School of Water, Energy and Environment (SWEE)

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Browsing School of Water, Energy and Environment (SWEE) by Subject "11 Sustainable Cities and Communities"

Now showing 1 - 4 of 4
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    Assessing diurnal land surface temperature variations across landcover and local climate zones: implications for urban planning and mitigation strategies on socio-economic factors
    (Elsevier, 2024-12-01) Palanisamy, Prathiba A.; Zawadzka, Joanna Ewa; Jain, Kamal; Bonafoni, Stefania; Tiwari, Anuj
    Rising temperatures and rapid urbanization globally reinforce the need to understand urban climates. We investigated the influence of land cover and local climate zones (LCZs) on diurnal land surface temperature (LST) in various seasons in greater Delhi region, India, and their implications on socio-economic factors. Day LST was the highest in the summer and night LST in the monsoon, which also had the lowest diurnal differences in LST. Higher height and density of built-up features contributed to greater heat at night. During the day, open built-up and vegetated areas experienced relatively less heat than their compact equivalents. The lowest diurnal difference was in medium height compact urban zones and tall vegetation. Social inequity in access to urban cooling was indicated by large low-income and heat-vulnerable populations inhabiting the hottest LCZs. This research highlighted that even in semi-arid and subtropical climates, spatial planning policy should consider both the seasonality and diurnal differences in temperature as much as appropriate morphologies for design of thermally comfortable and climate resilient urban spaces. These policies should address the evidenced social inequities in heat exposure to reduce the adverse health impacts on vulnerable groups and therefore contribute to wider societal and economic benefits of healthier populations.
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    Psychological modeling for community energy systems
    (Elsevier, 2025-06-01) Zhao, Alexis Pengfei; Alhazmi, Mohannad; Huo, Da; Li, Wanzi
    This paper introduces a novel framework for community energy system (CES) optimization that integrates Social Cognitive Theory (SCT) with Distributionally Robust Optimization (DRO) to address both behavioral and technical challenges. The rapid integration of renewable energy resources and the proliferation of peer-to-peer (P2P) trading platforms necessitate solutions that balance economic efficiency, environmental sustainability, and user engagement under uncertainty. While existing studies focus predominantly on technical optimization, they often neglect the significant influence of behavioral dynamics on community energy systems. This research bridges the gap by explicitly incorporating peer influence, observational learning, and engagement incentives into a robust optimization framework. The proposed methodology models user behavior through SCT, enabling dynamic adjustments to trading patterns and energy-sharing decisions. A DRO model is employed to handle uncertainties in renewable energy generation and demand, ensuring system reliability and resilience. To enhance computational efficiency, a primal–dual algorithm is developed, offering faster convergence compared to traditional DRO methods. The framework is validated through a comprehensive case study on a 50-household microgrid equipped with solar, wind, and storage systems, alongside a P2P trading platform. Results demonstrate the framework's ability to reduce carbon emissions by up to 30%, improve renewable energy utilization to over 90%, and increase trading participation by 25%, compared to baseline scenarios. This study makes four key contributions: (1) introducing SCT-based behavioral modeling to enhance user engagement in CES operations, (2) leveraging DRO to address uncertainties in renewable energy and demand profiles, (3) proposing a primal–dual algorithm for scalable and efficient optimization, and (4) presenting a unified framework that balances economic, environmental, and social objectives. The findings highlight the transformative potential of integrating behavioral and technical approaches for sustainable and resilient community energy management.
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    Unmanned aerial vehicles versus smart grids
    (Institution of Engineering and Technology (IET), 2025-01) Pengfei Zhao, Alexis; Li, Shuangqi; Huo, Da; Alhazmi, Mohannad
    The increasing threat of unmanned aerial vehicles (UAVs) to smart grid infrastructures poses critical challenges to energy systems security. This study examines smart grid vulnerabilities to UAV‐based attacks and proposes a novel optimisation framework to enhance grid resilience. Employing a multi‐objective optimisation approach using the Non‐dominated Sorting Genetic Algorithm III (NSGA‐III) and a game‐theoretic Stackelberg model, the research captures the strategic interplay between UAV operators and grid defenders. Key contributions include the development of a multi‐objective optimisation framework, integration of adversarial game theory, incorporation of dynamic environmental conditions, and generation of Pareto‐optimal solutions for strategic defence planning. This research makes four pivotal contributions: (a) the design of a comprehensive multi‐objective optimisation framework tailored for UAV strike optimisation, (b) the integration of game‐theoretic principles to model adversarial behaviours, (c) the inclusion of dynamic environmental factors to improve solution robustness, and (d) the application of NSGA‐III to generate trade‐off solutions, equipping decision‐makers with diverse strategies to enhance grid resilience. By addressing an urgent and timely challenge, this work offers practical guidance for fortifying smart grid infrastructures against emerging UAV threats in increasingly complex operational environments.
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    Urban air quality management at low cost using micro air sensors: a case study from Accra, Ghana
    (American Chemical Society , 2025-02-14) Hodoli, Collins Gameli; Mead, Iq; Coulon, Frederic; Ivey, Cesunica E.; Tawiah, Victoria Owusu; Raheja, Garima; Nimo, James; Hughes, Allison; Haug, Achim; Krause, Anika; Amoah, Selina; Sunu, Maxwell; Nyante, John K.; Tetteh, Esi Nerquaye; Riffault, Véronique; Malings, Carl
    Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (r2) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m–3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m–3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.