Innovative integration of sustainable technologies in educational programs: Fostering freshwater production and environmental preservation awareness.

This study highlights the integration of sustainable desalination technologies into educational programs to raise awareness of freshwater production and environmental preservation. Through a comprehensive curriculum, students explore innovative methods such as pressure-retarded osmosis, multi-effect desalination, and seawater source heat pumps, all powered by renewable seawater thermal energy. The curriculum emphasizes the importance of reducing greenhouse gas emissions, lowering reliance on fossil fuels, and protecting aquatic ecosystems. Students engage in practical evaluations of energy efficiency, economic viability, and environmental impact. Sensitivity analyses are incorporated to help students identify critical factors affecting system performance and optimize operational conditions for various modes. Through hands-on experiments, students learn that components like the heat pump condenser contribute the most to energy loss (29 %), followed by the expansion valve (12 %), compressor (11 %), and seawater heat exchanger (8 %). Economic analyses reveal that while the heat pump condenser and seawater heat exchanger have the lowest financial impact (0.33 % and 2.48 %, respectively), the pressure-retarded osmosis and compressor units have the highest (100 % and 60.3 %, respectively). The findings demonstrate that an optimally designed desalination system can produce freshwater at 80 % lower costs compared to traditional plants, while also reducing carbon emissions by 15 %. Educational experiments also show that integrating pressure-retarded osmosis downstream of multi-effect desalination significantly reduces brine salinity and temperature, highlighting the system's potential for environmental sustainability. This approach not only fosters a deeper understanding of desalination technologies but also equips students with the tools to address future global water challenges.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources.

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80-90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids.

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Techno-economic and environmental analysis of hybrid energy system for industrial sector of Pakistan.

The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution. A sustainable, cost-effective, and environment-friendly solution can help the industrial growth of Pakistan. This article proposes an optimal hybrid energy system (HES) for the industrial sector of Pakistan to overcome the mentioned challenges. The proposed HES is developed in HOMER Pro. Three different energy cases (Case I: Existing energy system including a utility grid and diesel generator, Case II: On-grid Biogas system, and Case III: On-grid PV system with batteries) are considered for the Gourmet food Industry in the Sundar Industrial estate, Pakistan. The Load profile of the selected site was calculated through on-site visits and data provided by the designated utility grid feeder. The analysis shows that Case III is more effective than other cases, indicating reduced Net Present Cost (NPC), Cost of Energy (COE), and Operating Cost (OC) to $ 19.2 million, $0.034/kWh, and $ 573,371/year respectively. Moreover, the On-grid PV system with batteries (Case III) provides an environmentally friendly solution by reducing 63.82% [Formula: see text] by and 62.22% [Formula: see text]. Comparing the sensitivity analysis for various grid sell-back prices ($0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh), Case III is more cost-effective than Case II. The revenue generation in Case III is $128,499.41/yr, considering the supply of excess electricity into nearby small industrial loads at $0.065/kWh, this indicates that installing optimal HES in industries will not only help in overall cost reduction but also support in mitigating environmental pollution and load shedding.

Performance comparison between PID and Fuzzy logic controllers for the hardware implementation of traditional high voltage DC-DC boost converter.

This research introduces a hardware implementation of DC-DC boost converter designed to elevate the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter application. The main objective is to boost the DC link voltage to the level of Vmax in the output AC voltage obtained from inverter circuits. This enables the inverters for transformer-less power conversion from DC to AC to reduce magnetic losses, size and weight of the inverter circuits used in the utility application. The proposed converter's topology and switching sequences play a crucial role in enhancing overall performance. Utilizing a Zero Current Switching (ZCS) technique, the converter efficiently recovers stored energy from the magnetics. The proposed converter attained the output voltage of 350 V at its current of 1A from the input voltage of 20 V at its current of 19 A. The ZCS technique and the topology of the converter enhances the efficiency to 92 %. The study employs traditional Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for effective voltage regulation, analysing time domain specifications. Additionally, a Fuzzy logic controller is introduced as an alternative to PID controllers to compare their performance metrics, evaluating the optimization of the converter's transient and steady-state behaviours. The proposed converter is designed, simulated and their performance metrics are analysed using MATLAB for both with and without controllers. The step-time characteristics of the proposed converter with load resistance of RL = 500 Ω and an input voltage of Vi = 20 V has been determined and analysed. The PID system attained a rise time of 88.781 ms, an overshoot value of 9.341 %, and a steady-state error of 0.00043. The fuzzy system achieved a low-rise time of 10.624 ms, a low overshoot of 0.55 %, and a steady-state error of 0.0584. The hardware prototype of the proposed converter is implemented with a FPGA based PID and Fuzzy logic controllers for providing better voltage regulation and to improve the performance metrics of the converter. The simulation and experimental findings are contrasted, examined, and confirmed to ensure improved consistency in performance measures.

Optimal techno-economic assessment of isolated microgrid integrated with fast charging stations using radial basis deep learning.

The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into  the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES's uncertainties on the selection criterion of the FCS's components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time.

Technoeconomic analysis of solar PV electrification to remote areas of Dera Ghazi Khan: A case study.

With the increase in population at an immense rate, electricity demand is growing exponentially. Researchers and policymakers are seeking alternating means of power generation to meet the load demand. These resources should be cost-effective, environmentally friendly and least carbon emissions. To mitigate the load demand Renewable Energy Sources (RES) are integrated into electrical networks. In this work, an off-grid solar photovoltaic (PV) system is designed for rural areas of Dera Ghazi Khan (DG Khan), Pakistan. These areas often lack access to reliable grid power. Installing PV systems in these areas can help provide a reliable source of electricity, reduce reliance on fossil fuels, and improve living conditions. This case study is simulated using PVsyst 7.2. of Roonghan village such as hospitals, shops, and residential houses for techno-economic analysis of off-grid solar PV. The economic viability shows that installing off-grid solar PV in DG Khan is much cheaper. The electricity taken from the grid that's almost 80.01%is expensive. This case study helps other researchers and policymakers to mitigate the electricity requirement of remote areas located far away from the national grid. To install a transmission line, having cost $26.98 corer but using an off-grid to provide electricity to the same area is $2.16 corer. Additionally, it integrates Battery Energy Storage (BES) with Renewable Energy Sources (RES) to achieve a 5 % annual energy cost reduction and enhanced self-sufficiency, filling a gap in existing literature.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks.

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Integration of renewable energy sources using multiport converters for ultra-fast charging stations for electric vehicles: An overview.

The rise of electric vehicles (EVs) necessitates an efficient charging infrastructure capable of delivering a refueling experience akin to conventional vehicles. Innovations in Extreme Fast Charging (EFC) offer promising solutions in this regard. By harnessing renewable energy sources and employing sophisticated multiport converters, EFC systems can meet the evolving demands of EV refueling. A single-stage topology simplifies the converter design, focusing on efficient DC-AC conversion, vital for feeding solar power into the grid or charging stations. It provides power factor correction, harmonics filtering, and mitigates power quality issues, ensuring stable and efficient operations. Converters with Maximum Power Point Tracking (MPPT) capability facilitate the efficient integration of solar PV systems in charging stations, ensuring maximum solar energy utilization for EV charging. The ability to operate in different modes allows seamless integration with energy storage systems, storing excess solar energy for use during night-time or peak demand periods, enhancing overall efficiency and reliability. Advanced converters support bidirectional energy flow, enabling EV batteries to discharge back to the grid, aiding grid stability and energy management. However, robust control algorithms are needed to handle dynamic conditions like partial shading more effectively. Our review focuses on integrating renewable energy sources with multiport converters, providing insights into a novel EV charging station framework optimized for EFC topology. We highlight the advantages of multiport non-isolated converters over traditional line frequency transformers, particularly in medium voltage scenarios, offering enhanced efficiency and versatility for EFC applications.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning.

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs' hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4-23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid.

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm.

The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network.

Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.

Rural electrification in Nigeria: A review of impacts and effects of frugal energy generation based on some of e-waste components.

Nigeria is the seventh most populous country in world being the highest in Africa. The country is blessed with vast natural resources and is one of the highest producers of oil in the world. However, the inadequate supply of electrical energy is a major setback in the nation's economic development. Thus, there is need for an urgent and immediate solution to address the electricity access situation in Nigeria. It is in view of this that we first present an overview of the electrical energy situation of Nigeria (especially in the rural areas). The benefits of rural electrification and it impacts are discussed to buttress the need for electrifying rural areas and an overview of the abundant renewable energy resources in Nigeria is presented. As a proposed solution to improve the electricity situation, the concept of a reuse solar photovoltaic system based on e-waste components and old materials is presented. The system comprises repurposed Power Supply Unit (PSU) from old desktop computers, old thermal car Lead-acid batteries, old solar panels and Uninterruptible Power Supply (UPS) units. The possibility of adopting this solution in Nigeria depends on the amount of e-wastes generated annually thus necessitating the need for an analysis to see the annual impact of this system on electricity access based on the amount of available e-waste. Using the huge amount of e-waste generated/received annually in Nigeria, the feasibility of our solution is assessed by estimating the possible number of households that could be electrified by the second life renewable energy systems we propose. Due to the lack of official data in this field, certain constraints and assumptions were defined for the purpose of this analysis which resulted in obtaining a range of results that showed the possible impacts of adopting the reuse system. The analysis showed the minimum and maximum impacts the reuse solution could have on electricity access in Nigeria, based on best and worst case scenario respectively. The results further showed that an average of 287,000 households can be electrified annually if this solution is adopted, causing 2.2 % increment in population with electricity access in a year (between 620 thousand and 4.1 million individuals). Thus, the result is an indication that it is possible to achieve immediate growth in electricity access based on renewable energy integration, frugal innovation and reuse/repurposing of e-waste materials. In addition, this extension of their lifespan reduces their ecological footprint. It is expected that the energy demands of the continuously growing population can be met by strict adherence to set targets including adoption of smart-grids, generation diversification and focusing on rural electrification.

Designing new energy markets to promote renewables.

The drive toward decarbonization has spurred the growth of renewable energy sources, reshaping energy production and consumption patterns. As the energy landscape evolves, so must the market design supporting it to steer the integration of renewable energy. Addressing the challenges of promoting distributed renewable energy is paramount for developing a cleaner energy system and meeting decarbonization targets. This study presents a modern market design that efficiently integrates renewable energy sources, long-term contracts, and flexibility technologies into a single evolved market framework. The approach described herein provides proper price signals for diverse assets and decouples renewable energy from fossil fuels, ensuring economic viability and efficient integration. Taking into consideration key barriers and drivers, the findings provide insights for perfecting energy markets, meeting decarbonization targets, and guiding policymaking to boost cleaner energy systems.

A comprehensive review of wind power integration and energy storage technologies for modern grid frequency regulation.

Integrating wind power with energy storage technologies is crucial for frequency regulation in modern power systems, ensuring the reliable and cost-effective operation of power systems while promoting the widespread adoption of renewable energy sources. Power systems are changing rapidly, with increased renewable energy integration and evolving system architectures. These transformations bring forth challenges like low inertia and unpredictable behavior of generation and load components. As a result, frequency regulation (FR) becomes increasingly important to ensure grid stability. Energy Storage Systems (ESS) with their adaptable capabilities offer valuable solutions to enhance the adaptability and controllability of power systems, especially within wind farms. This research provides an updated analysis of critical frequency stability challenges, examines state-of-the-art control techniques, and investigates the barriers that hinder wind power integration. Moreover, it introduces emerging ESS technologies and explores their potential applications in supporting wind power integration. Furthermore, this paper offers suggestions and future research directions for scientists exploring the utilization of storage technologies in frequency regulation within power systems characterized by significant penetration of wind power.

The crucial roles of ICT, renewable energy sources, industrialization, and institutional quality in achieving environmental sustainability in BRICS.

The BRICS countries-Brazil, Russia, India, China, and South Africa-are committed to achieving United Nations Sustainable Development Goal 13, which focuses on mitigating climate change. To attain this goal, it is crucial to emphasize the significance of ICT, renewable energy sources, industrialization, and institutional quality. This study contributes to the literature by examining the potential role of these factors in environmental sustainability in the BRICS economies from 2000 to 2021, utilizing cross-sectional augmented autoregressive distributed lag (CS-ARDL) estimation and other novel econometric techniques. Accordingly, the study suggests that BRICS governments and policymakers prioritize the use of ICT in the industrial and institutional sectors to achieve faster environmental sustainability in the short-run, as per the CS-ARDL results. However, the study advises caution in the long-term as the interaction between ICT and renewable energy sources, industrialization, and institutional quality may not favour environmental quality. Although the renewable energy sources interaction with ICT may not yield immediate progress, strong measures need to be taken to ensure that short-term gains are not nullified. In conclusion, the study highlights the potential of ICT, renewable energy sources, industrialization, and institutional quality in achieving environmental sustainability in the BRICS countries, while recommending cautious measures in the long run to safeguard the progress made.

Techno-economic and environmental analysis for the application of renewable energy sources in seaports.

Ports have an indisputable effect on the decarbonization of urban areas, helping to minimize air and environmental pollution and achieve sustainable development. In this instance, it is crucial to do research that can advance our understanding of how to increase ports' energy independence by utilizing renewable energy sources. The current study aims to study the environmental benefits and techno-economic challenges of converting three Egyptian ports to eco-friendly green ports by using solar panels, offshore wind turbines, and hydrogen fuel cells. The study shows that from a technical point of view, the required green power to be installed at Alexandria, Port Said, and Suez ports is around 13 MW, 5 MW, and 1.5 MW, respectively. Furthermore, the environmental analysis findings demonstrate that integrating green energy will significantly lower emissions in seaports. It is anticipated that the ports of Alexandria, Port Said, and Suez will achieve annual reductions in carbon dioxide emissions of roughly 68,7 k-tons, 25,8 k-tons, and 6,4 k-tons, respectively. From an economic point of view, the ports could be supplied with green energy from wind turbines for a cost of between 0.115 and 0.125 USD/kWh, while solar panels have a cost range of 0.098 to 0.129 USD/kWh. Additionally, hydrogen fuel cell systems cost about 0.102 USD/kWh.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07 M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.