A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant

Bendato, Ilaria; Mosca, Marco; Mosca, Roberto

doi:10.1016/j.rser.2015.07.074

Cited by 19 publications

(8 citation statements)

References 14 publications

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“…The main advantage of such approach is that helps to achieve a more comprehensive description of the problem and a more effective analysis, as already put in evidence by other recent studies conducted by the authors (Bendato et al, 2016) (Bendato et al, 2015).…”

Section: Introductionmentioning

confidence: 94%

RSM approach for stochastic sensitivity analysis of the economic sustainability of a methanol production plant using renewable energy sources

Bellotti

Mosca

Magistri

2019

Journal of Cleaner Production

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This study aims at investigating the economic viability, at the pre-feasibility level, of a 5MW electrolyser base-methanol production plant, coupled with a PV power plant. The Authors investigated the impact of different parameters, such as the PV plant size, the electrical energy cost and the components capital costs on the methanol production cost and on the system economic viability. It was also analyzed the minimum recommended sale price of the methanol in order to assure an adequate time frame for the return of the investment, considering a different combination of the investigated parameters. An economic sensitivity analysis, based on the RSM approach, was performed in order to define the most promising economic conditions under which the plant can be considered a profitable investment in terms of ARR. A guide for an economically viable plant design, allowing for the identification of the most suitable combination of the economic parameters, was proposed as a kind of "maps of existence". For the reference case, the Methanol Production Cost (MPC) resulted around 324 €/ton and the minimum methanol sale price to achieve a PBP of 10 years. The sensitivity analysis identified the cost of electricity and the capital cost of the electrolyser as the most affecting parameters for the system economic viability. In terms of ARR, the methanol price represents the most significant factor. Considering a methanol sale price ranging between 400 and 1200 €/ton, the ARR varied from 5% (20 year of PBP) to 20% (5years of PBP). From the environmental point of view, it is worth underling that the methanol production plant here proposed allows to recycle about 5800 tons of CO2 per year and to avoid the consumption of about 5.2 MNm 3 of NG per year (compared to the traditional production).

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Section: Introductionmentioning

confidence: 94%

RSM approach for stochastic sensitivity analysis of the economic sustainability of a methanol production plant using renewable energy sources

Bellotti

Mosca

Magistri

2019

Journal of Cleaner Production

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“…Shetty et al 22 developed Various parametric and nonparametric models based on wind power equations and concepts of the power curve, such as RSM and ANN, and the results have been compared in their study. Bendato et al 23 presented a case study about a 1 MWe PV plant to determine, in real-time, the investment parameters. They described that by suitably varying the independent variables, it is possible to obtain a sensitivity analysis on the dependent variables and also, by applying the Response Surface Methodology techniques, it has been possible to obtain mathematical functions that can explain the link among dependent and independent variables in the definition range.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of Box–Behnken and central composite designs to investigate the effective parameters of ammonia–water absorption refrigerant system

Kazemian

Nassab

Javaran

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, the statistical analyses of the ammonia-water absorption refrigerant system (ARS) are presented. For this purpose, different surface response methods, including the Central composite design (CCD) and Box- Behnken design (BBD), have been studied and compared first to obtain the best response. An attempt is made to study the effect of some factors (mass flow rate, isentropic efficiency of pump, pressure ratio, maximum pressure of cycle, concentration difference of strong and weak solution, concentration of strong solution and effectiveness factor of heat exchanger and pure ammonia concentration) on the system’s response. Based on the design of experiment analysis, regression models are presented to quantify the effects of these parameters on coefficient of performance (COP) and cooling load of the ammonia-water absorption refrigerant cycle. The proposed correlations obtained by the analysis have remarkably satisfactory performance for all simulation data. The error analysis is applied to find the maximum error of 0.482% in numerical computations for ARS by CCD method in midpoint values of parameters.

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“…Nevertheless, generally, these methods require a deepned knowledge of the related PV electronics or physics properties or are based on datasheets providing by the manufacturers, not specific to the considered panel under evaluation [1][2][3][4][5]. By else, these methods are based on relatively complex and specific mathematical models [6][7][8][9][10][11][12]. With a view to considerable time, financial, and material resources savings that we can expect in development of performing new photovoltaic systems, the Design of Experiments (DoE) approach should be adapted to offers a practical upstream way for the study, the modeling and characterizations 1141 of the influence of the pertinent parameters involved in the response of these systems.…”

Section: Introductionmentioning

confidence: 99%

Design of experiments approach for modeling the electrical response of a photovoltaic module

Kessaissia

Zégaoui

Taleb

et al. 2020

IJEECS

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In the current paper, modeling and evaluation of the significant effect of independent variables on the behavior of the electrical response of a multi- crystalline photovoltaic (PV) module using design of experiments (DoE) approach is simulated. The main purpose of this contribution is to evaluate the maximum power response dependence within the indoor conditions of both variations of solar irradiation and surface temperature and checking the pertinent one on the defined response. The DoE approach is used for estimating both main and combined effect of the two independents considered variables. Multiple linear regression was been introduced to justify the relationship between the independent input variables and dependent output variable, also to determine which input factor is the most significant on the output variable. The DoE model can be used for predicting the response variable at different operating condition in a considered domain study. In addition, DoE approach based on statistical tool for analyzing the accuracy of the predictive model, then the significance of coefficients in the predictive model using statistical and graphical analysis. Therefore, an ANOVA Table can summarize the results, detect the parameters influences on responses variations and determine the best predictive model then reproduce the most possible the experimental data.

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A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant

Cited by 19 publications

References 14 publications

RSM approach for stochastic sensitivity analysis of the economic sustainability of a methanol production plant using renewable energy sources

RSM approach for stochastic sensitivity analysis of the economic sustainability of a methanol production plant using renewable energy sources

Comparative study of Box–Behnken and central composite designs to investigate the effective parameters of ammonia–water absorption refrigerant system

Design of experiments approach for modeling the electrical response of a photovoltaic module

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