The growing adoption of clean solar energy in urban environments emphasizes the critical importance of efficient capacity planning in the development of building-integrated photovoltaic (BIPV) systems. Nevertheless, uncertainties associated with solar generation and demand pose substantial obstacles to systems planning. In response to these challenges, this paper introduces a novel approach: an optimization model that leverages ambiguity sets based on scenarios and is robust to distributional uncertainty for the expansion planning of building-integrated photovoltaic (BIPV) system. The distinctiveness of the model lies in its consideration of the non-linearity between global horizontal irradiance as well as PV solar power generation potential. A case study conducted in Singapore demonstrates the proposed model's substantial advantages including a 2 % -5 % improvement in self-sufficiency, 3 % -8 % electricity cost savings, and 3 % -8 % reduction in carbon dioxide emissions compared to benchmark models.@en

The expansion of solar energy in high density cities highlights the crucial need for optimal capacity planning in building-integrated photovoltaic (BIPV) systems. However, uncertainties present significant challenges for robust planning in these systems. Addressing this challenge, this paper proposes a distributionally robust optimization (DRO) model employing scenario robust ambiguity sets for BIPV system expansion planning to handle uncertainties in both demand and solar irradiance. This model stands out for its comprehensive incorporation of factors including the dynamics of climate change, the properties of building geometry, the intricate non-linear correlation between solar generation potential and global horizontal irradiance and uncertainty handling. The model can be transformed into a tractable formulation using the linear decision rule approach. In evaluating the model’s performance in data uncertainty handling, the study compares it against three alternative approaches – deterministic optimization, stochastic programming, and robust optimization – in a case study for BIPV system expansion planning for a college in Singapore. The findings demonstrate the superiority of the proposed model: it achieves a 5%–12% reduction in grid reliance, saves electricity cost by 3%–10% and reduces carbon dioxide (CO2) emission by 3%–10% compared to the benchmark approaches. These results underscore the capability of the proposed model in effectively handling data uncertainties in demand and irradiance in the BIPV system expansion planning.@en

Smart city initiatives have been a driving force for city-level dataset collection and the development of data-driven applications that benefit effective city management. There is a need to demonstrate use cases for effective city management using the available dataset. Urban Digital Twin (UDT) is a 3D city model that can integrate multi-disciplines and improve systems operability on a digital platform. However, UDTs are developed within organisations, and there is only limited availability of authoritative open 3D datasets to explore the potential of UDT concepts. This paper reports a methodology for creating a UDT platform for visualising and querying city energy data. We demonstrate a bottom-up approach to constructing an integrated 3D city dataset and create a query system for rapid access and navigation of the 3D city dataset through a visualisation platform using Cesium Ion. Various use cases are explored based on the dataset, such as building material stock management, energy demand simulation, electric vehicles (EV) demand and flexibility, and estimation of greenhouse gas (GHG) emissions. These use cases can help decision-makers and stakeholders involved in city planning and management. Furthermore, it provides a guideline for developers willing to create UDT applications for smart city initiatives.@en