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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.

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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.

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ß-hydroxybutyrate (BHB) is a ketone body that serves as an alternative energy source for various tissues, including the brain, heart, and skeletal muscle. As a metabolic intermediate and signaling molecule, BHB plays a crucial role in modulating cellular and physiological processes. Notably, BHB supplementation offers a novel and promising strategy to induce nutritional ketosis without the need for strict dietary adherence or causing nutritional deficiencies. This review article provides an overview of BHB metabolism and explores its applications in age-related diseases. This review conducted a comprehensive search of PubMed, ScienceDirect, and other relevant English-language articles. The main findings were synthesized, and discussed the challenges, limitations, and future directions of BHB supplementation. BHB supplementation holds potential benefits for various diseases and conditions, including neurodegenerative disorders, cardiovascular diseases, cancers, and inflammation. BHB acts through multiple mechanisms, including interactions with cell surface receptors, intracellular enzymes, transcription factors, signaling molecules, and epigenetic modifications. Despite its promise, BHB supplementation faces several challenges, such as determining the optimal dosage, ensuring long-term safety, identifying the most effective type and formulation, establishing biomarkers of response, and conducting cost-effectiveness analyses. BHB supplementation opens exciting avenues for research, including investigating molecular mechanisms, refining optimization strategies, exploring innovation opportunities, and assessing healthspan and lifespan benefits. BHB supplementation represents a new frontier in health research, offering a potential pathway to enhance well-being and extend lifespan.

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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.

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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.

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There are numerous studies dealing with olive oil management from ancient civilizations to the mid last century, but they are limited on the historical value of information. At the same time, much knowledge is widely available and accessible on the contemporaneous production of olive oil, the necessary inputs (water and energy) and outputs (by-products) of the production process. The present study aims to shed light on olive oil extraction management from antiquity to present and to bridge the gap between archaeological and modern agricultural, engineering, and environmental disciplines. For the purposes of this study, Crete, Greece, a well-known and traditional olive oil producing region is investigated. This study is dedicated to unveil practices concerning: (a) the processing of the olives, (b) the various energy aspects per era, (c) the role of water and energy at each stage of the extraction process, and (d) management of by-products per era. The main findings support that: (a) the evolution of the extraction processes was relatively slow and remained almost the same from Minoan times until the middle of the 20th century, (b) the importance of water has been demonstrated from the beginning in the efficient extraction of the maximum amount of olive oil, (c) wastewater was first reported during the Hellenistic-Roman period due to the increased quantities produced, (d) by-product management was only considered in the previous century for environmental purposes, (e) olive oil production has been a human-based process for centuries and was greatly increased by the introduction of animals, and (f) olive oil production was further increased with the utilization of mechanical and electrical energy. It can be therefore clearly concluded that past practices have both similarities and differences with the present ones, which in turn have been optimized in terms of energy sources, water uses, olive mill equipment, and environmental considerations, to result in maximum olive oil production with minimum environmental impacts. Based on this work, important lessons can be drawn that show the historical evolution of extraction and management practices.

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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.

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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.

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This research explores the relationship between energy poverty, industrial efficiency, and the energy transition within China's digital economy from 2010 to 2022, spanning 30 provinces. Addressing the significant issue of energy poverty, where many lack access to affordable and reliable energy, the study seeks to understand its impact on industrial productivity and the broader imperative of energy transition in the face of rapid digitalization in China. Using panel data analysis, the research examines how energy poverty affects industrial production efficiency and evaluates its influence on China's ability to shift to cleaner energy sources within the digital economic framework. Findings highlight a complex interplay between energy poverty, industrial efficiency, and energy transition. It is revealed that energy poverty significantly impedes industrial productivity, with notable differences across provinces. Furthermore, the study finds a positive link between industrial efficiency and the speed of energy transition, indicating that enhancing industrial efficiency can aid in a smoother shift to cleaner energy sources. The digital economy is identified as a crucial factor in this process, providing innovative solutions to reduce energy poverty, improve productive efficiency, and accelerate the energy transition. The study emphasizes the importance of integrated strategies to tackle energy poverty, enhance industrial efficiency, and support the energy transition, particularly through the utilization of digital economy tools.

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BACKGROUND: Monochamus saltuarius is a destructive trunk-borer of pine forest and an effective dispersal vector for pinewood nematode (PWN), a causative agent of pine wilt disease (PWD), which leads to major ecological disasters. Cold winter temperatures determine insect survival and distribution. However, little is known about the cold tolerance and potential physiological mechanisms of M. saltuarius. RESULTS: We demonstrated that dead Pinus koraiensis trunks do not provide larvae with insulation. The M. saltuarius larvae are freeze-tolerant species. Unlike most other freeze-tolerant insects, they can actively freeze extracellular fluid at higher subzero temperatures by increasing their supercooling points. The main energy sources for larvae overwintering are glycogen and the mid-late switch to lipid. The water balance showed a decrease in free and an increase in bound water of small magnitude. Cold stress promoted lipid peroxidation, thus activating the antioxidant system to prevent cold-induced oxidative damage. We found eight main pathways linked to cold stress and 39 important metabolites, ten of which are cryoprotectants, including maltose, UDP-glucose, d-fructose 6P, galactinol, dulcitol, inositol, sorbitol, l-methionine, sarcosine, and d-proline. The M. saltuarius larvae engage in a dual respiration process involving both anaerobic and aerobic pathways when their bodily fluids freeze. Cysteine and methionine metabolism, as well as alanine, aspartate, and glutamate metabolism, are the most important pathways linked to antioxidation and energy production. CONCLUSIONS: The implications of our findings may help strengthen and supplement the management strategies for monitoring, quarantine, and control of this pest, thereby contributing to controlling the further spread of PWD. © 2024 Society of Chemical Industry.

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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%.

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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.

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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.

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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.

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Renewable energy sources, such as wind, tide, solar cells, etc, are the primary research areas that deliver enormous amounts of energy for our daily usage and minimize the dependency upon fossil fuel. Paralley, harnessing ambient energy from our surroundings must be prioritized for small powered systems. Nanogenerators, which use waste energy to generate electricity, are based on such concepts. We refer to these nanogenerators as energy harvesters. The purpose of energy harvesters is not to outcompete traditional renewable energy sources. It aims to reduce reliance on primary energy sources and enhance decentralized energy production. Energy storage is another area that needs to be explored for quickly storing the generated energy. Supercapacitor is a familiar device with a unique quick charging and discharging feature. Encouraging advancements in energy storage and harvesting technologies directly supports the efficient and comprehensive use of sustainable energy. Yet, self-optimization from independent energy harvesting and storage devices is challenging to overcome. It includes instability, insufficient energy output, and reliance on an external power source, preventing their direct application and future development. Coincidentally, integrating energy harvesters and storage devices can address these challenges, which demand their inherent action. This review intends to offer a complete overview of supercapacitor-based integrated energy harvester and storage systems and identify opportunities and directions for future research in this subject.

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Pulsed field ablation (PFA) is an innovative approach in the field of cardiac electrophysiology aimed at treating cardiac arrhythmias. Unlike traditional catheter ablation energies, which use radiofrequency or cryothermal energy to create lesions in the heart, PFA utilizes pulsed electric fields to induce irreversible electroporation, leading to targeted tissue destruction. This state-of-the-art review summarizes biophysical principles and clinical applications of PFA, highlighting its potential advantages over conventional ablation methods. Clinical data of contemporary PFA devices are discussed, which combine predictable procedural outcomes and a reduced risk of thermal collateral damage. Overall, these technological developments have propelled the rapid evolution of contemporary PFA catheters, with future advancements potentially impacting patient care.

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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.

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The world faces several problems related to natural gas resource rents and energy production from renewable sources. One of the main problems is the influence of energy imports, manufacturing exports, and alternative energy sources on natural gas and electricity production from renewable sources. Energy imports, manufacturing exports, and alternative energy sources can impact natural gas and electricity production. This paper examines natural gas resource rents and electricity production from renewable sources nexus from 1971 to 2021, using energy imports, manufacturer's exports, and alternative energy sources in China. Electricity production from renewable sources and manufacturing exports are negatively associated with natural gas resource rents. Energy imports and alternative energy sources positively relate to natural gas resource rents in China. These results suggest that the energy sector in China is highly interconnected and that policies that seek to promote renewable energy sources and other alternatives can positively affect natural gas resource rents. China needs to develop an energy policy considering the policy implications of energy imports and natural gas resource rents. Such a policy should focus on increasing domestic production, reducing energy imports, and ensuring adequate revenue from natural gas resource rents. Additionally, regulations could be implemented that support the development of alternative energy sources, such as requiring utilities to purchase a certain percentage of their power from renewable sources.

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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.

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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.