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

Planning and real-time management of smart grids with high PV penetration in Italy

For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. 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, designed and developed following the microservice approach and providing REST web services, simulates real-sky solar radiation on rooftops and estimates the PV energy production; the second, based on a digital real-time power systems simulator, simulates the behaviour of the electric network under the simulated generation scenarios. The platform is tested on a case study based on real data for a district of the city of Turin, Italy. In the results, we show possible applications of the platform for power flow forecasting during real-time operation and to detect possible voltage and transformers capacity problems during planning due to high penetration of Renewable Energy Sources. In particular, the results show that the case study distribution network, in the actual configuration, is not ready to accommodate all the generation capacity that can be installed as, in certain hours of the day and in certain days of the year, the capacity of some transformers is exceeded

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

Implementing Air Pollution and Health Damage Costs in Urban Multi-Energy Systems Modelling

The growing global urbanization rate implies that the sustainability challenges are increasingly concentrated in cities. At today, around 75% of global energy is consumed in urban areas, so efforts must be addressed to transform existing urban energy systems into more sustainable systems. In this perspective, a key aspect to evolve toward a cleaner and affordable energy system is the development of Multi-Energy Systems (MES) modelling, whereby heat, electricity, fuels, transport, and other energy carriers closely interact with each other at various scales. MES can optimize technical, economic and environmental performance with respect to “traditional” independent energy systems, at both the operational and the planning stage. This paper presents a development of the existing MESsi modelling platform, consisting in the implementation of a model estimating the impacts on air quality and human health. MESsi is a novel distributed infrastructure for modelling and co-simulating Multi-Energy-Systems. It exploits modern software design patterns (i.e. microservices) to guarantee scalability, extendibility and easy maintenance of the system. Thus, MESsi is flexible in modelling and co-simulating different energy flows in a single solution made of different interoperable modules that can be deployed in a plug-and-play fashion. The module to be implemented in MESsi infrastructure is the DIATI integrated dispersion and externalities model (DIDEM). The DIDEM model is based on the impact pathway approach, linking the simulation of pollutants dispersion to the concentration-exposure-response functions provided by latest WHO recommendations. An overview of the potential integration steps in the modelling infrastructure is described in this paper. A discussion on possible application scenarios that have different spatio-temporal resolutions is also reported. The integration of DIDEM model in MESsi platform allows the inter-connection of a detailed impact assessment to a high-level energy system simulation

GIS-Based Optimal Photovoltaic Panel Floorplanning for Residential Installations

Shading is a crucial issue for the placement of PV installations, as it heavily impacts power production and the corresponding return of investment. Nonetheless, residential rooftop installations still rely on rule-of-thumb criteria and on gross estimates of the shading patterns, while more optimized approaches focus solely on the identification of suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work addresses the challenge of identifying an optimal (with respect to the overall energy production) placement of PV panels on a roof. The novel aspect of the proposed solution lies in the possibility of having a sparse, irregular placement of individual modules so as to better exploit the variance of solar data. The latter are represented in terms of the distribution of irradiance and temperature values over the roof, as elaborated from historical traces and Geographical Information System (GIS) data. Experimental results will prove the effectiveness of the algorithm through three real world case studies, and that the generated optimal solutions allow to increase power production by up to 28% with respect to rule-of-thumb solutions

A Microservices-based Framework for Smart Design and Optimization of PV Installations

The design of photovoltaic (PV) installations mostly relies on rule-of-thumb criteria and on gross estimates of the shading patterns, and the few optimized approaches are generally focused on the problem of identifying the most suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work proposes a framework to address the design and the optimization of PV installations through a set of microservices focusing on the different variables of the design: identification of the target surfaces, elaboration of weather data, modeling of the PV panel, and floorplanning of the panel on the surface. The microservices architecture ensures extensibility and generality, as the user may execute only a subset of the proposed services or provide novel algorithms to extend the existing ones. Additionally, the framework provides a set of built-in models that allow sensitivity to the distribution of shades and accurate modeling of the power production over time. We show the many benefits of the proposed framework on two different use cases

A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO2 emission

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

Optimal Configuration and Placement of PV Systems in Building Roofs with Cost Analysis

Following the Smart Grid view, current energy generation systems based on fossil fuels will be replaced with renewable energy sources. Photovoltaic (PV) is currently consid- ered the most promising technology, due to decreasing costs of the devices and to the limited invasiveness in existing infrastructures, that make PV installations quite common urban buildings’ roofs. To maximise both power production and Return Of Investment (ROI) of PV installations, new techniques and methodologies should be applied to limit sources of inefficiencies, like shading and power losses due to an incorrect installation. In this paper, we propose a novel solution for an optimal configuration and placement of PV systems in buildings’ roofs. Given a number of alternative configurations and a roof of interest, it combines detailed geographic and irradiance information to determine the optimal PV installation, by maximizing both power production and ROI. Our simulation results on two real-world roofs demonstrate an improvement on power generation up to 23% w.r.t. standard compact installations. These results also highlight that a cost analysis, often ignored by standard installation strategies, is nonetheless necessary to guarantee optimal results in terms of PV production and revenue

A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities

One of the ambitious goals of the ‘‘Smart city’’ paradigm is to design zero-energy buildings. Buildings can be considered as connected cyber-physical systems that require the construction of sound methodologies inherited from the Electronic Design Automation (EDA) research. In particular, aiming at autonomous buildings, the effective design of renewable energy sources is a key aspect for which such methodologies have to be developed. In this work, we propose a modeling strategy for the early estimation of the performance of photovoltaic (PV) arrays. Although a plethora of PV panel models there exists, most of these models suffer from accuracy/complexity tradeoffs. On one hand, building fast models forces to ignore either the correlation between temperature and irradiance, or the topology of panels, thus yielding inaccurate estimations. On the other, more accurate models are time consuming and require costly measurements or circuit analysis, that cannot be extracted from the sole datasheet. This paper proposes a compact semi-empirical model, suitable for real time simulation and built solely from information derived from the PV panel datasheet. The model is built by empirically fitting an expression of the panel operating point as a function of both irradiance and temperature, and of the adopted PV system topology. The accuracy and effectiveness of the proposed model have been validated w.r.t. the production traces of the PV systems of a real world industrial building