Dark energy records in lensed cosmic microwave background

We consider the weak lensing effect induced by linear cosmological perturbations on the cosmic microwave background (CMB) polarization anisotropies. We find that the amplitude of the lensing peak in the BB mode power spectrum is a faithful tracer of the dark energy dynamics at the onset of cosmic acceleration. This is due to two reasons. First, the lensing power is non-zero only at intermediate redshifts between the observer and the source, keeping record of the linear perturbation growth rate at the corresponding epoch. Second, the BB lensing signal is expected to dominate over the other sources. The lensing distortion on the TT and EE spectra do exhibit a similar dependence on the dark energy dynamics, although those are dominated by primary anisotropies. We investigate and quantify the effect by means of exact tracking quintessence models, as well as parameterizing the dark energy equation of state in terms of the present value ($w_{0}$) and its asymptotic value in the past ($w_{\infty}$); in the interval allowed by the present constraints on dark energy, the variation of $w_{\infty}$ induces a significant change in the BB mode lensing amplitude. A Fisher matrix analysis, under conservative assumptions concerning the increase of the sample variance due to the lensing non-Gaussian statistics, shows that a precision of order 10% on both $w_{0}$ and $w_{\infty}$ is achievable by the future experiments probing a large sky area with angular resolution and sensitivity appropriate to detect the lensing effect on the CMB angular power spectrum. These results show that the CMB can probe the differential redshift behavior of the dark energy equation of state, beyond its average.Comment: New version including substantial text change, three more figures and two more table

A novel internet-of-things infrastructure to support self-healing distribution systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we present a novel distributed software infrastructure to foster new services in smart grids with particular emphasis on supporting self-healing distribution systems. This infrastructure exploits the rising Internet-of-Things paradigms to build and manage an interoperable peer-to-peer network of our prototype smart meters, also presented in this paper. The proposed three-phase smart meter, called 3-SMA, is a low cost and open-source Internet-connected device that provides features for self-configuration. In addition, it selectively run on-board-algorithms for smart grid management depending on its deployment on the distribution network. Finally, we present the experimental results of Hardware-In-the-Loop simulations we performed

A Novel Integrated Real-time Simulation Platform for Assessing Photovoltaic Penetration Impacts in Smart Grids

© 2017 The Authors. For future planning and development of smart grids, it is important to evaluate the impacts of PV distributed generation, especially in densely populated urban areas. In this paper we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first simulates real-sky solar radiation of rooftops and estimates the PV energy production; the second simulates the behaviour of the network when generation and consumption are provided at the different buses. The platform is tested on a case study based on real data for a district of the city of Turin, Italy

PVInGrid: A distributed infrastructure for evaluating the integration of photovoltaic systems in smart grid

© IFIP International Federation for Information Processing 2017 Published by Springer International Publishing AG 2017. All Rights Reserved. Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator

An online grey-box model based on unscented kalman filter to predict temperature profiles in smart buildings

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions

Fast fault location for fast restoration of smart electrical distribution grids

© 2016 IEEE. Distribution systems are evolving towards fault self-healing systems which can quickly identify and isolate faulted components and restore supply to the affected customers with little human intervention. A self-healing mechanism can considerably reduce the outage times and improve the continuity of supply; however, such an improvement requires a fast fault location method and also a communication and measurement infrastructure. In this paper the feasibility of fast service restoration through a fast fault location method is studied. A fast fault location method is proposed which is applicable to any distribution network with laterals, load taps and heterogeneous lines. The performance of the proposed method is evaluated by simulation tests on a real 13.8 kV, 134-node distribution system under different fault conditions. The results verify the applicability of the proposed architecture. We show that the communication delay plays a less important role in overall restoration time, and we stress the contribution of a fast fault location method in keeping the overall interruption time less than 1 minute

Emerging smart meters in electrical distribution systems: Opportunities and challenges

© 2016 IEEE. High penetration of variable and non-programmable distributed generation has brought new challenges to the power system operation and is highlighting the need of a smarter grid. One of the key requirements in this regard is developing and deploying smart metering systems in distribution networks. In this paper we present the actual situation in the Italian distribution networks and we discuss the opportunities and challenges of applying new metering systems and introducing a flexible, multi-utility, multi-service metering architecture. Some off-the-shelf or prototype smart meters, selected to be tested in an ongoing European project, named FLEXMETER, are presented

Benchmarking a many-core neuromorphic platform with an MPI-based DNA sequence matching algorithm

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS)multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform

Halo Clustering with Non-Local Non-Gaussianity

We show how the peak-background split can be generalized to predict the effect of non-local primordial non-Gaussianity on the clustering of halos. Our approach is applicable to arbitrary primordial bispectra. We show that the scale-dependence of halo clustering predicted in the peak-background split (PBS) agrees with that of the local-biasing model on large scales. On smaller scales, k >~ 0.01 h/Mpc, the predictions diverge, a consequence of the assumption of separation of scales in the peak-background split. Even on large scales, PBS and local biasing do not generally agree on the amplitude of the effect outside of the high-peak limit. The scale dependence of the biasing - the effect that provides strong constraints to the local-model bispectrum - is far weaker for the equilateral and self-ordering-scalar-field models of non-Gaussianity. The bias scale dependence for the orthogonal and folded models is weaker than in the local model (~ 1/k), but likely still strong enough to be constraining. We show that departures from scale-invariance of the primordial power spectrum may lead to order-unity corrections, relative to predictions made assuming scale-invariance - to the non-Gaussian bias in some of these non-local models for non-Gaussianity. An Appendix shows that a non-local model can produce the local-model bispectrum, a mathematical curiosity we uncovered in the course of this investigation.Comment: 12 pages, 4 figures; submitted to Phys. Rev. D; v2: references added; v3: some more comments on kernel-bispectrum relation in appendi