Digital Twin-Based Resilience Evaluation Of District-Scale Archetypes
1. Introduction: District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. As a pilot, we developed a digital twin through a web map application for a district-scale university campus of area 170Ha. The impact on the built environment is simulated with pandemic (covid-19) and climate change scenarios. This can be observed through varying occupant rates and average cooling loads in the buildings during the lockdown period. We used a resilience assessment metric to measure the robustness of buildings to these disruptions. 2. District-scale digital twins: A digital twin is a virtual imitation of physical processes that can be updated in real-time with their physical counterparts. In 2002, Michael Grieves introduced the concept of digital twins in production engineering. Rapidly, explorations of digital twins took place in various scales and domains. However, the concept has widened and loosened to applications portraying digital simulation models, which relate to social and economic systems as well as physical systems . Numerous scholars emphasize the conflict in the definition of digital twins concerning a city or district. Furthermore, digital twins that represent the physical assets of the city rarely include processes that define its social and economic function. A city- or district-scale digital twin needs to contain geospatial urban environment data models in time series. The urban environment data models include building information modelling (BIM) for individual buildings, landscape, mobility, climate, demography, and infrastructure systems; these models are also known as city information modelling (CIM) . In our paper, we introduce a workflow to create a digital twin from the energy simulation software City Energy Analyst (CEA). 3. Methodology: In the first step, we modelled the campus using City Energy Analyst (CEA), which extracts the building footprint and height information (LOD0) from OpenStreetMap (OSM). Subsequently, the 2D shapefiles of the buildings were extruded with OSM heights and then converted to CityGML and 3D tiles format for web streaming. At the same time, detailed IFC models (LOD3) of a few buildings on the campus were converted and merged with the rest of the model with an IFC to CityGML pipeline. Lastly, the digital twin dashboard was built with visualisations of 3D campus, real-time data from sensors, energy demand simulation results from CEA, and occupancy rate from agent-based modelling. 4. Discussion: There is a need for conceptual and empirical work on digital twins that focuses on energy systems management on a district scale. In this paper, we identified issues related to building digital twins which can effectively contribute to the discussion. We have developed a use case of a digital twin through a web map application of district scale. We describe our resilience evaluation method for measuring the robustness of the district energy systems to disruptions caused by the covid-19 pandemic. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show the digital twin approach can support decarbonising initiatives for cities. References: 1. Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820.
Keywords: Digital Twin, City Information Modelling, Planning Support System, Energy Demand Model, Sgd11, Sustainable Cities And Communities, Sgd13, Climate Action