Using demand response to deal with unexpected low wind power generation in the context of smart grid

Demand response is assumed an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed aims the minimization of the operation costs in a smart grid operated by a virtual power player. It is especially useful when actual and day ahead wind forecast differ significantly. When facing lower wind power generation than expected, RTP is used in order to minimize the impacts of such wind availability change. The proposed model application is here illustrated using the scenario of a special wind availability reduction day in the Portuguese power system (8th February 2012).

Energy and reserve provision dispatch considering distributed generation and demand response

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot market´s efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

LMP triggered real time demand response events

Recent changes in power systems mainly due to the substantial increase of distributed generation and to the operation in competitive environments has created new challenges to operation and planning. In this context, Virtual Power Players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Demand response market implementation has been done in recent years. Several implementation models have been considered. An important characteristic of a demand response program is the trigger criterion. A program for which the event trigger depends on the Locational Marginal Price (LMP) used by the New England Independent System operator (ISO-NE) inspired the present paper. This paper proposes a methodology to support VPP demand response programs management. The proposed method has been computationally implemented and its application is illustrated using a 32 bus network with intensive use of distributed generation. Results concerning the evaluation of the impact of using demand response events are also presented.

Demand response in electrical energy supply: an optimal real time pricing approach

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer’s profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer’s profit. The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers’ decision making.

Determination of pharmocotherapeutical complexity in institutionalized elderly

The phenomenon of aging is nowadays society as acquired the status of a social problem, with growing attention and concern, leading to an increase number of studies dedicated to the elderly. The lack of domestic, familiar or social support often lead elderly to nursing homes. Institutionalization is in many cases the only opportunity to have access to health care and life quality. Aging is also associated with a higher prevalence of chronic diseases that require long term medication sometimes for life. Frequently the onset of multiple pathologies at the same time require different therapies and the phenomenon of polypharmacy (five ou more drugs daily) can occur. Even more, the slow down of physiological and cognitives mechanisms associated with these chronic diseases can interphere, in one hand, with the pharmacocinetic of many medications and, on the other hand, with the facility to accomplish the therapeutical regimen. All of these realities contribute to an increase of pharmacotherapeutical complexity, decreasing the adherence and effectiveness of treatment. The pharmacotherapeutical complexity of an individual is characterized by the conciliator element of different characteristics of their drug therapy, such as: the number of medications used; dosage forms; dosing frequency and additional indications. It can be measured by the Medication Regimen Complexity Index (MRCI), originally validated in English.

The role of demand response in future power systems

Power systems are planed and operated according to the optimization of the available resources. Traditionally these tasks were mostly undertaken in a centralized way which is no longer adequate in a competitive environment. Demand response can play a very relevant role in this context but adequate tools to negotiate this kind of resources are required. This paper presents an approach to deal with these issues, by using a multi-agent simulator able to model demand side players and simulate their strategic behavior. The paper includes an illustrative case study that considers an incident situation. The distribution company is able to reduce load curtailment due to load flexibility contracts previously established with demand side players.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Demsi - a demand response simulator in the context of intensive use of distributed generation

Demand response can play a very relevant role in future power systems in which distributed generation can help to assure service continuity in some fault situations. This paper deals with the demand response concept and discusses its use in the context of competitive electricity markets and intensive use of distributed generation. The paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes using a realistic network simulation based on PSCAD. Demand response opportunities are used in an optimized way considering flexible contracts between consumers and suppliers. A case study evidences the advantages of using flexible contracts and optimizing the available generation when there is a lack of supply.

Drug therapy complexity: the importance of pharmacotherapeutic follow-up

Nowadays, the phenomenon of population ageing represents an worldwide problem, which assumes particular significance in Portugal. As they get older, individuals present more comorbidities and consequently consume an increasing number of drugs, which contributes to a growing drug therapy complexity. The institutionalized elders are particularly affected by this occurrence. Drug therapy complexity is defined as the conciliator of several characteristics of the pharmacotherapy and can affect patient’s safety and medication adherence. It can be measured with Medication Regimen Complexity Index (MRCI). This study aims to determine the drug therapy complexity of institutionalized elders in order to assess the need of pharmacotherapeutic follow-up.

Simulação numérica aplicada ao tratamento de efluentes

A existência de estações de tratamentos de águas residuais (ETAR) é, nos dias de hoje, fundamental na medida em que permite, reduzir a poluição ambiental associada às águas e, também, a reutilização da água tratada para diversos fins. A constante necessidade de cumprir os limites de descargas nos meios recetores conduziu a um melhoramento nas técnicas e processos de tratamento de efluentes, nomeadamente, nos processos biológicos. O processo por lamas ativadas é um processo amplamente utilizado para a remoção de poluentes orgânicos presentes nas águas residuais, pelo que um estudo mais intensivo sobre estes tratamentos resultou na publicação de uma série de conceitos e pressupostos, através de modelos numéricos. A modelação numérica de processos de tratamento de águas residuais e a utilização de ferramentas de simulação numérica têm sido largamente utilizadas, a nível mundial, por exemplo em investigação, desenvolvimento de processos, atividade de consultoria e igualmente por entidades reguladoras, na medida em que os auxiliam no planeamento, dimensionamento e análise do comportamento de infraestruturas de tratamento. No presente trabalho, recorreu-se ao software de simulação GPS-X (versão 6.0) para implementar o esquema de tratamento da ETAR de Beirolas. O objetivo deste trabalho é verificar a aplicabilidade de modelos numéricos na simulação de unidades de tratamento de efluentes e avaliar a resposta dos diferentes modelos, assim como a influência na alteração de características das águas afluentes no comportamento dos modelos. Os resultados obtidos permitiram concluir que os modelos numéricos podem ser utilizados para prever a resposta dos sistemas biológicos e as suas perturbações. Conclui-se ainda que o comportamento, dos modelos estudados (ASM1, ASM2d, ASM3 e mantis), é semelhante, contudo deve-se referir que devido à complexidade do modelo e a falta de informação experimental sobre as características do efluente, não permitiram efetuar uma completa caracterização e calibração do caso de estudo, e toda a informação disponível sobre as características físico-químicas da água foram baseadas em estimativas de concentrações de carências de oxigénio e sólidos suspensos.

Cellular uptake of BCG-loaded chitosan microparticles and in vivo evaluation of immune response following intranasal immunisation

Attenuated Mycobacterium bovis bacillus Calmette-Guérin (BCG) is the only currently available vaccine against tuberculosis. It is highly effective in pre-exposure immunisation against TB in children when administered by subcutaneous route to newborns. However, it does not provide permanent protection in adults. In this work, polymeric chitosan-alginate microparticles have been evaluated as potential nasal delivery systems and mucosal adjuvants for live attenuated BCG. Chitosan (CS) has been employed as adjuvant and mucosal permeation-enhancer, and, together with alginate (ALG), as additive to enhance BCG-loaded microparticles (MPs) cellular uptake in a human monocyte cell line, by particle surface modification. The most suitable particles were used for vaccine formulation and evaluation of immune response following intranasal immunisation of BALB/c mice.

Mudanças climáticas nas regiões insulares

Tese de Doutoramento, Física, 27 de Novembro de 2013, Universidade dos Açores.

Auto-regulação na gestão de conflitos em retalho

Mestrado em Engenharia Informática

Topological Complexity and Predictability in the Dynamics of a Tumor Growth Model with Shilnikov's Chaos

Dynamical systems modeling tumor growth have been investigated to determine the dynamics between tumor and healthy cells. Recent theoretical investigations indicate that these interactions may lead to different dynamical outcomes, in particular to homoclinic chaos. In the present study, we analyze both topological and dynamical properties of a recently characterized chaotic attractor governing the dynamics of tumor cells interacting with healthy tissue cells and effector cells of the immune system. By using the theory of symbolic dynamics, we first characterize the topological entropy and the parameter space ordering of kneading sequences from one-dimensional iterated maps identified in the dynamics, focusing on the effects of inactivation interactions between both effector and tumor cells. The previous analyses are complemented with the computation of the spectrum of Lyapunov exponents, the fractal dimension and the predictability of the chaotic attractors. Our results show that the inactivation rate of effector cells by the tumor cells has an important effect on the dynamics of the system. The increase of effector cells inactivation involves an inverse Feigenbaum (i.e. period-halving bifurcation) scenario, which results in the stabilization of the dynamics and in an increase of dynamics predictability. Our analyses also reveal that, at low inactivation rates of effector cells, tumor cells undergo strong, chaotic fluctuations, with the dynamics being highly unpredictable. Our findings are discussed in the context of tumor cells potential viability.