Search Results (52)

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article

The Xiong’an New Area, a newly established national-level zone in China, faces the threat of land subsidence and ground fissure due to groundwater overexploitation and geothermal extraction, threatening urban safety. This study integrates time-series InSAR and GNSS monitoring to analyze spatiotemporal deformation patterns from 2017/05 to 2025/03. The key results show: (1) Three subsidence hotspots, namely northern Xiongxian (max. cumulative subsidence: 591 mm; 70 mm/yr), Luzhuang, and Liulizhuang, strongly correlate with geothermal wells and F4/F5 fault zones; (2) GNSS baseline analysis (e.g., XA01-XA02) reveals fissure-induced differential deformation (max. horizontal/vertical rates: 40.04 mm/yr and 19.8 mm/yr); and (3) InSAR–GNSS cross-validation confirms the high consistency of the results (Pearson’s correlation coefficient = 0.86). Subsidence in Xiongxian is driven by geothermal/industrial groundwater use, without any seasonal variations, while Anxin exhibits agricultural pumping-linked seasonal fluctuations. The use of rooftop GNSS stations reduces multipath effects and improves urban monitoring accuracy. The spatiotemporal heterogeneity stems from coupled resource exploitation and tectonic activity. We propose prioritizing rooftop GNSS deployments to enhance east–west deformation monitoring. This framework balances regional and local-scale precision, offering a replicable solution for geological risk assessments in emerging cities. Full article

Distributed photovoltaic (PV) generation is increasingly important for urban energy systems amid global climate change and the shift to renewable energy. Traditional PV deployment prioritizes maximizing energy output, often neglecting electricity price variability caused by time-of-use tariffs. This study develops a high-resolution planning and economic assessment model for building-integrated PV (BIPV) systems, incorporating hourly electricity real-time market prices, solar geometry, and submeter building spatial data. Wuhan (30.60° N, 114.05° E) serves as the case study to evaluate optimal PV placement and tilt angles on rooftops and façades, focusing on maximizing economic returns rather than energy production alone. The results indicate that adjusting rooftop PV tilt from a maximum generation angle (30°) to a maximum revenue angle (15°) slightly lowers generation but increases revenue, with west-facing orientations further improving returns by aligning output with peak electricity prices. For façades, south-facing panels yielded the highest output, while north-facing panels with tilt angles above 20° also showed significant potential. Façade PV systems demonstrated substantially higher generation potential—about 5 to 15 times that of rooftop PV systems under certain conditions. This model provides a spatially detailed, market-responsive framework supporting sustainable urban energy planning, quantifying economic and environmental benefits, and aligning with integrated approaches to urban sustainability. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO₂ emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article

This study explores the optimization of solar energy harvesting in Truro City in the UK using PVSyst simulations integrated with real-time meteorological data. Focusing on panel orientation, tilt angle, shading, and albedo, the research aimed to enhance both energy efficiency and economic viability of photovoltaic (PV) systems in green buildings. A 100 kWp rooftop solar installation served as the case study. Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. Optimal tilt angles were identified between 35° and 39°, and the azimuth angle of 0° yielded the highest energy gain without requiring solar tracking. Fixed configurations with a 5 m pitch showed only a 10% shading loss, requiring 1680 m² of space and generating an average of 646.83 kWh/m² monthly. Compared to recent works, our integration of real-time climate data improved simulation accuracy by 6–9%, refining operational planning and decision-making processes. This included better timing of high-load activities and enhanced prediction for grid feedback. The study demonstrates that data-driven optimization significantly improves performance reliability and system design, offering practical insights for solar infrastructure in similar temperate climates. These results provide a benchmark for urban energy planners seeking to balance performance and spatial constraints in PV deployment. Full article

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment. Full article

The deployment of solar photovoltaic (PV) systems on rooftops in urban environments utilizes the rooftop areas for electricity generation. Rooftops may provide a large amount of empty space that can reduce the use of land for large PV plant installations and other purposes. These deployments may encounter shading on the PV collectors from surrounding building walls, thus reducing the incident direct beam radiation on the PV collectors, resulting in shading losses. Moreover, walls and collector rows block part of the visible sky, reducing the incident diffuse radiation on the collectors, resulting in masking losses. The present study complements previous studies by the authors (see the references) by calculating the incident beam, diffuse and global radiation, and their distribution across the collector rows for four configurations of PV systems installed near obscuring walls. In addition, the article quantifies the shading problem by simulating the shading dimensions and their patterns caused by walls and collector rows. The article is of practical importance for designers of PV systems in urban environments. The simulation results indicate an almost uniform distribution of the incident radiation between the collector rows. On the other hand, the losses may reach 8 percent for a wall height of 4 m for the parameters used in the study. Full article

Despite the abundance of solar resources and significant electrical demand during the daytime, residential PV installations are rarely found in Saudi Arabia due to unfavorable economics, resulting from low electricity tariffs by global standards. This work reports on opportunities and challenges of residential PV installation in Saudi Arabia based on the deployment process and analyses of the performance of two 15 kWp PV systems installed on the rooftops of two similar villas in Jeddah, Saudi Arabia. For each villa, 18 months of electrical consumption and ambient temperature were available pre-installation, followed by 24 months of post-installation PV system monitoring, including incident radiation, generation, and import from the grid. A linear model of the consumption of the villas fitted between 0.016 and 0.019 kWh/m² per cooling degree day, with varying levels of interception. No significant change was observed post-installation of the PV system. On average, the reduction in overall electrical import from the grid was 20–30%. A financial analysis based on the real costs and performance of the installed systems found that the net billing feed-in tariff should be increased to SAR 1.0–1.5 (USD 0.27–0.40), depending on a range of other possible measures, in order to stimulate the growth in residential rooftop PVs. Full article

To address the growing integration of renewable energy sources and storage systems into distribution networks, there is a need for effective tools that can assess the impact of these technologies on grid performance. This paper investigates the impact of integrating residential rooftop photovoltaic (PV) systems and battery energy storage systems (BESSs) into low-voltage (LV) distribution networks. A stochastic approach, using the Monte Carlo method, is applied to randomly place PV systems across the network, generating multiple scenarios for power flow simulations in MATLAB Simulink R2024b. The method incorporates real-world consumer load data and grid topology, representing a novel approach in simulating distribution network behaviour accurately. The novelty of this paper lies in its ability to combine stochastic PV placement with real-world load data, providing a more realistic representation of network conditions. The simulation results revealed that widespread PV deployment can lead to overvoltage issues, but the integration of BESSs alongside PV systems mitigates these problems significantly. The findings of this paper offer valuable insights for Distribution Network Operators, aiding in the development of strategies for optimal PV and BESS integration to enhance grid performance. Full article

This study explores the integration of wind power generation into urban infrastructure via a rooftop vertical-axis wind turbine. A rigorous experimental framework was established by installing a small-scale turbine on an urban building for performance assessment under controlled conditions. Simulated environmental conditions were created using a pedestal fan and blower to evaluate mechanical interactions between the components and electrical output efficiency of the turbine. Extensive numerical modeling was conducted to analyze turbine performance, by computational fluid dynamics using ANSYS FLUENT. The results reveal that the turbine operates efficiently even at low to moderate wind speeds (0.5–6 m/s), demonstrating its feasibility for urban deployment. Performance tests indicated that, as the shaft rotational speed increased from 55 rpm to 115 rpm, the output voltage, current and power varied nonlinearly in the ranges of 3–11.9 V, 20–130 mA and 0.05–2.7 W, respectively. Vibration measurement at specified turbine locations revealed nonlinear variation in displacement, velocity, acceleration and frequency without fixed patterns. Good agreement was observed between the experimental and numerical results. The numerical model yielded interesting profiles related to velocity and turbulence distributions, apart from torque, mechanical power and electrical voltage. Important conclusions were drawn from the entire work. Full article

Rooftop photovoltaics (RPVs) are crucial in addressing energy shortages and environmental concerns caused by fossil fuel combustion. To promote the optimal deployment of RPVs in Tianjin, a region with abundant solar resources and dense buildings, this study proposes a framework that integrates building vector data with a deep learning model to extract currently installed RPVs from remote sensing images, and further estimate the development potential of RPVs. A total of 86,363 RPV polygons were extracted, covering an area of 10.34 km². More than 70% of these RPVs are concentrated on large and low-rise buildings, and a similar proportion is found in industrial buildings, as these buildings offer favorable installation conditions. Combining solar radiation and construction land development planning, we further determined the potential deployment zone of RPVs covering about 13% of the Tianjin’s land area, which represents 31.31 TWh per year of power generation potential. In the future, it is recommended to prioritize RPV installation on large and low-rise buildings or industrial buildings in the potential deployment zone, which could provide higher power generation and contribute significantly to environmental emission reduction goals. The proposed research framework can also be applied to other cities. Full article

Rooftop photovoltaic (RTPV) systems have the potential to significantly boost residential electricity self-sufficiency in urban areas. However, estimating the self-sufficiency potential of each city is challenging due to the trade-off between target accuracy and data availability, which limits the scalability of existing methods. This study aims to evaluate the potential of RTPV systems to enhance residential electricity self-sufficiency in major Japanese cities. The self-sufficiency analysis employs a balanced approach using statistical data to estimate RTPV and battery storage capacity in detached houses and hourly simulations to capture supply–demand variations. To project the penetration rate, a logistic curve is utilized to estimate the timeline for achieving a 100% installation rate in detached houses. The analysis reveals that RTPV systems could supply approximately 40% of the residential electricity demand in major cities, with some achieving self-sufficiency rates exceeding 65%. Densely populated cities like Tokyo, Osaka, and Kawasaki may only meet a quarter of their demand due to higher energy requirements. Including older detached houses in RTPV deployment boosted self-sufficiency by an average of 11.77%, with cities like Nagoya, Kyoto, and Kitakyushu achieving increases of 15–20%. Battery storage plays a critical role in enhancing self-sufficiency and reducing energy curtailment. Logistic curve projections suggest that most cities are unlikely to reach 100% RTPV penetration before 2050, though leading cities could achieve 75% penetration by then due to favorable growth rates. These findings reveal that while RTPV has substantial potential to improve residential electricity self-sufficiency, additional efforts are necessary to accelerate adoption. Further research is needed to refine capacity estimates, explore the socioeconomic and political context of the cities, and examine alternative pathways for cities like Tokyo, Osaka, and Kawasaki. Full article

Urban surfaces such as rooftops, facades, and infrastructure offer significant potential for solar energy integration, contributing to energy efficiency and sustainability in cities. This article introduces an advanced multi-criteria assessment (MCA) framework designed to evaluate the suitability of various urban surfaces for solar energy deployment. The framework extends beyond traditional economic, environmental, and technological factors to include social, political, legal, health and safety, cultural, and psychological dimensions, providing a comprehensive evaluation of photovoltaic (PV) applications in urban contexts. By synthesizing existing literature and applying this holistic MCA framework, this research offers valuable insights for urban planners, architects, and policymakers, enabling strategic optimization of solar energy integration in urban environments. The findings underscore the importance of sustainable urban development and climate resilience, highlighting key factors influencing solar technology deployment and proposing actionable recommendations to address existing challenges. Full article