ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems

Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Community’s Horizon 2020 Program (H2020/2014–2020) under project “ERIGrid” (Grant Agreement No. 654113)

Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re) afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals

The RE-Europe data set

This data set models the continental European electricity system, including demand and renewable energy inflows for the period 2012-2014. The main features of the data set are: High resolution (~50km, 1 hour) and large extent (Mainland Europe, 3 years) Technical & economic characteristics of generators from real-world data and best available estimates Synthetic wind and solar observations and forecasts from numerical weather prediction models, describing the full spatio-temporal structure of potential wind and solar production The transmission system comprises 1494 buses and 2156 lines, and is fitted based on [1]. The location, capacity and fuel type for 969 real-world generators are given based on the information in [2], and these are supplied with full cost specifications estimated based on fuel type [3]. For each bus, signals for load [4, 5], wind and solar production is given for each hour of the three years, with the wind and solar signals based on meteorological weather data from [6,7]. Further, at hour 00 and 12, forecasts for the solar and wind production are given for the following 91 hours, based on weather data from [6]. All spatially-distributed data is aggregated to the nodal domain by summation/averaging over the area closest to each node. Wind and solar signals and forecast are given as capacity factors, i.e. production relative to rated power. To use the renewable signals, a capacity layout must be specified, which assigns an installed solar and wind capacity to each node. We supply two sets of capacity layouts, both scaled so the mean yearly production of (solar, wind) is equal the mean yearly load across EU. The Uniform layout is scaled to make the capacity in each node proportional to the area aggregated by that node - i.e. capacity is distributed uniformly across EU. The Proportional layout is scaled to make the capacity in each node proportional to the area aggregated by that node times the mean yearly capacity factor of the resource at that node - i.e. capacity is installed preferentially in nodes with high capacity factors. The data is intended for use in, e.g: Operational studies on markets Investment studies (generation capacity and transmission) Evaluation of future energy scenarios The source code used to generate this data is available at [9]. Version History: V1.2: Line information extended with data on the number of parallel lines in each connection. Fixed generator capacity typos. V1.1: License relaxed to CC-BY V1.0: Initial Releas