Search Results (8,046)

The deployment of hydrogen-powered public transport requires operators to decide not only whether to adopt fuel-cell buses, but also how hydrogen should be supplied. This study compares two sourcing strategies, as follows: on-site hydrogen production (Make) and external procurement (Buy). An operator-centered framework combining Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO) is applied to two Italian cases, using project-specific procurement and technical data from Monfalcone/Gorizia and Ferrara together with explicit modeling assumptions. Under base-case assumptions, Make yields an LCOH of €13.67/kg H₂ and a fleet TCO of €2.34/km, compared with €2.51/km for Buy at a delivered green hydrogen price of €12/kg. The deterministic break-even price is €10.25/kg, while both hydrogen configurations remain more expensive than diesel (€1.24/km). A probabilistic analysis based on 10,000 Latin Hypercube simulations and six uncertain parameters shows that Make is less costly in 78.8% of cases and Buy in 21.2%. The median probabilistic break-even price is €10.38/kg, with a 5th–95th percentile range of €8.79–12.38/kg. Delivered hydrogen price is the main driver, followed by Make CAPEX, fleet utilization, and financing conditions. Hydrogen sourcing therefore remains a strategic investment choice, with implications for capital exposure, renewable-energy integration, economic sustainability, and supply resilience. Full article

High penetrations of wind and photovoltaic generation create simultaneous challenges for battery energy storage system (BESS) planning in distribution networks, including differences in nodal regulation value, power-energy configuration, and day-ahead operation under forecast uncertainty. This study develops a sequential planning-to-operation workflow comprising candidate-bus generation, siting and sizing within the candidate set, and finite-scenario day-ahead scheduling for a fixed configuration. First, nodal net-injection sensitivities, Jacobian-assisted pre-screening, and deterministic topology/support safeguards are used to generate the main candidate set, and alternating-current (AC) finite-difference refinement is performed only for the sensitivity-led fast set; in the IEEE-33 system, this refinement reduces the number of AC power-flow calls from 65 for full-node analysis to 17. Next, Sensitivity-Guided Envelope-Based Nonanticipative Adjustable Recourse Optimal Power Flow (SG-ENAR-OPF) is solved separately for each bus in the main candidate set; the BESS location and power/energy capacities are jointly determined subject to the P-Q LinDistFlow model, BESS duration constraints, a shared affine response, and finite-scenario constraints. The full-node audit serves only as an independent paper-level validation benchmark and is not part of the deployable workflow. After the configuration is fixed, interval forecasts for load, photovoltaic (PV) output, and wind-turbine (WT) output at // are used to construct 25 static load-renewable disturbance points and seven temporal stress paths, over which a shared finite-scenario day-ahead policy is optimized. The IEEE-33 MAIN case selects Bus 30, with BESS capacities of approximately 10.66 MW/10.66 MWh. Using scaled public time-series data, the final policy is replayed over 46 consecutive 24 h execution windows, comprising 1104 h actual trajectories; under the 0.002 MW/MWh storage-engineering criterion, all 46/46 windows pass storage engineering validation, and energy continuity is maintained across all 45/45 interday boundaries. Further nonlinear AC post-validation converges at all 1104/1104 operating points, of which 1058/1104 satisfy the complete voltage and branch-capacity constraints. Supplementary results for IEEE-69 show that the workflow can be executed on a second radial test feeder. However, the conclusions are strictly limited to the tested feeders, finite scenarios, scaled public-data settings, and stated engineering tolerances and do not constitute a formal guarantee over a continuous uncertainty domain or of general cross-system applicability. Full article

This paper presents a model predictive control (MPC)-based energy management strategy for hybrid power systems combining a proton-exchange membrane fuel cell (PEMFC) with a lithium iron phosphate (LFP) battery storage unit for renewable energy applications. The proposed framework optimizes power allocation between the two sources while respecting operational constraints, including current limits, power balance requirements, and state-of-charge (SOC) bounds with soft constraints to prevent overcharging and deep discharging. Unlike conventional rule-based approaches, the MPC formulation employs a quadratic cost function with tunable weighting factors that enable flexible prioritization of either fuel cell conservation or battery lifetime extension. Accurate yet computationally efficient models are developed for both components: an equivalent circuit model for the LFP battery and a theoretical electrochemical model for the PEMFC. The performance of the proposed strategy is validated through comprehensive simulations under realistic renewable generation and load profiles. Five case studies are examined, each representing different operational scenarios characterized by varying initial SOC conditions and component prioritization weights. The results demonstrate that the MPC-based approach effectively manages power distribution, maintains SOC within safe operating ranges, and adapts to changing system conditions. Quantitative analysis shows that the tunable weighting strategy successfully limits high-current events, reducing high-current operation and potentially mitigating current-related degradations. The proposed framework offers a scalable and flexible solution for improving the reliability of hybrid energy storage in modern renewable grids. Full article

The global energy transition is driving power systems toward lower-carbon electricity generation, requiring sustainability assessments that consider environmental burdens beyond direct carbon emissions. This study evaluates Mexico’s electricity system using life cycle assessment, resource analysis, and energy sustainability indices. The main novelty of this study is the development of four energy sustainability indices derived from EI99H damage results: the Index of Environmental Change per Energy Unit, Relative Environmental Change Index, Per Capita Environmental Impact, and Environmental Intensity Metric. These indices capture temporal environmental change, generation-related variation, population-related burden, and environmental impact per unit of electricity. Results show improvements in fuel oil, water, and biomass performance between 2013 and 2023, whereas natural gas and coal impacts increased. Mexico exhibits a lower per capita environmental burden than Germany and Spain, while France shows the lowest value, largely due to its nuclear-based electricity mix. Human Health damage is 55% higher than Ecosystem Quality, mainly due to fossil fuel combustion. Hydroelectric generation shows substantial water demand, while solar and wind have negligible requirements. Rising natural gas costs constrain competitiveness, whereas renewables maintain low operating costs. Full article

Biomass is widely recognized as a critical renewable energy source for supporting global decarbonization and energy diversification. However, the financial viability of bioenergy investments depends heavily on how climate policies and sustainable finance mechanisms interact under environmental uncertainty. This study develops a land-constrained bioenergy valuation model to examine the joint effects of carbon pricing, climate finance arrangements, and land-use regulations on the economic sustainability of biomass producers. Environmental and financial policies enter the framework through carbon costs, land-use constraints, and financial intermediation conditions, thereby linking climate regulation to firm-level capital performance. Using a contingent-claim framework, producer equity and financing risks are evaluated to capture the complex effects of market asset volatility on energy investment incentives. The framework is evaluated through a literature-informed baseline calibration and comparative-static numerical analysis; accordingly, the reported results are model-implied comparative-static mechanisms designed to evaluate bioenergy project viability under policy and market uncertainty. The numerical analysis employs a non-region-specific representative benchmark and is intended to identify structural and comparative-static mechanisms rather than to forecast outcomes for a particular geographical market. The results show that higher carbon prices reduce producer viability by increasing operating costs, although this financial strain is partially mitigated by the option-like nature of equity under higher asset volatility. In contrast, improvements in agronomic productivity, greater land availability, and higher biomass market prices enhance investment returns and financing conditions. The findings further indicate that climate policies shape bioenergy deployment not only through physical production costs but also through financial transmission channels and risk-sharing mechanisms. The study contributes to the energy policy and climate finance literature by integrating carbon pricing, land governance, and financial options within a unified analytical framework. The results highlight the importance of policy coordination between renewable energy incentives, sustainable finance management, and carbon regulation to mitigate investment risks in the bioenergy sector. Full article

This research examines the structural evolution of environmental assessment in Spain over a 35-year period (1991–2025), serving as a longitudinal case study for the transition of mid-sized developed economies from industrial-age infrastructure to a decarbonized energy model. The study introduces a novel approach by utilizing Environmental Assessment records to evaluate national strategic directions and quantify developmental intent via administrative proxies. The analysis utilizes a comparative quantitative study of Strategic Environmental Assessments and Environmental Impact Assessments of national and supranational projects. Administrative trends were contextualized within major socio-economic and geopolitical events, including global financial crises, public health emergencies, and energy security developments, to identify potential external influences. The data reveals a paradigm shift. While the period 1991–2010 was dominated by grey infrastructure (transport and civil engineering), the post-2018 era shows an unprecedented increase in renewable energy dossiers. Between 2021 and 2025, energy projects dominated the regulatory workload, accounting for 73.86% of total processed dossiers, and reaching a single-year peak of 84.56% in 2022. National and international legal, economic, and social events, such as European Green Deal mandates and the NextGenerationEU recovery funds, have an apparent alignment with a country’s development strategy. We highlight the challenges and risks of such an exponential change in the energy model. For mid-sized developed economies, these findings suggest that Environmental Assessment data is an essential tool for navigating the complexities of rapid decarbonization and identifying systemic gaps in strategic planning. Full article

Posidonia oceanica (P. oceanica) is a key marine seagrass that provides essential ecosystem services but managing its stranded biomass (banquettes) poses a challenge to local authorities due to high disposal costs. This review examines the ecological and bioenergetic potential of P. Oceanica, focusing on the Calabria region. Through the analysis of regional monitoring data (ARPACAL) and a comprehensive review of the literature on anaerobic digestion (AD) processes, the study analyzes the transition from waste to resource. The results highlight a worrying regression of Calabrian meadows due to siltation and high burial-induced mortality. Chemically, the biomass is characterized by high recalcitrance (lignin > 34%) and an unbalanced C/N ratio (~153), which limits the efficiency of standard AD. However, the review demonstrates that acidic thermochemical pretreatment (132 °C with 0.4% HCl) can increase methane yields by up to 575%, while co-digestion with nitrogenous substrates further stabilizes the process. The study concludes that, although technical challenges such as reactor corrosion and sediment loss remain, the transformation of P. oceanica into biomethane represents a technologically potential route toward a circular Blue Economy, provided that operational and economic trade-offs are carefully balanced. Adopting the “Ecological Beach” model could enable sustainable coastal protection while unlocking prospective renewable energy potential. Full article

Understanding the relationship between economic growth, energy use, and environmental degradation remains a key challenge for resource-dependent economies undergoing energy transition. This study re-examines the effects of economic growth, energy consumption, renewable energy, and capital formation on CO₂ emissions in Kazakhstan using annual data for 1992–2024. To account for structural changes and ensure robust inference, the analysis employs the Bai–Perron structural break test, the Autoregressive Distributed Lag (ARDL) approach, and alternative cointegration estimators including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR). The results reveal a stable long-run relationship among the variables. Economic growth and energy consumption significantly increase CO₂ emissions, indicating the persistence of a carbon-intensive growth pattern in Kazakhstan. The effect of renewable energy is not robust across estimation techniques and does not provide consistent evidence of an emissions-reducing impact. Capital formation also shows limited explanatory power for long-run environmental outcomes. Overall, the findings suggest that Kazakhstan has not yet achieved a decoupling between economic growth and environmental degradation. The study contributes to the literature by jointly incorporating structural breaks, multiple cointegration estimators, and an extended sample period within a country-specific framework. The results imply that expanding renewable energy capacity alone may be insufficient to improve environmental quality, highlighting the need for broader structural transformation and low-carbon development strategies. Full article

With the continuous increase in renewable energy penetration, the traditional distribution network is gradually evolving into the active distribution network. Facing increasingly severe regulation pressure, relying solely on resource allocation from a single side of the distribution network can no longer provide adequate flexibility support. As an effective carrier integrating sources and loads, collaborative mutual assistance between the microgrid and the distribution network has become an inevitable trend to tap the flexibility potential of multiple entities. However, the current insufficient coordination of multi-type flexibility resources and the lack of deep interaction between the distribution network and the microgrid limit system flexibility, affecting the secure operation of the power grid. Therefore, this paper proposes an active distribution network–microgrid collaborative planning method for flexibility enhancement. Firstly, a collaborative optimal allocation model of nodal and grid-level flexibility resources for the active distribution network is established. The upper tier minimizes the annualized comprehensive cost, while the lower tier minimizes the annual operational cost and optimizes flexibility indices, comprehensively considering constraints like equipment investment, system security, and flexibility supply–demand balance. Secondly, the coupling relationship between the distribution network and the microgrid is established through tie-lines to construct the active distribution network–microgrid collaborative planning model. Finally, an accelerated and robust analytical target cascading solution strategy is proposed. By constructing a balancing coefficient, it eliminates the algorithm’s sensitivity to initial penalty weights, effectively improving the stability and efficiency of the model solution. Case study analysis verifies the effectiveness of the proposed method. Full article

The widespread adoption of EVs is expected to intensify peak demand, stress distribution networks, and increase carbon emissions if traditional grid-based charging models continue. Therefore, this study proposes a smart-grid-connected microgrid (MG) architecture for EV charging stations (EVCSs) that integrates renewables and energy storage, while incorporating incentive-based response (iDR) and net energy metering schemes into a single optimization framework. To maintain the quality of EV charging services, optimize grid interactions and MG reliability, and explore the socioeconomic impact of establishing such infrastructures, a comprehensive 4E optimization framework incorporating energy, environmental, employment, and economic metrics is developed. The suggested framework is applied to an urban case study in Riyadh, including realistic load profiles, tariff structures, EV charging behavior, network outage scenarios, NEM conditions, and an assumed iDR incentive scenario. A grid-dependent BES/Conv/Grid configuration optimized without iDR is adopted as the common reference for consistently evaluating all system configurations. Compared with this common reference, the results show that while renewable hybridization improves performance, the assumed iDR scenario provides further economic and operational benefits. The optimal iDR-enabled configuration achieves an 87.5% reduction in TNPC and a standard HOMER Grid LCOE of $0.0287/kWh, corresponding to a 67% reduction relative to the common grid-dependent reference. When electricity exports are excluded from the LCOE normalization, the corresponding load-serving LCOE is approximately $0.0372/kWh, which remains approximately 57.3% lower than the reference value of $0.0872/kWh. In addition, the optimal MG generates $63,128 in revenue/year through participation in iDR events without degrading EV charging service performance relative to the non-iDR scenario. Employment analysis indicates that the optimal system supports approximately 49 job-years of direct project-associated employment over the 25-year project lifetime through infrastructure deployment, operation, and maintenance, while ecologically, it limits annual grid-related CO₂ emissions to approximately 280.18 tons, representing about an 84.7% reduction relative to the reference scenario. These findings confirm that coordinated demand-side flexibility and intelligent storage dispatch can partially substitute for infrastructure oversizing, enabling cost-effective, low-carbon, and investor-attractive EVCS-based MGs aligned with sustainability targets. Full article

Wind power forecasting is essential for the reliable and efficient operation of wind farms based on power grids, which is significantly important for stakeholders like wind farm owners, power pools, and power traders. Wind power prediction is also crucial for optimal power dispatch, grid security, and minimizing generation curtailment at wind farms. Hence, an accurate methodology for the power production of wind farms is needed for identifying long-term operational performance, failure detection, and ensuring grid integration. The present study proposes a hybrid machine learning (ML) approach that combines the strengths of random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVR), using linear regression (LR) as a meta-model for wind speed forecasting. This modeling approach was trained, validated, and tested on 87,600 hourly data points across 10 variables from high-wind potential sites in the Kingdom of Saudi Arabia: Damat Al Jandal, Taif, Abha, East Coast, and Red Sea. The key findings suggest that the hybrid RFR + ANN + SVR model performed better than individual models, including the simple persistence model, achieving R² scores up to 95.3 in testing, with train–test performance loss of less than 5% across all scenarios. The mean bias error (MBE) stayed within ±0.17 m/s, and the relative root mean square error (RRMSE) stayed below 15%. The hybrid model outperformed the metric-site standalone models and persistence model. Through feature importance analysis, this study also finds that temperature, relative humidity, and cyclical time-encoded features are the most important inputs. For offshore sites, thermal features like pressure and dew point were found to be more important. Due to its demonstrated superior performance across metric-site combinations, this hybrid framework is adaptable to other wind regions with intermittent renewables, with implications for reliable renewable energy integration and grid load stability. Full article

This study examines how multiple dimensions of climate variability are associated with irrigation-related water productivity in the six Gulf Cooperation Council (GCC) countries over 2000–2023. Water productivity is measured as economic output generated per unit of freshwater withdrawal and is interpreted as an economy-wide proxy for water-use efficiency rather than a crop-specific biophysical measure. Using a balanced panel of 144 country-year observations, the study combines seven annual climate indicators from NASA POWER—temperature, precipitation, evapotranspiration, relative humidity, soil moisture, wind speed, and solar radiation—with socioeconomic and agricultural indicators from the World Bank. A two-way fixed effects model is estimated with controls for agricultural land, rural population, renewable energy consumption, carbon emissions, GDP per capita, and agricultural employment. The results show that temperature, evapotranspiration, wind speed, and solar radiation are negatively associated with water productivity, whereas precipitation, relative humidity, and soil moisture are positively associated with it. Soil moisture and temperature display the largest estimated effects among the climate variables in the baseline specification. The analysis further finds that the estimated climate–water-productivity relationship differs in magnitude between the pre-COVID (2000–2019) and post-COVID (2020–2023) periods. Interaction models indicate that soil moisture, precipitation, and relative humidity partially moderate the adverse effects of temperature and evapotranspiration. These findings remain broadly stable across alternative dependent variables, lagged climate specifications, alternative estimators, and leave-one-country-out analyses. The study contributes GCC-wide evidence on the joint and conditional effects of climate variability and highlights the importance of climate-informed irrigation scheduling, soil-moisture monitoring, and precision water-management technologies for strengthening agricultural resilience in arid environments. Full article

Understanding the determinants of electricity consumption is essential for designing effective economic and energy policies in advanced economies. This study examines the macroeconomic, institutional, and environmental factors influencing electricity consumption in 19 Euro Area countries during 2006–2024. Using a balanced panel dataset and a fixed-effects regression model, the analysis examines the associations between electricity consumption and GDP per capita, manufacturing value added, regulatory quality, electricity prices, renewable energy share, and heating degree days. The results indicate that GDP per capita, manufacturing value added, and regulatory quality are positively associated with electricity consumption, suggesting that higher levels of economic development, industrial activity, and institutional quality are associated with higher electricity demand. In contrast, electricity prices and renewable energy are negatively associated with electricity consumption, highlighting the importance of market incentives and structural changes related to the energy transition. Heating degree days exhibited the strongest estimated association with electricity consumption, highlighting the importance of climatic conditions across the Euro Area. The study contributes to the energy economics literature by providing an integrated assessment of economic, institutional, and environmental drivers of electricity consumption within a highly integrated economic region. The findings offer relevant implications for policymakers seeking to balance economic growth, energy transition objectives, and long-term electricity demand management. Full article

Massive blooms of Sargassum spp. along Caribbean coasts have created an urgent need for sustainable valorization strategies. This study develops an integrated cascade biorefinery for biomass collected in Dominican Republic. First, bioactive compounds were extracted using methanol and high temperature/high pressure, followed by solvent-based fractionation according to molecular weight ($x<3 \mathrm{k}\mathrm{D}\mathrm{a}$). Bioactivity testing against human medulloblastoma cell line (HDMB03) revealed a promising cell death effect, up to 60% at 8.0 mg/mL fraction concentration. Then, residual biomass underwent lipid extraction using chemical method, followed by compositional analysis (GC–MS) that highlighted nutritionally relevant fatty acids. Finally, the exhausted biomass was subjected to anaerobic digestion, where its biochemical methane potential (BMP) was determined using an Automatic Methane Potential Test System, yielding 102.9 ± 19.4 mL CH₄ g⁻¹ VS, demonstrating the feasibility of renewable energy production from the residual biomass. Overall, this study demonstrates the effectiveness of a cascade biorefinery approach for pelagic Sargassum spp., obtaining a set of extracts with potential for biomedical and nutraceutical sectors. In addition, we also valorize the residual, leftover biomass using biomethane production to enhance and advance circular bioeconomy solutions for affected Caribbean regions. Full article

Sector coupling strategies are increasingly adopted to improve the flexibility and efficiency of renewable energy systems by integrating multiple end-users, energy sectors, and conversion technologies. However, their widespread deployment relies on additional infrastructure and energy conversion processes, raising questions about the associated environmental and economic trade-offs. Despite the growing interest in Power-to-X technologies, integrated assessments simultaneously evaluating energy, economic, and environmental performance at the district scale remain limited. This study proposes an integrated framework combining techno-economic analysis and life cycle assessment to evaluate alternative Power-to-X strategies within a regenerated renewable energy community in Southern Italy. Three sector coupling pathways are compared: Power-to-Power, Power-to-Gas, and Power-to-Gas-to-Power. The results highlight different trade-offs among the investigated configurations. The Power-to-Power scenario achieves complete renewable electricity self-consumption and increases system self-sufficiency from 46% to 71%, whereas the Power-to-Gas configuration provides the shortest payback period (6 years). From an environmental perspective, the baseline, Power-to-Power, and Power-to-Gas scenarios reduce average life cycle impacts by 53%, 44%, and 40%, respectively, compared with the business-as-usual configuration, while the Power-to-Gas-to-Power scenario exhibits slightly higher impacts due to the additional fuel cell system. Furthermore, the environmental ranking of the scenarios depends on the adopted system expansion assumptions, particularly the avoided burden approach. The proposed framework provides a comprehensive basis for evaluating sector coupling strategies in renewable energy communities, supporting decision making through the integrated assessment of environmental performance, economic viability, and energy self-sufficiency. Full article