Modelling and Co-simulation of Multi-Energy Systems: Distributed Software Methods and Platforms

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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

Design and development of a co-simulation platform for Multi-Energy System analysis

Multi-Energy Systems (MES) are complex systems where heterogenous energy vectors (e.g. electricity, heat exchanging fluids, natural gas) interact together in such a multi-faceted way that they are very difficult to be analysed comprehensively. Starting from a literature analysis, we identified the main challenges to be addressed to analyse in-depth these systems and let them interoperate in an efficient interconnection: i) the multi-fuel perspective to analyse the different input that a MES requires, ii) the multi-service perspective to identify the output of MES operations, iii) the multi-scale perspective to scale the analysis from small environments up to large scale scenarios (e.g. house, district, city, region, state), iv) the multi-time perspective to harmonize and synchronize different operational timings, v) the energy network perspective to take into account different vector’s distribution and transmission systems, vi) the ICT perspective to monitor and manage the overall system through data signalling, and vii) the economic and business perspective to study the impact of new solutions and services on the marketplace. These perspectives reflect on the difficult effort needed to simulate MES to assess their efficiency from an operational and planning viewpoint. Standalone solutions have been proposed in literature to analyse and simulate MES. However, these solutions focus only on some of the above-mentioned perspectives. Other solutions are more complete and allows analysis of all the aspects required by MES. Often, these solutions follow a vertical design in different field of technology (e.g. electrical and thermal engineering, distribution and transmission grid management and energy market analysis). Hence, MES scenario developers must dedicate a steep learning curve to master these solutions. In this work, we propose a co-simulation platform to simulate all the above-mentioned perspectives of a MES. The platform will allow to run energy-related simulations of specific elements of a MES, or to combine them in a homogenous simulation environment. The co-simulation platform will offer different functionalities to manage MES simulation, connecting in a plug-and-play fashion different software models, hardware and real-world devices. The functionalities will take into account: i) the scenario generation to design a MES interconnecting different ready-to-use models, ii) the simulation step and time management to manage the simulation of each model in a distributed simulation environment, iii) the exchange of information among different simulators, and iv) the optimization process to reach the best efficiency for a MES scenario

Non-Intrusive Load Disaggregation of Industrial Cooling Demand with LSTM Neural Network

As the telecommunication industry becomes more and more energy intensive, energy efficiency actions are crucial and urgent measures to achieve energy savings. The main contribution to the energy demand of buildings devoted to the operation of the telecommunication network is cooling. The main issue in order to assess the impact of cooling equipment energy consumption to support energy managers with awareness over the buildings energy outlook is the lack of monitoring devices providing disaggregated load measurements. This work proposes a Non-Intrusive Load Disaggregation (NILD) tool that exploits a literature-based decomposition with an innovative LSTM Neural Network-based decomposition algorithm to assess cooling demand. The proposed methodology has been employed to analyze a real-case dataset containing aggregated load profiles from around sixty telecommunication buildings, resulting in accurate, compliant, and meaningful outcomes

A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem

The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and dis- tributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simu- lation frameworks

A Distributed Software Solution for Demand Side Management with Consumer Habits Prediction

Future smart grids will open the marketplace to novel services for grid management, such as Demand Side Management (DSM). To achieve energy saving in distribution systems, DSM aims at modifying load profile patterns of electricity demand by involving actively customers. In particular, residential customers can participate to this service by shifting their energivourous appliances (e.g. washing machine and dishwasher). In this paper, we present a novel DSM service to manage a day ahead balance. It exploits a human-in-the-loop approach to provide suggestions on shifting their appliances based on Latent Dirichlet Allocation algorithm combining both i) the probability density function of each customer’s appliance usage and ii) the cost function. To assess our DSM service, we present our experimental results performed in a realistic environment where we simulated a virtual population of about 1′000 families

COMET: Co-simulation of Multi-Energy Systems for Energy Transition

The ongoing energy transition to reduce carbon emissions presents some of the most formidable challenges the energy sector has ever experienced, requiring a paradigm change that involves diverse players and heterogeneous concerns, includ- ing regulations, economic drivers, societal, and environmental aspects. Central to this transition is the adoption of integrated multi-energy systems (MES) to efficiently produce, distribute, store, and convert energy among different vectors. A deep understanding of MES is fundamental to harness the potential for energy savings and foster energy transition towards a low carbon future. Unfortunately, the inherent complexity of MES makes them extremely difficult to analyze, understand, design and optimize. This work proposes a digital twin co-simulation platform that provides a structured basis to design, develop and validate novel solutions and technologies for multi-energy system. The platform will enable the definition of a virtual representation of the real-world (digital twin) as a composition of models (co-simulation) that analyze the environment from multiple viewpoints and at different spatio-temporal scales

Design and accuracy analysis of multi-level state estimation based on smart metering infrastructure

While the first 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 multi-level state estimator that exploits smart meter measurements for monitoring both low and medium voltage grids. The goal of the paper is to present an architecture 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 were enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at the 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 the 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 multi-level estimator in coordination with the cloud-based platform

Synthetic Ground Truth Generation of an Electricity Consumption Dataset

The training of supervised Machine Learning (ML) and Artificial Intelligence (AI) algorithms is strongly affected by the goodness of the input data. To this end, this paper proposes an innovative synthetic ground truth generation algorithm. The methodology is based on applying a data reduction with Symbolic Aggregate Approximation (SAX). In addition, a Classification And Regression Tree (CART) is employed to identify the best granularity of the data reduction. The proposed algorithm has been applied to telecommunication (TLC) sites dataset by analyzing their electricity consumption patterns. The presented approach substantially reduced the dispersion of the dataset compared to the raw dataset, thus reducing the effort required to train the supervised algorithms

Load Profiles Clustering and Knowledge Extraction to Assess Actual Usage of Telecommunication Sites

Deep awareness of a particular industry sector represents a fundamental starting point for its energy efficiency enhancement. In this perspective, a huge amount of industrial facilities' energy measurements are collected thanks to the widespread usage of monitoring systems and Internet-of-Things infrastructures. In this context, data mining techniques allows an effective exploitation of data for knowledge extraction to automatically analyse such enormous amount of data. This paper investigates a large data set including real telecommunication sites' aggregate electrical demand provided by the largest telecommunication service provider in Italy. The goal is the assessment of the actual usage category of telecommunication sites, aiming at supporting the facility management of the company and the energy knowledge discovery of each site category. A novel methodology is proposed that includes i) a proper normalisation method focused on energy Key Performance Indicators for telecommunication network energy management, ii) a time series decomposition tool to extract trends and periodical fluctuation of telecommunication sites' aggregated electric demand, and iii) the application of a k-Means clustering algorithm to assess sites' actual usage. The proposed methodology results in accurate outcomes, which witness the potential for practical application and discloses opportunities for further developments