Solar energy resource assessment in Mexican states along the Gulf of Mexico

Renewable and Sustainable Energy Reviews 43 (2015) 216–238

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Solar energy resource assessment in Mexican states along the Gulf of Mexico Q. Hernández-Escobedo a, E. Rodríguez-García c, R. Saldaña-Flores b, A. Fernández-García e, F. Manzano-Agugliaro d,f,n a Facultad de Ingeniería, Universidad Veracruzana, Campus Coatzacoalcos, Avenida Universidad Veracruzana km. 7.5, CP 96536 Col. Santa Isabel, Coatzacoalcos, Veracruz, Mexico b Instituto de Investigaciones Eléctricas, Reforma 113, Col. Palmira, C.P. 62490 Cuernavaca, Morelos, Mexico c Centro de Investigaciones en Recursos Energéticos y Sustentables, Universidad Veracruzana, Campus Coatzacoalcos, Avenida Universidad Veracruzana km. 7.5, CP 96536, Col. Santa Isabel, Coatzacoalcos, Veracruz, Mexico d BITAL (Research Center on Agricultural and Food Biotechnology), CEIA3, University of Almeria, La Cañada de San Urbano, E-04120 Almería, Spain e Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Plataforma Solar de Almería, Ctra. Senés, km. 4, E04200 Tabernas, Almería, Spain f Department of Engineering, University of Almería, La Cañada de San Urbano, E04120 Almería, Spain

art ic l e i nf o

a b s t r a c t

Article history: Received 23 August 2013 Received in revised form 23 April 2014 Accepted 6 October 2014

The development of renewable energy has increased over the past few years due to the high cost of fossil fuels and our great dependence on them. Solar energy has been evaluated in the majority of developed countries. Mexico is known to possess large quantities of renewable energy resources, for example, approximately 6000 MW of wind energy resources. Nevertheless, solar energy is not sufficiently developed in Mexico. In this work, the global solar resources in Mexican states along the Gulf of Mexico were assessed. The data used in the analysis were obtained from the Automatic Meteorological Stations (AMEs) of the National Meteorological Service of Mexico (NMS) every 10 min over a period of 10 years, as well as from the Surface Meteorology and Solar Energy (SMSE) of the National Aeronautics and Space Administration (NASA) every month over 22 years. AMEs and SMSE validation data were compared to calculate their determination coefficient, R2, which was above 90%. A total of 13 maps generated by a Geographic Information System (GIS), one per month, and annually averaged global solar resources were used to determine the areas and the periods of the year with the greatest global solar energy resources. According to the results obtained in this study, the highest amount of solar energy, i.e., greater than 6.22 kWh/m2/day, was registered on July in the state of Tamaulipas. Based on the average annual energy map, the southern region of Veracruz State registered the largest resource, i.e., greater than 5.03 kWh/m2/day. From the foregoing analysis, the primary conclusion arrived at in the present work is that solar energy has significant potential for complementing energetic requirements in Mexican states along the Gulf of Mexico. It is recommended that the government adopt policies supporting and promoting the utilization of solar energy to maintain fossil fuel reserves and to reduce greenhouse gases. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Solar resource assessment Solar energy Gulf of Mexico GIS Determination coefficient Renewable energy Sustainable energy

Contents 1. Introduction . 2. Area studied. 3. Methods . . . . 4. Results . . . . . 5. Conclusions . References . . . . . .

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216 225 225 226 231 237

1. Introduction n

Corresponding author. E-mail address: [email protected] (F. Manzano-Agugliaro).

http://dx.doi.org/10.1016/j.rser.2014.10.025 1364-0321/& 2014 Elsevier Ltd. All rights reserved.

It is well-known that burning fossil fuels (coal, oil, and natural gas) generates pollutant gases (SO2, CO, NOX, HC, and CO2) that

Q. Hernández-Escobedo et al. / Renewable and Sustainable Energy Reviews 43 (2015) 216–238

Symbol

Nomenclature Abbreviations AMEs GIS NASA NMS SMSE ARIMA

217

Automatic Meteorological Stations Geographic Information System National Aeronautics and Space Administration National Meteorological Service of Mexico Surface Meteorology and Solar Energy Autoregressive Integrated Moving Average

cause environmental pollution problems [1]. The development of renewable energies has increased over the past few years due to the high price of fossil fuels and our great dependence on them [2]. Solar energy is considered one of the most promising alternative sources of energy for avoiding the dependency on fossil energy resources [3,4]. In addition to being free, clean, and abundant, the evaluation of solar energy resources is especially important because of the high cost and the degradation of the environment caused by the use of fossil fuels [5]. For example, in 2009, 43% of CO2 emissions from fuel combustion worldwide came from coal, 37% from oil, and 20% from gas [6]. Solar energy has gained the attention of many industries and areas of application in recent years [7]. Information about the

R2 β^ 0 β^ 1 Z(Si) λi S0 n

coefficient of determination squared minimum of the ordinate at the origin slope of the straight line value measured at position i unknown weight for the value measured at position i predicted location number of measured values

characteristics of solar energy throughout the world plays an important role in the study, planning, and design of applications of this energy [8,9]. Therefore, data on solar radiation over the surface of the Earth are essential for studying and designing systems utilizing energy from the sun [10]. Solar energy received above the atmosphere (that is, the radiation that would be received on the Earth's surface in the absence of the atmosphere) is called extraterrestrial solar energy, whereas solar energy that is received below the atmosphere is called global or total solar energy [11]. Global solar radiation is the sum of the beam or direct radiation and the diffuse solar radiation on a surface, where the former is radiation received from the sun without having been scattered by the atmosphere and the latter is radiation received from

Fig. 1. Geographical locations of the stations employed in the Gulf of Mexico study.

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Q. Hernández-Escobedo et al. / Renewable and Sustainable Energy Reviews 43 (2015) 216–238

Table 1 Global solar irradiation statistics in Mexican states along Gulf of Mexico. SMSE/AMEs

Point_01 Point_02 Point_03 Point_04 Point_05 Point_06 Point_07 Point_08 Point_09 Point_10 Point_11 Point_12 Point_13 Point_14 Point_15 Point_16 Point_17 Point_18 Point_19 Point_20 Point_21 Point_22 Point_23 Point_24 Point_25 Point_26 Point_27 Point_28 Point_29 Point_30 Point_31 Point_32 Point_33 Point_34 Point_35 Point_36 Point_37 Point_38 Point_39 Point_40 Point_41 Point_42 Point_43 Point_44 Point_45 Point_46 Point_47 Point_48 Point_49 Point_50 Point_51 Acayucan Monclova Presa La Cangrejera Cd. Aleman Calakmul Paraiso Escarcega Cd. del Carmen Alvarado Cordoba Yohaltum Perote Campeche Tantaquin Oxkutzcab Celestún Tuxpan Citlaltepec Dzilam Río lagartos Ciudad Mante Jaumave

Lat.

27.527 26.356 25.831 25.732 25.028 24.154 24.205 23.341 23.321 24.174 23.341 23.321 22.528 22.498 22.488 21.665 20.526 21.175 20.252 20.313 20.008 19.856 19.049 19.541 19.301 19.023 19.002 18.586 18.738 18.024 17.519 17.722 18.362 17.763 18.403 18.332 17.712 17.519 19.002 18.362 18.118 19.175 19.866 19.985 20.018 20.841 21.217 21.482 20.821 20.669 21.319 17.977 18.057 18.106 18.189 18.365 18.423 18.608 18.648 18.715 18.89 19.014 19.545 19.836 20.03 20.291 20.858 20.96 21.334 21.391 21.571 22.744 23.408

Long.


99.674
99.166
98.023
97.401
98.323
99.187
98.333
100
99.187
97.856
98.333
97.795
99.176
98.343
97.906
98.536
98.445
97.854
97.581
97.002
96.667
97.185
97.175
96.839
96.342
97.134
96.024
96.009
96.118
95.325
94.198
93.608
93.354
92.673
92.135
91.657
91.454
91.129
90.936
90.824
90.082
90.021
90.336
90.021
89.168
90.001
89.269
88.335
88.324
87.766
87.684
94.901
90.821
94.331
96.098
89.893
93.156
90.759
91.823
95.633
96.923
90.311
97.268
90.507
89.047
89.394
90.383
97.417
97.879
88.904
88.16
98.983
99.375

kWh/m2/day Max

Min

SD

6.67 6.49 6.45 6.44 6.07 5.87 5.73 5.74 5.72 5.71 5.45 5.39 5.64 5.35 5.15 5.32 5.60 5.05 4.96 4.90 4.95 5.02 4.80 4.98 5.03 4.80 5.10 5.12 4.98 5.32 5.50 5.35 5.30 5.30 5.36 5.29 5.26 5.23 5.56 5.39 5.47 5.63 5.68 5.59 5.61 5.64 5.56 5.52 5.64 5.47 5.49 5.87 5.84 6.39 6.35 5.84 6.56 6.35 6.62 5.92 5.84 6.62 6.07 6.62 6.35 6.35 6.26 5.77 5.93 6.66 6.57 5.73 6.31

2.83 2.85 2.81 2.79 3.00 3.20 3.14 3.29 3.23 3.12 3.24 3.18 3.27 3.27 3.25 3.37 3.85 3.35 3.34 3.15 3.13 3.23 3.20 3.05 3.47 3.20 3.65 3.74 3.50 4.02 4.17 3.86 3.75 3.67 3.74 3.69 3.64 3.64 3.69 3.64 3.68 3.61 3.63 3.57 3.48 3.60 3.79 3.71 3.48 3.59 3.68 3.16 3.91 3.31 4.04 3.91 3.67 4.04 4.05 3.45 3.91 4.24 4.38 4.24 3.98 3.98 4.21 3.30 3.09 4.03 4.03 2.54 3.85

1.36 1.26 1.27 1.28 1.06 0.90 0.91 0.88 0.85 0.92 0.80 0.82 0.83 0.75 0.70 0.71 0.59 0.63 0.57 0.60 0.61 0.60 0.55 0.63 0.53 0.55 0.50 0.46 0.49 0.44 0.45 0.51 0.53 0.54 0.55 0.55 0.56 0.55 0.59 0.57 0.58 0.64 0.65 0.67 0.69 0.70 0.63 0.65 0.76 0.67 0.64 0.95 0.69 1.06 0.77 0.69 1.06 0.77 0.95 0.86 0.69 0.94 0.58 0.94 0.82 0.82 1.09 0.85 0.99 1.02 1.04 1.14 0.88

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219

Table 1 (continued ) Villagran San Fernando Matamoros Tizimin Barra del Tordo

24.471 24.843 25.886 21.161 23.052


99.489
98.158
97.519
87.989
97.772

6.2 6.57 6.33 6.55 6.24

3.62 3.33 2.96 3.39 3.19

0.94 1.15 1.22 0.9 1.14

Table 2 Coefficients of the adjusted model of linear regression and R2 between AMEs and SMSE data of global solar irradiation. Meteo station

R2

β^ 1

β^ 0

x (kWh/m2/day)

Acayucan Alvarado Barra del Tordo Calakmul Campeche Cd. Aleman Cd. del Carmen Celestún Citlaltepec Ciudad Mante Cordoba Dzilam Escarcega Jaumave Matamoros Monclova Oxkutzcab Paraiso Perote Presa La Cangrejera Río Lagartos San Fernando Tantaquin Tizimin Tuxpan Villagran Yohaltum

0.9104 0.9033 0.8963 0.9168 0.9023 0.9653 0.9374 0.9386 0.9026 0.9355 0.9266 0.9217 0.9019 0.9265 0.9350 0.9014 0.9264 0.9058 0.9157 0.9156 0.9021 0.9189 0.9308 0.9466 0.9025 0.9322 0.9133

0.8462 0.8802 0.9753 1.0519 0.8299 0.5904 0.8260 0.9145 0.9342 0.8334 0.6142 0.9863 1.1036 0.7237 1.1059 1.1036 0.6913 0.8429 0.5722 0.9638 0.9342 0.9830 0.7359 0.7977 0.8061 0.7928 0.8489

0.3967 0.6401 0.6367
0.3298 1.1263 2.0106 0.9363 0.7233 0.3989 1.2838 1.8941 0.4569
0.4476 2.0710
0.7345
0.4476 1.2946 1.3825 2.0275 0.2503 0.3989 0.6858 1.3158 0.8298 0.8588 1.5855 0.9725

4.56 4.93 4.96 5.16 5.85 4.92 5.63 6.01 4.68 4.43 4.92 5.88 5.31 5.28 4.96 5.32 5.29 5.39 4.99 4.93 5.73 4.64 5.29 5.40 4.62 5.10 5.84

the sun after its direction has been changed by atmospheric scattering [12]. Photovoltaic and thermal systems without concentrators use the entirety of global solar radiation, that is, both beam and diffuse radiation. However, solar concentrating systems can only use beam solar radiation [13,14]. Solar irradiation is the rate at which radiant energy is incident on a surface per unit area while solar irradiation is the incident energy per unit area on a surface, found by integration of irradiance over a specified time, usually an hour or a day [12]. It is well known that the spatial and temporal distribution of global solar radiation over a horizontal surface depends on astronomical, physical, meteorological, and geographical factors such as extraterrestrial radiation, atmospheric transmittance, latitude, the inverse relative distance between the Earth and the Sun, the angle of the setting sun, the real duration of insolation, and the degree of cloudiness at the corresponding location [15–18]. Although there are several world maps of solar radiation, they are not sufficiently detailed to be used for the determination of available solar energy over small areas [19]. The phenomenon of daily global irradiation can be represented using data from meteorological stations [20]. These data are necessary for developing energy simulation codes to design and/or evaluate solar technologies, such as photovoltaic and thermal systems. In spite of the ever-increasing network of meteorological stations around the world, the availability of solar radiation data is still limited for many applications in terms of spatial distribution [21]. Consequently, it is widely accepted that global solar radiation data derived from satellites are an excellent tool for analyzing solar resources and for supplying time series of solar irradiation [22,23]; this application was demonstrated by Pedro and Coimbra [24], who evaluated techniques to prognosticate solar potential using nonexogenous data.

GIS have been successfully employed to locate geographical areas with suitable potential for cultivating renewable energies: wind power [25–29], biomass [30–36], or solar [37–41]. GIS have also been used for alternative studies of diverse renewable energies, e.g., a spatial analysis of renewable energy potential performed the greater southern Appalachian Mountains [42] and the development of a GIS multi-criteria model of wind and solar parks in Colorado, USA [43]. In 2012, the electricity production in Mexico was 280.40 TWh, distributed as follows: 231.54 fossil fuels, 31.58 hydroelectric, 8.43 nuclear, and 8.86 geothermal, wind, solar, and other [44]. The new Law of Mexico mandates that CO2 emissions have to be reduced by 30% from business-as-usual levels by 2020 and by 50% from 2000 levels by 2050 [41]. Mexico possesses resources of almost all types of renewable energy: oceanic [45], hydroelectric [46], geothermal [47], wind [25,28,48], and biomass [36,49] energy. However, the solar energy in Mexico has not been sufficiently exploited compared with other sources of renewable energy. Renewable energy projects hold enormous potential for the reduction of future emissions in Mexico, due not only to the country's abundant renewable energy resources but also to the lack of opportunities for reducing emissions that can be recovered from industrial mitigation projects, such as those that have met with success in Asia [50]. According to Riveros-Rosas et al. [51], Mexico is in an ideal position for the application of solar technologies, thanks to its favorable geographic location. There are some studies that have focused on solar energy in Mexico. One of these studies as conducted to reduce solar radiation through the use of windows covered with solar filters to increase comfort in the interiors of buildings [52]. In another study, solar energy was used for the desalination of sea water in the Mexican state of Baja California Sur [53]. Finally, it is also

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Table 3 Average solar resource (kWh/m2/day) at Tamaulipas State from SMSE data. Meteo Point

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

SMSE_1 SMSE_2 SMSE_3 SMSE_4 SMSE_5 SMSE_6 SMSE_7 SMSE_8 SMSE_9 SMSE_10 SMSE_11 SMSE_12 SMSE_13 SMSE_14

27.527 26.356 25.831 25.732 25.028 24.154 24.205 23.341 23.321 24.174 23.341 23.321 22.528 22.498


99.674
99.166
98.023
97.401
98.323
99.187
98.333
100.000
99.187
97.856
98.333
97.795
99.176
98.343

3.07 3.06 2.96 2.93 3.18 3.48 3.33 3.72 3.52 3.29 3.42 3.31 3.55 3.45

3.74 3.68 3.70 3.71 3.90 4.12 4.09 4.41 4.21 4.07 4.22 4.14 4.18 4.19

4.65 4.49 4.58 4.61 4.68 4.75 4.81 5.00 4.81 4.81 4.90 4.85 4.77 4.80

5.24 5.04 5.25 5.31 5.27 5.21 5.38 5.46 5.25 5.39 5.40 5.33 5.15 5.11

5.89 5.60 5.68 5.71 5.64 5.62 5.72 5.57 5.72 5.72 5.61 5.71 5.73 5.65

6.40 6.13 6.22 6.26 5.83 5.91 5.95 5.87 5.95 5.85 5.83 5.83 5.94 5.82

6.67 6.49 6.45 6.44 6.07 5.99 5.73 6.07 6.17 6.07 5.94 5.93 6.16 5.93

6.27 6.02 5.99 5.97 5.77 5.66 5.61 5.74 5.60 5.59 5.45 5.38 5.59 5.35

5.37 5.21 5.18 5.17 5.09 5.09 5.03 5.15 5.02 5.01 4.94 4.89 4.97 4.85

4.36 4.25 4.41 4.48 4.46 4.42 4.56 4.41 4.45 4.58 4.63 4.63 4.44 4.57

3.29 3.34 3.43 3.46 3.57 3.68 3.68 3.78 3.70 3.67 3.76 3.71 3.72 3.74

2.83 2.85 2.81 2.79 3.00 3.20 3.14 3.29 3.23 3.12 3.24 3.18 3.27 3.27

4.81 4.68 4.72 4.74 4.70 4.76 4.75 4.87 4.80 4.76 4.78 4.74 4.79 4.73

Fig. 2. Graph of monthly irradiation based on SMSE data in Tamaulipas, Veracruz, and Tabasco.

Table 4 Average solar resource (kWh/m2/day) at Veracruz State from SMSE data. Meteo Point

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

SMSE_15 SMSE_16 SMSE_17 SMSE_18 SMSE_19 SMSE_20 SMSE_21 SMSE_22 SMSE_23 SMSE_24 SMSE_25 SMSE_26 SMSE_27 SMSE_28 SMSE_29 SMSE_30

22.488 21.665 20.526 21.175 20.252 20.313 20.008 19.856 19.049 19.541 19.301 19.023 19.002 18.586 18.738 18.024


97.906
98.536
98.445
97.854
97.581
97.002
96.667
97.185
97.175
96.839
96.342
97.134
96.024
96.009
96.118
95.325

3.36 3.55 4.19 3.46 3.49 3.25 3.25 3.45 3.48 3.24 3.57 3.48 3.75 3.91 3.67 4.19

4.18 4.22 4.73 4.12 4.00 3.75 3.74 3.83 3.76 3.62 4.27 3.76 4.52 4.61 4.30 4.92

4.81 4.80 5.21 4.71 4.47 4.22 4.15 4.21 4.16 3.95 4.63 4.16 4.87 4.93 4.61 5.20

5.06 5.05 5.36 4.90 4.67 4.50 4.48 4.50 4.43 4.33 4.92 4.43 5.10 5.05 4.81 5.28

5.55 5.45 5.60 5.45 5.49 5.37 5.27 5.48 5.58 5.59 5.29 5.26 5.56 5.37 5.36 5.32

5.70 5.92 5.87 5.81 5.88 5.74 5.65 5.66 5.65 5.65 5.72 5.65 5.78 5.79 5.69 5.92

5.68 5.63 5.65 5.61 5.59 5.58 5.68 5.69 5.58 5.58 5.69 5.58 5.67 5.69 5.58 5.79

5.15 5.32 5.47 5.04 4.96 4.90 4.95 5.02 4.80 4.98 5.03 4.80 5.08 5.12 4.98 5.29

4.74 4.84 5.01 4.68 4.58 4.46 4.43 4.52 4.38 4.38 4.54 4.38 4.61 4.60 4.49 4.78

4.63 4.52 4.67 4.42 4.16 4.04 4.01 3.94 3.76 3.83 4.36 3.76 4.58 4.70 4.47 4.49

3.73 3.79 4.23 3.70 3.73 3.57 3.60 3.68 3.47 3.55 3.91 3.47 4.08 4.15 3.89 4.46

3.25 3.37 3.85 3.35 3.34 3.15 3.13 3.23 3.20 3.05 3.47 3.20 3.65 3.74 3.50 4.02

4.65 4.71 4.99 4.60 4.53 4.38 4.36 4.43 4.35 4.31 4.62 4.33 4.77 4.80 4.61 4.97

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Table 5 Average solar resource (kWh/m2/day) at Tabasco State from SMSE data. Meteo Point

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

SMSE_31 SMSE_32 SMSE_33 SMSE_36 SMSE_37

17.519 17.722 18.362 18.332 17.712


94.198
93.608
93.354
91.657
91.454

4.36 4.04 3.93 3.97 3.94

5.02 4.69 4.62 4.62 4.51

5.29 4.99 4.97 5.08 4.96

5.48 5.24 5.24 5.29 5.16

5.65 5.64 5.63 5.62 5.61

5.81 5.80 5.95 5.85 5.79

5.54 5.53 5.52 5.51 5.52

5.46 5.34 5.29 5.27 5.26

4.95 4.85 4.77 4.67 4.63

4.40 4.75 4.68 4.45 4.29

4.26 4.29 4.18 4.06 3.96

4.17 3.86 3.75 3.69 3.64

5.03 4.92 4.88 4.84 4.77

Table 6 Average solar resource (kWh/m2/day) at Campeche state from SMSE data. Meteo Point

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

SMSE_34 SMSE_35 SMSE_38 SMSE_39 SMSE_40 SMSE_41 SMSE_42 SMSE_43

17.763 18.403 17.519 19.002 18.362 18.118 19.175 19.866


92.673
92.135
91.129
90.936
90.824
90.082
90.021
90.336

3.89 4.00 4.11 3.97 4.28 4.28 4.59 4.59

4.74 4.69 4.84 4.75 5.05 5.05 5.45 5.45

5.80 5.71 5.72 5.83 5.88 5.88 6.21 6.21

6.10 5.84 6.20 5.96 6.35 6.35 6.75 6.75

6.17 6.30 6.10 6.36 6.25 6.25 6.92 6.92

5.90 5.89 5.82 5.88 5.87 5.87 6.68 6.68

6.12 6.16 6.06 5.97 5.78 5.78 6.66 6.66

5.30 5.29 5.98 5.16 5.65 5.65 6.54 6.54

4.72 4.71 5.27 4.65 5.33 5.33 6.06 6.06

4.47 4.56 4.68 4.55 4.78 4.78 5.29 5.29

4.04 4.14 4.30 4.10 4.46 4.46 4.75 4.75

3.67 3.74 3.86 3.69 4.04 4.04 4.24 4.24

5.08 5.09 5.25 5.07 5.31 5.31 5.85 5.85

Fig. 3. Graph of monthly irradiation based on SMSE data in Campeche and Yucatan. Table 7 Average solar resource (kWh/m2/day) at Yucatan state from SMSE data. Meteo Point

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

SMSE_44 SMSE_45 SMSE_46 SMSE_47 SMSE_48 SMSE_49 SMSE_50 SMSE_51

19.985 20.018 20.841 21.217 21.482 20.821 20.669 21.319


90.021
89.168
90.001
89.269
88.335
88.324
87.766
87.684

3.86 3.77 3.86 3.97 3.87 3.72 3.77 3.83

4.57 4.32 4.41 4.55 4.46 4.17 4.62 4.43

5.20 5.12 5.00 5.10 5.04 4.77 4.96 5.02

5.59 5.60 5.40 5.42 5.46 5.34 5.47 5.49

5.58 5.74 5.64 5.56 5.52 5.64 5.65 5.55

5.41 5.61 5.35 5.34 5.22 5.42 5.21 5.31

5.31 5.32 5.38 5.24 5.33 5.34 5.20 5.22

5.21 5.25 5.34 5.15 5.32 5.31 5.18 5.12

4.73 5.03 4.94 5.03 4.90 4.83 4.74 4.80

4.47 4.76 4.49 4.68 4.51 4.20 4.27 4.40

3.99 4.27 4.00 4.13 4.01 3.76 3.85 3.94

3.57 3.48 3.60 3.79 3.71 3.48 3.59 3.68

4.88 4.95 4.87 4.91 4.86 4.75 4.80 4.81

Table 8 Average solar resource (kWh/m2/day) per state from SMSE data. State

January

February

March

April

May

June

July

August

September

October

November

December

Annual

Tamaulipas Veracruz Tabasco Campeche Yucatan

3.30 3.58 4.05 3.95 3.83

4.02 4.15 4.69 4.64 4.44

4.75 4.57 5.06 5.16 4.90

5.27 4.80 5.28 5.41 5.39

5.39 4.94 5.31 5.31 5.54

5.71 4.78 4.93 4.87 5.26

5.90 4.94 5.25 5.06 5.28

5.71 5.06 5.32 5.20 5.29

5.07 4.59 4.77 4.65 4.90

4.47 4.32 4.64 4.44 4.47

3.61 3.81 4.22 4.03 3.99

3.09 3.41 3.82 3.66 3.61

4.69 4.41 4.78 4.70 4.74

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Table 9 Average solar resource (kWh/m2/day)at Tamaulipas state from AMEs data. Met station

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

AMEs_Mante AMEs_Juamave AMEs_Villagran AMEs_SanFer AMEs_Matamoros AMEs_BarraTordo

22.744 23.408 24.471 24.843 25.886 23.052


98.983
99.375
99.489
98.158
97.519
97.772

3.37 3.62 3.45 3.67 3.49 3.82

4.55 4.45 4.66 4.76 4.22 4.53

5.11 5.19 5.23 5.39 5.48 5.22

5.76 5.54 5.77 5.84 5.80 5.73

6.07 6.12 6.20 6.29 6.15 6.27

6.68 6.62 6.47 6.51 6.60 6.71

7.00 7.18 7.74 7.73 7.28 7.05

7.53 7.23 7.19 7.16 6.92 6.79

6.44 6.26 6.22 6.21 6.11 6.11

4.14 4.04 4.19 4.25 4.24 4.20

3.13 3.17 3.25 3.29 3.39 3.50

2.69 2.71 2.67 2.65 2.85 3.04

5.21 5.18 5.25 5.31 5.21 5.25

Fig. 4. Graph of monthly irradiation based on AME data in Tamaulipas, Veracruz and Tabasco. Table 10 Average solar resource (kWh/m2/day) at Veracruz state from AMEs data. Met station

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

AMEs_Acay AMEs_PLC AMEs_Aleman AMEs_Alva AMEs_Cordoba AMEs_Perote AMEs_Tuxpan AMEs_Citlal

17.977 18.106 18.189 18.715 18.89 19.545 20.96 21.334


94.901
94.331
96.098
95.633
96.923
97.268
97.417
97.879

3.48 4.11 3.39 3.42 3.18 3.18 3.38 3.41

4.13 4.64 4.03 3.92 3.68 3.67 3.75 4.05

4.71 5.11 4.61 4.38 4.13 4.06 4.13 4.61

4.95 5.25 4.80 4.58 4.41 4.39 4.41 4.85

5.31 5.51 5.42 5.30 5.15 4.94 4.93 5.02

5.31 5.42 5.58 5.08 4.89 4.74 4.77 4.88

5.38 5.59 5.79 5.30 5.20 5.04 5.03 5.20

5.40 5.58 5.75 5.29 5.21 5.14 5.20 5.27

4.98 5.08 5.26 4.91 4.81 4.68 4.65 4.74

4.54 4.43 4.58 4.33 4.08 3.96 3.93 3.86

3.66 3.71 4.14 3.63 3.65 3.50 3.53 3.61

3.19 3.30 3.78 3.28 3.27 3.09 3.07 3.17

4.59 4.81 4.76 4.45 4.30 4.20 4.23 4.39

Table 11 Average solar resource (kWh/m2/day) at Tabasco State from AMEs data. Met station

Lat

Long

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Annual

AMEs_Paraiso

18.423


93.156

4.00

4.71

5.02

5.36

5.54

5.70

5.63

5.42

5.10

4.89

4.50

4.10

5.00

Table 12 Average solar resource (kWh/m2/day) at Campeche STATE from AMEs data. Met station

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

AMEs_Monclova AMEs_Calakmul AMEs_Escarcega AMEs_Carmen AMEs_Yohaltum AMEs_Campeche

18.057 18.365 18.608 18.648 19.014 19.836


90.821
89.893
90.754
91.823
90.311
90.507

3.70 3.80 3.75 3.77 3.74 3.78

4.25 4.45 4.29 4.52 4.40 4.43

4.56 4.89 4.76 5.04 4.91 4.97

5.54 5.89 5.68 6.11 5.93 6.01

5.69 5.84 5.62 5.90 5.77 5.78

5.70 6.01 5.81 5.99 5.90 5.95

5.77 5.88 5.80 5.81 5.75 5.72

5.83 5.82 5.75 5.67 5.68 5.64

5.19 5.18 5.07 5.11 5.06 5.04

4.91 5.02 4.64 5.01 4.81 4.78

3.64 3.72 3.54 3.69 3.59 3.59

3.30 3.37 3.28 3.32 3.28 3.31

4.84 4.99 4.83 5.00 4.90 4.92

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223

Fig. 5. Graph of monthly irradiation based on AME data in Campeche and Yucatan.

Table 13 Average solar resource (kWh/m2/day) at Yucatan state from AMEs data. Met station

Lat.

Long.

January

February

March

April

May

June

July

August

September

October

November

December

Annual

AMEs_Tantan AMEs_Oxkutz AMEs_Celest AMEs_Dzilam AMEs_Lagart AMEs_Tizimin

20.030 20.291 20.858 21.391 21.571 21.161


89.047
89.394
90.383
88.904
88.160
87.989

3.67 3.58 3.67 3.77 3.68 3.53

4.48 4.24 4.33 4.46 4.37 4.09

5.46 5.33 5.25 5.36 5.29 5.22

5.81 5.85 5.94 5.96 6.00 5.87

5.71 5.63 5.72 5.75 5.64 5.75

5.53 5.56 5.63 5.69 5.56 5.62

5.42 5.43 5.48 5.55 5.44 5.51

5.31 5.30 5.24 5.29 5.16 5.15

5.02 5.16 5.23 5.33 5.20 5.12

4.60 4.90 4.62 4.82 4.64 4.32

3.91 4.18 3.92 4.05 3.93 3.68

3.68 3.58 3.71 3.90 3.82 3.58

4.88 4.89 4.89 4.99 4.89 4.79

Table 14 Average solar resource (kWh/m2/day) per state from AMEs data. State

January

February

March

April

May

June

July

August

September

October

November

December

Annual

Tamaulipas Veracruz Tabasco Campeche Yucatan

3.57 3.45 4.00 3.76 3.65

4.53 3.98 4.71 4.39 4.33

5.27 4.47 5.02 4.86 5.32

5.74 4.71 5.36 5.86 5.91

6.18 5.20 5.54 5.77 5.70

6.60 5.08 5.70 5.89 5.60

7.33 5.31 5.63 5.79 5.47

7.14 5.36 5.42 5.73 5.24

6.22 4.89 5.10 5.11 5.18

4.18 4.21 4.89 4.86 4.65

3.29 3.68 4.50 3.63 3.94

2.77 3.27 4.10 3.31 3.71

5.23 4.47 5.00 4.91 4.89

Table 15 Forecasting of solar energy resource to 2020. Meteo point (SMSE)

Radiation year 2014 kWh/m2/day

ARIMA (radiation) year 2020 kWh/m2/day

Residuals

Standardized residuals

Absolute %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

4.815 4.681 4.722 4.736 4.683 4.691 4.673 4.828 4.685 4.664 4.650 4.598 4.670 4.588 4.513 4.600 4.930 4.442 4.326 4.154 4.147

4.686 4.761 4.667 4.696 4.706 4.669 4.674 4.662 4.771 4.670 4.655 4.645 4.609 4.659 4.602 4.549 4.610 4.843 4.498 4.417 4.296

0.129
0.08 0.055 0.04
0.023 0.022
0.001 0.166
0.086
0.006
0.005
0.047 0.061
0.071
0.089 0.051 0.32
0.401
0.172
0.263
0.149

0.815
0.509 0.346 0.257
0.143 0.142
0.006 1.059
0.548
0.036
0.032
0.301 0.391
0.452
0.565 0.327 2.038
2.553
1.099
1.672
0.944

2.68 1.71 1.16 0.84 0.49 0.47 0.02 3.44 1.84 0.13 0.11 1.02 1.31 1.55 1.97 1.11 6.49 9.03 3.98 6.33 3.59

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Table 15 (continued ) Meteo point (SMSE)

Radiation year 2014 kWh/m2/day

ARIMA (radiation) year 2020 kWh/m2/day

Residuals

Standardized residuals

Absolute %

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

4.230 4.111 4.074 4.441 4.111 4.588 4.633 4.425 4.872 5.032 4.809 4.739 4.644 4.749 4.694 4.621 4.604 4.745 4.662 4.673 4.727 4.786 4.747 4.699 4.787 4.873 4.778 4.655 4.685 4.716

4.291 4.350 4.265 4.239 4.498 4.265 4.602 4.633 4.486 4.802 4.915 4.757 4.708 4.641 4.715 4.676 4.625 4.613 4.712 4.654 4.662 4.700 4.741 4.713 4.680 4.742 4.802 4.736 4.649 4.670


0.061
0.239
0.191 0.202
0.387 0.323 0.031
0.208 0.386 0.23
0.106
0.018
0.064 0.108
0.021
0.055
0.021 0.132
0.05 0.019 0.065 0.086 0.006
0.014 0.107 0.131
0.024
0.081 0.036 0.046


0.386
1.518
1.220 1.282
2.460 2.052 0.200
1.327 2.453 1.467
0.673
0.119
0.406 0.685
0.134
0.353
0.130 0.842
0.319 0.123 0.417 0.547 0.036
0.092 0.685 0.833
0.153
0.516 0.231 0.294

1.44 5.81 4.69 4.55 9.41 7.04 0.67 4.70 7.92 4.57 2.20 0.38 1.38 2.27 0.45 1.19 0.46 2.78 1.07 0.41 1.38 1.80 0.13 0.30 2.24 2.69 0.50 1.74 0.77 0.98

Fig. 6. Map of irradiation average of January.

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worth mentioning a study on the direct coupling of a solar-hydrogen system, conducted by the Electrical Research Institute [54]. However, none of these studies has included an in-depth assessment of global solar energy resources, which is crucial for the appropriate development and implementation of this renewable energy. In spite of its huge potential, solar radiation has not yet been the subject of largescale evaluation in Mexico [55,56]. The goal of this work was to assess global solar resources (in terms of global solar irradiation) for the Mexican states along the Gulf of Mexico.

2. Area studied The area studied comprised the Mexican states situated along the Gulf of Mexico. The global solar irradiation data analyzed were obtained from 27 stations belonging to the AMEs of the SMS, with recordings made every 10 min over a period of 10 years, from 2001 to 2011 [57]. In addition, data from the SMSE of NASA were also used, with data gathered at 51 points by means of monthly recordings obtained over a period of 22 years up to 2011 [58]. Both the AME stations and the SMSE points are situated along the Gulf of Mexico. The geographical locations of the AMEs and the SMSE stations in the Mexican Gulf Coast states used in the present study are presented in Fig. 1.

225

Table 1 presents all of the maximum and minimum recordings and the corresponding standard deviations for the stations and the points studied.

3. Methods A statistical analysis was performed to evaluate data obtained from SMSE and data measured by AMEs to determine whether the measured data are appropriate for use in developing global solar resource maps of the Gulf of Mexico. The method utilized in this work was the Autoregressive Integrated Moving Average (ARIMA) [59]. The precision of the models was determined by calculating the statistics of error as the coefficient of determination (R2) for the differences between the predicted values and the values measured for the year 2011. The analysis of regression based on R2 determined the relationship among data obtained by the stations studied to validate the applicability of the data in this work [59]. The regression model provides tools for determining the estimators of the squared minimum of the ordinate at the origin (β^ 0 ) and the slope of the straight line (β^ 1 ).

Fig. 7. Map of irradiation average of February.

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The adjusted model of simple linear regression used in this work is expressed as follows: y^ ¼ β^ 0 þ β^ 1 ðx
xÞ

ð1Þ

The models of the ARIMA family allow for the representation, in a synthetic manner, of phenomena that vary with time and for the prediction of future values within a certain confidence interval [60]. The mathematical expression of the ARIMA models differs from one author to another [61–63]. The law in Mexico establishes that in the year 2020, 30% of energy will come from renewable energy, and the ARIMA model can forecast the amount of solar resources for that year. Once data measured at the AMEs are validated with data obtained from SMES, they are employed to develop maps using a GIS tool. Using a methodology based on the Kriging spatial interpolation method to represent the values obtained in a GIS makes it possible to obtain spatial databases from continuous measurements made at isolated stations [64]. Kriging is an advanced geo-statistical procedure that generates a surface estimated from a disperse set of points with z values [65]. It ponders over the measured values to derive a prediction for an unmeasured location. The general formula for the two interpolators is

formed as a prudent sum of the data: n

Z^ ðS0 Þ ¼ ∑ λj Z ðSi Þ i¼1

ð2Þ

where Z(Si) is the value measured at position i place, λi is an unknown weight for the value measured at position i, S0 is the predicted location, and n is the number of measured values. With the Kriging method, the considerations are based not only on the distance between the points of measurement and the predicted locations but also on the general spatial disposition of the points measured. To utilize the spatial disposition of values, the spatial auto-correlation should be quantified. In Kriging, therefore, the value λi depends on a model adjusted to the points of measurement, the distance to the location of prediction, and the spatial relations between the values measured around the location of prediction [66].

4. Results The coefficient R2 was determined to adjust data regarding global solar irradiation obtained from AMEs and SMSE. Both sets of data, the AME and SMSE data, were employed at the same geographical locations to compare them, that is, at locations of

Fig. 8. Map of irradiation average of March.

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227

Fig. 9. Map of irradiation average of April.

the 27 AMEs. Table 2 shows the coefficients R2 between data from the AMEs and the SMSE and coefficients for Eq. (1). As shown in Table 2, the R2 coefficients are greater than 90% of the linear relation, except for the coefficient for Barra del Tordo, which is equal to 89.63%. These results validate the use of the data studied. The averages solar resources (kWh/m2/day) determined from the SMSE data are shown for Tamaulipas State (Table 3 and Fig. 2), Veracruz State (Table 4, and Fig. 2), Tabasco State (Table 5 and Fig. 2), Campeche State (Table 6 and Fig. 3), and Yucatan State (Table 7 and Fig. 3). To summarize these results, Table 8 shows the average obtained for each state along the Gulf of Mexico using SMSE data. The averages solar resources (kWh/m2/day) obtained from the AME data are shown for Tamaulipas State (Table 9 and Fig. 4), Veracruz State (Table 10 and Fig. 4), Tabasco State (Table 11 and Fig. 4), Campeche State (Table 12 and Fig. 5), and Yucatan State (Table 13 and Fig. 5). To summarize these results, Table 14 shows the average obtained at each state along the Gulf of Mexico using AME data. An ARIMA model forecasting the solar radiance in the year 2020 was developed based on data recorded from 51 meteorological stations (SMSE). Table 15 presents these data, the corresponding predictions computed with the model, and the residuals. Predictions were computed for the validation data and forecasts for future values. Standard deviations and confidence intervals were computed for validation predictions and forecasts. The last

column of Table 15 presents the absolute deviation as a percent between the 2014 data and the 2020 forecast data, where the average of the deviation obtained is less than 2.5%. With the evaluated and adjusted data, an interpolation was performed using Kriging techniques. These interpolations were represented using a GIS, and 13 maps were obtained, one per month, as well as the annual average global solar resources for Mexican states along the Gulf of Mexico. These figures demonstrate the solar irradiation potential of the region under study. Every map from Figs. 6–17 shows the representative behavior of the average global solar resource each month during a year. In addition, Fig. 18 shows the average annual global solar resource. Based on the data presented in Figs. 6–18, the following results are highlighted.

 Fig. 6 shows that in the month of January the north of



Tamaulipas State registers the lowest global solar resource, between 2.92 kWh/m2/day and 3.23 kWh/m2/day. On the other hand, the south of Veracruz, all of Tabasco and Campeche, and part of Yucatan have the highest global solar resource, greater than 4.07 kWh/m2/day. In Fig. 7, corresponding to February, it can be observed that the southeast region of Mexican states along the Gulf of Mexico has a solar resource greater than 4.56 kWh/m2/day. The areas with

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Fig. 10. Map of irradiation average of May.









the lowest resources are the central part of Veracruz and the north of Tamaulipas, with recordings of 3.75 kWh/m2/day to 3.93 kWh/m2/day. The highest global solar resource for the month of March, greater than 5.09 kWh/m2/day (see Fig. 8), is registered at the south of Veracruz, part of Tabasco, Campeche, and the north of Yucatan, whereas the lowest global solar resource, from 4.13 kWh/m2/day to 4.44 kWh/m2/day, is registered at the center of the state of Veracruz. According to the results obtained for the month of April, indicated in Fig. 9, the highest global solar resource (greater than 5.45 kWh/m2/day) is recorded in the east and west of Tamaulipas, the south of Veracruz, Campeche, and the north of Yucatan, and the lowest global solar resource is registered at the center of Veracruz. Fig. 10 presents the average global solar resource in May, where the highest resource, greater than 5.52 kWh/m2/day, is located at the north of Tamaulipas, the south of Veracruz, the north of Campeche, and all of Yucatan, and the lowest resource is at the center of Veracruz, 4.58 kWh/m2/day to 4.92 kWh/m2/day. As shown in Fig. 11, in the month of June, the global solar resource of the entire region is over 4.53 kWh/m2/day, 0.05 kWh/ m2/day less than that in the month of May. In this month, the maximum solar global resource of 5.89 kWh/m2/day occurs at the









north of Tamaulipas, and the lowest occurs at the center of Veracruz and the south of Tabasco and Campeche. The lowest solar resource in the month of July, between 4.69 kWh/m2/day and 5.11 kWh/m2/day (see Fig. 12), covers the state of Veracruz and Campeche, and the highest solar global resource, greater than 6.22 kWh/m2/day, is registered at the north of Tamaulipas. This month presents the highest solar resource of the whole year. As is indicated in Fig. 13, in the month of August, the lowest resource, from 4.86 kWh/m2/day to 5.08 kWh/m2/day, is presented at the center of Veracruz, and the highest resource, greater than 5.82 kWh/m2/day, is in Tamaulipas. Fig. 14 shows the results for the month of September. The areas highlighted for this month are the same as those highlighted in the month of August, but different values are obtained, i.e., the highest resource, greater than 5.12 kWh/m2/day, is in Tamaulipas, and the lowest resource, from 4.45 kWh/m2/day to 4.60 kWh/m2/day, at in the center of Veracruz. Fig. 15 shows that in the month of October the solar resource presents a significant decrease. In this case, the highest resource is greater than 4.72 kWh/m2/day and occurs at the south of Veracruz and the east of Tamaulipas, whereas the lowest resource, between 3.76 kWh/m2/day and 4.12 kWh/m2/ day, is presented at the center of Veracruz.

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229

Fig. 11. Map of irradiation average of June.

 The lowest resource in the month of November, between





3.33 kWh/m2/day and 3.58 kWh/m2/day (see Fig. 16), is registered at the north of Tamaulipas, and the highest resource, greater than 4.19 kWh/m2/day, is obtained at the south of Veracruz, Tabasco, Campeche, and Yucatan. The data for the month of December are presented in Fig. 17. As can be observed, the lowest resource, from 2.78 kWh/m2/day to 3.05 kWh/m2/day, is located at the north of Tamaulipas, and the highest, greater than 3.85 kWh/m2/day, covers the south of Veracruz, Tabasco, Campeche, and the north of Yucatan. This month shows the lowest results of the whole year. Finally, Fig. 18 presents the average annual global resource of Mexican states along the Gulf of Mexico. As can be noted in this figure, the lowest resource, from 4.08 kWh/m2/day to 4.33 kWh/m2/day, covers the center of Veracruz. On the other hand, the zones with the highest solar resource, greater than 4.85 kWh/m2/day, are the north of Tamaulipas, the south of Veracruz, the east of Tabasco, the north of Campeche, and Yucatan. The results obtained in this work are similar to those reported in a previous study [55].

Based on the results of previous studies, such as those of Pedro and Coimbra [24], in which non-exogenous data regarding global solar radiation were used, this study allowed for the use of data obtained from the SMSE that were validated using adequate recordings from the AMEs, based on the values of the coefficient R2. The solar resources of the Mexican nation have not yet been evaluated [56]. However, in this work, the solar resources held by Mexican states situated along the Gulf of Mexico were examined. As a consequence, this work contributes to the evaluation of solar irradiation throughout the country. It also confirms the concept expounded by Riveros-Rosas et al. [51], who indicated that Mexico enjoys an excellent general position for utilizing the solar energy received throughout most of the year. A GIS was used to generate solar resource maps of the area studied using SMSE and AMEs data, as in previous studies [30,38,42]. As previously mentioned [65], Kriging interpolation made it possible to obtain the desired interpolation among the points of study. The consensus is that the average annual solar irradiation registered in the area studied is greater than 4.85 kWh/m2/day [55]. It was also demonstrated that the month with the highest solar resource was July, with a

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Fig. 12. Map of irradiation average of July.

maximum energy greater than 6.22 kWh/m2/day at northern Tamaulipas. Assessing the solar resources of the Mexican states along the Gulf of Mexico allowed for the identification of the areas with the highest potential. Indeed, it was observed that the southeastern states possess great solar energy potential throughout most of the year. Regarding the average annual energy, the south of Veracruz has the greatest solar resource, with 4.85 kWh/m2/day. This result agrees with that of another study reported in the literature [55]. If the months with the highest global solar irradiation, July and August, are considered, the northern states are those with the best resources, reaching up to 6.22 kWh/m2/day and 5.82 kWh/m2/day, respectively. The month with the lowest solar resource is December, with a minimum energy of 2.78 kWh/m2/day presented at the north of Tamaulipas. Table 16 presents a comparison of the states along the Gulf of Mexico, in terms of monthly and annual average daily values of global solar radiation, with several representative cities worldwide, covering some of the most interesting areas for the deployment of solar technologies. To facilitate the comparison, the last column presents the relative annual solar resource, that is, the coefficient between every site's annual average global solar radiation and that of the Gulf of Mexico. Hence, values greater than the unit value designate locations with greater solar resources than

the Gulf of Mexico, and values less than the unit value indicate areas with relatively fewer solar resources. As shown in Table 16, only Phoenix (USA), Darwing (Australia), and Nouak Chott (Mauritania) present a significantly higher solar potential than that of the Gulf of Mexico. On the other hand, the region studied in this paper has a similar solar potential to other cities that are in the same area (such as San Juan in Puerto Rico and Fresno, Denver, Reno, and Austin in the United States), in South America (Maceió in Brazil and Buenos Aires in Argentina), in the Mediterranean area (Marrakech, Gardaïa, and Cairo in North Africa and Almería in Spain), and in the Middle East (Amman in Jordan and Abu Dhabi in UAE). Special attention can be drawn to areas such as the south of the United States and Spain, where the solar resources are similar to those of the Gulf of Mexico but the areas currently have much higher solar capacity installed. According to a study performed by the International Energy Agency [79], the thermal collector area in operation by the end of 2010 was 0.013 m2/inhabitant in Mexico, whereas it was 0.071 m2/inhabitant in the United States and 0.052 m2/inhabitant in Spain. In addition, the European Photovoltaic Industry Association published a study [80] indicating that the photovoltaic power installed in 2009 was 1.55 W/inhabitant in the United States and 1.48 W/ inhabitant in Spain, whereas the photovoltaic power installed in

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231

Fig. 13. Map of irradiation average of August.

Mexico was 3 times lower, 0.45 W/inhabitant [81]. Therefore, considering these data, it may be asserted that political strategies in Mexico regarding solar energy should reproduce the mechanisms followed in the United States and Spain [82]. Finally, the results presented in Table 16 demonstrate that the Gulf of Mexico exhibits a solar potential that is considerably superior to that of cities such as Adana (Turkey) and Calcuta (India) and European cities such as Athens, Stuttgart, and Brussels. In the case of European countries, the comparison with Mexico with respect to installed capacity is remarkable because despite the lower solar resource, the thermal collector area in operation by the end of 2010 was 0.361 m2/inhabitant in Greece, 0.173 m2/ inhabitant in Germany, and 0.033 m2/inhabitant in Belgium [79], and the photovoltaic power installed in 2009 was 3.19 W/inhabitant in Greece, 46.47 W/inhabitant in Germany, and 85.37 W/ inhabitant in Belgium [80]. These data suggest that the imitation of these countries is strongly recommended.

5. Conclusions Solar resources in Mexico have not been sufficiently exploited compared with other sources of renewable energy, such

as hydraulic, geothermal, and wind power. As a renewable source of energy, the development of solar can certainly contribute to the energetic structure of the nation and to a reduction of greenhouse gases, especially of CO2, in compliance with the new state-wide law requiring the reduction of such emissions by 30–50% in the coming years. The enormous solar potential of the Mexican states along the Gulf of Mexico has been corroborated in this work. According to results presented in this work, solar energy has significant potential to complement energy needs in the Mexican states along Gulf of Mexico. It can be asserted that political strategies in Mexico regarding solar energy should reproduce the mechanisms followed in the United States and Spain. Therefore, it is recommended that the government adopt policies of support that will motivate people to use solar energy to save the reserves of fossil energy. The Law of Renewable Energy in Mexico established that in 2020 the country will have to generate 30% of its electricity from renewable energy sources; in this study, it was observed that irradiation levels in 2020 will be the same as those measured today and could be used to generate power. Thus, the government should, for example, introduce subsidies to customers for solar water heaters or solar photovoltaic systems to reduce their price and thus make them more competitive and accessible to the public.

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Fig. 14. Map of irradiation average of September.

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Fig. 15. Map of irradiation average of October.

233

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Fig. 16. Map of irradiation average of November.

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Fig. 17. Map of irradiation average of December.

235

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Fig. 18. Map of annual irradiation average.

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Table 16 Comparison of Gulf of Mexico monthly and annual average daily values of global solar radiation with other countries (kWh/m2/day). Location

Country

Jan

Feb

Mar Apr

May Jun

Jul

Aug Sep

Oct

Nov Dec

Annual Reference Relative annual solar resource

Brussels Stuttgart Adana Calcuta Athens Austin (TX) Kuala Lumpur Durban Buenos Aires Sydney The Gulf of Mexico Madrid Denver (CO) Abu Dhabi

Belgium Germany Turkey India Greece USA Malaysia South Africa Argentina Australia Mexico Spain USA United Arab Emirates Puerto Rico Egypt Spain Mozambique USA Morocco USA Brazil Jordan Algeria USA Australia Mauritania

0.64 0.96 1.72 3.22 1.83 2.73 4.28 6.39 7 6.25 3.71 2.27 2.65 3.91

1.26 1.66 2.33 4.35 2.61 3.55 4.69 6.14 6.36 5.67 4.39 3.25 3.55 4.84

2.2 2.69 3.31 5.09 3.78 4.51 4.81 4.77 5.14 4.81 4.94 4.65 4.83 5.73

3.44 4.05 3.72 5.41 5.02 5.06 4.92 3.86 3.73 3.78 5.37 5.75 5.93 6.33

4.57 4.96 4.69 5.61 6.27 3 4.47 3.64 2.69 2.75 5.49 6.6 6.73 6.96

4.92 5.12 4.89 4.39 6.87 6.53 4.42 2.81 2.06 2.42 5.44 7.74 7.41 6.84

4.64 5.43 5.14 4.29 6.91 6.64 4.42 3.17 2.27 2.97 5.6 8.04 7.17 6.81

4.09 4.48 5.06 4.09 6.19 6.09 4.42 3.71 3.19 3.61 5.55 7 6.45 6.53

3.02 3.63 4.47 3.99 4.89 5.06 4.44 4.1 4.17 4.69 5.05 5.47 5.44 6.2

1.74 2.22 3.03 3.48 3.39 4.2 4.39 4.07 5.26 5.53 4.51 3.56 4.1 5.39

0.83 1.09 2.03 3.93 2.27 3.11 4 4.85 6.65 6.25 3.87 2.43 2.79 4.24

0.49 0.76 1.42 3.39 1.69 2.6 4.03 5.93 6.87 6.95 3.47 1.87 2.31 3.14

2.65 3.09 3.47 4.27 4.31 4.42 4.44 4.45 4.62 4.64 4.78 4.88 4.95 5.13

[12] [12] [67] [68] [12] [12] [69] [70] [12] [71] ––– [72] [12] [73]

0.55 0.65 0.73 0.89 0.9 0.92 0.93 0.93 0.97 0.97 1 1.02 1.04 1.07

4.18 3.11 2.84 6.9 2.07 3.4 2.53 6.36 2.7 3.52 3.22 5.64 5.7

4.84 3.97 3.72 6.6 3.19 4.2 3.63 6.17 3.7 4.79 4.33 5.67 6.2

5.64 4.94 4.93 5.8 4.94 5.2 5.2 6.14 5 6.19 5.72 5.64 7.2

5.96 6.17 6.52 4.9 6.6 6 6.81 5.47 6.8 7.07 7.43 6.33 7.8

5.72 7.2 7.21 4.1 7.83 6.7 7.96 4.39 7.8 7.55 8.44 5.56 7.7

5.73 7.45 7.94 3.8 8.62 7.3 8.52 4.25 8.4 8.07 8.64 5.47 7.7

5.91 7.2 7.89 3.8 8.46 7.6 8.49 4.11 8.2 7.1 7.84 5.75 7.2

5.8 6.67 7.02 4.5 7.64 7 7.59 4.67 7.5 5.61 7.23 6.14 7.1

5.28 5.89 5.71 5 6.26 5.9 6.3 5.81 6.4 5.6 6.36 6.56 6.7

4.78 4.44 4.15 5.7 4.51 4.6 4.51 6.11 4.8 4.87 4.97 6.75 6.3

4.31 3.39 3.02 6 2.8 3.6 2.88 6.67 3.6 4.17 3.63 6.64 5.5

3.9 2.92 2.46 6.8 1.81 3.2 2.23 6.53 2.7 3.29 2.94 6.25 5.1

5.17 5.28 5.29 5.3 5.39 5.4 5.55 5.56 5.6 5.65 5.9 6.03 6.7

[12] [74] [72] [75] [12] [76] [12] [77] [76] [78] [12] [71] [76]

1.08 1.1 1.11 1.11 1.13 1.13 1.16 1.16 1.17 1.18 1.23 1.26 1.4

San Juan Cairo Almería Maputo Fresno (CA) Marrakech Reno (NV) Maceió Amman Ghardaïa Phoenix (AZ) Darwing Nouak Chott

References [1] Bose B. Global energy scenario and impact of power electronics in 21st century. IEEE Trans Ind Electron 2013;60:2638–51. [2] Baños R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J. Optimization methods applied to renewable and sustainable energy: a review. Renew Sustain Energy Rev 2011;15:1753–66. [3] Cruz-Peragon F, Palomar JM, Casanova PJ, Dorado MP, Manzano-Agugliaro F. Characterization of solar flat plate collectors. Renew Sustain Energy Rev 2012;16:1709–20. [4] Ssen Z. Solar energy in progress and future research trends. Prog Energy Combust Sci 2004;367:16–30. [5] Manzano-Agugliaro F, Escobedo QCH, Sierra AJZ. Use of bovine manure for ex situ bioremediation of diesel contaminated soils in Mexico. Inf Tec Econ Agrar 2010;106:197–207. [6] IEA. CO2 emissions from fuel combustion. Paris, France: International Energy Agency; 2010 (p. 512). [7] Wang RZ, Zhai XQ. Development of solar thermal technologies in China. Energy 2010;16:4407–35. [8] Bannani FK, Sharif TA, Ben-Khalifa A. Estimation of monthly average solar radiation in Libya. Theor Appl Climatol 2006;83:211–5. [9] Montoya FG, Montoya MG, Gómez J, Manzano-Agugliaro F, AlamedaHernández E. The research on energy in Spain: a scientometric approach. Renew Sustain Energy Rev 2014;29:173–83. [10] Diodato N, Bellocchi G. Modelling solar radiation over complex terrains using monthly climatological data. Agric For Meteorol 2007;144:111–26. [11] Czekalski D, Chochowski A, Obstawski P. Parameterization of daily solar irradiation variability. Renew Sustain Energy Rev 2012;16:2461–7. [12] Duffie JA, Beckman WA. Solar engineering of thermal processes. third ed.. New York: John Wiley and Sons; 2006. [13] Mills D. Advances in solar thermal electricity technology. Sol Energy 2004;76:19–31. [14] Fernández-García A, Zarza E, Valenzuela L, Pérez M. Parabolic-trough solar collectors and their applications. Renew Sustain Energy Rev 2010;14: 1695–721. [15] Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, Rome; 1998. p. 56. [16] Allen RG, Trezza R, Tasumi M. Analytical integrated functions for daily solar radiation slopes. Agric For Meteorol 2006;139:55–73. [17] Ertekin C, Evrendilek F. Spatiotemporal modeling of global solar radiation dynamics as a function of sunshine duration for Turkey. Agric For Meteorol 2007;145:33–47. [18] Sahin AD, Sen Z. Statistical analysis of the Angstrôm formula coefficients and application for Turkey. Sol Energy 1998;62:29–38.

[19] Badescu V, Gueymard CA, Cheval S, Oprea C, Baciu M, Dumitrescu A, et al. Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models. Renew Sustain Energy Rev 2012;16:1636–56. [20] Gurel AE, Ergun A. Estimation of global solar radiation on horizontal surface using meteorological data. Energy Educ Sci Technol Part A – Energy Sci Res 2012;28:941–8. [21] Polo J, Zarzalejo LF, Cony M, Navarro AA, Marchante R, Martin L, et al. Solar radiation estimations over India using Meteosat satellite images. Sol Energy 2011;85:2395–406. [22] Zelenka A, Perez R, Seals R, Renne D. Effective accuracy of satellite-derived hourly irradiations. Theor Appl Climatol 1999;62:199–207. [23] Vignola F, Harlan P, Perez R, Kmiecik M. Analysis of satellite derived beam and global solar radiation data. Sol Energy 2007;81:768–72. [24] Pedro HTC, Coimbra CFM. Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol Energy 2012;86:2017–28. [25] Hernández-Escobedo Q, Manzano-Agugliaro F, Zapata-Sierra A. The wind power of Mexico. Renew Sustain Energy Rev 2010;14:2830–40. [26] Sliz-Szkliniarz B, Vogt J. GIS-based approach for the evaluation of wind energy potential: a case study for the Kujawsko-Pomorskie Voivodeship. Renew Sustain Energy Rev 2011;15:1696–707. [27] Aydin NY, Kentel E, Duzgun S. GIS-based environmental assessment of wind energy systems for spatial planning: a case study from Western Turkey. Renew Sustain Energy Rev 2010;14:364–73. [28] Hernández-Escobedo Q, Saldaña-Flores R, Rodríguez-García ER, ManzanoAgugliaro F. Wind energy resource in Northern Mexico. Renew Sustain Energy Rev 2014;32:890–914. [29] Ramachandra TV, Shruthi BV. Wind energy potential mapping in Karnataka, India, using GIS. Energy Convers Manag 2005;46:1561–78. [30] Su MC, Kao NH, Huang WJ. Potential assessment of establishing a renewable energy plant in a rural agricultural area. J Air Waste Manag Assoc 2012;62:662–70. [31] Agugliaro FM. Gasification of greenhouse residues for obtaning electrical energy in the south of spain: localization by GIS. Interciencia 2007;32:131–6. [32] Viana H, Cohen WB, Lopes D, Aranha J. Assessment of forest biomass for use as energy. GIS-based analysis of geographical availability and locations of woodfired power plants in Portugal. Appl Energy 2010;2551:60–87. [33] Gil MV, Blanco D, Carballo MT, Calvo LF. Carbon stock estimates for forests in the Castilla y León region, Spain. A GIS based method for evaluating spatial distribution of residual biomass for bio-energy. Biomass Bioenergy 2011;35: 243–52. [34] Hiloidhari M, Baruah DC. Rice straw residue biomass potential for decentralized electricity generation: a GIS based study in Lakhimpur district of Assam, India. Energy Sustain Dev 2011;15:214–22. [35] Zhang F, Johnson DM, Sutherland JW. A GIS-based method for identifying the optimal location for a facility to convert forest biomass to biofuel. Biomass Bioenergy 2011;35:3951–61.

238

Q. Hernández-Escobedo et al. / Renewable and Sustainable Energy Reviews 43 (2015) 216–238

[36] Ghilardi A, Guerrero G, Masera O. A GIS-based methodology for highlighting fuelwood supply/demand imbalances at the local level: a case study for Central Mexico. Biomass Bioenergy 2009;33:957–72. [37] Moreno A, Gilabert MA, Martinez B. Mapping daily global solar irradiation over Spain: a comparative study of selected approaches. Solar Energy 2011;85: 2072–84. [38] Arnette AN, Zobel CW. Spatial analysis of renewable energy potential in the greater southern Appalachian mountains. Renew Energy 2011;36:2785–98. [39] Gastli A, Charabi Y. Solar electricity prospects in Oman using GIS-based solar radiation maps. Renew Sustain Energy Rev 2010;14:790–7. [40] Liu M, Bárdossy A, Li J, Jiang Y. GIS-based modelling of topography-induced solar radiation variability in complex terrain for data sparse region. Int J Geogr Inf Sci 2012;26:1281–308. [41] Vance E. Mexico sets climate targets. Nature 2012;484:430. [42] Choi Y, Rayl J, Tammineedi C, Brownson JRS. PV analyst: coupling ArcGIS with TRNSYS to assess distributed photovoltaic potential in urban areas. Sol Energy 2011;85:2924–39. [43] Janke JR, Multicriteria GIS. Modeling of wind and solar farms in Colorado. Renew Energy 2010;35:2228–34. [44] Monthly Electricity Statistics. International Energy Agency; March 2013. 〈http://www.iea.org/stats/index.asp〉. [45] Pastor J., Liu Y. Hydrokinetic energy: overview and it's renewable energy potential for the Gulf of Mexico. In: IEEE Green Technologies Conference, art. no. 6200995; 2012. [46] De Jesus Ramos-Gutierrez L, Montenegro-Fragoso M. Hydroelectric centers in Mexico: past, present and future. Tecnol Cienc Agua 2012;3:103–21. [47] Easley E, Garchar L, Bennett M, Beasley B, Woolf R, Joyce H. Investigation of geothermal resource potential in the Northern Rio Grande Rift. Colo N M Trans – Geotherm Resour Counc 2011;35:761–7. [48] Hernandez-Escobedo Q, Manzano-Agugliaro F, Gazquez-Parra JA, ZapataSierra A. Is the wind a periodical phenomenon? The case of Mexico Renew Sustain Energy Rev 2011;15:721–8. [49] Valdez-Vazquez I, Acevedo-Benítez JA, Hernández-Santiago C. Distribution and potential of bioenergy resources from agricultural activities in Mexico. Renew Sustain Energy Rev 2010;14:2147–53. [50] Lokey E. Barriers to clean development mechanism renewable energy projects in Mexico. Renew Energy 2008;34:504–8. [51] Riveros-Rosas D, Herrera-Vázquez J, Pérez-Rábago CA, Arancibia-Bulnes CA, Vázquez-Montiel S, Sánchez-González M, et al. Optical design of a high radiative flux solar furnace for Mexico. Sol Energy 2010;84:792–800. [52] Chavez-Galan J, Almanza R. Solar filters based on iron oxides used as efficient windows for energy savings. Sol Energy 2007;81:13–9. [53] Bermudez-Contreras A, Thomson M, Infield DG. Renewable energy powered desalination in Baja California Sur, Mexico. Desalination 2008;220:431–40. [54] Arriaga LG, Martínez W, Cano U, Blud H. Direct coupling of a solar-hydrogen system in Mexico. Int J Hydrogen Energy 2007;32:2247–52. [55] Cancino-Solorzano Y, Villicana-Ortiz E, Gutierrez-Trashorras AJ, XibertaBernat J. Electricity sector in Mexico current status. Contribution of renewable energy sources. Renew Sustain Energy Rev 2010;14:454–61. [56] Huacuz J. The road to green power in Mexico-reflections on the prospects for the large-scale and sustainable implementation of renewable energy. Energy Policy 2005;33:2087–99. [57] SMN, Servicio Meteorológico Nacional. 〈http://smn.cna.gob.mx/emas/〉 [visited 29.08.12]. [58] SMSE, Surface Meteorology and Solar Energy. 〈http://eosweb.larc.nasa.gov/ sse/〉 [visited 31.08.12]. [59] Brockwell PJ, Davis RA. Introduction to time series and forecasting. New York: Springer; 2002.

[60] Box GEP, Jenkins GM, Reinsel GC. Time series analysis: forecasting and control. fourth ed. Hoboken, New Jersey: Wiley; 2008. [61] Brockwell PJ, Davis RA. Introduction to time series and forecasting. 2nd ed. New York: Springer Verlag; 2002. [62] Brockwell PJ, Davis RA. Time series: theory and methods. 2nd ed.. New York: Springer Verlag; 1991. [63] Fuller WA. Introduction to statistical time series. second ed.. New York: John Wiley & Sons; 1996. [64] Alsamamra H, Ruiz-Arias JA, Pozo-Vázquez D, Tovar-Pescador J. A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agric For Meteorol 2009;149:1343–57. [65] Burrough PA. Principles of geographical information systems for land resources assessment. New York: Oxford University Press; 1986. [66] Jerosch K, Schluter M, Pesch R. Spatial analysis of marine categorical information using indicator kriging applied to georeferenced video mosaics of the deep-sea Hakon Mosby Mud Volcano. Ecol Inform 2006;1:391–406. [67] Ogulata RT, Ogulata SN. Solar radiation on Adana, Turkey. Appl Energy 2002;71:351–8. [68] Katiyar AK, Pandey CK. Simple correlation for estimating the global solar radiation on horizontal surfaces in India. Energy 2010;35:5043–8. [69] Muzathik AM, Wan Nik WNM, Samo K, Ibrahim MZ. Reference solar radiation year and some climatology aspects of east coast of west Malaysia. Am J Eng Appl Sci 2010;3(2):293–9. [70] Zawilska E, Brooks MJ. An assessment of the solar resource for Durban, South Africa. Renew Energy 2011;36:3433–8. [71] Morrison GL, Litvak A. Condensed solar radiation data base for Australia. Solar Thermal Energy Laboratory, University of New South Wales; 1999 (Report no. 1). [72] Sancho Ávila JM, Riesco Martín J, Jiménez Alonso C, Sánchez de Cos Escuin MC, Montero Cadalso J, López Bartolomé M. Atlas de Radiación Solar en España utilizando datos del SAF de Clima de EUMETSAT; 2013. 〈http://www.aemet.es/ documentos/es/serviciosclimaticos/datosclimatologicos/atlas_radiacion_solar/ atlas_de_radiacion_24042012.pdf〉 [accessed March 2014]. [73] Islam MD, Kubo I, Ohadi M, Alili AA. Measurement of solar energy radiation in Abu Dhabi, UAE. Appl Energy 2009;86:511–5. [74] Trabea AA, Shaltout MAM. Correlation of global solar radiation with meteorological parameters over Egypt. Renew Energy 2000;21:297–308. [75] Cuamba BC, Chenene ML, Mahumane G, Quissico DZ, Lovseth J, O'Keefe P. A solar energy resources assessment in Mozambique. J Energy South Afr 2006;14(4):76–85. [76] Alnaser WE, Eliagoubi B, Al-Kalak A, Trabelsi H, Al-Maalej M, El-Sayed HM, et al. First solar radiation atlas for the Arab world. Renew Energy 2004;29: 1085–107. [77] De Souza JL, Nicacio RM, Moura MAL. Global solar radiation measurements in Maceió, Brazil. Renew Energy 2005;30:1203–20. [78] Gairaa K, Benkaciali A. Analysis of solar radiation measurements at Ghardaïa area, South Algeria. Energy Procedia 2011;6:122–31. [79] Mauthner F, Weiss W. Solar heat worldwide, markets and contribution to the energy supply 2011. Gleisdorf: SHC, IEA; 2013. [80] Despotou E, El Gammal A, Fontaine B, Fraile Montoro D, Latour M, Latour S, et al. Global market outlook for photovoltaics until 2014. EPIA: Brussels; 2010. [81] Mundo-Hernández J, de Celis Alonso B, Hernández-Álvarez J, de Celis-Carrillo B. An overview of solar photovoltaic energy in Mexico and Germany. Renew Sustain Energy Rev 2014;31:639–49. [82] Montoya FG, Aguilera MJ, Manzano-Agugliaro F. Renewable energy production in Spain: a review. Renew Sustain Energy Rev 2014;33:509–31.