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• A dynamic model for a 300 MW subcritical power plant boiler is developed. • A novel dynamic exergy analysis and assessment model is proposed. • The influence of drum level control on dynamic exergy behavior is evaluate. • The asynchronous responses are discussed in flow and heat transfer processes. • The dynamic exergy analysis reveals exergy destruction rates of heating surfaces. In the era of high penetration of renewable energy sources (RESs), traditional coal-fired power plants are facing increasing requirements of operational flexibility which is limited by the inherent slow dynamics of boiler. To this end, this paper presents a comprehensive dynamic model of the heating surfaces for a 300 MW power plant boiler and analyzes the exergy performance from the perspective of transient responses. Based on the object-oriented modelling language Modelica, the multi-domain first-principal model is developed by unifying the equations of thermal, mechanical, liquid and control components and interfaces. The model accuracy is validated by the field measurements. The model takes into account the multi-scale time constants in terms of the flow and heat transfer processes and drum level control. As an extension to the conventional steady-state thermodynamic analysis, a methodology of dynamic exergy analysis and evaluation is proposed. Dynamic simulation results well describe the initial reverse response of the drum level, i.e., false water level indicator, as a result of which the mass flow rate of feedwater exhibits the oscillatory performance. It is revealed that the step disturbances of flue gas mass flow and feedwater temperature, respectively, renders the temperature responses in different positions being of multi-scale setting-time and magnitudes. The exergy evaluation reveals that the water-wall suffers from the largest dynamic exergy destruction, approximately 8.2 and 8.5 times higher than the superheater and economizer, respectively. Economizer is the most sensitive component to the drum level control performance, whose dynamic exergy performance oscillates significantly. The proposed dynamic modelling and analysis method in this paper lays a solid foundation for the development of dynamic optimal control in power plants. [ABSTRACT FROM AUTHOR]

• The thermal, rheological, and optical properties of ferrofluids and how they change with magnetic fields were discussed. • The utilization of ferrofluids in solar energy systems was detailed in a state-of-the-art analysis. • The recommendations aimed to optimize the use of ferrofluids and magnetic fields in energy systems to enhance performance. Solar energy technologies play a crucial role in the transition to sustainable energy sources; however, there is an ongoing challenge in optimizing their efficiency and performance. This review paper thoroughly exposes the potential for enhancing solar systems by incorporating ferrofluids and utilizing magnetic fields. It provides a comprehensive examination of the rheological, thermal, and optical properties of ferrofluids, as well as their behavior when exposed to magnetic fields. The paper emphasizes the ability of ferrofluids to improve heat transfer efficiency, adapt to changing conditions, and enable precise control in the conversion of solar energy. Furthermore, the review addresses the obstacles and limitations associated with integrating ferrofluids into solar technologies, proposing potential solutions and outlining future research directions. [ABSTRACT FROM AUTHOR]

[Display omitted] • PCM energy storage and PV/T coupled multi-source heat pump systems were combined. • The thermal performance, energy efficiency, and economy of the system were analyzed. • The performance of paraffin C17 and water as energy storage materials were compared. • The performance of six low-temperature PCMs applied to the system were analyzed. • Compared to the existing system, it saves 56 % of energy and 27.7 % of operating costs. To improve the application of renewable energy in the heating zone, as well as to address the limitations of air and water source heat pump. The study presents a PV/T (photovoltaic/thermal) coupled air–water source heat pump system integrated with phase change energy storage. A theoretical analytical model and TRNSYS simulation model of the system were developed to evaluate the thermal performance, energy efficiency and economics of the system. The performance of phase change energy storage was compared with that of water storage, and the effect of different phase change materials on the system characteristics. The results show that the coupled system achieves a seasonal performance factor of 2.3, a 56 % reduction in energy consumption, and a 27.7 % reduction in operating costs compared to a conventional gas boiler system. Comparative analysis with the water storage system showed that the system equipped with phase change materials performed well in terms of energy consumption, operating costs and heating stability. In addition, the density of the phase change material has a biased effect on the overall performance of the system. This paper can provide a reference value for the study of renewable energy heating systems in cold zone. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility study of cold ironing implementation on the ferry ship. • Cooperative cold ironing with green energy to enhance emission neutrality. • Reducing system and operational costs with a strategic energy management approach. • Two-stage management strategy combining system sizing and operation optimization. The cold ironing system is gaining interest as a promising approach to reduce emissions from ship transportation at ports, enabling further reductions with clean energy sources coordination. While cold ironing has predominantly been applied to long-staying vessels like cruise ships and containers, feasibility studies for short-berthing ships such as ferries are limited. However, the growing demand for short-distance logistics and passenger transfers highlights the need to tackle emissions issues from ferry transportation. Incorporating electrification technology together with integrated energy management systems can significantly reduce emissions from ferry operations. Accordingly, this paper proposes a cooperative cold ironing system integrated with clean energy sources for ferry terminals. A two-stage energy management strategy combining sizing and scheduling optimization is employed to reduce the port's emissions while minimizing system and operational costs. The proposed system configuration, determined through the sizing method, yields the lowest net present cost of $9.04 M. The applied energy management strategy managed to reduce operational costs by up to 63.402 %, while significantly decreasing emissions from both shipside and shoreside operations. From the shipside, emissions reductions of 38.44 % for CO 2 , 97.7 % for NO X , 96.69 % for SO 2 , and 92.1 % for PM were achieved. From the shoreside, the approach led to a 28 % reduction across all emission types. Thus, implementing cold ironing powered by clean energy sources is a viable solution for reducing emissions generated by ferry operations. The proposed energy management approach enables emissions reduction and delivering cost-effectiveness at ferry terminals. [ABSTRACT FROM AUTHOR]

• Techno-economic analysis of hybrid solar-wind-biomass-battery system is proposed. • Numerical modelling and varied operational scenarios for the system are formulated. • Evaluating system feasibility across various power loss probabilities is conducted. • Giza Pyramids construction outperformed by $177 saving and 8% less simulation time. The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction. Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solar-hydrogen hybrid system integration proposed for oil & gas industry. • Techno-economic-environmental optimization using novel Fick's Law Algorithm. • System sizing using Fick's Law Algorithm show an optimization in efficiency and cost. • Study shows 45% solar, 35% fuel cell, 20% gas turbine power contribution. • Renewable energy integration in oil & gas cuts CO2 emissions, boosts sustainability. The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities. The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts. The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation. Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised. This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies. [ABSTRACT FROM AUTHOR]

• The paper introduces an IoT-enabled energy management system tailored for grid-connected solar PV-powered sustainable energy management. • System offers real-time monitoring and control capabilities, enabling utilization of resources while minimizing energy consumption and costs. • The implementation of energy management system contributes to enhanced sustainability and promoting the adoption of renewable energy sources. • The practical application of the system in residential settings, demonstrating its feasibility and effectiveness in harnessing solar power. • The research sets the stage for advancements in IoT-based energy management systems, paving the way for smarter in residential energy consumption. This manuscript proposes a hybrid approach depending on the internet of things (IoT) for grid-tied solar photovoltaic (PV)-fed dc residential buildings. The hybrid approach combines the Honey Badger Algorithm (HBA) with density clustering and graph neural network (DCGNN), commonly referred to as the HBA-DCGNN method. The primary objective of the proposed method is to reduce customers' electricity costs while maintaining the desired level of comfort, such as interior temperature and appliance operation characteristics. The HBA algorithm optimizes power flow between the solar photovoltaic system, battery storage, and grid, while the DCGNN approach predicts solar PV power generation and energy demand in residential buildings. The proposed approach is done in MATLAB and is compared with the existing techniques, including the Random Forest Algorithm (RFA), Seagull Optimization Algorithm (SOA), and Wild Horse Optimizer (WHO). The outcome demonstrate that the proposed method lessens the costs of electricity by 1.12$ than the existing methods. [ABSTRACT FROM AUTHOR]

• A stack-level multiphysics model with bubble and shunt current effects is proposed. • The proposed ER curve is applied to quantify various factors on stack performance. • The reduction of single cell area will be a trend in size design for on-grid stacks. • The practical scale-up AWE stack coupled with RES is investigated. Large-capacity hydrogen production systems are crucial for promoting green hydrogen development. However, the scaling-up of electrolysis stacks through larger electrode areas or additional electrodes may increase the bubble coverage effect and shunt current effect, leading to efficiency degradation. Therefore, a quantitative model that considers interrelated factors is needed to optimize the size design during the scaling-up process. This paper presents a stack-level multiphysics model that describes the electrochemical and two-phase flow processes within the stack. The model is validated experimentally, with a relative error of the current–voltage (IV) curve within 4%. Based on the model, the energy efficiency-reaction current density (ER) curves of different stacks are analyzed and used to provide performance optimization strategies for stacks coupled with renewable energy sources (RES). The findings demonstrate that in on-grid mode, the small cell design leads to a hydrogen production rate increase of over 6%, driving a trend towards miniaturization of cell areas. In addition, during off-grid operation, the hydrogen production variations among different designs can exceed 4% due to performance differences under heavy and light loads. Therefore, optimizing electrolyzer performance requires considering power source fluctuations and conducting specialized computational optimizations based on specific scenarios. In summary, this paper proposes optimization strategies of size design for scaling up electrolysis stacks to improve stack performance, with the goal of driving the advancement of green hydrogen. [ABSTRACT FROM AUTHOR]

• Presenting a summary of more than ten mostly used Artificial Intelligence (AI) and Machine Learning (ML) methods. • Reviewing the most recent research which has been conducted in the Renewable Energy Systems (RES) field. • Classifying various AI methods and their applications in RES. • Discussing widely used approaches in the different categories of RES. Recent advancements in data science and artificial intelligence, as well as the development of clean and sustainable energy sources, have created numerous opportunities for energy researchers to conduct their studies. Artificial Intelligence (AI) and Machine Learning (ML) techniques have been applied to Renewable Energy Systems (RES) for several years, and their intensity and scope have grown in recent years. Since artificial intelligence and machine learning have a wide range of applications, it may be difficult to select and implement suitable methods for future research. In order to address this issue, this study examines several of the most popular and well-known AI techniques in renewable energy. This paper describes over ten of the most prevalent RES modeling and optimization algorithms, including Artificial Neural Networks (ANN), Long and Short-Term Memory (LSTM), Recurrent and Convolutional Neural Networks (RNNs and CNNs), the Genetic Algorithm (GA), and the Particle Swarm Optimization algorithm (PSO). More than a hundred different studies between 2020 and 2022 have been compiled and organized in the results section according to the method used and the field of application. At the end, the results are discussed, and the limitations of the current study will be mentioned, followed by some suggestions to complete our work and make it more useful. [ABSTRACT FROM AUTHOR]

• "Transition to sustainable multi-building energy systems can reduce greenhouse gas emissions by synergising the multiple renewable energy sources with specific structural characteristics and occupancy constraints of buildings." • "To ensuring energy flexibility and resilience, optimization strategies for multi-building energy systems vary significantly with the climatic context and potentials for integrating energy storage technologies, highlighting the necessity for adaptable designs considering future energy demands and the expansion of district-level systems." • "There is a pressing need for more user-friendly multi-building energy system simulation tools to facilitate effective long-term planning and to optimize the design and management of energy flows among multiple buildings, ensuring maximum efficiency and sustainability. The transition to clean and sustainable energy systems in the building sector is vital to reduce global greenhouse gas emissions. In this regard, shifting the focus from single buildings to clusters of buildings, such as at the district level, can provide more effective sustainable solutions. In this study, the term Multi Building Energy Systems (MBES) was introduced to represent various scales, from local communities to districts and larger urban areas. This approach reflects a more inclusive understanding of sustainability potentials for large-scale building blocks that goes beyond traditional boundaries. Such systems encompass the aggregated energy impacts of multiple buildings whether physically proximate or connected via smart grid technologies that enable two-way energy and information flows. This paper comprehensively reviewed case studies on MBES that have achieved zero-energy, nearly-zero-energy, and positive-energy performance. The literature review, facilitated by a combination of database searches and snowball search techniques, has yielded 67 relevant case studies. The findings revealed diverse pathways towards achieving optimal renewable energy production depending on the location and the climate. Integration of renewable energy sources and energy storage systems is pivotal in achieving economies of scale, reducing operational costs, and enhancing resilience and reliability of energy supply at the neighbourhood scale. Total energy use and emissions reduction could be achieved by considering the architectural features of the building stocks. The study highlighted the need for adaptive strategies in MBES implementation to address challenges such as power grid overloads and the complexities of spatial arrangement, to meet current demands, while ensuring long-term energy resilience and sustainability in evolving urban landscapes. The limited adoption of MBES simulation software suggests a need for accessible tailored software. Future research should focus on developing advanced personalised energy management and control strategies, while continuously assessing emerging technologies for improving MBES performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • It analyzes the feasibility of achieving net zero emission in the Chinese power sector by 2050 and the required policy measures. • It is essential to foster the concurrent development of renewable energy, energy storage, and demand response technologies. • The carbon pricing mechanism with exponential growth is more suitable. • Referring to the European offshore wind power investment model is beneficial for the development of offshore wind power in China. The low-carbon transition in China's power sector plays a crucial role in the mitigation of climate change worldwide, and increasing renewable energy penetration rate is widely recognized as the key approach to decarbonize this sector. This paper proposes a renewable energy penetration model to examine the feasibility for net zero emission by 2050 in China's power sector taking into account of economic and social development. It also evaluates the impacts of carbon pricing and financial incentives on renewable energy penetration and carbon emissions. The results indicate that along with economic development, China's electricity demand will continue to grow, but at a gradually slowing rate. It is possible for the power sector to achieve net zero emission by 2050 while meeting this growing electricity demand, with a renewable energy penetration rate of 75%. To ensure the achievement of the goal, we recommend setting the carbon price with the current market price as the baseline and increasing it annually by a relative increment index of 10%. Furthermore, given the high initial cost and late start of offshore wind power, we suggest incorporating associated grid connection cost into the transmission and distribution price to ensure the comprehensive and healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources. However, the lack of a well-set operational framework and a cost-sharing model has hindered its widespread implementation and large-scale development. This paper introduces three distinct cost-sharing methods, namely the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method, which are laterly applied to develop a combined operational and cost distribution model. The objective is to improve the efficiency of the power generation system by incorporating shared energy storage assistance and allocating the associated costs based on the use of various renewable energy stations. Furthermore, the viability of the suggested operational framework for shared energy storage and the methods for allocating costs are confirmed through numerical simulation. The findings indicate that the uniform allocation method represents the most straightforward approach for distributing costs evenly. In contrast, the second model introduces a cost allocation method that addresses the issue of fairness by considering the predicted generation level of each renewable energy plant. However, the third model takes into account the actual generation level of the plants when determining their respective costs. Sensitivity analysis is further conducted to offer valuable insights into cost-saving policies for four representative regions in China. The proposed operation and cost-sharing model is anticipated to serve as a useful reference for the widespread implementation of shared energy storage in power generation side. • Design three cost-sharing methods to allocate costs among various entities. • Formulate a combined operational and cost allocation model. • Optimize operational and cost allocation strategies for various entities. • Reduce total costs by up to 36% through the dynamic weighted allocation method. [ABSTRACT FROM AUTHOR]

• Fast Tri-level optimization approach. • HRES includes PV panels, wind turbines and HESS. • Real weather and load data of the stand-alone HRES. • The optimal solution for sizing and EMS stages. • Minimize the capital cost and maximize the efficiency of the whole system. Renewable energy sources penetrate our recent life very deeply. They have more expansion chances in the nearest future, especially in stand-alone and micro grid fields. Due to the uncertainty of the weather and the non-matching between the generated energy and demand, a hybrid energy storage system (HESS) is required. The optimization computations are the best way to select the optimal values for the capacity, sizes and quantities of the stand-alone hybrid renewable energy system (HRES) to satisfy the load demands of the available climate conditions. In this paper, the HRES which includes PV panels, wind turbines, battery banks, hydrogen tanks with fuel cells and electrolyzers has been tested and optimized using a tri-level optimization problem. Using Matlab software, the simulation results validate that the proposed method converged quickly and accurately to the optimal solution. The total capital cost was between $127,400 to $231,750. The maximum average efficiency was 83.53% at a minimum total capital cost of $127,400 and a minimum operating cost of $67 which were achieved in the same summer month presented in July. [ABSTRACT FROM AUTHOR]

The use of water electrolysis to produce green hydrogen from renewable energy resources has attracted a significant attention, where hydrogen could be utilized as a reliable energy carrier and clean fuel. Yet, Green Hydrogen Plants (GHPs) are categorized as complex and high-cost systems and thus their mass deployment and integration with power grids necessitate the development of appropriate design models. As such, this paper introduces a novel mathematical formulation to optimize the design of grid-connected GHPs. The formulated optimization model aims to find (i) the rated powers of the electrolysis, power converters, and compressor units, (ii) specifications of the internal parameters of the electrolysis stacks (membrane thickness, cell area, and cathodic pressure), and (iii) capacities of the local hydrogen storage tank and an optional battery energy storage system (BESS). The objective of the design is to minimize the Levelized Cost of Hydrogen (LCOH) considering two scenarios for hydrogen production: pure green from renewable generation and dull green from a mix of renewable and conventional power sources. The proposed formulation also accounts for the PEME degradation cost and the recently announced clean hydrogen subsidies by different governments. In addition it takes into account the PEME safety constraints. The proposed model has been numerically validated using the IEEE 30 bus transmission test system and the results show that the consideration of PEME internal parameters in the optimization model has achieved up to 20% reduction in the LCOH and 29% reduction when combined with the BESS. Further, the results show that ignoring the PEME safety constraints leads to improper GHP design and PEME parameters selection. The results also show that clean hydrogen subsidies significantly change the GHP design and the selection of the PEME internal parameters. • Proper design of hydrogen plants requires detailed electrochemical modeling of PEME. • Manufacturers need to optimize PEME internal parameters design for each application. • PEME safety constraint must be considered in the optimal design of hydrogen plants. • Economic feasibility of hydrogen plants is highly sensitive to government subsidies. • Battery storage installation in a hydrogen plant enhances its overall performance. [ABSTRACT FROM AUTHOR]

• Consider grid demands in long-term hydro-wind-PV complementary system operation. • Multiple uncertainties in runoff, wind power and photovoltaic are considered. • Develop a multi-objective double-layer model for complementary system operation. • The method has been implemented on a real-world hydro-wind-PV complementary system. The effective synergy among hydropower, wind power, and photovoltaic enhances variable renewable energy (VRE) utilization. However, existing long-term hydropower operating rules that consider runoff uncertainty neglect escalating peak-shaving demands. This paper optimizes the long-term decision-making of hydropower by supplementing the demand to buffer short-term VRE uncertainty. Latin hypercube sampling and K-means clustering reduction are utilized to generate coupling scenario sets to quantify long-term runoff and short-term VRE uncertainties. Then, a double-layer multi-objective optimization model is developed, the outer layer maximizes the long-term expected energy production of the whole system, and the inner layer minimizes hourly residual load fluctuation of power grids under daily electricity boundaries. The daily electricity is dependent on the load profile and monthly generation. Moreover, A power-electricity coupling approach is proposed to bridge long- and short-term operations and reduce the computational burden. An optimal set of pareto solutions is finally obtained with different constraint boundaries, where a compromise solution is selected by a fuzzy decision method. The model has been implemented on the Lancang River hydro-wind-photovoltaic complementary system in Southwest China. The results indicate that: (1) the model can transfer hydropower electricity from flood season to dry season, facilitate VRE integration, increase overall benefits, and improve short-term hydropower regulation capacity, especially in wet years; (2) the obtained Pareto solution set has significant advantages. Under multi-year average conditions, energy production increases by 11 million kWh, hydropower curtailment decreases by 751 million kWh, and residual load fluctuations reduce by 2200 MW, and (3) increasing transmission capacity is more conducive to improving system efficiency in wet years than in dry years, and is helpful to enhance hydropower flexibility. [ABSTRACT FROM AUTHOR]

The growing demand for sustainable systems due to climate change has led to increased reliance on renewable energy sources. However, this transition has raised concerns about power quality in power systems due to climate variations and the intermittent nature of renewables, photovoltaic energy generation in particular. In this context, uninterruptible power supply systems play a crucial role in ensuring reliable and high-quality energy supply. As an added benefit, photovoltaic energy generation may be integrated into uninterruptible power supply systems by sharing the inverter already present and storing generated energy in the batteries. In this paper, it is presented the design and management of photovoltaic energy, integrated into double-conversion uninterruptible power supplies. A method for selecting the suitable number of panels to be used in series in a given application is developed, considering the panel model used, and the environmental mission profile. A management algorithm for tracking the maximum power point of the photovoltaic generation while ensuring the hold-up time is proposed, introducing the dynamic hold-up time concept. Using the point for maximum power instead of the dynamic hold-up time at two different locations leads to voltage sags of 12.6% in the first and 24.3% in the second. Using the method to select the number of panels in series, 13 panels are selected for the first location, resulting in an annual generation that is only 2.3% smaller than the maximum power point. The results are validated using a hardware-in-the-loop platform and the dSpace Microlabbox 1202. • Design and management of photovoltaic energy in uninterruptible power supplies. • Development of algorithms for selecting the appropriate number of panels in series. • Proposal of the dynamic hold-up time concept. [ABSTRACT FROM AUTHOR]

• HRES with PV-Wind and different types of energy storage systems. • Optimal size and efficient performance of HRES. • Bi-level mixed integer nonlinear programming optimization approach. • Real application of a stand-alone HRES in a real case study, Jordan. This paper aims to design and run a standalone hybrid renewable energy System (HRES) feeding residential building load. The suggested HRES encompasses photovoltaic panels and wind turbines as energy sources, combined with a hydrogen subsystem and a bank of batteries for energy storage. The meteorological input data includes irradiance, temperature, and wind speed for four seasonal weeks. These data sets are collected for a rural site situated in the town of Ras Munif, northern Jordan. The load profiles characterize a selected benchmark residential building. Consequently, a bi-level mixed integer nonlinear programming (BMINLP) problem is formulated to incorporate the designing problem with the energy management process. The upper level represents the sizing problem, while the lower level problem denotes the energy management strategy (EMS). Each problem has its independent objective function, constraints, and optimization variables. The EMS is an economic-based single objective optimization problem expressed as a mixed integer linear programming (MILP) problem. On the contrary, a mixed integer nonlinear programming (MINLP) approach frames the sizing problem to optimize a techno-economic-based multi-objective function. The interaction between the two problems is that the lower problem is an embedded constraint of the upper problem. Moreover, the candidate solutions from the upper problem are treated as fixed parameters in the lower problem. A multi-objective genetic algorithm is used to execute the overall problem by the MATLAB toolbox. The technical and economic performance parameters, including the investment cost, maintenance, operating costs, and others, are assessed based on the best findings. The key finding demonstrates that the summer week has the lowest total cost and energy loss factor, with around 132,000 $ and 0, respectively. Furthermore, the findings reveal that the entire system's energy loss factor for each sample period is less than 0.0009. This value is substantially less than the permitted limit of 0.01. [ABSTRACT FROM AUTHOR]

• An innovative approach that uses a Differential Evolution (DE) algorithm is used. • Hybrid PV-Wind system for powering RO desalination is optimized for 100% renewable contribution. • The optimized system comprises 26 PV panels, 2 wind turbines, and 9 batteries. • A Levelized Cost of Energy (LCOE) of 0.39 $/kWh is achieved. • The cost of desalinated water falls within the economic range of (1.98–2.16) $/m3. Access to clean water is a basic human right, and reverse osmosis (RO) is a common method for producing potable water from seawater. However, the high energy demands of RO systems make them expensive to operate. Hybrid systems that combine renewable energy sources (RES), such as wind and photovoltaic (PV) systems, can reduce the energy costs associated with powering RO systems. In this paper, a novel approach that uses a differential evolution (DE) algorithm to optimize wind-PV hybrid systems for RO desalination is proposed. (DE) is a heuristic, population-based algorithm that searches for the global optimal solution. The approach aims to minimize the levelized cost of energy (LCOE) and ensure that Desalinated water costs are within an appropriate range. The algorithm was used in a case study of the city of Dhahran, Saudi Arabia, which faces significant water scarcity challenges. The results show that for an RO load demand of 1 kW, the optimized hybrid system's configuration includes a 26 PV panels with generation capacity of 7.67 kW, 2 wind turbines with 1 kW production capacity each and 9-battery storage system with 38.79 kWh storage capacity. The LCOE is 0.39 $/kWh for Dhahran city, and the cost of desalinated water falls between 1.98 and 2.16 $/m3, which is lower than the costs reported in the literature. [ABSTRACT FROM AUTHOR]

• This work presents analysis of SNG production systems with higher carbon utilization. • Cold gas efficiency of proposed SNG production systems varies from 63.27% to 77.10%. • Calculated break-even price of SNG ranges from 58 €/MWh SNG to 115 €/MWh SNG. Due to the growth in the share of renewable energy sources (RES) in the power generation sector worldwide and their intermittency, storage of surplus electricity is needed. The technology known as Power to X (PtX) facilitates the extended storage of excess electricity by converting it into gaseous or liquid fuels such as hydrogen, methane, ammonia, or methanol. This study examines the potential of synthetic natural gas (SNG) technology as a viable energy storage solution. The paper introduces three distinct SNG production systems, all of which are based on the processes of biomass gasification and methanation. Case 1 assumes further CO 2 capture from generated SNG, and Cases 2 and 3 additionally assume hydrogen production and almost complete CO 2 utilization by syngas hydrogenation via the methanation process. The methanation process converts syngas and hydrogen into SNG with a high methane content (>90 vol% dry), that can be injected into the natural gas grid. The thermodynamic and economic potential of SNG production systems is presented in this work. The simulations were conducted using the AspenONE software. The methanation process was analyzed for various design conditions such as methanation temperature and pressure, and different H 2 :CO, and H 2 :CO 2 ratios. The estimated cold gas efficiency of proposed SNG production systems varies from 63.27% to 77.10% and can be increased up to about 69.10–75.58% when the recovery of heat from methanation is considered. A sensitivity analysis was conducted to determine the break-even price of SNG, considering different scenarios for the costs of feedstock, specifically biomass and electricity. The results indicate that under the most optimistic conditions, the break-even price of SNG is estimated to be 115 €/MWh SNG , 58 €/MWh SNG and 67 €/MWh SNG for Cases 1, 2, and 3, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility of using organic wastewater as renewable energy source is verified. • ＞99% COD, TOC removal rate in the experimental platform. • Up to 78.75% of the energy recovery efficiency of the SCWO system. The degradation of high concentration organic wastewater and the recovery of energy using supercritical water oxidation (SCWO) technology have the potential to become viable in the future. High concentration organic wastewater contains a significant amount of energy. This paper focuses on utilizing high concentrated wastewater as a renewable energy source, from an energy system perspective, an experimental rig was designed to determine the optimal working conditions. The experiments were conducted with an oxidation coefficient of 1.1, a flow rate of high concentration organic wastewater of 9 ml/min, a heating temperature of 400 °C, and a reaction pressure of 25 MPa. Under these conditions, the COD and TOC removal rates of the high concentration organic wastewater were found to be 99.383 % and 99.876 % respectively. Additionally, the phenomenon of "Abnormalities in pressure sensitivity" observed during high concentration organic wastewater the experiment was further explained through additional experiments. Finally, by comparing the experimental energy recovery with the simulation results, an energy recovery efficiency of 78.95 % was achieved. The feasibility of the simulated energy recovery in practice was verified, confirming the potential to convert the previous energy-consuming wastewater treatment process into a productive one. This study provides a theoretical and experimental foundation for further related research. [ABSTRACT FROM AUTHOR]

[Display omitted] • A smart energy harvesting tile was proposed with a combination of MP, MI, and MVF mechanisms. • Different real scenarios showed that the duration of energy extraction is increased compared to other mechanisms. • Experimental studies showed the possibility of harvesting energy with all the weight range of people. • Due to the increase in tile energy production, it can be used as a sustainable energy source. • The energy extracted from the proposed framework compared to the MP, MI, and MVF mechanisms showed significant growth. In this paper, a new framework based on combining three different mechanisms is proposed to improve electrical energy harvesting from piezoelectric smart tiles. These mechanisms are named magnetic plucking (MP), mechanical impact (MI), and mechanical vibration force (MVF). The MP mechanism uses the magnetic interaction between an iron rod and a magnet to excite the consoles used in the tile. In the MI mechanism, the piezoelectric cells are stimulated by the mechanical function of the cantilevers. Also, in the MVF mechanism, piezoelectric cells are stimulated by the cantilever oscillations of MP and MI. In the experimental design procedure of the proposed smart tile structure, at first, the smart tile based on consoles made of MP, MI, and MVF mechanisms are designed. Then, to compare the electrical energy harvesting of the proposed structure with other tiles, the proposed smart tile with 36 piezoelectric consoles, which is a combination of three mechanisms, is implemented. To protect the piezoelectric cells against excessive bending during the operation of the consoles, the swing of the consoles is limited by using a plastic stopper. The proper air gap between the magnet and the iron rod as well as the air gap between the console and the stoper are determined by actual tests. In the energy harvesting evaluations of the proposed structure, the energy and average power of 71.0 mJ and 13.6 mW were obtained at the optimal resistance load of 100 kΩ. Experiments on energy harvesting from tiles made of MP, MI, and MVF mechanisms were also conducted. When comparing the electrical energy harvested from the proposed framework, utilizing rectification with the diode bridge and capacitor filter, with that generated by the MP, MI, and MVF mechanisms, a substantial increase in energy yield was observed. Also, the average energy harvesting time of the proposed structure has increased by 59.4 % compared to the other three mechanisms. [ABSTRACT FROM AUTHOR]

• A control-oriented solid oxide electrolysis cell dynamic model was developed. • EFAST analysis identified the most sensitive control signals for SOEC temperature. • A real-time feedback control on SOEC temperature was proposed. • The control algorithm's effectiveness was verified by actual photovoltaic output. Power-to-liquid (PtL) technology through solid oxide electrolysis cells (SOECs) can efficiently convert renewable energy sources into synthetic fuels for long-time energy storage. However, due to its fluctuation and intermittence, renewable energy sources only produce time-varying currents, which directly impact the operating temperature of SOEC, generating high thermal stresses for SOEC and reducing its durability. To deal with this issue, a control-oriented SOEC dynamic model containing an electrochemical part, a reversible water gas shift (RWGS) part, and a thermodynamic part, was developed in this paper. Afterward, main control parameters, able to adjust SOEC operating temperature effectively, were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST). The corresponding results show that the air flow rate was the best choice to control SOEC temperature when it works in a low current density case, while the fuel flow rate becomes optimal when SOEC operates in a high current case. Given this knowledge, an adaptive feedback control strategy was proposed to stabilize the operating temperature of SOEC in practice. In the end, the effectiveness of the proposed control was demonstrated through step signals of current in both low- and high-current density cases and an actual fluctuating current from real solar cells. [ABSTRACT FROM AUTHOR]