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

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

Designing a Smart City Internet of Things Platform with Microservice Architecture

The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standard-ization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the microservice architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces

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

GIS-based Software Infrastructure to Model PV Generation in Fine-grained Spatio-temporal Domain

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists on reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such renewable systems by modelling variability and uncertainty of their generation profiles. In this paper, we present a distributed software infrastructure for modelling and simulating energy production of Photovoltaic (PV) systems in urban context. In its core, it performs simulations in a spatio-temporal domain exploiting Geographic Information Systems together with meteorological data to estimate Photovoltaic generation profiles in real operating conditions. This solution provides results in real-sky conditions with different time-intervals: i) yearly, ii) monthly and iii) sub-hourly. To evaluate the accuracy of our simulations, we tested the proposed software infrastructure in a real world case study. Finally, experimental results are presented and compared with real energy production data collected from PV systems deployed in the case study area

IoT Software Infrastructure for Remote Monitoring of Patients with Chronic Metabolic Disorders

Novel Information and Communication Technologies, such as Internet-of-Things (IoT), middleware and cloud computing, are providing innovative solutions ranging in different contexts. Smart health is one of these scenarios. Indeed, there is a rising interest in developing new healthcare services for remote patient assistance and monitoring. Among all, the main promised benefits consist on improving the patients’ quality of life, speeding up therapeutic interventions and reducing hospitalizations’ costs. This is also known as Telemedicine. In this paper, we present a novel distributed software infrastructure for remote monitoring of patients with chronic metabolic disorders: i) it collects and and makes available information coming from IoT devices, ii) it performs analysis to help medical diagnosis and iii) it promotes a bidirectional communication among the end-users (i.e. medical personnel and patients). In this paper, we also present our experimental results performed in a laboratory test environment to validate the proposed solution

An Energy-autonomous Wireless Sensor Network Development Platform

Internet-of-things enabled applications are increasingly popular and are expected to spread even more in the next few years. Energy efficiency is fundamental to support the widespread use of such systems. This paper presents a practical framework for the development and the evaluation of low-power Wireless Sensor Networks equipped with energy harvesting, aiming at energy-autonomous applications. An experimental case study demonstrates the capabilities of the solution

Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts

In recent years, many governments are promoting a widespread deployment of Renewable Energy Sources (RES) together with an optimization of energy consumption. The main purpose consists on decarbonizing the energy production and reducing the CO2 footprints. However, RES imply uncertain energy production. To foster this transition, we need novel tools to model and simulate Multi-Energy-Systems combining together different technologies and analysing heterogeneous information, often in (near-) real-time. In this paper, first we present the main challenges identified after a literature review and the motivation that drove this research in developing MESsi. Then, we propose MESsi, a novel distributed infrastructure for modelling and cosimulating Multi-Energy-Systems. This infrastructure is a framework suitable for general purpose energy simulations in cities. Finally, we introduce possible simulation scenarios that have different spatio-temporal resolutions. Space resolution ranges from the single dwelling up to districts and cities. Whilst, time resolution ranges from microseconds, to simulate the operational status of distribution networks, up to years, for planning and refurbishment activities

An online reinforcement learning approach for HVAC control

Heating, Ventilation and Air Conditioning (HVAC) optimization for energy consumption reduction is becoming ever more a topic of the utmost environmental and energetic concerns. The two most employed methodologies for optimizing HVAC systems are Model Predictive Control (MPC) and Reinforcement Learning (RL). This paper compares three different RL approaches to HVAC optimization: one based on a black-box system identification model trained on historical data, one based on a white-box model of a building and one online method based on an imitation learning pretraining phase on historical data. The three approaches are compared with a literature baseline and an EnergyPlus baseline. Results show that the overall best method in terms of energy consumption reduction (65% decrease) and thermal comfort increase (25% increase) is the approach based on the white-box model. However, the proposed methodology, based on online and imitation learning, demonstrates remarkable efficiency, achieving comparable improvements in energy consumption after just a few months of online training, while maintaining thermal comfort at around the same level as the baseline. These results prove a direct online RL approach, which avoid the use of costly simulations, can provide a reliable and inexpensive solution to the problem of HVAC optimization

A User-Centric View of a Demand Side Management Program: From Surveys to Simulation and Analysis

Residential Demand Side Management (DSM) strategies increase the efficiency of the smart grid. However, the efficacy of these strategies relies on the participation of customers in DSM programs, an issue usually neglected in the analysis. To encompass all aspects, we tried to identify what are the drivers for the user engagement, focusing on the social and psychological behaviour of the user in order to simulate and analyse a residential DSM program with a centralised approach. In particular, the DSM program minimises costs taking into account different energy sources and performing load shifting considering and learning users’ acceptance of requests. The results show the advantage of a preferences-aware approach, highlighting the importance of user satisfaction on participation