Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

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Cleanup of industrial effluents containing heavy metals: a new opportunity of valorising the biomass produced by brewing industry

Heavy metal pollution is a matter of concern in industrialised countries. Contrary to organic pollutants, heavy metals are not metabolically degraded. This fact has two main consequences: its bioremediation requires another strategy and heavy metals can be indefinitely recycled. Yeast cells of Saccharomyces cerevisiae are produced at high amounts as a by-product of brewing industry constituting a cheap raw material. In the present work, the possibility of valorising this type of biomass in the bioremediation of real industrial effluents containing heavy metals is reviewed. Given the autoaggregation capacity (flocculation) of brewing yeast cells, a fast and off-cost yeast separation is achieved after the treatment of metal-laden effluent, which reduces the costs associated with the process. This is a critical issue when we are looking for an effective, eco-friendly, and low-cost technology. The possibility of the bioremediation of industrial effluents linked with the selective recovery of metals, in a strategy of simultaneous minimisation of environmental hazard of industrial wastes with financial benefits from reselling or recycling the metals, is discussed.

Assigning real-time tasks on heterogeneous multiprocessors with two unrelated types of processors

A preliminary version of this paper appeared in Proceedings of the 31st IEEE Real-Time Systems Symposium, 2010, pp. 239–248.

The utilization bound of static-priority preemptive partitioned multiprocessor scheduling is 50%

This paper studies static-priority preemptive scheduling on a multiprocessor using partitioned scheduling. We propose a new scheduling algorithm and prove that if the proposed algorithm is used and if less than 50% of the capacity is requested then all deadlines are met. It is known that for every static-priority multiprocessor scheduling algorithm, there is a task set that misses a deadline although the requested capacity is arbitrary close to 50%.

CPMD-mindful task assignment for NPS-F

The multiprocessor scheduling scheme NPS-F for sporadic tasks has a high utilisation bound and an overall number of preemptions bounded at design time. NPS-F binpacks tasks offline to as many servers as needed. At runtime, the scheduler ensures that each server is mapped to at most one of the m processors, at any instant. When scheduled, servers use EDF to select which of their tasks to run. Yet, unlike the overall number of preemptions, the migrations per se are not tightly bounded. Moreover, we cannot know a priori which task a server will be currently executing at the instant when it migrates. This uncertainty complicates the estimation of cache-related preemption and migration costs (CPMD), potentially resulting in their overestimation. Therefore, to simplify the CPMD estimation, we propose an amended bin-packing scheme for NPS-F allowing us (i) to identify at design time, which task migrates at which instant and (ii) bound a priori the number of migrating tasks, while preserving the utilisation bound of NPS-F.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

Algoritmo genético para empacotamento de contentores

No âmbito da investigação operacional o problema de empacotamento de contentores é conhecido por procurar definir uma configuração de carga, de forma a otimizar a utilização de um espaço disponível para efetuar o empacotamento. Este problema pode ser apresentado em diversas formas, formas estas que variam em função das características de cada empacotamento. Estas características podem ser: o tipo de carga que se pretende carregar (homogénea ou heterogénea), a possibilidade de a carga poder sofrer rotações em todas as suas dimensões ou apenas em algumas, o lucro que está associado a cada caixa carregada ou restrições inerentes ao contentor como por exemplo dimensões. O interesse pelo estudo de problemas de empacotamento de contentores tem vindo a receber cada vez mais ênfase por várias razões, uma delas é o interesse financeiro dado que o transporte é uma prática que representa custos, sendo importante diminuir estes custos aproveitando o volume do contentor da melhor forma. Outra preocupação que motiva o estudo deste problema prende-se com fatores ambientes, onde se procura racionalizar os recursos naturais estando esta também ligada a questões financeiras. Na literatura podem ser encontradas varias propostas para solucionar este problema, cada uma destas dirigidas a uma variante do problema, estas propostas podem ser determinísticas ou não determinísticas onde utilizam heurísticas ou metaheurísticas. O estudo realizado nesta dissertação descreve algumas destas propostas, nomeadamente as metaheurísticas que são utilizadas na resolução deste problema. O trabalho aqui apresentado traz também uma nova metaheurísticas, mais precisamente um algoritmo genético que terá como objetivo, apresentar uma configuração de carga para um problema de empacotamento de um contentor. O algoritmo genético tem como objetivo a resolução do seguinte problema: empacotar várias caixas retangulares com diversos tamanhos num contentor. Este problema é conhecido como Bin-Packing. A novidade que este algoritmo genético vai introduzir nas diversas soluções apresentadas até à data, é uma nova forma de criar padrões iniciais, ou seja, é utilizada a heurística HSSI (Heurística de Suavização de Superfícies Irregulares) que tem como objetivo criar uma população inicial de forma a otimizar o algoritmo genético. A heurística HSSI tenta resolver problemas de empacotamento simulando, o comportamento da maioria das pessoas ao fazer este processo na vida real, contudo, tem um campo de busca reduzido entre as soluções possíveis e será então utilizado um algoritmo genético para ampliar este campo de busca e explorar novas soluções. No final pretende-se obter um software onde será possível configurar um dado problema de empacotamento de um contentor e obter, a solução do mesmo através do algoritmo genético. Assim sendo, o estudo realizado tem como principal objetivo contribuir com pesquisas e conclusões, sobre este problema e trazer uma nova proposta de solução para o problema de empacotamento de contentores.