Sustainable Power Production Research Articles

Techno-economic and advanced exergy analysis and machine-learning-based multi-objective optimization of the combined supercritical CO2 and organic flash cycles

In the past few years, attention has been directed towards the advancement of renewable-driven supercritical CO2 recompression Brayton (SCRB) cycles. This highlights the impacts of sustainable and efficient power production towards transition in the electricity sector. The combination of the SCRB cycle with the organic flash cycle (OFC) is comprehensively investigated from thermodynamic, techno-economic, environmental, and advanced exergy perspectives. Conventional thermodynamic analysis alone cannot provide information on the cause of irreversibilities, the interconnections between components, and the possibility of enhancing their performance; therefore, advanced exergy analysis is implemented. Furthermore, an examination has been conducted into the effects of different decision parameters on the exergoenvironmental indicators. Multi-objective optimization based on machine learning is performed on the SCRB/OFC plant to maximize exergy efficiency, net present value, and net power output. Results from advanced exergy analysis revealed that the reactor has the worst performance, with just 2.8 % of its total exergy destruction being avoidable. Furthermore, it is feasible to decrease its total exergy destruction rate by 21.6 % through enhancements in the performances of other components. Under the optimized conditions, exergy efficiency, net present value, and net power output are found to be 70.8 %, 1347.1 M$, and 306.4 MW, respectively, representing a 21.1 %, 68.9 %, and 21.1 % enhancement compared with the base conditions, respectively.

Machine learning and deep learning models based grid search cross validation for short-term solar irradiance forecasting

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results.

The Use of Hydrogen as Fuel Gives Rise to Emerging Environmental Concerns

Given the urgent global warming situation and the need to decrease carbon emissions, this research project is essential for examining alternative energy sources, like hydrogen, for sustainable transportation and power production. The increasing urbanization and enhanced urban mobility emphasize the significance of building energy-efficient transport systems, such as Light Rail Transport, to offset environmental consequences. This study seeks to enhance the current discussion on moving towards a more sustainable and ecologically aware energy model by examining the practicality and advantages of these developing energy alternatives.

AI-assisted optimal energy conversion for cost-effective and sustainable power production from biomass-fueled SOFC equipped with hydrogen production/injection

This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425 kW, 48.5 %, and 2.3 M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.

(FA) Fly Ash And (CAD) Coal Ash Dams: A Potential Hotspot For Environmental Pollution Study Of Resources Emission And TheirConsequences Among Humans Inhabiting In Developmental Areas Of Over Bridges Across National Highway India

FA (FA) is a major output of Thermal Power Plants (TPP) formed when coal is burnt it produces Fly Ash (FA) trapped in Electrostatic Precipitator termed as EPA and left over at bottom of power plants. As per Government of India, this Fly ash can be directly used as better option for landfill material during any construction of Overbridges and Underpass in and around Maharashtra State. During our study, we do find apart of its major use as Land filler option it create havoc in environment. Study comprises two distinct methods Direct Observation and Line Transect Methods. To accomplish objective overall six villages were selected in and around site of construction. Our result suggest chemical contents of FA are extremely hazardous for local villagers it contains mercury,antimony , silica, aluminum oxide , ferric oxide and other oxides material acting as passive contributing factor in physical environment causing all three major types of pollution viz. air pollution ,water pollution , and soil pollution resultant consequences of human health problems .residing nearby construction highway areas .For sustainable power production there are recommended package and practices as measure to be adapted by Government of India .

Sustainable biomass-driven heating, Bio-hydrogen, and power production scheme with desalination unit

In this study, a multi-generation energy system is modeled for the purpose of simultaneous producing distilled water as well as biofuels, electrical power, and thermal energy from wood chip biomass. The system is triggered by a gas turbine cycle with two different agents of steam and air. The system performance is evaluated by changing the gas turbine cycle working fluid and their performances are compared in details. The research findings indicate that the air-powered system demonstrated an efficiency of 67.38 %, surpassing the steam-powered system which exhibited a slightly lower efficiency rate of 67.17 %. The results indicated that the air-driven system displayed superior emission characteristics in comparison to the steam-driven system. The air-based system was found to release 510.3 g/kWh of carbon dioxide, whereas the steam-based system exhibited a marginally higher emission rate of 511.9 g/kWh. The study's results indicated that the air-powered system exhibited superior performance in comparison to the steam-powered system. The study illustrated that the integration of wood chip biomass into energy systems offers an environmentally sustainable and economically viable solution to address the increasing need for clean technologies.

Optimal 4E evaluation of an innovative solar-wind cogeneration system for sustainable power and fresh water production based on integration of microbial desalination cell, humidification- dehumidification, and reverse osmosis desalination

Reliable freshwater production is vital for tackling two of the most critical challenges the world is facing today: climate change and sustainable development. Present work presents an innovative cogeneration system based on solar and wind energies for sustainable production of freshwater, power, and wastewater treatment. For freshwater production and wastewater treatment in this system, the integration of a microbial desalination cell with a humidification-dehumidification and reverse osmosis desalinations has been used. The mentioned systems provide their heat demand from solar energy, and when solar radiation is unable to provide this heat, a hydrogen internal combustion engine drive produces the required heat for the freshwater plant. Excess heat from the hydrogen internal combustion engine is fed into the organic Rankine cycle for more power generation in the whole system to reduce system waste heat and increase efficiency. PEM electrolyzer has been used to supply the hydrogen needed by the internal combustion engine, and this system uses wind turbines to supply the power demand. To evaluate the performance of the whole system, energy, exergy, exergeoeconomic, and exergoenvironmental (4E) analyses have been carried out. Finally, to improve the performance parameters of the system, multi-objective optimization using the Salp swarm algorithm has been used. Investigation of the results show that the proposed system can produce 720 kW of electricity and 5.36 m3/h of freshwater. The energy efficiency of the system is 22.09 %, and its overall cost rate and overall environmental impact rate are 540.33 $/hr and 17.37 Pt/h, respectively. Among the qualitative results obtained in this research, it is possible to mention the high exergy destruction, cost destruction, and environmental impact destruction of the internal combustion engine compared to other equipment used in the proposed system, and this point shows the need to improve this equipment, similar to previous researches. The five-objective optimization results of the proposed system showed that the performance parameters of the system, such as polygeneration energy efficiency, total cost rate, and total environmental impact rate, can be improved by 6.2 %, 1.44 %, and 0.52 %, respectively. The payback period of the proposed system in the optimal state is 6.95 years.

Valorizacija otpadne poljoprivredne biomase kao goriva za održivu proizvodnju energije kosagorevanjem

Waste agricultural biomass, like the crop residue, is an abundant indigenous renewable energy source in Serbia. As such, the agricultural residue might be utilized for sustainable power production. Direct co-combustion of biomass with coal/lignite offers a viable option to remove this kind of waste and, in the same time, to use its energy potential, mitigate harmful emissions and retrofit existing coal-fired power plants cost-effectively. However, there are serious issues regarding the waste management and the co-firing process itself, yet to be solved. The main aim of this paper is to present benefits and challenges of co-firing the waste agricultural biomass, as a promising technology for biomass valorization and decarbonization in energy, focusing the agricultural residues energy potential, as well as characteristics, preparation and efficient utilization of the biomass as an alternative fuel in power sector.

Penjanaan Kuasa yang Bersih: Kajian Mendalam Penilaian Kitaran Hayat bagi Sel Bahan Api Oksida Pepejal

Nowadays, there is a growing emphasis on creating alternative power generation methods to replace outdated technologies like coal-fired power and hydroelectric plants. One promising solution is the Solid Oxide Fuel Cell (SOFC), which offers high energy efficiency, low carbon emissions, and cost-effectiveness. This study presents a focused examination of the Life Cycle Assessment (LCA) of SOFCs, with the objective of reducing dependence on nonrenewable energy sources. The review encompasses a review of published articles between years 2016 to 2023 and an analysis of research gaps in SOFCs' environmental performance. A systematic literature review underpins this investigation, where several publication selection criteria were considered including plants that can produce hydrocarbon such as biogas and biomass, utilization of LCA software as a method of assessment and incorporation of the life cycle impact assessment in the articles. The results suggest that using SOFC powered by biogas as a stationary power generator is a viable option. This is supported by recent LCA studies demonstrating that SOFCs using biogas have a reduced impact on climate change compared to other fuels. Implementing SOFCs holds great potential for a cleaner energy future, aligning with society's goals for sustainable power production. For a more precise comparison of results, future LCA studies should adopt a multi-criteria environmental impact analysis using a consistent functional unit across different SOFC operations.

Influence of Inclination Angle at the Chimney Inlet on the Power Generation in Solar Chimney Power Plants through 3D CFD Model

The sun is an abundantly available and clean renewable energy source. Therefore, solar energy offers significant potential for mitigating climate change and reducing emissions from burning fossil fuels in the future. Solar chimney power plants (SCPPs) have a technical capability for meeting the massive sustainable power production. Basic parts of SCPP system are the chimney, turbine, and collector. The geometric dimensions of the components are the crucial factors for improving the solar chimney efficiency. The goal of this work is to analyse the influences of the inclination angle (θ) at chimney inlet on performance characteristics of the system by employing RNG k−ε turbulence model coupled with discrete ordinate (DO) solar ray tracing method via ANSYS Fluent CFD software. The model is built by taking into consideration geometric parameters of Manzanares plant and verified with its measurements. The innovative chimney entrance configurations are produced by altering the chimney entrance slope (θ=50°–80°) with the geometrical dimensions of the chimney, collector, and fillet keeping constant. The computational results display that the new chimney configurations improve the maximum velocity, system power output, and turbine pressure drop. The peak velocity of 18.1 m/s is gained for the configuration with θ=80° compared to that of 14.3 m/s obtained for the base model having θ=45° at 1000 W/m2. Besides, this configuration enhances power output to 61.5 kW with a rise of 24.5% compared to the base model with a power output of 49.1 kW at 1000 W/m2.

A novel multi-generation system for sustainable power, heating, cooling, freshwater, and methane production: Thermodynamic, economic, and environmental analysis

Addressing environmental concerns and developing efficient systems pose significant challenges for researchers. This paper presents a groundbreaking multi-generation system designed to generate power, heating, cooling, freshwater, and methane. The system comprises a flash-binary power plant, a modified transcritical CO2 cycle, a proton exchange membrane electrolyzer, and a methanation unit. Integration of the modified transcritical CO2 cycle with the ejector refrigeration cycle, solar still desalination unit, and heating unit enhances its functionality. The proton exchange membrane electrolyzer supplies hydrogen to the methanation unit. The thermodynamic, economic, and environmental approaches are employed to analyze the system, and three scenarios are considered to assess the optimum state. Accordingly, the exergy efficiency, products' total cost, and exergoenvironmental impact rates are obtained at about 28.30%, 4.153 $/h, and 73.48 mPts/h, respectively. The gas heater unit has the highest exergy destruction rate and cost rate and exergoenvironmental impact rate by 37.6%, 36.4%, and 22.7% portions, respectively. The optimal state provides 33.25% exergy efficiency, 450.9 kW net power, 10.07 kg/h freshwater, and 2.149 kg/h methane. The cooling and heating production rates are attained 28.16 kW and 183.5 kW. The product's cost rate and exergoenvironmental impact rates are estimated at about 4.357 $/h and 76.64 mPts/h.

The impacts of nanoscale silica particle additives on fuel atomisation and droplet size in the internal combustion engines: A review

The combustion process in compression ignition (CI) engines is complex and affects their efficiency and emission levels. Internal combustion engines (ICE) are being studied to find better ways to burn fuel and produce less pollution to meet the growing demand for these qualities. However, one intriguing avenue is the utilisation of nanoparticle additives, such as silica nanoparticles, to enhance fuel atomisation and droplet size. This study aimed to comprehensively review the impact of silica nanoparticle additives on fuel atomisation and droplet size in internal combustion engines. This review explores the researchers' underlying mechanisms and experimental techniques to determine nanoparticle fuel additives' overall impact on engine performance. The results achieved from the literature study indicated that incorporating these nanoparticles (following the engine design and fuel formulations) can enhance combustion efficiency and reduce exhaust emissions, thereby contributing to developing more sustainable transportation and power production systems.

Thermodynamic and Thermo-Economic Analysis of a Solar-Integrated Double-Turbine Kalina Cycle for Varying Solar Flux Conditions

Abstract Kalina cycle is established as a reliable low-grade energy cycle working on solar, geothermal, and other waste heat recovery sources. This work aims to develop a novel methodology for optimizing a Kalina cycle according to the solar irradiation. A comprehensive analysis of performance is conducted by varying the parameters of the Kalina system, modeled with high- and low-pressure turbines. The present work implements and analyzes the performance of a multi turbine Kalina cycle with cylindrical parabolic collectors for energy input at different time, on a particular day, for a location. The proposed cycle is modeled to simulate the working. The dependency of parameters—separator pressure, concentration of ammonia in boiler, intermediate separator temperature and vapor fraction at condenser side turbine exit—on the system performance is investigated. Optimization is conducted using genetic algorithm with net power as objective function for different solar irradiations. The optimized power values are 282.62, 246.75, 222.31, and 180.0 kW for solar influxes 507.7, 461.8, 413.9, and 321.0 W/m2 respectively. The results show that the proposed model can be adopted for better performance. A thermo-economic analysis of an optimized output is conducted to conclude on capital investment and operation cost for sustainable power production. The analysis yields highest cost rate of exergy destruction of 58936.41$/year for the boiler. The investment cost of the turbines together is 89% of the total capital investment, and hence, thermo-economic factor is highest for these components.

Fresnel Lens Solar Pumping for Uniform and Stable Emission of Six Sustainable Laser Beams under Non-Continuous Solar Tracking

A multirod solar laser approach is here proposed to attain uniform and stable multibeam emission under non-continuous solar tracking. A Fresnel lens was used as the primary concentrator. The laser head was composed of a second-stage aspherical lens with a light-guide homogenizer and a third-stage conical pump cavity with six Nd:YAG rods. The solar laser system was optimized through numerical analysis in both Zemax® and LASCAD™ software to obtain six 1064 nm laser beams of similar multimode power. To investigate the effect of the homogenizer on the laser performance, the laser head was compared with a similar one that only used the aspherical lens in the second stage. The approach with the light guide attained a slightly lower efficiency than the one without the light guide; however, the tracking error width at 10% laser power loss was higher and, most importantly, only a 2.17% coefficient of variation of the laser power emitted by the six rods at the tracking error angle of ±0.5° was obtained. This is 4.2 times better than the 52.31% obtained with the laser head without the homogenizer and 76 times better than that of the previous numerical work. The light guide is thus essential to ensure uniform and stable solar laser power extraction from all rods even under non-continuous solar tracking, making this prototype the ideal for multibeam laser applications where uniformity and stability of the laser power are indispensable. This renewable multibeam solar laser may replace the classical lamp- and diode-pumped lasers, therefore ensuring a sustainable laser power production pattern for both space and terrestrial applications.

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

Humidity-Tolerant Moisture-Driven Energy Generator with MXene Aerogel-Organohydrogel Bilayer.

Free-standing and film-type moisture-driven energy generators (MEGs) that harness the preferential interaction of ionized moisture with hydrophilic materials are interesting because of their wearability and portability without needing a water container. However, most such MEGs work in limited humidity conditions, which provide a substantial moisture gradient. Herein, we present a high-performance MEG with sustainable power-production capability in a wide range of environments. The bilayer-based device comprises a negatively surface-charged, hydrophilic MXene (Ti3C2Tx) aerogel and polyacrylamide (PAM) ionic hydrogel. The preferential selection on the MXene aerogel of positive charges supplied from the salts and water in the hydrogel is predicted by the first-principle simulation, which results in a high electric output in a wide relative humidity range from 20% to 95%. Furthermore, by replacing the hydrogel with an organohydrogel of PAM that has excellent water retention and structural stability, a device with long-term electricity generation is realized for more than 15 days in a broad temperature range (from -20 to 80 °C). Our MXene aerogel MEGs connected in series supply sufficient power for commercial electronic components in various outdoor environments. Moreover, an MXene aerogel MEG works as a self-powered sensor for recognizing finger bending and facial expression.

Electricity Production and Sustainable Development: The Role of Renewable Energy Sources and Specific Socioeconomic Factors

An eco-friendly and sustainable power production system constitutes the cornerstone of every country’s strategic plan to tackle climate change and enhance energy resource autonomy. Carbon dioxide abatement in electricity generation, in addition to being a necessary condition for t “green” energy transition, can contribute greatly to cleaner industrial production and sustainable development. Emphasizing this key role of the power sector, the present research focuses on shedding light on the impact of renewable energy resources (RES), per capita gross domestic product (GDP), electricity gross fixed capital formation (GFCF) and urbanization in the CO2 intensity, and the sustainability level of electricity production. The analysis is based on a comprehensive dataset of 31 countries including 26 European countries, U.S.A., Japan, Australia, Canada, and New Zealand from 1995 until 2018. The econometric outcomes revealed the strong statistical significance of all variables and a plethora of causality relationships, upon which several policy suggestions are made. Interestingly, GDP per capita beyond a certain level can gradually become an aggravating factor for the electricity carbon footprint. Similarly, the vital role of RES in clean electricity production was confirmed as expected, yet surprisingly, this effect also appears to reverse after a certain percentage of total RES reliance. In contrast to urbanization, the electricity GFCF parameter is estimated to have an adverse effect on electricity CO2 intensity, indicating that the vast amount of new investments in the power sector concerns carbon-intensive technologies. Finally, a dynamic analysis is carried out, revealing to policy makers the necessary time frame after which the implementation of new energy policies can have the full impact on the carbon emissions of electricity generation.

Poly(amidoxime)-graft-magnetic chitosan for highly efficient and selective uranium extraction from seawater

Adsorbents with highly efficient and selective recovery performance towards uranium are significantly demanded for the sustainable nuclear power production. Herein, poly(amidoxime)-graft-magnetic chitosan (P(AO)-g-MC) was synthesized through functionalizing magnetic chitosan with polyacrylonitrile followed by amidoximation process. Under magnetic field, P(AO)-g-MC can be separated from the solution in 10 s. Owing to the strong affinity of high-density amidoxime groups towards uranium, P(AO)-g-MC showed remarkable adsorption capacity, rapid kinetics and good regeneration performance in uranium spiked aqueous solution. Notably, the 7-day uranium adsorption capacity of P(AO)-g-MC from natural seawater in column mode was up to 5.14 mg/g, 12 times that of vanadium. The excellent uranium uptake performance over vanadium originated from the strong coordination by N and O in amidoxime groups according to theoretical simulation. The advantages of easy separating and high selectivity make P(AO)-g-MC a very potential uranium adsorbent in natural seawater.

Sensitivity-Analysis-Driven Surrogate Model for Molten Salt Reactors Control

The numerical analysis for the controllability assessment of a new design nuclear reactor is typically carried out by means of complex multiphysics codes, solving high fidelity partial differential equations governing the system neutronics as well as the fluid dynamics. Multiphysics codes deliver very accurate solutions at the expense of high computational times, which could be of several hours depending on the specific case study. In this work, to efficiently reduce runtimes, a sensitivity analysis (SA) is carried out to identify the most important input parameters affecting the solution of a multiphysics model developed for the controllability assessment of molten salt reactors (MSRs). The numerical modeling of these innovative systems is fundamental to allow for a safer and more sustainable power production (e.g., due to the lower radiotoxicity of the actinide inventory in MSRs and to the possibility of operation at atmospheric pressure). In this paper, four global sensitivity measures are calculated first, including the Pearson correlation coefficient, δ, Kolmogorov–Smirnov and Kuiper indices, whose results are aggregated by an ensemble strategy and confirmed by the CUmulative SUm of NOrmalized Reordered Output (CUSUNORO) plot. The results of the SA point out that the fuel density is the most important parameter yielding the largest variations in the system reactivity, fundamental for guaranteeing the MSR controllability. In light of this result, a simplified, surrogate model is then developed, which uses density as the only input parameter to determine reactivity, guaranteeing runtime reductions from several hours to a few seconds and, at the same time, a comparable level of accuracy of the multiphysics model. This result demonstrates the capability of global sensitivity analysis approaches to effectively identify the most relevant parameters in MSR systems, supporting the development of simplified, control-oriented models for these innovative reactors.

Applicable Smart City Strategies for a Smart Sustainable City to Ensure Energy Efficiency and Renewable Energy Integration: Casablanca Case Study

A Smart city is essentially expected to diminish the utilization of assets and upgrade efficiencies. In practically any region, effectiveness brings about energy saving, diminished energy force, supportable monetary turn of events, upgraded usefulness, a safeguarded climate, and in particular, participation with the environmental change fight. In spite of the fact that financial plan, innovation, and the necessary framework are significant imperatives for helpless urban areas to accomplish shrewd and economical city objectives, the advantages of brilliant urban areas are numerous for helpless urban areas contrasted with creating and created urban areas. Helpless urban areas accomplish worked on living conditions, security, wellbeing, financial turn of events, administration, and personal satisfaction as well as accomplishing supportable energy objectives, and this study tries to distinguish those shrewd sustainable power and energy production systems that are monetarily achievable and in fact relevant in helpless urban communities. Renewable energies are a sustainable, unlimited, and decarbonized solution to address future energy challenges. In this context, Morocco has considerable lead vantage to position itself on this promising market. Furthermore, renewable energies have been highlighted as a key strategic source for the country’s green growth. Morocco has adopted the renewable energy path through a strategy targeted at the development of solar, wind, and hydroelectric power to boost its energy policy by adapting it to the challenges posed by today’s world. Nowadays, Morocco is facing a challenge to reach 52% by 2030 of its total renewable energy capacity, which will exceed 42% by the end of 20221