Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Renewable power forecast to scheduling of thermal units

In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

The electric vehicles as a mean to reduce Co2 emissions and energy costs in isolated regions: the São Miguel (Azores) case study

Most of small islands around the world today, are dependent on imported fossil fuels for the majority of their energy needs especially for transport activities and electricity production. The use of locally renewable energy resources and the implementation of energy efficiency measures could make a significant contribution to their economic development by reducing fossil fuel imports. An electrification of vehicles has been suggested as a way to both reduce pollutant emissions and increase security of supply of the transportation sector by reducing the dependence on oil products imports and facilitate the accommodation of renewable electricity generation, such as wind and, in the case of volcanic islands like Sao Miguel (Azores) of the geothermal energy whose penetration has been limited by the valley electricity consumption level. In this research, three scenarios of EV penetration were studied and it was verified that, for a 15% LD fleet replacement by EVs with 90% of all energy needs occurring during the night, the accommodation of 10 MW of new geothermal capacity becomes viable. Under this scenario, reductions of 8% in electricity costs, 14% in energy, 23% in fossil fuels use and CO2 emissions for the transportation and electricity production sectors could be expected.

Impacto da introdução de baterias num SEE isolado: o caso da ilha da Madeira

Com a crescente integração de energias renováveis variáveis nos sistemas elétricos de energia surgem novos desafios à maneira como se efetua a exploração dos mesmos, devido à dificuldade na previsão produção e na controlabilidade destas energias. Estes desafios são ainda maiores quando se analisa uma rede elétrica isolada de pequenas dimensões sem possibilidade de ser interligada a uma rede continental, por apresentar uma maior fragilidade e dai resultarem critérios de exploração muito mais apertados, de forma a garantir da melhor forma a segurança e estabilidade da rede. Consequentemente existe a necessidade de serem adotadas medidas que atenuem os impactos da variabilidade e tornem mais previsíveis as energias renováveis. É neste âmbito que surgem as tecnologias de armazenamento de energia elétrica. O presente documento apresenta um estudo aprofundado ao sistema eletroprodutor da ilha da Madeira e às suas especificidades, analisando a viabilidade técnica da introdução de baterias em larga escala no sistema. De forma a realizar esta análise, criou-se uma ferramenta de simulação em Matlab, que visou quantificar o impacto da introdução de baterias, quer ao nível da integração de energia eólica, quer ao nível da redução da produção térmica. Esta ferramenta permite ainda uma análise gráfica do diagrama de produção agregado diário, assim como a evolução de potência e energia na bateria.

Production of recombinant protein hLIF in static and dynamic systems of animal cell culture

Thesis for the Degree of Master of Science in Biotechnology Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

Power-and-area efficient 14-bit 1.5 MSample/s two-stage algorithmic ADC based on a mismatch-insensitive MDAC

IEEE International Symposium on Circuits and Systems, pp. 220 – 223, Seattle, EUA

TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services

he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

TEA: Timing and Energy Aware compression architecture for Efficient Configuration in CGRAs

Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern applications (e.g. 4G, CDMA, etc.). Recently proposed CGRAs offer time-multiplexing and dynamic applications parallelism to enhance device utilization and reduce energy consumption at the cost of additional memory (up to 50% area of the overall platform). To reduce the memory overheads, novel CGRAs employ either statistical compression, intermediate compact representation, or multicasting. Each compaction technique has different properties (i.e. compression ratio, decompression time and decompression energy) and is best suited for a particular class of applications. However, existing research only deals with these methods separately. Moreover, they only analyze the compaction ratio and do not evaluate the associated energy overheads. To tackle these issues, we propose a polymorphic compression architecture that interleaves these techniques in a unique platform. The proposed architecture allows each application to take advantage of a separate compression/decompression hierarchy (consisting of various types and implementations of hardware/software decoders) tailored to its needs. Simulation results, using different applications (FFT, Matrix multiplication, and WLAN), reveal that the choice of compression hierarchy has a significant impact on compression ratio (up to 52%), decompression energy (up to 4 orders of magnitude), and configuration time (from 33 n to 1.5 s) for the tested applications. Synthesis results reveal that introducing adaptivity incurs negligible additional overheads (1%) compared to the overall platform area.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

A Multiple Criteria Utility-based Approa h for the Unit Commitment with Wind Power and Pumped Storage Hydro

The integration of wind power in eletricity generation brings new challenges to unit commitment due to the random nature of wind speed. For this particular optimisation problem, wind uncertainty has been handled in practice by means of conservative stochastic scenario-based optimisation models, or through additional operating reserve settings. However, generation companies may have different attitudes towards operating costs, load curtailment, or waste of wind energy, when considering the risk caused by wind power variability. Therefore, alternative and possibly more adequate approaches should be explored. This work is divided in two main parts. Firstly we survey the main formulations presented in the literature for the integration of wind power in the unit commitment problem (UCP) and present an alternative model for the wind-thermal unit commitment. We make use of the utility theory concepts to develop a multi-criteria stochastic model. The objectives considered are the minimisation of costs, load curtailment and waste of wind energy. Those are represented by individual utility functions and aggregated in a single additive utility function. This last function is adequately linearised leading to a mixed-integer linear program (MILP) model that can be tackled by general-purpose solvers in order to find the most preferred solution. In the second part we discuss the integration of pumped-storage hydro (PSH) units in the UCP with large wind penetration. Those units can provide extra flexibility by using wind energy to pump and store water in the form of potential energy that can be generated after during peak load periods. PSH units are added to the first model, yielding a MILP model with wind-hydro-thermal coordination. Results showed that the proposed methodology is able to reflect the risk profiles of decision makers for both models. By including PSH units, the results are significantly improved.

Perylene Diimide acceptors: Fabrication and characterization of electron-only, hole-only devices and solar cells

The thrust towards energy conservation and reduced environmental footprint has fueled intensive research for alternative low cost sources of renewable energy. Organic photovoltaic cells (OPVs), with their low fabrication costs, easy processing and flexibility, represent a possible viable alternative. Perylene diimides (PDIs) are promising electron-acceptor candidates for bulk heterojunction (BHJ) OPVs, as they combine higher absorption and stability with tunable material properties, such as solubility and position of the lowest unoccupied molecular orbital (LUMO) level. A prerequisite for trap free electron transport is for the LUMO to be located at a level deeper than 3.7 eV since electron trapping in organic semiconductors is universal and dominated by a trap level located at 3.6 eV. Although the mostly used fullerene acceptors in polymer:fullerene solar cells feature trap-free electron transport, low optical absorption of fullerene derivatives limits maximum attainable efficiency. In this thesis, we try to get a better understanding of the electronic properties of PDIs, with a focus on charge carrier transport characteristics and the effect of different processing conditions such as annealing temperature and top contact (cathode) material. We report on a commercially available PDI and three PDI derivatives as acceptor materials, and its blends with MEH-PPV (Poly[2-methoxy 5-(2-ethylhexyloxy)-1,4-phenylenevinylene]) and P3HT (Poly(3-hexylthiophene-2,5-diyl)) donor materials in single carrier devices (electron-only and hole-only) and in solar cells. Space-charge limited current measurements and modelling of temperature dependent J-V characteristics confirmed that the electron transport is essentially trap-free in such materials. Different blend ratios of P3HT:PDI-1 (1:1) and (1:3) show increase in the device performance with increasing PDI-1 ratio. Furthermore, thermal annealing of the devices have a significant effect in the solar cells that decreases open-circuit voltage (Voc) and fill factor FF, but increases short-circuit current (Jsc) and overall device performance. Morphological studies show that over-aggregation in traditional donor:PDI blend systems is still a big problem, which hinders charge carrier transport and performance in solar cells.