Renewable Systems Research Articles

Synergistic Integration of Multiple Wave Energy Converters with Adaptive Resonance and Offshore Floating Wind Turbines through Bayesian Optimization

We developed a synergistic ocean renewable system where an array of Wave Energy Converters (WEC) with adaptive resonance was collocated with a Floating Offshore Wind Turbine (FOWT) such that the WECs, capturing wave energy through the resonance adapting to varying irregular waves, consequently reduced FOWFT loads and turbine motions. Combining Surface-Riding WECs (SR-WEC) individually designed to feasibly relocate their natural frequency at the peak of the wave excitation spectrum for each sea state, and to obtain the highest capture width ratio at one of the frequent sea states for annual average power in a tens of kilowatts scale with a 15 MW FOWT based on a semi-submersible, Bayesian Optimization is implemented to determine the arrangement of WECs that minimize the annual representation of FOWT’s wave excitation spectra. The time-domain simulation of the system in the optimized arrangement is performed, including two sets of interactions: one set is the wind turbine dynamics, mooring lines, and floating body dynamics for FOWT, and the other set is the nonlinear power-take-off dynamics, linear mooring, and individual WECs’ floating body dynamics. Those two sets of interactions are further coupled through the hydrodynamics of diffraction and radiation. For sea states comprising Annual Energy Production, we investigate the capture width ratio of WECs, wave excitation on FOWT, and nacelle acceleration of the turbine compared to their single unit operations. We find that the optimally arranged SR-WECs reduce the wave excitation spectral area of FOWT by up to 60% and lower the turbine’s peak nacelle acceleration by nearly 44% in highly occurring sea states, while multiple WECs often produce more than the single operation, achieving adaptive resonance with a larger wave excitation spectra for those sea states. The synergistic system improves the total Annual Energy Production (AEP) by 1440 MWh, and we address which costs of Levelized Cost Of Energy (LCOE) can be reduced by the collocation.

Techno-economic analysis of deploying a short or mixed energy storage strategy in a 100 % green power grid

A fully decarbonised electricity grid with extensively deployed renewable systems is a fundamental step in transitioning to a net-zero world. Unlike fossil energy, renewable energy systems are subject to meteorological intermittency. However, few studies have investigated the techno-economic performance of integrating short- and promising long-duration energy storage into a 100 % renewable energy grid to balance short-term and inter-seasonal demand.This research developed an economic model to investigate the techno-economic performance of standalone and combined energy storage solutions for a fully green grid in three defined scenarios. Lithium-ion batteries (Li-ion), hydrogen, and electrical thermal energy storage (ETES) were selected as promising storage technologies due to their maturity, commercial availability, and scalability. The three examined scenarios are: 1) Li-ion battery only, 2) Li-ion battery with ETES, and 3) Li-ion battery with hydrogen. The research aims to determine whether combining long-duration energy storage (e.g., ETES and hydrogen) with Li-ion batteries offers greater economic and technical benefits, resulting in a more affordable, resilient, and secure power supply.The results show that the Li-ion battery only scenario (Scenario 1) requires a capacity of 234,956 MWh. Adding ETES and hydrogen reduces this capacity to 53,304 MWh (Scenario 2) and 7020 MWh (Scenario 3). The addition of ETES and hydrogen improves power supply flexibility, increasing the proportion of generated electricity used to meet demand from 32.7 % in Scenario 1 to 41.2 % and 52.3 % in Scenarios 2 and 3, respectively. These technologies also help avoid electricity deficits, reduce the loss of power probability (LOPP), and lower the cost of electricity supply losses from $8365 million in Scenario 1 to $1793 million in Scenario 2 and only $4 million in Scenario 3. Power delivery costs decrease from $99.5/MWh in Scenario 1 to $77.6/MWh and $76.6/MWh in Scenarios 2 and 3, respectively. The research concludes with four policy recommendations to advance long-duration energy storage and transition to a fully green power supply system.

A deep generative model for selecting representative periods in renewable energy-integrated power systems

The extensive integration of renewable energies into power systems has led to a challenging and computationally demanding scenario for the system planning. This is due to the increased number of time series involved and the greater complexity of time series data integrated into power systems. In this paper, a new deep learning-based time aggregation method is proposed for selecting representative periods for renewable energy-integrated power system datasets where multiple variable energy resources are present. Relying on the capabilities of deep generative models, especially Generative Adversarial Network (GAN), the proposed method selects a representative period, including one or more representative days, obtained from multiple time series with spatio-temporal correlations among them. The proposed approach contains the Long Short-Term Memory (LSTM) Network in both generator and discriminator parts. Also, it includes a loss term, specifically designed for clustering tasks, in the minimax objective of the vanilla GAN loss function to enhance the clustering performance in the latent space. Furthermore, the learning rate of the proposed model, as the most important parameter of the learning algorithm, is adaptively fine-tuned during the training process, based on the training error, to enhance its learning performance. Two real-world test cases with various datasets and time series are used for data-based and model-based evaluations. Results obtained on these two real-world test cases confirm the superiority of the proposed model with respect to the state-of-the-art conventional and deep learning-based models, obtaining the performance improvement in the range of [38.55 %, 46.14 %] in terms of Mean Absolute Percentage Error (MAPE), in the range of [45.75 %, 70.16 %] in terms of Root Mean Squared Error (RMSE), and in the range of [65.81 %, 74.58 %] in terms of Mean Absolute Error (MAE). The proposed model can significantly enhance the tractability and scalability of the planning problem of renewable energy-integrated power systems, facilitating renewable energy integration into power systems which is a key issue for net zero transitioning of communities.

Enhancing on-grid renewable energy systems: Optimal configuration and diverse design strategies

This study explores the optimization of hybrid renewable energy systems in smart grids, incorporating configurations involving multiple sources such as solar photovoltaic, wind, and combined PV/wind systems with advanced battery storage strategies. The goal is to develop optimal sizing and energy management solutions that not only reduce the grid use factor but also enhance the sustainability and reliability of energy systems through innovative energy-saving methods and carbon-neutral approaches. Utilizing a novel methodology that integrates particle swarm optimization, long short-term memory, and genetic algorithms, this research identifies the most effective mix of renewable energy capacities alongside versatile energy storage solutions, to align energy production with consumption efficiently. The study demonstrates significant environmental benefits, crucial for sustainable management aimed at achieving carbon neutrality, including reducing CO2 emissions by 122.29 tons per year and maintaining a notably low levelized cost of energy at $0.0417/kWh. The model proves accurate in forecasting renewable energy generation, emphasizing its potential as a sustainable and cost-effective alternative to traditional energy sources. These findings underscore the critical importance of integrating optimal hybrid renewable systems that incorporate strategic energy storage, innovative optimization for energy savings, and robust measures towards achieving carbon neutrality, thus enhancing both energy sustainability and economic feasibility.

Renewable Power Systems: A Comprehensive Meta-Analysis

The ongoing amplification of climate change necessitates the exploration and implementation of effective strategies to mitigate ecological issues while simultaneously preserving economic and social well-being. Renewable power systems offer a way to reduce adverse anthropogenic effects without hindering economic growth. This study aims to conduct a comprehensive bibliometric analysis of renewable power systems to explore their historical context, identify influential studies, and uncover research gaps, hypothesizing that global contributions and policy support significantly influence the field’s dynamics. Following Preferred Reporting Items For Systematic Reviews And Meta-Analyses guidelines, this study utilized Scopus tools analysis and VOSviewer 1.6.20 software to examine the metadata sourced from scientific databases in Scopus. The outcomes of this investigation facilitate the identification of the most prolific countries and authors, as well as collaborative efforts that enrich the theoretical landscape of renewable power systems. The study also traces the evolution of research on renewable power systems. Furthermore, the results reveal key scientific clusters in the analysis: the first cluster concentrates on renewable energy and sustainable development, the second on the relationship between government policies and renewable power systems, and the third on the role of incentives that catalyse the advancement of renewable power systems. The findings of this meta-analysis not only contribute valuable insights to existing research but also enable the identification of emerging research areas related to renewable power system development.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

System-level optimisation of hybrid energy powered irrigation system

Renewable energy-powered irrigation systems have emerged as sustainable solutions, particularly for farmers in off-grid areas. While existing research often highlights tank storage-based systems as the most cost-effective option, large-scale deployment of water tanks incurs significant costs and maintenance challenges. Additionally, there is limited research on the feasibility and optimisation of battery-based irrigation systems, which are often deemed costly despite their potential benefits. This study addresses this gap by identifying the optimal storage solution for hybrid energy-powered irrigation systems through a system-level optimisation model. The model evaluates the suitability of three storage options: direct-coupled water tank storage, battery-coupled storage, and a hybrid battery-tank storage system. Optimisation criteria include life cycle cost (LCC), loss of power supply probability (LPSP), and loss of load probability (LOLP), ensuring a comprehensive assessment of both cost and reliability. Results indicate that the hybrid battery-tank storage system is the most reliable, followed by battery-only storage, while tank-only storage, despite its lower initial cost, poses scalability and maintenance challenges. The LCC over a 25-year project lifetime is £31 k for battery-tank, £26 k for battery-only, and £23.3 k for tank-only systems. Despite the lower cost of tank storage, its complexity and maintenance make it the least preferred option for large-scale systems.

Titanium-Cerium Redox Flow Batteries for Grid-Scale Electrical Energy Storage

The development of redox flow batteries (RFBs) has gained increased attention due to the need of mitigating emission pollution from fossil fuels by developing renewable energy storage system. Among all the different types of RFBs, all vanadium one (VRFBs) are the most prevalent type because crossover contamination is avoided by using only one metal element. However, VRFBs still suffer from limitations such as low output voltage, low efficiency, and high material cost. Titanium (Ti) and cerium (Ce), on the contrary, are available in abundance and have lower cost. Moreover, the standard potential of Ti-Ce combination is much higher than that of VRFBs (1.61 vs. 1.23 V). Herein, we have studied and compared the performance of Ti-Ce RFBs with sulfuric acid (SA) and methanesulfonic acid (MSA) serving as the supporting electrolyte. We applied an asymmetric electrode configuration, i.e., carbon felt for Ti side and carbon paper for Ce side, to further promote the batteries performance. Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to analyze the mass transport and reaction kinetics of the Ti-Ce system. Electrochemical impedance spectroscopy (EIS) was used to analyze the contribution of different types of polarization. Ti-Ce RFBs demonstrated an energy efficiency around 80 % at 100 mA/cm2, and excellent capacity retention.

Oahu as a case study for island electricity systems relying on wind and solar generation instead of imported petroleum fuels

A macro-scale electricity model was used to evaluate whether wind and solar generation, combined with short- and long-duration energy storage, could cost-effectively provide 100% of Oahu's electricity. A least-cost renewable electricity system was developed with 100% of hourly averaged demand met, based on 14 consecutive years of hourly wind and solar resource and electricity demand data. The system used current asset costs for wind generation, solar generation, short-duration battery storage, and long-duration hydrogen energy storage. Our results indicate that Oahu could transition to an electricity system reliant on wind and solar generation and battery and hydrogen storage with electricity costs lower than today's electricity costs. For Oahu, a least-cost wind-solar-battery electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.2458 per kWh. In comparison, from October 2022 through September 2023, electricity generation costs from the petroleum-dominated electricity system on Oahu were between $0.2126/kWh and $0.2987/kWh. This wind-solar-battery system would require 332 km2 (21.4%) of Oahu's land. Moreover, including hydrogen storage as a second form of energy storage led to reductions in system cost and land use. At current asset costs, a least-cost wind-solar-battery‑hydrogen electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.1673/kWh and require 265 km2 (17.1%) of Oahu's land. This case study indicates that other isolated regions with similar resource profiles and a reliance on imported petroleum fuels could realize a decrease in electricity system costs when switching to renewables in conjunction with short- and long-duration energy storage, at current asset costs, while meeting demand in full over extended periods of renewable resource variability.

Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system

Electric vehicles (EVs) are positioned as an alternate green energy technology, which potentially can enable the efficient transition to sustainable low-carbon emission transportation systems. However, different barriers hamper the public adoption of EVs. The degree to which these barriers prevent the adoption of EVs and the potential alternatives to these barriers are mostly unknown. To fill this research gap, the current study contributes by systematically identifying those barriers based on their importance. Initially, several barriers were identified using existing literature, and then by employing the modified Delphi method, a total of twenty-five barriers were classified into five significant dimensions. The Analytical Hierarchical Process (AHP) determined the rankings of these barriers based on weight allocation. As a next step, the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) ranked alternative solutions to these barriers. The findings indicate that 'technological barrier' is the top-ranked barrier among all major dimensions. The overall ranking indicated that 'battery technology' is most influential among sub-dimensions. It is recommended that "investment in research and development" is the best alternative to overcome these barriers. In order to improve the renewable and sustainable urban transportation system, all stakeholders should cooperate in a consistent and coherent manner.

Power electronic converters in the unbalance compensation method for renewable energy-powered active distribution systems: AOA-RERNN approach

Power electronic converters in the unbalance compensation method for renewable energy-powered active distribution systems: AOA-RERNN approach

Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way

Abstract Since Russia’s 2022 invasion, Ukraine’s civilian energy infrastructure has faced systematic attack and requires urgent and strategic reconstruction. This study confronts the dual challenges of rebuilding Ukraine’s energy system rapidly to mitigate civilian and economic disruption while aligning this to long-term goals of sustainability and energy security. We demonstrate that Ukraine can readily meet future energy demands through a fully renewable electrified system at costs comparable to those from fossil fuels and nuclear power. Contrary to previous re- liance on high-carbon energy sources, we find a diversified renewable energy portfolio, including significant solar photovoltaic and wind contributions, can ef- ficiently meet growing energy demands and position Ukraine as an energy ex- porter, leveraging its geographical advantages. The approach of our study, based on open data and models, extends beyond national borders and offers a model for post-conflict reconstruction that harmonizes immediate recovery with sustain- able energy transition.

Optimization of dual-stage controllers in renewable energy sources-based interconnected power systems through refinement of the African Vultures Optimization Algorithm

With an emphasis on a three-region system, this paper explores automaticgeneration control within a conventional framework. Area 2 uses thermal-thermal sources, Area 3 uses gas-thermal sources, and Area 1 uses thermal-biodiesel sources. IPD(1 + I) is a unique cascade controller that combines integral-proportional-derivative (IPD) components with one-plus-integral (1 + I) components. To find the best gains and settings for the IPD(1 + I) controller, a meta-heuristic method called the African Vulture Optimization Algorithm (AVOA) is applied. Reducing the integral squared error (ISE), as determined by the performance index (Pi), is the main goal. The performance of the IPD(1 + I) controller is evaluated by contrasting it with a range of secondary controllers. Hybrid Peak Area-ISE (hPA-ISE), a novel performance metric, is also assessed in addition to the conventional ISE. The incorporation of renewable energy sources, such solar thermal energy and solid oxide fuel cells, significantly improves the system’s performance. Interestingly, under normal conditions, the IPD(1 + I) controller’s parameter settings continue to work for both sinusoidal and random load situations, obviating the need for additional tuning.

Transitioning to a renewable hydrogen system: Optimal infrastructure for self-sufficient hydrogen supply in Austria by 2030

In this study, we employ an optimization model to optimally design a self-sufficient, independent of any imports and exports, hydrogen infrastructure for Austria by 2030. Our approach integrates key hydrogen technologies within a detailed spatial investment and operation model – coupled with a European scale electricity market model. We focus on optimizing diverse infrastructure components including trailers, pipelines, electrolyzers, and storages to meet Austria's projected hydrogen demand. To accurately estimate this demand in hourly resolution, we combine existing hydrogen strategies and projections to account for developments in various industrial sectors, consider demand driven by the transport sector, and integrate hydrogen demand arising from its use in gas-powered plants. Accounting for the inherent uncertainty linked to such projections, we run the analysis for two complementary scenarios. Our approach addresses the challenges of integrating large quantities of renewable hydrogen into a future energy system by recognizing the critical role of domestic production in the early market stages. The main contribution of this work is to address the gap in optimizing hydrogen infrastructure for effective integration of domestic renewable hydrogen production in Austria by 2030, considering sector coupling potentials, optimal electrolyzer placement, and the design of local hydrogen networks.

Design and Development of an Unmanned/Autonomous Ocean Surface Vehicle using Self-Sustaining Dual Renewable Energy Harvesting System

Design and Development of an Unmanned/Autonomous Ocean Surface Vehicle using Self-Sustaining Dual Renewable Energy Harvesting System

Aerodynamic effects of an offshore floating photovoltaic platform undergoing pitch and heave motions

The offshore floating photovoltaic (OFPV) platform is a renewable energy generation system that has gained attention in recent years due to several advantages. OFPV exhibits higher sensitivity to wind loads than other offshore platforms, owing to its lightweight design and large aerial coverage. In contrast to the fixed photovoltaic systems, the OFPV platforms experience continuous oscillations induced by waves, which significantly influence the aerodynamic characteristics. In this study, the aerodynamic effects of an oscillating OFPV are investigated using the computational fluid dynamics (CFD) simulation method, including the three-dimensional effects, the Reynolds number effects, and the aerodynamic loads with various kinematic parameters. Some major findings are as follows. Firstly, a large-scale tip vortex exists when OFPV oscillates in pitch and heavy directions, which cannot be detected in two-dimensional simulations. Consequently, the lift coefficient, CL, and the drag coefficient, CD, are highly overestimated in 2-D simulation. Secondly, CL and CD decrease significantly as the Reynolds number increases for cases where Re < 106 while there is only a slight decrease for Re > 106. Thirdly, the aerodynamic forces are highly related to the amplitude of the pitch motion and the phase lag between the pitch and the heave motions. A fitting formula is proposed to calculate CL and CD based on the maximum angle of attack. Lastly, the evolution of the flow fields around the oscillating OFPV is analyzed. The separation of the leading edge mainly causes the aerodynamic force.

Operational Verification of DC-DC Converters Used in Ecologically Sound Non Polluting Renewable Systems

This research delves into the intricate control mechanisms governing renewable energy sources through the application of three distinct DC-DC converter technologies: the buck, boost, and buck-boost converters. Renewable energy sources like wind and solar can be pivotal in the future and become the primary source of energy for global energy requirements. Renewable sources are pollution-free and have minimal impact on the environment. This research illuminates the efficacy of these converters in orchestrating the optimal utilization of renewable energy sources, offering valuable contributions to the advancement of sustainable power systems. Open and closed loop control of a buck converter is studied along with its operation for the voltage regulation of an emulated PV panel. For the boost converter, a comprehensive exploration ensues through two closed-loop control methods: Perturb and Observe (P&amp;O) algorithm and the incremental conductance method. Simulations are meticulously executed in MATLAB Simulink, interfacing with PV arrays and directing power toward battery charging. Meanwhile, the buck-boost converter undergoes simulation and evaluation, fed by a DC source and delivering power to a resistive load. Experimental validation is conducted for the buck converter, providing tangible insights into real-world performance. Conventional sources, primarily coal, which is the most used source of energy in Asian countries, including India, have adverse effects on the environment. Coal contributes to a huge proportion of pollutants in the environment and also the release of large quantities of greenhouse gases. Renewable energy resources can play a huge role in addressing the energy demands of Asian countries. They hold the ability to alleviate the environmental burden caused by conventional energy sources. Renewable energy sources also mitigate pollution and eliminate various hazardous effects of conventional energy sources. This study emphasises the adoption of such sources and underscores the pivotal role of DC-DC converter technologies in this adoption.

Improving Distributed Renewable Energy Power Planning Through Particle Swarm Algorithm

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.

Robust power converters for renewable generation systems in future networks with short circuit ratio wide variations

The European Directive (EU) 2018/2001 requests the connection of a new renewable inverter-based resources (RIR) but ensuring that the connection point is strong enough to minimize and avoid possible interaction phenomena. Historically, the indicator used to determine the strength of a network has been the Short Circuit Ratio (SCR). Until now, the tendency had been to ensure that the network remained strong enough with High SCR, but another possibility is to ensure that RIR can operate stably and reliably at different SCR values, both high and low. The inverters currently deployed in European power systems use grid following inverter technology (GFI). The main objective of the present work is the redesign of the traditional current regulators of JEMA Energy PV family inverters, based on grid following technology, to guarantee the stability of electrical systems with a variable short-circuit ratio in the range [1÷20]. To achieve this, the current loops of the inverter are redesigned, introducing an additional first-order controller that improves the dynamics of the system and allows stable and efficient behaviour. For validation purposes, the system is analysed under adverse fault scenarios. The results obtained in all cases are satisfactory, but the stability with PLL (phase lock loop) slower than those originally used, is better.

Novel integrated system for power, hydrogen, and ammonia production using direct oxy-combustion sCO2 power cycle with automatic CO2 capture, water electrolyzer, and Haber-Bosch process

Renewable energy-based multigeneration systems for power, hydrogen, and ammonia production suffer from intermittency issues and high production costs. On the other hand, conventional fossil-fuel-based systems yield high levels of CO2 emissions. Thus, to eliminate the intermittency issue with low production costs and near zero CO2 emissions, a novel integrated, efficient, compact, and interactive system for power, hydrogen (H2), and ammonia (NH3) production is developed and investigated in this study. A direct oxy-combustion supercritical carbon dioxide (DOC-sCO2) power plant is integrated with a water electrolyzer (WE) and Haber-Bosch process (HBP) system for the cogeneration of power, H2, and NH3, respectively. The power consumption of the air separation unit (ASU) of the DOC-sCO2 cycle is significantly reduced by the utilization of the oxygen produced from the WE system. Moreover, the cost of ammonia production is minimized by the internal utilization of the nitrogen gas produced by the ASU. Furthermore, the feedwater of the WE system is supplied internally from the DOC-sCO2 power block. In addition, the CO2 emissions of the system are captured to realize near-zero emissions to the ambiance. Based on these features, thorough energy, exergy, economic, and environmental (4E) analyses were performed to evaluate the performance of the proposed system. The obtained results showed an impressive levelized cost of electricity of 3.72¢/kWh, levelized cost of hydrogen of 1.162$/kgH2, and ammonia of 0.197$/kgNH3 at an overall energy and exergy efficiencies of 44.09 %, and 63.6 %, respectively. The system has no emissions of nitrogen oxides (NOx) and reduces the CO2 emissions of the H2 production by 49.6 % and of the NH3 production by 48.8 % compared to the conventional gasification, SMR, and HBP systems, respectively. This research work also provides insights into wet versus dry cooling, part load operation, and the advantages over renewable-based systems in terms of stability, reliability, and economic feasibility.