Demand response — a different form of distributed storage?

Reduced flexibility of low carbon generation could pose new challenges for future energy systems. Both demand response and distributed storage may have a role to play in supporting future system balancing. This paper reviews how these technically different, but functionally similar approaches compare and compete with one another. Household survey data is used to test the effectiveness of price signals to deliver demand responses for appliances with a high degree of agency. The underlying unit of storage for different demand response options is discussed, with particular focus on the ability to enhance demand side flexibility in the residential sector. We conclude that a broad range of options, with different modes of storage, may need to be considered, if residential demand flexibility is to be maximised.

CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

Characterization and antimicrobial efficacy against E. coli of a helium/air plasma at atmospheric pressure created in a plastic package

A plasma source, sustained by the application of a floating high voltage (±15 kV) to parallel-plate electrodes at 50 Hz, has been achieved in a helium/air mixture at atmospheric pressure (P = 105 Pa) contained in a zip-locked plastic package placed in the electrode gap. Some of the physical and antimicrobial properties of this apparatus were established with a view to ascertain its performance as a prototype for the disinfection of fresh produce. The current–voltage (I–V) and charge–voltage (Q–V) characteristics of the system were measured as a function of gap distance d, in the range (3 × 103 ≤ Pd ≤ 1.0 × 104 Pa m). The electrical measurements showed this plasma source to exhibit the characteristic behaviour of a dielectric barrier discharge in the filamentary mode and its properties could be accurately interpreted by the two-capacitance in series model. The power consumed by the discharge and the reduced field strength were found to decrease quadratically from 12.0 W to 4.5 W and linearly from 140 Td to 50 Td, respectively, in the range studied. Emission spectra of the discharge were recorded on a relative intensity scale and the dominant spectral features could be assigned to strong vibrational bands in the 2+ and 1− systems of N2 and ${\rm N}_2^+$ , respectively, with other weak signatures from the NO and OH radicals and the N+, He and O atomic species. Absolute spectral intensities were also recorded and interpreted by comparison with the non-equilibrium synthetic spectra generated by the computer code SPECAIR. At an inter-electrode gap of 0.04 m, this comparison yielded typical values for the electron, vibrational and translational (gas) temperatures of (4980 ± 100) K, (2700 ± 200) K and (300 ± 100) K, respectively and an electron density of 1.0 × 1017 m−3. A Boltzmann plot also provided a value of (3200 ± 200 K) for the vibrational temperature. The antimicrobial efficacy was assessed by studying the resistance of both Escherichia coli K12 its isogenic mutants in soxR, soxS, oxyR, rpoS and dnaK selected to identify possible cellular responses and targets related with 5 min exposure to the active gas in proximity of, but not directly in, the path of the discharge filaments. Both the parent strain and mutants populations were significantly reduced by more than 1.5 log cycles in these conditions, showing the potential of the system. Post-treatment storage studies showed that some transcription regulators and specific genes related to oxidative stress play an important role in the E. coli repair mechanism and that plasma exposure affects specific cell regulator systems.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Fat and fatty acid composition of cooked meat from UK retail chickens labelled as from organic and non-organic production systems

This study compared fat and fatty acids in cooked retail chicken meat from conventional and organic systems. Fat contents were 1.7, 5.2, 7.1 and 12.9 g/100 g cooked weight in skinless breast, breast with skin, skinless leg and leg with skin respectively, with organic meat containing less fat overall (P < 0.01). Meat was rich in cis-monounsaturated fatty acids, although organic meat contained less than did conventional meat (1850 vs. 2538 mg/100 g; P < 0.001). Organic meat was also lower (P < 0.001) in 18:3 n−3 (115 vs. 180 mg/100 g) and, whilst it contained more (P < 0.001) docosahexaenoic acid (30.9 vs. 13.7 mg/100 g), this was due to the large effect of one supermarket. This system by supermarket interaction suggests that poultry meat labelled as organic is not a guarantee of higher long chain n−3 fatty acids. Overall there were few major differences in fatty acid contents/profiles between organic and conventional meat that were consistent across all supermarkets.

Effect of feeding intensity and milking system on nutritionally relevant milk components in dairy farming systems in the North East of England

There is increasing concern that the intensification of dairy production reduces the concentrations of nutritionally desirable compounds in milk. This study therefore compared important quality parameters (protein and fatty acid profiles; α-tocopherol and carotenoid concentrations) in milk from four dairy systems with contrasting production intensities (in terms of feeding regimens and milking systems). The concentrations of several nutritionally desirable compounds (β-lactoglobulin, omega-3 fatty acids, omega-3/omega-6 ratio, conjugated linoleic acid c9t11, and/or carotenoids) decreased with increasing feeding intensity (organic outdoor ≥ conventional outdoor ≥ conventional indoors). Milking system intensification (use of robotic milking parlors) had a more limited effect on milk composition, but increased mastitis incidence. Multivariate analyses indicated that differences in milk quality were mainly linked to contrasting feeding regimens and that milking system and breed choice also contributed to differences in milk composition between production systems.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Elementary Petri net inside RFID distributed database (PNRD)

Usually, a Petri net is applied as an RFID model tool. This paper, otherwise, presents another approach to the Petri net concerning RFID systems. This approach, called elementary Petri net inside an RFID distributed database, or PNRD, is the first step to improve RFID and control systems integration, based on a formal data structure to identify and update the product state in real-time process execution, allowing automatic discovery of unexpected events during tag data capture. There are two main features in this approach: to use RFID tags as the object process expected database and last product state identification; and to apply Petri net analysis to automatically update the last product state registry during reader data capture. RFID reader data capture can be viewed, in Petri nets, as a direct analysis of locality for a specific transition that holds in a specific workflow. Following this direction, RFID readers storage Petri net control vector list related to each tag id is expected to be perceived. This paper presents PNRD cornerstones and a PNRD implementation example in software called DEMIS Distributed Environment in Manufacturing Information Systems.

Towards a Protocol for the Attachment of Metadata to Service Descriptions and its Use in Semantic Discovery

Service discovery in large scale, open distributed systems is difficult because of the need to filter out services suitable to the task at hand from a potentially huge pool of possibilities. Semantic descriptions have been advocated as the key to expressive service discovery, but the most commonly used service descriptions and registry protocols do not support such descriptions in a general manner. In this paper, we present a protocol, its implementation and an API for registering semantic service descriptions and other task/user-specific metadata, and for discovering services according to these. Our approach is based on a mechanism for attaching structured and unstructured metadata, which we show to be applicable to multiple registry technologies. The result is an extremely flexible service registry that can be the basis of a sophisticated semantically-enhanced service discovery engine, an essential component of a Semantic Grid.

QoS-aware model driven architecture through the UML and CIM

The specification of Quality of Service (QoS) constraints over software design requires measures that ensure such requirements are met by the delivered product. Achieving this goal is non-trivial, as it involves, at least, identifying how QoS constraint specifications should be checked at the runtime. In this paper we present an implementation of a Model Driven Architecture (MDA) based framework for the runtime monitoring of QoS properties. We incorporate the UML2 superstructure and the UML profile for Quality of Service to provide abstract descriptions of component-and-connector systems. We then define transformations that refine the UML2 models to conform with the Distributed Management Taskforce (DMTF) Common Information Model (CIM) (Distributed Management Task Force Inc. 2006), a schema standard for management and instrumentation of hardware and software. Finally, we provide a mapping the CIM metamodel to a .NET-based metamodel for implementation of the monitoring infrastructure utilising various .NET features including the Windows Management Instrumentation (WMI) interface.

Corporate governance and its influence on the investment decision process of equity market's professionals: An inside view of how corporate governance issues influence the decision-making process of sell-side and buy-side professionals when analyzing Brazilian listed companies

Corporate governance has been in the spotlight for the past two decades, being subject of numerous researches all over the world. Governance is pictured as a broad and diverse theme, evolving through different routes to form distinct systems. This scenario together with 2 types of agency problems (investor vs. management and minorities vs. controlling shareholders) produce different definitions for governance. Usually, studies investigate whether corporate governance structures influence firm performance, and company valuation. This approach implies investors can identify those impacts and later take them into consideration when making investment decisions. However, behavioral finance theory shows that not always investors take rational decisions, and therefore the modus operandi of those professionals needs to be understood. So, this research aimed to investigate to what extent Brazilian corporate governance standards and practices influence the investment decision-making process of equity markets' professionals from the sell-side and buy-side. This exploratory study was carried out through qualitative and quantitative approaches. In the qualitative phase, 8 practitioners were interviewed and 3 dimensions emerged: understanding, pertinence and practice. Based on the interviews’ findings, a questionnaire was formulated and distributed to buy-siders and sell-siders that cover Brazilian stocks. 117 respondents from all over the world contributed to the study. The data obtained were analyzed through structural equation modeling and descriptive statistics. The 3 dimensions became 5 constructs: definition (institutionalized governance, informal governance), pertinence (relevance), practice (valuation process, structured governance assessment) The results of this thesis suggest there is no definitive answer, as the extent to which governance will influence an investment decision process will depend on a number of circumstances which compose the context. The only certainty is the need to present a “corporate governance behavior”, rather than simply establishing rules and regulations at firm and country level.

Species Diversity of Myrmecofauna and Araneofauna Associated with Agroecosystem and Forest Fragments and their Interaction with Carabidae and Staphylinidae (Coleoptera)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)