Economic and Environmental Analyses of an Integrated Power and Hydrogen Production Systems Based on Solar Thermal Energy

Abstract

This study introduces a novel hybrid solar–biomass cogeneration power plant that efficiently produces heat, electricity, carbon dioxide, and hydrogen using concentrated solar power and syngas from cotton stalk biomass. Detailed exergy-based thermodynamic, economic, and environmental analyses demonstrate that the optimized system achieves an exergy efficiency of 48.67% and an exergoeconomic factor of 80.65% and produces 51.5 MW of electricity, 23.3 MW of heat, and 8334.4 kg/h of hydrogen from 87,156.4 kg/h of biomass. The study explores four scenarios for green hydrogen production pathways, including chemical looping reforming and supercritical water gasification, highlighting significant improvements in levelized costs and the environmental impact compared with other solar-based hybrid systems. Systems 2 and 3 exhibit superior performance, with levelized costs of electricity (LCOE) of 49.2 USD/MWh and 55.4 USD/MWh and levelized costs of hydrogen (LCOH) of between 10.7 and 19.5 USD/MWh. The exergoenvironmental impact factor ranges from 66.2% to 73.9%, with an environmental impact rate of 5.4–7.1 Pts/MWh. Despite high irreversibility challenges, the integration of solar energy significantly enhances the system’s exergoeconomic and exergoenvironmental performance, making it a promising alternative as fossil fuel reserves decline. To improve competitiveness, addressing process efficiency and cost reduction in solar concentrators and receivers is crucial.

1. Introduction

The energy supply sector is the largest contributor to global greenhouse gas emissions [1]. The pollution and harmful emissions from fossil fuels have prompted a shift toward renewable energy (RE) sources [2]. Among these, solar and biomass energy stand out due to their global availability and abundance. Biomass is a carbon-neutral renewable energy source that offers several advantages for the energy sector. It can be used as a primary feedstock or in co-gasification processes due to its abundant reserves, renewability, high reactivity, and low pollutant emissions [3]. While solar energy is not consistently available and high-temperature solar technology incurs higher costs than other RE sources, it is entirely carbon-free [4]. In contrast, biomass energy is cost-effective and does not share the intermittent issues of solar energy [5]. Combining solar and biomass energy as heat sources for power generation systems is a relatively new approach. This method maximizes the advantages of both energy sources while mitigating the drawbacks of using them individually [6].

Studies indicate a growing interest among researchers in using RE sources and integrating them to reduce pollutant emissions and control climate change [7]. However, solar energy’s main drawback is its fluctuating and intermittent supply. A solution to this issue is the use of hybrid resources, such as combining solar energy with thermal energy storage (TES) [8] and hydrogen (H₂) production [4,6]. In this respect, H₂ can be stored and utilized for continuous power generation through systems like fuel cells [9] and internal combustion gas turbines (GT) [4,10], ensuring a steady power supply.

Recently, there has been significant attention given to hybridizing solar and biomass energies in power generation systems. For most of the investigations, concentrating solar power (CSP) plants present CO₂ emissions of less than 40 gCO₂/kWh [11]. Incorporating low-carbon fuels like H₂ is crucial for achieving net-zero goals through clean energy technologies. H₂ is also seen as a future fuel source for the transportation sector and is applicable in the petroleum and chemical industries [12]. Consequently, global spending on H₂ energy research and development has increased, as it has begun to represent a major green energy opportunity in recent decades.

Numerous studies have examined different configurations of biomass–solar hybrid systems aimed at generating multiple valuable outputs in power production. The combined cycle approach is a proven and efficient method for improving the performance of traditional high-temperature power plants. Within this framework, some research has centered on proposing and exploring combined cycle systems [13] and hybrid solar and fuel-fired plants [14], particularly in Uzbekistan. Zarif et al. [15] examined the hybridization of biomass with solar energy from CSP systems to operate a syngas, H₂, and power production cycle in a combined heat and power (CHP) plant. Their findings demonstrated that using a hybrid resource significantly increases energy and exergy efficiency by 37.07% and 32.52%, respectively, compared with the natural gas-fired Tashkent CHP plant.

Combining multiple energy sources can ensure stability and energy balance. The economic potential of these technologies is promising enough to meet increasing demand [16]. However, if thermal systems are optimized solely based on thermodynamic factors, the overall cost can rise significantly. Thus, it is crucial to evaluate thermal systems from thermodynamic, economic, and environmental perspectives to enhance the efficiency of energy systems [17,18].

Energetic, exergetic, exergoeconomic, and exergoenvironmental (4E) analyses [19,20] have recently emerged as valuable methods for evaluating and assessing the feasibility of energy systems. These analyses offer a comprehensive overview of performance by considering both the quantity and quality of energy, the amount of natural resources consumed, and the resulting cost of a specific product. They allocate costs to exergy and provide insights into cost distribution based on irreversibility, which is beneficial for the optimization, design, control, and diagnostics of energy systems [16]. Additionally, 4E analyses facilitate benchmarking and comparison between energy systems based on environmental aspects, using primary factors and life cycle assessment (LCA) considerations [7].

Several studies have explored the use of biomass and solar energies as simultaneous heat sources for energy conversion systems. Notably, Cao et al. [4] and Cen et al. [16] proposed hybrid systems that integrated H₂ post-firing, achieving carbon dioxide (CO₂) emissions of 0.475 kg/kWh and 0.45 kg/kWh, respectively. The levelized cost of electricity (LCOE) for these systems was determined to be 61.37 USD/MWh and 65.8 USD/MWh. Another study [7] that investigated solar–biomass waste poly-generation systems demonstrated energy and exergy efficiencies of 29.25% and 23.59%, respectively, with a payback period of 4.28 years.

Mohammad et al. [5] studied a hybrid heat source combining a solar power tower (SPT) and biomass gasification in a multi-generation system. Their findings showed an enhancement in total exergy efficiency of 9.04% points, attributable to waste heat recovery from the GT. The total cost rate of the proposed system was 7799 USD/h, with a unit cost of the multi-generation system of 8.26 USD/GJ. Another study proposed a multi-generation system utilizing a CO₂ Brayton cycle, which resulted in a 76.9% reduction in the exergy destruction cost and a 25.9% decrease in the unit cost of the system. A poly-generation plant driven by a solid oxide fuel cell (SOFC) was found to have CO₂ emissions of 10.79 kmol/MWh, with the unit cost of exergy and the total cost rate of the products being 11 USD/GJ and 223 USD/h, respectively [21].

In the CO₂ transcritical cogeneration cycle [17], the product cost decreased by 9% when the H₂ production rate and refrigeration power were reduced from 6.425 kW to 6.103 kW. In another system focused on H₂ production and CO₂ emission reduction using solar energy [6], H₂ injection achieved an output power of 346.2 kW, an exergy efficiency of 24.85%, CO₂ emissions of 0.257 kg/kWh, and a levelized cost of product (LCOP) of 0.0911 USD/kWh. Ehsanolah et al. [22] analyzed a solar–biomass integrated energy system that produced 7967 kW of power and 10.74 kg/h of liquid H₂ with an exergy efficiency of 11.06%. The results demonstrated an LCOE of 40.16 USD/MWh, and CO₂ emissions of 0.39 t/MWh. Modified exergy-based economic analyses showed a 0.24% reduction in exergy destruction, a 3.36% decrease in the total exergy destruction cost rate, a 2% reduction in the CO₂ discharge rate, and a 3% decrease in the total unit product cost [23].

To boost H₂ production, a plant incorporated steam reforming technology and a proton-exchange membrane (PEM) [24]. The results indicate that the unit product cost and environmental penalty cost rate were approximately 23.7 USD/GJ and 141.7 USD/h, respectively. Rahbari et al. [25] explored the production of solar fuels through supercritical water gasification (SCWG) of algae, estimating a fuel cost of 2.99 AUD/L (2.06 USD/kg), with H₂ costing 2.1 AUD/kg (1.45 USD/kg).

Ding et al. [26] developed a multi-generation energy system that achieved an exergetic efficiency of 31.1% and a net product unit cost of 51.7 USD/GJ. The system’s levelized cost of hydrogen (LCOH) and LCOE were 5.67 USD/kg and 0.098 USD/kWh, respectively. Another study investigated an integrated system using biomass and solar energy for H₂ production with an electrolyzer [27]. Their results indicated power generation costs of 0.014 USD/kWh and environmental impacts of 0.002 Pts/kWh for biomass. Wang et al. [9] assessed an SOFC CHP system integrated with biomass gasification and solar-assisted carbon capture and energy utilization.

In the evaluation of integrating solar thermal and alternative energy into the SCWG of microalgae [28,29], minimum fuel selling prices (MFSPs) ranged from 52 to 73 AUD/GJ (39–54.85 USD/GJ).

A study on the solar-driven steam gasification of biomass for large-scale H₂ production estimated the minimum H₂ prices to be 2.99 EUR/kgH₂ (2.62 USD/kgH₂) for all thermal processes and 2.48 EUR/kgH₂ (2.17 USD/kgH₂) for hybrid processes compared with 2.25 EUR/kgH₂ (1.97 /kgH₂) for conventional methods [30].

Karthikeyan et al. [31] investigated a system that harnesses solar power during the day and uses abundant cotton waste biomass at night. The system exhibits an energy efficiency of 19% and an exergy efficiency of 11%, with a plant cost of 37 USD/h. Another study [32] reported an LCOE and LCOH at optimal total product unit costs of 0.045 USD/kWh and 28.86 USD/kg, respectively. Results indicated that the hybrid RE system offers a levelized cost of electricity and heat at 0.38 USD/kWh. Additionally, Lin et al. [33] introduced an electricity and liquid H₂ cogeneration power plant with a total cost rate of 86.61 USD/h and an optimal environmental index of 1.09 kgCO₂/kWh.

Mohammad et al. [34] proposed a system integrating sorption-enhanced biomass chemical looping gasification with solar energy, waste heat recovery, and power generation subsystems. Using rice straw biomass, the system proved to be more economical and environmentally friendly than a microalgae-based system, with a fuel cost of 116.9 USD/h and CO₂ emissions of 5.22 t/h.

Matin et al. [35] designed and optimized a system for generating power, heat, and H₂ via electrolysis. Their optimization achieved the highest exergy efficiency of 31.15% and the lowest LCOE at 0.0211 USD/kWh. The study revealed that the main advantage of integrating a biomass gasification–SOFC system with H₂ production and injection is the significant reduction in CO₂ emissions and the increase in power output, with CO₂ emissions of 0.257 kg/kWh and an LCOE of 0.0911 USD/kWh [36].

Rahbari et al. [37,38] conducted research on a solar fuel plant utilizing SCWG integrated with Fischer–Tropsch synthesis, employing steady-state modeling and system-level dynamic simulation. The proposed systems demonstrated a levelized cost of fuel (LCOF) that was as low as 2.44 USD/L with a 15 h syngas storage capacity and 2.50 USD/L with a 20 h syngas storage capacity.

Hashemian et al. [39] examined an assessment and multi-criteria optimization of a solar- and biomass-based multi-generation system. Their study demonstrated improvements of 82.4% in energy efficiency and 14% in exergy efficiency, a total product cost rate of 0.84 USD/s, and an exergoenvironmental impact of 0.15.

The solar-driven thermochemical conversion of empty cotton boll biomass to syngas and potassic fertilizer has been evaluated based on experimental results [40]. The study reports that the LCOF via Fischer–Tropsch synthesis was estimated at 0.90 USD/L for a 20 MW syngas plant. Additionally, an economic analysis [41] of H₂ production revealed that the LCOH was reduced to approximately 3.67 USD/kg, aligning with the 2030 target value of 4000 KRW/kgH₂ (3.06 USD/kgH₂) set by the Korean government’s H₂ economy revitalization roadmap. The study confirms that integrating various energy sources into the system enhances resource flexibility [20]. The results showed that the overall unit product cost was 21.79 USD/GJ, while the overall social–ecological factor was calculated to be 1.37.

The literature review on solar-powered plants integrated with biomass for power and hydrogen production reveals that most research has focused on specific hybrid power plants. These studies often combine different cycles, such as multi-generation and poly-generation systems, to produce a variety of fuels and products. Additionally, research has investigated the use of solar energy through PV/PVT panels or medium-temperature solar systems. However, there is a significant gap in studies that assess the techno-economic and environmental aspects of various biomass-fueled CHP systems, including hydrogen generation, using a unified energy mix approach that prioritizes biomass and solar energy sources.

Moreover, the feasibility analysis of dynamic simulations in biomass-fired GT systems remains underexplored in the existing literature, as most studies have concentrated on steady-state modeling, primarily involving solar energy. To address these gaps, this paper introduces an innovative configuration of a hybrid biomass–solar system, where solar energy is integrated as a co-feed to support the energy requirements of a biomass-fueled CHP plant. This integration aims to lower CO₂ emissions while increasing product output through hydrogen production. The required solar thermal energy is generated using SPT technology equipped with a TES system that operates at high temperatures. Building on previous studies that analyzed thermodynamic performance in steady-state conditions, this research conducts exergoeconomic and exergoenvironmental analyses of hydrogen and power production from various processes using a CSP-system-level dynamic simulation.

Four scenarios were considered to evaluate the proposed models, namely conventional biomass gasification (CBG), sorption-enhanced reforming (SER) with calcium sorbent, chemical looping reforming (CLR) using iron oxygen carrier (OC), and SCWG for CO₂ capture. The main objectives of this study were to:

• Design a new solar-based cotton stalk gasification system using system-level dynamic simulation that produces renewable H₂, heating, and electricity that can effectively avoid solar fluctuation and achieve efficient solar thermochemical conversion.
• Investigate the performance of different biomass-conversion-based systems by calculation of the exergoeconomic and exergoenvironmental impact factors.
• Analyze the impact of related parameters on an optimized H₂ and power production system with a CSP plant to evaluate the overall system’s performance.

2. System Description and Simulation

The integrated CHP plant with biomass and solar energies for H₂ and power production investigated in this study is based on a combination of CSP-derived thermal energy with different biomass conversion plants, a CO₂ capture plant, a H₂ purification unit, a steam cycle, a H₂S remover unit and a CHP plant. A detailed validated steady-state physical model of the plant has been developed in Aspen Plus^® [42], which was discussed in the thermodynamic modeling study by the authors [15]. In this regard, optimal parameters were obtained to maximize the H₂-rich syngas production and CO₂ capture in the proposed models. However, future research should also account for factors like the catalytic performance in H₂ production to achieve higher activity and stability in the production process [43]. Additionally, various co-catalysts have been extensively utilized in CO₂ reduction, effectively lowering the energy barrier of thermal reactions and enabling milder reaction conditions [44].

To analyze the dynamic behavior of the plant at a system level under a variable solar thermal source, an energy-based model was developed using the Ebsilon^® Professional software [45], which is widely used for the modular modeling and simulation of complex dynamic systems. The proposed model utilizes polynomial performance curves and design point parameters to simulate key component metrics such as mass flows, energy flows, and efficiencies. These elements are integrated to create a system-level dynamic model of the entire plant. These dynamic models for each proposed system have been developed with the Time Series Calculation from the design and off-design simulation in Ebsilon^® Professional [45].

The schematic diagrams of the proposed hybrid biomass–solar driven CHPs with H₂ production are presented in Figure 1, Figure 2, Figure 3 and Figure 4. Each configuration consists of a GT cycle and a ST cycle with capacities of 30 MW and 27 MW of electricity, respectively. The systems are powered by hybrid biomass–solar resources. Cotton stalk biomass is the primary fuel, while solar energy is used for meeting the heat demand of biomass drying, preheating, syngas production via gasification and reforming, steam generation, H₂S cleaning, and the regeneration of solvent.

Except for the SCWG process, the systems work by firstly drying the biomass fuel to convert it into a higher calorific biomass, followed by the production of synthesized gas via various biomass conversion procedures, namely gasification with air and steam agents (Figure 1), calcium looping reforming based on a reversible reaction between CO₂ and CaO limestone (Figure 2), chemical looping reforming with an iron oxygen carrier (Figure 3), and technology that connects to a process where a metal with variable oxidative states is applied as the oxygen carrier (OC) and SCWG is carried out with a pressurized water feed (Figure 4) in the reactor. The oxygen required for the gasification is obtained from the air separation unit (ASU) in the conventional biomass gasification process (Figure 1).

After the gasification and reforming stages, the syngas is cooled to the operational temperature before being introduced into each reactor to improve the CO conversion and efficient heat recovery. H₂S and other sulfur compounds are absorbed by the ZnO fixed bed [17,46].

The gaseous mixture is separated through the PSA unit after cooling to 350 °C to produce a hydrogen stream with 99.99% purity. The purification efficiency of the PSA unit is about 80%, resulting in purged gases that are recycled for combustion in the GT to produce electricity. The exhaust gas after the GT combustion is utilized by the HRSG to supply the heat. The produced steam ensures the availability of steam for the steam cycle for two turbines, one with low pressure (LP) and one with high pressure (HP), which are used to generate power. The HP ST is used only for power generation, but the LP ST is also used for heat production in addition to power generation.

In the calcination reactor, CO₂ is first captured and then regenerated via a sorbent, followed by a highly endothermic calcium carbonate decomposition reaction to produce CO₂. In other models, the gas is decarbonized using post-combustion (model 1 and 4) and pre-combustion (model 3) MEA-based gas–liquid absorption reactions. The heat demand for solvent regeneration by the CO₂ capture unit is provided by solar energy.

The main demand for solar energy is from the gasifier reactors, with the highest consumption in models 1, 2, and 4. In the case of the iron-based chemical looping model (model 3), the heat demand of the reduction process is provided by the thermal balance of exothermic reactions in the air reactor.

To simulate the proposed integrated systems, the following assumptions were made:

-

The compositions of ambient air are 21% O₂ and 79% N₂ molar fractions.

-

Compressors, pumps, and turbines are simulated using isentropic efficiency values.

-

Calculation of the produced syngas is carried out using the principle of minimizing Gibbs free energy.

The main input parameters used for the modeling are listed in

Table 1. The main input parameters used for the modeling [15,22,47,48].

Parameter	Value
C, H, N, S, O (%)	44.54, 6.74, 0.8, 0.23, 40.68
LHV of cotton stalk (MJ/kg)	16.82
Moisture (%)	10.32
Location	Tashkent, Uzbekistan
Direct normal irradiation (W/m2)	845
Optical efficiency of the heliostats (%)	71
Optical efficiency of the receiver (%)	92
Operating temperature of the working fluid (°C)	600–870
Operating hours (hours)	10, 12, 14, 16
Gas turbine inlet pressure (bar)	16
Ambient temperature (°C)	25
Turbine and compressor isentropic efficiency (%)	85
Steam turbine inlet pressure (bar)	30
Steam turbine inlet temperature (°C)	430
Interest fate (%)	10
Economic life (years)	25

. The weather data source from the Meteonorm Database [47] was used to create a more detailed and accurate dynamic model of the whole system with the CSP plant, as it fills in the gaps between known data points, providing a continuous representation of the system’s performance across a range of conditions. A sub-component instance can be created within any component that requires weather or solar position data, and it is then connected to its corresponding counterpart in the weather data source component. The case study in the present work is based on an annual year of hourly weather data for Tashkent city, Uzbekistan. This site was chosen due to the high annual average of solar resources and the reference power plant in the nearby area.

The solar field utilizes central tower technology, employing an array of heliostats to reflect concentrated sunlight onto a receiver positioned at the tower’s apex. A cavity receiver was preferred in this study and a polar field layout was chosen as it is more adapted to cavity receiver designs [38]. The CSP plant was designed using the Ebsilon^® Professional [45] commercial package. The EbsSolar model was used to simulate the performance of the SPT plant with the heliostat field, storage tank, and its other components [45].

Since the chosen SPT plant is operated at high temperatures (600–870 °C), which is possible in a large-scale production [49], the NaCl:KCl:ZnCl₂ combination was considered in this study for the heat transfer fluid (HTF) [15]. The optimized solar field layout has 88.41% of effective reflectivity heliostats, each being 10 × 10 m² in area size around a tower of 126.9 m height at a DNI design value of 845 W/m², which is varied up to 1014 W/m² in dynamic simulations of the CSP system. According to a report from the World Bank [50], the economic lifespan of a CSP is approximately 25–30 years. For this reason, the current study examined 25 years of plant life.

The field outputs a flux density distribution on a defined aperture surface that serves as an interface between the optical concentrator and the solar receiver. Each heliostat’s efficiency, defined as the ratio of reflected irradiance reaching the receiver to the irradiance accepted by its mirror surface, depends on its location within the field, which affects its cosine angle and distance to the aperture plane. In the solar receiver, the heat flux arriving at the aperture is transferred to the heat transfer medium, raising the fluid temperature or steam fraction in a steam receiver. Optical and thermal losses occur between the aperture and the fluid, which are modeled within the receiver component.

The third main unit of CSP plant consists of the thermal energy storage tanks (hot and cold tanks). Thermal energy is stored to mitigate intermittency, ensuring continuous operation during periods of low solar energy supply [20]. This approach enhances the overall system efficiency and reduces both the initial and operating costs.

3. Modeling

This section is divided into five subsections, covering the methodologies for the thermodynamic investigations, economic analysis, exergoeconomic analysis, environmental assessment, and exergoenvironmental analysis. The assumptions and input data used in the exergy-based economic and environmental modeling are outlined in Table 1. Moreover, the detailed modeling performance results are included in Appendix A.

3.1. Thermodynamic Modeling

Aspen Plus^® [42] and Ebsilon^® Professional [45] software were used to create the thermodynamic model of the proposed models integrated with the solar-assisted system. For system feasibility assessment from the first- and second-law standpoints, thermodynamic models were developed.

According to the principle of the conventional state, knowing two independent physical properties allows for the calculation of other thermodynamic properties [27].

The energy balance principle for a control volume is applied as

$$\sum _i{m}_i{h}_i+\sum _i{Q}_i=\sum _e{m}_e{h}_e+\sum _e{Q}_e+\sum _e{W}_e$$

(1)

where $Q_i$ and $W_e$ stand for the heat and electrical power, respectively, while i and e denote input and output streams of the system.

The energy efficiency information measures the value of the power, H₂ production, and cooling capacity generated by the system relative to the energy used. It evaluates system performance variations based on the solar radiation and biomass flow rate in the trigeneration mode [31].

Exergy analysis is a more robust method for evaluating thermal systems, as it helps in formulating strategies for efficient energy use and in identifying the magnitude and locations of inefficiencies. This analysis requires the exergy to be determined for all the streams. Neglecting kinetic and potential exergies [24], the steady-state exergy rate balance equation is typically expressed as follows:

$$\sum _i{\left(\dot{m}\dot{e}\right)}_i+\sum {\dot{E}}_i^Q=\sum _e{\left(\dot{m}\dot{e}\right)}_e+\sum {\dot{E}}_e^W+\sum {\dot{E}}_D$$

(2)

In the above relationship, ${\dot{E}}_i^Q$, ${\dot{E}}_e^W$, ${\dot{E}}_D,$ and $\dot{e}$ are the exergies caused by the heat transfer flow and power exchange, exergy destruction, and specific exergy rate flow rate, respectively. The nature of the mass flow exergy is separated into physical and chemical forms that can be calculated by using references [7,19].

Therefore, the overall exergy efficiency of the proposed hybrid system, which indicates the quality of their energy performance, is defined as follows:

$${\mathsf{\epsilon }}_{sys}={\displaystyle \frac{{\dot{E}}_P}{{\dot{E}}_F}}={\displaystyle \frac{{\dot{E}}_{net}^W+{\dot{E}}_{H2}+{\dot{E}}_{th}^Q}{{\dot{E}}_{biomass}+{\dot{E}}_{solar}^Q+{\dot{E}}_{water}^Q+{\dot{E}}_{air}}}$$

(3)

3.2. Economic Modeling

The National Energy Technology Laboratory (NETL) approach for the economic analysis of power plants was utilized to determine the capital expenditure (CAPEX) and operational and maintenance (O&M) costs of investment for the whole power plant construction [9]. To evaluate total investment, we need to determine the investment cost of the equipment, so the relationship used to calculate the purchase equipment cost (PEC) of each piece of equipment is given in

Table A1. Calculation of the cost and weight of the equipment in the system.

Component	Cost Equation	Ref.	CEPCI	Weight Equation [7,27,80]
Solar tower	${PEC}_{rec}=A_{rec}(79T_{rec}-\mathrm{42,000})$	[22]	381.7	${Weight}_{Rec}=0.019\times Q_{rec}$
Heliostat	${PEC}_{helio}=100A_{helio}{N}_{helio}$	[40]	567.3	${Weight}_{Helio}=6\times N_{helio}$
Solar HE	${PEC}_{SHE}=\mathrm{12,000}{\left({\displaystyle \frac{\sum A_{helio}}{100}}\right)}^{0.6}$	[80]	541.7	${{Weight}_{SHE}=2.14\times \left({\dot{Q}}_{ SHE}\right)}^{0.7}$
TES	${PEC}_{TSE}=113V_{TES}$	[20]	596.2	${Weight}_{Reactor}=8.050\times V_{Rector}$
Water HE	${PEC}_{WHE}=6.534Q_{HE}$	[9]	468.2	${{Weight}_{HE}=2.14\times \left({\displaystyle \frac{{\dot{Q}}_{ HE}}{1000}}\right)}^{0.7}$
HEX	${PEC}_{HEX}=235{\dot{Q}}_{HEX}^{0.75}$	[27]	567.5	${{Weight}_{HE}=2.14\times \left({\displaystyle \frac{{\dot{Q}}_{ HEX}}{1000}}\right)}^{0.7}$
Gasifier	${PEC}_{gasifier}=1600{\left({\dot{m}}_{dry,biomass}\right)}^{0.67}$	[4]	525.4	$Weight=8.050\times V_{Rector}$
Reformer	${PEC}_{Ref}=4.78\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{CH4,in}}{\mathrm{44,910}}}\right)}^{0.67}$	[34]	556.8	${Weight}_{Ref}=8.050\times V_{Ref}$
Calciner	${PEC}_{Cal}=0.59\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{CaCO3,in}}{\mathrm{59,875}}}\right)}^{0.67}$	[34]	556.8	${Weight}_{Cal}=8.050\times V_{Cal}$
Carbonator	${PEC}_{Car}=0.59\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{CO2,out}}{\mathrm{59,875}}}\right)}^{0.67}$	[34]	556.8	${Weight}_{Car}=8.050\times V_{Car}$
Fuel reactor	${PEC}_{FR}=2.15\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{Fe2O3,in}}{\mathrm{1,009,800}}}\right)}^{0.67}$	[34]	541.7	${Weight}_{Reactor}=8.050\times V_{Reactor}$
Steam reactor	${PEC}_{SR}=0.63\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{steam,in}}{\mathrm{213,840}}}\right)}^{0.67}$	[34]	541.7	${Weight}_{Reactor}=8.050\times V_{Reactor}$
Air reactor	${PEC}_{AR}=0.63\times {10}^6{\left({\displaystyle \frac{{\dot{m}}_{air,in}}{\mathrm{213,840}}}\right)}^{0.67}$	[34]	541.7	${Weight}_{Reactor}=8.050\times V_{Reactor}$
Water pump	${PEC}_{WP}=1120{\left({\dot{W}}_{WP}\right)}^{0.8}$ ${PEC}_{SCW,P}=3450{\left({\dot{W}}_{WP}\right)}^{0.71}$	[32] [4]	607.5 596.2	${Weight}_{WP}=0.0061\times {\dot{W}}_P^{0.95}$ ${Weight}_{WP}=0.175\ln \left(W\right)$
Solar pump	${PEC}_{SP}=3540{\left({\dot{W}}_{SP}\right)}^{0.67}$	[24]	607.5	${Weight}_{SP}=0.0061\times {\dot{W}}_P^{0.95}$
Air compressor	${PEC}_{AComp}=71.1{\dot{m}}_{air}{\displaystyle \frac{1}{0.9-{\eta }_{AComp}}}\left({\displaystyle \frac{P_e}{P_i}}\right)\ln \left({\displaystyle \frac{P_e}{P_i}}\right)$	[70]	567.5	${Weight}_{Acomp}=4.9\times {\dot{W}}_{GT}^{0.73}$
Cooler	${PEC}_{cooler}=\mathrm{12,000}{\left({\displaystyle \frac{{\dot{m}}_1\left(h_1-h_2\right)}{100U∆T_{LM}}}\right)}^{0.6}$	[81]	607.5	${{Weight}_{HE}=2.14\times \left({\displaystyle \frac{{\dot{Q}}_{ HE}}{1000}}\right)}^{0.7}$
Flash	${PEC}_{sep}=0$	[21]	596.2	$Weight=8.050\times V_{flash}$
CC	${PEC}_{CC}={\displaystyle \frac{46.08}{0.995-(P_e/P_i)}}\left(1+exp\left(0.018T_e-26.4\right)\right)$	[80]	567.5
GT	${PEC}_{GT}=479.34{\dot{m}}_{gas}\left({\displaystyle \frac{3600}{0.92-{\eta }_{GT}}}\right)\ln \left({\displaystyle \frac{P_i}{P_e}}\right)\left(1+exp\left(0.036T_i-54.4\right)\right)$	[6]	402	${Weight}_{ST}=4.9\times {\dot{W}}_{GT}^{0.73}$
Generator	${PEC}_{Gen}=26.18{\left({\dot{W}}_{Gen}\right)}^{0.95}$	[21]	607.5	${Weight}_{WP}=0.125\ln \left(W\right)-0.0415$
HRSG	${PEC}_{HRSG}=6570\left({\left({\displaystyle \frac{{\dot{Q}}_{ECO}}{{∆T}_{ECO}}}\right)}^{0.8}+{\left({\displaystyle \frac{{\dot{Q}}_{EVA}}{{∆T}_{EVA}}}\right)}^{0.8}+{\left({\displaystyle \frac{{\dot{Q}}_{SH}}{{∆T}_{SH}}}\right)}^{0.8}\right)+\mathrm{21,276}{\dot{m}}_{water}+1184.4{\dot{m}}_{fg}^{1.2}$	[27]	567.3	${Weigh{t}_{HRSG}=8.42\left({\displaystyle \frac{{\dot{Q}}_{ SH}}{1000}}\right)}^{0.87}+13.91{\left({\displaystyle \frac{{\dot{Q}}_{ EVA}}{1000}}\right)}^{0.68}+2.989\times {\left({\displaystyle \frac{{\dot{Q}}_{ ECO}}{1000}}\right)}^{0.98}$
ST	${PEC}_{ST}=2210\times {{\dot{W}}_{ST}}^{0.7}$	[80]	541.7	${Weight}_{ST}=4.9\times {\dot{W}}_{ST}^{0.73}$
Compressor	${PEC}_{Comp}=\mathrm{91,562}{\left({\displaystyle \frac{{\dot{W}}_{Comp}}{455}}\right)}^{0.67}$	[32]	402	${Weight}_{comp}=4.9\times {\dot{W}}_{comp}^{0.73}$
Expander	${PEC}_{EXP}=479.34{\dot{m}}_{gas}\left({\displaystyle \frac{3600}{0.92-{\eta }_{EXP}}}\right)\ln \left({\displaystyle \frac{P_i}{P_e}}\right)\left(1+exp\left(0.036T_i-54.4\right)\right)$	[6]	402	${Weight}_{exp}=4.9{\times W}^{0.73}$
Condenser	${PEC}_{Cond}=\mathrm{10,000}+324{\left(A_{Cond}\right)}^{0.91}$	[82]	556.8	${Weight}_{Cond}=0.073{\times \left({\displaystyle \frac{{\dot{Q}}_{Cond}}{1000}}\right)}^{0.99}$
Valve	${PEC}_{Valve}=114.5{\dot{m}}_{flow}$	[70]	607.5
Boiler	${PEC}_{Boiler}=130{\left({\displaystyle \frac{A_{Boiler}}{0.093}}\right)}^{0.78}$	[32]	499.6	${{Weight}_{boiler}=\left({\displaystyle \frac{{\dot{Q}}_{ HE}}{1000}}\right)}^{0.7}$
Cyclone	${PEC}_{Cycl}=0.31\times {10}^6{\left({\displaystyle \frac{m_{CaCO3}}{47.85\times 3.6\times {10}^6}}\right)}^{0.8}$	[51]	567.3	${Weight}_{cycl}=8.050\times V_{Column}$
Dryer	${PEC}_{Dryer}=0.01\times {10}^6{\left({\displaystyle \frac{m_i}{\mathrm{119,047}}}\right)}^{0.67}$	[51]	395.6	${Weight}_{Dryer}=8.050\times V_{Column}$
ASU	${PEC}_{ASU}=29.5\times {10}^6{\left({\displaystyle \frac{24m_{O_2}}{432\times {10}^3}}\right)}^{0.65}$	[54]	521.9	${Weight}_{ASU}=8.050\times V_{Column}$
WGS, kmol/h	${PEC}_{WGS}=17\times {10}^6{\left({\displaystyle \frac{m_i}{\mathrm{33,415}}}\right)}^{0.6}$	[83]	541.7	${Weight}_{WGS}=8.050\times V_{Column}$
H2S remover	${PEC}_{H_{2}S}=46.24\times {10}^6{\left({\displaystyle \frac{m_{H_{2}S}}{2400}}\right)}^{0.75}$	[52] 1	402	${Weight}_{H2S}=8.050\times V_{Column}$
PSA, kmol/h	${PEC}_{PSA}=38.3\times {10}^6{\left({\displaystyle \frac{m_i}{\mathrm{17,069}}}\right)}^{0.6}$	[53] 2	525.4	${Weight}_{PSA}=8.050\times V_{Column}$
CO2 capture, kmol/h	${PEC}_{{CO}_{2}capture}=18.1\times {10}^6{\left({\displaystyle \frac{m_{{CO}_2}}{1227}}\right)}^{0.65}$	[54]	521.9	${Weight}_{ABS}=8.050\times V_{Column}$

¹ Average exchange rate: EUR 1 = USD 1.13 in 2003. ² Average exchange rate: EUR 1 = USD 1.37 in 2007.

in Appendix B.

The CAPEX included the total direct and indirect costs (TDICs), as well as the contingency cost ($C_{Cont}$) and the land cost ($C_{Land}$) [9,40,51]. TDICs involved direct costs (DCs) and indirect costs (IDC); the detailed breakdown of the CAPEX is shown in

Table 2. Primary premise of the CAPEX and OPEX estimates and economic assumptions.

CAPEX	Method		Ref.
Purchased Equipment Cost (PEC)	Purchased equipment cost of all equipment as per Table A1 of Appendix B
Direct Cost (DC)	$\left[{PEC}_{sys}^{tot}-{PEC}_{CSP plant}\right]\times \varphi +{PEC}_{CSP plant}$
	$\mathrm{Installation} \mathrm{factor}, \varphi$:
	Purchased equipment Installation	$30\% \mathrm{of} PEC$	[51]
	Piping	$5\% \mathrm{of} PEC$	[9]
	Electrical system	$10\% \mathrm{of} PEC$	[40]
	Yard improvement	$10\% \mathrm{of} PEC$	[40]
	Building and services	$4\% \mathrm{of} PEC$	[9]
Indirect cost (IDC)	Engineering and construction	$10\% \mathrm{of} PEC$	[51]
Total Direct and Indirect Cost (TDIC)	$DC+IC$
Contingency (CCont)	$15\% \mathrm{of} TDIC$		[51]
CAPEX	$TDIC+C_{Cont}+C_{Land}$
OPEX	Value	CEPCI	Ref.
Variable cost
Cooling water price	0.01 USD/t	800.8	[48]
Boiler water treatment cost	95,000 USD/month	800.8	[48]
Cost of biomass disposal	16 USD/t	816	[52]
Biomass cost	2 USD/GJ	800.8	[40]
Fe-based OC solvent cost	91.2 USD/t	596.2 1	[53]
Limestone CaO solvent cost	22.8 USD/t	596.2 1	[53]
MEA solvent	1250 USD/t	607.5	[54]
MEA make-up	1.5 kgMEA/tCO2	607.5	[54]
Distilled water	0.08 USD/kg	800.8	[55]
Electricity cost	0.08 USD/kWh	800.8	[48]
Hot water cost	2.02 USD/GJ	800.8	[48]
Reformer catalyst	57,000 USD/m3	596.2 1	[53]
WGS catalyst	15,960 USD/ta	596.2 1	[53]
ZnO catalyst	1570 USD/t	596.2	[51]
NaCl	49 USD/t	541.7	[56]
KCl	455 USD/t	541.7	[56]
ZnCl2	1870 USD/t	541.7	[56]
Fixed cost
Capital annuity cost (CAnn)	$CRF\times CAPEX$		[40]
O&M	$9.33 {\mathrm{U}\mathrm{S}\mathrm{D}m}^{-2}{y}^{-1}\times {\mathrm{A}}_{\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{o}}+3\mathrm{\%}\mathrm{o}\mathrm{f}\left[{\mathrm{P}\mathrm{E}\mathrm{C}}_{\mathrm{I}\mathrm{n}\mathrm{s}}\right]$		[40]
Property, taxes, and insurance costs (CPTI)	$2\% \mathrm{of} CAPEX$		[9]
Interest rate (ir)	3%		[9]
Annual operation hours, τ	Calculated based on daily operation time (10 h, 12 h, 14 and 16 h)
Plant life (n)	25 years		[52]
Discount rate	8%		[40]

¹ Average exchange rate: EUR 1 = USD 1.14 in 2020.

.

$$C_{Land}={UP}_{Land}{\displaystyle \frac{A_{Helio}}{f_{Land}}}$$

(4)

OPEX costs are estimated considering two components: fixed and variable costs. The fixed costs cover the annuity cost, maintenance, property, taxes, and insurance costs. Variable costs cover the materials directly linked to the production process (e.g., biomass, catalysts, solvent, waste disposal, make-up etc.). Table 2 summarizes the key economic assumptions employed for the OPEX calculation in the economic analysis.

The specific CAPEX was computed as the investment cost (CAPEX) reported for the production of H₂ and power [52]:

$$Specific CAPEX={\displaystyle \frac{Total investment cost}{\dot{m}{LHV}_{H2}+{\dot{W}}_{net}+Q_{heating}}}$$

(5)

To analyze the economic performance of the whole proposed system, the LCOP was assessed. Since the proposed system in this work is the co-generation of heat, H₂, and electricity, a similar definition was used, which was

$$LCOP={\displaystyle \frac{Annualized CAPEX+{\sum }_{y=1}^{N}OPEX}{\sum _{y=1}^N\left({\dot{Q}}_e+{\dot{W}}_{net}\right)}}$$

(6)

where $\dot{m}$, ${LHV}_{H_2}$, and ${\dot{W}}_{net}$ are the mass flow rate and LHV of the produced H₂ and the produced heating and electricity power, and $r$ and $N$ are the discount rate and the life of plant for $y^{th}$ year.

3.3. Exergoeconomic Modeling

The performance of the energy system in this study was extensively assessed using the specific exergy costing (SPECO) method of Lazzaretto et al. [34]. The examination of all the exergy flows and irreversible processes of the system are the basis of this method for exergy analysis. The procedure is to define the production structure, including the exergy fuel (${\dot{E}}_F$), exergy product (${\dot{E}}_P$), and exergy destruction (${\dot{E}}_D$) for each system.

To estimate the cost of the system, the unit cost of each stream must be determined by employing the following overall exergoeconomic balance:

$$\sum c_{Q,k}{\dot{E}}_i^Q+\sum _i{\left(c_i{\dot{E}}_i\right)}_k+{\dot{Z}}_k=\sum _e{\left(c_e{\dot{E}}_e\right)}_k+\sum c_{W,k}{\dot{E}}_e^W$$

(7)

where $c_{Q,k}$ and $c_{W,k}$ represent the cost rate for the thermal energy input and work output rates, respectively.

The relative difference in cost unit ($r_k$) and cost factor ($f_k$) of the plant is defined as below for the exergoeconomic evaluation of the system [20,35,57,58]:

$$r_k={\displaystyle \frac{c_{P,k}-c_{F,k}}{c_{F,k}}}$$

(8)

$$f_k={\displaystyle \frac{{\dot{Z}}_k}{{\dot{C}}_{D,k}+{\dot{Z}}_k+{\dot{C}}_L}}$$

(9)

3.4. Environmental Modeling

CO₂ emissions include both direct and indirect emissions [34], as well as emissions associated with the syngas, H₂, and power generation. The total CO₂ emissions are calculated as follows:

$${CO}_2 emission={{CO}_2 emission}^{direct}+{{CO}_2 emission}^{indirect}$$

(10)

$${{CO}_2 emission}^{indirect}={{CO}_2 emission}_{thermal}^{indirect}+{{CO}_2 emission}_{electrical}^{indirect}$$

(11)

Here, ${{CO}_2 emission}_{thermal}^{indirect}$ refers to the CO₂ produced from thermal energy (205.3 lbCO₂/MMBtu), and ${{CO}_2 emission}_{electrical}^{indirect}$ refers to the CO₂ generated from the electricity (0.89 t/MWh) supplied to the system [59]. It is important to note that direct CO₂ emissions refer to the CO₂ released directly into the environment, while the captured CO₂ stored in tanks is not considered as CO₂ emissions.

To assess the environmental performance across different energy levels, the CO₂ and CO emissions of the proposed models are estimated using Equations (12) and (13) [60,61].

$${\xi }_{{CO}_2}={\displaystyle \frac{{\dot{m}}_{{CO}_2}}{{\dot{e}}_{H_2}{\dot{m}}_{H_2}+{\dot{E}}_{heating}^Q+{\dot{E}}_{net}^W}}$$

(12)

$${\xi }_{CO}={\displaystyle \frac{{\dot{m}}_{CO}}{{\dot{e}}_{H_2}{\dot{m}}_{H_2}+{\dot{E}}_{heating}^Q+{\dot{E}}_{net}^W}}$$

(13)

where ${\dot{m}}_{H_2}$, ${\dot{m}}_{CO},$ and ${\dot{m}}_{{CO}_2}$ indicate the mass flow rate of the produced H₂, CO, and CO₂ emissions, and ${\dot{e}}_{H_2}$ denotes the specific exergy rate of H₂.

To fully understand and evaluate the CO₂ emissions of a process, a life cycle assessment (LCA) should be conducted as shown in Figure A1 [62]. Figure A2 shows the life cycle boundary of the proposed power systems.

The environmental impact of different hydrogen production technologies is typically assessed using metrics like the acidification potential (AP) and the global warming potential (GWP) [12]. These modules are then integrated into the OpenLCA model [63].

The system’s environmental impact is evaluated through the social ecological factor and a CO₂ emission reduction indicator using Equation (14):

$$SEF={\displaystyle \frac{1}{1-{\mathsf{\epsilon }}_{sys}}}$$

(14)

where $SEF$ is the social ecological factor.

The total environmental impact points of material, Pts, can be determined by Equation (15). The total time-dependent environmental impact points of material are calculated by Equation (16), assuming that the annual operating time ($n$) and the life of the system ($N$) are about 3600 h and 25 years, respectively.

$$y_M=\sum \left({m_{CO}w}_{CO}+{m_{OM}w}_{OM}+{m_{DI}w}_{DI}\right)$$

(15)

$$Y_M={\displaystyle \frac{y_M}{N\times n\times 3600}}$$

(16)

Here, $m$ and $w$ are the equipment weight (in kg) and the standard environmental impact indicator (SEI; Pts/kg). $CO, OM$ and $DI$ represent the life cycle of a system containing the construction, operation and maintenance, and disposal and dismantling stages, during which the materials (for construction) and relevant services will lead to an environmental impact [64,65].

The average yearly LCC assessment of H₂ production can be given as follows:

$${LCC}_{H_2}={\displaystyle \frac{\sum _{n=0}^N\frac{CAPEX+OPEX}{{(1+r)}^n}+{\dot{C}}_{env}}{{\dot{m}}_{H_2}}}$$

(17)

3.5. Exergoenvironmental Modeling

The overall exergoenvironmental balance used for the studied systems is as follows [65]:

$$\sum _i{\left(b\dot{E}\right)}_i+\sum {\dot{B}}_i^Q+{\dot{Y}}_k=\sum {\dot{B}}_e^W+\sum _e{\left(b\dot{E}\right)}_e$$

(18)

In the above Equation (18), $b$ and ${\dot{Y}}_k$ are the environmental impacts of flow per unit of exergy and the environmental impacts of equipment during their life cycle, respectively.

A thorough exergoenvironmental balance equation is established following the calculations in Equations (19)–(22) below [27]. According to Equation (19), ${\dot{B}}_D$ considers the inefficiency of the k^th component or system in terms of the environmental impact rate, while ${\dot{B}}_{tot}$ depicts the overall environmental impact of the units [64,66,67].

$${\dot{B}}_D=b_F{\dot{E}}_D$$

(19)

$${\dot{B}}_{tot}={\dot{B}}_D+{\dot{Y}}_k$$

(20)

$$r_{b.k}={\displaystyle \frac{b_{p.k}-b_{f.k}}{b_{f.k}}}$$

(21)

$$f_{b.k}={\displaystyle \frac{{\dot{Y}}_k}{{\dot{Y}}_k+{\dot{B}}_{D.k}}}={\displaystyle \frac{{\dot{Y}}_k}{{\dot{Y}}_k+b_{f.k}\dot{E}{x}_{D.k}}}$$

(22)

where $Y_k,$ ${\dot{B}}_{D.k}$, r_b,k, and f_b,k are the total environmental impact of the system’s LCA, environmental impact destruction, exergoenvironmental coefficient, and exergoenvironmental factor of the plant are defined as below for the exergoenvironmental evaluation of the system.

Furthermore, the principal exergoenvironmental indicators are formulated as follows [20,39,58].

Exergoenvironmental impact improvement:

$${\Omega }_{ii}={\displaystyle \frac{1}{{\Omega }_{dei}}}$$

(23)

where ${\Omega }_{ii}$ clarifies the connection between the system and the environment. Unlike ${\Omega }_{dei}$, the ${\Omega }_{ii}$ parameter should be as high as possible.

The next important metric is the exergetic sustainability index:

$${\Omega }_{si}={\Omega }_{sf}{\Omega }_{ii}$$

(24)

In this regard, the exergetic sustainability index for the desired system should be as high as possible. The exergy stability factor (${\Omega }_{sf}$) can be calculated by Equation (25):

$${\Omega }_{sf}={\displaystyle \frac{\sum {\dot{E}}_P}{\sum {\dot{E}}_P+{\dot{E}}_{D,tot}+\sum {\dot{E}}_F}}$$

(25)

where $\sum {\dot{E}}_P$ denotes the total product exergy in the system. The ${\Omega }_{sf}$ of a favorable system approaches one.

4. Results and Discussion

4.1. Energy and Exergy Evaluations

In this section, the modeling results of the presented system for 4E analyses of the proposed plants for the Tashkent CHP plant will be examined in detail. To perform 4E analyses of the hybrid power system, a comprehensive computer modeling was developed using the Aspen Plus^® [42], Ebsilon^® Professional [45], and openLCA [63] tools, and the results of the analyses are given in detail below.

As defined in Section 3, the overall mass, energy, and exergy balances were utilized to quantify the most relevant plant performance indexes.

Table 3. Main thermodynamic performance indexes.

Parameter	Unit	Model 1	Model 2	Model 3	Model 4
Biomass input	t/h	104.07	104.75	93.24	87.16
Biomass exergy rate	MW	559.39	563.02	501.15	468.52
Average hourly exergy rate of solar energy	MW	171.35	183.45	188.88	194.57
Exergy rate of air	MW	123.98	136.5	135.94	106.24
Exergy rate of water feed	MW	0.74	1.29	1.07	0.84
Net power output	MWe	46.83	55.37	66.36	51.48
Exergy rate of heating	MWth	23.3	23.3	23.26	23.3
Hydrogen exergy rate	MW	281.07	255.13	188.88	283.46
Exergy efficiency of H2 production	%	32.86	28.85	22.84	36.8
Energy penalty	% point	−2.34	+1.67	+7.65	−5.98
Exergy rate of CO2	MW	17.78	15.13	15.2	16.6
Exergy efficiency of the overall system	%	43.13	39.46	35.61	48.67
Total exergy destruction rate	MW	463.84	507.77	415.44	336.89
Total exergy loss	MW	22.63	27.55	116.95	58.43
Exergy destruction yield	%	54.22	57.42	50.25	43.74

shows the most significant technical indexes calculated for the assessed power and green H₂ generation concepts via decarbonized solar-based biomass gasification and reforming. Multiple energy resources, such as solar and biomass, are used in the system to enable sustainable operation without intermittence issues. Also, there is a thermal energy storage tank installed for the solar cycle to provide output even during low solar energy levels. All the concepts have the same biomass feedstock with different thermal inputs as well as the same steam and gas turbine capacities.

The exergy input rate of the biomass is the highest exergetic contribution to the system, comprising 60.61–65.39% of the total exergy fuel. The highest average hourly solar exergy fuel required is 194.57 MW due to the highest energy demand in model 3. In addition, the exergy product of the system consists of four parts heat and electricity generation by the turbines, captured CO₂, and H₂ production. In the case of CLR and SCWG, an extra expander contributes to the net power output of models 3 and 4. Therefore, the net power output of model 3 is 66.36 MW, with a lower energy consumption.

The system-level dynamic simulation design of the proposed plants indicates that the net power output can be decreased to 5.52 MW points when the H₂ and power production effectiveness reach the highest index. The H₂ production energy penalty is approximately 1.67 and 7.65% exergy efficiency in models 2 and 3, respectively.

It is obvious that model 4 has the highest exergy performance with 48.67% exergy efficiency, 13.16% more than that for model 3, which illustrates the high energy quality used in the systems. This is due to the greater amount of H₂ production and a lower biomass exergy fuel requirement of the system. In addition, the system shows less exergy destruction and internal irreversibility (353.5 MW) compared with the other cases (models 1–3). In this regard, the exergy efficiency was calculated to be 35.61% in the system with the CLR biomass conversion. The main reasons for this showing the lowest performance are the low H₂ production and higher entropy generation in a cooler. This is because the temperature difference between steam and the ambient temperature is higher. The largest exergy destruction yield occurs in model 2, whereas the coupling system with the SER process for biomass conversion contributes 57.42% of the exergy destruction yield of the exergy fuel. This result is attributable to the significant irreversibility associated with the chemical reaction and the heat transfer across the large temperature differences between the exergy fuel and the exergy product of the reactors and combustion chambers.

An annual simulation using an hourly time step was carried out for each of the proposed models. For each operating mode, the daily average solar heat energies attributable to each energy source by month and over the entire year are presented in Figure 5. The energy supply by the CSP drops significantly in the winter due to a lower DNI by up to 25.6 W/m² (on 20 January). Note that while this mode reduces the exergy efficiency, the efficiency of the overall system was reduced in the range of 14.58–51.7% points in all cases.

As shown in Figure 6, the monthly exergetic efficiencies for the power plant and the CSP plant are presented. The highest DNI peak was observed for the first ten days in July (Figure 5). However, when considering the average monthly solar field exergetic efficiency, this ranges from 41.25% to 57.53%, with a peak in September for all models. This is to be expected, given the lower cosine losses and larger solar resources during the summer until the end of August. The performance of CSP plant 3 results in the highest index, which reaches 57.53% of the average monthly efficiency.

These findings provide valuable insights into the exergy analysis of the systems. To enhance the exergy efficiency, it is crucial to assess and optimize the irreversibility of each unit and subsystem in future studies.

4.2. Economic and Exergoeconomic Evaluations

The primary simulation results needed for the PEC calculations are listed in

Table A5. Comparison of the performance between the presented models and the models proposed in past studies.

System	Useful Output	Main Outcomes	Ref.
Solar-driven hydrogen and power production system based on biomass gasification	Electricity, heating, CO2, and hydrogen	Exergy efficiency of 41.62%; Exergoeconomic factor of 69.4%; Total cost rate of 7603.5 USD/h; LCOE of 83.4 USD/MWh; LCOH of 13.9 USD/MWh; LCOP of 10.6 USD/MWh; Unit exergy cost of hydrogen of 5.28 USD/GJ; LCC of 1.67 USD/kgH2; Specific CO2 emission of 28.9 kgCO2/MW; Total environmental impact rate of 1899.7 Pts/h; Exergoenvironmental factor of 67.6%; Social ecological factor of 1.71	This study
Solar-driven hydrogen and power production system based on sorption-enhanced reforming	Electricity, heating, CO2, and hydrogen	Exergy efficiency of 37.98%; Exergoeconomic factor of 59.2%; Total cost rate of 6076.8 USD/h; LCOE of 49.2 USD/MWh; LCOH of 10.7 USD/MWh; LCOP of 7.8 USD/MWh; Unit exergy cost of hydrogen of 3.3 USD/GJ; LCC of 1.23 USD/kgH2; Specific CO2 emission of 93.82 kgCO2/MW; Total environmental impact rate of 1942.7 Pts/h; Exergoenvironmental factor of 66.2%; Social ecological factor of 1.61	This study
Solar-driven hydrogen and power production system based on iron chemical reforming	Electricity, heating, CO2, and hydrogen	Exergy efficiency of 34.22%; Exergoeconomic factor of 67.9%; Total cost rate of 6941 USD/h; LCOE of 55.4 USD/MWh; LCOH of 19.5 USD/MWh; LCOP of 12.5 USD/MWh; Unit exergy cost of hydrogen of 4.69 USD/GJ; LCC of 2.24 USD/kgH2; Specific CO2 emission of 32.76 kgCO2/MW; Total environmental impact rate of 1983 Pts/h; Exergoenvironmental factor of 70.7%; Social ecological factor of 1.52	This study
Solar-driven hydrogen and power production system based on supercritical water gasification	Electricity, heating, CO2, and hydrogen	Exergy efficiency of 43.99%; Exergoeconomic factor of 76.9%; Total cost rate of 7981.4 USD/h; LCOE of 88.4 USD/MWh; LCOH of 16.1 USD/MWh; LCOP of 12.1 USD/MWh; Unit exergy cost of hydrogen of 5.19 USD/GJ; LCC of 1.95 USD/kgH2; Specific CO2 emission of 26.56 kgCO2/MW; Total environmental impact rate of 2176 Pts/h; Exergoenvironmental factor of 73.9%; Social ecological factor of 1.78	This study
Hybrid biomass–solar-driven triple combined cycle	Electricity and hydrogen	Exergy efficiency of 30.44%; LCOE of 61.37 USD/MWh; CO2 emission of 0.4579 kg/kWh	[4]
Multi-generation system with solar power tower	Electricity, cooling, heating, and hydrogen	Exergy efficiency of 43.11%; Total cost rate of 7799 USD/h; Unit cost of multi-generation of 8.26 USD/GJ; H2 unit cost of 45 USD/GJ; Environmental cost rate of 20%	[5]
Biogas-driven poly-generation plant	Electricity, fresh water, cooling, and hydrogen	Exergy efficiency of 31.69%; Product unit cost of 20.1 USD/GJ; Exeregoeconomic factor of 38.64%	[24]
Solar–biomass integrated energy system	Electricity, hot water, and liquid hydrogen	Exergy efficiency of 11.06%; Total cost rate of 320 USD/h; LCOE of 40.16 USD/MWh; CO2 emission of 0.39 t/MWh	[22]
Multi-generation energy system driven by integrated RES	Electricity and hydrogen fuel	Exergy efficiency of 35.9%; Product unit cost of 36.95 USD/h; LCOE of 98 USD/MWh; LCOH of 170.11 USD/MWh	[26]
Poly-generation system based on the integration of biomass and solar	Electricity, fresh water, and hydrogen	Exergy efficiency of 32.01%; LCOE of 14 USD/MWh; LCA of 0.002 Pts/kWh	[27]
Solar-driven steam gasification of biomass	Electricity and hydrogen	Hydrogen minimum price of 2.56 EUR/kgH2	[30] 1
Geothermal–solar–wind micro-multi-energy system	Electricity, hydrogen, and hot water	Exergy efficiency of 88.31%; Total product unit cost of 1859 USD/GJ; LCOE of 45 USD/MWh; LCOH of 865.89USD/MWh.	[27]
Solar–biomass cascade ORC system	Cooling, power, and hydrogen	Exergy efficiency of 11%; Total cost of 42–75 USD/h; Reduced CO2 emission from 364 kg/h to 43 kg/h	[31]
Solar–biomass multi-generation system	Electricity, hydrogen, and cooling	Exergy efficiency of 43.12%; Hydrogen unit cost of 39.49 USD/GJ; Product unit cost of 8.35 USD/GJ (30 USD/MWh)	[70]
Cogeneration system based on biomass and solar	Electricity, hot water, and hydrogen	Exergy efficiency of 30.91%; Total cost rate of 633 USD/h; Unit product cost of 16.77 USD/MWh; LCOE of 21.08 USD/MWh	[35]
Decarbonized green hydrogen production using biomass gasification system	Hydrogen and CO2	LCOH of 71.47–78.66 USD/MWh	[52] 2
Multi-generation plant powered by geothermal energy	Electricity, fresh water, hydrogen, and cooling	Unit cost of 124 USD/GJ; Exergy efficiency of 24.4%; Exergoeconomic factor of 38.85%; Exergy stability factor of 0.6	[58]
Biomass air–steamgasification with methane co-feeding	Syngas	Exergy efficiency of 71.8%; Unit hydrogen cost of 2.69 USD/kg. Unit exergy cost of hydrogen of 18.89 USD/GJ	[71]
Decarbonized hydrogen production based on direct biogas conversion using thermo-chemical looping cycles	Electricity, CO2, and hydrogen	Cumulative energy efficiency of 68.56–73.35%LCOH of 33 EUR/MWh (34.7 USD/MWh); CO2 specific emissions of 0.4–159.3 kg/MWh	[53]
Solar, geothermal, and biomass multi-generation	Power, hydrogen, heating, cooling, and fresh water	Exergy efficiency of 27.09%; Unit product cost of 21.79 USD/GJ; Unit cost of hydrogen of 15.74 USD/GJ; Social ecological factor of 1.37	[20]
Renewable energy-driven poly-generation system	Electricity, hydrogen, fuel, hot water, and chilled water	Exergy efficiency of 32.3%; Unit exergy cost of hydrogen fuel of 4.29 USD/GJ	[57]
Solar-powered setup	Natural gas, electricity, and liquid hydrogen	Second-law efficiency of 17.05%; LCOH of 107.7 USD/MWh; CO2 emission reduction of 0.0711 kg/s	[60]
Solar thermal-operated power cycle	Electricity and hydrogen	Thermal efficiency of LCOH of 6.88 USD; SI of 1.65–1.88	[75]
Integrated solar-powered electricity generation system	Electricity	LCOE of 0.342 USD/kWh; Specific CO2 emissions of 102 kgCO2/MWh; Ecological effect factor of 1.05; Exergoenvironental sustainability index of 9.78	[85]
Solar-based sorption-enhanced gasification co-generation system	Electricity, hydrogen, and CO2	Exergy efficiency of 62.09%; LCOE of 60.1 USD/MWh; CO2 emission of co-generation and electricity production of 8.5 kgCO2/MWh and 38.37 kgCO2/MWh	[61]
Solar–biomass drive integrated energy system	Desalinated water, hydrogen, and electricity	Exergy efficiency of 33.58%; LCOE of 13.75 USD/MWh; Total cost rate of 1889 USD/h; Environmental impact of 1.96 Pts/MWh; Total environmental impact rate of 0.274 mPts/h	[66]
Multi-generation system	NG, fresh water, hydrogen, and electricity	Exergy efficiency of 20.7%; Cost rate of 142 USD/h; CO2 emission cost rate of 7 USD/h	[86]
Fresnel collector solar-driven multi-generation plant	Electricity, methanol, oxygen, fresh water, and hydrogen	Exergy efficiency of 27.5%; Total cost rate of 3378 USD/h; LCOE of 49.28 USD/MWh; Environmental impact of 294.1 mPts/s; LCA of 4.377 Pts/GJ	[69]

¹ Average exchange rates: EUR 1 = USD 1.14 in 2020 ² Average exchange rates: EUR 1 = USD 1.05 in 2022.

in Appendix B. Based on Equations (A4) and (A5), each cost is calculated and updated to the year 2023 using the CEPCI cost index. This allows for the calculation of each PEC for the proposed models, which is essential for determining the total investment cost.

Figure 7 shows the breakdown of the PEC for the proposed systems. The lowest PEC for hydrogen and power production is USD 159.59 million for model 2 operating for 10 h daily. This figure highlights that the heliostat cost makes up the largest proportion of the PEC, being 24.54%, 41.79%, 37.05%, and 29.15% for models 1–4, respectively. The next most costly equipment is the MEA-based CO₂ capture unit, except in model 2, which does not need extra decarbonization technology. This allows the SER-based system to significantly reduce the total investment cost. The PECs are detailed in

Table A3. Total purchased equipment cost of the components.

Unit	Model 1	Model 2	Model 3	Model 4	Unit	Model 1	Model 2	Model 3	Model 4
Receiver	23.37	23.78	12.73	17.23	GT	0.94	0.69	0.38	0.93
Heliostat	62.32	66.70	69.03	70.86	HRSG	0.00	0.00	0.00	0.00
S-HE	2.73	2.84	2.90	2.95	ST	0.00	0.00	0.00	0.00
TES	2.44	2.43	4.28	4.08	Gen	0.00	0.00	0.51	0.46
S-pump	0.59	0.59	0.69	0.87	Cooler	0.76	0.00	2.57	1.74
Dryer	0.02	0.02	0.02	0.00	ABS	51.98	0.00	48.38	49.72
Gasifier	5.21	2.61	0.00	4.98	Cond	0.00	0.00	0.00	0.00
ASU	31.64	0.00	0.00	0.00	Boiler	0.00	0.00	0.00	0.00
W-HE	0.38	0.56	1.35	1.47	FR	0.00	0.00	3.21	0.00
WGS	10.17	0.00	8.90	10.88	SR	0.00	0.00	1.57	0.00
HEX	0.46	0.00	0.10	1.82	AR	0.00	0.00	0.85	0.00
W-pump	0.12	0.11	0.16	0.42	Cycl	0.00	0.01	0.03	0.00
H2S	15.65	13.92	12.75	13.16	Exp	0.00	0.00	6.78	9.95
PSA	38.77	35.97	0.00	38.49	Scomp	0.00	0.00	1.76	2.64
CC	0.06	0.06	0.08	0.06	Calciner	0.00	1.99	0.00	0.00
AComp	6.32	7.33	7.30	5.70	Reformer	0.00	0.00	0.00	4.65
Total cost, USD (in millions)						253.9	159.59	186.32	243.07

in Appendix B. Additionally, 15.26%, 22.53%, and 15.83% of the PEC of models 1, 2 and 4, respectively, are attributed to the pressure swing absorption unit for the H₂ purification process. The equipment costs for the H₂S removal section amount to USD 12.75–15.67 million.

For the CAPEX, the cost correlation method was conducted. The specific CAPEX costs were calculated using Equation (5), as presented in

Table 4. Estimated costs for the solar-based biomass gasification power plants (in USD millions).

	Model 1	Model 2	Model 3	Model 4
Total purchased equipment cost (TPEC)	253.91	159.59	186.32	243.07
Direct cost (DC)	350.11	197.25	243.77	330.36
Indirect cost (IDC)	25.39	15.96	18.63	24.31
Total direct and indirect cost	375.51	213.21	262.4	354.67
Contingency cost (Ccont)	56.33	31.98	39.36	53.2
The cost of land (Cland)	1.26	1.35	1.39	1.43
Total CAPEX	433.09	246.54	303.16	409.3
Capital annuity cost (CAnn)	24.87	14.16	17.41	23.51
Property, taxes, and insurance cost (CPTI)	8.66	4.93	6.06	8.19
Material cost (Material)	16.4	15.25	14.72	13.93
Variable cost (O&M)	9.01	6.32	7.48	9.12
Total OPEX	52.84	35.75	39.24	51.68
Total investment cost of the system	485.93	282.29	342.4	460.98

. Figure 8a presents the results for all the proposed models. Case 2, with the SER technology, has the lowest CAPEX of about 706.55 USD/kW of H₂ and power, followed by the CLR model, which presents small differences between them (1029.68 USD/kW). The model with the conventional biomass gasification process has the highest CAPEX, which is 1173.74 USD/kW. This can be explained by the fact that the solar energy demand here is the greatest compared with the other cases, which results in increased costs for the heliostats and TES units. Moreover, an extra oxygen separation cost (ASU cost) and syngas production cost (WGS cost) was required, which affected the total cost of the model.

The next relevant economic performance parameter was the OPEX costs. The main economic assumptions used for OPEX evaluation are displayed in Table 2. The OPEX costs for renewable H₂ and power generation systems with CO₂ capture are described in Figure 8b.

The OPEX costs are enhanced by the decarbonization of H₂ and by the power production based on biomass gasification. Model 1 has the greatest OPEX, mainly due to the high O&M cost of the CSP plant and the property, taxes, and insurance costs of the overall system. However, the material costs for the operation show a lower cost than for model 4. The lowest O&M costs were observed for the case using SER (model 2).

The LCOE, LCOH, and LCOP were calculated using Equations (12)–(14) as presented in Section 4. These key economic parameters are indicated in

Table 5. Levelized cost of the products for renewable hydrogen and power production processes.

	Model 1	Model 2	Model 3	Model 4
LCOE, USD/MWh	117.6	58.9	59.0	101
LCOH, USD/MWh	19.6	12.8	20.7	18.3
LCOP, USD/MWh	14.9	9.4	13.3	13.9
LCC, USD/kg	2.38	1.47	2.39	2.24

. As observed from Table 5, CaO- and Fe₂O₃-based chemical looping technologies applied in biomass gasification exhibited the lowest levelized cost of electricity. Between models 3 and 4, the SCWG case using gas–liquid CO₂ absorption technology had a slightly lower electricity production cost by about 3.7%. Conventional biomass gasification had the highest electricity production cost.

In the case of hydrogen production, the lowest LCOH was observed in model 2, which was 12.8 USD/MWh for hydrogen production. However, the systems with conventional gasification and gasification with SCW processes had the greatest hydrogen production costs of 19.6 USD/MWh and 20.7 USD/MWh, respectively.

The calculated solar-based levelized cost of product (hydrogen, electricity, and heating) for each model was in the range of 9.4–14.9 USD/MWh. As can be seen in

Table 6. Exergoeconomic data of the solar- and biomass-based CHP plant.

	${\dot{\mathit{E}}}_{\mathit{D}}$, MW	${\dot{\mathit{C}}}_{\mathit{t}\mathit{o}\mathit{t}}$, USD/h	${\mathit{c}}_{\mathit{F}}$, USD/GJ	${\mathit{c}}_{\mathit{P}}$, USD/GJ	${\dot{\mathit{C}}}_{\mathit{D}}$, USD/h	${\dot{\mathit{C}}}_{\mathit{L}}$, USD/h	${\mathit{Z}}_{\mathit{T}}$, USD/h	${\mathit{r}}_{\mathit{k}}$	${\mathit{f}}_{\mathit{k}}$, %
Model 1	463.85	14,124.23	1.31	8.80	2191.07	106.91	7649.65	5.707	76.9
Model 2	507.78	11,626.09	1.28	6.78	2341.16	127.01	4445.5	4.297	64.3
Model 3	415.44	11,053.28	1.21	8.51	1822.73	513.13	5390.81	5.982	69.77
Model 4	336.89	13,247.48	1.22	8.55	1482.19	257.06	8152.99	5.998	82.42

, the sorption-enhanced reforming-based system offered the most cost-economic result with 9.4 USD/MWh of the LCOP. The same was seen with electricity production, with the model with conventional biomass gasification indicating the highest hydrogen and power production cost compared with that for the other cases (14.9 USD/MWh).

The LCC of hydrogen was calculated based on the cost inventory data given in Table 5, and monetization of the life cycle environmental impacts was performed using the environmental cost unit price calculated from Equation (A18). Taking into account the discounting factor (r = 8%), the annual average LCC of 1 kg of hydrogen production from cotton stalk biomass was in the range of 1.47–2.39 USD over the plant lifetime of 25 years. Hydrogen production with the SER process was about 38.5% cheaper than for model 1 and 3, which were 71 USD/MWh and 72 USD/MWh, respectively. This is attributed to the low capital investment operating costs related to syngas production (WGS and cooling units) and there being no necessity for CO₂ capture in the system.

The discounting factor (r) has been reported as 10% in a study [68]. At r = 10%, the LCC of 1 kg of hydrogen production increased by 34.4–36.4% compared with the base case (r = 8%) and resulted in 0.97–1.6 USD/kg in models 1–4.

From the LCC perspective, to make biomass- and solar-based hydrogen production competitive, the CAPEX of the SCWG system technology should be reduced to 50% of its present value. And, to reduce variable OPEX costs, energy-efficient biomass pyrolysis and methane reforming are vital. Whereas in the case of SCWG, to reduce both the variable CAPEX and OPEX costs, improvements should focus on the solar energy demand and heat recovery of the system. This can be achieved by several means, but a simple approach could be to reduce the energy losses in the units and streams of the system.

The major outcomes of the exergoeconomic study for the system are presented in Table 6. These parameters are provided to validate the exergoeconomic models of the processes. The cost stream flows consist of investment, work, exergy, and heat cost flows. SPECO was used to calculate the hydrogen unit costs using the exergy-related costs in thermal systems. In this work, the unit costs of the four cases using different biomass conversion methods were obtained. To calculate the specific exergy fuel ($c_F$) and product ($c_P$) costs, the exergy destruction (${\dot{C}}_D$), total operational cost rate (${\dot{C}}_D+Z_T$), relative difference ($r_k$), and exergoeconomic factor ($f_k$) were used.

The lowest total cost rate of the proposed system (model 3) is 11,053.28 USD/h, in which the contribution of investment, exergy destruction, biomass consumption, and environmental cost rates are 48.8%, 16.5%, 32.6%, and 21%. As can be observed, the highest contribution to the total cost rate is related to the investment cost, which is followed by the cost rate of the biomass. However, the exergoeconomic factor is 69.77%, which indicates the difference between the cost rate of exergy destruction and the total cost rate of the system. In this regard, model 4 shows the best performance, with an 82.42% exergoeconomic factor.

Exergoeconomic analysis can predict the cost rate of the product. For example, by evaluation of the unit cost of products, it is determined that in the SER case, the cost for electricity, heating, and hydrogen production is equal to 6.78 USD/GJ; hence, the unit cost of hydrogen is obtained to be 15 USD/MW, which is much cheaper than that of conventional biomass gasification (27.2 USD/MW) and supercritical water gasification (22 USD/MW) for hydrogen production. Owing to the high rate of hydrogen generation compared with in other cases, the unit cost of the whole system with the biomass gasification process is 8.8 USD/GJ. Accordingly, model 2 has the highest contribution to the exergy destruction rate and, consequently, the cost rate of the total exergy destruction (2341.16 USD/h). However, the largest cost rate of exergy loss in the system is related to the CLR process due to the greatest loss due to cooling.

Figure 9 describes the amount of exergy efficiency, unit exergy cost of hydrogen, and the summation of the exergy destruction and investment cost rates $\left({\dot{C}}_D+Z_T\right)$ for the system. From a thermodynamic point of view, among the systems with an influential impact on the performance of the system, model 4, with an exergy efficiency above 48%, has the best thermodynamic performance; on the other hand, model 2, with the lowest exergy efficiency, has the worst performance. However, model 2 has the lowest value of unit exergy cost of hydrogen and $\left({\dot{C}}_D+Z_T\right)$, which is equal to 6786.6 USD/h, 3054 USD/h, and 1943.5 USD/h points lower than for case 1 and case 4, respectively.

Based on the above description, to reduce the overall system cost, efforts should focus on lowering the investment cost of high-cost components and the exergy destruction cost of low-cost components. These findings can guide future process optimization. Additionally, to enhance systems with moderate cost factors, exergoeconomic analyses of each component should be conducted.

4.3. Environmental and Exergoenvironmental Evaluations

The environmental assessment of the proposed solar–biomass system includes evaluating both direct and indirect CO₂ emissions. Direct CO₂ emissions can be further divided into captured (using various technologies in models 1–4) and uncaptured CO₂. Indirect CO₂ emissions, arising from the consumption of electricity, steam, and heat, can be calculated using Equation (11). In this study, the electrical CO₂ emission is zero since the required electricity for compressors and pumps is generated by the turbine. Thermal CO₂ emissions are calculated by multiplying the thermal energy needed for heating and cooling the streams by 205.3 lbCO₂/MMBtu (0.088 kgCO₂/MJ).

As shown in Figure 10a, the total CO₂ emissions for models 1–4 are 179.84, 153.31, 161.1, and 167.93 t/h, respectively. Model 1 has the highest direct and indirect CO₂ emissions among the models. Models 1 and 2 have higher direct CO₂ emissions because more CO₂ is produced during the production and combustion of syngas. Since CO₂ is captured via a CaO-based chemical looping process, model 2 does not require an additional CO₂ capture unit, such as an absorption plant, resulting in zero thermal energy consumption and, thus, zero indirect CO₂ emissions. Therefore, model 2 is more environmentally friendly than the other three systems. However, if we consider that the thermal energy consumed comes from an RE source, such as solar energy, indirect CO₂ emissions can be neglected. In this scenario, model 3, which utilizes a CLR process, can be considered an environmentally friendly option for hydrogen and power production.

CO₂ emissions are a crucial indicator of the thermo-environmental performance of energy systems. In the proposed system, which considers the poly-generation of electricity, heating, and hydrogen, emissions range from 26.56 to 93.82 kgCO₂/MWh. Among the four models, the SCWG case has the lowest specific CO₂ emission at 26.56 kgCO₂/MWh. According to a report by the International Renewable Energy Agency (IRENA), biomass-based power plants had an average CO₂ emission of 55 kg/MWh in 2020 [61]. Therefore, the emissions from the integrated system 4, followed by models 1 and 3, are within acceptable limits. Notably, the proposed system achieves a 93% CO₂ capture efficiency, significantly reducing CO₂ emissions. This highlights the environmental benefits of the system and underscores its potential as a sustainable energy solution.

Figure 10b illustrates the ecological footprint of various hydrogen production technologies based on the human toxicity potential (HTP), AP, and GWP from renewable energy sources. The values of HTP, AP, and GWP are normalized using similar equations across all the models. The CaO-based reforming process for H₂ production is the most environmentally destructive, with a relatively high GWP of 3996.19 kgCO₂/tH₂, primarily because post-combustion CO₂ capture technology is not applied. GWP values can vary in the literature due to differences in system boundary assumptions. The solar-based sorption-enhanced reforming model, however, is among the best technologies for producing clean hydrogen. Biomass gasification with supercritical water has a higher AP (1.056 kgSO₂/tH₂). In terms of CO₂ emissions, gasification with steam–oxygen and supercritical water are the most eco-friendly technologies studied, as shown in Figure 10b.

Based on the SEI value of materials with the weight of each equipment in Table A1 (Appendix B), the results of the environmental impact points of components ($Y_M$) for the system are obtained and listed in

Table 7. Environmental impact points of the components.

Unit	Model 1	Model 2	Model 3	Model 4	Unit	Model 1	Model 2	Model 3	Model 4
Receiver	3090.5	3246.8	3374.7	3461.6	GT	67,257.2	77,417.6	73,574.3	68,150.4
Heliostat	173,509	371,385	384,354	394,572	HRSG	185,538	185,538	185,538	185,538
S-HE	1,548,039	195,541.2	182,109.5	215,786.5	ST	35,085.1	35,085.1	35,085.1	35,085.1
TES	1,005,375	1,002,745	176,5370	1,684,474	Gen	95.2	95.2	98.6	98.3
S-pump	767.3	766.2	963.8	1346.6	Cooler	79.8	41.9	387.4	212.8
Dryer	23,458.1	23,610.4	21,015.8	0.0	ABS	31,929.3	0.0	28,584.3	29,816.1
Gasifier	41,924.4	39,049.9	0.0	34,857.2	Cond	106.5	106.5	106.5	106.5
ASU	2338.1	0.0	0.0	0.0	Boiler	96.9	96.9	96.9	96.9
W-HE	89,257.0	116,523.0	216,709.8	229,842.7	FR	0.0	0.0	36,848.6	0.0
WGS	5521.6	0.0	855.6	3892.0	SR	0.0	0.0	2804.7	0.0
HEX	51,103.8	0.0	12,077.1	185,605.5	AR	0.0	0.0	19,278.2	0.0
Wpump	147.8	136.4	208.0	233.5	Cycl	0.0	206.1	827.3	0.0
H2S	33,949.1	19,566.5	40,687.0	31,882.2	Exp	0.0	0.0	35,380.6	32,834.5
PSA	33,898.1	19,515.1	0.0	31,834.6	Scomp	0.0	0.0	2330.4	3638.1
CC	75,491.6	99,416.9	48,668.7	78,319.6	Calciner	0.0	28,851.2	0.0	0.0
AComp	4978.3	5093.6	4867.9	4241.9	Refor	0.0	0.0	0.0	18,401.7
Total EI, Pts/h						37.42	24.39	34.02	35.86

. The total environmental impact points of material are in the range of 24.39–37.42 mPts/h. The first system has the highest time-dependent environmental impact points of material, which is equal to 37.42 mPts/h, followed by model 4, with a somewhat similar index (35.86 Pts/h). The utility of the system containing biomass, hydrogen, heating, and electricity, according to Equation (6), and the environmental impact points of utility ($Y_U$) in

Table 8. Exergoenvironmental parameters of the proposed power plants.

System	${\mathit{b}}_{\mathit{F}}$	${\dot{\mathit{B}}}_{\mathit{F}}$	${\dot{\mathit{B}}}_{\mathit{P}}$	${\dot{\mathit{B}}}_{\mathit{D}}$	${\mathit{Y}}_{\mathit{U}}$	${\dot{\mathit{B}}}_{\mathit{t}\mathit{o}\mathit{t}}$	${\mathit{r}}_{\mathit{b}}$	${\mathit{f}}_{\mathit{b}}$	${\mathit{Y}}_{\mathit{M}}$	$\acute{\mathit{Y}}$
	mPts/GJ	Pts/h	Pts/h	Pts/h	Pts/h	Pts/h	-	%	Pts/h	Pts/MWh
Model 1	500.00	1006.90	2863.89	834.92	1856.99	2691.91	3.53	68.98	46.61	0.995
Model 2	500.00	1013.43	2928.06	914.00	1914.62	2828.62	3.87	67.69	43.82	0.792
Model 3	500.00	902.07	2849.62	747.79	1947.55	2695.34	4.68	72.26	54.12	0.816
Model 4	500.00	843.33	2979.28	606.40	2135.95	2742.35	3.62	77.89	56.47	1.097

are obtained.

The environmental impact points of utility are 40, 44, 36, and 38 times that of material for models 1–4, respectively. The greatest total environmental impact points of the LCA system are 2192.42 mPts/h in the SCWG-based system.

Table 8 presents the results of the exergoenvironmental analysis for the combined cycle using different syngas and hydrogen production plants. The findings reveal that the scenario with SER (model 2) has the highest environmental impact due to significant destruction, highlighting the substantial environmental consequences of the hydrogen and power production cycle in the hybrid system. Since the destruction ratio for each piece of equipment is based on fuel and product, the relative differences in environmental impacts are detailed, with the CLR-based power plant showing a notably high value of 4.68. Furthermore, the environmental impact of the product in case 2, at 2979.28 mPts/h, underscores the environmental significance of this model. The exergoenvironmental assessment of fuel, product, and destruction across all the cases demonstrates that the conventional biomass gasification plant has the least environmental impact compared with the other three models.

As shown in Table 8, the exergoenvironmental analysis results of the combined cycle with biomass conversion processes indicate that the SER case (model 2) has the highest destructive environmental impact, reflecting the significant environmental consequences of the hydrogen and power production cycle in the hybrid system. The destruction ratio for each piece of equipment is defined by its fuel and product, and the CLR-based model exhibits a particularly high relative environmental impact. The product environmental impact of the CLR process, at 2979.28 mPts/h, highlights the environmental significance of this model. Additionally, the exergoenvironmental evaluation of fuel, product, and destruction across all systems shows that the conventional biomass gasification plant has the least environmental impact compared with the other three models.

Implementing measures to enhance exergy efficiency is crucial for achieving lower ${\dot{B}}_D$ values and for minimizing the environmental impact. Among the three components discussed, model 3 has the highest $r_b$ value of 4.68, indicating that its environmental impact can be relatively easily reduced by increasing hydrogen production and minimizing the physical exergy loss. On the other hand, model 4, with its lower $r_b$ and already high exergy efficiency, offers limited room for improvement, making it more challenging to reduce its environmental impact compared to model 3.

Sustainability and environmental metrics were defined for analysis. Using the exergoenvironmental impact factor (${\Omega }_{if}$), the amount of exergy fuel converted into irreversibility can be calculated. Figure 11a shows that the model with the SER process has the largest ${\Omega }_{if}$. The ${\Omega }_{if}$ values were calculated to be 56.9%, 60.5%, 64.4%, and 51.3% for cases 1–4, respectively. According to Equation (A26), by decreasing the exergy destruction rate while keeping the exergy fuel of the system constant, the ${\Omega }_{if}$ is reduced. A reduction in the impact factor leads to improved system exergoenvironmental performance.

Higher exergetic efficiency results in a lower exergoenvironmental damage effectiveness index (${\Omega }_{dei}$). Similarly, a lower ${\Omega }_{if}$ factor corresponds to a lower ${\Omega }_{dei}$ index. As shown in Figure 11a, model 3 has the largest ${\Omega }_{dei}$ index. The ecological effect factor (${\theta }_{ef}$) is a function of the exergetic efficiency of the systems. Plants with the highest ${\theta }_{ef}$ values should be reviewed to improve their exergetic efficiency and, thus, reduce their ecological effect factor. Figure 11b shows the largest ${\theta }_{ef}$ for the models, with the system using the CLR process having a factor of 2.808.

Exergoenvironmental impact improvement (${\Omega }_{ii}$) is inversely related to the ${\theta }_{ef}$. If the ${\theta }_{ef}$ is higher, the ${\Omega }_{ii}$ is lower. Figure 11a indicates that model 3 has the smallest ${\Omega }_{ii}$, with a value of 0.553, followed by model 2 with 0.652. To achieve a higher ${\Omega }_{ii}$ for any system, the ${\theta }_{ef}$ must be reduced.

As hydrogen production increases and the total exergetic inefficiency of the system decreases, the exergetic sustainability index (${\Omega }_{si}$) improves, assuming that the exergy fuel remains constant. This enhancement in the sustainability index benefits the solar- and biomass-based system, making it more efficient in reducing exergy losses by achieving higher values of the index.

The final metric for evaluating the environmental and sustainability aspects is the social ecological factor (SEF), which incorporates an exergetic efficiency index. Model 3 has the smallest SEF, determined to be 1.55. As the system’s exergetic efficiency increases, the SEF also increases. Figure 11b shows the SEF of all the systems. The overall SEF of model 3 could be improved by increasing the exergetic efficiencies of its lower subsystems, such as the cooling units.

4.4. Parametric Studies

A parametric analysis was conducted to identify the key design and operating variables and to examine their effects on system performance.

Solar energy is dynamic and varies over time, making it crucial to study its effects on solar-based energy systems. Researchers have conducted various analyses to investigate solar radiation, with one common method being the monthly average analysis of solar radiation. This approach can report the operating conditions of energy systems on a monthly average basis. In this context, the radiation conditions of solar energy were examined for the most important functional parameters of the CSP plant. In addition, the influence of working hours of the CSP plant and the overall system on the techno-economic and environmental performance of the system was evaluated.

Figure 12a illustrates how time and exergy efficiency variations influence the ecological effect factor of the CSP plant, which is dependent on the system’s exergetic efficiency. This environmental indicator is derived from the average monthly exergetic performance of the CSP plant. During the monthly peak radiation period, the CSP plant in model 3, with its higher exergy efficiency, achieved the lowest ecological effect factor of approximately 1.738. The highest ecological effect factor, 1.974, was observed in the CSP plant of model 2. Conversely, CSP plant 1 had the highest ecological effect factor of 2.423 during a month with lower radiation, indicating that the availability of solar radiation significantly impacts this environmental indicator.

Figure 12b shows another environmental metric, the social ecological factor, which is based on the exergy efficiency of the CSP plant. The social ecological factor demonstrated a linear relationship with exergy efficiency and solar radiation, showing higher values during the average peak radiation month. The CSP plant in model 3, with its higher exergy utilization, had the highest social ecological factor value, ranging from 1.78 to 2.35, compared with other cities. The other CSP plants also maintained adequate values throughout the year, ranging from 1.70 to 2.25, indicating that this CSP system has exergy sustainability.

Figure 13a shows the variations in the exergy destruction rate and exergy destruction cost rate with the operation time of the system. It is seen that operation time has the same effects on the exergy destruction rate and the exergy destruction cost rate. Increasing operation hours increases the exergy destruction rate in the cycle as well as the exergy cost rate. On other hand, model 4 has the lowest cost of exergy destruction (1482.2–16,665.5 USD/h), especially in the 10 h, 12 h, and 14 h modes, followed by model 3, which can be attributed to the lower irreversibility due to the lower solar energy demand of the system. As indicated in Figure 13b, enhancement of the operating life of the CSP plant results in opposite influences on the specific CAPEX and investment cost rate of the system. As time increases, the investment cost rate decreases in all cases until it reaches a minimum; then, it rapidly increases due to the high cost of heliostats in models 3 and 4.

Referring to Figure 14, as the daily operation of the CSP plant increases, the LCOE firstly decreases via enhancement of the output power, and then decreases, resulting in the achievement of an optimum value for heat storage time (14 h), which minimizes the LCOE in the systems with CLR and SCWG processes. Such a trend for W_net can be attributed to its direct relation with the operation time and investment cost of the system (Equation (A8)). The LCOH and LCOP have similar trends, according to Equations (A9) and (6). Thus, the lowest LCOE of the models was observed as 83.4, 49.2, 55.4, and 88.4 USD/MWh and as 13.9, 10.7, 19.5, and 16.1 USD/MWh for the LCOH for models 1–4, respectively. It shows the same results as the work by [41]. Based on this, the LCOH can be reduced by decreasing the CAPEX and investment cost rate of the system, suggesting that economic feasibility can be secured.

Figure 15 shows the impact of operation time variation on the unit cost of exergy products, relative differences, and exergoeconomic factors. As operation hours of the CSP plant increase, all the unit costs decrease, resulting in reductions of 8.2, 3.1, 1.4, and 3.3 USD/MW points for hydrogen costs in each case. Models 3 and 4 exhibit the lowest hydrogen costs in the 14 h mode. Regarding the exergoeconomic factor of the system, it also decreases with longer operation time. However, the exergoeconomic factor improves due to the high solar energy consumption at zero cost in the 16 h mode for models 3 and 4.

The next significant parameter is the environmental index, which is evaluated based on the exergoenvironmental criteria of the proposed solar- and biomass-based system, as shown in Figure 16. With a longer operation time, the impact factor and damage effectiveness index increase, while the impact improvement, sustainability index, ecological effect factor, and social ecological factor decrease. This is due to the reduced exergy efficiency and constant destroyed exergy, leading to worsened environmental impacts for the hybrid systems. Ultimately, it can be concluded that improving system performance can lead to a decrease in adverse environmental parameters.

According to Figure 17, as the working hours of the system increase, exergy destruction grows, and the environmental impact of this exergy destruction slightly increases, leading to a decrease in the exergoenvironmental factor. Additionally, increasing the capacity of the CSP plant results in an increase in both its weight and environmental impact rate. Notably, the exergoenvironmental factors of models 3 and 4 dramatically decrease in the 14 h mode.

4.5. Comparison with Other Solar- and Biomass-Based Power Plants

To better understand the performance of the proposed hybrid biomass–solar system, a comparison is made with various hybrid systems for multi- and poly-generation explored in previous studies. Each of these earlier studies investigated different processes and cycles for producing multiple products, such as electricity, fresh water, heating, cooling, CO₂, hydrogen, and more. However, all these systems share the same goals: to reduce the cost of electricity and hydrogen and to minimize the system’s environmental impact. Table A5 presents a comparison of these previous works under optimal operating conditions. The comparison considers factors such as exergetic performance, levelized costs of electricity, hydrogen, and products, unit CO₂ emissions, total costs, environmental impact rates, unit hydrogen costs, and exergoeconomic and exergoenvironmental factors.

In terms of thermodynamic performance, most of the previously studied systems have lower overall exergy efficiencies that range from 11% to 33%. In contrast, the models in the current study achieve exergy efficiencies of 34.2% to 44%. Model 4, with the highest exergy efficiency of 44%, also has the lowest CO₂ emissions (26.56 kgCO₂/MWh) compared with the other models in this study and those in previous works.

Referring to this table, it is evident that the proposed models 2 and 3, which utilize chemical looping reforming processes, demonstrate superior performance in terms of the LCOE, with values of 49.2 USD/MWh and 55.4 USD/MWh, respectively, compared with previous studies [4,26,61]. Although solar energy is an expensive technology, using it as the primary resource increases the system’s LCOE. A solar–biomass integrated system [22] and a multi-generation system [69] studied previously reported electricity production costs of 40.16 USD/MWh and 49.28 USD/MWh for the LCOE, respectively. On the other hand, other systems [27,35,66] have reported significantly lower LCOE values. It is suggested that these systems primarily produce electricity and other products through various cycles, with hydrogen production achieved via water electrolysis technology. Additionally, the proposed systems in this study include cost units for syngas production, hydrogen purification, and CO₂ capture.

Due to the innovative nature of the proposed systems, which focus on producing green hydrogen by decarbonizing conventional CHP plants, it is important to provide insights into hydrogen production costs using different technologies and systems, such as biomass gasification and water electrolysis (utilizing solid oxide and proton-exchange membrane technologies). The proposed models have proven to be the most cost-effective processes to date, with LCOHs ranging from 10.7 to 19.5 USD/MWh and a unit exergy cost of hydrogen between 3.3 USD/GJ and 5.28 USD/GJ. Among the reviewed research, one plant [57] achieved the lowest unit exergy cost of hydrogen at 4.29 USD/GJ, while others [5,20,70,71] reported hydrogen unit costs between 15.74 and 45 USD/GJ. These cost differences are primarily due to three factors: the cost of the primary resource, the plant’s capacity, and the hydrogen production technology employed. Most of the reviewed studies utilized hydrogen production based on water electrolysis technology, which generally incurs higher production costs than other methods. This study proposes a system for syngas and hydrogen production using mature, commercialized technologies that offer higher efficiency and lower product costs [72]. Despite this, natural gas, mainly methane, is predominantly used for hydrogen production through steam reforming and partial oxidation processes, followed by coal gasification technology.

The total investment cost of a system is significantly impacted by the various processes, equipment, and units required. Due to these differences, the proposed model 2, which uses a CaO-based chemical looping process, has the lowest total cost rate at 6076.8 USD/h, with an LCOP of 7.8 USD/MWh and an LCC of 1.23 USD/kgH₂. This cost is competitive with another system [68] that reports costs ranging from 0.47 to 3.48 USD/kg. Additionally, the biomass gasification method has the highest eco-cost at 11.82 USD/kgH₂ [73].

Another important outcome of the study is the environmental impact indexes, which help to differentiate the environmental impact between the LCA and energy consumption. Table A5 provides a detailed comparison of the exergoenvironmental impact factor, with positive values from this study directly illustrating the relationship between the LCA impact and the exergy loss contribution.

A cradle-to-gate life cycle assessment was conducted by Chisalita et al. [74] to evaluate the environmental impact of producing 1 kg of hydrogen using innovative steam methane reforming and chemical looping technologies. They reported a GWP ranging from 0.31 to 9.65 kgCO₂/kgH₂. Ahmed et al. [8] proposed an enhancement in steam-only gasification, achieving the lowest economic annualized cost and environmental emissions per hydrogen production capacity of approximately 0.12 USD/kgH₂ and 20 kgCO₂/kgH₂. In this study, model 2 demonstrates a relatively lower GWP of 3.99 kgCO₂/kgH₂.

The exergoenvironmental impact factor ranged from 66.2% to 73.9%, indicating that the exergy and environmental aspects are comparable in terms of environmental impact, consistent with industrial conditions. A small exergoenvironmental factor value for chemical looping reforming-based models indicates that the exergy loss contributes to the main environmental impact and should be given priority in future improvements. According to the results [69], the LCA values for the proposed multi-generation plant were estimated at 4.377 Pts/GJ (15.73 Pts/MWh), whereas the proposed models achieved LCA values of 5.4–7.1 Pts/MWh. However, another integrated solar- and biomass-driven energy system [66] reported an environmental impact of 1.96 Pts/MWh, suggesting that the proposed models need improvement not only thermodynamically but also in reducing environmental impacts. Given that the exergetic performance shows better results, the social ecological factors (1.52–1.78) are comparable to some studies [20,31,75]. Although the environmental impacts of the proposed systems are lower than those of current systems, their range appears appropriate compared with other values.

5. Conclusions

This study presents a poly-generation renewable system that uses solar and biomass energy to produce electricity, heating, carbon dioxide, and hydrogen. The primary innovation of this analysis lies in the comprehensive integration of technical, economic, and environmental assessments for various green hydrogen production pathways utilizing decarbonized biomass gasification.

The key findings from the design and analysis are as follows:

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Comprehensive dynamic simulations have been developed for CO₂ capture and hydrogen production units.

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The most significant exergy destruction occurs in the biomass conversion and syngas production processes, with the highest associated costs and environmental impacts seen in hydrogen production.

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The dynamic results indicate that extending the operation of CSP systems enhances hydrogen production by reducing the levelized costs of electricity, hydrogen, and total products.

Overall, the optimized system achieves an exergy efficiency of 48.67%, an exergoeconomic factor of 80.65%, and an exergoenvironmental factor of 77.89%. It can produce 51.5 MW of electricity, 23.3 MW of heat, and 8334.4 kg/h of hydrogen through the supercritical water gasification of 87,156.4 kg/h of biomass.

In terms of thermodynamic performance, the models in the current study achieve exergy efficiencies of 34.2% to 44%. Model 4, with the highest exergy efficiency of 44%, also has the lowest CO₂ emissions (26.56 kgCO₂/MWh) compared with other models in this study and those in previous works.

The proposed systems 2 and 3, which utilize chemical looping reforming processes, demonstrate superior performance in terms of the LCOE, with values of 49.2 USD/MWh and 55.4 USD/MWh, respectively. Moreover, the proposed models have proven to be the most cost-effective processes to date, with LCOHs ranging from 10.7 to 19.5 USD/MWh and a unit exergy cost of hydrogen between 3.3 USD/GJ and 5.28 USD/GJ.

The exergoenvironmental impact factor ranged from 66.2% to 73.9% with an environmental impact rate of 5.4–7.1 Pts/MWh, indicating that exergy and environmental aspects are comparable in terms of their environmental impact, consistent with industrial conditions. An extensive comparison demonstrated that the designed hybrid system with solar energy integration can outperform other similar plants in terms of exergoeconomic and exergoenvironmental performance and, sometimes, even be competitive with them.

Finally, it is important to note that the systems studied have high irreversibility, necessitating analysis of the units and components in each model. To make solar-driven hybrid power plants more competitive, it is essential to address issues relating to improving process efficiency and reducing the costs associated with CSP plants (heliostats, solar receivers, etc.). Solar concentrators contribute substantially to the investment, while receivers must endure high temperatures and corrosive chemicals.