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This review article provides a thorough examination of the factors affecting energy consumption in India and around the world, focussing on suburban areas and their role in the broader context of building construction and energy efficiency. The study examines the switch from conventional energy sources to more diversified energy portfolios, including electricity, natural gas, oil, biomass, and waste. Moving deeper into the subject, the review delves into the realm of carbon emissions resulting from building and construction. It spotlights the critical contribution of residential energy consumption to the overall carbon footprint and discusses the dual significance of operational and embodied emissions in the construction sector. This research underscores the necessity for integrated strategies to mitigate carbon emissions in building practices. Furthermore, the paper critically evaluates the evolution of energy regulations, with a special focus on the shift from prescriptive to performance-based Building Energy Codes (BECs). It assesses the capacity of current energy policies to curtail global warming and provides an in-depth analysis of energy usage patterns, policies, and CO2 emissions in the Indian suburban context. The study highlights India's efforts to modernise building codes and improve energy efficiency while promoting multifaceted strategies that include building code improvements, the use of energy-efficient technologies, and sustainable urban design, and ends with a plea for sustainable development and major reductions in carbon emissions, thereby highlighting the critical role that both local and global initiatives can play. This study provides a thorough understanding of sustainable building practices and energy efficiency, especially in suburban development. [ABSTRACT FROM AUTHOR]

The rapid depletion of fossil reserves and their rising costs necessitate that consumers collaborate and use their resources judiciously. This paper proposes a demand response-based centralized control strategy that facilitates optimal resource management. A central controller (CC) is developed to overlook the buy/sell strategies based on load, generation status and utility retail prices. This community ecosystem consists of smart and regular houses, renewable energy sources (RES) and a community-wide energy storage system (ESS). The CC facilitates community energy brokering with the grid via these smart houses while still aligning with community requirements through the integration of RES and ESS. A novel CC leveraging Fuzzy Double Q-Learning method is introduced, and it is benchmarked against a Deterministic method and an established Fuzzy Single Q-Learning method. A substantial improvement in energy trading performance, buying and selling flexibility, and cost to community (C2C) is observed via this model–methodology combination. The comprehensive comparative analysis also showcases the significance of the cost reductions, the improved efficiencies, and the sustainable and economic approach toward the community ESS. The results indicate an overall improvement of 18.3% in C2C and a day saving of $5.34 when compared against Deterministic approach. Additionally, a 3.4% improvement in C2C is observed when compared against an established Fuzzy Single Q-Learning method. [ABSTRACT FROM AUTHOR]

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM's simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems. [ABSTRACT FROM AUTHOR]

The ongoing transition to energy systems with high shares of variable renewables motivates the development of novel thermal power cycles that operate economically at low capacity factors to accommodate wind and solar intermittency. This study presents two recuperated power cycles with low capital costs for this market segment: (1) the near-isothermal hydrogen turbine (NIHT) concept, capable of achieving combined cycle efficiencies without a bottoming cycle through fuel combustion in the expansion path, and (2) the intercooled recuperated water-injected (IRWI) power cycle that employs conventional combustion technology at an efficiency cost of only 4% points. The economic assessment carried out in this work reveals that the proposed cycles increasingly outperform combined cycle benchmarks with and without CO2 capture as the plant capacity factor reduces below 50%. When the cost of fuel storage and delivery by pipelines is included in the evaluation, however, plants fired by hydrogen lose competitiveness relative to natural gas-fired plants due to the high fuel delivery costs caused by the low volumetric energy density of hydrogen. This important but uncertain cost component could erode the business case for future hydrogen-fired power plants, in which case the IRWI concept powered by natural gas emerges as a promising solution. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework's capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future. [ABSTRACT FROM AUTHOR]

The electric distribution sector is facing significant challenges in integrating distributed energy resources (DERs), with most assessment methodologies being primarily tested on standard feeders. However, given the nature of electric distribution systems, each feeder is unique and constantly changing, emphasizing the need for customized and updated feeder models for local quantitative studies. These studies encompass network operation, expansion planning, and DER integration. In response to this context, this paper proposes a methodology for extracting and processing information from public data to model up-to-date electric feeders. As these feeder models represent real operating circuits, they can be utilized by utilities, investors, and consumers. The proposed approach involves processing data from the Brazilian utilities' geographic database (BDGD, in Portuguese) to develop structured simulations of distribution feeders using OpenDSS software. The BDGD standards are established by the Brazilian National Electric Energy Agency (ANEEL). In order to validate the methodology, two case studies are presented evaluating the impacts of distributed generation on real feeders from the Brazilian utilities: Minas Gerais Electric Power Company (CEMIG) and Bahia State Electricity Company (Neoenergia COELBA). [ABSTRACT FROM AUTHOR]

Introduction: The integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging. Methods: To address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis. Results: The proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (N PC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case. Discussion: Seasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption. [ABSTRACT FROM AUTHOR]

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system's energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia's electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS). [ABSTRACT FROM AUTHOR]

The global pursuit of sustainable energy solutions has led to an increased interest in green hydrogen as a significant contributor to the transition towards a low-carbon economy. This comprehensive analysis delves into the progress made in green hydrogen production using alkaline water electrolysis, shedding light on the fundamental principles, materials, design, operational conditions, and integration with renewable energy sources. The paper critically evaluates the economic and environmental aspects, providing valuable insights into the challenges, future prospects, and real-world applications. The importance of green hydrogen as a clean energy carrier is emphasized in the context of mitigating climate change and achieving energy independence. The paper outlines various methods for hydrogen production, with a focus on alkaline water electrolysis, highlighting its role in sustainable hydrogen generation. The structure of the review paper is well-detailed, guiding the reader through a systematic exploration of key aspects related to green hydrogen production. The fundamentals of alkaline water electrolysis are explained in detail, covering the basic principles, reactions at electrodes, components, working mechanisms, efficiency calculations, and existing challenges. An in-depth examination of materials and electrodes is presented, showcasing recent innovations that enhance performance. Electrolysers design and system configurations are discussed, incorporating recent developments aimed at improving efficiency and scalability. The paper explores operating conditions and optimization strategies, emphasizing the impact of temperature, pressure, and current density on electrolysis efficiency. The integration of renewable energy sources, such as wind and solar power, with alkaline water electrolysis is analyzed, addressing both the benefits and challenges. The economic feasibility of green hydrogen production and its environmental impacts, including life cycle assessments and carbon footprint, are meticulously examined. The paper concludes with an overview of current challenges and limitations, ongoing research insights, and future prospects for alkaline water electrolysis technology. Real-world case studies are presented to showcase the practical applications of this technology in industries, transportation, and energy storage. In summary, this review highlights the pivotal role of alkaline water electrolysis in advancing green hydrogen production, offering a promising pathway towards a sustainable and decarbonized energy future. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs' potential in global energy sustainability fully. [ABSTRACT FROM AUTHOR]

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change. [ABSTRACT FROM AUTHOR]

Energy sources are critical in the industrial sector, particularly as population growth intensifies the pressure on industries to scale up production to meet increasing demands. Integrating renewable energy sources such as biomass, biogas, and photovoltaic systems in the palm oil production process can be considered a pivotal strategy for mitigating carbon emissions. However, load fluctuations due to mill size and insufficient supply of biogas and photovoltaic are significant challenges in energy management for the palm oil industry. Adopting renewable energy solutions is hindered by process disturbances and climatic variability caused by weather, ambient temperature, relative humidity, and solar radiation. These factors pose constraints on the implementation of renewable energy solutions. Therefore, this study aims to fill the gap in terms of renewable energy management within the palm oil industry by applying reinforcement learning to achieve effective energy management and reduce carbon emissions under five climatic scenarios. The result shows that reinforcement learning effectively combines renewable energy sources to generate electricity and steam while efficiently managing energy storage without exceeding predefined levels. Additionally, deploying a photovoltaic system contributes to energy savings from biomass and biogas of 2.9% and reduces average daily carbon dioxide emissions by 3137 kg. The proposed method has proved beneficial across all climate scenarios for energy savings and emission reduction. [ABSTRACT FROM AUTHOR]

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system. [ABSTRACT FROM AUTHOR]

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies. [ABSTRACT FROM AUTHOR]

This review highlights the latest advancements in thermal energy storage systems for renewable energy, examining key technological breakthroughs in phase change materials (PCMs), sensible thermal storage, and hybrid storage systems. Practical applications in managing solar and wind energy in residential and industrial settings are analyzed. Current challenges and research opportunities are discussed, providing an overview of the field's current and future state. Following the PRISMA 2020 guidelines, 1040 articles were initially screened, resulting in 49 high-quality studies included in the final synthesis. These studies were grouped into innovations in TES systems, advancements in PCMs, thermal management and efficiency, and renewable energy integration with TES. The review underscores significant progress and identifies future research directions to enhance TES's efficiency, reliability, and sustainability in renewable energy applications. [ABSTRACT FROM AUTHOR]

This study explores the integration and optimization of battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) within an energy management system (EMS), using Kangwon National University's Samcheok campus as a case study. This research focuses on designing BESSs and HESSs with specific technical specifications, such as energy capacities and power ratings, and their integration into the EMS. By employing MATLAB-based simulations, this study analyzes energy dynamics, grid interactions, and load management strategies under various operational scenarios. Real-time data from the campus are utilized to examine energy consumption, renewable energy generation, grid power fluctuations, and pricing dynamics, providing key insights for system optimization. This study finds that a BESS manages energy fluctuations between 0.5 kWh and 3.7 kWh over a 24 h period, with battery power remaining close to 4 W for extended periods. Grid power fluctuates between −5 kW and 75 kW, while grid prices range from 75 to 120 USD/kWh, peaking at 111 USD/kWh. Hydrogen energy storage varies from 1 kWh to 8 kWh, with hydrogen power ranging from −40 kW to 40 kW. Load management keeps power stable at around 35 kW, and PV power integration peaks at 48 kW by the 10th h. The findings highlight that BESSs and HESSs effectively manage energy distribution and storage, improving system efficiency, reducing energy costs by approximately 15%, and enhancing grid stability by 20%. This study underscores the potential of BESSs and HESSs in stabilizing grid operations and integrating renewable energy. Future directions include advancements in storage technologies, enhanced EMS capabilities through artificial intelligence and machine learning, and the development of smart grid infrastructures. Policy recommendations stress the importance of regulatory support and stakeholder collaboration to drive innovation and scale deployment, ensuring a sustainable energy future. [ABSTRACT FROM AUTHOR]

[Objective] In the context of promoting energy structure transformation and accelerating the construction of a new energy system, clarifying the energy input strategies and their influencing factors across different regions is of great significance for formulating region-specific energy policies and orderly advancing regional renewable energy integration. [Methods] Based on a game theory framework, this study theoretically deduced the energy input strategies and influencing factors of different regions in the context of energy structure transformation. It also conducted empirical tests corresponding to the implementation effects of wind power subsidy policies in different regions of China and the progress of renewable energy integration in Europe. [Results] (1) Under the fossil fuel energy scenario, regions exhibited non-cooperative game behavior, where regional energy input strategies were characterized by non-cooperative games, displaying path dependency and influenced by the scale of capital and labor factors in the region. (2) Under the renewable energy scenario, regions engaged in cooperative games, with energy input strategies reflecting cooperative games, influenced by the unit cost of renewable energy, the scale of capital and labor factors, and the differences in the scale of capital and labor factors within the region. (3) When there are larger disparities in the scale of capital and labor factors among different regions within an area, lower costs of renewable energy, and a higher overall scale of capital and labor factors, the area is suitable for prioritizing the renewable energy integration, which is more conducive to the orderly achievement of energy efficiency improvement and energy structure transformation. [Conclusion] Dynamically adjusting energy input strategies, emphasizing the alignment of renewable energy deployment with regional factor endowments, and leveraging the leading role of central cities are crucial strategies for promoting China’s energy structure transformation. The findings of this study provide policy insights for the orderly advancement of renewable energy integration and the construction of a new energy system.

Abstract Energy sources are critical in the industrial sector, particularly as population growth intensifies the pressure on industries to scale up production to meet increasing demands. Integrating renewable energy sources such as biomass, biogas, and photovoltaic systems in the palm oil production process can be considered a pivotal strategy for mitigating carbon emissions. However, load fluctuations due to mill size and insufficient supply of biogas and photovoltaic are significant challenges in energy management for the palm oil industry. Adopting renewable energy solutions is hindered by process disturbances and climatic variability caused by weather, ambient temperature, relative humidity, and solar radiation. These factors pose constraints on the implementation of renewable energy solutions. Therefore, this study aims to fill the gap in terms of renewable energy management within the palm oil industry by applying reinforcement learning to achieve effective energy management and reduce carbon emissions under five climatic scenarios. The result shows that reinforcement learning effectively combines renewable energy sources to generate electricity and steam while efficiently managing energy storage without exceeding predefined levels. Additionally, deploying a photovoltaic system contributes to energy savings from biomass and biogas of 2.9% and reduces average daily carbon dioxide emissions by 3137 kg. The proposed method has proved beneficial across all climate scenarios for energy savings and emission reduction.

This paper introduces a cutting-edge deep learning-based model aimed at enhancing the short-term performance of microgrids by simultaneously minimizing operational costs and emissions in the presence of distributed energy resources. The primary focus of this research is to harness the potential of demand response programs (DRPs), which actively engage a diverse range of consumers to mitigate uncertainties associated with renewable energy sources (RES). To facilitate an effective demand response, this study presents a novel incentive-based payment strategy packaged as a pricing offer. This approach incentivizes consumers to actively participate in DRPs, thereby contributing to overall microgrid optimization. The research conducts a comprehensive comparative analysis by evaluating the operational costs and emissions under scenarios with and without the integration of DRPs. The problem is formulated as a challenging mixed-integer nonlinear programming problem, demanding a robust optimization technique for resolution. In this regard, the multi-objective particle swarm optimization algorithm is employed to efficiently address this intricate problem. To showcase the efficacy and proficiency of the proposed methodology, a real-world smart microgrid case study is chosen as a representative example. The obtained results demonstrate that the integration of deep learning-based demand response with the incentive-based pricing offer leads to significant improvements in microgrid performance, emphasizing its potential to revolutionize sustainable and cost-effective energy management in modern power systems. Key numerical results demonstrate the efficacy of our approach. In the case study, the implementation of our demand response strategy results in a cost reduction of 12.5% and a decrease in carbon emissions of 14.3% compared to baseline scenarios without DR integration. Furthermore, the optimization model shows a notable increase in RES utilization by 22.7%, which significantly reduces reliance on fossil fuel-based generation. [ABSTRACT FROM AUTHOR]

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner. [ABSTRACT FROM AUTHOR]

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

This study presents a novel approach to integrating combined cooling, heating, and power (CCHP) systems with water desalination for enhanced energy and water management in educational buildings. Two distinct layouts for CCHP and desalination systems are introduced: one prioritizing efficient power generation to meet electricity demands while providing waste heat for desalination, and the other focusing on balancing cooling and heating loads alongside water desalination. Both layouts are tailored to meet the building's energy and water demands while considering operational efficiency. Optimization of these layouts against traditional systems using the bat search algorithm emphasizes economic viability and the gas engine's operational flexibility, which are crucial for partial load operation. In addition, an environmental assessment compares the proposed CCHP-desalination systems with conventional setups, assessing CO2 emission reductions and overall sustainability. The evaluation encompasses key environmental metrics, such as resource consumption and the integration of renewable energy sources. Results highlight significant CO2 emission reductions across various gas engine capacities, with notable enhancements in economic and environmental performance achieved by selecting a 3,250 kW gas engine within the CCHP-desalination system. This choice not only maximizes the annual profit but also reduces CO2 emissions by 57% compared to conventional systems, underscoring the system's sustainability benefits. HIGHLIGHTS This study introduces a novel approach to integrate CCHP systems with water desalination specifically tailored for use in educational buildings.; Two distinct layouts for CCHP and desalination systems are introduced in the article, each tailored to meet the building's energy and water demands while considering operational efficiency.;

Abstract The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems.

Abstract This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

The global transition to decentralized energy systems signifies a fundamental transformation toward sustainable energy paradigms. This study specifically focuses on the Swiss energy system, analyzing how dynamic pricing influences the strategic decisions of different actors. The main contributions include 1) a detailed examination of pricing models tailored to the Swiss context, 2) an exploration of strategic financial burden shifts among end-users, TSOs, and DSOs, and 3) a comparison of decentralized versus centralized energy models, highlighting their respective efficiencies and resilience. This research differentiates from existing literature by providing an in-depth actor-based analysis within a Swiss context, offering valuable insights into decentralized energy system optimization. This study tackles the problem of how pricing influences strategic decisions across different actors in Switzerland's evolving decentralized energy landscape. Here we show that a carefully tailored pricing model, designed for the Swiss context, enables optimized strategies that balance local efficiencies with systemic equity and resilience. The analysis reveals that decentralized approaches, in contrast to centralized models, not only accommodate diverse stakeholder preferences but also enhance system robustness against market and operational disruptions. Moreover, the study illustrates the strategic financial burden shifting where end-users compensate for cost shifts, with observed additional costs up to 5200 CHF/year cap when service providers are prioritized as objective actors. Notably, the most frequently selected system configuration in the primal problem, which optimizes the total system costs, aligns with the preferences of TSO and DSO for a 47.1 GW PV deployment. However, end-users demonstrate a preference for increased PV installations, constrained by urban grid capacities. Additionally, the study highlights significant regional disparities across Switzerland, necessitating tailored pricing approaches that reflect varied urban forms. The emergence of prosumers catalyzes new business models, redistributing investments across TSOs (256-261 CHF/cap/year), DSOs (244-413 CHF/cap/year), and prosumers (556-764 CHF/cap/year), showcasing the evolving dynamics of energy system economics. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

The growing interest in solar energy stems from its potential to reduce greenhouse gas emissions. Global horizontal irradiance (GHI) is a crucial determinant of the productivity of solar photovoltaic (PV) systems. Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimization of solar PV energy systems. This study evaluates the performance of six machine learning (ML) regression models—artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR), Random Forest (RF), and support vector regression (SVR)—in predicting GHI for a site in northern Saudi Arabia known for its high solar energy potential. Using historical data from the NASA POWER database, covering the period from 1984 to 2022, we employed advanced feature selection techniques to enhance the predictive models. The models were evaluated based on metrics such as R-squared (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The DT model demonstrated the highest performance, achieving an R2 of 1.0, MSE of 0.0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0. Conversely, the EN model showed the lowest performance with an R2 of 0.8396, MSE of 0.4389, RMSE of 0.6549, MAPE of 9.66%, and MAE of 0.5534. While forward, backward, and exhaustive search feature selection methods generally yielded limited performance improvements for most models, the SVR model experienced significant enhancement. These findings offer valuable insights for selecting optimal forecasting strategies for solar energy projects, contributing to the advancement of renewable energy integration and supporting the global transition towards sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper focuses on the topic of renewable energy integration and carbon neutrality green technology innovation based on intelligent microgrid and focuses on the design and optimization of intelligent microgrid control to improve the utilization efficiency of renewable energy and achieve the goal of carbon neutrality. In this paper, the hardware structure of the microgrid is designed, and the solar, wind and biomass power generation systems are integrated, and the battery energy storage and supercapacitor energy storage systems are optimized. Subsequently, a complete software control system was developed, and based on the application of big data processing and machine learning technology, the intelligent management of the microgrid was realized. Then, based on the research of intelligent control strategy and optimization algorithm, the overall response speed and operation efficiency of the system were improved, and a simulation platform was built to achieve effective simulation experiments, and then the efficiency and stability of the system were verified. [ABSTRACT FROM AUTHOR]

The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems. [ABSTRACT FROM AUTHOR]

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence's role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles' energy management systems is discussed. The findings underscore artificial intelligence's potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications. [ABSTRACT FROM AUTHOR]

The hydrogen pressure typically increases to approximately 700-bar for storage in high-pressure tanks. Some industry applications use such a high-pressure hydrogen but some others need lower-pressure hydrogen where the pressure is reduced through pressure reduction valves. Some energy is lost during pressure reduction which would be significant in large-scale systems. A multi-level hydrogen storage can address this issue, where the hydrogen is stored at various pressures. This paper addresses an electricity-hydrogen nexus system integrated with multi-level hydrogen storage and solar PV sites. The designed system supplies electric and fuelcell vehicles at 3 different charging points including an electric vehicle charging station, fuelcell car refueling station, and fuelcell truck refueling station. The hydrogen is stored at 30, 350, and 700-bar pressure levels and each level feeds various users. The water electrolysis is employed to generate hydrogen at 30-bar pressure and 2 compressors increase the pressure to 350 and 700-bar. The cost function, defined as the daily energy cost, is optimized at $1045.65 per day. The water electrolyzer produces the most hydrogen during nighttime when electricity is inexpensive. Under normal operating conditions, the fuel cell is not utilized due to its low efficiency of about 60%. However, it serves as a backup resource in the event of a system outage, such as when the external grid fails, in hours 22 to 24. During this time, the fuel cell is activated and produces 154 kWh. • Multi-Level Hydrogen Storage. • Electricity-Hydrogen Nexus System Integration. • Optimized Hydrogen Production and Pressure Management. • Cost Optimization and Efficiency. • Backup Resource Utilization. [ABSTRACT FROM AUTHOR]

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid's ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method's superiority over conventional moving averages and ramp-rate filters. [ABSTRACT FROM AUTHOR]

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid. [ABSTRACT FROM AUTHOR]

This study examines the design and performance comparison of Proportional-Integral (PI) and Fuzzy Logic Controllers (FLC) for a Dual Active Bridge (DAB) converter, specifically within the framework of solar-integrated off-board fast charging for electric vehicles (EVs). The main goal is to create and assess control strategies that facilitate efficient and stable power transfer between the PV System and the EV battery, effectively managing the challenges associated with fluctuating solar irradiance and changing battery load conditions. The approach includes designing both PI and FLC controllers specifically for the DAB converter and then conducting a simulation-based assessment across various operating conditions. The PI controller is adjusted using traditional tuning methods, whereas the FLC is crafted to handle the system's nonlinearities. The evaluation focuses on key performance indicators such as voltage regulation, response time, and overall system performance. The results indicate that although PI controller performs adequately in steady-state scenarios, the FLC outperforms it in terms of dynamic response, especially when managing disturbances and system uncertainties. Additionally, the FLC improves the overall efficiency of the solar-powered charging system by optimizing power flow amidst fluctuating solar input and load conditions. The innovation of this study is rooted in applying a FLC to a DAB converter within the unique setting of solar-integrated fast charging for EVs, an area that appears underexplored in current literature. The proposed FLC provides a strong solution to the challenges of incorporating renewable energy into fast EV charging systems, contributing to the development of more sustainable and dependable charging infrastructure. [ABSTRACT FROM AUTHOR]

The paper examines the evolving landscape of energy sources within sports facilities. Traditionally reliant on electrical power and other forms of power from non-renewable resources such as coal, petroleum, and natural gas, these facilities face the inevitability of resource depletion and environmental repercussions. In response, this paper advocates for the adoption of new avenues, including energy conservation, emission reduction, energy recovery, and embracing renewable energy sources. A pioneering focus is on the implementation of a piezoelectric power generation system, tapping into typically wasted sources such as sound and human locomotion. An experimental approach explored the feasibility of such a system in order to ascertain its reliability, efficiency, and sustainability. With applications ranging from powering low-power electronic devices to environmental monitoring sensors, the successful integration of this technology promises a substantial advancement in the use of other renewable energy sources, diminishing reliance on non-renewable sources albeit on a small scale. The paper endeavours to shed light on the potential application of piezoelectric power generation in the electricity production field, aligning with the overarching objective of harvesting energy from unconventional sources and converting it into usable electrical energy. Moreover, it aims to shed light on the potential integration of piezoelectric and audio-electric generators in the energy or renewable energy mix of sports facilities, and address emerging technologies, societal impacts, and strategies to overcome barriers to adoption. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources and distributed energy resources (DERs) in the Malaysian low voltage distribution network has introduced voltage stability challenges, particularly voltage rises, leading to detrimental impacts on network performance. This paper presents a novel reactive power control strategy for addressing these challenges. Unlike conventional methods with fixed reactive power references, the proposed technique dynamically adjusts the reactive power reference in real time, considering voltage and active power injection. It calculates the rate of change in reactive power reference (ΔQ) per second by analyzing Volt-VAR and Watt-VAR components and updates the reference accordingly. Simulations conducted on a low voltage distribution network in Taman Impian Putra, Malaysia, showcase the adverse effects of high photovoltaic (PV) penetration on voltage stability and highlight the success of the proposed strategy in mitigating voltage rise. The technique effectively reduces average voltage, maintains voltage regulation during high sun irradiance and low load demand periods, and surpasses the adaptability of existing methods dependent on PV active injection or network voltage alone. The proposed strategy ensures accurate control and efficiently addresses dynamic network changes by accounting for both PV active power injection and network voltage. This approach offers enhanced voltage regulation, adaptability to varying network conditions, and reduced losses, making it a promising solution for mitigating voltage rise in the Malaysian low voltage distribution network. The simulations, validated using MATLAB Simulation and OpenDSS, confirm the strategy’s efficacy and potential for real-world implementation. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock's direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm's performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model. [ABSTRACT FROM AUTHOR]

Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption forecasting models need to capture feature nonlinearities, analyze system dynamics and conserve evolving temporal patterns in order to minimize the impact of noise and adapt to concept drift. Prominent estimators and standalone decomposition-based approaches may not fully address those challenges as they often yield small error rate improvements and omit optimal time series evolution. Therefore, in this work we propose a combinatorial component decomposition method focused on the selection of important renewable generation component sequences extracted from the combined output of seasonal-trend decomposition using locally estimated scatterplot smoothing, singular spectrum analysis and empirical mode decomposition methods. The proposed method was applied on five well-known kernel models in order to evaluate day-ahead consumption forecasts on linear, tree-based and neural network structures. Moreover, for the assessment of pattern conservation, an intuitive metric function, labeled as Weighted Average Unanchored Chain Divergence (WAUCD), based on distance scores and unanchored time series chains is introduced. The results indicated that the application of the combinatorial component method improved the accuracy and the pattern conservation capabilities of most models substantially. In this examination, the long short-term memory (LSTM) and deep neural network (DNN) kernels reduced their mean absolute percentage error by 46.87% and 42.76% respectively and predicted sequences that consistently evolved over 30% closer to the original target in terms of daily and weekly patterns. [ABSTRACT FROM AUTHOR]

Intensive increases in electrical energy storage are being driven by electric vehicles (EVs), smart grids, intermittent renewable energy, and decarbonization of the energy economy. Advanced lithium–sulfur batteries (LSBs) are among the most promising candidates, especially for EVs and grid-scale energy storage applications. In this topical review, the recent progress and perspectives of practical LSBs are reviewed and discussed; the challenges and solutions for these LSBs are analyzed and proposed for future practical and large-scale energy storage applications. Major challenges for the shuttle effect, reaction kinetics, and anodes are specifically addressed, and solutions are provided on the basis of recent progress in electrodes, electrolytes, binders, interlayers, conductivity, electrocatalysis, artificial SEI layers, etc. The characterization strategies (including in situ ones) and practical parameters (e.g., cost-effectiveness, battery management/modeling, environmental adaptability) are assessed for crucial automotive/stationary large-scale energy storage applications (i.e., EVs and grid energy storage). This topical review will give insights into the future development of promising Li–S batteries toward practical applications, including EVs and grid storage. [ABSTRACT FROM AUTHOR]

Today the integration of renewable energy production technologies into power systems brings a new challenge in terms of optimal usage of renewable. It is known that due to the discontinuous nature of renewable energy, the challenge can be associated with the presence of unwanted voltage fluctuations and power losses in power grids. This study performs optimization of the calculation of hosting capacity to determine the maximum amount of renewable energy that can be further expanded. This can be possible by energy storage deployment on the same power grids. The battery storages are expected to have power injection and absorption associated with discharging and charging of energy storages, respectively, to solve voltage fluctuations and power disturbances. Thus, to enhance the probability of distributed generation penetration, it is necessary to have optimal allocation and size during the renewable energy penetration in low-voltage distribution networks. Global optimization as a genetic algorithm (GA) method is used during hosting capacity assessment. The results show that using GA-based optimization, total cost and loss can be reduced by 38.8 and 73%, respectively. The practical results are verified by the IEEE-33 bus system. [ABSTRACT FROM AUTHOR]

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analyzed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic (PV) plant distributed on some modular shelters which serves to power the same routes, is carried out. The resulting system has a power of the order of 190–200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated. [ABSTRACT FROM AUTHOR]

Achieving climate neutrality requires reducing energy consumption and CO2 emissions in the building sector, which has prompted increasing attention towards nearly zero energy, zero energy, and positive energy communities of buildings; there is a need to determine how individual buildings up to communities of buildings can become more energy efficient. This study addresses the scientific problem of optimizing energy efficiency strategies in building areas and identifies gaps in existing theories related to passive design strategies, active energy systems, and renewable energy integration. This study delineates boundaries at the building and community scales to examine the challenges of attaining energy efficiency goals and to emphasize the intricate processes of selecting, integrating, and optimizing energy systems in buildings. The four boundaries describe: (B1) energy flows through the building envelope; (B2) energy flows through heating, ventilation, air conditioning and energy systems; (B3) energy flows through individual buildings; (B4) energy flows through a community of buildings. Current theories often treat these elements in isolation, and significant gaps exist in interdisciplinary integration, scalable frameworks, and the consideration of behavioral and socioeconomic factors. Achieving nearly zero energy, zero energy, and positive energy communities requires seamless integration of renewable energy sources, energy storage systems, and energy management systems. The proposed boundaries B1–B4 can help not only in analyzing the various challenges for achieving high energy efficiency in building communities but also in defining and evaluating these communities and establishing fair methods for energy distribution within them. The results demonstrate that these boundaries provide a comprehensive framework for energy-efficient designs, constructions, and operational practices across multiple buildings, ensuring equitable energy distribution and optimized performance. In addition, the definition of boundaries as B1-B4 contributes to providing an interface for energy-efficient designs, constructions and operational practices across multiple buildings. [ABSTRACT FROM AUTHOR]

This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system's resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow. [ABSTRACT FROM AUTHOR]

Leveraging the dispatchability of 5G base station energy storage (BSES) not only enables the mobile network operator (MNO) to gain additional revenue, but also facilitates the integration of renewable energy sources in distribution network (DN). However, since BSES and DN are owned by different stakeholders, integrating BSES into DN operations poses significant challenges. In this regard, this paper proposes a DN optimal dispatch model that incorporates the adaptive aggregation of 5G base stations (BSs) through a cooperative game framework. Firstly, the dispatchability of BSESs is analyzed and modelled. Considering it is difficult to dispatch every single unit optimally, an adaptive aggregation model of 5G BSs is established, where the electrical coupling degree and the communication service similarity are taken as comprehensive metrics. On this basis, an optimal dispatch model of DN based on cooperative game is constructed, where the total operational costs of 5G BSs and DN are considered as the characteristic function. The number of 5G BS clusters and the aggregating results are adjusted adaptively during optimization. The optimal aggregation of 5G BSs is achieved using the Affinity Propagation (AP) clustering algorithm. Furthermore, to solve the optimal dispatch model of the DN with enhanced computational efficiency, the particle swarm optimization algorithm integrated with second-order cone programming (PSO-SOCP) is employed. After dispatching, the benefit allocation between MNO and distribution system operator (DSO) is conducted using the Shapley value method and the Equal Profit Method to obtain an entire range of allocation results. Finally, simulations are carried out with results proving the effectiveness of the proposed method.

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

_Predicting solar energy is essential for efficient power system planning and the successful integration of renewable energy sources. This study aims to develop a framework for evaluating various machine learning models and feature selection strategies for solar energy prediction. The research applies six machine learning models, i.e., linear regression (LR), random forest (RF), neural networks (NN), K-nearest neighbor (KNN), gradient boosting (GB), and AdaBoost, to datasets from 2019 to 2021 collected at the Abiod Sid Cheikh solar station in southern Algeria. Various statistical indicators, including R2, RMSE, MAE, and Adj-R, were analyzed to assess model performance. The analysis revealed that R2 values ranged from 0.591 to 0.996 kW/m2, RMSE from 0.510 to 1.78 kW/m2, and MAE from 0.357 to 0.856 kW/m2 across different models. KNN and NN models showed significant errors, while GB and RF models demonstrated strong accuracies (RMS = 0.9). AdaBoost and LR excelled in real-time and short-term predictions, exhibiting an RMS of 0.99. This framework offers a comprehensive evaluation method for selecting the most suitable machine learning models and feature selection strategies for solar energy prediction. The findings can assist energy planners and engineers in choosing the appropriate models for accurate solar energy prediction, thereby enhancing the efficiency of power systems. Improved solar energy prediction can contribute to more reliable integration of renewable energy into power grids, supporting the transition to cleaner energy sources and reducing environmental impacts.

The global energy landscape is undergoing a significant transformation, driven by the need to reduce greenhouse gas emissions, enhance energy security, and provide sustainable and clean power solutions. Renewable energy sources (RES) such as Solar Photovoltaic (SPV), wind, fuel cells, and tidal power play a crucial role in this transition due to their abundant availability and low environmental impact. However, integrating these diverse energy sources into power systems requires advanced control strategies to ensure stability, efficiency, and reliability. Existing systems, however, face significant challenges such as unstable voltage profiles, prolonged fault settling time (FST), and high total harmonic distortion (THD), which compromise their efficiency and reliability. To overcome these limitations, a novel methodology is proposed, integrating Solar PV (SPV), wind, fuel cell, and tidal energy sources into a seven-level converter (SLC) system, which is named as Four Level RES based SLC (FLRES-SLC) system. This system is managed by an Adaptive Neuro-Fuzzy Inference System (ANFIS), fine-tuned by Cuckoo Search adopted Grey Wolf Optimizer (CS-GWO). This hybrid control strategy ensures a robust voltage profile, significantly reduces FST, and lowers THD, thereby markedly improving the overall performance of the converter system. The innovative approach offers a reliable and efficient solution for modern power systems, enhancing their integration with diverse RES and ensuring high-quality power delivery.