Search Results (7,177)

This study analyzes the intersection of energy, urban planning, decarbonization, and sustainability as a central axis for addressing urban development challenges in Latin America. A systematic search of the Scopus database selected 509 articles published between 2019 and 2024. The documents were thematically classified into urban planning (274), energy (79), and decarbonization (147), identifying only 10 studies that simultaneously integrate at least two of these dimensions in Latin American contexts. While this sample of 10 articles does not allow for generalizations about the region, the article selects representative cases to contextualize the type of research conducted, rather than offering extrapolable results. An exploratory multivariate analysis was applied to identify patterns, thematic gaps, and convergence trends, including Principal Component Analysis (PCA) to reduce the dimensionality of the set of key concepts and Hierarchical Clustering (HCC) to group terms according to their semantic proximity. These results are complemented by co-occurrence and thematic concentration maps generated from keywords extracted from the selected articles. The findings reveal a low level of integration among the topics analyzed, justifying the need to establish new lines of interdisciplinary research. The study proposes a replicable analytical tool that guides future regional research and contributes to the achievement of the Sustainable Development Goals, especially SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Green Public Procurement (GPP) is a key tool in European strategies to promote sustainability in public procurement. This study examines the integration of GPP criteria in the National Action Plans (NAPs) of the European Union Member States, with a focus on the construction and urbanizing fields. Through a comparative analysis of policies and regulatory instruments, the main differences in terms of mandatory application between countries in the monitoring and implementation of GPP are highlighted. The comparative analysis of 27 countries reveals significant variation in mandatory execution, regulatory frameworks and monitoring mechanisms. This study proposes an integrated set of indicators that align GPP performance with the Sustainable Development Goals (SDGs) of the 2030 Agenda, providing an holistic framework for the evaluation of policy and strategic planning. The research also explores the role of environmental performance indicators—such as energy efficiency, CO₂ emissions reduction and the use of sustainable construction materials—highlighting how these criteria support the ecological transition and contribute to the achievement of the EU climate objectives. The findings offer insights to strengthen the strategic role of GPP in sustainable environmental and territorial planning. Full article

There is a need for simultaneous attention to sustainability and resilience objectives while making energy decisions because of the need to address disruptions or shocks that can result from system-wide changes due to transitioning and existing threats to system performance. Owing to this emerging research area, this systematic review used the Scopus database to address the central question: What are the trends and practices that can enhance the integration of sustainability and resilience for energy decisions? The articles used are peer-reviewed, empirical research in the energy field and written in English. Articles that did not explicitly address energy systems (or any of the value chains) and gray literature were excluded from the study. The final screening of records resulted in the selection of 75 articles that effectively addressed the decision objective, context, and implementation (D-OCI), a classification scheme that supports 18 specific questions to identify practices for integrating the sustainability and resilience objectives. The highlighted practices are advantageous for decision evaluation and can provide valuable insights for formulating energy policies. This is particularly relevant because energy-related decisions affect households, organizations, and both national and international development. The study proposes ideas for future research based on the highlighted practices. Full article

Transmission lines in electrical power systems are studied and analyzed to improve the electrical system’s safety, stability, and optimal operation. Past research has proposed various optimization methods to address the problem of active and reactive power; however, they do not consider the voltage at the nodes, which causes losses in the system. By proposing a reduction in voltage at the nodes of the electrical system, it is possible to minimize voltage variation in the system using mixed integer nonlinear programming. The proposed methodology was tested on the IEEE 30-bus test system, where the objective function was modeled and simulated independently to test the results achieved through an AC OPF and reducing energy loss in the system. One of the most important investments was to demonstrate that the proposed methodology reduces voltage deviation at the system nodes, effectively confirming and maintaining lower active and reactive power production losses, resulting in a new type of energy planning that effectively benefits the electrical system voltage. Full article

Energy-efficient consumption has become a strategic priority to mitigate global climate change and enhance national energy security. While social media has reshaped online consumption behavior, the mechanisms through which these digital platforms influence energy-efficient purchasing remain underexplored. This study extends the Theory of Planned Behavior (TPB) by integrating price perception variables and applies multi-group structural equation modeling to examine how social media shapes Chinese consumers’ intentions to purchase energy-efficient air conditioning. The results show that (1) social media exposure strengthens energy-efficient purchasing intentions indirectly via behavioral attitude, subjective norm, and perceived behavioral control; (2) price perception is negatively associated with purchase intention; and (3) these effects vary by age cohort, gender, and income—Generation Z and female consumers are more susceptible to social media influence, while low-income groups exhibit heightened price sensitivity. These findings advance TPB theory and offer guidance for digital platform policies aimed at promoting energy-efficient consumption. Full article

With the increasing penetration of renewable energy, the scale of AC/DC hybrid transmission systems continues to grow, intensifying risks such as line overloads under N-1 contingencies, short-circuit current violations, and operational stability challenges arising from multi-DC coupling. This paper explores the complex coupling characteristics between AC/DC and multi-DC systems in hybrid configurations, proposing innovative evaluation indicators for coupling properties and a comprehensive assessment scheme for multi-DC coupling degrees. To enhance system stability, coordinated planning strategies are proposed for AC/DC hybrid transmission systems with multi-infeed High-voltage direct-current (HVDC) based on the AC/DC strong–weak balance principle. Specifically, planning schemes are developed for determining the locations, capacities, and converter configurations of newly added DC lines. Furthermore, to mitigate multi-DC simultaneous commutation failure risks, we propose an AC-to-DC conversion planning scheme and a strategy for adjusting the DC system technology route based on a through comprehensive multi-DC coupling strength assessment, yielding coordinated planning strategies applicable to the AC/DC hybrid transmission systems with multi-infeed HVDC. Finally, simulation studies on the IEEE two-area four-machine system validate the feasibility of the proposed hybrid transmission grid planning strategies. The results demonstrate its effectiveness in coordinating multi-DC coupling interactions, providing critical technical support for future hybrid grid development under scenarios with high renewable energy penetration. Full article

To promote low-carbon transformation and achieve carbon peak and neutrality in the energy field, this study proposes an operational optimization model considering the energy- and load-side dual response (ELDR) mechanism for electrothermal coupled virtual power plants (VPPs) containing a carbon capture device. The organic Rankine cycle (ORC) waste heat boiler (WHB) is introduced on the energy side. The integrated demand response (IDR) of electricity and heat is performed on the load side based on comprehensive user satisfaction (CUS), and the carbon capture system (CCS) is used as a flexible resource. Additionally, a carbon capture device operation mode that makes full use of new energy and the valley power of the power grid is proposed. To minimize the total cost, an optimal scheduling model of virtual power plants under ladder-type carbon trading is constructed, and opportunity-constrained planning based on sequence operation is used to address the uncertainty problems of new energy output and load demand. The results show that the application of the ELDR mechanism can save 27.46% of the total operating cost and reduce CO₂ emissions by 45.28%, which effectively improves the economy and low carbon of VPPs. In particular, the application of a CCS in VPPs contributes to reducing the carbon footprint of the system. Full article

This study investigates the significant energy optimization gaps in hotel retrofits in a subtropical climate, quantifying the missed energy-saving opportunities through advanced simulation techniques. Utilizing Design Builder software, the energy consumption of a hotel in Cienfuegos (Cuba) was assessed both before and after renovation, focusing on passive strategies (e.g., replacing single-glazed windows with double glazing) and active interventions (e.g., upgrading the air conditioning system). The results reveal that current retrofit strategies fail to reduce energy consumption substantially. Replacing single-glazed windows with double glazing could reduce annual energy use by 42%. Additionally, upgrading the existing chiller system or implementing a Variable Refrigerant Flow (VRF) system could result in 40% and 59.5% energy savings, respectively. The most significant energy reduction, 71%, is achieved when both interventions—upgrading the chiller and installing double-glazed windows—are implemented, reducing the energy consumption index (ECI) to a quarter of its current value. The life cycle cost (LCC) analysis demonstrates that energy-efficient investments offer considerable economic returns. For instance, an investment of USD 508,600 in a modern chiller system would generate net savings of USD 1,373,500 over its operational lifespan. This study underscores substantial economic and environmental losses from omitting energy efficiency considerations in hotel renovations. It calls for integrating comprehensive energy optimization strategies in retrofit planning, with each dollar invested in energy-saving measures potentially yielding USD 2.5 in life cycle savings. This approach is crucial for global hotel markets facing energy challenges. Full article

The expanding integration of wind and photovoltaic (PV) energy is disrupting the power system planning processes. Their incorporation poses limitations to forecasting due to their inherent variability. This review compiles a total of ninety studies conducted and published between 2019 and 2025, presenting for the first time an integrated approach that simultaneously optimizes the generation, transmission, storage, and flexibility of resources given high ratios of renewable generation. We present a systematic taxonomy of conflicting optimization approaches—deterministic, stochastic, robust, and AI-enhanced optimization—outlining meaningful mathematical formulations, real-world case studies, and the achieved trade balance between optimality, scale, and runtime. Emerging international cooperation clusters are identified through quantitative bibliometric analysis, and method selection in practice is illustrated using a table with concise snapshots of case study excerpts. Other issues analyzed include long-duration storage, centralized versus decentralized roadmap delineation, and regulatory and market drivers of grid expansion. Finally, we identified gaps in the literature—namely, resilience, sector coupling, and policy uncertainty—that warrant further investigation. This review provides critical insights for researchers and planners by systematically integrating methodological perspectives to tackle real-world, application-oriented problems related to generation and transmission expansion models amid significant uncertainty. Full article

Swift urbanization and technical progress in Cambodia, specifically in Phnom Penh, require underground transmission lines (UGTL) as a viable substitute for overhead transmission lines (OHTL). However, the substantial cost of UGTL has prevented its extensive integration. In this respect, we identified the most cost-effective technological route for an underground transmission line between substations. Using geographic information system (GIS) data, we generated algorithms to define the optimal route for the installation of a UGTL and minimize the costs of the material and labor required. The research results presented an automated tool for route optimization which simplifies the planning of energy projects and partially relieves the financial burden of UGTL integration. The proposed method radically changes the planning of urban energy infrastructure, as it provides a technology-based, cost-efficient, and environmentally favorable decision for UGTL routing. It also fosters the development of sustainable and resilient urban energy systems in similar urban locations. Full article

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

Conventional approaches for distributed generation (DG) planning often fall short in addressing operational demands and regional control requirements within distribution networks. To overcome these limitations, this paper introduces a cluster-oriented DG planning method. In terms of cluster partitioning, this study breaks through the limitations of traditional methods that solely focus on electrical parameters or single functions. Innovatively, it partitions the distribution network by comprehensively considering multiple critical factors such as system grid structure, nodal load characteristics, electrical coupling strength, and power balance, thereby establishing a unique multi-level grid structure of **distribution network—cluster—node**. This partitioning approach not only effectively reduces inter-cluster reactive power transmission and enhances regional power self-balancing capabilities but also lays a solid foundation for the precise planning of subsequent distributed energy resources. It represents a functional expansion that existing cluster partitioning methods have not fully achieved. In the construction of the planning model, a two-layer coordinated siting and sizing planning model for distributed photovoltaics (DPV) and energy storage systems (ESS) is proposed based on cluster partitioning. In contrast to traditional models, this model for the first time considers the interaction between power source planning and system operation across different time scales. The upper layer aims to minimize the annual comprehensive cost by optimizing the capacity and power allocation of DPV and ESS in each cluster. The lower layer focuses on minimizing system network losses to precisely determine the PV connection capacity of each node within the cluster and the grid connection locations of ESS, achieving comprehensive optimization from macro to micro levels. For the solution algorithm, a two-layer iterative hybrid particle swarm algorithm (HPSO) embedded with power flow calculation is designed. Compared to traditional single particle swarm algorithms, HPSO integrates power flow calculations, allowing for a more accurate consideration of the actual operating conditions of the power grid and avoiding the issue in traditional methods where the current and voltage distribution are often neglected in the optimization process. Additionally, HPSO, through its two-layer iterative approach, is able to better balance global and local search, effectively improving the solution efficiency and accuracy. This algorithm integrates the advantages of the particle swarm optimization algorithm and the binary particle swarm optimization algorithm, achieving iterative solutions through efficient information exchange between the two layers of particle swarms. Compared with conventional particle swarm algorithms and other related algorithms, it represents a qualitative leap in computational efficiency and accuracy, enabling faster and more accurate handling of complex planning problems. Case studies on a real 10 kV distribution network validate the practicality of the proposed framework and the robustness of the solution technique. Full article

This study presents a wind speed prediction model using monthly average wind speed data, employing the Extreme Gradient Boosting (XGBoost) algorithm to enhance forecasting accuracy for wind farm operations. Accurate wind speed forecasting is crucial for optimizing energy production, ensuring grid stability, and improving operational planning. Existing studies on enhancing wind speed prediction using ML algorithms have some drawbacks based on accuracy, efficient prediction, and stuck-in-local-optima parameters. The dataset comprises monthly average wind speed measurements, and extensive preprocessing is conducted to prepare the data for machine learning. Various hyperparameter tuning techniques, including Randomized Search, Grid Search, and Bayesian Optimization, are applied to improve prediction accuracy. The performance of the model is evaluated utilizing key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Continuous Ranked Probability Score (CRPS), and Maximum Error. The results indicate that hyperparameter tuning significantly improves model accuracy. Specifically, Grid Search demonstrates superior performance for short-term (one-month) forecasting, while Randomized Search is more effective for long-term (six-month) forecasting. The findings emphasize the critical importance of hyperparameter tuning strategies in the development of reliable wind speed forecasting models, which have significant implications for the efficient management of wind energy resources. Full article

Sustainable development requires efficient planning and management of both natural and built resources. The identification of urban forms that best balance exposure to solar radiation and urban noise, ensuring compliance with residential construction regulations and European directives may be carried out through simulations. The proposed methodology involves simulating various scenarios and adjusting parameters of selected urban forms to evaluate the availability of solar radiation and the noise exposure on building façades within a specific context. In addressing the requirements for solar and noise optimization, predictive models (solar and noise) were employed, utilizing urban form indicators to relate these three variables. The case study demonstrates the inverse behavior of these variables in relation to the same urban forms. The findings highlight the optimal urban forms for each scenario. The enclosed form was identified as the most suitable for minimizing noise exposure, while the linear form is optimal for maximizing solar radiation exposure. This approach allows the designer to make informed decisions that balance these competing requirements, achieving a compromise between optimizing thermal and acoustic performance. The ultimate goal is to enhance the overall comfort of the building, reduce energy consumption, and promote a sustainable building solution. Full article