Use Of Clean Energy Research Articles

Differential Game Theory in Electric Vehicle Charging Optimization

Differential Game Theory is a strong way to solve the hard optimization problems that come up when charging electric vehicles (EVs) in smart grids. This method takes into account the strategic exchanges between many parties, such as EV users, charging station operators, and grid operators, each of whom has their own goals. In this study, we look at how differential game theory can be used to find the best charging plans for electric vehicles. Our goal is to lower total energy costs, make the grid less crowded, and get more energy from green sources. We take into account how power prices change, the different types of green energy that are available, and how EVs move around by modeling the charging process as a difference game. The game-theoretic method lets you come up with plans where each player maximizes their own reward while taking into account what other players do. To find the best price methods for different situations, such as peak and off-peak hours, different amounts of green energy usage, and different pricing plans, we use Nash equilibrium solutions. Our findings show that differential game theory can successfully balance the different needs of parties, which can lead to EV charging systems that work better and last longer. Furthermore, the suggested way not only makes smart grids work better, but it also helps lower carbon emissions by supporting the use of clean energy. In addition, this method helps us understand how to create reward systems that match individuals' goals with world improvement goals. This makes it possible for EV charging strategies to work together better. This study shows that differential game theory could be a very useful tool in the development of smart and environmentally friendly transportation systems.

Regional disparities and socio-demographic determinants of clean cooking energy use among women in Nigeria

Regional disparities and socio-demographic determinants of clean cooking energy use among women in Nigeria

How can interventions of building renovation and household energy conversion effectively improve the physical and mental health outcomes? A systematic review and meta-analysis

Household energy efficiency measures can effectively reduce indoor air pollution and positively impact the health of residents, but there is no conclusive evidence on the effectiveness of different energy efficiency measures for various diseases. This study used systematic review and meta-analysis to research the effects of two interventions on residents’ physical and mental health: building renovation (heating, insulation, double-glazed windows and ventilation) and household energy conversion (stove upgrading and clean energy use). A total of 79 papers published between 1977 and 2023 were reviewed in this study, providing comprehensive meta-analytic evidence on the effectiveness of energy efficiency measures. The results show that household energy-saving measures have a minor but significant positive influence on residents’ physical and mental health, with the effect on mental health being larger than the impact on physical health. Stove upgrading is advantageous in respiratory and cardiovascular diseases; insulation can greatly reduce the risk of skin diseases; heating is beneficial in sensory and digestive diseases; and clean energy use can successfully mitigate integrative diseases. Furthermore, a combination of heating and insulation measures has the most favourable influence on mental health conditions such as depression, anxiety and social isolation. The study findings can help residents, policymakers and relevant organizations take appropriate interventions to promote residents’ physical and mental health.

A review of load forecasting technology based on artificial intelligence

Abstract. Driven by the "dual carbon" goals and the construction of new-type power systems, the importance of power load forecasting technology has become increasingly prominent. Accurate power load forecasting can not only ensure the safe and stable operation of the power system, but also promote the efficient use of clean energy and reduce carbon emissions. Traditional power load forecasting methods have become insufficient in dealing with the complexity and nonlinear growth of power systems, and the development of artificial intelligence technology has brought new possibilities for power load forecasting. Power load forecasting methods based on machine learning and deep learning have gradually become a hot topic of research due to their powerful nonlinear mapping capabilities and advantages in processing big data. These methods not only perform well in short-term forecasting, but also show potential in medium- and long-term forecasting. This paper discusses the application of different artificial intelligence technologies in power load forecasting by sorting out the development status of related technologies, and provides a reference direction for future research.

Solar PV powered DC charger for electric vehicles with reconfigurable power electronic interface

ABSTRACT Global expansion of the EV market has led to widespread DC charging infrastructure. Consumers prefer DC charging over AC to reduce charging time per kilometer. However, grid-connected DC chargers can cause issues such as grid instability, power quality problems, demand imbalance, and conversion losses. This paper proposes an off-grid photovoltaic (PV) fed DC charger for EVs along with a battery energy storage system (BESS) to mitigate these challenges. The work aims to increase the use of clean energy for EV charging, to ensure uninterrupted EV charging with minimum power conversion stages, and to improve PV utilization. BESS serves as a backup power source that charges or discharges based on PV availability. Additionally, the system powers the non-critical adjustable loads of domestic/industrial sites with the available PV power when both EV and BESS are fully charged. The system can be reconfigured to operate in six distinct modes using two DC-DC converters and five relays. Among six modes, four modes operate in single-stage power conversion. A buck converter is used to extract power from PV. A bidirectional buck-boost converter is used to charge and discharge the BESS. The proposed system is validated through hardware prototype for both steady-state and transient conditions.

Dual Metal Sites Coengineered Graphitic Carbon Nitride as Green Electrocatalyst for Highly Selective Overall Water Splitting—A Sustainable Approach

The development of efficient, low‐cost, non‐noble metal‐oxide‐based nanohybrid materials for overall water splitting is a critical strategy for enhancing clean energy use and addressing environmental issues. In this study, an interfacial engineering strategy for the development of bimetallic Co–Ni nanoparticles on graphitic carbon nitride (g‐C3N4) using ultrasonication followed by coprecipitation is conveyed. These nanoparticles demonstrate high efficacy as bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline conditions. Co–Ni nanoparticles on graphitic carbon nitride demonstrate an increased surface area via ultrasonication and subsequent coprecipitation. The g‐C3N4 combined with Co–Ni nanoparticles leads to the development of bifunctional catalysts that exhibit significant efficiency in both HER and OER, and their interfacial properties are investigated for the first time. The chemical composition and morphology of g‐C3N4 integrated with Co–Ni nanoparticles significantly influence the modulation of redox‐active sites and the facilitation of electron transfer, resulting in improved splitting efficiency. The interactions between the Co–Ni bimetal and g‐C3N4 demonstrate exceptional electrochemical performance for water splitting. Consequently, the 20% 20–Co–Ni–graphitic carbon nitride electrode demonstrated superior HER performance, comparable to the other electrodes. In the results, it is indicated that an increased molar ratio of Co and Ni incorporated in graphitic carbon nitride significantly improves HER performance.

PROPOSTA DE ELETROPOSTOS MODULARES PARA CARROS ELÉTRICOS ABASTECIDOS POR ENERGIA SOLAR

In recent years, the growing demand for electric vehicles has intensified the need for adequate infrastructure for their charging. Charging stations, dedicated to supplying energy to these vehicles, play a crucial role in the transition to sustainable urban mobility. This study proposes the development of modular charging stations powered by solar energy, focusing on solutions that favor sustainability and efficiency. The primary objective is to create systems that integrate photovoltaic technology, promoting the use of clean energy and contributing to the reduction of air pollution. The research evaluates the viability of on-grid and off-grid systems, aiming to optimize the efficiency of solar panels and reduce operational costs. Factors such as ideal location, shading prevention, and the environmental impacts of electric mobility are analyzed. Additionally, existing charging station examples are examined to identify best practices and innovations in urban design. The methodology includes exploratory and bibliographic research, utilizing case studies and literature reviews to support the proposal. As a result, the study culminates in a sustainable modular charging station proposal that is adaptable to different locations, promoting more efficient and eco-friendly mobility.

Sustainable Supply Chain Management and Green Logistics Practice Analysis: Case Study of Wahaha and Patagonia

In globalization and digitization, supply chains have become key for companies to gain competitive advantages and achieve sustainable development. This study provides a detailed analysis of the sustainable supply chain management practices of China's leading food and beverage company, Wahaha, and the American outdoor brand, Patagonia. It explores their successes in green production, logistics, packaging, and environmental governance. The research finds that Wahaha has achieved efficient resource utilization and cost reduction through digital and intelligent transformation, enhanced recycling, and the use of clean energy. On the other hand, Patagonia maintains a strong environmental commitment by using organic cotton and recycled materials, supporting environmental projects, and promoting a green supply chain. Despite their different strategies, both companies have achieved significant sustainable supply chain management results, offering valuable insights for other businesses. Future research should include different regions and industries, explore the challenges and solutions in sustainable development, and optimize evaluation systems through quantitative analysis and model building to enhance practical application value.

Research progress of hydrogen blocking coatings

In the context of carbon peak and carbon neutrality, the use of clean energy has become the focus of attention. Hydrogen energy as the most potential secondary energy has a very important position in various fields, so the storage and transport of hydrogen energy is one of our most important concerns. Long distance pipeline is regarded as an effective means to transport hydrogen, but the high pressure hydrogen environment is easy to cause hydrogen embrittleness and other problems in the pipeline, which seriously threatens the safety of pipeline transportation. Hydrogen blocking coatings are one of the most effective ways to solve such problems. In this paper, the research results in recent years are reviewed and summarized. The article points out the hydrogen barrier mechanism, advantages and disadvantages of various types of hydrogen barrier coatings, the current status of research, as well as future research focus and development trend.

Design and performance evaluation of a solar-assisted biogas and air-source heat pump hybrid system for radiant heating applications

Energy and environmental crises have become critical challenges, making the efficient use of clean energy a focus of public attention. Although emerging clean energies have high economic and environmental values, these single energy sources are not well-developed. In colder climates, maintaining a stable fermentation temperature can be difficult, leading to the idling of many biogas digesters. Furthermore, despite extensive research on solar-assisted air-source heat pumps, these systems perform poorly in cold climates with low solar irradiance. To address these issues, this study investigates the integration of a solar-assisted biogas and air-source heat pump hybrid heating system (SBHP-HHS) for capillary radiant heating in rural North China, specifically adapted to local conditions. The system aims to utilize the combined benefits of various clean energy sources to improve system reliability in harsh climates. By combining experimental analysis with TRNSYS simulation, the system performance during the heating season in rural northern China was evaluated. Experimental results show that the hybrid system, utilizing the radiant capillary terminal, is capable of maintaining room temperatures above 20 °C, demonstrating its effectiveness. Simulation results show that the air-source heat pump achieves a coefficient of performance (COP) of 4.6. This efficiency level is generally favorable, ensuring stable operation. Additionally, the system achieves a solar-biogas hybrid utilization rate exceeding 75 % and a primary energy-saving rate of 78 %. Economically, the system offers an 8.23-year dynamic payback period and annual cost savings of 3055 CNY. Environmentally, it reduces CO2 emissions by 4263.6 kg annually. This study provides a solid experimental foundation for further research, offering empirical evidence to optimize system performance.

Scheduling and assessment of multi-area virtual power plant including flexible resources using swarm intelligence technique

A virtual power plant (VPP) makes the distributed energy resources (DERs) visible to support grid services, promoting clean energy use by offering an alternative to reduce reliance on conventional sources. This paper proposes optimized operation and scheduling of VPP by forming a group of flexible resources that includes renewables i.e., solar, wind power, fuel cell, and co-generation units along with storage facilities to form a holistic network in a multi-area interconnected system framework. Initially, the scheduling is performed for a single area system in which a comparison is made with the available work relevant to the proposed formulation. In addition, the scheduling is further extended to the proposed multi-area based virtual power plant (MAVPP) performed on a day-ahead (DA) basis and the target objective(s) i.e., net profit and emission are optimized. Furthermore, a recently developed marine predator optimization algorithm (MPOA) is employed to solve the proposed problem formulation and the results are compared and discussed extensively with conventional techniques. Also, the analysis pertaining to MAVPP is carried out which is beneficial in terms of economic and environmental perspective. The result reveals that the maximum value of net profit for MAVPP is converging to an optimum value of 59,351.88 $ followed by the attained value of minimum emission of 51,700.73 Kg by consuming an elapsed time of 92.96 and 104.76 s., respectively in 200 iterations. The outcomes are promising and upon comparison with the available literature work, the superiority of the selected technique is evident in terms of computational time, improvement of net profit and reduction in emission followed by the ease of convergence behaviour in fewer iterations.

Impact of urban vitality on carbon emission—an analysis of 222 Chinese cities based on the spatial Durbin model

Urban development is undergoing rapid growth, and the increasingly prominent environmental protection challenge underscores the crucial need to balance urban vitality development with carbon emissions for sustainable goals. how urban vitality affect carbon emissions? By collecting and analyzing data from 222 prefecture-level cities, utilizing long-time series data from 2011 to 2019, and employing Spatial Durbin Method (SDM) to study. The study explores various dimensions of urban vitality, including economic, social, and population indicators. The direct impact of economic vitality on carbon emissions is negative, while the spillover effect is significantly positive, resulting in an overall positive total effect. There are noticeable spillover effects associated with economic vitality; a rise in one city’s economic vitality may result in a rise in carbon emissions in nearby prefecture-level cities. The reason behind the increase in carbon emissions in nearby metropolitan areas is the departure of some energy-intensive and highly polluting enterprises from the central sections of nearby cities. While enhancing the economic vitality of cities, it is recommended to strive for the development of a green economy, aiming for a sustainable development balance between economic growth and environmental conservation. Population vitality has a favorable impact on carbon emissions both directly and indirectly, with the direct effect being particularly significant. The population expansion of prefecture-level cities exerts a substantial influence on urban carbon emissions. Additionally, social vitality exhibits positive direct and spillover effects on carbon emissions, as well as a significant overall positive effect. Policymakers are urged to prioritize clean energy use while fostering economic growth, prevent high-polluting industries’ migration to neighboring urban areas, manage population expansion, promote environmental awareness, and implement integrated urban planning through collaborative governance for sustainable development.

Minimizing Carbon Emissions by Improving Water and Energy Use Efficiencies in AI Servers: A Green Cloud Computing Strategy for Sustainable Artificial Intelligence Systems

The advent of Artificial Intelligence systems, in particular of generative models like ChatGPT, has resulted in one more area requiring heavy computational resources which in turn consumes a lot of energy and water. By estimations, one interaction with ChatGPT for instance will take an estimate of 2.9 watt hour, which is ten times higher than the amount of energy consumed to conduct an ordinary googling task that is 0.3 watt hours. This stands as a call for action toward improving the water to energy ratio of the AI systems and therefore the recent carbon emissions. This paper explores the energy efficiency patterns of AI languages such as chatbots compared with the other means of searching the internet like Google and how the effects of the AI machines on the environment can be reduced. In this connection, green cloud computing methods have been suggested as possible solutions that can be effectively combined with the principles of clean energy use; on this list are both advanced systems for maintaining low temperatures and the optimization of AI systems. Finally, using of resources could also play a crucial part in the ultimate decrease in the adverse effects that the AI industry has on our environment.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

Aluminum demand and low carbon development scenarios for major countries by 2050

Aluminum is a widely used energy-intensive material and the second largest source of carbon emissions from metal after steel. Global demand for aluminum has grown rapidly since 1970 and has shown a high correlation with population and economic growth. These factors have posed challenges to carbon reduction of aluminum industry. Therefore, it is crucial to investigate global aluminum demand and emissions to develop low carbon pathways. However, few studies have conducted a comprehensive analysis of the global aluminum industry. We develop different scenarios of joint abatement measures to explore pathways for the aluminum industry to significantly reduce carbon emissions by 2050. Three scenarios are considered, namely, moderate mitigation scenario (MMS), enhanced mitigation scenario (EMS), and deep mitigation scenario (DMS). The results show that: 1) Aluminum industry has a high potential for carbon emission reduction. The emissions in the three scenarios will be reduced by 33%, 77% and 85% by 2050 compared to 2020, respectively. 2) The carbon emission reduction effect of energy structure optimization and low carbon technology application is significant, with the use of clean energy playing a decisive role. 3) There is an inverse U-shaped curve relationship between per capita primary aluminum demand and per capita GDP. The aluminum demand in China and India is still growing at a high rate, and carbon emissions are always at the top. This study will effectively support the development of a low-carbon transition path for the global aluminum industry.

Role of Climate Change in Afghanistan’s Geopolitical Power

The most significant environmental threats today are global warming and climate change. The effects of climate change include water and food scarcity, disease, unemployment, migration, poverty, resource conflicts, and global instability. This study examines the role of climate change in Afghanistan’s geopolitical power. This study employs a descriptive-analytical approach and a survey method. The statistical research community includes experts and specialists in geopolitics, political science, climate science, and university professors. The questionnaire was distributed among them based on indicators related to climate change, development, and geopolitics. 80 questionnaires were collected. Based on these findings, a significant and inverse relationship exists between the impacts of climate change and development. Furthermore, the reduction in the development process has a significant and negative impact on Afghanistan’s geopolitical power. Finally, the reduction in development affects the change. Climate plays a mediating role in geopolitics. Then, the priority of parameters influencing climate change in geopolitics was discussed using Friedman’s comparison test. These parameters include 1) poverty and social anomalies, drought, and migration. In the suggestions section, it is emphasized that there is a need to implement effective methods, such as irrigation and the use of clean energy, to reduce greenhouse gas emissions and adapt to the impacts of climate change.

Exploring communities’ utilization of Jatropha based biofuels to transition towards cleaner energy sources

Abstract Ethiopia is committed to using sustainable energy sources due to the limited availability of traditional fuel and their environmental damage. This study examines the viability of biofuels as an alternative fuel source in Ethiopia for community’s sustainable transition to cleaner energy. The research reviews relevant publications, policies, initiatives, and programs, identifies barriers to implementation, and collects data through literature reviews, community discussions and observations of energy experts. Ethiopia has 8.6 million hectares available for energy crops and governmental policies are encouraging the use of clean fuels. The study suggests that biofuel can support rural development and environmental sustainability. However, challenges to the widespread adoption of alternative fuels by communities exist such as flawed policies, limited technical skills, insufficient maintenance experts, a scarcity of biofuel production companies, and inadequate markets for biofuel seeds. To achieve a sustainable future through the use of clean energy in the country, policy interventions and investment strategies are necessary. All stakeholders must adopt policies and develop capacity-building programs while providing incentives for developers and users.

Productive Performance of Biomass Sorghum (Sorghum bicolor (L.) Moench) and Cowpea (Vigna unguiculata (L.) Walp) Cultivars in Different Cropping Systems and Planting Times

Global projections indicate that the demand for fresh water, energy, and food will increase significantly in the coming decades under the pressure of population growth, economic development, climate change, and other factors. Faced with this, technologies that promote sustainable development through the use of clean energy will be imperative. That way, this study aimed at evaluating the productive performance of biomass sorghum and cowpea cultivars in different cropping systems and planting seasons. The experiment was conducted at the Caatinga Experimental Field at Embrapa Semiarid, Petrolina—PE. Four cowpea (BRS Itaim, BRS Gurguéia, BRS Guariba, and BRS Carijó) and two biomass sorghum cultivars (BRS 716 and AGRI-002E) were used in intercropping and monoculture systems. The cultivars were sown during two different seasons: June (season 1—winter) and December (season 2—summer) of 2021. The biometric and productive parameters and land equivalent ratios (LERs) of sorghum and cowpea were evaluated. The data were subjected to multivariate analysis. The productive performance of biomass sorghum cultivars Agri-002E and BRS 716 was higher when planted in December, with an increase of 37% due to the planting season. Cowpea productivity was not influenced by sowing seasons or the cultivation system. Based on the calculation of efficient land use, the intercropping between biomass sorghum cultivar BRS 716 and cowpea cultivars BRS Gurguéia, BRS Guariba, and BRS Carijó was advantageous when compared to monocultures planted in the hottest season. This study showed the importance of cultivar selection, the planting time, and land use efficiency in intercropping systems.

Khung pháp lý cho nền kinh tế xanh: Hướng đến giảm thiểu khí thải carbon

During the period 2018 - 2023, Vietnam has witnessed a close correlation between economic growth, expressed through GDP, and carbon emissions (CO2), posing many challenges and opportunities in the journey towards Sustainable Development. Although Vietnam’s economy has achieved impressive growth, the question of the dark side of growth - rising CO2 emissions - is also seriously concerned. The current state of green economic development during this period reflects Vietnam’s efforts in balancing economic development and environmental protection. However, the transition to a green economic model still faces many challenges, from infrastructure, technology to people’s and businesses’ awareness. Carbon emissions in Vietnam during this period also recorded a significant increase, reflecting that lifestyles and development models are not truly sustainable. This further emphasizes the importance of implementing solutions to minimize environmental impacts while developing the economy in a sustainable way. The relationship between green economic development and carbon emissions shows that converting to a green economic model not only reduces emissions, but also brings long-term economic benefits through savings. energy, reducing production costs and increasing competition. To promote green economic development and reduce carbon emissions in Vietnam, a combination of solutions from government, businesses and the community is needed. Solutions include: increased use of clean energy, development of green technology, improved energy efficiency, and increased environmental awareness. This requires a strong and synchronized strategy from all stakeholders to create a green and sustainable future for Vietnam.

A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs.