Abstract
A digital twin is regarded as a potential solution to optimize positive energy districts (PED). This paper presents a compact review about digital twins for PED from aspects of concepts, working principles, tools/platforms, and applications, in order to address the issues of both how a digital PED twin is made and what tools can be used for a digital PED twin. Four key components of digital PED twin are identified, i.e., a virtual model, sensor network integration, data analytics, and a stakeholder layer. Very few available tools now have full functions for digital PED twin, while most tools either have a focus on industrial applications or are designed for data collection, communication and visualization based on building information models (BIM) or geographical information system (GIS). Several observations gained from successful application are that current digital PED twins can be categorized into three tiers: (1) an enhanced version of BIM model only, (2) semantic platforms for data flow, and (3) big data analysis and feedback operation. Further challenges and opportunities are found in areas of data analysis and semantic interoperability, business models, data security, and management. The outcome of the review is expected to provide useful information for further development of digital PED twins and optimizing its sustainability.
Highlights
Positive Energy Districts (PED) require integration of different systems and infrastructures for the optimal interactions among buildings, stakeholders, mobility, energy systems, and communication systems
According to European Strategic Energy Technology (SET) Plan Action 3.2, PEDs are the essential part of comprehensive approaches toward sustainable urbanization including technology, spatial, regulatory, financial, legal, social, and economic perspectives (Urban Europe, 2019)
In a digital twin platform, sensors will be set up to collect all kind of information, such as occupancy, temperature, moisture, energy consumption, renewable production, CO2 concentration, costs, waste, carbon footprint, etc., creating the “brain of PEDs.”
Summary
Positive Energy Districts (PED) require integration of different systems and infrastructures for the optimal interactions among buildings, stakeholders, mobility, energy systems, and communication systems. In a digital twin platform, sensors will be set up to collect all kind of information, such as occupancy (mobility), temperature, moisture, energy consumption, renewable production, CO2 concentration, costs, waste, carbon footprint, etc., creating the “brain of PEDs.” With such big data sets, digital twin model can be used to assess energy demand/supply, indoor air quality, thermal comfort, carbon emissions, expenses for operating and maintenance, building renovation and replacement needs (including recycle of waste construction materials), carbon emissions, and payback periods of energy saving measures over lifetime. The information and data within a digital PED twin can be collected and transferred by various sensor networks to create real-time monitoring, which often includes weather conditions (temperature, solar irradiation, etc.), material (new or wasted), cost, carbon emissions and footprint, facility/system status, indoor air quality (IAQ), inhabitant behavior, electric vehicle (EV) mobility, energy demand, local energy supply, and social structural information These data will be further analyzed by and exchanged in actions/decisions with different stakeholders for operations. How inhabitants change their mobility behavior in response to the increase of EV numbers; the impact of distributed PV installation on local network and storage systems, as well as local electricity market via different business models; Frontiers in Sustainable Cities | www.frontiersin.org
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