Stochastic optimisation-based valuation of smart grid options under firm DG contracts

Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies

In pursuit of an expressive vocabulary for preserved new media art

The status of the new media, interactive and performance art context appears to complicate our ability to follow conventional preservation approaches. Documentation of digital art materials has been determined to be an appropriate means of resolving associated difficulties, but this demands high levels of expressiveness to support the encapsulation of the myriad elements and qualities of content and context that may influence value and reproducibility. We discuss a proposed Vocabulary for Preserved New Media Works, a means of encapsulating the various information and material dimensions implicit within a work and required to ensure its ongoing availability

Reflections on preserving the state of new media art

As part of its work to explore emerging issues associated with characterisation of digital materials, Planets has explored vocabularies and information structures for expressing the properties integral to the value of digital art. Value encompasses those qualities that must be understood and captured in order to ensure that art works’ sensory, emotional, mental and spiritual resonance remain. Facets of interactivity, modularity and temporality associated with digital art present some critical questions that the preservation community must increasingly be equipped to answer. Because digital art materials exhibit fundamental multidimensionality, validating the successful preservation of creative experience demands the explication of more than just file characteristics. Understanding relationships between objects also implies an understanding of their respective functional qualities. This paper presents a Planets’ vocabulary for encapsulating contextual and implicit characteristics of digital art, optimised for preservation planning and validation

C-Vine copula mixture model for clustering of residential electrical load pattern data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method

The DigCurV curriculum framework for digital curation in the cultural heritage sector

The Digital Curator Vocational Education (‘DigCurV’) project was funded by the European Commission’s Leonardo da Vinci lifelong learning programme . It aimed to establish a curriculum framework for vocational training in digital curation. DigCurV brought together a network of partners to address the availability of vocational training for digital curators in the library, archive, museum and cultural heritage sectors, with a particular focus on the training needed to develop new skills that are essential for the long-term management of digital collections. In 2013, the DigCurV collaborative network completed development of this Curriculum Framework for digital curation skills in the European cultural heritage sector. Drawing on a variety of established skills and competence models in the digital curation and cultural heritage sectors, DigCurV synthesised such expertise with input from those in the digital curation professions to develop a new Curriculum Framework. As a result, the Framework can help develop digital curation training offerings, provide a benchmark against which to map and compare existing offerings, and motivate training providers to continue to develop and refresh training. Our paper will describe the salient points of this work, including how the project team conducted the research necessary to develop the Framework, the structure of the Framework, the processes used to validate the Framework, and three ‘lenses’ onto the Framework. Our paper will also provide suggestions as to how the Framework might be used, including a description of potential audiences and purposes. As such, this paper draws on various DigCurV project deliverables. The contributions of members of the network to these deliverables is gratefully acknowledged