Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

Optimization of Generation and Aggregated Consumption Shifting for Demand Response Programs Definition

The concept of demand response has drawing attention to the active participation in the economic operation of power systems, namely in the context of recent electricity markets and smart grid models and implementations. In these competitive contexts, aggregators are necessary in order to make possible the participation of small size consumers and generation units. The methodology proposed in the present paper aims to address the demand shifting between periods, considering multi-period demand response events. The focus is given to the impact in the subsequent periods. A Virtual Power Player operates the network, aggregating the available resources, and minimizing the operation costs. The illustrative case study included is based on a scenario of 218 consumers including generation sources.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Contextual and Environmental Awareness Laboratory for Energy Consumption Management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Cation transporters/channels in plants: Tools for nutrient biofortification

Cation transporters/channels are key players in a wide range of physiological functions in plants, including cell signaling, osmoregulation, plant nutrition and metal tolerance. The recent identification of genes encoding some of these transport systems has allowed new studies toward further understanding of their integrated roles in plant. This review summarizes recent discoveries regarding the function and regulation of the multiple systems involved in cation transport in plant cells. The role of membrane transport in the uptake, distribution and accumulation of cations in plant tissues, cell types and subcellular compartments is described. We also discuss how the knowledge of inter- and intra-species variation in cation uptake, transport and accumulation as well as the molecular mechanisms responsible for these processes can be used to increase nutrient phytoavailability and nutrients accumulation in the edible tissues of plants. The main trends for future research in the field of biofortification are proposed.

Production of polyhydroxyalkanoates from cheese whey - pH effect on the acidogenic fermentation stage and nutrient needs of the culture selection stage

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Drivers of participation in collaborative consumption ventures

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

The consumption of media contents through mobile devices in Finland

O campo dos media na Finlândia encontra-se num processo de mudanças aparentemente imparáveis, com os dispositivos móveis, tal como os smartphones e tablets, a influenciarem cada vez mais os padrões de produção e consumo de media. Nesta dissertação é defendido que esta situação é influenciada por factores históricos, sociais e culturais: desde os livros e jornais como meio de manter a língua finlandesa até aos benefícios da Segurança Social que permitiram que até as pessoas com menos rendimentos comprassem o jornal para se manterem a par dos desenvolvimentos do país, bem como a grande tradição de leitura que é associada aos finlandeses, assim como as condições que ao longo da história fazem fizeram com que os finlandeses sejam fascinados pelas novas tecnologias. Adicionalmente, presto especial atenção às estratégias que são adoptadas pelas cada vez mais convergentes companhias de media para enfrentarem a competição de elementos como os social media.

How to optimize Lipton’s business model in order to stimulate tea consumption in Portugal and increase Lipton Leaf tea market share?: The importance of distribution and sales channels management in a B2B2C market

Field Lab Entrepreneurial Innovative Ventures

Ethical consumption (in)decisons

Consumers’ indecisions about the ethical value of their choices are amongst the highest concerns regarding ethical products’ purchasing. This is especially true for Fair Trade certified products where the ethical attribute information provided by the packaging is often unacknowledged by consumers. While well-informed consumers are likely to generate positive consumer reactions to ethical products and increase its ethical consumption, less knowledgeable buyers show different purchasing patterns. In such circumstances, decisions are often driven by socio-cultural beliefs about the low functional performance of ethical or sustainable attributes. For instance, products more congruent with sustainability (e.g., produce) are considered to be simpler but less tasty than less sustainable products. Less sustainable products instead, are considered to be more sophisticated and to provide consumers with more hedonic pleasures (e.g., chocolate mousse). The extent that ethicality is linked with experiences that provide consumers with more pain than pleasure is also manifested in pro-social social behaviors. More specifically through conspicuous self-sacrificial consumption experiences like running for charity in marathons with wide public exposure. The willingness of consumers to engage in such costly initiatives is moderated by gender differences and further, mediated by the chronic productivity orientation of some individuals to use time in a productive manner. Using experimental design studies, I show that consumers (1) use a set of affective and cognitive associations with on-package elements to interpret ethical attributes, (2) implicitly associate ethicality with simplicity, and that (3) men versus women show different preferences in their forms of contribution to pro-social causes.

Measuring the effects of price movements on private consumption

This paper studies how shocks in the prices of Food, Energy and Financial Assets affect private consumption using a VAR Model. Then, the total effects are broken into direct and indirect effects, using the coefficients taken from the previous model. We use quarterly data for the Portuguese economy from the last 20 years. We found that energy prices and financial assets have a strong connection with consumption, suggesting that the economy may be too exposed to shocks in these markets.