Adewale Wasiu Adegboyega, Emmanuel Chigozie Ani, Stephen Opeyemi Oladunni, Abrar Shahriar Pramanik
The integration of renewable energy into the U.S. electricity grid is a multifaceted challenge that demands innovative project management approaches. This review article explores the current landscape of renewable energy integration in the United States, emphasizing the diversity of renewable sources such as solar, wind, hydropower, geothermal, and biomass. It examines the state of integration, highlighting significant progress and persistent challenges, particularly in grid stability, regional variations, and regulatory frameworks. Key stakeholders, including federal and state agencies, utilities, independent power producers, and advocacy organizations, play pivotal roles in shaping the renewable energy sector. The article delves into the critical importance of project management, outlining how effective coordination, risk mitigation, and performance optimization are essential for successful project execution. Various tools and techniques, including advanced analytics, agile methodologies, and stakeholder engagement strategies, are analyzed for their effectiveness in navigating the complexities of renewable energy projects. The review also addresses the emerging trends and innovations in project management, such as the adoption of artificial intelligence, machine learning, and sustainability-focused frameworks, which are poised to revolutionize the sector. It concludes with practical recommendations for project managers, emphasizing the need for flexibility, technological awareness, stakeholder collaboration, and data-driven decision-making to overcome challenges and drive the transition to a sustainable energy future. By providing a comprehensive overview and actionable insights, this article aims to equip project managers and stakeholders with the knowledge needed to successfully integrate renewable energy into the U.S. grid, thereby contributing to the nation's goal of a resilient and sustainable energy system. Keywords: Renewable Energy, Project Management, Grid Integration, Stakeholder Engagement, Sustainability, Technological Advancements, Energy Transition, Review.
{"title":"Project management tools in renewable energy integration: A review of U.S. perspectives","authors":"Adewale Wasiu Adegboyega, Emmanuel Chigozie Ani, Stephen Opeyemi Oladunni, Abrar Shahriar Pramanik","doi":"10.51594/estj.v5i7.1359","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1359","url":null,"abstract":"The integration of renewable energy into the U.S. electricity grid is a multifaceted challenge that demands innovative project management approaches. This review article explores the current landscape of renewable energy integration in the United States, emphasizing the diversity of renewable sources such as solar, wind, hydropower, geothermal, and biomass. It examines the state of integration, highlighting significant progress and persistent challenges, particularly in grid stability, regional variations, and regulatory frameworks. Key stakeholders, including federal and state agencies, utilities, independent power producers, and advocacy organizations, play pivotal roles in shaping the renewable energy sector. The article delves into the critical importance of project management, outlining how effective coordination, risk mitigation, and performance optimization are essential for successful project execution. Various tools and techniques, including advanced analytics, agile methodologies, and stakeholder engagement strategies, are analyzed for their effectiveness in navigating the complexities of renewable energy projects. The review also addresses the emerging trends and innovations in project management, such as the adoption of artificial intelligence, machine learning, and sustainability-focused frameworks, which are poised to revolutionize the sector. It concludes with practical recommendations for project managers, emphasizing the need for flexibility, technological awareness, stakeholder collaboration, and data-driven decision-making to overcome challenges and drive the transition to a sustainable energy future. By providing a comprehensive overview and actionable insights, this article aims to equip project managers and stakeholders with the knowledge needed to successfully integrate renewable energy into the U.S. grid, thereby contributing to the nation's goal of a resilient and sustainable energy system. \u0000Keywords: Renewable Energy, Project Management, Grid Integration, Stakeholder Engagement, Sustainability, Technological Advancements, Energy Transition, Review.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"15 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chijioke Paul Agupugo, Husseini Musa Kehinde, Helena Nbéu Nkula Manuel
Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Oper
{"title":"Optimization of microgrid operations using renewable energy sources","authors":"Chijioke Paul Agupugo, Husseini Musa Kehinde, Helena Nbéu Nkula Manuel","doi":"10.51594/estj.v5i7.1360","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1360","url":null,"abstract":"Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. \u0000Keywords: Optimization, Microgrid, Oper","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adindu Donatus Ogbu, Kate A. Iwe, Williams Ozowe, Augusta Heavens Ikevuje
Advances in rock physics have significantly enhanced pore pressure prediction, a critical aspect of subsurface exploration and drilling operations. This comprehensive review delves into the latest developments in rock physics methodologies, integrating empirical, theoretical, and computational approaches to predict pore pressure more accurately. Traditional pore pressure prediction methods often rely on well log data and seismic attributes, but recent advancements have introduced innovative techniques that leverage the physical properties of rocks to provide more reliable predictions. Key advances include the development of improved rock physics models that better account for the complexities of subsurface environments, such as heterogeneity and anisotropy. These models integrate data from various sources, including well logs, core samples, and seismic surveys, to create a more comprehensive understanding of the subsurface. Additionally, the application of machine learning and artificial intelligence to rock physics has opened new avenues for analyzing large datasets, identifying patterns, and refining predictive models. This review also examines the role of laboratory experiments and field studies in validating and calibrating rock physics models. High-pressure and high-temperature experiments have provided valuable insights into the behavior of rocks under different conditions, which are essential for accurate pore pressure prediction. Field studies, on the other hand, offer real-world data that help in fine-tuning models and methodologies. Future directions in rock physics for pore pressure prediction include the integration of advanced geophysical techniques, such as full-waveform inversion and distributed acoustic sensing, which offer higher resolution data and more detailed subsurface imaging. The use of cloud computing and high-performance computing platforms is also expected to enhance the processing and analysis of large datasets, making predictive models more efficient and scalable. The comprehensive review concludes by highlighting the importance of interdisciplinary collaboration in advancing rock physics methodologies. By combining expertise from geophysics, petrophysics, geomechanics, and data science, the field can continue to innovate and improve the accuracy and reliability of pore pressure predictions, ultimately enhancing exploration and production efficiency in the oil and gas industry. Keywords: Advances, Rock Physics, Pore Pressure, Prediction, Future Directions.
{"title":"Advances in rock physics for pore pressure prediction: A comprehensive review and future directions","authors":"Adindu Donatus Ogbu, Kate A. Iwe, Williams Ozowe, Augusta Heavens Ikevuje","doi":"10.51594/estj.v5i7.1345","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1345","url":null,"abstract":"Advances in rock physics have significantly enhanced pore pressure prediction, a critical aspect of subsurface exploration and drilling operations. This comprehensive review delves into the latest developments in rock physics methodologies, integrating empirical, theoretical, and computational approaches to predict pore pressure more accurately. Traditional pore pressure prediction methods often rely on well log data and seismic attributes, but recent advancements have introduced innovative techniques that leverage the physical properties of rocks to provide more reliable predictions. Key advances include the development of improved rock physics models that better account for the complexities of subsurface environments, such as heterogeneity and anisotropy. These models integrate data from various sources, including well logs, core samples, and seismic surveys, to create a more comprehensive understanding of the subsurface. Additionally, the application of machine learning and artificial intelligence to rock physics has opened new avenues for analyzing large datasets, identifying patterns, and refining predictive models. This review also examines the role of laboratory experiments and field studies in validating and calibrating rock physics models. High-pressure and high-temperature experiments have provided valuable insights into the behavior of rocks under different conditions, which are essential for accurate pore pressure prediction. Field studies, on the other hand, offer real-world data that help in fine-tuning models and methodologies. Future directions in rock physics for pore pressure prediction include the integration of advanced geophysical techniques, such as full-waveform inversion and distributed acoustic sensing, which offer higher resolution data and more detailed subsurface imaging. The use of cloud computing and high-performance computing platforms is also expected to enhance the processing and analysis of large datasets, making predictive models more efficient and scalable. The comprehensive review concludes by highlighting the importance of interdisciplinary collaboration in advancing rock physics methodologies. By combining expertise from geophysics, petrophysics, geomechanics, and data science, the field can continue to innovate and improve the accuracy and reliability of pore pressure predictions, ultimately enhancing exploration and production efficiency in the oil and gas industry. \u0000Keywords: Advances, Rock Physics, Pore Pressure, Prediction, Future Directions.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jephta Mensah Kwakye, Darlington Eze Ekechukwu, Olorunshogo Benjamin Ogundipe
Climate change poses an imminent threat, necessitating innovative and sustainable strategies for mitigation. This paper explores the potential of Bioenergy with Carbon Capture and Storage (BECCS) as a promising approach. The introductory section sets the stage by elucidating the urgency of climate action. The background section surveys existing climate mitigation strategies, introducing bioenergy and carbon capture technologies. The paper delves into the distinctive contributions of bioenergy to carbon emission reduction and assesses the viability of various bioenergy sources. Simultaneously, the discussion on Carbon Capture and Storage (CCS) provides insight into the technological aspects of carbon capture. An integral focus is the integration of bioenergy and carbon capture technologies in BECCS, exploring synergies that enhance their combined efficacy. Real-world examples and case studies illustrate successful BECCS projects. Environmental and social impacts are critically examined, considering sustainability and ethical dimensions. Challenges and criticisms associated with BECCS are discussed comprehensively, addressing concerns and proposing potential solutions. The paper concludes by outlining future prospects for BECCS, offering recommendations for policymakers and stakeholders. It also suggests avenues for further research and development in this evolving field. Keywords: Bioenergy, Carbon Capture and Storage (BECCS), Climate Mitigation.
{"title":"Reviewing the role of bioenergy with carbon capture and storage (BECCS) in climate mitigation","authors":"Jephta Mensah Kwakye, Darlington Eze Ekechukwu, Olorunshogo Benjamin Ogundipe","doi":"10.51594/estj.v5i7.1346","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1346","url":null,"abstract":"Climate change poses an imminent threat, necessitating innovative and sustainable strategies for mitigation. This paper explores the potential of Bioenergy with Carbon Capture and Storage (BECCS) as a promising approach. The introductory section sets the stage by elucidating the urgency of climate action. The background section surveys existing climate mitigation strategies, introducing bioenergy and carbon capture technologies. The paper delves into the distinctive contributions of bioenergy to carbon emission reduction and assesses the viability of various bioenergy sources. Simultaneously, the discussion on Carbon Capture and Storage (CCS) provides insight into the technological aspects of carbon capture. An integral focus is the integration of bioenergy and carbon capture technologies in BECCS, exploring synergies that enhance their combined efficacy. Real-world examples and case studies illustrate successful BECCS projects. Environmental and social impacts are critically examined, considering sustainability and ethical dimensions. Challenges and criticisms associated with BECCS are discussed comprehensively, addressing concerns and proposing potential solutions. The paper concludes by outlining future prospects for BECCS, offering recommendations for policymakers and stakeholders. It also suggests avenues for further research and development in this evolving field. \u0000Keywords: Bioenergy, Carbon Capture and Storage (BECCS), Climate Mitigation.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"21 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aliu Olalekan Olatunji, Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul
Antimicrobial resistance (AMR) poses a significant threat to global public health, necessitating innovative strategies to combat this escalating issue. This review outlines next-generation approaches integrating genomics, CRISPR technology, and novel therapeutics to effectively address AMR. Genomic techniques enable comprehensive understanding of the genetic mechanisms underpinning resistance, facilitating the development of targeted interventions. By sequencing the genomes of resistant pathogens, researchers can identify resistance genes, track their spread, and predict emerging resistance patterns. CRISPR-Cas systems offer a revolutionary tool for combating AMR through precise genome editing. This technology can disrupt resistance genes, restore antibiotic sensitivity, and develop bacteriophage therapies that selectively target resistant bacteria. Moreover, CRISPR-based diagnostics enable rapid, accurate detection of resistant strains, enhancing infection control measures. The advent of novel therapeutics, such as antimicrobial peptides, bacteriophage therapy, and synthetic biology-derived compounds, provides alternative treatment options. These therapeutics can bypass traditional resistance mechanisms and exhibit efficacy against multi-drug resistant organisms. Additionally, integrating artificial intelligence (AI) and machine learning with genomics and CRISPR can accelerate the discovery of new antibiotics and predict resistance trends, optimizing treatment regimens. Implementing these next-generation strategies requires robust global collaboration, regulatory frameworks, and investment in research and development. By combining genomics, CRISPR, and novel therapeutics, we can create a multifaceted approach to overcome AMR, ensuring effective treatments and safeguarding public health. This integration represents a paradigm shift in antimicrobial strategy, offering hope for a future where resistant infections can be effectively managed and treated. Keywords: Integrating Genomics, Antimicrobial Resistance, CRISPR, Therapeutic
{"title":"Next-Generation strategies to combat antimicrobial resistance: Integrating genomics, CRISPR, and novel therapeutics for effective treatment","authors":"Aliu Olalekan Olatunji, Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul","doi":"10.51594/estj.v5i7.1344","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1344","url":null,"abstract":"Antimicrobial resistance (AMR) poses a significant threat to global public health, necessitating innovative strategies to combat this escalating issue. This review outlines next-generation approaches integrating genomics, CRISPR technology, and novel therapeutics to effectively address AMR. Genomic techniques enable comprehensive understanding of the genetic mechanisms underpinning resistance, facilitating the development of targeted interventions. By sequencing the genomes of resistant pathogens, researchers can identify resistance genes, track their spread, and predict emerging resistance patterns. CRISPR-Cas systems offer a revolutionary tool for combating AMR through precise genome editing. This technology can disrupt resistance genes, restore antibiotic sensitivity, and develop bacteriophage therapies that selectively target resistant bacteria. Moreover, CRISPR-based diagnostics enable rapid, accurate detection of resistant strains, enhancing infection control measures. The advent of novel therapeutics, such as antimicrobial peptides, bacteriophage therapy, and synthetic biology-derived compounds, provides alternative treatment options. These therapeutics can bypass traditional resistance mechanisms and exhibit efficacy against multi-drug resistant organisms. Additionally, integrating artificial intelligence (AI) and machine learning with genomics and CRISPR can accelerate the discovery of new antibiotics and predict resistance trends, optimizing treatment regimens. Implementing these next-generation strategies requires robust global collaboration, regulatory frameworks, and investment in research and development. By combining genomics, CRISPR, and novel therapeutics, we can create a multifaceted approach to overcome AMR, ensuring effective treatments and safeguarding public health. This integration represents a paradigm shift in antimicrobial strategy, offering hope for a future where resistant infections can be effectively managed and treated. \u0000Keywords: Integrating Genomics, Antimicrobial Resistance, CRISPR, Therapeutic","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a comprehensive analysis of integrating smart grids with renewable energy sources, focusing on technological advancements, economic impacts, and policy frameworks. The primary objective is to explore how smart grid technologies can efficiently incorporate renewable energy sources, thereby enhancing grid reliability, efficiency, and sustainability. Utilizing a multidisciplinary approach, the study examines successful case studies, pilot projects, and innovative practices that highlight the potential and challenges of this integration. Key findings reveal that advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain are crucial for the real-time monitoring, predictive maintenance, and optimized management of energy systems. These technologies address the inherent variability and intermittency of renewable energy sources like solar and wind power. Case studies, including the Brooklyn Microgrid and Germany’s Energiewende, demonstrate significant improvements in energy resilience, efficiency, and consumer empowerment through decentralized energy systems. Economic analysis underscores the dual impact of cost savings from operational efficiencies and the financial challenges posed by substantial upfront investments in smart grid infrastructure. Policy frameworks play a pivotal role, with recommendations for supportive regulatory policies, increased funding for research and development, and enhanced public-private partnerships to drive innovation and consumer engagement. The study concludes that overcoming the technical, economic, and regulatory barriers requires coordinated efforts among stakeholders. Recommendations include developing consistent regulatory frameworks, fostering public-private partnerships, and implementing educational programs to encourage consumer participation in renewable energy initiatives. By addressing these challenges, the integration of smart grids with renewable energy sources can pave the way for a more sustainable, resilient, and efficient energy future. Keywords: Smart Grids, Renewable Energy Integration, Technological Advancements, Economic Impacts, Policy Frameworks, Grid Management Innovations.
{"title":"Comprehensive analysis of integrating smart grids with renewable energy sources: Technological advancements, economic impacts, and policy frameworks","authors":"Nwankwo Charles Uzondu, Dominic Dummene Lele","doi":"10.51594/estj.v5i7.1347","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1347","url":null,"abstract":"This study presents a comprehensive analysis of integrating smart grids with renewable energy sources, focusing on technological advancements, economic impacts, and policy frameworks. The primary objective is to explore how smart grid technologies can efficiently incorporate renewable energy sources, thereby enhancing grid reliability, efficiency, and sustainability. Utilizing a multidisciplinary approach, the study examines successful case studies, pilot projects, and innovative practices that highlight the potential and challenges of this integration. Key findings reveal that advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain are crucial for the real-time monitoring, predictive maintenance, and optimized management of energy systems. These technologies address the inherent variability and intermittency of renewable energy sources like solar and wind power. Case studies, including the Brooklyn Microgrid and Germany’s Energiewende, demonstrate significant improvements in energy resilience, efficiency, and consumer empowerment through decentralized energy systems. Economic analysis underscores the dual impact of cost savings from operational efficiencies and the financial challenges posed by substantial upfront investments in smart grid infrastructure. Policy frameworks play a pivotal role, with recommendations for supportive regulatory policies, increased funding for research and development, and enhanced public-private partnerships to drive innovation and consumer engagement. The study concludes that overcoming the technical, economic, and regulatory barriers requires coordinated efforts among stakeholders. Recommendations include developing consistent regulatory frameworks, fostering public-private partnerships, and implementing educational programs to encourage consumer participation in renewable energy initiatives. By addressing these challenges, the integration of smart grids with renewable energy sources can pave the way for a more sustainable, resilient, and efficient energy future. \u0000Keywords: Smart Grids, Renewable Energy Integration, Technological Advancements, Economic Impacts, Policy Frameworks, Grid Management Innovations.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"33 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance. Keywords: Artificial Intelligence Technology, Digital Media Interaction Design, Generate Adversarial Network, User Experience.
{"title":"Transforming User Experience (UX) through Artificial Intelligence (AI) in interactive media design","authors":"Yingchia Liu, Yang Xu, Runze Song","doi":"10.51594/estj.v5i7.1325","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1325","url":null,"abstract":"This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance. \u0000Keywords: Artificial Intelligence Technology, Digital Media Interaction Design, Generate Adversarial Network, User Experience.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"65 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital Twin Technology (DTT) is an emerging innovation poised to revolutionize the management and optimization of renewable energy microgrids. A digital twin is a virtual replica of a physical system, integrating real-time data, simulations, and machine learning to provide a dynamic, interactive model of the actual environment. In the context of renewable energy microgrids, DTT offers significant benefits in efficiency, reliability, and sustainability. Renewable energy microgrids, which include solar panels, wind turbines, and energy storage systems, are complex networks that require precise management to balance supply and demand, maximize energy efficiency, and ensure stability. By creating a digital twin of these microgrids, operators can monitor real-time performance, predict potential failures, and optimize operations. This virtual model enables predictive maintenance, reducing downtime and extending the lifespan of equipment by identifying issues before they lead to critical failures. Furthermore, DTT facilitates advanced energy management strategies. Through simulations, it can evaluate various scenarios, such as fluctuating energy demands, changing weather conditions, and equipment performance variations. These simulations help in designing robust control strategies and improving the integration of renewable energy sources, leading to better energy storage utilization and reduced reliance on fossil fuels. Another critical advantage is the enhancement of grid resilience. Digital twins can simulate the impact of extreme weather events and other disruptions, allowing operators to develop and test contingency plans in a risk-free environment. This capability is vital for ensuring continuous energy supply and mitigating the effects of unexpected outages. Digital Twin Technology offers a transformative approach to managing renewable energy microgrids. By providing a comprehensive, real-time virtual model, DTT enhances operational efficiency, predictive maintenance, energy management, and grid resilience. As the renewable energy sector continues to grow, the integration of digital twins will be instrumental in optimizing the performance and sustainability of microgrid systems. Keywords: Digital Twin, Renewable, Energy, Microgrids.
{"title":"Digital twin technology for renewable energy microgrids","authors":"Kelvin Edem Bassey, Jesse Opoku-Boateng, Bernard Owusu Antwi, Afari Ntiakoh, Ayanwunmi Rebecca Juliet","doi":"10.51594/estj.v5i7.1319","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1319","url":null,"abstract":"Digital Twin Technology (DTT) is an emerging innovation poised to revolutionize the management and optimization of renewable energy microgrids. A digital twin is a virtual replica of a physical system, integrating real-time data, simulations, and machine learning to provide a dynamic, interactive model of the actual environment. In the context of renewable energy microgrids, DTT offers significant benefits in efficiency, reliability, and sustainability. Renewable energy microgrids, which include solar panels, wind turbines, and energy storage systems, are complex networks that require precise management to balance supply and demand, maximize energy efficiency, and ensure stability. By creating a digital twin of these microgrids, operators can monitor real-time performance, predict potential failures, and optimize operations. This virtual model enables predictive maintenance, reducing downtime and extending the lifespan of equipment by identifying issues before they lead to critical failures. Furthermore, DTT facilitates advanced energy management strategies. Through simulations, it can evaluate various scenarios, such as fluctuating energy demands, changing weather conditions, and equipment performance variations. These simulations help in designing robust control strategies and improving the integration of renewable energy sources, leading to better energy storage utilization and reduced reliance on fossil fuels. Another critical advantage is the enhancement of grid resilience. Digital twins can simulate the impact of extreme weather events and other disruptions, allowing operators to develop and test contingency plans in a risk-free environment. This capability is vital for ensuring continuous energy supply and mitigating the effects of unexpected outages. Digital Twin Technology offers a transformative approach to managing renewable energy microgrids. By providing a comprehensive, real-time virtual model, DTT enhances operational efficiency, predictive maintenance, energy management, and grid resilience. As the renewable energy sector continues to grow, the integration of digital twins will be instrumental in optimizing the performance and sustainability of microgrid systems. \u0000Keywords: Digital Twin, Renewable, Energy, Microgrids.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"114 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kelvin Edem Bassey, Jesse Opoku-Boateng, Bernard Owusu Antwi, Afari Ntiakoh
Digital twins have emerged as pivotal tools for reshaping renewable energy investments by enhancing operational efficiency, predictive maintenance, and grid management. This review explores the economic impact of digital twins specifically in the context of renewable energy investments. Digital twins, virtual replicas of physical assets and processes, facilitate improved decision-making through advanced simulations and real-time data analytics. In renewable energy sectors, they optimize the performance of solar, wind, and storage assets by predicting energy production, optimizing maintenance schedules, and enhancing overall asset lifecycle management. By providing accurate forecasts and scenario analyses, digital twins minimize operational risks and maximize energy yield, thereby increasing profitability and return on investment for renewable energy projects. Moreover, digital twins streamline operational workflows and reduce costs associated with manual monitoring and maintenance. They enable proactive maintenance strategies, identifying potential faults before they cause disruptions, thus minimizing downtime and repair expenses. This efficiency contributes to lower operational expenditures and enhances asset longevity, further bolstering economic viability. In addition to operational efficiencies, digital twins support strategic decision-making in renewable energy investments. They facilitate detailed financial modelling and risk assessment, allowing stakeholders to assess project feasibility, optimize capital allocation, and attract financing. Enhanced transparency and data-driven insights provided by digital twins increase investor confidence and reduce perceived risks, thereby lowering the cost of capital for renewable energy projects. Furthermore, digital twins foster innovation and technological advancements in the renewable energy sector. They enable continuous improvement through iterative learning and adaptation based on real-world data, driving innovation in energy efficiency and performance optimization. Digital twins represent a transformative technology with significant economic implications for renewable energy investments. By enhancing operational efficiency, reducing costs, facilitating informed decision-making, and fostering innovation, digital twins play a crucial role in accelerating the transition towards a sustainable and economically viable renewable energy future. Keywords: Economic Impact, Digital Twins, Renewable Energy.
{"title":"Economic impact of digital twins on renewable energy investments","authors":"Kelvin Edem Bassey, Jesse Opoku-Boateng, Bernard Owusu Antwi, Afari Ntiakoh","doi":"10.51594/estj.v5i7.1318","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1318","url":null,"abstract":"Digital twins have emerged as pivotal tools for reshaping renewable energy investments by enhancing operational efficiency, predictive maintenance, and grid management. This review explores the economic impact of digital twins specifically in the context of renewable energy investments. Digital twins, virtual replicas of physical assets and processes, facilitate improved decision-making through advanced simulations and real-time data analytics. In renewable energy sectors, they optimize the performance of solar, wind, and storage assets by predicting energy production, optimizing maintenance schedules, and enhancing overall asset lifecycle management. By providing accurate forecasts and scenario analyses, digital twins minimize operational risks and maximize energy yield, thereby increasing profitability and return on investment for renewable energy projects. Moreover, digital twins streamline operational workflows and reduce costs associated with manual monitoring and maintenance. They enable proactive maintenance strategies, identifying potential faults before they cause disruptions, thus minimizing downtime and repair expenses. This efficiency contributes to lower operational expenditures and enhances asset longevity, further bolstering economic viability. In addition to operational efficiencies, digital twins support strategic decision-making in renewable energy investments. They facilitate detailed financial modelling and risk assessment, allowing stakeholders to assess project feasibility, optimize capital allocation, and attract financing. Enhanced transparency and data-driven insights provided by digital twins increase investor confidence and reduce perceived risks, thereby lowering the cost of capital for renewable energy projects. Furthermore, digital twins foster innovation and technological advancements in the renewable energy sector. They enable continuous improvement through iterative learning and adaptation based on real-world data, driving innovation in energy efficiency and performance optimization. Digital twins represent a transformative technology with significant economic implications for renewable energy investments. By enhancing operational efficiency, reducing costs, facilitating informed decision-making, and fostering innovation, digital twins play a crucial role in accelerating the transition towards a sustainable and economically viable renewable energy future. \u0000Keywords: Economic Impact, Digital Twins, Renewable Energy.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":"123 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Augusta Heavens Ikevuje, David Chinalu Anaba, Uche Thankgod Iheanyichukwu
The offshore engineering industry plays a critical role in global energy production and infrastructure development. Ensuring quality, safety, and efficiency in offshore operations is paramount to the success and sustainability of these projects. This paper explores the significant impact of professional engineering certifications on offshore industry standards and practices, specifically focusing on how certifications contribute to enhancing quality, safety, and efficiency. Professional engineering certifications validate the skills and expertise of individuals working in the offshore sector, providing assurance to stakeholders that industry standards are being met. Through a comprehensive review of existing literature and case studies, this paper examines the role of certifications in maintaining quality assurance, ensuring safety protocols, and enhancing operational efficiency. Certified professionals play a crucial role in implementing and adhering to stringent quality standards throughout offshore projects, ultimately reducing the risk of costly errors and project delays. Moreover, certifications equip professionals with the necessary knowledge and training to identify and mitigate safety hazards, thereby minimizing the likelihood of accidents and environmental disasters. In addition to quality and safety considerations, certifications also contribute to improving efficiency in offshore operations. Certified professionals are equipped with the latest industry best practices and technologies, enabling them to streamline processes, optimize resource utilization, and deliver projects on time and within budget. While professional certifications are instrumental in raising industry standards and practices, challenges such as access to training, certification maintenance, and evolving regulatory requirements remain. Addressing these challenges and promoting the adoption of certification programs are essential steps towards further enhancing the quality, safety, and efficiency of offshore engineering operations. Overall, this paper underscores the critical role of professional engineering certifications in shaping the future of the offshore industry, providing a roadmap for stakeholders to leverage certifications effectively to achieve their operational objectives. Keywords: Professional Engineering Certifications, Offshore Industry Standards, Quality, Safety, Efficiency, Offshore engineering operations.
{"title":"The influence of professional engineering certifications on offshore industry standards and practices","authors":"Augusta Heavens Ikevuje, David Chinalu Anaba, Uche Thankgod Iheanyichukwu","doi":"10.51594/estj.v5i7.1311","DOIUrl":"https://doi.org/10.51594/estj.v5i7.1311","url":null,"abstract":"The offshore engineering industry plays a critical role in global energy production and infrastructure development. Ensuring quality, safety, and efficiency in offshore operations is paramount to the success and sustainability of these projects. This paper explores the significant impact of professional engineering certifications on offshore industry standards and practices, specifically focusing on how certifications contribute to enhancing quality, safety, and efficiency. Professional engineering certifications validate the skills and expertise of individuals working in the offshore sector, providing assurance to stakeholders that industry standards are being met. Through a comprehensive review of existing literature and case studies, this paper examines the role of certifications in maintaining quality assurance, ensuring safety protocols, and enhancing operational efficiency. Certified professionals play a crucial role in implementing and adhering to stringent quality standards throughout offshore projects, ultimately reducing the risk of costly errors and project delays. Moreover, certifications equip professionals with the necessary knowledge and training to identify and mitigate safety hazards, thereby minimizing the likelihood of accidents and environmental disasters. In addition to quality and safety considerations, certifications also contribute to improving efficiency in offshore operations. Certified professionals are equipped with the latest industry best practices and technologies, enabling them to streamline processes, optimize resource utilization, and deliver projects on time and within budget. While professional certifications are instrumental in raising industry standards and practices, challenges such as access to training, certification maintenance, and evolving regulatory requirements remain. Addressing these challenges and promoting the adoption of certification programs are essential steps towards further enhancing the quality, safety, and efficiency of offshore engineering operations. Overall, this paper underscores the critical role of professional engineering certifications in shaping the future of the offshore industry, providing a roadmap for stakeholders to leverage certifications effectively to achieve their operational objectives. \u0000Keywords: Professional Engineering Certifications, Offshore Industry Standards, Quality, Safety, Efficiency, Offshore engineering operations.","PeriodicalId":113413,"journal":{"name":"Engineering Science & Technology Journal","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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