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

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Nowadays, integration of more renewable energy resources into distribution systems to inject more clean en- ergy introduces new challenges to power system planning and operation. The intermittent behaviour of variable renewbale resources such as wind and PV generation would make the energy balancing more difficult, as current forecasting tools and existing storage units are insufficient. Transmission system operators may withstand some level of power imbalance, but fluctuations and noise of profiles are undesired. This requires local management performed or encouraged by distribution system operators. They could try to involve aggregators to exploit flexibility of loads through demand response schemes. In this paper, we present an optimal power flow-based algorithm written in Python which reads flexibility of different loads offered by the aggregators from one side, and the power flow deviation with respect to the scheduled profile at transmission-distribution coupling point from the other side, to define where and how much load to adjust. To demonstrate the applicability of this core, we set-up a real- time simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network. © 2020 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

IoT-Enabled Real-Time Management of Smart Grids with Demand Response Aggregators

Integration of widely distributed small-scale Renewable Energy Sources like rooftop Photovoltaic panels and emerging loads like plug-in Electric Vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this paper, we present a developed real-time management schema based on Internet of Things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified Optimal Power Flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulation- based test bed and realised the performance of this approach in a real-like environment using real data of a network with residential buildings

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

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

Real-Time Simulation of small-scale power grids with software in-the-loop and hardware in-the-loop experiments

As the term “Smart Grid” defines, the electricity supply network uses smart devices to monitor the state quantities, and digital communication technologies to support fast decisions and control. This work is focused on developing a flexible IoT architecture to support real-time tests and to validate some algorithms for monitoring and maintenance of micro-systems as intelligent smart functions on the virtual model of a small-scale power grid, through Real-Time Simulation Software in-the-loop (SIL) and Hardware in-the-loop (HIL). We aim to explain the implementation aspects of a microgrid in MATLAB/Simulink, based on real grid data and “smart customers” as end-users (capable of exchanging data with the outside of the simulation environment), then compiled in Real-Time environment (RT-LAB software). The overall communication infrastructure relies on different protocols for the data exchange between grid and application components (TCP and MQTT protocol). Furthermore, the presence of an MQTT broker makes the architecture flexible, since it allows the integration of different services

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

Lactoferrin's anti-cancer properties. Safety, selectivity, and wide range of action

Despite recent advances in cancer therapy, current treatments, including radiotherapy, chemotherapy, and immunotherapy, although beneficial, present attendant side effects and long-term sequelae, usually more or less affecting quality of life of the patients. Indeed, except for most of the immunotherapeutic agents, the complete lack of selectivity between normal and cancer cells for radio- and chemotherapy can make them potential antagonists of the host anti-cancer self-defense over time. Recently, the use of nutraceuticals as natural compounds corroborating anti-cancer standard therapy is emerging as a promising tool for their relative abundance, bioavailability, safety, low-cost effectiveness, and immuno-compatibility with the host. In this review, we outlined the anti-cancer properties of Lactoferrin (Lf), an iron-binding glycoprotein of the innate immune defense. Lf shows high bioavailability after oral administration, high selectivity toward cancer cells, and a wide range of molecular targets controlling tumor proliferation, survival, migration, invasion, and metastasization. Of note, Lf is able to promote or inhibit cell proliferation and migration depending on whether it acts upon normal or cancerous cells, respectively. Importantly, Lf administration is highly tolerated and does not present significant adverse effects. Moreover, Lf can prevent development or inhibit cancer growth by boosting adaptive immune response. Finally, Lf was recently found to be an ideal carrier for chemotherapeutics, even for the treatment of brain tumors due to its ability to cross the blood-brain barrier, thus globally appearing as a promising tool for cancer prevention and treatment, especially in combination therapies

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

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