.

Developing a Technoself System to Improve Lifelong Learning Engagement

Lifelong learning exists today in the context of a cultural and societal shift to a knowledge-based, technology-enhanced, and rapidly-changing economy. It has a significant impact on people’s lives and has become of vital importance with the emergence of new technologies that change how people communicate, collect information, and collaborate with others. The emerging technologies, such as social networking, interactive media and game technology, have expanded a new dimension of self – ‘technoself’ driven by socio-technical innovations and taken an important step forward in lifelong learning through the Technology Enhanced Learning (TEL). The TEL encourages learners as producers to embed personalized knowledge and collective experience on individualized learning within professional practice. It becomes more personal and social than traditional lifelong learning, especially about the ‘learning as socially grounded’ aspects. This paper studies the development of technoself system during lifelong learning and introduces technoself enhanced learning as a novel sociological framework of lifelong learning to couple the educational dimension with social dimension in order to enhance learner engagement by shaping personal learning focus and setting. We examine how people construct their own inquiry and learn from others, how people shift and adapt in these technoself-enhanced learning environments, and how learner engagement is improving as the involvement of learners as producers in lifelong learning. We further discuss the barriers and the positive and negative unintended consequences of using technology for lifelong learning.

Religion and returns in Europe

Drawing on social identity and social impact theory, this paper is the first to investigate the impact of religious preferences on share prices and expected returns at the country level. Using data from 12 European countries, our findings suggest that religion has a significant effect on the share price of companies whose activities are considered unethical, i.e., tobacco manufacturers and alcohol producers. The share price of these companies (called sin stocks) is depressed when they are located in a predominantly Protestant environment (relative to a Catholic environment). With investors in Protestant countries being more sin averse than in Catholic countries, they insist upon higher expected returns on sin stocks. Conversely, religious preferences do not have the same impact on the performance of other companies, e.g. socially responsible companies. Our results are robust to various methodologies and controlling for several firm-specific, industry-specific and country-specific characteristics.

Reflections Upon the Experience of Longitudinal Research into Cultural Event Production in a Developing Destination

Longitudinal studies have the capacity to provide more nuanced explanations of tourism and event phenomena, taking account of complexity, change and context. This paper is a self-reflexive, methodological study of research practice. It investigates my experience of engaging with cultural event producers in an emerging destination over a seven-year period. Focussing on my research journey, it considers the social and relational dynamics associated with longitudinal research. Reciprocal relations and co-production of cultural events reveal nuanced information and expose fluid relationships and networks. Long-term engagement uncovers evolving practices and develops understanding of event processes embedded within their wider context.

A televisão na era digital: qual o modelo mais adequado para o serviço público de televisão na plataforma internet?

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

VPPs information needs for effective operation in competitive electricity markets

The large increase of renewable energy sources and Distributed Generation (DG) of electricity gives place to the Virtual Power Producer (VPP) concept. VPPs may turn electricity generation by renewable sources valuable in electricity markets. Information availability and adequate decision-support tools are crucial for achieving VPPs’ goals. This involves information concerning associated producers and market operation. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, focusing mainly in the information requirements for adequate decision making.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Coordination between mid-term maintenance outage decisions and short-term security-constrained scheduling in smart distribution systems

Distribution systems are the first volunteers experiencing the benefits of smart grids. The smart grid concept impacts the internal legislation and standards in grid-connected and isolated distribution systems. Demand side management, the main feature of smart grids, acquires clear meaning in low voltage distribution systems. In these networks, various coordination procedures are required between domestic, commercial and industrial consumers, producers and the system operator. Obviously, the technical basis for bidirectional communication is the prerequisite of developing such a coordination procedure. The main coordination is required when the operator tries to dispatch the producers according to their own preferences without neglecting its inherent responsibility. Maintenance decisions are first determined by generating companies, and then the operator has to check and probably modify them for final approval. In this paper the generation scheduling from the viewpoint of a distribution system operator (DSO) is formulated. The traditional task of the DSO is securing network reliability and quality. The effectiveness of the proposed method is assessed by applying it to a 6-bus and 9-bus distribution system.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Transmission cost allocation using cooperative game theory: a comparative study

In this paper is presented a Game Theory based methodology to allocate transmission costs, considering cooperation and competition between producers. As original contribution, it finds the degree of participation on the additional costs according to the demand behavior. A comparative study was carried out between the obtained results using Nucleolus balance and Shapley Value, with other techniques such as Averages Allocation method and the Generalized Generation Distribution Factors method (GGDF). As example, a six nodes network was used for the simulations. The results demonstrate the ability to find adequate solutions on open access environment to the networks.

Multi-agent based electricity market simulator with VPP: Conceptual and implementation issues

This paper presents a new architecture for the MASCEM, a multi-agent electricity market simulator. This is implemented in a Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) provides a DLL interface between Win-Prolog and other applications. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Avaliação da rentabilidade económica dos sistemas de microgeração fotovoltaica em Portugal (1990-2010)

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica - ramo de Energia