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The University of Alcalá in Madrid will organize the "III International Congress on Green Public Procurement and Climate Change" on October 4th and 5th, 2023. The congress aims to receive oral communications on topics related to green public procurement and climate change, as well as tangential aspects such as sustainable food, legislation on climate change, public procurement and depopulation, sustainable infrastructure and energy efficiency, and sustainable or low-emission mobility. The Scientific Committee will review the communications and select the best ones for possible publication in the journal Actualidad Jurídica Ambiental. The deadline for submitting abstracts is May 15th, 2023. [Extracted from the article]

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Bitcoin is a complex phenomenon, whether in terms of the macro factors affecting its price or its role in the global energy infrastructure. However, extant literature pays too little attention to exploring the internal mechanisms of the protocol to be able to link them to how they affect the visible characteristics of Bitcoin. This paper uses secondary data from highly reputable Bitcoin-focused sources to systematically map the processes that enable Bitcoin to function as a peer-to-peer cash system. Novelty is achieved by applying the established and versatile "4I" organisational learning framework to provide a new lens through which to understand how the processes within Bitcoin enable and facilitate different types of changes to the protocol. Further insights are provided to organisational learning from Bitcoin, in relation to managing mission-critical changes to organisational systems. In addition, it presents an option for dealing with irreconcilable internal differences to "hard-fork" part of the organisation. While the scope of this paper is limited to secondary data, opportunities for further research, including primary data collection, are outlined to explore how Bitcoin knowledge disseminates within communities or companies. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO2 savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability. [ABSTRACT FROM AUTHOR]

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors. [ABSTRACT FROM AUTHOR]

Much of the attention on bitcoin relates to its ability to store value over time or whether you will one day by able to buy a cup of coffee with it. Much less attention is given to bitcoin's potential role as a unit of account. This opinion piece proposes that bitcoin has potential to provide a consistent unit of account for organisations to adopt, but also to assist them in making and measuring meaningful business developments. The paper draws from the business improvement philosophy of Theory of Constraints to propose that unit of account, particularly within high inflation environments, is critical to consider. An illustrative case of a well-known publicly traded company, Microstrategy, provides an example and logic for a company choosing to integrate bitcoin into a business. The paper also gives attention to how the adoption of bitcoin can promote the development of renewable energy infrastructure and provide staff with opportunities for personal development to support their well-being. Opportunities for further research are identified to explore the integration of bitcoin within a business as well as with Theory of Constraints. [ABSTRACT FROM AUTHOR]

Renewable energy infrastructures, such as wind and solar farms, require land on which they can be deployed. While politics and conflicts over accessing land for renewables are well documented, the role, conditions and potential agency of landownership have been often overlooked or oversimplified as a powerful terrain in the field of renewables development. In this paper, we explore the relationship between landed property and community renewable energy projects. In particular, we focus on how landed property variously influences the development modes of renewables by acting as a mediator, barrier and enabler for different types of wind energy projects. We show how this takes place through appropriation of rents in processes of assetisation and value grabbing by landowners. In this way, value grabbing acts as a vital intermediary process to understand green grabbing and wider processes of capital accumulation through renewables. We draw on insights from the Netherlands and Scotland to illuminate different mechanisms, social and historical conditions, and policies through which landed property constrains or enables community wind energy projects. The paper finishes by sketching out some alternative ways of allocating land for the deployment of renewable energy projects, which could help shift the balance of power in favour of community energy developments. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters. [ABSTRACT FROM AUTHOR]

The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS. [ABSTRACT FROM AUTHOR]

Energy security is a crucial issue for political, environmental, and economic reasons. This article presents a modern approach to securing critical infrastructure in energy transmission networks, which are managed by advanced IT systems. This paper focuses on the integration of cryptographic mechanisms with biometric data, providing an additional layer of protection against cyber threats. The discussed solutions enable the protection of management systems in energy transmission networks, enhancing their resilience to cyberattacks. The use of the command-line interface (CLI) in combination with biometrics allows for precise execution of security tasks such as network monitoring, firewall management, and automation of security tasks. This makes these systems more reliable and secure, which is essential for the stability of energy systems. [ABSTRACT FROM AUTHOR]

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization. [ABSTRACT FROM AUTHOR]

Water security in arid and semi-arid Middle Eastern climates has been severely impacted by effects of climate change such as reduced precipitation, diminished storage, increased evapotranspiration, and prolonged heat waves. These climate effects are compounded in Iraq, where populations, agriculture, industry, and energy rely heavily on varying transboundary water flows to meet water demands. Iraq's most profitable sector, energy, is especially threatened by insufficient water supply, which complicates government decision making in energy infrastructure development. The goal of this paper is to develop a scenario-based multi-criteria analysis framework to prioritize infrastructure investments in the context of climate change and scarcity of natural resources. Infrastructure facilities are evaluated against social, economic, climate, and hydrologic criteria across a set of disruptive climatological, economic, and social scenarios to identify robust initiatives and the most and least disruptive scenarios to the system. A particular innovation of this paper is the evaluation of hydrology data derived from satellites in determining water scarcity impact on individual energy facilities. The methods are demonstrated for a critical sector of Iraq's economy: oil and natural gas. The demonstration includes 13 system-order criteria, 44 oil and gas initiatives within Iraq, and seven risk scenarios. The results include an accounting of the most and least disruptive scenarios to energy sector priorities and scenario-based system orderings to guide stakeholders in investment prioritization. [ABSTRACT FROM AUTHOR]

Topics of interest for publication included, but were not limited to, advanced simulation and modeling methods for complex energy problems, soft computing and artificial intelligence in energy systems, machine learning, multi-energy systems, co-simulation, distributed energy resources, and energy demand prediction. The second paper, "A statistical assessment of blending hydrogen into gas networks" [[6]], provides a tool for the statistical assessment of deploying hydrogen in existing gas networks. The need for a "smarter" energy grid infrastructure, with the large-scale integration of renewables and a better demand-response mechanism, is leading to an ever-increasing complexity of next-generation energy networks [[1]]. [Extracted from the article]

Although the severity of the COVID-19 pandemic has appears to have subsided in most parts of the world, nevertheless, in addition to six million deaths, it has yielded unprecedented challenges in the economy, energy, education, urban services, and healthcare sectors. Meanwhile, based on some reports, smart solutions and technologies have had significant success in achieving pandemic-resilient cities. This paper reviews smart city initiatives and contributions to the prevention and treatment of coronavirus disease, as well as reducing its destructive impact, leading towards pandemic-resilient economic and health systems. Furthermore, the situational awareness contributions are reviewed in pandemic-resilient governance. The main contribution of this study is to describe the construction of social capital in smart cities as a facilitator in creating a pandemic-resilient society in crisis through two analyses. Moreover, this research describes smart cities' energy as interconnection of energy hubs (EHs) that leads to a high level of resiliency in dealing with the main challenges of the electricity industry during the pandemic. Energy-hub-based smart cities can contribute to designing pandemic-resilient energy infrastructure, which can significantly affect resilience in economic and health infrastructure. In brief, this paper describes a smart city as a pandemic-resilient city in the economic, energy, and health infrastructural, social, and governmental areas. [ABSTRACT FROM AUTHOR]

The development of offshore wind farms requires robust bonding solutions that can withstand harsh marine conditions for the easy integration of secondary structures. This paper investigates the durability performance of two adhesives: Sikadur 30 epoxy resin and Loctite UK 1351 B25 urethane-based adhesive for use in offshore wind environments. Tensile tests on adhesive samples and accelerated aging tests were carried out under a variety of temperatures and environmental conditions, including both dry and wet conditions. The long-term effects of aging on adhesive integrity are investigated by simulating the operational life of offshore installations. The evolution of mechanical properties, studied under accelerated aging conditions, provides an important indication of the longevity of structures under normal conditions. The results show significant differences in performance between the two adhesives, highlighting their suitability for specific operating parameters. It should also be noted that for both adhesives, their exposure to different environments (seawater, distilled water, humid climate) over a prolonged period showed that (i) Loctite adhesive has a slightly faster initial uptake than Sikadur adhesive, but the latter reaches an asymptotic plateau with a lower maximum absorption rate than Loctite adhesive; and (ii) a progressive deterioration in the tensile properties occurred following an exponential function. Therefore, aging behavior results showed a clear correlation with the Arrhenius law, providing a predictive tool for the aging process and the aging process of the two adhesives followed Arrhenius kinetics. Ultimately, the knowledge gained from this study is intended to inform best practice in the use of adhesives, thereby improving the reliability and sustainability of the offshore renewable energy infrastructure. [ABSTRACT FROM AUTHOR]

The global electricity network and natural gas have been developed worldwide. So far, various investigations in relation to energy infrastructures have been performed; however, the combination of these systems has not been examined much. For this reason, electric and thermal loads have been interconnected by CHP units, and the combined study of power and heat has helped the operator to propose a more optimal structure for energy management. Therefore, integrating different energy carriers and using the benefits of each one can increase the efficiency and reliability along with optimizing system performance. Such a capability in structure and operation requires a comprehensive framework to describe the effects of combining different energy carriers on the economic and environmental indicators of energy systems. In this paper, the microgrid has been modeled as an energy hub in order to combine different energy carriers and perform conversions in them to provide the optimal electric and thermal load required by the consumer. This structure has been created by managing the incoming carriers of gas, electricity and renewable energy and managing their conversion and storage in order to minimize the cost of energy supply and environmental pollutants. In addition to managing generation units, in order to improve the performance of the microgrid and help the operator of the microgrid, a responsive load management strategy and charging and discharging of electric vehicles have been proposed. Epsilon constraints and fuzzy decision-making methods have been used to solve this issue. The results of the study indicated that in the optimal state with the application of responsive loads, the total cost of operation has been reduced by more than 6% and the cost of environmental pollutants has been mitigated by about 13%. [ABSTRACT FROM AUTHOR]

Energy storage systems are essential in modern energy infrastructure, addressing efficiency, power quality, and reliability challenges in DC/AC power systems. Recognized for their indispensable role in ensuring grid stability and seamless integration with renewable energy sources. These storage systems prove crucial for aircraft, shipboard systems, and electric vehicles, addressing peak load demands economically while enhancing overall system reliability and efficiency. Recent advancements and research have focused on high-power storage technologies, including supercapacitors, superconducting magnetic energy storage, and flywheels, characterized by high-power density and rapid response, ideally suited for applications requiring rapid charging and discharging. Hybrid energy storage systems and multiple energy storage devices represent enhanced flexibility and resilience, making them increasingly attractive for diverse applications, including critical loads. This paper provides a comprehensive overview of recent technological advancements in high-power storage devices, including lithium-ion batteries, recognized for their high energy density. In addition, a summary of hybrid energy storage system applications in microgrids and scenarios involving critical and pulse loads is provided. The research further discusses power, energy, cost, life, and performance technologies. [ABSTRACT FROM AUTHOR]

The reliable prediction of solar energy production and surplus is crucial for the stability of the electricity grid and effective energy distribution, particularly during peak consumption periods. Encouraging solar adoption by customers is also important despite fluctuations in solar energy generation. This paper addresses the need for an intelligent surplus solar energy prediction model, referred to as the Green Model, which aims to accurately predict surplus electrical energy that can be returned to the distribution grid. The Green Model utilizes a combination of a developed long short-term memory (LSTM) neural network and a novel optimization technique called deterministic selection network by gain sharing knowledge (DSN-GSK). The DSN-GSK optimizes the structure of LSTM by determining the optimal number of hidden layers, nodes in each layer, biases, weights, and activation functions based on the most important features that represent the relationship between generated solar energy and weather factors. The Green Model was evaluated and analysed, demonstrating high accuracy in the short-term prediction of surplus electrical energy with a minimal percentage of error. The model shows promising prospects for the development of returned electrical energy prediction. The main novelty of this paper lies in the development of the Green Model, which combines LSTM and DSN-GSK to enhance the accuracy of surplus solar energy prediction. The benefits of the Green Model include improved stability and efficiency of the electricity grid, effective utilization of renewable energy sources, and the reduction of negative environmental impacts. The contributions of this paper include advancing the field of intelligent energy prediction, optimizing the structure of LSTM, and providing valuable insights for the integration of renewable energy into the existing energy infrastructure. [ABSTRACT FROM AUTHOR]

Woody biomass has gained increased attention as a source of renewable energy. However, its categorization as a sustainable source of energy remains controversial, as its carbon neutrality depends on its end use, moisture content, bulk density, and the distance between the source of biomass and its end use. Due to these mediating factors, policies largely shape its sustainability and ability to mitigate emissions. This paper organizes and evaluates the current state of research through a systematic review of global literature from 2011 to 2021 (n = 345) on the governance of the production, transportation, sale, and use of woody biomass for energy. Peer-reviewed literature emphasizes the role of woody biomass as a decentralized energy source for individual households and communities, focusing on its harvest, transport, and localized energy conversion. Policies primarily address land management and energy infrastructure rather than direct emissions reduction. Research gaps in policy within the Global South underscore the need to examine biomass regulation in regions with limited energy diversity. The key factors that drive the sustainable uptake of woody biomass include strict sustainability criteria, fiscal incentives, technological development, and wood utilization from across its value chain. Effective policy implementation increases rural employment, boosts rural economies, provides energy security to remote areas, improves overall environmental sustainability, reduces emissions, and improves land management practices. Our systematic review reveals that future research should focus on improving conversion efficiencies in small-scale systems, reducing emissions in the international trade of woody biomass, and using the wood value chain to increase the profitability of bioenergy products. [ABSTRACT FROM AUTHOR]

This paper examines the financing of green energy initiatives as a critical element in strengthening Ukraine's energy security, especially in the context of post-war reconstruction and the need for sustainable and resilient energy infrastructure. The study aims to assess the potential and challenges of investing in green energy in Ukraine, considering the destruction caused by Russian military actions in the winter of 2022- 2023, and the need for recovery based on sustainable models. In this regard, the obtained results emphasize the importance of creating a reliable and decarbonized energy sector, driven by both domestic needs and international cooperation with the EU and other partners. The analysis shows that despite strong support for the green energy transition, the development of the sector is hindered by the high capital intensity required for green technologies, which are often not competitive without government subsidies. This requires a careful balance between innovation and commercial viability of green energy projects, highlighting the need for public and private investments in technologies that can compete with traditional sources of energy in the long term and developing new financing tools for Ukraine. The conclusions indicate that a multifaceted approach is needed to ensure Ukraine's energy future and minimize risks. Such an approach should include policy reforms, strategic investments in proven green technologies, and fostering partnerships to bridge the commercialization gap. Policymakers face the dual challenge of accelerating the energy transition while managing financial constraints exacerbated by military spending, highlighting the trade-off between rapid development and the risks associated with financing innovation. Overall, this study contributes to the understanding of how green energy financing mechanisms can enhance Ukraine's energy security, suggesting broader implications for policy, investment, and technological innovation in the context of Ukraine's post-war recovery and future resilience. [ABSTRACT FROM AUTHOR]

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R. [ABSTRACT FROM AUTHOR]

Poland is actively exploring the application of nuclear power as a substitute for its present reliance on fossil fuels for the generation of heat and electricity. This change reflects a calculated attempt to reduce carbon emissions, diversify the nation's energy sources, and enhance the sustainability of its energy infrastructure. However, the implementation of nuclear technology faces many challenges, such as radiation exposure, the production of radioactive waste, the off-site effects of nuclear accidents, and high capital costs. Addressing such nuclear-safety-related issues is crucial for nuclear technology's successful deployment. An extended analysis of the "coal-to-nuclear" process in terms of its safety has to be performed. Therefore, this review paper covers multidisciplinary studies related to the rollout of nuclear energy in Poland. The first stage of this study was the identification of the key areas of analysis, which included (i) formal requirements and recommendations imposed by international and national organizations on the process of designing and operating nuclear power systems; (ii) potential nuclear hazards for the personnel working at a nuclear reactor unit and the local population; (iii) the applied solutions of the security systems of a reactor itself, the steam turbine cycle, and the auxiliary infrastructure; and (iv) the management of spent nuclear fuel and radioactive waste. This methodology, developed based on a review of the literature and international standards, was tested for the selected country—Poland. [ABSTRACT FROM AUTHOR]

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low-voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom–up approach. District energy systems with a building scale resolution are modeled in a mixed-integer linear programming problem. The Dantzig–Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of a renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale reveals a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Placed in perspective with the optimal PV capacity forecast at 15.4 GW   p e a k at the national level, coordinated investment between local and national actors is needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems from 12% to 83% and curtails 48% of local renewable electricity. [ABSTRACT FROM AUTHOR]

Offshore wind energy is becoming an increasingly important element in the decarbonisation of energy systems. This study aims to estimate the technical potential for offshore wind energy in the Polish exclusive economic zone (EZZ) of the Baltic Sea. Such estimates are necessary to plan the expansion of energy infrastructure. This paper proposes a multi-criteria analysis for the estimation of offshore wind potential and site selection using QGIS software (QGIS Desktop 3.28.6). The criteria include wind velocity, water depth, distance from nature conservation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, and distance from the coastline. The best sites are identified through the Analytic Hierarchy Process (AHP). The results show that the total suitable marine area of 16,218.67 square kilometres can be divided into three parts, in which the highly suitable area for offshore wind farms accounts for 17.55%, the moderately suitable area accounts for 48.56%, and the marginally suitable area accounts for 10.26%. Two different wind turbine placement configurations are being considered, in which wind turbines with a nominal capacity of 15 MW are laid out: 2.5 km and 2 km apart from each other. The corresponding overall electrical capacities only in highly suitable areas equal ca. 9 GW and 15 GW, respectively. If this area is extended to also include the moderately suitable parts, these values increase to ca. 31 GW and 50 GW. The results indicate that the technical potential of offshore wind in the Polish EEZ is significant and should be taken into account when designing the future electricity generation mix. [ABSTRACT FROM AUTHOR]

Purpose: This paper analyzes the European Bank for Reconstruction and Development's (EBRD) strategic financing roles in Kosovo from 2013 to 2020, focusing on economic impacts and developmental progress. Methodology: Employing a quantitative and comparative approach, this study utilizes secondary data analysis, supported by statistical tools in Microsoft Excel, to evaluate the effectiveness of EBRD investments in various sectors within Kosovo. Results: The findings reveal significant EBRD investments, primarily in SMEs and infrastructure, contributing to competitive gains and sustainable development. The study details the volume of investments and their distribution across different sectors, highlighting substantial impacts on employment and green energy initiatives. Theoretical Contribution: This research enriches the literature on international financial institutions' roles in post-conflict economic development, providing a nuanced understanding of the EBRD's strategic priorities and operational outcomes in transitional economies. Practical Implications: The study offers policymakers and development agencies insights into practical strategies for leveraging international finance for economic growth and sustainability. Recommendations are provided to enhance the focus on renewable energy and infrastructure, aligning with global sustainable development goals. [ABSTRACT FROM AUTHOR]

We have designed a real-world smart building energy fault detection (SBFD) system on a cloud-based Databricks workspace, a high-performance computing (HPC) environment for big-data-intensive applications powered by Apache Spark. By avoiding a Smart Building Diagnostics as a Service approach and keeping a tightly centralized design, the rapid development and deployment of the cloud-based SBFD system was achieved within one calendar year. Thanks to Databricks' built-in scheduling interface, a continuous pipeline of real-time ingestion, integration, cleaning, and analytics workflows capable of energy consumption prediction and anomaly detection was implemented and deployed in the cloud. The system currently provides fault detection in the form of predictions and anomaly detection for 96 buildings on an active military installation. The system's various jobs all converge within 14 min on average. It facilitates the seamless interaction between our workspace and a cloud data lake storage provided for secure and automated initial ingestion of raw data provided by a third party via the Secure File Transfer Protocol (SFTP) and BLOB (Binary Large Objects) file system secure protocol drivers. With a powerful Python binding to the Apache Spark distributed computing framework, PySpark, these actions were coded into collaborative notebooks and chained into the aforementioned pipeline. The pipeline was successfully managed and configured throughout the lifetime of the project and is continuing to meet our needs in deployment. In this paper, we outline the general architecture and how it differs from previous smart building diagnostics initiatives, present details surrounding the underlying technology stack of our data pipeline, and enumerate some of the necessary configuration steps required to maintain and develop this big data analytics application in the cloud. [ABSTRACT FROM AUTHOR]

Urban and regional development is indispensable from energy systems that function in a sustainable and resilient manner. The generation of renewable energy has seen significant upturn in the last decade. Renewables can be exploited to meet the global energy needs and climate change mitigation goals. Therefore, development of urban and regional energy systems must take into account the possibilities and challenges posed by the increasing penetration of the renewable. The achievement of goals of the energy system in the context of transition to low-carbon energy society rises numerous complex decisions over development of infrastructure and technologies. Although renewable energy devices generate clean energy, they also change landscape. The term of energy-scape encourages discussion about interactions between the energy system and the environment, and other ecosystem services at local level. The paper reviews previous studies analysing energy-scape elements and provides the insights towards the transition to low-carbon energy and low-carbon society in general. The systematic literature review allows clearly outlining the problem and gives reliable background for further studies. The main contribution of the article to the existing literature is the creation of framework for the analysis and assessment of new renewable energy technologies installation and infrastructure. The presented framework can be applied for all renewable energy technologies and infrastructure in both urban and rural areas and allows to attain a decision acceptable to the local community. [ABSTRACT FROM AUTHOR]

E-mobility represents an important part of the EU's green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector's development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market. [ABSTRACT FROM AUTHOR]

The USA energy market is a complex and evolving system that profoundly impacts the nation's economy and environmental landscape. This literature review offers a concise overview of the USA energy market, drawing insights from existing research. The review explores energy consumption trends, the diversity of energy sources, the influence of policies and regulations, market competition, pricing dynamics, and the challenges faced by energy infrastructure. The transition towards sustainable and resilient energy solutions in the face of growing demand and climate concerns remains a key challenge. This paper provides a glimpse into the USA energy market's multifaceted nature and its significance for policymakers, stakeholders, and researchers working towards a more sustainable future. In this article, the author tries to make an overview of USA electricity market from market from its appearance until today. The electricity market legislation and the authorities which assure market regulation are identified and presented. Federal and states transport and distribution system is overview and their connection. Finally, it is presented the market situation is shown together with the legal steps which was taken in order to promote the renewable energies. [ABSTRACT FROM AUTHOR]

Energy infrastructure connectivity is a key implementation area for the success of the Belt and Road Initiative. In the current complex international environment, China's energy infrastructure investment in the countries along the Belt and Road is often restricted due to varying degrees of heated disputes with the host countries, thus facing the political risk of investment loss or even failure. This paper used the Heckman two-stage selection model to overcome the sample selection problem in traditional literature and systematically studied the political risks to China's energy infrastructure investment in countries along the Belt and Road within a unified framework. The results showed that Chinese energy infrastructure investments exhibited market-seeking and energy resource-seeking features; while political risks had a significant impact on choosing the locations of energy infrastructure investments, socio-environmental factors were not given sufficient attention. At the national level, the implementation of the Belt and Road Initiative was beneficial to China's foreign energy infrastructure investments under certain regulatory levels and environmental policy performance. [ABSTRACT FROM AUTHOR]

This study focuses on the specific topic of assessing the negative visual impacts associated with renewable energy infrastructure that may prevent their wider deployment in energy mix. The main objective of the paper is to quantify the perception of the visual impact of renewable energy infrastructure and to estimate potential changes in the visitation of a location after the construction of power plants. The research was conducted using a questionnaire survey in which 449 respondents evaluated edited photographic materials of seven locations with a fictitious power plant. The collected data served as input for the statistical testing of eight defined hypotheses using the U-Mann–Whitney test. The results confirmed trends regarding the influence of age, educational level, and power plant proximity on the overall acceptance of renewable energy infrastructure. Landscape-forming factors affecting the acceptance rate of power plants were also defined at the local level. [ABSTRACT FROM AUTHOR]

Modern power and transportation systems are subject to high requirements for reliability and performance in performing their specified functions. At the same time, these requirements are constantly increasing with the increasing complexity of technology and the introduction of electronics and computer technology into its structure. This is fully applicable to energy and transportation infrastructure, including electric vehicles. The complexity of the systems and increasing requirements for them have led to the fact that the problem of increasing their operational reliability has acquired great importance. The article presents a review of methods and justification of ensuring a high level of reliability and serviceability of technical systems as one of the most important tasks in the creation and operation of complex systems, such as modern energy and transportation systems. It is shown that a significant reserve in solving the problem of increasing the reliability and performance of technical systems is the information on failures and malfunctions of these systems obtained from the field of operation. The methodology of collection and processing of statistical information on failures of vehicles described by different distribution laws is outlined. [ABSTRACT FROM AUTHOR]

Planning the best path for the energy system decarbonization is currently one of the issues of high global interest. At the European level, the recent policies dealing with the transportation sector have decided to ban the registration of light-duty vehicles powered by internal combustion engines fed by fossil fuels, from 2035. Regardless of the official positions, the major players (industries, politicians, economic and statistical institutions, etc.) manifest several opinions on this decision. In this paper, a mathematical model of a nation's energy system is used to evaluate the economic impact of this decision. The model considers a superstructure that incorporates all energy conversion and storage units, including the entire transportation sector. A series of succeeding simulations was run and each of them was constrained to the achievement of the decarbonization level fixed, year by year, by the European community road-map. For each simulation, an optimization algorithm searches for a less costly global energy system, by including/excluding from the energy system the energy conversion units, storage devices, using a Mixed Integer Linear approach. Three optimization scenarios were considered: (1) a "free" scenario in which the only constraint applied to the model is the achievement of the scheduled decarbonization targets; (2) an "e-fuels" scenario, in which all new non-battery-electric light-duty vehicles allowed after 2035 must be fed with e-fuels; (3) a "pure electric" scenario, in which all new light-duty vehicles allowed after 2035 are battery-electric vehicles. The comparison of the optimum solutions for the three scenarios demonstrates that the less costly transition to a fully renewable energy system decarbonizes the transportation sector only when the share of renewable energy sources exceeds 90%. E-fueled light-duty vehicles always turn out to be a less expensive alternative than the electric vehicles, mainly because of the very high cost of the energy supply infrastructure needed to charge the batteries. Most of all, the costs imposed to society by the "e-fuels" and "pure electric" light-duty-vehicle decarbonizing scenarios exceed by 20% and 60%, respectively, the "free" transition scenario. [ABSTRACT FROM AUTHOR]

Multicarrier energy systems have systematic flexibility for energy and load management. The integration of different types of energy under the title "energy hub" (EH) in multiple energy infrastructures such as electricity, natural gas, and heat in an integrated manner will meet demand at a lower cost. On the other hand, the desire for distributed generation (DG) systems has increased due to their many economic and technical advantages, as well as the technical development of these units and the reduction of environmental pollution. In this paper, a proposed EH system is optimized in terms of cost using linear programming, considering transformer converters, combined heat and power (CHP), heat exchangers, and energy storage, including electric vehicle charging stations (EVCSs) and heat storage. In this regard, four proposed scenarios are defined and evaluated from the aspect of cost in order to mitigate load shedding while considering uncertainty in order to bring the problem closer to reality. The results show that the total cost in scenarios 1, 2, and 3 (the best case) is reduced by 15.47%, 26.29%, and 27.93% compared to Scenario 0 (the base case), and Scenario 3 is the most economical possible case. This method can be a suitable tool for energy hub management, especially for solving possible load shedding. [ABSTRACT FROM AUTHOR]

Drones are becoming a greater threat to modern electrical grids with the capability to cause expensive and time-consuming damage repairs to substations and transmission lines. Consumer drones have the potential to cause harm at a low cost, and finding methods to counter these threats is becoming more crucial to keep grids secure. In 2021, there was an attempted attack on a substation with a consumer drone which highlighted the need for research in this area. Previously, there has been a large focus on counter drones around places such as airports; however, more focus is warranted to analyze drone impact on the grid infrastructure. Methods to counter drones' harmful impacts vary from physical methods to using electromagnetic waves. This article looks to identify and propose potential applications for existing technologies, as well as developing anti-drone technologies. These methods have not been adopted yet; thus, there is a great opportunity to utilize these existing technologies to defend the grid. The methods investigated were surveillance cameras, patrolling drones, nets, signal jammers, and energy weapons. The existing technology is currently lacking in the area of drone defense and can be improved with existing studies. However, there is a need to identify those methods and find ways to apply them to the power grid. Different defending technologies vary concerning their potential implementation. This paper also identifies and categorizes different results these methods produce to counter drones and their associated costs. [ABSTRACT FROM AUTHOR]

Local integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms. [ABSTRACT FROM AUTHOR]

Large-scale clean-heating renovations are being carried out in northern China as part of important measures to optimize energy infrastructure, improve the atmospheric environment, and minimize peak CO2 emissions. Because rural areas in the urban–rural fringe are close to the city center, they have several advantages in centralized heating reforms, but there are no effective means to measure whether a transformation is economical or has good environmental effects. In this paper, based on an actual reconstruction project, 100 typical households were selected for a related test. These typical households evinced convergence and possessed certain regional characteristics; therefore, the applicability of the models had certain limitations. To make sure that the relevant factors were fully considered in establishing the model, a prediction model group was established regarding heat consumption in winter (four models in total) that could predict the actual effect well. Lastly, the authors used the model group to calculate economic effects and CO2 emission reduction in rural heating transformations in Beijing under different heating methods. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are gaining more and more traction as a viable option in the automotive sector. This mode of transportation is currently on track, according to current trends, to totally replace internal combustion engine (ICE) cars in the not-too-distant future. The economic system, the energy infrastructure, and the environment are just a few of the areas where electric vehicles could have a major impact. The transportation industry produces the second-most carbon dioxide gas from the combustion of fossil fuels, making it the second-highest contributor to global warming. A lot of people are looking to EVs, or electric vehicles, as a possible game-changing answer to this problem. Since an electric motor drives the electric vehicle's propeller instead of an internal combustion engine, electric vehicles can reduce their carbon dioxide (CO2) emissions compared to traditional automobiles. If coupled with renewable energy sources, EVs might theoretically become emission-free automobiles. In this paper, we will examine the various EV drive circuit types, including their construction and the benefits and drawbacks of employing each. This article discusses the current state of battery technology with an emphasis on EV batteries. This article discusses the best electric motor for EVs in terms of efficiency, power density, fault tolerance, dependability, cost, and more. Next, we conduct in-depth research into the difficulties and potential rewards of EV adoption in the future. While improvements in areas like charging times and battery performance are encouraging, government regulation of EVs remains a big non-technical barrier. [ABSTRACT FROM AUTHOR]

The global economy faces increasing environmental challenges and economic instability, prompting the adoption of innovative energy technologies as a crucial strategy. This study addresses the urgent quest for sustainable development in South Africa, specifically by evaluating renewable energy solutions. This study utilizes a comprehensive literature analysis to examine the current state of renewable energy infrastructure, policy frameworks, technological advancements, and economic viability within the South African context. Synthesizing insights from the existing literature on the interplay between energy, economy, and technology, this study aims to provide a refined understanding of renewable energy solutions' feasibility and integration potential. The exploration of these solutions in South Africa identifies key opportunities, challenges, and implications for sustainable development. These findings offer valuable guidance for policymakers, researchers, and stakeholders in advancing a country's transition towards a sustainable energy future. [ABSTRACT FROM AUTHOR]

Severe events such as floods, earthquakes, and hurricanes cause disruption in the distribution networks and cause them to be islanded. In such circumstances, microgrids can be separated from the main grid with the help of their technical and communication infrastructure and transfer energy with each other to reduce their operation and outage costs. Therefore, energy management in a networked microgrid requires a modern operating framework to cause microgrids to supply each other with the energy they need. So, in this paper, a cooperative framework to improve the resilience response of microgrids has been proposed based on peer‐to‐peer energy transactions. In the developed model, since the microgrids are operated in a cooperative and networked manner, they sell energy to each other at the lowest possible cost. Utilizing the developed model, microgrids can improve their resilience response by peer‐to‐peer energy transactions in addition to reduce their operating costs, compared to the independent and non‐cooperative models. To evaluate the efficiency of the proposed method, simulations have performed on a system with 14 microgrids, and the obtained results have compared and analysed. The results obtained have shown the efficiency of the proposed method in increasing resilience and reducing operating costs. [ABSTRACT FROM AUTHOR]

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electricity cost by approximately 21.04%, and decreases the compressor switching frequency by 50.00% without changing the CNG filling demand, thus significantly extending the compressor's service life. Moreover, the average comprehensive power cost of processing one unit of CNG reduces 20.62%. [ABSTRACT FROM AUTHOR]

The ambitious goal of the European Union (EU) countries is to achieve carbon neutrality by providing inclusive economic growth, which requires the development of relevant incentives and initiatives. Furthermore, such incentives and initiatives should guarantee the achievement of the declared goals. Energy sectors are the core determinant of inclusive economic growth. Traditional energy resources (coal oriented) have a higher negative impact on nature and people's well-being than on economic and social benefits. However, the transition to renewable energy raises new issues in achieving goals of inclusive economic growth: affordable and clean energy, responsible energy consumption, and energy infrastructure. The analysis of the theoretical framework found that the digitalization of government could be a core instrument for handling the abovementioned issues. The paper aims to justify the role of green energy in achieving inclusive economic growth empirically. The study applies the following methods: fully modified Ordinary Least Square (OLS) and canonical cointegrating regression. The findings allow concluding that institutional quality passively affects inclusive economic growth and that the digitalization of government has a U-shaped impact on inclusive economic growth. In this case, countries should boost the digital transformation of public services and continuously increase the quality of institutions. [ABSTRACT FROM AUTHOR]

Smart Cities are complex systems where digital innovations are applied to design enhanced city services and enable public value creation across various domains (e.g., mobility, energy and infrastructure, environment, and so). Such complexity challenges the alignment between city strategies with city services and underlying information systems, leading to providing services that may fail to achieve city goals and meet the needs of citizens. This paper defines a set of design principles to ensure strategic alignment in Smart City Enterprise Architectures (SCEA). The design principles were derived from the literature and validated by smart city domain experts, following a design-oriented research approach. The main contribution of this paper is the formulated design principles that deepen the knowledge of Enterprise Architecture design in the smart cities field. These design principles can be used by researchers and practitioners to guide the design of innovative digital services aligned with urban strategies. [ABSTRACT FROM AUTHOR]

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers' energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost. [ABSTRACT FROM AUTHOR]