Search Results (86)

The life-cycle economics and greenhouse-gas (GHG) emissions of forest biomass harvesting and utilization for value-added bioproducts were comprehensively evaluated via the development of an integrated modeling framework. Taking the eastern U.S. as the case region, the model innovatively integrated field studies, a Bayesian-based statistical learning model, techno-economic analysis, and life-cycle assessment. In specific, by investigating and summarizing the typical forest biomass harvesting systems across the region, the forest biomass harvesting costs were spatially grouped and mapped for four classified subregions across the eastern US. Overall, with 95% confidence the forest biomass harvesting cost is between USD 21.99 and USD 44.33/dry Mg, while the GHG emissions are between 14.79 and 98.80 kg CO₂ eq./dry Mg. Furthermore, for the forest biomass utilization for four alternative value-added bioproducts, the minimum selling price (MSP) is USD 177.82/Mg for pellet fuel, USD 110.24/MWh for biopower, USD 1059.4/Mg for biochar, and USD 4.98/gallon for aviation fuel. The life-cycle GHG emissions are 149.80 kg CO₂ eq./Mg pellet fuel, 52.22 kg CO₂ eq./MWh biopower, 792.12 kg CO₂ eq./Mg biochar, and 2.13 kg CO₂ eq./gallon aviation fuel, respectively. Considering the uncertainties, 95% confidence intervals of MSPs range from USD 164.77 to USD 190.97/Mg for pellet fuel with an 81.85% probability to be profitable, from USD 100.20 to USD 120.21/MWh for biopower with a 49.38% probability to be profitable, from USD 1000.91 to USD 1109.25/Mg for biochar with a 79.51% probability to be profitable, from USD 4.86 to USD 5.54/gallon for aviation fuel with an 0.03% probability to be profitable. Moreover, the MSPs of pellet fuel and biochar are much less affected by the market changes than those of biopower and aviation fuel. However, the production of biopower and aviation fuel has lower carbon intensities than that of pellet fuel and biochar. Full article

A model is proposed to investigate the effects of power generation source diversification and CO₂ emission control in the presence of dispatchable fossil fuel sources and non-dispatchable carbon-free renewables. In a stochastic environment in which three random factors are considered, namely fossil fuels (gas and coal) and CO₂ prices, we discuss a planning methodology for power system portfolio selection that integrates the non-dispatchable renewables available in a given energy system and optimally combines cost, risk and CO₂ emissions. By combining the deep neural network probabilistic forecasting of fossil fuel path prices with a geometric Brownian motion model for describing the CO₂ price dynamics, we simulate a wide range of plausible market scenarios. Results show that under CO₂ price volatility, optimal portfolios shift toward cleaner energy sources, even in the absence of explicit emission targets, highlighting the implicit regulatory power of volatility. The results suggest that incorporating CO₂ price volatility through market mechanisms can serve as an effective policy tool for driving decarbonization. Our model offers a flexible and reproducible approach to support policy design in energy planning under uncertainty. Full article

About 50% of Italian households’ overall energy consumption is satisfied by natural gas, mainly for space heating, leading to substantial CO₂ emissions. In Italy’s mild climate, fourth-generation district heating (4GDH) networks coupled with renewable energy sources (RESs) could represent a viable option for reaching the ambitious space heating decarbonization objectives set by the EU. In this paper, such a decarbonization pathway, consisting in a centralized heat pump (HP)-powered 4GDH network, with and without the addition of a distributed PV plant, is assessed and compared with the individual natural gas boilers-based Italian reference scenario. A cluster of buildings, comprising 200 dwellings, representative of common households in Rome, has been chosen as the case study. Starting from the cluster’s hourly space heating demand, a semi-dynamic MATLAB/Simulink model has been developed to size the technological components and evaluate their performance with respect to outdoor environmental conditions. The scenario comparison is carried out by means of techno-economic and environmental indicators: the levelized cost of heat (LCOHE), CO₂ emissions, and carbon avoidance cost (CAC). Moreover, a sensitivity analysis has been carried out to address the uncertainty regarding the main economic parameters, namely the electricity and natural gas price and the HP and DH investment cost. The results show that 4GDH-based layouts significantly reduce CO₂ emissions, at the expense of the LCOHE. The sensitivity analysis highlights how a significant reduction in both the electricity price and the DH network capital cost are required for achieving price parity with the fossil-fuel based scenario. Full article

The use of novel technologies for low-emission and more efficient aviation requires not only the achievement of a given technology readiness level, but also their integration into aircraft concepts. Furthermore, the assessment of unconventional configurations requires robustness considerations already in the conceptual aircraft design phase. In this context, the next developmental milestone of the Advanced Morphological Approach (AMA) as a conceptual aircraft design method is presented by introducing design parameter uncertainties for disruptive technologies. The purpose of this work is the integration verification of Bayesian networks (BNs) into the AMA process for semi-quantitative system modeling and uncertainty propagation. This allowed for the visualization of uncertainties in the solution space, and therefore the depiction and initial estimation of configuration robustness. The verification is demonstrated on an existing conceptual design use case of a regional aircraft for 50 passengers, similar to the ATR 42-600. It investigated hybrid-electric and fuel-cell-based hybrid propulsion systems for 2030, 2040, and 2050 as potential years of entry into service. A BN-based system model has been developed by verifying its quality, adding parameter uncertainty and three energy price scenarios. The executed Bayesian inference propagated the uncertainties through the system and allowed for the visualization of a solution space. The presented uncertainties for the mission energy, mission energy price, and emission criteria for each design solution yield a more reliable basis for robustness analysis and decision-making. Full article

Capacity expansion models for electricity grids typically use deterministic optimization, addressing uncertainty through ex-post analysis by varying input parameters. This paper presents a stochastic capacity expansion model that integrates uncertainty directly into optimization, enabling the selection of a single strategy robust across a defined range of uncertainties. Two cost-based risk objectives are explored: “risk-neutral” minimizes expected total system cost, and “risk-averse” minimizes the most expensive 5% of the cost distribution. The model is applied to the U.S. Midwest grid, accounting for uncertainties in electricity demand, natural gas prices, and wind generation patterns. While uncertain gas prices lead to wind additions, wind variability leads to reduced adoption when explicitly accounted for. The risk-averse objective produces a more diverse generation portfolio, including six GW more solar, three GW more biomass, along with lower current fleet retirements. Stochastic objectives reduce mean system costs by 4.5% (risk-neutral) and 4.3% (risk-averse) compared to the deterministic case. Carbon emissions decrease by 1.5% under the risk-neutral objective, but increase by 3.0% under the risk-averse objective due to portfolio differences. Full article

With the global shift to sustainability, the energy sector faces pressure to adopt low-carbon solutions. Blue ammonia (BA), derived from natural gas (NG) with carbon capture, presents significant opportunities but requires a holistic sustainability assessment. This study conducts a novel life cycle sustainability assessment (LCSA) of BA, evaluating environmental, economic, and social impact performance from feedstock processing to maritime transport for a 1.2 MMTPA production capacity. Process simulations in Aspen HYSYS V12 and the ammonia maritime transport operations’ sustainability assessment model provide critical insights. The ammonia converter unit contributes the highest emissions (17.9 million tons CO₂-eq), energy use (963.2 TJ), and operational costs (USD 189.2 million). CO₂ removal has the most considerable land use (141.7 km²), and purification records the highest water withdrawal (14.8 million m³). Carbon capture eliminates 6.5 million tons of CO₂ annually. Economically, ammonia shipping dominates gross surplus (USD 653.9 million, 72%) and tax revenue (USD 65.3 million) despite employing just 43 workers. Socially, the ammonia converter unit has the highest human health impact (16,621 DALY, 54%). Sensitivity analysis reveals transport distance (46.5% CO₂ emissions) and LNG fuel prices (63.8% costs) as key uncertainties. Findings underscore the need for optimized logistics and alternative fuels to enhance BA sustainability. Full article

The energy transition towards achieving carbon neutrality is marked by the decarbonization of the power system and a high degree of electrification in end-use sectors. The decarbonization of the power system primarily relies on large-scale renewable energy, nuclear power, and fossil fuel-based power with carbon capture technologies. This structure of power supply introduces significant uncertainty in electricity supply. Due to the technological progress in end-use sectors and spatial reallocation of industries in China, the load curve and power supply curve is very different today. However, most studies’ analyses of future electricity systems are based on today’s load curve, which could be misleading when seeking to understand future electricity systems. Therefore, it is essential to thoroughly analyze changes in end-use load curves to better align electricity demand with supply. This paper analyzes the characteristics of electricity demand load under China’s future energy transition and economic transformation pathways using the Integrated Energy and Environment Policy Assessment model of China (IPAC). It examines the electricity and energy usage characteristics of various sectors in six typical regions, provides 24-h load curves for two representative days, and evaluates the effectiveness of demand-side response in selected provinces in 2050. The study reveals that, with the transition of the energy system and the industrial relocation during economic transformation, the load curves in China’s major regions by 2050 will differ notably from those of today, with distinct characteristics emerging across different regions. With the costs of solar photovoltaic (PV) and wind power declining in the future, the resulting electricity price will also differ significantly from today. Daytime electricity prices will be notably lower than those during the evening peak, as the decrease in solar PV and wind power output leads to a significant increase in electricity costs. This pricing structure is expected to drive a strong demand-side response. Demand-side response can significantly improve the alignment between load curves and power supply. Full article

This paper examines the effectiveness of a pumped storage hydropower plant (PSHP) when combined with other plants. System 1 examines the contribution of the PSHP to reducing fuel costs for thermal power plants. System 2 examines the optimization of operations for power systems with energy storage and uncertain renewable energies to maximize total profit based on four test system cases: Case 1: neglect the PSHP and consider wind and solar certainty; Case 2: consider the PSHP and wind and solar certainty; Case 3: neglect the PSHP and consider wind and solar uncertainty; and Case 4: consider the PSHP and wind and solar uncertainty. Cases 1 and 2 focus on systems that assume stable power outputs from these renewable energy sources, while Cases 3 and 4 consider the uncertainty surrounding their power output. The presence of a PSHP has a key role in maximizing the system’s total profit. This proves that Case 2, which incorporates a PSHP, achieves a higher total profit than Case 1, which does not include a PSHP. The difference is USD 17,248.60, representing approximately 0.35% for a single day of operation. The total profits for Cases 3 and 4 are USD 5,089,976 and USD 5,100,193.80, respectively. Case 4 surpasses Case 3 by USD 10,217.70, which is about 0.2% of Case 3’s total profit. In particular, the PSHP used in Cases 2 and 4 is a dispatching tool that aims to achieve the highest profit corresponding to the load condition. The PSHP executes its storage function by using low-price electricity at off-peak periods to store water in the reservoir through the pumping mode and discharge water downstream to produce electricity at periods with high electricity prices using the generating mode. As a result, the total profit increases. A modified slime mould algorithm (MSMA) is applied to System 2 after proving its outstanding performance compared to the jellyfish search algorithm (JS), equilibrium optimizer (EO), and slime mould algorithm (SMA) in System 1. Full article

Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector. Full article

After the onset of the armed conflict between Russia and Ukraine, Poland was forced to change its markets for sourcing raw materials, specifically oil and gas. Simultaneously, as a member of the EU and due to its geographical location in Europe, Poland must meet emission standards and ensure energy security. The aim of this publication is to analyze and evaluate the management of the fuel supply chain (FSC) in Poland in the context of energy security. The main research question formulated is to what extent the management of the FSC can ensure Poland’s energy security. The publication employs two models: MAED (Model for Analysis of Energy Demand) and CDM (canonical distribution model). The research is based on data from the Statistical Office and data provided by the fuel industry. Between 2021 and 2023, Poland diversified its supply sources, mainly from Saudi Arabia (45.2%) and Norway (35.2%), which together account for 80.4% of imports. The current fuel storage capacity (15.05 million m³) is capable of securing production logistics in the event of SC disruptions and market uncertainties. The shift in fuel supply logistics during the discussed period, along with the increase in the fuel safety stock coefficient to quantities exceeding current demand in case of further disruptions caused by external factors, affects the security of the Polish state as well as neighboring countries in Central Europe. Distribution logistics are managed domestically through networks of fuel stations operated by Polish and foreign corporations, including a group of independently owned private fuel stations (47.5%). The fuel industry in Poland has risen to the challenge, maintaining the stability of fuel supplies and their prices. Full article

In this study, we conducted an in-depth analysis of the dynamic cointegration relationship between international crude oil, natural gas, and coal price indices from 2009 to 2023, revealing the changes and differences in the cointegration relationship between these three prices during different periods. Utilizing statistical analysis and economic modeling, we found significant cointegration among these energy prices during the initial decade-long observation period, indicating their close interaction in the global energy market influenced by supply and demand fundamentals, macroeconomic conditions, and geopolitical landscapes. However, since 2020, this long-standing stable cointegration relationship has been severely disrupted due to the global spread of the COVID-19 pandemic and escalating geopolitical tensions, leading to a notable increase in volatility and uncertainty in the energy market. Further analysis highlights that, in recent years, with the strengthening of global climate governance and the advancement of the low-carbon transition trend, fossil fuel markets, particularly high-carbon-emitting crude oil and coal markets, have undergone significant adjustments. Meanwhile, the role of natural gas as a transitional clean energy source has become increasingly prominent. The findings of this study have significant implications for energy policy formulation, market risk management, and strategic planning in the energy industry, while providing directions for future research on resilience and adaptability in the transition process of energy systems. Full article

Ammonia can be considered a relevant compound in the future energy sector, playing a significant role as an energy carrier, storage, or carbon-free fuel. However, the production of this molecule has a high energy demand, and the use of natural gas, which is not free of controversy due to the accidental leakage into the atmosphere produced during extraction and the fact that it is a nonrenewable source, contributes to increasing greenhouse gas emissions. Reducing the process’s energy demand and carbon footprint will be essential to making ammonia a clear alternative for a carbon-free economy. Given the vast research in ammonia production and handling, this gas seems to be the logical step forward in the evolution of the energy sector. However, the current uncertainty in the global market requires cautiousness in decision making. Several factors may impact economic growth and human welfare, thus needing a careful assessment before making any transcendental decisions that could affect worldwide energy prices and raw material availability. Full article

Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation. Full article

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs. Full article

Effective decarbonization of the power generation sector requires a multi-pronged approach, including the implementation of CO₂ capture and storage (CCS) technologies. The Graz cycle features oxy-combustion CO₂ capture in a power production scheme which can result in higher thermal efficiencies than that of a combined cycle. However, the auxiliary consumption required by the air separation unit to provide pure O₂ results in a significant energy penalty relative to an unabated plant. In order to mitigate this penalty, the present study explores the possibility of chemical looping combustion (CLC) as an alternative means to supply oxygen for conversion of the fuel. For a midscale power plant, despite reducing the levelized cost of electricity (LCOE) by approximately 12.6% at a CO₂ tax of EUR 100/ton and a natural gas price of EUR 6.5/GJ and eliminating the energy penalty of CCS relative to an unabated combined cycle, the cost reductions of CLC in the Graz cycle were not compelling relative to commercially available post-combustion CO₂ capture with amines. Although the central assumptions yielded a 3% lower cost for the Graz-CLC cycle, an uncertainty quantification study revealed an 85.3% overlap in the interquartile LCOE range with that of the amine benchmark, indicating that the potential economic benefit is small compared to the uncertainty of the assessment. Thus, this study indicates that the potential of CLC in gas-fired power production is limited, even when considering highly efficient advanced configurations like the Graz cycle. Full article