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Hydrogen price significantly impacts its potential as a viable alternative in the sustainable energy transition. This study introduces a synergy-based Hydrogen Pricing Mechanism (HPM) within an integrated framework. The HPM leverages synergy between a Renewable-Penetrated Electric Power System (RP-EPS) and a Hydrogen Energy System (HES). Utilizing the Alternating Direction Method of Multipliers (ADMM), it facilitates data exchange, quantifying integration levels and simplifying the complexities. The study assesses the HPM's operational sensitivity across various scenarios of hydrogen generation, transportation, and storage. It also evaluates the benefits of synergy-based versus stand-alone HPMs. Findings indicate that the synergy-based HPM effectively integrates infrastructure and operational improvements from both EPS and HES, leading to optimized hydrogen pricing. • Hydrogen pricing based on integration of hydrogen and electric power system. • Lower hydrogen prices by aligning operational targets in integrated configuration. • Enhances market flexibility by transitioning from cost-plus pricing. • Quantified integration level using data exchange method enabled by ADMM. [ABSTRACT FROM AUTHOR]

This paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants' output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method. [ABSTRACT FROM AUTHOR]

The demand for the integration of renewable energy sources (RESs) with the existing distribution grid is increasing rapidly because of the growing power requirement. The variable power generation from RESs and changing power demand make it necessary to integrate energy storage units. To get stable and trouble-free operation in both transient state and steady state, a combination of battery and supercapacitor storage devices are considered in this work as hybrid energy storage devices (HESDs). But to ensure the power balance in the grid-connected microgrid is a critical aspect nowadays. Hence, a real power management algorithm is proposed in this work to ensure a balance between energy production and demand and provide stability in the microgrid. Both simulation and experimental implementation of the proposed scheme confirm the efficacy and smooth operation of the hybrid microgrid. [ABSTRACT FROM AUTHOR]

Integrating renewable sources with conventional power grids is not just a necessity but a crucial step toward implementing sustainable energy solutions. However, the effective management and control of such systems, which are highly integrated during maintenance and fault, has been a persistent challenge. This paper presents a ground-breaking development—an Internet of Things (IoT)-enabled system designed to revolutionize the control and monitoring of renewable energy generation and net metering operations. The proposed system caters to three prime objectives: to develop a method for enabling utility companies to control power supply related to renewable energy generation in the case of maintenance or faults using IoT, to allow owners of renewable energy generation to track and control net-metering operations, and to develop an Android application for global monitoring and controlling of net metering parameters. It uses advanced IoT devices for real-time data acquisition, processing, and communication with renewable energy sources. The Android application provides users with an effective remote monitoring and control interface. Experiments show that the system can significantly improve operational efficiency and reliability with effective energy management. This IoT-enabled solution is scalable and cost-effective, enhancing the integration and management of distributed renewable energy sources in the power grid. [ABSTRACT FROM AUTHOR]

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR's mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles. [ABSTRACT FROM AUTHOR]

This review provides comprehensive information on the synergistic valorization of fruit and vegetable waste (FVW) for bioenergy production, addressing the urgent need for sustainable waste management and renewable energy sources. Due to their high organic content and substantial methane formation potential, FVW presents significant challenges. This review examines anaerobic digestion, composting, and thermochemical conversion processes, including pyrolysis and gasification. The emphasis is given to the combined use of these pathways, which allows maximizing the energy recovery and the resource utilization factor as well as reducing the environmental burden. The critical analysis of the main factors influencing the effectiveness of these processes is provided waste composition, process adjustments, and technical advancements. Recent studies indicated that pre-treatment methods improved conversion efficiency by up to 30%, and integrating multiple conversion pathways enhanced energy recovery by 20-40%. This comprehensive review concludes by discussing the prospects and challenges of commercial bioenergy production from FVW, integrating findings from recent scientific investigations and technological breakthroughs. The results of this work aim to enhance sustainable waste management strategies and contribute to a holistic circular bioeconomy vision. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

Motivated by South Africa's need for the transition to a net-zero economy, this study investigates the integration of renewable energy sources (RESs) into oil refineries, considering the unique challenges and opportunities therein. The research focuses on optimising RES allocation using particle swarm optimisation (PSO), a data-driven approach that adapts to real-time operational conditions. Traditional energy management systems often struggle with the inherent variability of RESs, leading to suboptimal energy distribution and increased emissions. Therefore, this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil refineries. It considers factors like the levelised cost of energy, geographical location, and available technology. The methodology involves formulating the optimisation problem, developing a PSO model, and implementing it in a simulated oil refinery environment. The results demonstrate significant convergence of the PSO algorithm, leading to an optimal configuration for integrating RESs and achieving cost reductions and sustainability goals. The optimisation result of ZAR 4,457,527.00 achieved through iterations is much better than the result of ZAR 4,829,638.88 acquired using linear programming as the baseline model. The mean cost, indicating consistent performance, has remained at its original value of ZAR 4,457,527.00, highlighting the convergence. The key findings include the average distance measurement decreasing from 4.2 to 3.4, indicating particle convergence; the swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; the average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the optimum cost function achieved at ZAR 4,457,527 with zero standard deviation, highlighting stability and optimal solution identification. This research offers a valuable solution for oil refineries seeking to integrate RESs effectively, contributing to South Africa's transition to a sustainable energy future. [ABSTRACT FROM AUTHOR]

In recent years, the demand side micro-grid had a lot of challenges, most of them being the uninterrupted power supply. The effective energy management of residential structures concerning diverse and often conflicting objectives is one of the most challenging problems associated with hybrid renewable energy sources (HREs) generation, an energy storage system (ESS), and electric vehicles (EV). Therefore, an intelligent home energy management system (HEM) is essential with consumers who can change their load according to their comfort with demand response program schemes (DRP). The key contribution is as follows: (1) Home energy management (HEM) through optimal scheduling of home appliances to reduce energy consumption costs with DRP. (2) The net present cost of the HREs has been minimized for the smart HEM appliances. (3) The techno-economic feasibility and optimal energy management analysis of household loads have been analyzed. Grid and without a grid-connected mode of operation of HEM is analyzed. The HOMER, an energy management tool, is used to solve energy management based on RES and ESS in this study. The HEM has considered DRP and RES to reduce the net present cost (NPC) by 21.95% and 5.71%, with grid and without grid-connected, respectively. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present a designed and implemented an integrated system for the sale, installation and monitoring of photovoltaic systems in a company that is one of the leaders on the Polish renewable energy market. The study also conducted a literature review on photovoltaic systems and green jobs. The basic architecture of the system envisages the use of a number of components responsible for: data preparation in the sales module; implementation in the installation module; supervision and monitoring of actions by management in the monitoring module. The integrated approach presented: increases efficiency through streamlined workflows; improves transparency through a central source of information; enables efficient management of resources; while monitoring the progress of management activities. In addition, the implementation of the integrated system has enabled real-time synchronisation of work between the sales department and the investment implementation and maintenance department. [ABSTRACT FROM AUTHOR]

The article celebrates the 30th anniversary of the journal Meteorological Applications and provides a review of its accomplishments and historical context. The journal focuses on meteorological science, including weather and climate, and is aimed at forecasters, applied meteorologists, climate scientists, and users or providers of meteorological and climate services. Over the years, the journal has made changes to its submission and review processes, including moving to a free-format submission process, providing guidance on the use of color in figures, and implementing a double-blind review approach. The journal has seen an increase in the quality of submissions and its impact factor. Looking ahead, the journal plans to launch an Early Career Researcher Board and continue to publish papers on emerging technologies and issues related to climate change, renewable energy, geographic areas of focus, hydrology, economics, and the climate-water-food nexus. The success of the journal is attributed to the dedication and hard work of the editorial team, reviewers, and authors. [Extracted from the article]

Leveraging the Vehicle-to-Grid (V2G) concept, this research explores how a decentralized energy reserve from hybrid electric vehicles can enhance the power system, particularly in large-scale implementations. The study introduces a V2G solution designed for effective microgrid frequency control over a full day. Targeting a scenario with minimal usage, typically in spring or fall, the microgrid is scaled to represent a community of 2000 homes. This is exemplified by integrating 500 Electric Vehicles (EVs) based on a 1:4 vehicle-to-household ratio, reflecting a plausible future scenario. The research conducts a comprehensive examination of the microgrid's voltage, current, and active power. By synchronizing the management of diesel and Renewable Energy Source (RES) generation, power transactions, and EV generation, the microgrid's frequency is effectively regulated through V2G devices adjusting load demand. The implemented V2G-enriched microgrid demonstrates improved energy management and mitigates the inconsistencies and fluctuations inherent in RES power generation, showing notable performance enhancements. In various operational contexts, system parameter fluctuations have been analyzed, revealing that deviations are maintained below a 5% threshold. [ABSTRACT FROM AUTHOR]

The steady-state and dynamic phases of operation can both be improved, the transmission corridors have been controlled using FACTS and flexible AC systems. To provide variable AC power flow regulation in the distribution corridors of an MV distribution system, Using the FACTS idea, this project suggests extending power distribution networks. The management of renewable energy sources (RERs) and batteries as a type of energy storage will be the main focus. The distribution network operator (DNO) can use the suggested FACPFCS outputs to help with peak shaving, supporting voltage and amperage, reducing long line sag, grid mitigation of reverse power flow, relieving congestion, delaying distribution upgrade, and a storage bus with regulation that can act as a load or a generator. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI's role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption. [ABSTRACT FROM AUTHOR]

Rural farms constitute a vital component of a country's agricultural landscape, traditionally reliant on energy installations known for their reliability yet notorious for their energy-intensive and inefficient characteristics. While the smart farm concept, integrating renewable energy sources and resource management technologies, has seen widespread adoption in domestic and industrial sectors, rural farms have been slower to embrace these innovations. This study presents a groundbreaking solution, deployed on a rural farm in Portugal, resulting in an impressive 83.24% reduction in energy consumption sourced from the grid. Notably, this achievement translates to a substantial reduction in CO2 emissions, aligning with the growing need for environmentally sustainable farming practices. The technical intricacies of this pioneering solution are comprehensively described and juxtaposed with other scientific case studies, offering valuable insights for replication. This initiative represents a vital first step towards the integration or combination of conventional farming with photovoltaic energy production, exemplified by agrivoltaic systems. In conclusion, this research showcases the potential for rural farms to significantly enhance energy efficiency and financial viability, thereby contributing to a more sustainable and cost-effective agricultural sector. These findings serve as a model for similar endeavors, paving the way for a greener and more economically viable future for rural farming practices. [ABSTRACT FROM AUTHOR]

The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

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The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s. [ABSTRACT FROM AUTHOR]

Zhang JD, Li C, Zhu MQ, et al. B Stable and environmentally friendly perovskite solar cells induced by grain boundary engineering with self-assembled hydrogen-bonded porous frameworks. b I Nano Energy i 2023; B 108 b : 108217. Hanmandlu C, Paste R, Tsai H, et al. B 3D nanographene precursor suppress interfacial recombination in PEDOT:PSS based perovskite solar cells. b I Nano Energy i 2023; B 107 b : 108136. TANDEM CELLS Zhou XM, Lai HW, Huang T, et al. B Suppressing nonradiative losses in wide-band-gap perovskites affords efficient and printable all-perovskite tandem solar cells with a metal-free charge recombination layer. b I Acs Energy Letters i 2023; B 8 b (1): 502-512. Rehman Au, Van Kerschaver EP, Aydin E, et al. B Electrode metallization for scaled perovskite/silicon tandem solar cells: Challenges and opportunities. b I Progress in Photovoltaics: Research and Applications i 2023; B 31 b (4): 429-442. [Extracted from the article]

Effective ways of reducing harmful emissions in the energy sector include, in particular, increasing the efficiency of energy conversion from non-renewable energy sources, the wider use of renewable energy sources and on the other hand, the rational use of energy by consumers. This study deals with the application of renewable energy sources (green energy), which, in addition to environmental benefits, also increase the state's independence from the import of fossil fuels. The world trend is clearly moving towards more intensive use of these clean green energies. Therefore, their higher use is included among the strategic goals of energy policy in most countries of the world, in EU countries, including Slovakia. The paper aims to examine the most important renewable energy sources producing green energy in the EU-27 member states in the period of 2010-2020. The subject of the research is the indigenous production of geothermal energy, solar thermal energy, primary solid biofuels, biogases, and renewable municipal waste. The analyzed EU countries differ in the most important renewable energy production. The countries of northern Europe are the Union leaders in producing energy from waste. Italy is a leader in the use of geothermal energy. The countries of northern Europe, including the Baltic States, are making progress in the production of solar energy. The cluster analysis resulted in the identification of countries with similar characteristics in the case of the development of domestic production of selected renewable sources in the examined period. The analysis showed the development of indicators had not changed significantly over the last decade, although some countries have moved within the established clusters. [ABSTRACT FROM AUTHOR]

In the automated world of business, a modern engineer should be characterized by a passion for technology, creativity, innovative capacity, initiative and commitment to scientific and technical progress, ease of adaptation, but also conviction about the mission of technology. Among these, a lack of competence in recognition of, and response to, anthropogenic ecological threats and destruction of biodiversity and ecosystems was noticed. The aim of this article was to draw attention to the problems of educating engineers, to include provision with specialist knowledge and development of necessary competences. The article also refers to the growth of waste production, including packaging waste, presenting an example of the use of activating methods in adult education, deployed in the subject "Waste management" for students of "Renewable energy sources and waste management", implemented at the University of Rzeszów. Such teaching activity is of great importance in shaping the aforementioned competences. [ABSTRACT FROM AUTHOR]

The high proportion of Electric vehicles (EV) escalates the demand for rapid charging stations, putting a strain on the present grid infrastructure. Installing charging stations can perform certain jobs, lowering grid effect and assisting the system in maintaining power balance for an extended period of time. The involvement among several sources of energy for powering an automobile is coordinated by a power management technique. Solar panels, wind turbines, and a bidirectional vehicle-to-grid interface comprise up the system. Vehicle to grid technology enhances the use of sustainable power while also assuring stability of the system and a transformation to a healthier atmosphere. Motivated by the growing development of electric cars (EVs) upon this market and indeed the lack of predictability of renewable generation. This paper suggests a voltage stability design of a photo - voltaic (PV) array and wind turbine, storage facility of an electric car, as well as the grid via a controller to prevent voltage instabilities at the source. The power circuit consists of a dc-to-dc converter, a bidirectional ac-to-dc converter, a car battery, a load, and switches. The results of the generated computer simulation suggested synchronising the grid, batteries, wind, and solar electricity under various loading scenarios, solar irradiance conditions, and wind conditions [ABSTRACT FROM AUTHOR]

Ghana is experiencing an increase in energy demand as a result of increased industrialization activities. Nonrenewable energy sources, such as combustible fuels like petroleum, are the primary source of energy. Nonrenewable energy resources are associated with a number of issues, including environmental pollution. Renewable energy is a sustainable source of energy that is critical to the energy sector and the economy’s progress. Hydropower, biomass, solar energy, and wind energy are among the renewable energy resources available in Ghana. In Ghana, key institutions are responsible for the management and development of energy sources in the renewable energy sector. Among these institutions is the Ministry of Energy, which is responsible for the formulation and implementation of laws and policies, for instance, the Renewable Energy Act of 2011. Volta River Authority (VRA), Ghana Grid Company (GGC), and Electricity Company of Ghana (ECG) are among the institutions under the Ministry of Energy. There are also regulatory agencies established by parliamentary act to ensure that all actors in the sector are working properly. Among these are the Energy Commission, the Public Utilities Regulatory Commission (PUR), and the National Petroleum Authority. Active nongovernmental organisations (NGOs), research institutes and universities, and industry are also involved in renewable energy activities. Ghana is experiencing an increase in energy demand as a result of increased industrialization activities. Nonrenewable energy sources, such as combustible fuels like petroleum, are the primary source of energy. Nonrenewable energy resources are associated with a number of issues, including environmental pollution. Renewable energy is a sustainable source of energy that is critical to the energy sector and the economy’s progress. Hydropower, biomass, solar energy, and wind energy are among the renewable energy resources available in Ghana. In Ghana, key institutions are responsible for the management and development of energy sources in the renewable energy sector. Among these institutions is the Ministry of Energy, which is responsible for the formulation and implementation of laws and policies, for instance, the Renewable Energy Act of 2011. Volta River Authority (VRA), Ghana Grid Company (GGC), and Electricity Company of Ghana (ECG) are among the institutions under the Ministry of Energy. There are also regulatory agencies established by parliamentary act to ensure that all actors in the sector are working properly. Among these are the Energy Commission, the Public Utilities Regulatory Commission (PUR), and the National Petroleum Authority. Active nongovernmental organisations (NGOs), research institutes and universities, and industry are also involved in renewable energy activities. [ABSTRACT FROM AUTHOR]

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy's effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy's effectiveness in optimizing BESS operations under real-world conditions. [ABSTRACT FROM AUTHOR]

This study focuses on modelling and controlling hybrid Photovoltaic (PV) and wind energy systems for Electric Vehicle (EV) battery charging stations. A load shedding mechanism based on Deep Neural Networks (DNN) has been developed. The integration of this mechanism with the grid through a voltage source converter (VSC) has been examined. The study emphasizes the integration of a DNN-based controller and maximum power point tracking (MPPT) algorithm. The proposed system aims to reduce energy costs and optimize the load on the grid by increasing the use of renewable energy sources. This innovative approach has been developed to address load balancing and stability issues in existing energy management systems. The primary goal of the research is to ensure more efficient use of renewable energy sources and to optimize energy demand management. Simulations conducted under various operating conditions have evaluated the ability of the proposed DNN controller to manage loads dynamically according to three priority levels. According to the results, the proposed system improved load shedding by 25% and increased overall system stability by 15%. The performance rate of the DNN controller was measured at 98%. This indicates that the system operates with high accuracy. In conclusion, the proposed system validates its effectiveness in managing load shedding, maintaining system stability in energy management and optimizing renewable energy sources. [ABSTRACT FROM AUTHOR]

The second paper, authored by M. Kara, A. Laouid, M. AlShaikh, M. Hammoudeh, A. Bounceur, R. Euler, A. Amamra, and B. Laouid proposed a multi-round Proof of Work (PoW) consensus algorithm for preserving energy consumption and resisting attacks [[9]]. Special Features on AMC The most commonly seen AMC algorithms include genetic algorithm (GA), genetic programming (GP), evolutionary strategy (ES), evolutionary programming (EP), memetic algorithm (MA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), simulated annealing (SA), tabu search (TS), scatter search (SS), variable neighborhood search (VNS), and GRASP, to name a few. The first paper, authored by J. Chou, T. Pham, and C. Ho, developed a metaheuristic-optimized machine-learning algorithm for multi-level classification in civil and construction engineering [[1]]. The adaptive shrinking grid search chaos wolf optimization algorithm was proposed to optimize the parameters of the neural network to enhance the image recognition accuracy. [Extracted from the article]

The article considers the problems in the field of socioeconomic systems and institutions to ensure sustainable and environmentally safe economic growth of the country. The author presents the results of analysis of electricity consumption, transition to renewable energy sources and management systems for sustainable socio-economic development of the country, which describes the impact of environmental factors on electricity production, describes corruption processes, touches on current issues of favorable institutional environment in Ukraine and participation in economic development of the country. The author makes proposals (scenarios) for Ukraine's transition to renewable sources, one of which is cardinal, which will achieve high efficiency of electricity from renewable sources and high rates of economic development and ecology in the country. The author concludes that with the use of a systematic transition plan there will be an opportunity for balanced management of energy processes, taking into account the impact of environmental factors. [ABSTRACT FROM AUTHOR]

Optimizing energy management for distributed renewable energy sources (DRESs) with battery energy storage systems, energy trading, and emission trading schemes (ETSs) within the Internet of Energy (IoE) has garnered considerable attention. ETSs have been extended to include sectors with dispersed emissions, notably targeting households, aligning with the growing adoption of DRESs for household electricity. However, previous studies focused on how renewable portfolio standard (RPS) that mandates energy suppliers to include a percentage of renewable energy into their energy portfolios influence industrial and large-scale energy systems, neglecting the potential of implementing RPS at the household level. In addition, previous studies analyzed energy markets of traded green certificates (TGCs), certified through their separation from renewable energy generation, but rarely investigated their potential within households. Consequently, this study introduces house-based RPS (HRPS) and the unbundling of TGCs into a dynamic energy management optimization problem for a smart house with DRESs, a home energy storage system (HESS), and an electric vehicle, where HRPS mandates daily consumption of green energy, allowing for energy and TGC trading through their respective trading platforms. A mathematical programming model is formulated for determining HESS charging/discharging decisions, energy trading, and TGC trading under HRPS, while minimizing costs and penalties for HRPS non-compliance. Since TGC unbundling makes this model much complex, a hybrid simplified harmony search (SHS) and double-adaptive general variable neighborhood search (DAGVNS) algorithm is proposed. Simulation results demonstrate that the introduction of RPS and TGC trading can effectively reduce the smart house's carbon emissions by approximately 19.4 % weekly. [Display omitted] • House-based renewable portfolio standard (HRPS) is included for more emission cuts. • Unbundling of traded green certificates (TGCs) enhances household energy markets. • Energy optimization of a house battery, energy, & TGC trading under HRPS is modeled. • Hybrid SHS and DAGVNS is devised to tackle model complexity due to TGC unbundling. • This method with HRPS and TGC unbundling reduces house emissions by 19.4 % weekly. [ABSTRACT FROM AUTHOR]

The microgrid energy management with renewable energy is efficiently integrating intermittent sources like solar and wind while ensuring grid stability and reliability is difficult. The gravitational sear search method is employed in MG energy management with renewable energy sources (RESs) to address these problems. The gravitational search technique is used in the proposed method (GSA). In order to build a database of control signals that take into account the power differential between the source and load sides, GSA is used to precisely identify the control signals for the system. The proposed technique’s main goal is to deliver the best performance at the lowest possible cost. The constraints are the availability of the RESs, energy consumption as well as the storage elements’ level of charge. Batteries are utilized as an energy source to steady and allow the renewable power system components to continue operating at a constant and stable output power. The proposed method cost is 1.1$ that is lower than the existing methods. The MATLAB platform is used to implement the proposed method, and its efficacy is assessed in comparison to established techniques like modified PSO (MPSO), genetic algorithm (GA), particle swarm optimization (PSO), and proportional integral controller (PI) (MPSO). [ABSTRACT FROM AUTHOR]

The electrification and extension of conventional grid in remote areas is still a major challenge in developing countries. This can be addressed with an integration and management of renewable energy sources and energy storage systems to the remote network. This paper aims to develop a Rule-based Smart Energy Management System (RBSEMS) paradigm for Remote area power supply (RAPS) systems to implement simultaneous source-side and demand-side energy management. The uninterrupted power supply and reduction of electricity cost is the primary objective of the work. Besides, in the multi-objective framework, reduction of dependency on the grid is considered along with the primary objective. The remote area power system controllers are modelled to provide a seamless transition between different modes of operation of energy sources and respond to RBSEMS signals without much delay. The RAPS test system consisting of schedulable and non-schedulable loads, solar PV, wind energy system, battery energy storage system, utility grid along with its controllers are modelled in MATLAB Simulink to validate the RBSEMS. The comprehensive analysis and simulation results of various cases present the effectiveness of the proposed approach on the modelled RAPS system. Four performance indices are also presented to highlight the merits of the proposed work. The overall cost is decreased by 11.56% in case 2B, when primary objective is considered alone. When primary and secondary objectives are considered together, the overall cost is decreased by 3.69% in case 3B but the independent performance index is improved from 0.7815 to 0.836 indicating the reduced grid dependency. The response time of the modelled local controllers of the system is found to be 24.2 ms, which is acceptable for an interval of 0.25 s. [ABSTRACT FROM AUTHOR]

This study introduces an innovative compressed CO 2 energy storage (CCES) system poised to significantly enhance the management of fluctuating renewable energy sources. Unlike traditional system layouts, a high-temperature thermal energy storage (HTES), an ejector cycle and a flash process are adopted in the system and innovatively integrated into its charging and discharging process. These above methods aim to reduce geographic limitations and improve round-trip efficiency. Thermodynamic and economic models are established to conduct a comprehensive evaluation, engaging in detailed parameter analysis followed by multi-objective optimization. The analysis indicates that increasing turbine inlet pressure and temperature significantly improves system performance. While increasing flash pressure and the temperature of low-pressure storage benefit round-trip efficiency (RTE) and levelized cost of electricity (LCOE), it negatively impacts the energy storage density (ESD). Additionally, setting the ejector back pressure at approximately 7.5 MPa optimizes performance. Multi-objective analysis indicates that the optimal low-pressure storage temperature is approximately 295.07 K, with the associated flash pressure and ejector back pressure set at 3.94 MPa and 7.48 MPa, respectively. Under these conditions, the proposed system achieves the optimal RTE , ESD and LCOE of 50.43 %, 16.60 kWh/m³, and 0.0992 $/kWh, respectively. Compared to other similar systems, the proposed system exhibits preferable balance performance. [ABSTRACT FROM AUTHOR]

The necessity of making reliable decisions is exceptionally important in the field of sustainable development, particularly when evaluating the management of renewable energy sources. A thorough analysis becomes imperative in complex decision problems where various factors influence outcomes. Multi-Criteria Decision Analysis (MCDA) methods have emerged as valuable tools for addressing such challenges, enabling decision-makers to navigate through conflicting criteria and make informed choices. Combined with sensitivity analysis approaches, comprehensive assessments can be achieved, ensuring the robustness of decision-making processes. To increase the reliability of the results, different aspects of input data fluctuations should be examined to provide a broader view of the stability of the results. This paper proposes a comprehensive multi-dimensional sensitivity analysis approach to assess the robustness of renewable energy source (RES) development in selected European countries. By evaluating RES management in terms of electricity and energy consumption and generation, the study addresses key components of sustainable development. It offers a holistic perspective on result reliability and stability by analyzing five sensitivity dimensions: a comparative analysis of four MCDA methods, varying criteria weights scenarios, probabilistic modifications, criteria relevance identification, and ranking stability. This approach enhances decision-making in sustainable energy development, providing valuable insights for policymakers on prioritizing sustainable technologies. The provided open-source implementation promotes transparency and accessibility in decision support systems. • Novel sensitivity analysis for robust RES in Europe. • Advances decision-making in sustainable energy. • Guides policy with insights & open-source framework. [ABSTRACT FROM AUTHOR]

• Novel GRU-LIME prediction method boosts accuracy. • GRU consistently beats traditional models for reliable predictions. • Renewable energy insights inform sustainable urban development. This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of electric vehicles. • Using probabilistic modeling of wind, solar and wind-solar power. • Reporting superior solutions in short-term scheduling and Micro-grid energy management. In light of electric vehicles (EVs) and demand-side management, a combined optimization paradigm for microgrid (MG) resource scheduling is proposed in this study. Renewable energy sources, including wind and solar power, fulfill a fraction of the consumption demand, while the MG maintains a connection to the electrical network to facilitate power transactions. By utilizing EVs, we can mitigate the inherent volatility of renewable energy production with manageable loads and level out the system's demand profile. The proposed system employs a bi-level programming strategy, wherein the initial level is dedicated to the minimization of aggregate costs, encompassing those related to energy and reserves. Additionally, costs associated with resource redistribution due to the uncertainty of solar and wind power output are minimized at the second level. Subsequently, the objective function is addressed using the enhanced adapted approach of modified marine predators algorithm (MMPA), chosen for its robustness and effectiveness as an optimization technique. The outcomes obtained for a microgrid underscore the superior performance of the results achieved through the application of the MMPA algorithm when contrasted with alternative established methodologies. The outcomes have demonstrated that integrating EVs and adaptable loads contributes to a decrease in operational expenses and emissions. Furthermore, the uncertainties associated with WT and PV are effectively counterbalanced. Additionally, the results garnered from the simulations indicate that the recommended MMPA technique has the capacity to yield superior solutions compared to several other widely recognized optimization algorithms. Scheduling with both DR and EVs with three cost components are considered, generation cost, reserve cost, and initial unit cost, amounting to $740.11, $9.61, and $6.07, correspondingly and it can lead to decreased emission levels and increased incentives for proprietors of EVs, responsive load participants, and RES. [ABSTRACT FROM AUTHOR]

The article titled "An efficient control strategy for optimal power flow management from a renewable energy source to a generalized three-phase microgrid system: A hybrid squirrel search algorithm with whale optimization algorithm approach" has been retracted by the Journal Editor and SAGE Publishing. The retraction was prompted by concerns raised on PubPeer that a language translation software was used to avoid plagiarism checks. The article was found to contain similar ideas and language to other publications, and attempts to contact the authors for clarification were unsuccessful. The retraction was made due to concerns about the authorship of the research and the presence of unattributed text. [Extracted from the article]

This paper proposes a study on the potential of waste-to-energy (WTE) possibility in the Arab Republic of Egypt. WTE is a viable option for municipal solid waste (MSW) management and a renewable energy source. The issue of waste spread is a chronic environmental challenge in the Arab Republic of Egypt. The MSW practices in Egypt are simply done by collecting the waste and dumping it in open landfill sites. This research aims to assess the potential contribution of the WTE facility to meet electricity demand in Cairo City, which is Egypt's capital and the biggest city in population as a sample, and then apply the results to all of Egypt. The paper introduced a step-by-step calculation for the electrical power that can be generated from Cairo MSW. Four scenarios for WTE utilization were developed: Mass Burn without any waste processing, Mass Burn with 25% recycling, Mass Burn excluding 50% of organic material, and Mass Burn excluding 50% of organic material with 25% recycling. The Mass Burn scenario implies full MSW stream incineration; the Mass Burn with recycling scenario considers a partial separation of reusable materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material scenario considers a partial separation of organic materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material with 25% recycling scenario considers a partial separation of organic materials and partial separation of reusable materials and the waste leftover for incineration. The analyses were done for the period 2011–2031 for Cairo with a total population of about 10 million in Nov 2021. The results show the huge potential of WTE conversion as a source of renewable energy, which is a key factor in Egypt's sustainable development strategy 2030. Available data have been theoretically processed to show the decision-makers in Egypt the amount of electrical power that can be generated by using the WTE options to manage the MSW in Egypt. [ABSTRACT FROM AUTHOR]

The thermal power of the total power demand accounts for the total of 60% in India, with the penetration of the nonconventional energy sources the grid management becomes a tedious job in maintaining the grid stability, and adding to the woes the environmental norms become stricter as it is a tough job to maintain to lesser CO2 emissions in the atmosphere. Hence this article aims with the integration of the grid management with the renewable energy sources and reduction of CO2 and also strives for the grid stability with the smart grid. And the thermal scheduling optimization is done with the novel bat algorithm. [ABSTRACT FROM AUTHOR]

Biogas is a renewable energy source which plays an important role in addressing rural energy and environmental pollution problems. However, more and more rural households are reluctant to use biogas digesters and they have been generally rejected. This research focused on Guangxi Zhuang autonomous region of China, and used the Q-methodology to analyze the reasons behind the barriers for adopting biogas digesters for rural households in China. By visiting and interviewing rural households and government managers, we found that there are four main reasons for the limited use of rural household biogas digesters: (1) reductions in the rural work force and land area; (2) a poor system of follow-up service for biogas digesters; (3) the advantages of alternative energy; and (4) raw material shortages. Moreover, there is blame shifting among government managers and farmers on the reasons for abandonment of household biogas digesters in rural China. This study is helpful to understand the actual reasons behind the abandonment of biogas digesters and the rural renewable energy source management in China. [ABSTRACT FROM AUTHOR]

The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers. The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations. The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts. [ABSTRACT FROM AUTHOR]

Summary: With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end‐energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources‐based microgrid and develops its centralized energy management model for both off‐grid and on‐grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector‐valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi‐parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self‐consumption, energy efficiency, and myopic decision‐making model using the energy level of storage devices obtained from the full look‐ahead optimization model. Highlights: The optimization problem combining electricity, heating, and cooling is formulatedThe electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model.Accurate energy storage level information into the myopic energy management model improves the model decisions.The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding. [ABSTRACT FROM AUTHOR]

Renewable energy-based smart grids are famous nowadays due to their high intellectual properties. The world is starting new inventions in renewable energy-based electrical power generation systems to reduce global warming. However, a single renewable energy source cannot maintain a proper energy management system and reliability of power towards loads. Hence, integrating two or more systems is very important and can form a smart grid with an appropriate energy management system. Effective energy management system for a 4-wire 1-MW smart grid system is proposed in this paper. The system is composed of three solar plants and three wind farms with a battery bank. The battery energy management system can operate the complete system as a smart grid with the proper design of the controllers. The maximum power points of PV plants are tracked using a hybrid algorithm that merges the merits of Modified Invasive Weed Optimization and Perturb and Observe (P&O). Thus, the maximum power is obtained under partial shading conditions. The P&O algorithm is also developed to track the maximum power of wind farms. All the loads and generation units are connected in a ring-configuration distribution with a centralized battery energy management system. The loads are selected to be unbalanced, nonlinear and reactive to simulate practical cases. TS-Fuzzy based common inverter controller is implemented to maintain acceptable power quality, which is linked to the battery. The proposed inverter controller can work as a reactive power compensator, active power filter, voltage regulator under unbalanced load, and power balancing device between generation and load. Extensive Hardware-in-Loop (HIL) results are presented to validate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Smart grids are electricity networks that deliver electricity in a controlled way, offering multiple benefits such as growth and effective management of renewable energy sources. The present article is a review of smart grids/smart technologies in relation to Photovoltaic (PV) systems, storage, buildings and the environment. In the frame of PV/smart applications, factors such as promotion of building-integrated PV/smart-grid configurations and evaluation of the systems in different countries/markets play a pivotal role. Regarding storage, parameters such as recycling and non-interconnected territories are critical. Moreover, the development of smart grids/smart systems in the building sector is associated with zero-energy buildings, indoor-environment control, polygeneration, policy and so on. From an environmental point of view, the vast majority of the investigations discuss general environmental issues (CO 2 emissions; energy savings) and there is a lack of environmental Life Cycle Assessment (LCA) studies based on multiple life-cycle impact assessment methods and environmental indicators. In light of the issues mentioned above, it can be seen that smart grids/smart systems pose many challenges: residential microgeneration, flexibility and active participation of the users, promotion of renewable energy systems (offshore wind power plants, building-integrated solar systems, etc.), energy transition and so on. • A review of smart grids, Photovoltaics (PVs), storage, buildings & the environment. • Critical factors have been identified: Building-integrated PVs, policies, etc. • As for storage, parameters such as recycling and toxicity should be considered. • Regarding smart buildings, key issues have been presented and discussed. • Smart grids pose challenges such as decrease in CO 2 emissions & promotion of PVs. [ABSTRACT FROM AUTHOR]

These works are introduced to avoid electrical problems in literary works. An intelligent hybrid control technique for optimal power flow management (OPFM) in grid‐associated hybrid renewable energy sources (HRES) with energy storage is proposed in this dissertation. For stabilizing power fluctuations, the intelligent controller is proposed. The proposed intelligent controller is joint performance of Big Bang‐Big Crunch (BB‐BC) and random forest algorithm known as BCRFA controller. In the proposed controller, the BB‐BC method reproduces the evaluation process to establish the exact control signals (ECS) system depending on power variations with source, load side. With this appropriate control, HRES can significantly improve the dynamic safety of the power system. Finally, the proposed system is executed on MATLAB/Simulink work site. The efficiency of the BCRFA system compares the existing system. The result of the comparison reveals that HRES power flow using the proposed system is direct efficiently compared with the existing systems. The efficiency of the various techniques and the proposed technique has been analysed in three case studies. In Case 1, the PV, wind, battery and FC gives the efficiency using proposed technique is 95.3617%, 99.1235%, 98.1264% and 99.4677%. In Case 2, the photovoltaic (PV), wind, battery and fuel cell (FC) give the efficiency using the proposed technique is 91.4365%, 93.6537%, 98.5151% and 97.1562%. In Case 3, the PV, wind, battery and FC give the efficiency using the proposed technique is 92.5479%, 91.2369%, 97.5631% and 90.1245%. [ABSTRACT FROM AUTHOR]