Urban Sustainability: a holistic approach for energy planning and operational dimensions

Strategic energy planning in cities is an emerging research field that is key to shift toward smarter and more sustainable communities. Increased awareness of environmental risks and human engagement can promote the communities toward natural and social flourishing, regarding domains namely ecology, economics, politics, and culture. Therefore, cities energy planning must bring together all the sustainable requirements toward integrated solutions and it needs new methodologies with a multi-perspective and holistic approach regarding the subjects, objects, and spatiotemporal domain of the communities. While macro-scale energy planning methodologies are well consolidated, the small-scale application still faces technical challenges such as the dynamic of an energy system with increasing penetration of distributed RES and the interaction of different functional layers (technology, policy, environment and communication layers) as well as multiple and diverse stakeholders. There is also the need for long‐term cross‐sectoral analysis and a fine disaggregation of the energy demand on a spatio-temporal domain. In this regard, it is important to develop a method to analyze the technology penetration, in order to understand the adoption mechanisms and develop policy strategies to act on accordingly. To address the above-mentioned issues, there is the necessity of combining different modeling frameworks and ICT solutions. The aim is to integrate temporal and spatial aspects, capturing the interactions between energy technologies and the physical infrastructure that distributes energy from producers to consumers while keeping into account constraints and feedback from regulators, economic drivers, and social behavior. This will require a bi‐directional amalgamation of planning and operational perspectives, working toward the interoperability of models. In addition, Agent‐Based Modeling (ABM) approach should be addressed because it is a suitable modeling technique in order to study real-world Complex Adaptive System (CAS), such as the urban communities. Specifically, ABM can feature concepts like heterogeneity, complexity, autonomy, explicit space and local interactions. The final goal is better understanding and prediction of: i) how consumers use energy, ii) how individuals react to information about the costs and benefits of energy choices and iii) how energy policies affect the behavior of the individual and, consequently, of the whole society

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

Modelling and techno-economic analysis of Peer-to-Peer electricity trading systems in the context of Energy Communities

The increasing penetration of Renewable Energy Resources (RES) is an opportunity to empower citizens to actively participate in energy markets through energy communities. At the local level, the Peer-to-Peer (P2P) trade and exchange of renewable energy represents a valid solution to fulfil the energy demand of the members, increase self-consumption and obtain economic benefits. However, a proper evaluation of the benefits for the community would require new considerations in designing typologies, composition, sharing and pricing mechanisms. Based on these premises, this paper explores the possible influences of different community-based P2P trading systems by examining several categories, ranging from aggregation structures, market mechanisms, sharing policies and pricing mechanisms internal to the local market. Furthermore, a flexible Mixed Integer Linear Programming model was formulated to optimise the day-ahead scheduling of community members participating in the P2P energy market. In this way, different community types, sharing policies, and pricing mechanisms were tested. Finally, the optimisation results were evaluated based on several key parameters

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

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18 720 households distributed over 1 290 building blocks and a surface area of 2.47 km2 is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of `prosumers' within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings

A Distributed Multi-Model Platform to Cosimulate Multi-Energy Systems in Smart Buildings

Nowadays, buildings are responsible for large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this paper, we present a flexible and distributed co-simulation platform that exploits a multi-modelling approach to simulate and evaluate energy performance in smart buildings. The developed platform exploits the Mosaik co-simulation framework and implements the Functional Mock-up Interface (FMI) standard in order to couple and synchronise heterogeneous simulators and models. The platform combines in a shared simulation environment: i) the thermal performance of the building simulated with EnergyPlus; ii) a heat pump integrated with a PID control strategy modelled in Modelica to satisfy the heating demand of the building; iii) an electrical energy storage system modelled in MATLAB Simulink; and iv) different Python models used to simulate household occupancy, electrical loads, photovoltaic production and smart meters, respectively. The platform guarantees a plug-and-play integration of models and simulators, in which one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities, capability and usability of the developed platform and discuss future developments of our framework

A Distributed Platform for Multi-modelling Co-simulations of Smart Building Energy Behaviour

Nowadays, buildings are responsible of a large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this paper we present a flexible and distributed co-simulation platform that exploits a multi-modelling approach to simulate and evaluate energy performance in smart build- ings. The developed platform exploits the Mosaik co-simulation framework and implements the Functional Mock-up Interface (FMI) standard in order to couple and synchronise heterogeneous simulators and models. The platform integrates: i) the thermal performance of the building simulated with EnergyPlus, ii) the space heating and hot water system modelled as an heat pump with PID control strategy in Modelica, and iii) different Python models used to simulate household occupancy, electrical loads, roof-top photovoltaic production and smart meters. The platform guaranties a plug-and-play integration of models and simulators, hence, one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities and capabilities of the developed platform, and discuss future developments of our framework

GAMES: a General-purpose Architectural model for Multi-Energy System engineering applications

The growing interest in Multi-Energy Systems (MES) leads the scientific community to implement innovative technologies to analyse and simulate these complex systems. Two main research trends are identified in such analysis: i) improve the usability and capability of preexisting reference architectures in the energy field to cope with high-level use case descriptions, and ii) study the interoperability of such reference architectures in order to increase systematic and functional analysis of MES use cases. GAMES is a a general-purpose architectural model for MES engineering application. The aim is twofold: i) GAMES implements an extension of Smart Grid Architecture Model (SGAM) to cope with MES use case descriptions, and ii) it offers a methodology to deal with a systemic description of the use case through a combination of UML and SysML integrated in the proposed architectural model. Furthermore, GAMES will allow the implementation of Domain Specific Language (DSL) and hardware configuration for the specific components described by UML/SysML diagrams. Compared to other solutions, GAMES allows to assess both research trends in a single hierarchical ICT infrastructure

Towards Energy Systems Integration: a holistic methodology, software platform, and toolset for modelling and simulation of Cyber-Physical-Social Energy Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities (RECs) involves maximizing the self-consumption within a community, particularly in regulatory contexts in which shared energy is incentivized. In many countries, the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users (e.g., residential and commercial users) makes the design of a new energy community a challenging task. This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data (i.e., billed energy), with the aim of estimating the energy shared by RECs. The proposed methodology involves three phases: first, identifying the typical load patterns of residential users through k-Means clustering, then implementing a Random Forest algorithm, based on monthly energy bills, to identify typical load patterns and, finally, reconstructing the hourly electrical load profile through a data-driven rescaling procedure. The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant. The Normalized Mean Absolute Error (NMAE) and the Normalized Root Mean Squared Error (NRMSE) were evaluated over an entire year and whenever the energy was shared within the REC. The Relative Absolute Error was also measured when estimating the shared energy at both a monthly (MRAE) and at an annual basis. (RAE). A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04 %, an NRMSE of 26.17 %, and errors of 18.34 % and 23.87 % during shared energy timeframes, respectively. Furthermore, our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31 % and 0.12 %, respectively