Fault Detection, Isolation and Restoration Test Platform Based on Smart Grid Architecture Model Using Intenet-of-Things Approaches

© 2018 IEEE. To systematically shift existing distribution outage management paradigms to smart and more efficient schemes, we need to have an architectural overview of Smart Grids to reuse the assets as much as possible. Smart Grid Architecture Model offers a support to design such emerging use cases by representing interoperability aspects among component, function, communication, information, and business layers. To allow this kind of interoperability analysis for design and implementation of Fault Detection, Isolation and Restoration function in outage management systems, we develop an Internet-of-Things-based platform to perform real time co-simulations. Physical components of the grid are modeled in Opal-RT real time simulator, an automated Fault Detection, Isolation and Restoration algorithm is developed in MATLAB and an MQTT communication has been adopted. A 2-feeder MV network with a normally open switch for reconfiguration is modeled to realize the performance of the developed co-simulation platform

A co-ultramicronized palmitoylethanolamide/luteolin composite mitigates clinical score and disease-relevant molecular markers in a mouse model of experimental autoimmune encephalomyelitis

Background: Persistent and/or recurrent inflammatory processes are the main factor leading to multiple sclerosis (MS) lesions. The composite ultramicronized palmitoylethanolamide, an endogenous N-acylethanolamine, combined with the flavonoid luteolin, PEALut, have been found to exert neuroprotective activities in experimental models of spinal and brain injury and Alzheimer disease, as well as a clinical improvement in human stroke patients. Furthermore, PEALut enhances the expression of different myelin proteins in oligodendrocyte progenitor cells suggesting that this composite might have protective effects in MS experimental models. Methods: The mouse model of experimental autoimmune encephalomyelitis (EAE) based on active immunization with a fragment of myelin oligodendrocyte glycoprotein (MOG35-55) was used. The daily assessment of clinical score and the expression of serum amyloid A (SAA1), proinflammatory cytokines TNF-\u3b1, IL-1\u3b2, IFN-\u3b3, and NLRP3 inflammasome, as well as TLR2, Fpr2, CD137, CD3-\u3b3, and TCR-\u3b6 chain, heterodimers that form T cell surface glycoprotein (TCR), and cannabinoid receptors CB1, CB2, and MBP, were evaluated in the brainstem and cerebellum at different postimmunization days (PIDs). Results: Vehicle-MOG35-55-immunized (MOG35-55) mice developed ascending paralysis which peaked several days later and persisted until the end of the experiment. PEALut, given intraperitoneally daily starting on day 11 post-immunization, dose-dependently improved clinical score over the range 0.1-5 mg/kg. The mRNA expression of SAA1, TNF-\u3b1, IL-1\u3b2, IFN-\u3b3, and NLRP3 were significantly increased in MOG35-55 mice at 14 PID. In MOG35-55 mice treated with 5 mg /kg PEALut, the increase of SAA1, TNF- \u3b1, IL-1\u3b2, and IFN-\u3b3transcripts at 14 PID was statistically downregulated as compared to vehicle-MOG35-55 mice (p < 0.05). The expression of TLR2, Fpr2, CD137, CD3-\u3b3, TCR-\u3b6 chain, and CB2 receptors showed a significant upregulation in vehicle-MOG35-55 mice at 14 PID. Instead, CB1 and MBP transcripts have not changed in expression at any time. In MOG/PEALut-treated mice, TLR2, Fpr2, CD137, CD3-\u3b3, TCR-\u3b6 chain, and CB2 mRNAs were significantly downregulated as compared to vehicle MOG35-55 mice. Conclusions: The present results demonstrate that the intraperitoneal administration of the composite PEALut significantly reduces the development of clinical signs in the MOG35-55 model of EAE. The dose-dependent improvement of clinical score induced by PEALut was associated with a reduction in transcript expression of the acute-phase protein SAA1, TNF-\u3b1, IL-1\u3b2, IFN-\u3b3, and NLRP3 proinflammatory proteins and TLR2, Fpr2, CD137, CD3-\u3b3, TCR-\u3b6 chain, and CB2 receptors

A SGAM-based test platform to develop a scheme for wide area measurement-free monitoring of smart grids under high PV penetration

© 2019 by the authors. In order to systematically shift existing control and management paradigms in distribution systems to new interoperable communication supported schemes in smart grids, we need to map newly developed use cases to standard reference models like Smart Grid Architecture Model (SGAM). From the other side, any new use cases should be tested and validated ex-ante before being deployed in the real-world system. Considering various types of actors in smart grids, use cases are usually tested using co-simulation platforms. Currently, there is no efficient co-simulation platform which supports interoperability analysis based on SGAM. In this paper, we present our developed test platform which offers a support to design new use cases based on SGAM. We used this platform to develop a new scheme for wide area monitoring of existing distribution systems under growing penetration of Photovoltaic production. Off-the-shelf solutions of state estimation for wide area monitoring are either used for passive distribution grids or applied to the active networks with wide measurement of distributed generators. Our proposed distribution state estimation algorithm does not require wide area measurements and relies on the data provided by a PV simulator we developed. This practical scheme is tested experimentally on a realistic urban distribution grid. The monitoring results shows a very low error rate of about 1% by using our PV simulator under high penetration of PV with about 30% error of load forecast. Using our SGAM-based platform, we could propose and examine an Internet-of-Things-based infrastructure to deploy the use case

A cloud-based smart metering infrastructure for distribution grid services and automation

© 2017 The Authors The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applications are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud

Design and Accuracy Analysis of Multilevel State Estimation Based on Smart Metering Infrastructure

© 1963-2012 IEEE. While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-Time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

© 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 framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy

Neuroscience application for the analysis of cultural ecosystem services related to stress relief in forest

The paper presents an integrated methodology to assess psychological and physiological responses of people when exposed to forests, with the main objective of assessing the suitability of different stands for stress recovery on the basis of tree species and density. From the methodological viewpoint, the study applies both a Restoration Outcome Scale (ROS) questionnaire and a neuroscientific technique grounded on electro-encephalographic (EEG) measurement. Results show different outcomes for conifers and broadleaves as well as a statistical significance of density in the evaluation of an individual&rsquo;s emotional state. A forest with a high density of conifers and low density of broadleaves seems to be the proper combination for stress recovery. The differences among psychological stated preferences and EEG trends highlights potential conflict among &ldquo;needs&rdquo; and &ldquo;wants&rdquo; of people in the topic of stress relief. Potential applications of the research for health care and territorial marketing operations are suggested

Low voltage system state estimation based on smart metering infrastructure

© 2016 IEEE. The accurate monitoring of distribution grids is essential to enable the intelligent management and control of future Smart Grids. Several challenges prevent an easy development of the state estimation tools needed to assess the operating conditions of distribution networks. The lack of a suitable measurement infrastructure is one of the most challenging aspects to face. However, in last years, several utilities started a massive deployment of smart meters in their networks. The proper use of these measurements is key to enhance the performance of distribution system state estimators. This paper presents a two-level approach conceived to efficiently include smart meter measurements in low voltage grid state estimation. The proposed solution relies on a cloud-based smart metering architecture, which allows scalability and interoperability among different off-the-shelf meters. Moreover, a suitable design of the estimation algorithm, using the uncertainty propagation theory, is proposed in order to maximize the accuracy of the estimation results. Tests performed on a sample low voltage network show the performance and the main features of the proposed state estimation solution

Serum amyloid A primes microglia for ATP-dependent interleukin-1\u3b2 release

Acute-phase response is a systemic reaction to environmental/inflammatory insults and involves production of acute-phase proteins, including serum amyloid A (SAA). Interleukin-1\u3b2 (IL-1\u3b2), a master regulator of neuroinflammation produced by activated inflammatory cells of the myeloid lineage, in particular microglia, plays a key role in the pathogenesis of acute and chronic diseases of the peripheral nervous system and CNS. IL-1\u3b2 release is promoted by ATP acting at the purinergic P2X7 receptor (P2X7R) in cells primed with toll-like receptor (TLR) ligands

Expression and Differential Responsiveness of Central Nervous System Glial Cell Populations to the Acute Phase Protein Serum Amyloid A

Acute-phase response is a systemic reaction to environmental/inflammatory insults and involves hepatic production of acute-phase proteins, including serum amyloid A (SAA). Extrahepatically, SAA immunoreactivity is found in axonal myelin sheaths of cortex in Alzheimer's disease and multiple sclerosis (MS), although its cellular origin is unclear. We examined the responses of cultured rat cortical astrocytes, microglia and oligodendrocyte precursor cells (OPCs) to master pro-inflammatory cytokine tumour necrosis factor (TNF)-\u3b1 and lipopolysaccaride (LPS). TNF-\u3b1 time-dependently increased Saa1 (but not Saa3) mRNA expression in purified microglia, enriched astrocytes, and OPCs (as did LPS for microglia and astrocytes). Astrocytes depleted of microglia were markedly less responsive to TNF-\u3b1 and LPS, even after re-addition of microglia. Microglia and enriched astrocytes showed complementary Saa1 expression profiles following TNF-\u3b1 or LPS challenge, being higher in microglia with TNF-\u3b1 and higher in astrocytes with LPS. Recombinant human apo-SAA stimulated production of both inflammatory mediators and its own mRNA in microglia and enriched, but not microglia-depleted astrocytes. Co-ultramicronized palmitoylethanolamide/luteolin, an established anti-inflammatory/neuroprotective agent, reduced Saa1 expression in OPCs subjected to TNF-\u3b1 treatment. These last data, together with past findings suggest that co-ultramicronized palmitoylethanolamide/luteolin may be a novel approach in the treatment of inflammatory demyelinating disorders like MS