Search Results (5,670)

Raised personal health awareness and social environmental responsibility put pressure on the agri-food industry to adopt more sustainable ways of production, including the use of more natural ingredients, reducing waste, conservation and the regeneration of resources and energy. Plant-based colorants are ecologically friendly alternatives to artificial food dyes, especially with regards to the current reports on the adverse effects of some of the latter on human health. Various plants are traditionally used by many cultures to obtain vivid food coloration; however, the knowledge and means to produce them becomes less and less accessible to urbanized societies, and affordable organic alternatives are not always available on the market. An online questionnaire was performed to explore the awareness on plant-based dyes and pro-environmental attitudes of Bulgarian customers through the lens of Orthodox Easter eggs dyeing and the obtaining of plant dyes. From a total of 294 adult participants, only 5% reported a strict preference for natural dyes, while more than half of them (54%) were found to use natural and artificial ones concomitantly or switching between both. Of 45 plant taxa used for egg coloration, 12 were most frequently cited with many new additions of imported plants. Most of the used plant-based dyes were common fruits, spices, herbal infusions and even food waste like onion peels and avocado pits that were readily available from home gardens, markets and food shops. Additionally, we made a review of the scientific literature regarding their antioxidant and antimicrobial activity against food spoilage bacteria and foodborne pathogens isolated from eggs. All frequently used taxa were reported to exhibit antibacterial activities against Gram-positive and Gram-negative bacteria and possess antioxidant activity due to the presence of various polyphenols, essential oils and other compounds. Grape and red wine, roselle and stinging nettle are the species with the most diverse antibacterial activity, effective against 15 out of the 16 bacterial species of spoilage and foodbourn microorganisms included in our focus. The antimicrobial activities, however, were found mostly tested against bacterial strains in vitro, and further studies are needed to confirm their potential antibacterial activity when applied to Easter/boiled eggs or other food products. Our findings suggest that traditional cultural practices, as a multifaceted and engaging phenomenon, have the potential to promote environmental responsibility and a healthy lifestyle using both contemporary and traditional knowledge. Full article

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges. Full article

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources. Full article

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. Full article

In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making. Full article

As power system reforms deepen, direct trading with large power consumers has emerged as a crucial aspect of opening up the power sales market. In light of this trend, power sales enterprises should accelerate their digital transformation in response to the growing demand for personalized services from large consumers and continuous advancements in energy digitalization and smart technologies. In particular, big data technology is critical for power enterprises to satisfy users and increase profitability as it can help enterprises gain deeper insights into user needs and behavioral characteristics. The application of big data to provide customized value-added services for large power consumers has become a key development focus. In this paper, we develop a two-party evolutionary game model involving power sellers using big data technology to profile large consumers and offer them customized value-added power packages. We conduct a detailed analysis of the local stability of equilibrium points and employ MATLAB.R2021a to examine the impact of changes in the benefits of value-added services for large consumers and the cost coefficients associated with big data on the system’s evolutionary outcomes. The study results indicate that big data technology can enhance the competitiveness of power sellers in the market. Value-added services based on user-profiling using big data have become a crucial factor in influencing the decision-making behavior of large consumers. Additionally, the investment cost in big data infrastructure by power sellers impacts system evolution, with the cost coefficient being inversely proportional to their willingness to offer customized services. Full article

New energy vehicle (NEV) power batteries are experiencing a significant “retirement wave”, making second-life utilization (SLU) a crucial strategy to extend their lifespan and maximize their inherent value. This study focuses on prominent enterprises in China’s SLU sector, including BAIC Group, BYD, China Tower, and Zhongtian Hongli. Employing a multi-case study approach, a variety of business models and applicable scenarios developed through the cooperation between NEV manufacturers and SLU enterprises are effectively identified, including “co-constructing and purchase”, “co-constructing and leasing”, “self-constructing and purchase”, and “self-constructing and leasing”. The choice of collaborative business model is closely linked to the developmental stage of the NEV manufacturers and SLU enterprises. Additionally, this paper finds that the achievement of collaboration is influenced by the interplay between market dynamics and government policies. The theoretical framework developed from this study offers valuable insights for NEV manufacturers and SLU enterprises to establish stable and effective collaborative business models. Full article

In many energy policies, including Poland’s, environmental priorities clash with the issue of energy security. With these contradictions in mind, the main objective of the article is a comparative analysis of domestic production and imports of hard coal in Poland and the formulation of conclusions for energy policy and competitiveness. The analysis covers the years 2018–2023 and concerns three issues: the volume and directions of coal imports to Poland, the qualitative and price competitiveness of coal, and the possibility of substituting imported coal with domestic coal. The research used statistical analysis. Indicators of structure and dynamics as well as comparative analysis were also used. The analysis shows that the structure of coal importers to Poland is quite diverse and includes many geographic directions. However, until 2021, it was dominated by Russia, followed by Colombia, indicating a fairly homogeneous supply market and a continuing tendency to depend on a single importer. Analysis of qualitative competitiveness confirms the existence of balance and industrial resources whose quality parameters (sulfur content, ash content, and calorific value) are comparable to and better than those of imported coal. Polish hard coal can also compete with imported coal in terms of price. From 2021 to 2023, it was clearly cheaper than foreign coal. In the above circumstances, it is quite difficult to unequivocally assess the reasons for importing coal to Poland and to justify dependence on external suppliers. This is especially relevant since domestic mining in 2020–2023 remains stable (periodically even increasing), which does not indicate a decisive shift away from coal as an energy resource. Full article

To boost the sustainable development of energy and the environment, a new power system with clean energy sources has been proposed by the Chinese government and traditional coal-fired power units are being transformed into regulation service providers for this new energy power system. Then, in this study, complementary power generation cooperation between traditional coal-fired power and new energy power producers is analyzed and discussed, and the energy quota agents, power sellers, are also included. Based on the cooperation game idea, different decision-making models of the tripartite power entities are elaborately constructed. Then, according to the price linkage mechanism between new energy and traditional thermal power, the profit of all power subjects is calculated and the profit allocation process is also analyzed. The conclusions show that the similarity of the two wholesale power price coefficients verifies the symmetry of the cooperative status of power producers. For BPC and SPC quota patterns, for example, BPC is bundled with new energy power and green certificates, whereas SPC is separate. Under the SPC pattern, there is a critical value for effective cooperation between the two power producers in the price range of traditional thermal power or new energy, which can achieve a win–win situation of increasing economic benefits and the consumption scale. Under the BPC pattern, the dynamic benefit compensation mechanism, which is the corrected Shapley value based on the RPS quota ratio, can solve the compressed profit of traditional coal-fired power producers. In contrast, the overall effect of profit allocation using the nucleolar method is not ideal. This study aims to give full play to the elastic induction effect of RPS to promote the sustainable transformation of traditional thermal power energy, especially combining the market mechanism to encourage traditional coal-fired power units to improve green technology to advance the construction of the green power market in China. Full article

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant. Full article

A notable trend in the modern automotive market is the increased interest in hybrid cars. Hybrid cars combine a standard internal combustion engine with an electric motor solution. Research into increasing the energy efficiency of a conventional unit while meeting increasingly stringent exhaust emission standards is becoming a key postulate in this matter. This article discusses an analysis of modifying the fuel used by hybrid vehicles using the example of a selected drive unit equipped with a spark-ignition engine. This effect was tested after the Eco Fuel Shot liquid catalyst was added to the fuel. The research process was carried out in two stages, as follows: in road conditions using the Dynomet road dynamometer; and on the V-tech VT4/B2 chassis dynamometer. Tests were carried out to replicate road tests with a catalytic additive in the fuel. A mathematical model was created and the following energy efficiency parameters of the hybrid vehicle were calculated: the torque of the internal combustion engine, electric motor, and generator; the rotational speeds of the internal combustion engine, electric motor, and generator; the power of the internal combustion engine, electric motor, and generator; the equivalent fuel consumption of the electric motor and generator; the fuel consumption of the internal combustion engine, electric motor, and generator; and the mileage fuel consumption of the internal combustion engine, electric motor, and generator. The results of the tests made it possible to identify the benefits of using the tested liquid catalyst on the operation of the drive system of the analyzed hybrid vehicle. This research will be of benefit to both the demand side in the form of users of this category of vehicles, and the supply side represented by the manufacturers of power units. Full article

Sub-Saharan economies often rely heavily on a narrow range of commodities, making them particularly vulnerable to price fluctuations in global markets. This volatility predisposes these countries to economic instability, threatening short-term growth and long-term development goals. As a result, this study examines the sustainability implications of commodity price volatility and commodity dependence for 31 Sub-Saharan African countries from 2000 to 2023. Eleven agricultural commodity-dependent countries, six energy commodity-dependent countries, and fourteen mineral and metal ore-dependent countries were chosen. This study uses balanced annual panel data from World Development Indicators, World Bank Commodity Price Data, and Federal Reserve Bank Data. The data were analyzed using the VECM, and this study’s findings were threefold and unanimous for all three categories of commodities (agricultural, energy and mineral, and metal ore). First, commodity dependence is positively related to economic growth, suggesting that higher commodity prices benefit the economy in the long run. Second, commodity price volatility is negatively related to economic growth, indicating adverse impacts on economic stability in the long run. Third, commodity dependence is positively related to commodity price volatility in the long run. By analyzing the interconnectedness of these factors, this study underscores the need for diversified economic policies and sustainable practices to reduce vulnerability and promote sustainable development in the region. The findings highlight the critical role of strategic resource management and policy interventions in achieving economic stability and ensuring the well-being of future generations. Full article

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC’s safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC. Full article

As more companies strive for net-zero emissions, mitigating indirect greenhouse gas emissions embedded in value chains—especially in logistics activities—has become a critical priority. In the European logistics sector, sustainability and energy efficiency are receiving growing attention, given the sector’s intersectional role in both transportation and construction. This transition toward low-carbon logistics design not only reduces carbon emissions but also yields financial benefits, including operational cost savings and new market opportunities. This study examines the impact of passive design strategies and low-carbon technologies in a Swedish logistics center, assessed using the low-carbon design criteria from the BREEAM International standard, version 6. The findings show that passive energy-efficient measures, such as the installation of 47 skylights for natural daylighting, reduced light power density in accordance with AHSHARE 90.1-2019 and the integration of free night flushing, contribute to a 23% reduction in total energy consumption. In addition, the integration of 600 PV panels and 480 batteries with a capacity of 268 ampere-hours and 13.5 kWh storage, operating at 50 volts, delivers a further 56% reduction in carbon emissions. By optimizing the interaction between passive design and active low-carbon technologies, this research presents a comprehensive feasibility analysis that promotes sustainable logistics practices while ensuring a future-proof, low-carbon operational model. Full article

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure. Full article