Search Results (21)

Hydrogen is being increasingly integrated into the international trade system as a clean and flexible energy carrier, motivated by the global energy transition and carbon neutrality objectives. The rapid expansion of the global hydrogen trade network has simultaneously exposed several sustainability challenges, including a centralized structure, overdependence on key countries, and limited resilience to external disruptions. Based on this, we develop a risk propagation model that incorporates the absorption capacity of nodes to simulate the propagation of supply shortage risks within the global hydrogen trade network. Furthermore, we propose a composite sustainability index constructed from structural, economic, and environmental resilience indicators, enabling a systematic assessment of the network’s sustainable development capacity under external shock scenarios. Findings indicate the following: (1) The global hydrogen trade network is undergoing a structural shift from a Western Europe-dominated unipolar configuration to a more polycentric pattern. Countries such as China and Singapore are emerging as key hubs linking Eurasian regions, with trade relationships among nations becoming increasingly dense and diversified. (2) Although supply shortage shocks trigger structural disturbances, economic losses, and risks of carbon rebound, their impacts are largely concentrated in a limited number of hub countries, with relatively limited disruption to the overall sustainability of the system. (3) Countries exhibit significant heterogeneity in structural, economic, and environmental resilience. Risk propagation demonstrates an uneven pattern characterized by hub-induced disruptions, chain-like transmission, and localized clustering. Accordingly, policy recommendations are proposed, including the establishment of a polycentric coordination mechanism, the enhancement of regional emergency coordination mechanisms, and the advancement of differentiated capacity-building efforts. Full article

Integrating Electric Vehicle (EV) charging stations into buildings is becoming increasingly important due to the rapid growth of private EV ownership and prolonged parking durations in residential areas. This paper proposes robust, building-integrated charging solutions that combine mobile energy storage systems (ESSs), station linkage data, and traffic volume data. The proposed system promotes eco-friendly EV usage, flexible energy management, and carbon neutrality through a polyfunctional Vehicle-to-Grid (V2G) architecture that integrates decentralized energy networks. Two core strategies are implemented: (1) configuring Virtual Power Plant (VPP)-based charging packages tailored to station types, and (2) utilizing EV batteries as distributed ESS units. K-means clustering based on spatial proximity and energy demand is followed by heuristic algorithms to improve the efficiency of mobile ESS operation. A three-layer framework is used to assess improvements in energy demand distribution, with demand-oriented VPPs deployed in high-demand zones to maximize ESS utilization. This approach enhances station stability, increases the load factor to 132.7%, and reduces emissions by 271.5 kgCO₂. Economically, the system yields an annual benefit of USD 47,860, a Benefit–Cost Ratio (BCR) of 6.67, and a Levelized Cost of Energy (LCOE) of USD 37.78 per MWh. These results demonstrate the system’s economic viability and resilience, contributing to the development of a flexible and sustainable energy infrastructure for cities. Full article

With the increasing demand for electricity, it is causing a growing burden on the power grid. In order to alleviate the pressure on the power system, a series of demand response (DR) strategies have emerged. This paper studied the DR potential and energy flexibility on city-scale building clusters under pre-cooling combined with temperature reset. This study firstly selected 18 types of buildings, each containing three construction years as prototype buildings, to represent the 228,539 buildings in Shenzhen. Then several pre-cooling strategies were developed, and after comparative analysis, the optimal strategy was obtained and applied to the entire Shenzhen building cluster, with simulation and analysis conducted for the nine administrative districts. Among them, this paper used AutoBPS-DR and added pre-cooling code based on the Ruby language to automatically generate building models with DR strategies and finally simulated the energy consumption results by EnergyPlus. The results showed that a pre-cooling duration of 0.5 h and a change of 2 °C in both pre-cooling temperature and reset temperature was the optimal strategy. Under this strategy, small and medium prototype buildings can achieve better results, with a maximum load reduction of 23.89 W/m² and a reduction rate of 56.82%. In the simulation results of the building cluster, Guangming District showed the best results. Finally, the peak electricity reduction amount and reduction rate of the entire building cluster were calculated to be 0.007 kWh/m² and 21.87%, respectively, with the maximum cost saving and saving percentage of 0.081 CNY/m² and 15.05%, respectively. From this, it can be seen that the Shenzhen building cluster had shown considerable DR potential under the pre-cooling strategy. Full article

This paper aims to determine the optimal construction strategies for new-build houses in the UK to minimize heating energy demand and discomfort hours. This research utilizes a previously calibrated model of “The Future Home” in Energy House 2.0’s environmental chamber. Eight design variables were optimized including multiple building fabric specifications, air permeability rates, and heating setpoint temperatures. Three optimization scenarios were investigated: fixed heating setpoints, variable heating setpoints, and variable setpoints with comfort constraints. The analysis revealed that while fixed heating setpoints showed limited optimization potential, variable setpoint scenarios identified three distinct clusters of optimal solutions. The optimization consistently favored superior building fabric parameters, though air permeability solutions became more nuanced with variable heating control. When constrained to a maximum of 400 discomfort hours, solutions required elevated heating setpoints (22–23 °C) while maintaining high fabric specifications. These findings advance building optimization methodology by demonstrating the importance of heating control flexibility and comfort constraints in achieving optimal performance, while the use of a calibrated model in controlled conditions overcomes the limitations of previous studies that relied on uncalibrated or hypothetical models. As in situ field measurements of short- and long-term building performance are often subjected to disruptions, delays, and uncertainties, the building performance research under controlled conditions reported in this article will lead towards the achievement of net zero targets in a timelier manner and with more certainty. Full article

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock. Full article

The deeper penetration of renewables in the energy mix is an intense requirement in order to reduce global carbon dioxide emissions. In addition, new technologies are being developed, such as electric mobility and Distributed Generation (DG) in urban areas. However, the unpredictable fluctuations in energy generation from roof-installed PVs and the switching operation of their inverters greatly aggravate the already-present grid quality problems. In this paper, the Smart Boiler (SB) concept for grid quality improvement is presented. Furthermore, its experimental verification is implemented on a flexible testbed that accurately emulates several realistic scenarios for the low voltage distribution grid, under complex operating conditions. The proposed low-cost electronic kit, which contains a converter of fairly simple topology and requires connection to the internet, is used to upgrade conventional domestic boilers to smart devices. The SB automatically regulate the local reactive power flow, helping to stabilize the voltage level and suppress the grid current harmonic content, with both services provided in a matter of seconds. The higher the active power consumed and the denser the SB cluster, the wider the beneficial impact on the affected network area. While this service is provided, excess energy generated by PVs is temporarily stored as heat in the boiler tanks, given the users’ hot water consumption habits. The whole application, as a powerful demand-side management tool, proves beneficial for both the network operator and the end-user, especially when self-consumption is desirable in order to achieve a Nearly Zero Energy Building. Full article

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits. Full article

A superatom is a cluster of atoms that acts like a single atom. Two main groups of superatoms are superalkalis and superhalogens, which mimic the chemistry of alkali and halogen atoms, respectively. The ionization energies of superalkalis are smaller than those of alkalis (<3.89 eV for cesium atom), and the electron affinities of superhalogens are larger than that of halogens (>3.61 eV for chlorine atom). Exploring new superalkali/superhalogen aims to provide reliable data and predictions of the use of such compounds as redox agents in the reduction/oxidation of counterpart systems, as well as the role they can play more generally in materials science. The low ionization energies of superalkalis make them candidates for catalysts for CO₂ conversion into renewable fuels and value-added chemicals. The large electron affinity of superhalogens makes them strong oxidizing agents for bonding and removing toxic molecules from the environment. By using the superatoms as building blocks of cluster-assembled materials, we can achieve the functional features of atom-based materials (like conductivity or catalytic potential) while having more flexibility to achieve higher performance. This feature paper covers the issues of designing such compounds and demonstrates how modifications of the superatoms (superhalogens and superalkalis) allow for the tuning of the electronic structure and might be used to create unique functional materials. The designed superatoms can form stable perovskites for solar cells, electrolytes for Li-ion batteries of electric vehicles, superatomic solids, and semiconducting materials. The designed superatoms and their redox potential evaluation could help experimentalists create new materials for use in fields such as energy storage and climate change. Full article

This paper proposes an active–reactive power collaborative scheduling model with cluster division for the flexible distributed energy resources (DERs) of smart-building systems to resolve the high complexity of the centralized optimal scheduling of massive dispersed DERs in the distribution network. Specifically, the optimization objective of each cluster is to minimize the operational cost, the power-loss cost, and the penalty cost for flexibility deficiency, and the second-order cone-based branch flow method is utilized to convert the power-flow equations into linearized cone constraints, reducing the nonlinearity and heavy computation burden of the scheduling model. Customized virtual battery models for building-integrated flexible DERs are developed to aggregate the power characteristics of flexible resources while quantifying their regulation capacities with time-shifting power and energy boundaries. Moreover, a cluster division algorithm considering the module degree index based on the electrical distance and the flexible balance contribution index is formulated for cluster division to achieve information exchange and energy interaction in the distribution network with a high proportion of building-integrated flexible DERs. Comparative studies have demonstrated the superior performance of the proposed methodology in economic merits and voltage regulation. Full article

Renewable, decentralised, and citizen-centred energy paradigms have emerged as feasible and reliable alternatives to the traditional centralised fossil-based infrastructure. In this scenario, energy storage systems (ESSs) are enabling technologies to boost the stability and flexibility of the power grid in the short-to-medium term, allowing local communities to envision energy autonomy in the medium term. Traditionally, ESSs have been installed in individual households for their own benefit. However, new storage paradigms focusing on building clusters and district scale have illustrated the need to revise the role of ESSs and to pay close attention to the social factors, while devising implementation strategies for scaling up these new energy infrastructural models. This study reviews recent research trends (2021–2023), proposing three integrated social pillars for the implementation of ESSs: (i) multi-dimensional geographical and institutional scales of ESSs; (ii) social components of spatial and temporal flexibility of ESSs; and (iii) co-creation approaches to devising ESS implementation strategies. These pillars point out the necessary social factors for the implementation of ESSs at scale, highlighting future research perspectives to operationalise such factors, with a particular focus on the importance of citizens’ perception, participation, and collaboration, which are critical for maximising the benefit of sharing and exchanging renewable energy locally. Development of flexible and agile digital platforms that facilitate the co-creation of adaptable socio-technical solutions to adopting ESSs is proposed. The need to tailor these solutions to suit the stakeholders’ capabilities is emphasized. Full article

Electric vehicles (EVs) can provide important flexibility to the integration of local energy generation in buildings. Although most studies considering the integration of EVs and buildings are focused on residential buildings, the number of publications regarding large buildings, in particular, public buildings (PBs), has increased. However, the quantity of studies regarding the integration of EVs and PBs is still limited. Additionally, there are no review studies approaching the integration of EVs and buildings in one single framework. In this sense, this review aims to address the challenges and trends associated with optimizing the charging of EVs in PBs by conducting a systematic review of the existing literature. As contributions, this work develops a review that approaches the integration of EVs and PBs using multiple strategies and structures, presents an integrated picture of the technical and economic constraints, and addresses the future trends and research perspectives related to the subject. Through the use of an open-access search engine (LENS), a cluster of 743 publications was analyzed using two strings and a timeframe restriction. The most important contributions regarding optimization strategies and their evolution are presented, followed by a comparison of the findings with other review papers. As key findings, technical and economic constraints are identified (uncertainties of driving behavior and local generation, battery degradation, “injection tariffs”, etc.), as are future trends and perspectives (local generation legislation, incentives for purchasing EVs, energy communities, etc.). Full article

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance. Full article

The paper presents the analysis of energy retrofitting, integration of renewable energy and activation of energy flexibility in a cluster of buildings in the surroundings of a port on the Mediterranean Sea in Southern Italy, with the aim of checking the potential for it to achieve the status of positive energy district (PED). The objective of this study is to improve the contemporaneity between local energy generation and energy demand and reduce CO_2eq emissions by considering signals that reflect the environmental variability of the electricity grid, through flexibility solutions applied to the HVAC system. The proposed scenarios are based on the dynamic simulation of the district and analyze the effect of actions that activate the energy flexibility of buildings through advanced control strategies of the air conditioning system. The results show that the joint action of energy efficiency strategies, integration of solar energy and energy flexibility improves the environmental sustainability of the district and the balance of energy flows. Specifically, the activation of energy flexibility contributes to a 10% reduction in operational CO_2eq emissions and increases in self-consumption of energy per year. The operational emissions of the district vary from the base value of 33.37 tons CO_2eq/y to 19.52 tons CO_2eq/y in the scenario based on the integration of solar energy systems and energy efficiency measures, and to 17.39 tons CO_2eq/y when also the demand-side energy flexibility is activated. Full article

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems. Full article

The protection of the natural environment and countering global warming are crucial worldwide issues. The residential sector has a significant impact on overall energy consumption and associated greenhouse gas emissions. Therefore, it is extremely important to focus on all of the activities that can result in more energy efficient and sustainable city scale areas, preventing global warming. The highest improvement in the energy efficiency of existing buildings is possible by combining their deep refurbishment and the use of renewable energy sources (RES), where solar energy appears to be the best for application in buildings. Modernizations that provide full electrification seem to be a trend towards providing modern, energy efficient and environmentally friendly, smart buildings. Moreover, switching from an analysis at the single building level to the district scale allows us to develop more sustainable neighborhoods, following the urban energy modelling (UEM) paradigm. Then, it is possible to use the energy cluster (EC) concept, focusing on energy-, environmental- and economic-related aspects of an examined region. In this paper, an actual Polish suburban district is examined using the home-developed TEAC software. The software is briefly described and compared with other computer codes applied for UEM. In this study, the examined suburban area is modernized, assuming buildings’ deep retrofitting, the application of RES and energy storage systems, as well as usage of smart metering techniques. The proposed modernizations assumed full electrification of the cluster. Moreover, the examined scenarios show potential electricity savings up to approximately 60%, as well as GHG emission reduction by 90% on average. It is demonstrated that the proposed approach is a valid method to estimate various energy- and environment-related issues of modernization for actual residential clusters. Full article