Search Results (1,647)

Standalone DC microgrids offer a promising solution for providing reliable and sustainable electricity to remote communities in developing countries. However, the intermittent nature of solar and wind resources, combined with continuously varying load demand, presents considerable operational challenges in maintaining real-time power balance, stable DC bus voltage, and ensuring reliable continuous supply. Therefore, there is dire need for user-friendly control solutions tailored to the specific needs of isolated communities. As such, this paper presents a coordinated control and power management strategy for an isolated hybrid solar–wind–battery integrated DC microgrid for rural electrification applications. A comprehensive mathematical model of the standalone DC microgrid incorporating photovoltaic generation, wind energy conversion, battery storage, bidirectional DC-DC conversion, and common DC bus dynamics is developed at the very first stage of the proposed coordinated control framework. The framework utilizes principal local device loops and a secondary dynamic power management strategy to ensure efficient renewable power extraction, dynamic source–storage–load coordination, stable DC bus voltage, and real-time energy management within the developed standalone DC microgrid. It is worthwhile to mention that, instead of using synthesized or online available wind speed and solar irradiance data, this research utilized real-time recorded metrological data obtained from the Mehran University Jamshoro, Pakistan. The obtained results establish a stable DC bus voltage regulation within acceptable operating limits, continuous power balance, seamless bidirectional battery operation, and safe battery state-of-charge (SoC) management to prevent deep discharging or overcharging, thus ensuring reliable operation. The overall performance confirms the technical robustness, operational flexibility, and practical suitability of the proposed standalone hybrid DC microgrid architecture for its resilient operation and rural electrification applications. Full article

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out as an essential foundation for maintaining long-term stability of urban human well-being and ecosystems. Green-space supply is defined as the stock of various existing urban parks within the city, while green-space demand is quantified via grids generated based on residential communities in Changsha. Existing research on urban park site selection lacks a full-process coupled framework, fails to accommodate differentiated layout demands for multi-level parks, and struggles to reconcile the sustainable operation and long-term ecological empowerment of urban green-space systems. Taking the main urban area of Changsha as the research scope, this study divides the study area into grid units to analyze the spatial differentiation of green-space accessibility and identify service blind zones. The XGBoost model is adopted to predict areas suitable for green-space construction, and the NSGA-III algorithm is applied to realize collaborative multi-objective optimization covering service efficiency, ecological benefits, and land development costs. The results reveal that the 15-min walking coverage of community parks in central Changsha only reaches 57.29%. Respectively, 34.52% and 41.04% of residential communities record accessibility levels below the municipal average of urban parks and forest parks, with prominent shortages of green-space supply in peripheral urban areas. This study optimizes and screens twenty-eight candidate sites for community parks, twelve candidate sites for urban parks, and eight candidate sites for forest parks. The proposed scheme effectively narrows the gap in green-space accessibility across the whole city and coordinates ecological conservation with land development costs. Compared with research relying on a single model or two-stage coupling frameworks, this paper constructs a systematic workflow spanning supply–demand status assessment to multi-objective layout decision-making, enabling differentiated optimized layout of multi-tiered parks. The integrated framework effectively enhances the spatial resilience and resource utilization efficiency of urban green-space systems, facilitates high-quality and sustainable upgrading of urban living environments, and provides a referable innovative approach for multi-level urban park arrangement and refined multi-objective planning. Full article

The rapid integration of distributed renewable energy and flexible loads significantly intensifies supply and demand uncertainty in active distribution networks (ADNs), threatening economic and secure grid operations. Existing deep reinforcement learning (DRL) dispatch methods fail to extract spatial features properly, leading to a local optimal solution. To address these limitations, this paper proposes a state-adaptive topology-aware continuous-dispatch framework via multi-head graph attention network and deep deterministic policy gradient (GAT-DDPG). A multi-head graph attention network is embedded within a centralized Actor–Critic training paradigm to adaptively update spatial message-passing weights based on operational states. Extensive simulations on a modified IEEE 33-bus ADN over a 125-day unseen test set demonstrate that the proposed framework achieves lower comprehensive operating costs and fewer voltage violations compared with representative DRL-based dispatch baselines. Visualizations of state-dependent attention shifts confirm the model’s capability to track dynamically shifting network vulnerabilities, providing physical interpretability. Based on this, and combined with the optimized dispatch method of balancing responsibility, the ability of different flexible resources to support safe and stable operation and the ideal dispatch results under the temporary reduction in new energy output are further simulated. Full article

The Baltic states disconnected from the Soviet-era BRELL ring and synchronized with the Continental European grid in February 2025, completing a decade of new electricity and gas interconnections in the Poland–Lithuania–Latvia–Estonia corridor. This study examines how energy security evolved across the four markets as those interconnections were added, drawing on ENTSO-E cross-border flow data, Eurostat energy balances and ENTSOG gas transmission statistics for 2018–2025, and builds a composite Baltic Regional Electricity Security Index (BRESI) from three dimensions of electricity security: supply diversification, interconnection utilization and import dependency, with price convergence analyzed separately. The gains were uneven. Lithuania still imported 47% of the electricity it consumed in 2024, whereas Poland covered almost all of its own demand. Synchronization first sent prices sharply higher, with Lithuanian peaks of EUR 325/MWh against a January average of EUR 88/MWh, before the market settled. Across the corridor, electricity links ran at about 60 to 70 percent of capacity and gas links at 35 to 50; security improved where flows and market coupling were in place, while idle capacity added little. The index rose for all four countries between 2019 and 2024; the improvement holds under every aggregation and weighting variant tested, and monthly price spreads averaged EUR 19/MWh between Poland and Lithuania against under EUR 5/MWh inside the Baltic market, a pattern that persisted after synchronization. On this basis the study argues for more storage, earlier delivery of the Harmony Link, and shared balancing to cope with variable renewable output. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

The increasing integration of renewable energy is a key pathway toward sustainable energy transitions, but it also introduces supply-side uncertainty that makes demand flexibility management increasingly important. In this context, accurate load forecasting becomes a key task for balancing uncertain supply with demand-side behavior and supporting more reliable energy management. Most existing studies approach load forecasting by developing advanced forecasting models. However, in real-world commercial-user forecasting, load data often exhibit inter-user heterogeneity and temporal dynamics, making forecasting performance depend not only on model architecture, but also on how models are trained, shared, combined, and used; we refer to these design choices as forecasting strategies. Therefore, we study day-ahead individual load forecasting from a strategy-level perspective using historical load observations only. We compare homogeneous strategies, inter-user-heterogeneity-aware strategies, and temporal-dynamics-aware strategies using three real-world electricity consumption datasets from China. The results show that forecasting strategy design substantially affects prediction performance, and that both inter-user heterogeneity and temporal dynamics provide useful and complementary information for improving day-ahead individual load forecasting. We find that appropriate strategy design can improve MSE by more than 6% compared with using only a single model. Overall, the study provides useful methodological tools and practical insights for load forecasting and sustainable energy management. Full article

Sustainable urban development increasingly depends on the efficient and equitable provision of municipal infrastructure, and waste bins, as critical end-point facilities in urban solid waste management systems, directly influence waste disposal accessibility, collection efficiency, and service equity. However, existing studies remain limited in achieving effective demand–supply matching, controlling spatial redundancy, and evaluating equity in waste facility allocation. This study developed a multi-source spatial evaluation framework integrating waste generation demand, road accessibility, and building functional characteristics, and proposed a two-stage waste bin allocation approach by coupling Adaptive Non-Maximum Suppression (ANMS) with an improved Coverage Location Problem with Overlap Control (CLPOC) model. The proposed framework first generated a theoretical candidate site pool and applied ANMS to achieve spatially balanced candidate refinement, followed by CLPOC-based optimization to improve service coverage and reduce redundant configurations. The results demonstrated that the optimized scheme substantially reduced redundant facility locations while maintaining high service coverage, thereby improving spatial allocation efficiency. Although urban areas exhibited notable improvements in service equity, the enhancement in mountainous and scenic areas was constrained by terrain conditions and spatial clustering effects. This study provides an efficiency–equity balanced framework for fine-scale waste bin planning in hilly cities, offering methodological support for sustainable urban solid waste management and contributing to the broader goal of sustainable urban development. Full article

Tourism seasonality remains one of the most persistent structural challenges of Mediterranean destinations, intensifying environmental, economic and social pressures during peak months while leaving tourism capacity underused in the remainder of the year. This paper examines the level, spatial distribution and structural determinants of tourism seasonality in 61 Mediterranean EU NUTS–2 regions from Croatia, Spain, Greece, Portugal and Italy over the period 2020–2024. Using harmonised Eurostat data, annual Gini coefficients are calculated from monthly overnight stays and analysed within a balanced panel of 305 region-year observations, testing whether seasonality is associated with international tourism dependency, hotel accommodation share and island geography, while controlling for regional GDP per capita, tourism intensity and the COVID-19 disruption period. Fixed and random effects models are estimated, with model choice guided by the Hausman test and cluster-robust standard errors applied at the regional level, complemented by a correlated random effects (Mundlak) specification. The results show substantial regional heterogeneity: Greece and Italy record the highest average seasonal concentration, while Spain and Portugal display more balanced patterns, partly due to regions with year-round demand, and Jadranska Hrvatska emerges as the most seasonally concentrated non-island region in the sample. A higher hotel accommodation share is significantly associated with lower seasonality, while the COVID-19 years significantly increased seasonal concentration, with concentration levels returning to recovery-phase values from 2022 onwards; as the panel does not include pre-pandemic baseline years, the findings compare the pandemic phase (2020–2021) with the recovery phase (2022–2024) and do not directly test a return to pre-pandemic equilibrium. The study contributes to comparative destination research by operationalising the Gini coefficient as a dependent variable in a multi-country regional panel framework and provides policy-relevant evidence for supply-side deseasonalisation strategies embedded in integrated destination management. Full article

With the rapid development and widespread deployment of advanced metering infrastructures, massive amounts of fine-grained data about aggregated residential load have been collected by power network operators, further helping improve forecasting accuracy. Accurate aggregated residential load forecasting plays an increasingly important role in various interactions between power networks and electricity customers as well as in maintaining the continuous balance between electricity supply and demand. However, most existing short-term aggregated residential load forecasting methods only take into consideration the temporal correlation of historical load profiles, ignoring the potential spatial correlation between electricity consumption behaviors of adjacent residential customers. Thus, to fill this gap, this paper proposes a short-term aggregated residential load forecasting method based on graph neural networks and K-means clustering. Specifically, K-means clustering is firstly used to divide residential customers into different groups according to the similarity of their electricity consumption behaviors. Then, the spatial–temporal graph series for aggregated residential load forecasting is constructed, based on the number of groups of residential customers, the aggregated historical load profile of each group of residential customers, and the correlation between the aggregated historical load profiles of different groups of residential customers. Finally, adaptive spatial–temporal synchronous graph convolutional networks are applied to perform short-term aggregated residential load forecasting. The proposed method is evaluated on a real-life Irish residential load dataset, and the experimental results demonstrate that it can improve forecasting accuracy significantly in comparison with a number of traditional benchmark methods. Full article

Recovery of critical materials from industrial byproducts is often presented as a near-term United States (U.S.) supply-security strategy, yet contained inventories, pilot output, announced capacity, and commercial production are not equivalent. This study evaluates eight U.S. pathways for gallium, germanium, tellurium, lithium, magnesium, and cobalt, classified as operational, demonstration/pilot, announced target-year, or technical upper-bound cases. Facility- and stream-specific quantities were converted to qualifying domestic output and incorporated into same-stage material balances under explicit assumptions for utilization, eligibility, demand, and import displacement. Supply risk was calculated from the net import dependence and governance-adjusted production and trade concentration using a geometric index, with arithmetic formulations as robustness checks. The operational U.S. copper-refining tellurium pathway yielded the largest central reduction (49.29%). Announced 2030 Clarksville capacity reduced modeled risk by 13.87% for gallium and 8.75% for germanium, conditional on project completion, feed attribution, product qualification, utilization, and demand. All other central cases produced reductions of 4.63% or less; the current lithium demonstration, Stillwater cobalt, and aluminum-residue magnesium cases had negligible national effects. Policy support should therefore be differentiated by qualifying output, market scale, project maturity, and evidence quality rather than by contained material or nameplate capacity alone. Full article

Urban agglomerations rely on both direct physical water intake and substantial virtual water embedded in cross-regional traded commodities and services to meet water demand. It is important to analyze the complex virtual water flows in urban agglomerations to advance sustainable water utilization and coordinated regional sustainable development. In this study, we constructed a multi-scale hierarchical analysis framework covering industrial sectors, internal cities and external hinterland regions by coupling multi-regional input–output (MRIO), ecological network analysis (ENA), and structural decomposition analysis (SDA). The framework was adopted to quantify consumption-driven virtual water flows across the Pearl River Delta (PRD) urban agglomeration in 2012, 2017 and 2022. Furthermore, we quantified the economic value of virtual water flows by considering water rights trading prices, which mainly reflect the scarcity value of regional water resources, aiming to provide new perspectives for water resources compensation and sustainable water management decision making. The results showed that the virtual water flows mainly satisfied food-related water requirements in the PRD. Declining water use intensity in external supply regions was the dominant factor reducing the water footprint in the PRD. The virtual water flows had greater economic value in the water-scarce northern provinces. On this basis, we put forward targeted financial subsidy recommendations for water-supplying external regions; the above economic quantification serves as the core evidence supporting this subsidy proposal. This work provides decision support for formulating sustainability-oriented water policies, balancing interregional water ecological benefits, and realizing long-term sustainable utilization of water resources. Full article

This paper presents an energy-management and state-of-charge (SoC) balancing scheme, denoted OEMSS, for a DC microgrid comprising photovoltaic generation, a fuel-cell source, two energy storage systems (ESSs), and six household loads. A demand-driven power-allocation layer first determines whether generation is sufficient, ESS support is required, or priority-based load scheduling must be activated. A supervisory balancing layer then allocates the fleet charging or discharging request by using a capacity-weighted average SoC and separate mode-dependent correction laws. The balancing command is dimensionally expressed as an energy-capacity deviation divided by the control interval and is projected onto the SoC and power limits. A Python simulation driven by recorded generation profiles is used to evaluate four seasonal operating conditions. In the tested equal-capacity case, the maximum inter-ESS SoC deviation is reduced from 18% to 4.8%, synchronization is reached within approximately 2 to 4 h, and simulated over-discharge events are avoided. The reported increase from 45% to approximately 70% is interpreted as a 25-percentage-point increase in the ESS storage contribution rate, rather than an increase in conversion efficiency. During shortage intervals, the retained priority demand is supplied, whereas satisfaction of the original uncurtailed demand is not claimed. A discrete-time Lyapunov analysis gives the nominal convergence condition $0<{\gamma }_b<2$, and the online implementation has $\mathcal{O}(J+K+H)$ time complexity. The study provides simulation evidence for a simple coordinated allocation rule; hardware performance, battery-life extension, converter-level stability, and global optimality remain to be established. Full article

Electrochemical conversion of carbon dioxide (CO₂) to ethanol offers a potential route for integrating carbon utilization with low-carbon electricity; however, its environmental performance is governed by the complete process system rather than by catalytic selectivity alone. This study presents a detailed attributional cradle-to-gate life cycle assessment of anion-exchange-membrane (AEM) and bipolar-membrane (BPM) electrolyzer systems using a functional unit of 1 kg of ethanol at the plant gate. The foreground inventory combines stoichiometric balances, peer-reviewed electrochemical evidence, process-energy estimates, and transparent engineering assumptions, while background processes are represented using ecoinvent 3.7.1. Climate-change impacts are evaluated with the IPCC 2021 100-year global warming potential method. The modeled AEM and BPM systems require 23.32 and 27.92 kWh of electricity per kilogram of ethanol, respectively. Wind-powered operation yields the lowest reported impacts, at 0.318 kg CO₂-eq kg⁻¹ ethanol for AEM and 0.442 kg CO₂-eq kg⁻¹ for BPM. Photovoltaic scenarios yield 1.812 and 2.231 kg CO₂-eq kg⁻¹, whereas the Austrian-grid scenarios yield 1.349 and 4.686 kg CO₂-eq kg⁻¹, respectively. Electricity supply is the dominant environmental driver, while separation heat, carbon utilization, component lifetime, and oxygen co-product treatment remain important secondary parameters. The BPM Austrian-grid result is disproportionately high relative to the 19.7% increase in modeled electricity demand and therefore requires exchange-level verification before it can be interpreted as a physical membrane effect. Overall, environmentally credible CO₂-to-ethanol deployment requires low-carbon electricity, reduced cell voltage, efficient carbon management, concentrated product streams, durable components, and transparent co-product accounting. Full article

Very small islands face freshwater scarcity due to limited catchment areas, rainfall variability, and saline intrusion. In Indonesia, rainwater harvesting (RWH) can reduce household water stress, but its reliability as an independent source remains uncertain. This study used field surveys and a daily water-balance model to assess household RWH systems on the Belakang Padang and Mecan Islands. Representative wet (2023) and dry (2019) years were selected from a 30-year rainfall record to evaluate storage performance, rainwater supply reliability, and water saving efficiency (WSE). Simulations considered 200, 500, and 1000 L tanks under household-demand scenarios of 60–300 L/day. During the wet year, rainfall frequently refilled storage tanks and improved water availability, although overflow occurred when harvested water exceeded storage capacity. Larger tanks reduced overflow losses and extended water availability after rainfall events. In contrast, dry-year storage performance declined because rainfall was limited and concentrated within a short period. Even 1000 L tanks could not maintain sufficient storage under high demand during prolonged rainless periods. Rainwater supply reliability and water saving efficiency were higher during the wet year, ranging from 31 to 99.2% and 0.231 to 1.029 on Belakang Padang and 25.2 to 98.6% and 0.211 to 1.024 on Mecan, while dry-year reliability declined markedly as demand increased. Belakang Padang showed higher reliability than Mecan because its larger roof catchment area harvested more rainwater from the same rainfall event. Overall, household RWH is unlikely to provide a dependable independent domestic water supply on very small islands but can serve as an important supplementary source during disruptions to centralized systems. Reintroducing and maintaining household RWH, together with improvements in tank capacity, effective roof catchment area, and conveyance systems, could strengthen household water security on the Belakang Padang and Mecan Islands. Full article

Steel production contributes significantly to global emissions, making its decarbonization essential. Electrified steelmaking based on electric arc furnaces (EAF) using hydrogen-based direct reduced iron (H₂-DRI) and scrap is a promising pathway. This study analyzes how the H₂-DRI:scrap ratio affects electricity demand, CO₂ emissions, slag formation, and oxygen management. To address limitations of approaches based on aggregated data and linear scaling assumptions, a detailed bottom-up mass and energy balance model is developed, explicitly resolving process interactions between electrolysis, direct reduction, and EAF steelmaking. Eight H₂-DRI:scrap ratios ranging from 0:100 to 100:0 are evaluated. Electricity demand increases from 1.1 GJ/t_Steel (0.31 MWh/t_Steel) for scrap-based operation to 13.9 GJ/tSteel (3.86 MWh/tSteel) for fully H₂-based production, largely driven by hydrogen generation. Consequently, emissions strongly depend on electricity carbon intensity, with reductions of up to 95% under renewable supply. Electrolytic oxygen can fully cover process demand at ~10–13% H₂-DRI, enabling system integration benefits. A sensitivity analysis evaluates the influence of key process parameters on electricity demand, CO₂ emissions, and oxygen management, demonstrating the robustness of the proposed modelling approach. Full article