System Modeling and Performance Simulation of a Full-Spectrum Solar-Biomass Combined Electricity-Heating-Cooling Multi-Generation System

Abstract

The reliance on fossil fuels poses significant challenges to the environment and sustainable development. To address the heating requirements of the pyrolysis process in a biomass gasification-based multi-generation system, this study explored the use of low-grade solar energy across the full solar spectrum to supply the necessary energy for biomass pyrolysis while leveraging high-grade solar energy in the short-wavelength spectrum for power generation. The proposed multi-generation system integrates the full solar spectrum, biomass gasification, gas turbine, and waste heat recovery unit to produce power, cooling, and heating. A detailed thermodynamic model of this integrated system was developed, and the energy and exergy efficiencies of each subsystem were evaluated. Furthermore, the system’s performance was assessed on both monthly and annual timescales by employing the hourly weather data for Hohhot in 2023. The results showed that the solar subsystem achieved its highest power output of around 2.5 MWh in July and the lowest of 0.7 MWh in December. The annual electrical output peaked at 10 MWh, occurring around noon in July and August, while the winter peak was typically 2–3 MWh. For the wind power subsystem, the power output was maximized in April at 5.17 MWh and minimized in August at 0.7 MWh. Additionally, considering the overall multi-generation system performance, the highest power output of 14.9 MWh was observed in April, with lower outputs of 10.9, 11.3, and 11.4 MWh from August to October, respectively. Overall, the system demonstrated impressive annual average energy and exergy efficiencies of 74.05% and 52.13%, respectively.

1. Introduction

The global energy system is currently grappling with two critical challenges [1]: the ever-increasing demand for energy and the urgent need for sustainable development [2]. According to the International Energy Agency (IEA), global energy consumption reached 439 exajoules (EJ) in 2021 and is projected to rise to 543.6 EJ by 2050, representing a 24% increase. While fossil fuels still account for 80% of global energy demand [3], their combustion processes are responsible for 76% of anthropogenic carbon dioxide emissions [4], underscoring the urgent need for renewable energy integration strategies [5].

To address this dual challenge, biomass energy stands out among the renewable options due to its unique strategic advantages [6,7], with an annual global production exceeding 150 billion tons, accounting for approximately 10% of total energy supply and 70% of renewable energy output [8]. The gasification process converts biomass into syngas with an optimal H₂/CO ratio ranging from 0.45 to 1.2 [9], enabling efficient energy conversion [10]. Such systems demonstrate a superior performance, enabling energy conversion efficiencies surpassing 60% in multi-generation systems [11]. Notably, Xie, N. et al. [12] demonstrated a 57.4% efficient fuel cell-integrated tri-generation system, while Jalili, M. et al. [13] achieved 29.8% thermal efficiency with biomass-ORC hybrids. However, standalone biomass systems face limitations in scalability and energy density, motivating hybrid approaches.

Parallel advancements in solar energy further enable synergistic solutions. The global photovoltaic capacity must surge from 402 GWh (2017) to 8519 GWh by 2050 to meet carbon neutrality [14], with regions like Peru holding untapped potential equivalent to 25 GW [15]. However, traditional photovoltaic (PV) systems, with efficiencies constrained by 16% to 21%, fall well below the theoretical limit of 86.8% [16]. Additionally, their intermittent nature, with capacity factors below 25% for large-scale plants, limits their standalone applications [17]. In contrast, photovoltaic/photothermal (PV/T) systems address this by utilizing the full solar spectrum, achieving 15–18% electrical and 50–60% thermal efficiencies [18], with thermal storage enabling 85% annual solar guarantee rates in heating applications [19].

The integration of solar energy and biomass offers an innovative pathway for sustainable energy systems [20]. Multi-generation systems that combine solar and biomass energy have been shown to reduce biomass consumption, increase efficiency, and extend the operational duration [21]. Thermodynamic studies indicate that solar-assisted biomass gasification can reduce raw material consumption by 22–30% and improve system efficiency by 15–25% compared with standalone systems [19]. A trough-type solar pyrolysis system achieved energy and exergy efficiencies of 61.53% and 41.31%, respectively [6]. Separately, an enhanced heliostat field design improved the gas turbine combined cycle efficiency by 30% [22]. Reference [23] described eight multi-generation systems based on biomass gasification and solar/wind energy subsystems, with the average energy and thermal efficiencies ranging from 60 to 80% and 40–50%, respectively. However, these systems often overlook the PT conversion process of solar energy, simplistically converting all solar energy into electrical energy. Additionally, Reference [19] developed a PV/T-assisted biomass gasification multi-generation system, conducting comprehensive thermodynamic and economic analyses that demonstrated energy and exergy efficiencies of 86.44% and 17.12%, respectively. Nevertheless, this configuration critically omitted the utilization of medium-temperature solar thermal energy to drive biomass pyrolysis reactions [24]. Accordingly, this highlights three key limitations in the existing research: (1) the insufficient utilization of spectral frequency division technology for simultaneous power generation and heating; (2) an oversimplification of the dynamic irradiation effects using fixed direct normal irradiance (DNI); and (3) inadequate thermal integration between the biomass conversion stages and waste heat recovery. These limitations fundamentally constrain the utilization potential of hybrid renewable systems.

To address these gaps, this study introduced a novel multi-generation system integrating full-spectrum solar energy, biomass gasification, and waste heat recovery. By synergizing spectral frequency division PV/T technology, thermal integration, and dynamic analysis, the system optimizes energy utilization: shortwave radiation (280–1200 nm) drives PV generation at 18.2% efficiency while it recovers heat from longwave infrared (1200–4000 nm) for biomass pyrolysis. The system combines biomass gasification, gas turbines, an ORC, a absorption heat pump/chiller (AH/C), and a domestic hot water (DHW) subsystem, enabling dynamic performance analysis under hourly DNI variations and thermal storage integration. The main contributions of this study are as follows:

(1)

Long-wavelength solar spectrum heat was utilized for biomass pyrolysis while short-wavelength solar energy was converted to electricity via the PV module.

(2)

An integrated electricity-heating-cooling multi-generation system was proposed that combines the full solar spectrum, wind turbine, biomass pyrolysis and gasification, and waste heat recovery.

(3)

A thermodynamic model was developed to evaluate the performances of the proposed system including the heat and electricity output of the solar subsystem, the heat consumption of the biomass pyrolysis process, and the overall energy and exergy efficiencies under the fluctuating direct normal irradiance conditions.

The rest of this paper is organized as follows. Section 2 details the system configuration, Section 3 establishes the thermodynamic models and evaluation criteria, Section 4 analyzes the system’s technical performance under dynamic irradiation, and Section 5 presents the conclusions.

2. System Description

The structure of the cooling, heating, and electricity multi-generation system, driven by full-spectrum solar energy for biomass pyrolysis, is illustrated in Figure 1. This system serves multiple functions: (1) it facilitates biomass pyrolysis through the combined photoelectric and photothermal conversion processes (PV/T) utilizing the full solar spectrum, and (2) it employs ORC, AH/C, and DHW subsystems to achieve cascaded recovery of flue gas exhaust, thereby supplying electricity, heat, and cooling. The system is composed of six primary components: PV/T, wind turbine (WT), biomass pyrolysis and gasification reactor, gas turbine (GT), and a waste heat recovery unit. The waste heat recovery unit primarily includes ORC, AH/C, and DHW subsystems.

PV cells convert high-grade solar energy from the short-wavelength spectrum (280–1200 nm) into electricity. Meanwhile, solar collectors transform low-grade energy from the long-wavelength spectrum (1200–4000 nm) into thermal energy through the PT process. The heat recovered by the circulation channel within the PV cells, combined with the thermal energy converted by the PT process, collectively provides the necessary heat for the biomass pyrolysis reaction. Subsequently, the syngas produced from the gasification reaction is combusted and directed into the GT for expansion and power generation. Therefore, the system’s power output is derived from the PV, WT, GT, and ORC subsystems.

3. Model Construction

This section presents a detailed introduction to the system design, the analysis models for each subsystem, the evaluation of the overall system efficiency, and the meteorological data utilized. Both energy and exergy analysis tools were employed to comprehensively evaluate the performance of the integrated system and its components.

The exergy (kJ/kg) in the proposed system can be expressed in four forms: chemical, physical, kinetic, and potential energy, as shown in Equation (1). In this paper, only the chemical exergy ($e{x}^{chemical}$) and physical exergy ($e{x}^{physical}$) were considered, because the other forms of exergy (e.g., kinetic or potential) were small enough to be ignored [25].

$$Ex=e{x}^{chemical}+e{x}^{physical}+e{x}^{kinetic}+e{x}^{potential}$$

(1)

$$e{x}_i^{physical}={\displaystyle \sum _i{y}_i\cdot \left[\left(h_i-h_0\right)-T_0\left(s_i-s_0\right)\right]}$$

(2)

$$e{x}_i^{chemical}={\displaystyle \sum x_i\cdot e{x}_{ch,i}}+R\cdot T_0\cdot {\displaystyle \sum x_i\cdot ln{x}_i}$$

(3)

where $h_i$ and $h_0$ is the enthalpy value at any temperature and the specific enthalpy value under standard conditions, respectively. $s_i$ and $s_0$ are the entropy at any temperature and the specific entropy under standard conditions, respectively. $e{x}_i^{physical}$and $e{x}_i^{chemical}$ denote the physical and chemical exergy of substance i, respectively. $y_i$ is the molar fraction of each substance i. $x_i$ represents the molar fraction of component $i$; $e{x}_{ch,i}$ denotes the specific chemical exergy with a reference state of 293.15 K and 1 atm; R represents the universal gas constant, with a value of 8.314 kJ/(kmol∙K). The exergy of electric energy is equal to power.

3.1. Model Assumptions

The steady-state model was built and run using Aspen V12 [26]. Then, the performance analysis on the annual and monthly time scales was realized by self-made code, and the dynamic simulation was carried out in MATLAB (https://www.mathworks.com/products/matlab.html) based on the hourly meteorological data of Hohhot in 2023. In the modeling process, the following key assumptions were made:

(1)

The ambient temperature and pressure were set at 25 °C and 101.3 kPa, respectively.

(2)

Due to the relatively low flow velocity, there was no obvious height difference, so the potential energy and kinetic energy and energy use were ignored.

(3)

The pump and steam turbine operated adiabatically, while the compressor operated isothermally.

(4)

The impacts of non-ideal radiation and ambient temperature conditions from the meteorological data on heat losses and power generation efficiency were neglected in this analysis.

(5)

In a custom-designed beam splitting system, shortwave radiation (λ < 1200 nm) and longwave radiation (λ > 1200 nm) were fully separated using a short-pass filter (Thorlabs FES1200, Thorlabs, Newton, NJ, USA) and a long-pass filter (Thorlabs FEL1200).

3.2. Biomass Pyrolysis and Gasification Subsystem

Biomass gasification involves two key reaction processes: pyrolysis and gasification. During pyrolysis, organic components in biomass decompose under anoxic or anaerobic conditions, yielding solid carbon, liquid fuels, and combustible gases. The biomass pyrolysis reaction can be represented in the following form.

$$Biomass\to C+CO+C_x{H}_y+H_2+C{O}_2+H_{2}O+S+ASH$$

(4)

$$C_x{H}_y\to C{H}_4,C_2{H}_6,or\hspace{0.17em}{C}_3{H}_8$$

(5)

Subsequently, the biomass gasification reaction further converts the biomass into combustible gas and carbon. The principal components of the gas are carbon monoxide, hydrogen, and methane. The composition and proportion of the gasification products are regulated by different gasification agents (such as steam and carbon dioxide) as well as reaction conditions. The combustible gas generated during this process can be utilized as a fuel source. The biomass gasification reaction can be expressed by the following equations:

$$C+0.5O_2\to CO\hspace{0.33em}\Delta H_{298K}=-111kJ/mol$$

(6)

$$C+O_2\to C{O}_2\hspace{0.33em}\Delta H_{298K}=-394kJ/mol$$

(7)

$$H_2+0.5O_2\to H_{2}O(g)\hspace{0.33em}\Delta H_{298K}=-241.8kJ/mol$$

(8)

$$C+H_{2}O(g)\to CO+H_2\hspace{0.33em}\Delta H_{298K}=131kJ/mol$$

(9)

$$C+C{O}_2(g)\to 2CO\hspace{0.33em}\Delta H_{298K}=172kJ/mol$$

(10)

$$C+2H_2\to C{H}_4\hspace{0.33em}\Delta H_{298K}=-75kJ/mol$$

(11)

$$CO+H_{2}O(g)\to C{O}_2+H_2\hspace{0.33em}\Delta H_{298K}=-42.4kJ/mol$$

(12)

Furthermore, the industrial analysis and elemental analysis of the selected biomass (CH_1.62O_0.67) feedstocks are shown in

Table 1. Proximate and ultimate analysis of cotton stalks.

Item	Proximate Analysis	Item	Ultimate Analysis
Fixed carbon, $F{C}_{ad}\left(wt\%\right)$	11.004	$C_{daf}\left(wt\%\right)$	49.037
Volatile matter, $V_{ad}\left(wt\%\right)$	80.785	$H_{daf}\left(wt\%\right)$	6.639
Water content, $M_{ad}\left(wt\%\right)$	5.525	$O_{daf}\left(wt\%\right)$	43.622
Ash content, $A_{ad}\left(wt\%\right)$	2.686	$N_{daf}\left(wt\%\right)$	0.655
$LHV\left(kJ/kg\right)$	17,565	$S_{daf}\left(wt\%\right)$	0.047

[6], where the subscripts ad and daf denote the air dry basis and dry ash free basis, respectively.

The biomass gasification process was simulated in Aspen Plus using the following framework: thermodynamic properties were modeled with the RK–Soave equation for the gas phase, NRTL for liquid-phase electrolytes, and HCOALGEN/DCOALIGT for the solid biomass properties. In the computational modeling approach, biomass materials were treated as special components. The simulation employed a yield reactor for pyrolysis and a Gibbs reactor for the gasification processes. The numerical analysis of biomass gasification was conducted following the fundamental rules of element conservation and thermodynamic equilibrium through Gibbs free energy minimization. The effectiveness of the simulation approach was verified in Reference [27]. Furthermore, the key assumptions included: (i) an isothermal gasifier operation at 800 °C, (ii) char treated as pure carbon (ash inert), (iii) negligible tar formation (verified via sensitivity analysis), and (iv) instantaneous pyrolysis kinetics. For biomass representation, an equivalent coal gasification model was adopted. The temperature was determined through sensitivity analysis. The pyrolysis and gasification process were conducted at 200 °C and 800 °C. Moreover, the Gibbs free energy minimization method [28,29] was employed to calculate the product yields.

For clarity, the power-related parameters were expressed in kW, while the energy quantities were consistently reported in kWh. The related energy balance equations of biomass pyrolysis and the gasification process are described as follows:

$$E{x}_{sol}=Q_{sol}\left(1-{\displaystyle \frac{T_0}{T_{pyrolysis}}}\right)$$

(13)

$$m_{bio}\cdot e{x}_{bio}+E{x}_{sol}=m_{syngas}\cdot e{x}_{syngas}+E{x}_{des,gasification}$$

(14)

$$m_{bio}\cdot LH{V}_{bio}+Q_{sol}=m_{syngas}\cdot LH{V}_{syngas}+Q_{loss,gasification}$$

(15)

In the above equations, $E{x}_{sol}$ (kWh) represents the full spectrum of the solar exergy input into the pyrolysis reactor; $T_{pyrolysis}$ (K) denotes the temperature of the pyrolysis reaction; $e{x}_{bio}$ and $e{x}_{syngas}$ (kJ/kg) represent the specific exergy of biomass and syngas, respectively; ${\eta }_{ex,sol}$ represents and describes the exergy efficiency of the solar collector; $Q_{sol}$ and $Q_{loss,gasification}$ (kW) represent the energy input into the pyrolysis reactor and the energy loss in the gasification process of the full-spectrum solar subsystem, respectively; $E{x}_{des,gasification}$ indicates the exergy loss of the vaporizer. Reference [30] described the method of calculating these parameters.

3.3. Solar Photovoltaic/Thermal System and Wind Power System

In the optical subsystem, the solar concentrator harvests full-spectrum solar energy. Due to the highly stable reflectivity of the solar concentrator, the influence of solar spectrum variations is negligible. The concentrated solar energy is then divided into two streams through a beam splitter: one directed toward the PV cell, and the other entering the vaporizer. The process is an idealized operation without any energy dissipation. The AM1.5 spectral irradiance [31] is the solar irradiance received closest to the Earth’s surface, and its energy distribution is shown in Figure 2a. As a solar-to-electric conversion component, ${\lambda }_1=$280 nm, ${\lambda }_2=$ 1200 nm [32].

In this study, the silicon PV cell with the largest commercial scale was selected, and its external quantum efficiency is shown in Figure 2b. The total energy received ($E_{solar}$, kWh) in the concentrating region ($A_{SC}$, m²) is expressed as [33]:

$$E_{solar}=A_{SC}{\displaystyle {\int }_{280nm}^{4000nm}E(\lambda )}d\lambda$$

(16)

$$E_{opt,loss}=DNI\cdot A_{sc}\cdot \left(1-{\eta }_{opt}\right)$$

(17)

$$E_{PV}=DNI\cdot A_{sc}\cdot {\eta }_{opt}\cdot {\displaystyle \frac{{\displaystyle {\int }_{280}^{{\lambda }_g}E\left(\lambda \right)d\lambda }}{E_{sunlight}}}$$

(18)

where $E(\lambda )$ represents the spectral irradiance in units of W·m⁻² μm⁻¹ [34]; $E_{opt,loss}$ is the optical loss in this subsystem; $DNI$ represents the direct normal irradiance (W/m²); ${\eta }_{opt}$ denotes the average optical efficiency in the optical subsystem, taking 0.93; $E_{PV}$ describes the PV subsystem absorbs part of the solar energy of the short-wavelength spectrum; ${\lambda }_g$ denotes the wavelength (nm) at the band gap of the PV cell; $E_{sunlight}$ is the full-spectrum solar irradiance (W/m²). In theory, for Si PV cells, light with a wavelength of 280–1200 nm can be converted into electrical energy [35].

For PV applications, both the spectral power distribution (E(λ)) and photon energy distribution must be considered. While E(λ) determines the available power, the photon energy spectrum affects carrier generation in the semiconductor. Hereafter, ‘solar irradiance’ refers specifically to the power per unit area (W/m²) received from the Sun, while ‘solar energy’ is reserved for integrated energy quantities (kWh).

In the PV subsystem, a portion of the solar irradiance is converted into electrical energy through the PV modules. The heat generated by the PV cells is recovered through the fluid channels to maintain the PV modules at an operating temperature of approximately 60 °C. The temperature difference between the PV modules and the circulating fluid is maintained at greater than 5 °C. The energy balance within the PV subsystem can be expressed as:

$$E_{PV}=E_{PV-opt,loss}+Q_{PV-heat,loss}+P_{PV}+Q_{Rec}$$

(19)

In Equation (19), $E_{PV-opt,loss}$ (kWh) is the light loss generated in this process; $Q_{PV-heat,loss}$ (kWh) represents the heat loss of PV cells; $P_{PV}$ represents the electrical output of the full-spectrum solar subsystem; $Q_{Rec}$ represents the circulating heat recovered through the fluid channel in the PV cell. To simplify modeling, the energy source for the cooling system (circulation pump) was ignored. Typically, the temperature difference between the PV module and the fluid exceeds 5 °C. The calculation methods of$P_{PV}$, $E_{PV-opt,loss}$, $Q_{PV-heat,loss}$ and $Q_{Rec}$ are as follows [17].

$$P_{PV}=E_{PV}\cdot {\eta }_{PV,280-1200nm}$$

(20)

$$E_{PV-opt,loss}=E_{PV}\cdot \left(1-{\tau }_{glass}\cdot {\alpha }_{PV}\cdot {\gamma }_{PV}\right)$$

(21)

$$Q_{PV-heat,loss}={\mu }_{PV,heat,loss}\cdot DNI\cdot A_{SC}$$

(22)

$$Q_{Rec}=E_{PV}-E_{PV-heat.loss}-P_{PV}-E_{PV-opt,loss}$$

(23)

In these equations, ${\tau }_{glass}$ represents the transmittance of the cover glass of the PV module, 0.96 [17]; ${\alpha }_{PV}$ and ${\gamma }_{PV}$ denote the absorption factor and the PV intercept factor of the PV cell, respectively, taking 0.98 [36]; ${\mu }_{PV,heat,loss}$ represents the heat loss rate of the PV module under ideal conditions, taking 0.38% [16].

In this paper, the cooling liquid absorbed the waste heat from the PV cells. Subsequently, this portion of thermal energy was transferred to the biomass pyrolysis reactor via radiation or convection, directly supplying the heat required for the pyrolysis reaction. However, this approach necessitates addressing the inherent intermittency of solar energy such as integrating thermal energy storage (TES) systems to compensate for periods of low solar irradiance (e.g., cloudy days or nighttime). Additionally, the coupled design of the reactor and solar collector introduces significant complexity. To simplify the model, this aspect of the system was omitted in the present study.

Additionally, ${\eta }_{PV,280-1200nm}$, the PV efficiency of sunlight in the wavelength range of 280–1200 nm at operating temperature, is expressed as [36]:

$${\eta }_{PV,280-1200nm}={\eta }_{PV}{\displaystyle \frac{{\displaystyle {\int }_{280nm}^{4000nm}E(\lambda )}d\lambda }{{\displaystyle {\int }_{280nm}^{1200nm}E(\lambda )}d\lambda }}$$

(24)

where ${\eta }_{PV}$ describes the PV efficiency of the full spectrum (280–4000 nm) under AM1.5 direct spectrum, taken as 21.30% [37].

In addition to the use of solar and biological energy to achieve the power output, the wind power generation scenario was also considered in this work. According to Reference [38], a technical model of the wind power subsystem was constructed. The analysis model of wind turbines uses the cubic power curve expression modeling of wind turbines [39,40]:

$$P_{WT}\left(t\right)=0.5N_{WT}\cdot {\rho }_{air}\cdot A_{WT}\cdot C_P\cdot {V_{WT}}^3\cdot {\eta }_{WT}$$

(25)

$${\rho }_{air}=1.293\cdot {\displaystyle \frac{273.15}{T_0}}$$

(26)

Herein, $P_{WT}\left(t\right)$ (kWh) represents the output power of the wind turbine; $N_{WT}$ denotes the number of wind turbines; $A_{WT}$ (m²) stands for the rotor area of a single wind turbine; $C_P$ is the power coefficient; $V_{WT}$ (m/s) is the wind speed at the height of the rotor; ${\eta }_{WT}$ is the mechanical efficiency of wind turbines. It should be noted that without considering the wake effect, the cut-in speed, rated speed, and cut-off speed (all measured in m/s) were 3, 12, and 25, respectively [40]. Additionally, the influence of ambient temperature on air density was also factored in. ${\rho }_{air}$ ($kg\cdot m^3$) is the air density and $T_0$ is the ambient temperature, 25 °C.

The key parameters of the renewable energy generation model (PV and WT subsystems) are shown in

Table 2. Design parameters of the main parameters of the PV cell and WT [39].

Design Parameters	Values	Design Parameters	Values
Solar PV		Operating temperature of PV cell, $T_{PV}$ (K)	340.15 [36]
Light-receiving area, $A_{SC}$ (m2)	36,000	Average temperature,
Optical efficiency of the optical subsystem, ${\eta }_{opt}$ (%)	93	Transmissivity, ${\tau }_{pv}$	0.2
Transmissivity of glazed glass, ${\tau }_{glass}$ (%)	96	Power coefficient, $C_P$	0.49
Absorption factor of PV cells, ${\alpha }_{PV}$ (%)	98	Mechanical efficiency, ${\eta }_{WT}$	0.9
Optical parameter of the cell, ${\gamma }_{PV}$ (%)	98	Number of wind turbines, $N_{WT}$	10
Ideal factor, n	1.07 [35]	Rotor area of a single wind turbine, $A_{WT}$ (m2)	5281

.

The system was simulated under steady-state conditions using Aspen Plus V12, as illustrated in Figure 3. In the RYield reactor, biomass is initially converted into water and various compounds, with the biomass conversion rate set at 100%. The parameters for the RYield reactor were specified according to guidelines provided in the literature [29]. To estimate the physical properties of all materials, the Peng–Robinson equation of state with the PR-BM property method was employed due to its computational efficiency, numerical stability, and adaptability [41]. For the AH/C subsystem, the ELECNRTL property method was utilized. The validity of the biomass gasification subsystem model was confirmed by comparing the results with the reference data on biomass composition, gasification temperature, and carbon conversion rate, as documented in [27,42]. Furthermore, the simulation results for the ORC and AH/C subsystems were benchmarked against experimental data from [43,44], demonstrating the model’s reliability.

3.4. Waste Heat Recovery Unit

The flue gas at the outlet of the GT still retains a certain utilization value. In this study, the flue gas was further recycled by employing the waste heat recovery device. The waste heat recovery unit consists of ORC, AH/C, and DHW.

For ORC, R245FA was selected as the organic working fluid of the cycle. The detailed AH/C model is shown in [45]. In ORC, the relevant equilibrium equation is expressed as follows:

$$W_{Pump}=h_{R3}-h_{R2}$$

(27)

$$W_{NET,ORC}=W_{MT2}-W_{Pump}$$

(28)

$$Q_{in,\hspace{0.33em}ORC}=m_{Flue\hspace{0.33em}gas}\cdot h_{Flue\hspace{0.33em}gas}-m_{A1}\cdot h_{A1}$$

(29)

$$m_{Flue\hspace{0.33em}gas}\cdot e{x}_{Flue\hspace{0.33em}gas}-E{x}_{des,ORC}=W_{MT2}-W_{Pump}+m_{A1}\cdot e{x}_{A1}$$

(30)

where $W_{Pump}$ (kWh) represents the power consumed by the organic working medium pump; $W_{NET,ORC}$ and $W_{MT2}$ represent the net power output by ORC and the power output by medium pressure turbine (MT2), respectively; $Q_{in,\hspace{0.33em}ORC}$ (kWh) represents the heat input to the ORC; $h_{Flue\hspace{0.33em}gas}$ and $h_{A1}$ both represent the enthalpy flow of flue gas, kJ/kg; $E{x}_{des,ORC}$ indicates the exergy loss of ORC, kWh.

For the refrigeration system, to facilitate the expansion of the system, the AH/C (for refrigeration) was selected in this study to further utilize the ORC outlet flue gas. In addition, its coefficient of performance (COP) was also higher than that of single-effect absorption chillers [41]. COP is mainly used to evaluate the performance of the system, which can be expressed as [45,46]:

$$COP=Q_{chilled water}/Q_{HG}$$

(31)

$$Q_c=m_{A1}\left(h_{A1}-h_{A2}\right)$$

(32)

$$Q_h=m_{Space heating water}\left(h_{Space heating water}-h_{Water,AH/C}\right)$$

(33)

$$E{x}_c=Q_c\left({\displaystyle \frac{T_o}{T_{chi}}}-1\right)$$

(34)

$$E{x}_h=Q_h\left(1-{\displaystyle \frac{T_o}{T_h}}\right)$$

(35)

$$E{x}_{des,AH/C}=m_{A1}\cdot e{x}_{A1}+E{x}_c-m_3\cdot e{x}_3$$

(36)

$$E{x}_{in,AH/C}=m_{A1}\cdot e{x}_{A1}-m_3\cdot e{x}_3$$

(37)

where $Q_c$ (in kW) denotes the load of chilled water in refrigeration mode; $Q_{HG}$ (kW) represents the load of the high voltage generator; $Q_h$ represents the load of space heating water in the heating mode of AH/C; $E{x}_c$ and $E{x}_h$ (kW) denote cooling and heating exergy, respectively; $T_{chi}$ and $T_h$ (K) represent the average temperature of chilled water and space heating water, respectively.

For the DHW subsystem, the heat input ($Q_{\hspace{0.33em}DHW}$, kW) and heating exergy capacity ($E{x}_{in,DHW}$, kW) can be expressed as:

$$Q_{DHW}=m_4\cdot h_4-m_5\cdot h_5$$

(38)

$$E{x}_{DHW}=Q_{DHW}\left(1-{\displaystyle \frac{T_0}{T_{DHW}}}\right)$$

(39)

$$m_{Water,HE}\cdot e{x}_{Water,HE}+m_4\cdot e{x}_4-E{x}_{des,HE}=m_5\cdot e{x}_5+E{x}_{DHW}$$

(40)

where $T_{DHW}$ denotes the average temperature of domestic hot water produced for ORC, set to 14 °C; $T_L$ and $T_H$ (K) denote the inlet temperature of the cold and heat sources, respectively.

The process simulation specifications of each section including the biomass gasification, GT, and waste heat recovery subsystems are shown in

Table 3. Key parameters of biomass gasification, gas turbine, and WHR unit.

Subsystem	Design Parameters	Values
Biomass gasification	Pyrolysis temperature, (°C)	200
	Pyrolysis pressure, (bar)	1
	Operating temperature, (°C)	800
	Operating pressure, (bar)	2
	The ratio of O2 and biomass, (%)	10
	The ratio of steam and biomass, S/B (%)	20
Gas turbine	Compressor isentropic efficiency (%)	89.5
	Compressor mechanical efficiency (%)	99
	Combustor operating pressure (bar)	17.33
	Combustor chamber efficiency (%)	99
	Isentropic efficiency (%)	90
	Pressure ratio (%)	5.883
Organic Rankine cycle	Organic medium	R245FA
	Pump chamber efficiency (%)	58
	Pump discharge pressure (bar)	10.7
Absorption heat pump/chiller	Cooling water/backwater temperature (°C)	7/14
Domestic hot water product system	Normal water/domestic hot water temperature (°C)	25/50

.

3.5. Overall Efficiency

The overall energy and exergy efficiency of the whole system were as follows:

(1) Energy efficiency was used to analyze the thermodynamic performance of each scenario. Energy efficiency (${\eta }_{en}$) is defined as

$${\eta }_{en}={\displaystyle \frac{Q_c+Q_h+P_{PV}+P_{WT}+P_{GT}+P_{ORC}-P_{Pump}-P_{CP}}{Q_{sol}+P_{wind}+m_{bio}\cdot LH{V}_{bio}}}$$

(41)

where $P_{GT}$ is the output power of GT, kW; $P_{ORC}$ is the output power of the ORC, kW; $m_{bio}$ is the mass flow rate of biomass, kg·s⁻¹; $LH{V}_{bio}$ is the low heating value of biomass, 17,565 kJ·kg⁻¹ [29]; $Q_{sol}$ is the solar energy produced, kW; $P_{wind}$ is the wind energy generated, kW; $P_{Pump}$ and $P_{CP}$ are the input power of the pump and the consumed power of the compressor, respectively, kW.

(2) The thermodynamic performance of the proposed co-production system was analyzed by exergy efficiency. Exergy efficiency (${\eta }_{ex}$) is defined as:

$${\eta }_{ex}={\displaystyle \frac{P_{PV}+P_{WP}+P_{GT}+P_{ORC}+E{x}_C+E{x}_{DHW}-\left(P_{Pump}+P_{CP}\right)}{Q_{sol}+P_{wind}+m_{bio}\cdot e{x}_{bio}}}$$

(42)

where $e{x}_{bio}$ is the specific exergy of biomass at 17.565 MJ/kg.

3.6. Meteorological Data

In this study, the MERRA-2 database was used for analysis in Hohhot (40.84° N, 111.67° E) to evaluate the long-term performance of this system [47]. Figure 4 depicts the meteorological data of Hohhot in 2023 including the annual hourly wind speed, ambient temperature, and total incident solar irradiance. The time resolution was 1 h, with a total of 8760 datasets. Each dataset was the average of the hourly time intervals.

4. Result and Discussion

This section describes the technical performance of each subsystem and the overall system based on thermodynamic analysis and dynamic simulation. It includes the annual and monthly changes in energy input and output, released heat, and the energy and exergy efficiency.

4.1. Full-Spectrum Solar and Power Output of Wind Energy Subsystem

The monthly average daily electric output and monthly total output of the PVT are shown in Figure 5a,b, respectively, while the thermal power output is shown in Figure 5c,d. As shown in Figure 5a, the hourly power output varied significantly across months. Winter months (Jan, Feb, Dec) showed notably lower output due to shorter sunshine duration, smaller solar elevation angles, and reduced radiation intensity, with output nearing zero in the early mornings and evenings. In contrast, spring (Mar, Apr, May) and autumn (Sep, Oct, Nov) exhibited a stable output, gradually increasing as the sunshine duration and radiation intensity rose. Summer (Jun, Jul, Aug) achieved the highest output, particularly at noon, driven by a longer sunshine duration, larger solar elevation angles, and stronger radiation intensity, with peaks observed around midday in July and August.

The annual electricity output reached its peak value primarily at noon during the summer season. Specifically, the power output could attain approximately 2.22 MW in July. In contrast, the peak value of power output in December was 0.88 MW. In contrast, the peak value of power output in winter was relatively low, typically ranging from 2 to 3 MW. Moreover, it exhibited significant fluctuations, with the power output decreasing rapidly in the morning and evening. During the summer months, however, the fluctuation in power output was minimal, and the power output remained relatively stable from noon until the afternoon.

As shown in Figure 5b, the monthly electricity output peaked in July at nearly 2.5 MW and dropped to its lowest in December at around 0.7 MW. Seasonal trends revealed stable outputs in spring and autumn (1.0–2.5 MW), with summer (June–August) achieving the highest outputs (1.5–2.5 MW). The annual average output of the solar subsystem was 1.56 MW.

The solar subsystem’s output is highly seasonal, driven by intense solar radiation and longer daylight in summer, contrasting with winter’s lower and fluctuating levels. This variability may affect the system stability, necessitating energy storage or auxiliary sources for reliability. Summer’s stable, high output offers opportunities for optimized transmission and storage.

Similarly, the solar heat output followed the same trend, with an annual average of 5.74 MW, consistently exceeding the monthly power output levels.

4.2. System Power Output and Heating Consumption of Pyrolysis Subsystem

Figure 6a shows the average hourly power output of the wind power subsystem, highlighting seasonal variations. April had the highest output, peaking at 11 MW at 3 PM, while August remained below 2 MW due to low wind speeds. Peak output typically occurred between 2 PM and 4 PM, with the lowest output from 12 AM to 4 AM.

As depicted in Figure 6b, April led the annual output at 5.17 MWh, followed by November at 3.78 MW, while August was the lowest at 0.7 MW. Spring (Mar–May) and autumn (Sep–Nov) showed stable outputs of 3–4 MW, whereas the summer (Jun–Aug) averages were slightly lower. The annual average output was 2.60 MW. Both the solar and wind subsystems exhibited clear seasonal and daily patterns, offering valuable insights for cogeneration system management.

Figure 7 illustrates the annual thermal energy provided by the solar subsystem for the biomass pyrolysis process. With a biomass input rate of 1 kg/s, the heat required for the pyrolysis process was 5.74 MW, and the average annual heat supply from the solar subsystem was 5745.14 kW. Therefore, under the given collector area conditions, the local application of the system in Hohhot can theoretically meet the energy requirements for the biomass pyrolysis reaction.

4.3. Overall Power Output of Pyrolysis Subsystem

In this paper, an in-depth study was conducted on the total power output of the electric heating and cooling multi-generation system, which was driven by the full-spectrum solar biomass gasification coupled with the waste heat recovery process. The overall monthly average hourly power output of this system is depicted in Figure 8a,b.

As illustrated in Figure 8a, the hourly power output of the system remained at a relatively low level during the winter months (Jan, Feb, and Dec). This phenomenon can be attributed to the weak solar radiation intensity and the relatively low wind speed in winter. From 14:00 to 23:00, the average power output in April reached its peak, attaining 22 MWh (specifically at 15:00). In comparison with Figure 6, the number of leading hours in April decreased. This reduction was primarily due to the increase in the power output of the solar subsystem within the period from 8:00 to 14:00. During the summer season, the fluctuation of the power output within a single day was minimal. Moreover, it maintained a relatively stable high-output state between 12:00 and 15:00. This is because the solar elevation angle is large in summer, resulting in more uniform solar radiation throughout the day. In contrast, the daily fluctuation of the power output in winter was significant, with the power output in the morning and evening dropping sharply. This is mainly caused by the short sunshine duration in winter and the substantial variation in solar radiation over a day.

As depicted in Figure 8b, the month with the highest power output throughout the year was April, reaching 14.9 MW. This was followed by March and November, both of which recorded 13.7 MW. Conversely, the months with the lowest electricity output were August, September, and October, with 10.9 MW, 11.3 MW, and 11.4 MW, respectively. The results show that the system’s combined wind and solar power generation remained relatively low from August to October. This reduced output may hinder the large-scale operation of the wind and solar units during these months. Furthermore, during the January to June period, the power output in February was the lowest at 11.8 MW, potentially due to the unfavorable solar energy resources and environmental conditions experienced in the winter months. Although the summer months (Jun–Aug) exhibited high daily peak power outputs, the average power output during this period was approximately 13–14 MW. This was not significantly higher than the spring and autumn seasons, likely due to the lack of solar energy input at night. The average power output in the winter months (Jan, Feb, Dec) was the lowest, ranging around 10–11 MW.

The power output of the integrated system primarily originates from three key components: gas turbines, solar subsystems, and wind turbines. The fluctuations in the electricity output are mainly driven by changes in the solar radiation intensity and wind speed. During the summer months, the abundant solar radiation helps to improve the efficiency of the biomass gasification process, thereby increasing the overall power output. In contrast, the power output fluctuations are more pronounced in the winter, and the average output level is lower, which can have a certain impact on the stability of the power system. To ensure a stable power supply, it may be necessary to consider increasing the biomass fuel supply or utilizing energy storage systems to buffer the fluctuations in power output.

4.4. Overall Energy and Exergy Efficiency

As illustrated in Figure 9a, the average hourly energy efficiency of the proposed system exhibited distinct variations. Obviously, during the 10:00 to 13:00 period, the energy efficiency was at its lowest, around 38–44%. In contrast, the system’s energy efficiency peaked from 8:00 PM to 5:00 AM, reaching 58–61%. The monthly energy efficiency of the proposed system is shown in Figure 9b and demonstrated notable fluctuations. Energy efficiency was at its lowest in March and May, around 71%. However, due to the changes in direct normal irradiance, the system’s energy efficiency reached its highest point in July, up to 77%. Regarding exergy efficiency, a trough period was observed in February, March, and November, with values between 51 and 52%. December recorded the peak exergy efficiency, around 55%.

Additionally, the annual average energy efficiency and exergy efficiency of the electricity-heating-cooling multi-generation system proposed in this paper were 74.05% and 52.13%, respectively, which were higher than those of some recent multi-generation systems of the same type. This was attributed to the integration of the full-spectrum solar utilization and biomass pyrolysis processes as well as the cascade utilization of the high-temperature waste heat from the flue gas, which collectively improved the overall efficiency and stability of the system.

The discrepancy between the energy and exergy efficiency highlights the difference between the quantity and quality of energy utilization during the conversion process. High energy efficiency does not necessarily equate to high exergy efficiency, and vice versa. When optimizing the system, it is necessary to consider both the energy and exergy efficiency comprehensively to achieve more efficient overall energy utilization. For example, in July, when energy efficiency is at its peak, further investigations can be conducted to improve the exergy efficiency and reduce the loss of energy quality.

5. Conclusions

This paper proposed a solar spectral-splitting coupled biomass gasification-based combined electricity-heating-cooling multi-generation system. This system utilizes the solar PV/T process to simultaneously achieve PV power generation and biomass pyrolysis heat supply. Through a thermochemical cycle integration architecture, it couples a gasification reactor, gas turbine, double-effect absorption chiller, organic Rankine cycle, and domestic hot water subsystem to achieve graded waste heat recovery from the system’s exhaust gases. Subsequently, the energy and exergy efficiency of the proposed system were evaluated based on the monthly and annual time scales. The main conclusions are as follows:

(1)

For the solar subsystem, the summer (Jun–Aug) power output was high, peaking in July at nearly 2.5 MW, while the winter (Jan, Feb, Dec) power output was low, reaching a minimum of 0.7 MW in December. The annual electrical output peaked at around 10 MW during the noon hours in July and August, while the winter peak was typically 2–3 MW.

(2)

In the wind power subsystem, the power output was the highest in April at 5.17 MW and the lowest in August at 0.7 MW. In April, the hourly power output peaks at 3 PM, reaching 11 MWh, while in August, the average power output from 0 to 24 h was low, not exceeding 2 MW.

(3)

The proposed multi-generation system exhibited the highest power output in April at 14.9 MW, while the output was lower from August to October, ranging from 10.9 to 11.4 MW. The annual average energy efficiency and exergy efficiency of the system were 74.05% and 52.13%, respectively, which were higher than those of some recent multi-generation systems of the same type.

Future research will focus on optimizing the fraction of incident light harnessed for PV conversion with the remaining portion utilized for thermal energy generation. To address the intermittency of solar energy, thermal energy storage devices can be employed. A more comprehensive and integrated design approach should be taken for pyrolysis reactors and solar collectors, optimizing their synergy and overall performance.