Gas Turbine System Research Articles

Constant temperature line identification on prototype gas turbine combustor with multi-colour change coating

Abstract This research focuses on identifying isotherms on the surface of a gas turbine combustion chamber using a colour-changing solvent known as temperature-indicating paint (TIP). The study employs a Multi Colour Change (MCC) 350-8 TIP to detect surface temperature variations. An innovative algorithm is proposed to identify these variations through irreversible colour changes, utilizing a colour-matching technique. The surface temperature data obtained from this method is compared with experimental calibration and computational analysis, showing not just agreement, but a high level of agreement, reinforcing the reliability of the method. This approach aims to enhance the accuracy of temperature detection in combustion chambers, which is crucial for optimizing performance and reducing emissions. The findings contribute to developing more efficient gas turbine systems by providing a reliable method for monitoring and controlling combustion temperatures. This study’s novelty lies in its use of colour-changing technology to achieve precise temperature mapping on complex surfaces.

Triangular neutrosophic set and its application to reliability analysis of gas turbine system

Triangular neutrosophic set and its application to reliability analysis of gas turbine system

Energetic and exergetic analysis of an AE-T100 micro gas turbine system fuelled by partially cracked ammonia

Abstract In recent years, ammonia has gained attention as a fuel for power generation purposes, especially due to its chemical characteristics which ensure reaching the target of zero-carbon emissions. Ammonia is also a powerful hydrogen carrier, with a solid and consolidated production method based on the Haber-Bosh process. By exploiting the ease of transport of ammonia that is liquid at moderately low pressures and temperatures, it can be converted to hydrogen and nitrogen through a cracking reaction directly at the power production plant site. Moreover, to reduce greenhouse gas emissions (GHG) in the production energy sector, small-scale power generation systems are today gaining more attention. In this context, the use of partially cracked ammonia mixtures in micro gas turbine fields represents an interesting solution in the distributed generation sector. In this work, an energetic and exergetic analysis of an AE-T100 mGT fuelled by partially cracked ammonia mixtures is carried out. The analysis has been performed by varying the ammonia cracking percentage according to the maximum achievable conversion by exploiting the thermal energy contained in mGT exhaust gases. The whole system has been modeled in MATLAB/Simulink environment and it includes not only the turbine but also the ammonia conditioning system required to bring the ammonia at the condition imposed by the mGT. Moreover, a comparison with the mGT fuelled by conventional fuel is carried out both from a thermodynamic and exergetic point of view.

Coordinated intelligent control strategy and power management for marine gas turbine under pulsed load using optimized neural network

Faced with the challenges of potentially irreversible damage due to instantaneous large pulsed power loads onboard, considering all-electric ship's small power grid capacity and complex gas turbine system, this study proposes a coordinated intelligent control strategy. It utilizes multi-objective optimization neural network to control a validated 20MW ship three-shaft gas turbine, ensuring global optimization tailored to pulsed load variations. Research findings reveal that the gas turbine efficiency at the design point is 32.3%, with a steady-state error within 2.8% and a maximum steady time error of 3.4 seconds, demonstrating acceptable accuracy. Under slope-type pulsed load of 20MW change in 5s, the proposed control strategy effectively manages speed while utilizing the energy storage system to smooth load inputs. It is worth noting that, increasing the fuel flow rate to 1.28 kg/s and inlet guide vane to 5.8 degrees reduces overshoot by 0.7% and rotor steady time by 15.4 seconds. Under transient-type pulsed load of 10MW back and forth change in 5s, the coordinated inlet guide vane control strategy mitigates surge margin and exhaust temperature issues. Adjusting the fuel flow to 1.17 kg/s and inlet guide vane to 9.96 degrees enables the gas turbine to operate within safety boundaries, reducing rotor speed overshoot by 0.4% and steady time by 6.1 seconds. Importantly, the optimized control strategy without energy storage system provides faster and safer dynamic regulation and power transmission for ship acceleration processes under radar load. However, considering only the coordinated strategy of the fuel sub-control loop, it is advisable to avoid compressor surge and over-temperature protection for electromagnetic ejection load scenarios. The research results will lay a valuable technical foundation for the intelligent control and smooth power transfer of the all-electric ship gas turbine power system with pulse loads in the extreme missions of ships in the future.

Biomass-Based Gas Turbine Systems for Power Generation: Sustainable Feedstocks and Environmental Benefits

This paper explores the significance and operational mechanisms of biomass-based gas turbine systems, which present a renewable and sustainable substitute for conventional fossil fuel-based electricity production methods. These systems utilize biomass as a source of fuel to produce heat, which is then converted into power through a gas turbine. The process involves the combustion of biomass fuel to heat a working fluid, such as air, which subsequently drives the turbine to produce electricity. Biomass can be derived from various organic materials, encompassing urban solid trash, forest residues, and agricultural residues, making it a versatile and sustainable fuel option. The paper also includes a practical case study, highlighting the system's advantages and disadvantages. Additionally, it identifies future trends, emphasizing the importance of efficiency enhancement. To achieve this, scientists are increasingly proposing hybrid systems that combine biomass-based gas turbines with other technologies. This approach aims to optimize performance, reduce emissions, and contribute to guaranteeing a future with more sustainable energy. The study underscores the potential of biomass-based gas turbine systems to play a crucial part in the shift to renewable energy sources.

Investigation of pollution formation mechanism through the application of a diesel surrogate model for developed for a diesel-operated gas turbine system

Investigation of pollution formation mechanism through the application of a diesel surrogate model for developed for a diesel-operated gas turbine system

Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks

Efficient cooling technology is crucial for high-temperature gas turbines which are significant applications in thermal processes particularly in industries such as power generation aerospace and marine propulsion. Enhancing cooling efficiency in these systems not only improves energy utilization but also plays a critical role in reducing fuel consumption increasing operational reliability and minimizing environmental impacts. This research focuses on optimizing the thermal performance of internal passage cooling channels with extended ribs using a combination of the response surface method and artificial neural networks. By varying the extended rib length and rib width the study aims to enhance heat transfer and minimize flow loss leading to improvements in overall system efficiency. Latin Hypercube Sampling is employed to strategically select variables and automated numerical simulations are conducted to perform a thorough parametric analysis. A steady-state k-ω shear stress transport turbulence model is applied in the simulations and the numerical results are validated against experimental data ensuring accuracy. The combined response surface method–artificial neural network approach integrated with TensorFlow-based modeling allows for the prediction of heat transfer and friction factor ultimately generating a comprehensive response surface to identify the optimal design point. Applying the optimized design leads to a 23.99 % improvement in the thermal performance factor compared to the reference case. The findings of this study highlight a significant enhancement in the thermal performance of the cooling channel structure particularly in high heat flux environments. This research provides valuable insights into the design of more efficient thermal management systems for gas turbines and has broader implications for other high-temperature thermal processes. The proposed optimization methodology offers a reliable approach for improving cooling technologies contributing to the development of the next generation of energy-efficient high-performance gas turbines and other industrial applications where effective thermal management is essential.

Investigating the performance of a gas turbine system combined with steam methane reforming as a thermochemical recuperator in a power, cooling, freshwater, and hydrogen production multigeneration system

Depletion of fossil fuel resources at a high speed and increasing demand for energy supply for various purposes in today's life integrated with industry has led to collective contemplation for the optimal and maximum utilization of fossil fuels through the implementation of multigeneration systems. In the presented study, a multigeneration system for cooling, power, freshwater, and Hydrogen production is proposed and investigated from exergy, energy, and economic viewpoints through a specific scenario. A case study is performed, revealing that the system can deliver a net output power of 16,251.8 kW, a cooling load of 15,905.0 kW, produce freshwater at a rate of 3.891 kg/s, and generate hydrogen at 0.284 kg/h. The energy and exergy efficiencies of the system are 60.48 % and 32.55 %, respectively. An economic analysis shows a payback period of 0.474 years. A parametric evaluation is performed to perceive the system operation in various conditions. Subsequently, a multi-objective optimization using the MOPSO-LINMAP method is conducted to identify the optimal operating conditions for the system. The optimization results are compared with the case study findings, demonstrating further improvements in performance metrics. The optimized system achieves a slightly higher energy efficiency of 61.62 % and a lower payback period of 0.40 years, underscoring the benefits of the optimization process. This study highlights the potential of the proposed system to efficiently and economically meet the demands for power, hydrogen, cooling, and freshwater in various applications.

Experiment and dynamic simulation of micro gas turbine combined with concentrated solar power system

The solar micro gas turbine (SMGT) system is a promising solution to address the instability and intermittency of renewable energy sources. Its dynamic characteristics under unstable input conditions and uncertain output load demands are crucial for peak shaving. This paper constructs an SMGT system based on experiments with micro gas turbine (MGT) and concentrated solar power (CSP), analyzing the impact of integrating CSP system on the MGT. Then the performance of the SMGT under varying ambient conditions and load demands is studied. Results show that in grid-connected mode, the turbine speed of MGT and SMGT is determined by ambient temperature and set power, with electrical efficiency decreasing as temperature and DNI increase. The solar receiver in the SMGT should maximize temperature increase while maintaining pressure loss below 55 kPa. Additionally, a higher heat capacity can reduce sensitivity to ambient changes but also extends stabilization time. Under real solar radiation, the SMGT system can keep constant power within 0.6 % fluctuation, with a solar share of 36.5 %. When combined with photovoltaic, the hybrid system's maximum power fluctuation does not exceed 2.6 %, with a solar share of 48.8 %. This study provides guidance for optimizing SMGT systems and their application in peak shaving scenarios.

Exploring explainable ensemble machine learning methods for long-term performance prediction of industrial gas turbines: A comparative analysis

In today's modern life, where electricity demand is one of the fundamental necessities, gas turbines play a pivotal role in meeting this demand. As such, it is imperative to address the challenges faced in the field. Current models often rely on simplifying assumptions, neglecting the intricate relationships between variables. This limitation leads to reduced accuracy and reliability, ultimately affecting the overall efficiency of gas turbine systems. Furthermore, the complexity of gas turbine behavior, coupled with the scarcity of comprehensive datasets, exacerbates the problem.To address these challenges, this research aimed to develop an advanced model capable of accurately forecasting real gas turbine behavior. The proposed approach leveraged ensemble decision trees, robust preprocessing techniques, and rigorous evaluation using an extensive dataset spanning from 2011 to 2015. The training and validation phases were conducted on data from 2011 to 2014, with the 2015 dataset reserved for evaluation.The results demonstrated that the bagging structure outperformed the boosted structure, exhibiting lower complexity and higher reliability. Remarkably, the bagging approach with only 30 estimators achieved a superior root mean square error of 1.4176, outperforming the boosted trees with 200 learners. The model effectively captured the overall gas turbine performance, though it encountered limitations in certain specific operating ranges.To further investigate the model's behavior, an evaluation was conducted to assess the effects of the input variables on the output power. While the interpretability of the results posed some challenges, the overall findings were deemed acceptable and provide valuable insights for optimizing gas turbine performance. The significance of this research lies in its potential to inform decision-making and enhance the efficiency of gas turbine systems, ultimately contributing to the reliable and sustainable supply of electricity.

Repairing weld for directly solidified Ni-based superalloy substrates using directed energy deposition of Inconel 625

Although the maintenance, repair, and operation of gas turbine systems are important issues in enhancing the operation of power units, advanced welding repair processes have limitations in depositing directionally solidified Ni-based superalloy components. To overcome this problem, in this study, a directed energy deposition method was applied to repair directionally solidified components using an Inconel 625 alloy. The rapid cooling rate and small laser beam diameter during directed energy deposition minimized the heat-affected zone and duration in the mushy zone, resulting in crack inhibition at the interface. The first laser scan partially melted the substrate and formed a mixing zone with a chemical composition gradient. By combining the chemical compositional gradient and dislocation accumulation due to repetitive solidification, that is, with the melting cycles of directed energy deposition, the thermal stability decreased, resulting in recrystallization and grain growth in the mixing zone. Consequently, the crack-free continuous interface of the partially repaired components did not induce tensile fracture near the interface. Although the mechanical properties of the IN625 deposit layer were degraded by severe heat treatment of the substrate, the results demonstrated that energy deposition with post-heat treatment is an effective approach in repairing Ni-based superalloy components without cracks and inclusion initiation.

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

A novel dual-stage intercooled and recuperative gas turbine system integrated with transcritical organic Rankine cycle: System modeling, energy and exergy analyses

Gas-fired power generation, characterized by high efficiency, low carbon emissions, and flexibility, contributes significantly to the global energy transition. In the present work, a novel dual-stage intercooled and recuperative gas turbine system integrated with transcritical organic Rankine cycle (dICR-GT-tORC) was proposed. System modeling was carried out based on Thermoflex software for different configurations to evaluate the energetic and exergetic performances. It was found that the dICR-GT-tORC exhibited improved thermodynamic performance compared to the gas turbine simple cycle and dICR-GT system, with the system net energy efficiency/exergy efficiency/specific work increasing from 43.88%/41.80%/567 kJ·kgair−1 and 57.21%/54.49%/684 kJ·kgair−1 to 62.48%/59.52%/745 kJ·kgair−1, respectively. An energy utilization diagram analysis revealed that the energy cascade utilization was achieved by coupling a bottoming tORC to fully utilize the waste heat from intercoolers and the exhaust gas. Furthermore, the dICR-GT-tORC demonstrated enhanced environmental performance, reducing the CO2 emission rate by a maximum value of 29.8%. Additionally, the impacts of key parameters, including the organic working fluid selection, the minimum temperature difference at the pinch point of recuperators and the terminal exhaust gas temperature on the system performance were investigated. It was indicated that the proposed system could be applicable in various practical scenarios from thermodynamic and environmental perspectives.

Thermodynamic and economic analysis of the combined cooling, heating, and power system coupled with the constant-pressure compressed air energy storage

Combined cooling, heating, and power (CCHP) technology represents a widely adopted and promising approach for achieving high energy efficiency. However, the conventional operational mode of electricity determined by heating often leads to poor partial load efficiency, strong heat-electricity coupling, and inflexible regulation in the gas turbine system. This paper proposes a novel CCHP system coupling the gas turbine and constant-pressure compressed air energy storage. Thermodynamic and economic models are built to evaluate the four schemes of the coupled system based on the simple cycle or regenerative cycle gas turbine with a 10 MW power output, respectively. The results show that different coupling methods offer advantages in various aspects. The coupled scheme employing the simple cycle gas turbine and the ejector demonstrates the highest primary energy ratio of 68.94 %. Meanwhile, the coupled scheme using the regenerative cycle gas turbine and expander exhibits superior economic performance, with the fuel cost output rate and system profit reaching 1.426 and $734.43, respectively. Integrating constant-pressure compressed air energy storage effectively widens the heat-electricity ratio adjustment range of the gas turbine CCHP system. Among them, the coupled scheme employing the regenerative cycle gas turbine and ejector achieves the highest relative variation rate of the heat-electricity ratio, reaching 13.68 %.

A techno-economic analysis of future hydrogen reconversion technologies

The transformation of fossil fuel-based power generation systems towards greenhouse gas-neutral ones based on renewable energy sources is one of the key challenges facing contemporary society. The temporal volatility that accompanies the integration of renewable energy (e.g. solar radiation and wind) must be compensated to ensure that at any given time, a sufficient supply of electrical energy for the demands of different sectors is available. Green hydrogen, which is produced using renewable energy sources via electrolysis, can be used to chemically store electrical energy on a seasonal basis. Reconversion technologies are needed to generate electricity from stored hydrogen during periods of low renewable electricity generation. This study presents a detailed techno-economic assessment of hydrogen gas turbines. These technologies are also superior to fuel cells due to their comparatively low investment costs, especially when it comes to covering the residual loads. As of today, hydrogen gas turbines are only available in laboratory or small-scale settings and have no market penetration or high technology readiness level. The primary focus of this study is to analyze the effects on gas turbine component costs when hydrogen is used instead of natural gas. Based on these findings, an economic analysis addressing the current state of these turbine components is conducted. A literature review on the subsystems is performed, considering statements from leading manufactures and researchers to derive the cost deviations and total cost per installed capacity (€/kWel). The results reveal that a hydrogen gas turbine power plant has an expected cost increase of 8.5% compared to a conventional gas turbine one. This leads to an average cost of 542.5 €/kWel for hydrogen gas turbines. For hydrogen combined cycle power plants, the expected cost increase corresponds to the cost of the gas turbine system, as the steam turbine subsystem remains unaffected by fuel switching. Additionally, power plant retrofit potentials were calculated and the respective costs in the case of an upgrade were estimated. For Germany as a case study for an industrialized country, the potential of a possible retrofit is between 2.7 and 11.4 GW resulting to a total investment between 0.3 and 1.1 billion €.

Intelligent optimization of eco-friendly H2/freshwater production and CO2 reduction layout integrating GT/rankine cycle/absorption chiller/TEG unit/PEM electrolyzer/RO section

The present study introduces an innovative approach tailored for gas turbine power plants to provide residential freshwater and energy, while also taking into account environmental concerns. The innovative design integrates sophisticated combustion technologies and effective heat recovery systems to enhance energy conversion processes and mitigate emissions, thereby facilitating more sustainable and environmentally-conscious power generation. The proposed system demonstrates capability in generating electrical power, producing desalinated water, and providing cooling capacity, thereby presenting a feasible substitute to conventional gas turbine systems. This research investigates the fundamental case of a gas turbine power plant and compares it to a modified system that includes heat recovery and hydrogen blending. The proposed system has been rigorously designed, analyzed, and optimized, with performance outcomes evaluated in comparison to the base case. Numerous effective parameters are taken into consideration in the exploration of the system's performance and the implementation of a multi-objective optimization procedure. The aims of this study encompass the optimization of overall exergy efficiency, the minimization of total cost rate, and the reduction of normalized CO2 emissions. The findings indicate a noteworthy enhancement in the suggested system when compared to the conventional base system. The overall exergy efficiency saw an increase from 31. 34–4273 %, accompanied by a decrease in the total cost rate from 1847 to 1514 $/h, and a reduction in the CO2 emission rate from 2. 22 kg/s to 1. 62 Moreover, through a comparative analysis, it is evident that augmenting the production capacity of the modified system results in a significant decrease in the power plant's payback period by 0. 8 years. The outcomes of this study offer significant insights for the creation of power generation systems that are both more effective and environmentally sustainable.

Research on load prediction of low-calorific fuel fired gas turbine based on data and knowledge hybrid model

The high-precision load prediction technology plays a vital role in load control and health management for gas turbines. Low-calorific fuel fired gas turbines pose an especially significant challenge for load prediction due to the frequent and wide fluctuations of gas parameters. First, an improved hybrid-dimension physical-based model is proposed, which is corrected based on segmented operation data and integrates zero-dimensional thermodynamic knowledge of gas turbines with three-dimensional fluid dynamics simulation knowledge. Secondly, an optimal data-driven model is proposed through conducting a systematic comparative study on four popular machine learning models with two types of input variables feature extraction. Then, a novel hybrid model is proposed based on the improved physical-based model, optimal data-driven model, and the principle of minimizing hybrid model errors. The average relative prediction error of the hybrid model over the complete operation range is 0.68%. Compared with the physical-based model and the data-driven model, the proposed hybrid model can improve the prediction accuracy by 63% and 29%, respectively. As the high-calorific fuel fired gas turbine system is relatively simple and has a small range of fuel fluctuations, the method established in this paper can be extended to high-calorific fuel fired gas turbines, which is of great significance for promoting the dynamic balance of loads in a new type of electric power system based on new energy.

Features of a gas turbine combustion chamber in operation with gaseous ammonia

Achieving climate neutrality requires the use of alternative hydrogen carriers for energy purposes, with ammonia currently being considered. One potential application in the energy sector is gas turbine systems, for which combustion chambers need to be designed to efficiently combust ammonia within an airflow. This paper focuses on theoretical investigations of fuel combustion completeness and environmental cleanliness of a gas turbine combustion chamber with a thermal power output of 1.0 MW, operating on gaseous ammonia. A prospective scheme for organizing processes is proposed, based on pre-mixing ammonia with a portion of air in a special pre-chamber, the sequential introduction of the remaining air through a radial-axial swirler and a series of holes in the flame tube, ensuring stable fuel combustion. Two approaches were used for modeling combustion processes in such a combustion chamber: one based on chemical reactor theory and the other on three-dimensional hydrodynamic analysis. After conducting a detailed kinetic analysis, it was concluded that to maintain stable ammonia combustion while varying the air excess coefficient in the primary combustion zone from 1.4 to 2.0, the reactants need to remain in this zone for a duration exceeding 0.025 s. Based on these parameters, design schemes for two combustion chamber variants were developed, differing in combustion tube length and air distribution pattern, for operation with an ammonia flow rate of approximately 50 g/s and a pressure of 0.3 MPa, determining a gas outlet temperature of 1490 K. Three-dimensional calculations of aerodynamic flow structures, ammonia combustion, and formation of toxic components were conducted, revealing that the proposed process organization scheme ensures complete ammonia combustion and small nitrogen oxide emissions. These results have potential applications in the development of new gas turbine combustion chamber designs for decarbonized energy systems, including those integrated with solid oxide fuel cells.

Effects of compositional uncertainties in cracked NH3/biosyngas fuel blends on the combustion characteristics and performance of a combined-cycle gas turbine: A numerical thermokinetic study

Blending of partially cracked ammonia with biosyngas is an attractive strategy for improving NH3 combustion. In practice, products of biomass gasification and those of thermo-catalytic cracking of NH3 are subject to some compositional uncertainties. Despite their practical importance, so far, the effects of such uncertainties on combustion systems remained largely unexplored. Hence, this paper quantifies the effects of small compositional uncertainties of reactants upon combustion of partially cracked NH3/syngas/air mixtures. An uncertainty quantification method, based on polynomial chaos expansion and a data-driven model, is utilised to investigate the effects of uncertainty in fuel composition on the laminar flame speed (SL) and adiabatic flame temperature (Tad) at different inlet pressures (Pi). The analysis is then extended to the power output of a combined-cycle gas turbine fuelled by the reactants. It is found that 1.5% fuel compositional uncertainty can cause 12–21% of SL uncertainty depending on the inlet pressure. Furthermore, the effect of compositional uncertainty on Tad increases at higher ratios of H2 to NH3. Sensitivity analysis reveals that the uncertainty of CO contribution to SL uncertainty is higher than that of NH3, while the trend is reversed for the Tad uncertainty. In addition, the power output from the combined-cycle gas turbine system varies between 4 and 6% with 1.5% of fuel compositional uncertainty. This become more noticeable at elevated Pi [5–10 atm], particularly when the fuel mixture contains high H2 which is the main contributor to Tad variability.

Annual performance evaluation of a hybrid concentrated solar–micro gas turbine based on off-design simulation

As the adoption of solar hybrid systems continues to rise due to their potential to compensate for the fluctuation of solar irradiation, it becomes imperative to accurately evaluate their performance, considering the variation of off-design conditions. This paper introduces a detailed analysis method for evaluating the annual performance of a solar-MGT system under transient boundary conditions for a whole-year operation range. A hybrid system of a micro gas turbine, recuperator, and solar dish is considered, and an off-design simulation model is developed and verified with available experimental results. Two different configurations for a recuperated cycle are considered, and simulations are conducted for a test case in Pretoria, SA. The results for Jun.21 and Dec.21 as low and high solar energy days are reported with more details to compare the configurations and demonstrate the effect of ambient temperature on the heat loss of the solar receiver and the overall performance of the system. The alternative configuration reduces heat loss with a lower temperature receiver but has higher fuel consumption compared to the conventional configuration. Operating strategies for different hours of operation from 1 h to 24 h per day are simulated for 365 days, based on real meteorological data, and compared with the operating in solar available hours. It is shown that the whole-year simulation of the system considering the variation of boundary conditions can change the estimation of fuel consumption by 25 %.