Search Results (223)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

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

Evaluating the renewable energy potential, such as that of solar photovoltaics (PV), is important for developing renewable energy policies. This study investigated rooftop solar PV potential in Thailand based on open-source geographic information system (GIS) building footprints, solar PV power output, and the most recent land use data (2022). GIS-based overlay analysis, buffering, fishnet modeling, and spatial join operations were applied to assess rooftop availability across various building types, taking into account PV module installation parameters and optimal panel orientation. Economic feasibility and sensitivity analyses were conducted using standard economic metrics, including net present value (NPV), internal rate of return (IRR), payback period, and benefit–cost ratio (BCR). The findings showed a total rooftop solar PV power generation potential of 50.32 TWh/year, equivalent to 25.5% of Thailand’s total electricity demand in 2022. The Central region contributed the highest potential (19.59 TWh/year, 38.94%), followed by the Northeastern (10.49 TWh/year, 20.84%), Eastern (8.16 TWh/year, 16.22%), Northern (8.09 TWh/year, 16.09%), and Southern regions (3.99 TWh/year, 7.92%). Both commercial and industrial sectors reflect the financial viability of rooftop PV installations and significantly contribute to the overall energy output. These results demonstrate the importance of incorporating rooftop solar PV in renewable energy policy development in regions with similar data infrastructure, particularly the availability of detailed and standardized land use data for building type classification. Full article

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar systems can exacerbate voltage unbalance in these networks. This paper investigates the application of a Static Synchronous Compensator (STATCOM) for the improvement of voltage regulation in four-wire LV distribution feeders through reactive power management as a means of mitigating voltage regulation and unbalance challenges. To demonstrate the performance of the STATCOM with varying loads and PV output, a Q-V droop curve is applied to specify the level of reactive power injection/absorption required to maintain appropriate voltage regulation. A practical four-wire feeder from New South Wales, Australia, has been used as a case study network to analyse improvements in system performance through the use of the STATCOM. The outcomes indicate that the STATCOM has a high degree of efficacy in mitigating voltage regulation and unbalance excursions. In addition, compared to other solutions identified in the existing literature, the STATCOM-based solution requires no sophisticated communication infrastructure. 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

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. 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

Accurate assessment of urban rooftop solar photovoltaic (PV) potential is critical for the low-carbon energy transition. This study presents a deep learning-based approach using high-resolution (0.5 m) aerial imagery to automatically identify building rooftops in the central urban area of Wuhan, China (covering seven districts), and to estimate their PV installation potential. Two state-of-the-art semantic segmentation models (DeepLabv3+ and U-Net) were trained and evaluated on a local rooftop dataset; U-Net with a ResNet50 backbone achieved the best performance with an overall segmentation accuracy of ~94%. Using this optimized model, we extracted approximately 130 km² of suitable rooftop area, which could support an estimated 18.18 GW of PV capacity. These results demonstrate the effectiveness of deep learning for city-scale rooftop mapping and provide a data-driven basis for strategic planning of distributed PV installations to support carbon neutrality goals. The proposed framework can be generalized to facilitate large-scale solar energy assessments in other cities. 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

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Rooftop photovoltaic (PV) panel systems have become a key component in green building design, driven by new building sustainability measures advocated worldwide. The shading generated by the rooftop PV panel arrays can impact their annual heating and cooling load, as well as their overall thermal performance. This paper presents a long-term experimental investigation into the changes in roof temperature caused by PV panels. The experiment was conducted over the course of a year, with measurements taken on four sample days each month. The study is based on measurements of the covered roof temperature, the uncovered roof temperature, PV surface temperature, ambient air temperature, as well as solar irradiation, wind speed, and rainfall. The results reveal that the annual energy savings (MJ/m²) in the cooling load due to the covered roof are about 26% higher than the energy loss from the heating load due to shading. The study shows that the effect of the rooftop PV panels on the house’s total heating and cooling load savings is between 5.3 to 6.1%. This difference is significant in thermal performance analyses, especially if most of the roof is covered by PV panels. 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

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage. Full article

The transition to renewable energy is critical to enhance Puerto Rico’s energy resilience and reduce dependence on imported fossil fuels. Rooftop photovoltaic (PV) systems provide a scalable opportunity to meet these objectives. This study evaluates the potential of rooftop PV systems across Puerto Rico using the National Renewable Energy Laboratory’s (NREL) PV Rooftop Database, processing detailed roof surface data to estimate installed capacity, energy generation, Levelized Cost of Electricity (LCOE), and solar resource potential at municipal and barrio levels. Findings reveal high solar rooftop capacity in urban neighborhoods, with areas like Sabana Abajo and Hato Tejas each exceeding 450 GWh/year in potential generation. Solar rooftop resource values peak at 3.67 kWh/kW in coastal areas, with LCOE values (0.071–0.215 USD/kWh) below current electricity rates. All municipalities demonstrate technical potential to meet their electricity demand with rooftop PV system alone. This research contributes through (1) developing Puerto Rico’s first comprehensive solar rooftop potential map; (2) providing unprecedented barrio-level analysis; (3) introducing a methodology for estimating missing post-disaster consumption data; and (4) integrating technical, economic, and equity indicators to inform energy policy. These findings demonstrate the importance of rooftop solar in achieving renewable energy goals and provide an understanding of spatial energy inequalities. Full article