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28 pages, 1795 KiB  
Article
From Policy to Prices: How Carbon Markets Transmit Shocks Across Energy and Labor Systems
by Cristiana Tudor, Aura Girlovan, Robert Sova, Javier Sierra and Georgiana Roxana Stancu
Energies 2025, 18(15), 4125; https://doi.org/10.3390/en18154125 - 4 Aug 2025
Abstract
This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log [...] Read more.
This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article
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25 pages, 2100 KiB  
Article
Flexible Demand Side Management in Smart Cities: Integrating Diverse User Profiles and Multiple Objectives
by Nuno Souza e Silva and Paulo Ferrão
Energies 2025, 18(15), 4107; https://doi.org/10.3390/en18154107 - 2 Aug 2025
Viewed by 170
Abstract
Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, [...] Read more.
Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO2 emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO2 emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article
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27 pages, 525 KiB  
Article
An Analytical Review of Cyber Risk Management by Insurance Companies: A Mathematical Perspective
by Maria Carannante and Alessandro Mazzoccoli
Risks 2025, 13(8), 144; https://doi.org/10.3390/risks13080144 - 31 Jul 2025
Viewed by 127
Abstract
This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, [...] Read more.
This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions. Full article
17 pages, 706 KiB  
Article
Empirical Energy Consumption Estimation and Battery Operation Analysis from Long-Term Monitoring of an Urban Electric Bus Fleet
by Tom Klaproth, Erik Berendes, Thomas Lehmann, Richard Kratzing and Martin Ufert
World Electr. Veh. J. 2025, 16(8), 419; https://doi.org/10.3390/wevj16080419 - 25 Jul 2025
Viewed by 351
Abstract
Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational [...] Read more.
Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO2 emissions compared to diesel buses is estimated. Full article
(This article belongs to the Special Issue Zero Emission Buses for Public Transport)
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27 pages, 4008 KiB  
Article
Evolutionary Dynamics and Policy Coordination in the Vehicle–Grid Interaction Market: A Tripartite Evolutionary Game Analysis
by Qin Shao, Ying Lyu and Jian Cao
Mathematics 2025, 13(15), 2356; https://doi.org/10.3390/math13152356 - 23 Jul 2025
Viewed by 193
Abstract
This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three [...] Read more.
This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article
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23 pages, 406 KiB  
Article
Periodically Kicked Rotator with Power-Law Memory: Exact Solution and Discrete Maps
by Vasily E. Tarasov
Fractal Fract. 2025, 9(7), 472; https://doi.org/10.3390/fractalfract9070472 - 21 Jul 2025
Viewed by 388
Abstract
This article discusses the transformation of a continuous-time model of the fractional system into a discrete-time model of the fractional system. For the continuous-time model, the exact solution of the nonlinear equation with fractional derivatives (FDs) that has the form of the damped [...] Read more.
This article discusses the transformation of a continuous-time model of the fractional system into a discrete-time model of the fractional system. For the continuous-time model, the exact solution of the nonlinear equation with fractional derivatives (FDs) that has the form of the damped rotator type with power non-locality in time is obtained.This equation with two FDs and periodic kicks is solved in the general case for the arbitrary orders of FDs without any approximations. A three-stage method for solving a nonlinear equation with two FDs and deriving discrete maps with memory (DMMs) is proposed. The exact solutions of the nonlinear equation with two FDs are obtained for arbitrary values of the orders of these derivatives. In this article, the orders of two FDs are not related to each other, unlike in previous works. The exact solution of nonlinear equation with two FDs of different orders and periodic kicks are proposed. Using this exact solution, we derive DMMs that describe a kicked damped rotator with power-law non-localities in time. For the discrete-time model, these damped DMMs are described by the exact solution of nonlinear equations with FDs at discrete time points as the functions of all past discrete moments of time. An example of the application, the exact solution and DMMs are proposed for the economic growth model with two-parameter power-law memory and price kicks. It should be emphasized that the manuscript proposes exact analytical solutions to nonlinear equations with FDs, which are derived without any approximations. Therefore, it does not require any numerical proofs, justifications, or numerical validation. The proposed method gives exact analytical solutions, where approximations are not used at all. Full article
26 pages, 505 KiB  
Article
Cost Modeling for Pickup and Delivery Outsourcing in CEP Operations: A Multidimensional Approach
by Ermin Muharemović, Amel Kosovac, Muhamed Begović, Snežana Tadić and Mladen Krstić
Logistics 2025, 9(3), 96; https://doi.org/10.3390/logistics9030096 - 17 Jul 2025
Viewed by 395
Abstract
Background: The growth of parcel volumes in urban areas, largely driven by e-commerce, has increased the complexity of pickup and delivery operations. To meet demands for cost efficiency, flexibility, and sustainability, CEP (Courier, Express, and Parcel) operators increasingly outsource segments of their [...] Read more.
Background: The growth of parcel volumes in urban areas, largely driven by e-commerce, has increased the complexity of pickup and delivery operations. To meet demands for cost efficiency, flexibility, and sustainability, CEP (Courier, Express, and Parcel) operators increasingly outsource segments of their last-mile networks. Methods: This study proposes a novel multidimensional cost model for outsourcing, integrating five key variables: transport unit type (parcel/pallet), service phase (pickup/delivery), vehicle category, powertrain type, and delivery point type. The model applies correction coefficients based on internal operational costs, further adjusted for location and service quality using a bonus/malus mechanism. Results: Each cost component is calculated independently, enabling full transparency and route-level cost tracking. A real-world case study was conducted using operational data from a CEP operator in Bosnia and Herzegovina. The model demonstrated improved accuracy and fairness in cost allocation, with measurable savings of up to 7% compared to existing fixed-price models. Conclusions: The proposed model supports data-driven outsourcing decisions, allows tailored cost structuring based on operational realities, and aligns with sustainable last-mile delivery strategies. It offers a scalable and adaptable tool for CEP operators seeking to enhance cost control and service efficiency in complex urban environments. Full article
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17 pages, 986 KiB  
Article
Safety-Oriented Coordinated Operation Algorithms for Natural Gas Pipeline Networks and Gas-Fired Power Generation Facilities
by Xinyi Wang, Feng Wang, Qin Bie, Wenlong Jia, Yong Jiang, Ying Liu, Yuanyuan Tian, Yuxin Zheng and Jie Sun
Processes 2025, 13(7), 2184; https://doi.org/10.3390/pr13072184 - 8 Jul 2025
Viewed by 234
Abstract
The natural gas pipeline network transmission system involved in the coordinated operation of pipeline networks and gas-fired power generation facilities is complex. It consists of multiple components, such as gas sources, users, valves, compressor stations, and pipelines. The addition of natural gas-fired power [...] Read more.
The natural gas pipeline network transmission system involved in the coordinated operation of pipeline networks and gas-fired power generation facilities is complex. It consists of multiple components, such as gas sources, users, valves, compressor stations, and pipelines. The addition of natural gas-fired power generation facilities overlaps with the high and low peak periods of civil gas, imposing dual peak-shaving pressures on pipeline networks and requiring more stringent operational control strategies for maintaining system stability. To address the aforementioned issues and improve the overall operating revenues of the system, we proposed the coordinated optimization model of gas-fired power generation facilities, pipeline networks, gas storage, and compressor stations. The optimization algorithm is written using the penalty function method of the Interior Point OPTimizer (IPOPT) solver. Meanwhile, the basic parameters of the system’s pipeline networks, users, gas storage, natural gas-fired power generation facilities, compressors, and electricity prices were input into the solver. The research results reveal that the algorithm ensures solution accuracy while accounting for computational efficiency and practical applicability. The algorithm can be used to effectively calculate the ideal coordinated operation solution, significantly improve the operating revenues of the system, and achieve safe, stable, coordinated, and efficient operation of the system. Full article
(This article belongs to the Section Energy Systems)
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41 pages, 3512 KiB  
Article
Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting
by Michalis Patsiarikas, George Papageorgiou and Christos Tjortjis
Information 2025, 16(7), 584; https://doi.org/10.3390/info16070584 - 7 Jul 2025
Viewed by 1035
Abstract
Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are [...] Read more.
Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R2 scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)
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18 pages, 2771 KiB  
Article
Short-Term Forecasting of Crop Production for Sustainable Agriculture in a Changing Climate
by Vincenzo Guerriero, Anna Rita Scorzini, Bruno Di Lena, Mario Di Bacco and Marco Tallini
Sustainability 2025, 17(13), 6135; https://doi.org/10.3390/su17136135 - 4 Jul 2025
Viewed by 302
Abstract
Globally, crop productive systems exhibit climatic adaptation, resulting in increased overall yields over the past century. Nevertheless, inter-annual fluctuations in production can lead to food price volatility, raising concerns about food security. Within this framework, short-term crop yield predictions informed by climate observations [...] Read more.
Globally, crop productive systems exhibit climatic adaptation, resulting in increased overall yields over the past century. Nevertheless, inter-annual fluctuations in production can lead to food price volatility, raising concerns about food security. Within this framework, short-term crop yield predictions informed by climate observations may significantly contribute to sustainable agricultural development. In this study, we discuss the criteria for historical monitoring and forecasting of the productive system response to climatic fluctuations, both ordinary and extreme. Here, forecasting is intended as an assessment of the conditional probability distribution of crop yield, given the observed value of a key climatic index in an appropriately chosen month of the year. Wheat production in the Teramo province (central Italy) is adopted as a case study to illustrate the approach. To characterize climatic conditions, this study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) as a key indicator impacting wheat yield. Validation has been carried out by means of Monte Carlo simulations, confirming the effectiveness of the method. The main findings of this study show that the model describing the yield–SPEI relationship has time-varying parameters and that the study of their variation trend allows for an estimate of their current values. These results are of interest from a methodological point of view, as these methods can be adapted to various crop products across different geographical regions, offering a tool to anticipate production figures. This offers effective tools for informed decision-making in support of both agricultural and economic sustainability, with the additional benefit of helping to mitigate price volatility. Full article
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18 pages, 2731 KiB  
Article
Effects of Co-Feeding Dry and Live Feed from the Onset of Exogenous Feeding on Red Seabream Pagrus major Larviculture and Pre-Growing
by Sofia Brinkmann Bougali, Nafsika Karakatsouli, Kostas Ntomalis, Anemos Kastelis, Vasiliki-Anna Alexopoulou, Alkisti Batzina and Isidoros Markakis
Fishes 2025, 10(7), 324; https://doi.org/10.3390/fishes10070324 - 3 Jul 2025
Viewed by 307
Abstract
Current feeding protocols used in commercial hatcheries of Mediterranean fish species are, for a species-dependent period after hatching, based on live feeds which have often raised issues of biosecurity, stability, availability, price and nutrients content. Instead, dry feeds can offer stability in nutritional [...] Read more.
Current feeding protocols used in commercial hatcheries of Mediterranean fish species are, for a species-dependent period after hatching, based on live feeds which have often raised issues of biosecurity, stability, availability, price and nutrients content. Instead, dry feeds can offer stability in nutritional value, price and supply. The aim of the present study was to evaluate an alternative feeding protocol by co-feeding live and dry feed at first feeding red seabream larvae (on 3 days post-hatching—3 dph, DF3) and to compare it to a standard feeding protocol (i.e., dry feed introduced on 22 dph, DF22). Larvae productivity and functional development of the digestive system were evaluated under actual production conditions in a commercial hatchery. Additionally, post-larvae efficiency during pre-growing was evaluated under controlled laboratory conditions. The results obtained showed that the experimental protocol (DF3) promoted larval growth without affecting larval survival and the efficiency of the digestion processes. After pre-growing, DF3-produced juveniles showed a decreased incidence of spinal cord malformations, while the initially gained growth advantage was maintained. The present study shows the feasibility of an alternative feeding protocol for red seabream intensive larviculture and points out the critical role that larval rearing may have for later production stages. Full article
(This article belongs to the Section Nutrition and Feeding)
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34 pages, 4495 KiB  
Article
Charging Ahead: Perceptions and Adoption of Electric Vehicles Among Full- and Part-Time Ridehailing Drivers in California
by Mengying Ju, Elliot Martin and Susan Shaheen
World Electr. Veh. J. 2025, 16(7), 368; https://doi.org/10.3390/wevj16070368 - 2 Jul 2025
Viewed by 731
Abstract
California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), [...] Read more.
California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article
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24 pages, 2253 KiB  
Article
Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach
by Rongshang Chen and Zhiyong Chen
Entropy 2025, 27(7), 715; https://doi.org/10.3390/e27070715 - 1 Jul 2025
Viewed by 318
Abstract
Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model [...] Read more.
Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. Through an approximation of the nonparametric functions with free-knot splines, we develop a Bayesian sampling approach that can be applied by the Markov chain Monte Carlo (MCMC) approach and design an efficient Metropolis–Hastings within the Gibbs sampling algorithm to explore the joint posterior distributions. Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. The simulation results demonstrate that our estimator exhibits robustness to alternative spatial weight matrices and outperforms both quantile regression (QR) and instrumental variable quantile regression (IVQR) in a finite sample at different quantiles. The effectiveness of the proposed model and estimation method is demonstrated by the use of real data from the Boston median house price. Full article
(This article belongs to the Special Issue Bayesian Hierarchical Models with Applications)
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9 pages, 3096 KiB  
Proceeding Paper
Development of AC-DC Converter for Hybrid PV Integrated Microgrid System
by Ramabadran Ramaprabha, Sakthivel Sangeetha, Raghunathan Akshitha Blessy, Ravichandran Lekhashree and Pachaiyappan Meenakshi
Eng. Proc. 2025, 93(1), 10; https://doi.org/10.3390/engproc2025093010 - 30 Jun 2025
Viewed by 138
Abstract
The amount of energy consumed worldwide is raising at a startling rate. This has led to a global energy crisis and a hike in fuel prices and has caused environmental jeopardy. Renewable energy resources offer a promising solution to the above situation. Solar [...] Read more.
The amount of energy consumed worldwide is raising at a startling rate. This has led to a global energy crisis and a hike in fuel prices and has caused environmental jeopardy. Renewable energy resources offer a promising solution to the above situation. Solar energy is examined to be the most liberal source of renewable energy. The efficiency of solar PV cells show nonlinear characteristics and deliver poor performance. Consequently, it is imperative to use the maximum power point tracking (MPPT) technique to extract the optimum amount of energy from photovoltaic (PV) cells. Perturb and Observe (P&O) and Incremental Conductance (INC) are examples of MPPT algorithms. The performance of MPPT schemes below varying climatic ambience should be predominantly considered. The workings of these schemes under various load conditions becomes critical to analyze. This work deals with this issue and compares the conventional P&O MPPT and INC MPPT schemes for various solar irradiation and load conditions and designing solar panels optimized for maximum power generation. The designed MPPT scheme is carried out in the control circuit of a boost converter, evaluating and designing a converter to convert solar panel DC power into grid-compatible AC power. By analyzing different methods for managing and tracking PV power, this method proves to be fast and gives better results under changes in solar insolation. Full article
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19 pages, 1272 KiB  
Article
Waste to Biofuel: Process Design and Optimisation for Sustainable Aviation Fuel Production from Corn Stover
by Nur Aina Najihah Halimi, Ademola Odunsi, Alex Sebastiani and Dina Kamel
Energies 2025, 18(13), 3418; https://doi.org/10.3390/en18133418 - 29 Jun 2025
Viewed by 599
Abstract
Addressing the urgent need to decarbonise aviation and valorise agricultural waste, this paper investigates the production of Sustainable Aviation Fuel (SAF) from corn stover. A preliminary evaluation based on a literature review indicates that among various conversion technologies, fast pyrolysis (FP) emerged as [...] Read more.
Addressing the urgent need to decarbonise aviation and valorise agricultural waste, this paper investigates the production of Sustainable Aviation Fuel (SAF) from corn stover. A preliminary evaluation based on a literature review indicates that among various conversion technologies, fast pyrolysis (FP) emerged as the most promising option, offering the highest fuel yield (22.5%) among various pathways, a competitive potential minimum fuel selling price (MFSP) of 1.78 USD/L, and significant greenhouse gas savings of up to 76%. Leveraging Aspen Plus simulation, SAF production via FP was rigorously designed and optimised, focusing on the heat integration strategy within the process to minimise utility consumption and ultimately the total cost. Consequently, the produced fuel exceeded the American Society for Testing and Materials (ASTM) limit for the final boiling point, rendering it unsuitable as a standalone jet fuel. Nevertheless, it achieves regulatory compliance when blended at a rate of up to 10% with conventional jet fuel, marking a practical route for early adoption. Energy optimisation through pinch analysis integrated four hot–cold stream pairs, eliminating external heating, reducing cooling needs by 55%, and improving sustainability and efficiency. Economic analysis revealed that while heat integration slashed utility costs by 84%, the MFSP only decreased slightly from 2.35 USD/L to 2.29 USD/L due to unchanging material costs. Sensitivity analysis confirmed that hydrogen, catalyst, and feedstock pricing are the most influential variables, suggesting targeted reductions could push the MFSP below 2 USD/L. In summary, this work underscores the technical and economic viability of corn stover-derived SAF, providing a promising pathway for sustainable aviation and waste valorisation. While current limitations restrict fuel quality during full substitution, the results affirm the feasibility of SAF blending and present a scalable, low-carbon pathway for future development. Full article
(This article belongs to the Special Issue Biomass and Waste-to-Energy for Sustainable Energy Production)
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