├── LICENSE ├── README.md ├── ass_res ├── CHN_adm_shp │ └── CHN_adm_shp.txt ├── __init__.py ├── ass_singlesite_lsy.py ├── ass_singlesite_ws.py ├── ass_widearea_dis.py ├── ass_widearea_wsy.py ├── draw_geo_fig.py ├── init_nasa_solar.py ├── init_nasa_wind.py ├── sd_solar_data │ └── sd_solar_data.txt ├── sd_temp_data │ └── sd_temp_data.txt └── sd_wind_data │ └── sd_wind_data.txt └── freq_a2c ├── README.md ├── c0122.out └── dsusr.dll /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PowerXLab-Tools 2 | ## Simulation and Computing Toolkit for Power System with High Penetration of Renewable Energy Sources 3 | Maintainer: Yongji Cao yongji@sdu.edu.cn from Shandong University, China. 4 | ## Introduction 5 | ### QSAT V2.1 6 | Quantitative Synergy Assessment Toolkit for Renewable Energy Sources 7 | A software toolbox developed in Python for assessment and visualization of the spatiotemporal characteristics of 8 | regional renewable energy sources (wind and solar energy) to support preliminary regional planning. 9 | - Requirements 10 | PYTHON 2.7 (or higher) 11 | NUMPY (PYTHON package) 12 | MATPLOTLIB (PYTHON package) 13 | BASEMAP (PYTHON package) 14 | SCIPY (PYTHON package) 15 | PYHDF (PYTHON package) 16 | SKLEARN (PYTHON package) 17 | METEOROLOGICAL DATA (Data) 18 | GEOGRAPHICAL DATA (Data) 19 | IN-SITU MEASURED DATA for CORRECTION (Data) 20 | ### AFAC V2.3 21 | Auxiliary Decision-Making Toolkit for Power System Frequency stability Analysis and Control 22 | A software toolbox developed in Python and Fortran for frequency stability analysis and control of 23 | the power system with high penetration of renewable energy sources. 24 | - Requirements 25 | PYTHON 2.7 (or higher) 26 | PSS/E 33.1 (or higher) or STEPS (https://github.com/changgang/steps) 27 | NUMPY (PYTHON package) 28 | MATPLOTLIB (PYTHON package) 29 | SCIPY (PYTHON package) 30 | SKLEARN (PYTHON package) 31 | POWER FLOW RAW DATA FILES (PSS/E Input Data Files) 32 | DYNAMICS DATA FILES (PSS/E Input Data Files) 33 | ### DLPF V1.1 34 | Deep Learning-Based Toolbox for Power Flow Analysis 35 | A software toolbox developed in Python for optimal search direction and high-convergence and 36 | high-efficiency power flow analysis. 37 | - Requirements 38 | PYTHON 3.9 (or higher) 39 | TKINTER (PYTHON package) 40 | PANDAS (PYTHON package) 41 | TIME (PYTHON package) 42 | SUBPROCESS (PYTHON package) 43 | SKLEARN (PYTHON package) 44 | JOBLIB (PYTHON package) 45 | NUMPY (PYTHON package) 46 | PYPOWER (PYTHON package) 47 | TQDM (PYTHON package) 48 | MATPLOTLIB (PYTHON package) 49 | PYPOWER (PYTHON package) 50 | TQDM (PYTHON package) 51 | TRAIN-LABEL (Data) 52 | TEST-LABEL (Data) 53 | 54 | ## Reference 55 | ## License 56 | [GNU General Public License v3.0](LICENSE) 57 | -------------------------------------------------------------------------------- /ass_res/CHN_adm_shp/CHN_adm_shp.txt: -------------------------------------------------------------------------------- 1 | The default path of the files of geographical data. -------------------------------------------------------------------------------- /ass_res/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sun Jun 11 15:42:16 2017 5 | ######################################################################################## 6 | # @ File name: __init_.py 7 | # @ Function: Assess and visualize the spatiotemporal characteristics of regional wind 8 | and solar energy sources to support perliminary regional planning. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Requirements： 11 | # Python 2.7 (or higher) 12 | # numpy (Python package) 13 | # matplotlib (Python package) 14 | # basemap (Python package) 15 | # scipy (Python package) 16 | # pyhdf (Python package) 17 | # sklearn (Python package) 18 | # Ten-year wind speed, solar irradiation and ambient temperature data (Data) 19 | # Geographical data in shapefile file format (Data) 20 | # In-situ measured data for correction (Data) 21 | # @ Version: 1.0 22 | # @ Revision date: Jan/19/2018 23 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 24 | ######################################################################################## 25 | """ 26 | 27 | 28 | __all__ = ['init_nasa_wind', 'init_nasa_solar', 'ass_singlesite_ws', 29 | 'ass_singlesite_lsy', 'draw_geo_fig', 'ass_widearea_dis', 'ass_widearea_wsy'] 30 | -------------------------------------------------------------------------------- /ass_res/ass_singlesite_lsy.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Fri Jun 09 18:54:30 2017 5 | ######################################################################################## 6 | # @ File name: ass_singlesite_lsy.py 7 | # @ Function: Perform single-site assessment. 8 | # Assess the local synergy effects. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | import matplotlib.pyplot as plt 19 | from scipy import stats 20 | from mpl_toolkits.axes_grid1 import host_subplot 21 | from sklearn import preprocessing 22 | 23 | import init_nasa_wind as mwind 24 | import init_nasa_solar as msolar 25 | import ass_singlesite_ws as masw 26 | 27 | 28 | def cass_varh_monthly(iwind_data, isolar_data, isiteth_plotted=3, iyear_plotted=2010, plot_flag=False): 29 | '''Assess local synergy of selected sites in month style. 30 | Output graphs 31 | Args: 32 | iwind_data: the source wind data. 33 | isolar_data: the source solar data. 34 | isiteth_plotted: the serial number of selected site. 35 | iyear_plotted: the selected year 36 | plot_flag: True or False. Draw graphs or not. 37 | Returns: 38 | the graphs of monthly variation. 39 | ''' 40 | year_index = range(iwind_data.start_year, iwind_data.end_year + 1) 41 | year_num = len(year_index) 42 | site_num = len(iwind_data.site_index) 43 | month_index = iwind_data.month_name 44 | wfc_mean_monthly = np.empty((site_num, 12), np.float32) 45 | wfc_sum_monthly = np.empty((site_num, 12, year_num), np.float32) 46 | sfc_mean_monthly = np.empty((site_num, 12), np.float32) 47 | sfc_sum_monthly = np.empty((site_num, 12, year_num), np.float32) 48 | wfc_ploted = np.empty((1, 12), np.float32) 49 | sfc_ploted = np.empty((1, 12), np.float32) 50 | for each_siteth in range(1, site_num + 1): 51 | for each_monthth in range(0, 12, 1): 52 | for each_year in year_index: 53 | wfc_mean_monthly[each_siteth-1,each_monthth] = \ 54 | wfc_mean_monthly[each_siteth-1,each_monthth] + \ 55 | np.sum(iwind_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 56 | wfc_mean_monthly[each_siteth-1,each_monthth] = \ 57 | wfc_mean_monthly[each_siteth-1,each_monthth] * 1.0 / year_num 58 | wfc_sum_monthly[each_siteth - 1, each_monthth, each_year - iwind_data.start_year] = \ 59 | np.sum(iwind_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 60 | 61 | sfc_mean_monthly[each_siteth-1,each_monthth] = \ 62 | sfc_mean_monthly[each_siteth-1,each_monthth] + \ 63 | np.sum(isolar_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 64 | sfc_mean_monthly[each_siteth-1,each_monthth] = \ 65 | sfc_mean_monthly[each_siteth-1,each_monthth] * 1.0 / year_num 66 | sfc_sum_monthly[each_siteth - 1, each_monthth, each_year - iwind_data.start_year] = \ 67 | np.sum(isolar_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 68 | for each_monthth in range(0, 12, 1): 69 | wfc_ploted[0, each_monthth] = \ 70 | np.sum(iwind_data.cselect_1site_1month(isiteth_plotted, iyear_plotted, month_index[each_monthth]), axis=1) 71 | sfc_ploted[0, each_monthth] = \ 72 | np.sum(isolar_data.cselect_1site_1month(isiteth_plotted, iyear_plotted, month_index[each_monthth]), axis=1) 73 | if plot_flag == True: 74 | plt.rcParams['font.family'] = 'Times New Roman' 75 | plt.figure(dpi=300) 76 | ax1_varann_wfc = host_subplot(111) 77 | ax2_varann_sfc = ax1_varann_wfc.twinx() 78 | width = 1 * 0.43 79 | ind = np.arange(0, 12, 1) 80 | ax1_varann_wfc.bar(ind-width/2.0, wfc_ploted[0, :], width, color='k', label='Wind') 81 | ax2_varann_sfc.bar(ind+width/2.0, sfc_ploted[0, :], width, color='b', label='Solar') 82 | ax1_varann_wfc.set_xticks(np.linspace(0, 11, 12)) 83 | ax1_varann_wfc.set_xticklabels(('Jan.', 'Feb.', 'Mar.', 'Apr.', 'May.', 'Jun.', 'Jul.', \ 84 | 'Aug.', 'Sep.', 'Oct.', 'Nov.', 'Dec.')) 85 | ax1_varann_wfc.set_xlabel('Month') 86 | ax1_varann_wfc.set_ylabel('Wind capacity factors') 87 | ax2_varann_sfc.set_ylabel('Solar capacity factors') 88 | ax1_varann_wfc.set_xlim(-0.575, 11.575) 89 | #ax1_varann_wfc.grid(True) 90 | ax1_varann_wfc.legend(loc=1) 91 | plt.title('Annual variation') 92 | plt.show() 93 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 94 | 95 | 96 | def cass_varh_hourly(iwind_data, isolar_data, isiteth_plotted=3, imonth_ploted='Apr', plot_flag=False): 97 | '''Assess local synergy of selected sites in hour style. 98 | Output graphs. 99 | Args: 100 | iwind_data: the source wind data. 101 | isolar_data: the source solar data. 102 | isiteth_plotted: the serial number of selected site. 103 | imonth_ploted: the selected month 104 | plot_flag: True or False. Draw graphs or not. 105 | Returns: 106 | the graphs of hourly variation. 107 | ''' 108 | year_index = range(iwind_data.start_year, iwind_data.end_year + 1) 109 | year_num = len(year_index) 110 | site_num = len(iwind_data.site_index) 111 | wfc_mean_hourly = np.zeros((site_num, year_num, 24), np.float32) 112 | sfc_mean_hourly = np.zeros((site_num, year_num, 24), np.float32) 113 | for each_siteth in range(1, site_num + 1): 114 | for each_yearth in range(0, year_num, 1): 115 | for each_day in range(2, 29, 1): 116 | wfc_mean_hourly[each_siteth - 1, each_yearth, :] = \ 117 | wfc_mean_hourly[each_siteth - 1, each_yearth, :] + \ 118 | np.hstack((iwind_data.cselect_1site_1day(each_siteth, year_index[each_yearth], imonth_ploted, each_day - 1)[0, 16:24], \ 119 | iwind_data.cselect_1site_1day(each_siteth, year_index[each_yearth], imonth_ploted,each_day)[0, 0:16])) 120 | sfc_mean_hourly[each_siteth - 1, each_yearth, :] = \ 121 | sfc_mean_hourly[each_siteth - 1, each_yearth, :] + \ 122 | np.hstack((isolar_data.cselect_1site_1day(each_siteth, year_index[each_yearth], imonth_ploted, each_day - 1)[0, 16:24], \ 123 | isolar_data.cselect_1site_1day(each_siteth, year_index[each_yearth], imonth_ploted, each_day)[0, 0:16])) 124 | wfc_ploted = np.mean(wfc_mean_hourly, axis=1) / 27.0 125 | sfc_ploted = np.mean(sfc_mean_hourly, axis=1) / 27.0 126 | if plot_flag == True: 127 | plt.rcParams['font.family'] = 'Times New Roman' 128 | plt.figure(dpi=300) 129 | ax1_varhour_wfc = host_subplot(111) 130 | ax2_varhour_sfc = ax1_varhour_wfc.twinx() 131 | ind = np.arange(0, 24, 1) 132 | ax1_varhour_wfc.plot(ind, wfc_ploted[isiteth_plotted - 1, :], 'k-o', label='Wind') 133 | ax2_varhour_sfc.plot(ind, sfc_ploted[isiteth_plotted - 1, :], 'b-^', label='Solar') 134 | ax1_varhour_wfc.set_xticks(np.linspace(0, 23, 24)) 135 | x_lables=('00:30', '', '02:30', '', '04:30', '', '06:30', '', '08:30', '', '10:30', '', '12:30', '', '14:30', '', '16:30', \ 136 | '', '18:30', '', '20:30', '', '22:30', '') 137 | ax1_varhour_wfc.set_xticklabels(x_lables) 138 | ax1_varhour_wfc.set_xlabel('Time') 139 | ax1_varhour_wfc.set_ylabel('Wind capacity factors') 140 | ax2_varhour_sfc.set_ylabel('Solar capacity factors') 141 | ax1_varhour_wfc.set_xlim(-0.7, 23.7) 142 | #plt.grid() 143 | ax1_varhour_wfc.legend(loc=1) 144 | plt.title('Hourly variation') 145 | plt.show() 146 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 147 | 148 | 149 | def cass_configh(iwind_data, isolar_data, isiteth_plotted=3, plot_flag=False): 150 | '''Assess local synergy of selected sites. 151 | Search the optimal ratio of wind and solar. 152 | Output statistical indices and graphs. 153 | Args: 154 | iwind_data: the source wind data. 155 | isolar_data: the source solar data. 156 | isiteth_plotted: the serial number of selected site. 157 | plot_flag: True or False. Draw graphs or not. 158 | Returns: 159 | the graphs of hourly variation. 160 | optimal_raio: optimal ratio of wind and solar 161 | ''' 162 | wfc_10years = iwind_data.cselect_1site_10year(isiteth_plotted) 163 | sfc_10years = isolar_data.cselect_1site_10year(isiteth_plotted) 164 | wfc_variable_coef = np.std(wfc_10years, axis=1) / np.mean(wfc_10years, axis=1) 165 | sfc_variable_coef = np.std(sfc_10years, axis=1) / np.mean(sfc_10years, axis=1) 166 | hfcvc_list=[] 167 | ind = np.arange(0, 1.02, 0.02) 168 | for each_ratio in ind: 169 | hfc_10years = wfc_10years * each_ratio + sfc_10years * (1 - each_ratio) 170 | hfc_variable_coef = np.std(hfc_10years, axis=1) / np.mean(hfc_10years, axis=1) 171 | hfcvc_list.append(hfc_variable_coef) 172 | optimal_raio = ind[np.argmin(hfcvc_list)] 173 | if plot_flag == True: 174 | plt.rcParams['font.family'] = 'Times New Roman' 175 | plt.figure(dpi=300) 176 | ax = plt.subplot(111) 177 | ax.axhline(y=wfc_variable_coef, xmin=0, xmax=1, color='k', ls='-.', label='Wind') 178 | ax.axhline(y=sfc_variable_coef, xmin=0, xmax=1, color='b', ls='--', label='Solar') 179 | ax.plot(ind, hfcvc_list, color='r', marker='.', label='Wind-Solar') 180 | ax.set_xticks(np.arange(0, 1.1, 0.1)) 181 | ax.set_xticklabels((0.0, '', 0.2, '', 0.4, '', 0.6, '', 0.8, '', 1.0)) 182 | ax.set_yticks(np.linspace(0.8, 1.4, 4)) 183 | ax.set_yticklabels((0.8, 1.0, 1.2, 1.4)) 184 | ax.legend(loc=1) 185 | #ax.grid(True) 186 | ax.set_ylabel('Variable coefficient') 187 | ax.set_xlabel('Matching coefficient') 188 | ax.set_xlim(0, 1) 189 | ax.set_ylim(0.7, 1.5) 190 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 191 | plt.title('The curve of matching coefficient and variable coefficient') 192 | plt.show() 193 | return optimal_raio 194 | 195 | 196 | def cass_rampopt(iwind_data, isolar_data, ioptimal_ratio, isiteth_plotted=3, plot_flag=False): 197 | '''Assess local synergy of selected sites. 198 | The probability distribution of ramp rate of optimal wind and solar configuration. 199 | Output statistical indices and graphs. 200 | Args: 201 | iwind_data: the source wind data. 202 | isolar_data: the source solar data. 203 | isiteth_plotted: the serial number of selected site. 204 | plot_flag: True or False. Draw graphs or not. 205 | Returns: 206 | the graphs of hourly variation. 207 | hfc_ramp_rate: the ramp rate of optimal wind and solar configuration. 208 | ''' 209 | wfc_10years = iwind_data.cselect_1site_10year(isiteth_plotted) 210 | sfc_10years = isolar_data.cselect_1site_10year(isiteth_plotted) 211 | hfc_10years = wfc_10years * ioptimal_ratio + (1 - ioptimal_ratio) * sfc_10years 212 | hfc_10years_tmp = np.hstack((hfc_10years[0, 1:].reshape(-1, 1), hfc_10years[0, 0:-1].reshape(-1, 1))) 213 | hfc_ramp_rate = np.subtract.reduce(hfc_10years_tmp, axis=1) 214 | if plot_flag == True: 215 | plt.rcParams['font.family'] = 'Times New Roman' 216 | plt.figure(dpi = 300) 217 | axh = plt.subplot(111) 218 | cnh,binsh,steph = axh.hist(hfc_ramp_rate, bins=40, normed=True, histtype='barstacked', color='b') 219 | axh.set_ylabel('Probability density') 220 | axh.set_xlabel('Wind-solar ramp rate') 221 | axh.set_xticks(np.arange(-0.2, 0.3, 0.1)) 222 | axh.set_xticklabels((-0.2, -0.1, 0.0, 0.1, 0.2)) 223 | axh.set_yticks(np.linspace(0, 15, 4)) 224 | axh.set_yticklabels((0, 5, 10, 15)) 225 | #axh.grid(True) 226 | plt.title('The distribution of ramp rate') 227 | plt.show() 228 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 229 | return hfc_ramp_rate 230 | 231 | 232 | def cass_single_site(iwind_data, isolar_data, isiteth=3, ikw=0.60, ikf=0.98, ikc=-0.5, igama=0.1): 233 | '''Assess local synergy of selected sites. 234 | Output statistical indices. 235 | Args: 236 | iwind_data: the source wind data. 237 | isolar_data: the source solar data. 238 | isiteth: the serial number of selected site. 239 | ikw: the profit coefficient of electricity selling of wind energy. 240 | ikf: the profit coefficient of electricity selling of solar energy. 241 | ikc: the negative cost coefficient of ramp reserve. 242 | igama: the weight factor. 243 | Returns: 244 | matching_coef_opt: optimal ratio of wind and solar. 245 | hfc_mean_yearly: means of optimal wind and solar configuration in year scale. 246 | hfc_mean_hourly: means of optimal wind and solar configuration in hour scale. 247 | hfc_std_hourly: std of optimal wind and solar configuration in hour scale. 248 | hfc_varcoef_hourly: variation coefficient of optimal wind and solar configuration in hour scale. 249 | hfc_half_prob: half power probability. 250 | hfc_ramp_rate: the ramp rate of optimal wind and solar configuration. 251 | hfc_ramp_mean: mean of ramp rate. 252 | hfc_ramp_std: std of ramp rate. 253 | hfc_ramp_max: max of ramp rate. 254 | hfc_ramp_min: min of ramp rate. 255 | hfc_ramp_upper: upper value of 95% confidence interval ramp rate. 256 | hfc_ramp_lower: lower value of 95% confidence interval ramp rate. 257 | improving_coef: the improving coefficient. 258 | local_synergy_coef: the local synergy coefficient. 259 | hfc_comp_profits: the comprehensive profit coefficient. 260 | ''' 261 | matching_coef_opt = cass_configh(iwind_data, isolar_data, isiteth, False) 262 | hfc_ramp_rate = cass_rampopt(iwind_data, isolar_data, matching_coef_opt, 3, False) 263 | wfc_10years = iwind_data.cselect_1site_10year(isiteth) 264 | sfc_10years = isolar_data.cselect_1site_10year(isiteth) 265 | hfc_10years = wfc_10years * matching_coef_opt + (1 - matching_coef_opt) * sfc_10years 266 | hfc_mean_yearly = np.sum(hfc_10years) / 10.0 267 | hfc_mean_hourly = np.mean(hfc_10years) 268 | hfc_std_hourly = np.std(hfc_10years) 269 | hfc_varcoef_hourly = hfc_mean_hourly * 1.0 / hfc_std_hourly 270 | hfc_half_prob = (hfc_10years[0, hfc_10years[0, :] > 0.5].shape[0]) / (hfc_10years.shape[1]) 271 | hfc_ramp_rate = cass_rampopt(wind_data, solar_data, matching_coef_opt, 3, False) 272 | hfc_ramp_mean = np.mean(hfc_ramp_rate) 273 | hfc_ramp_std = np.std(hfc_ramp_rate) 274 | hfc_ramp_max = np.max(hfc_ramp_rate) 275 | hfc_ramp_min = np.min(hfc_ramp_rate) 276 | hfc_ramp_upper = hfc_ramp_mean + 1.96 * hfc_ramp_std 277 | hfc_ramp_lower = hfc_ramp_mean - 1.96 * hfc_ramp_std 278 | wfc_mean_hourly = np.mean(wfc_10years) 279 | wfc_std_hourly = np.std(wfc_10years) 280 | wfc_varcoef_hourly = wfc_mean_hourly * 1.0 / wfc_std_hourly 281 | sfc_mean_hourly = np.mean(sfc_10years) 282 | sfc_std_hourly = np.std(sfc_10years) 283 | sfc_varcoef_hourly = sfc_mean_hourly * 1.0 / sfc_std_hourly 284 | improving_coef = 1 - hfc_varcoef_hourly / (matching_coef_opt * wfc_varcoef_hourly + \ 285 | (1 - matching_coef_opt)* sfc_varcoef_hourly) 286 | norwfc_10years = preprocessing.scale(wfc_10years[0, :]) 287 | norsfc_10years = preprocessing.scale(sfc_10years[0, :]) 288 | local_synergy_coef = 1 - 0.5 * stats.pearsonr(wfc_10years[0, :], sfc_10years[0, :])[0] 289 | Kp = ikw * matching_coef_opt + ikf * (1-matching_coef_opt) 290 | hfc_comp_profits = 1000.0 * (igama * Kp * hfc_mean_hourly + (1 - igama) * ikc * np.mean(hfc_ramp_rate)) 291 | return matching_coef_opt, hfc_mean_yearly, hfc_mean_hourly, hfc_std_hourly, hfc_varcoef_hourly, hfc_half_prob, hfc_ramp_rate, \ 292 | hfc_ramp_mean, hfc_ramp_std, hfc_ramp_max, hfc_ramp_min, hfc_ramp_upper, hfc_ramp_lower, improving_coef, local_synergy_coef, hfc_comp_profits 293 | 294 | 295 | def cass_attr_constr(iwind_data, isolar_data, imode=0, ikw=0.60, ikf=0.98, ikc=-0.5, igama=0.1): 296 | '''Construct new attirbutes. 297 | Output statistical indices 298 | Args: 299 | iwind_data: the source wind data. 300 | isolar_data: the source solar data. 301 | imode: 0~15 302 | 0 - optimal ratio of wind and solar. 303 | 1 - means of optimal wind and solar configuration in year scale. 304 | 2 - means of optimal wind and solar configuration in hour scale. 305 | 3 - std of optimal wind and solar configuration in hour scale. 306 | 4 - variation coefficient of optimal wind and solar configuration in hour scale. 307 | 5 - half power probability. 308 | 6 - the ramp rate of optimal wind and solar configuration. 309 | 7 - mean of ramp rate. 310 | 8 - std of ramp rate. 311 | 9 - max of ramp rate. 312 | 10 - min of ramp rate. 313 | 11 - upper value of 95% confidence interval ramp rate. 314 | 12 - lower value of 95% confidence interval ramp rate. 315 | 13 - the improving coefficient. 316 | 14 - the local synergy coefficient. 317 | 15 - the comprehensive profit coefficient. 318 | ikw: the profit coefficient of electricity selling of wind energy. 319 | ikf: the profit coefficient of electricity selling of solar energy. 320 | ikc: the negative cost coefficient of ramp reserve. 321 | igama: the weight factor. 322 | Returns: 323 | the constructed attributes. 324 | ''' 325 | wf_10yearstl = iwind_data.c2style_10year(True) 326 | sf_10yearstl = isolar_data.c2style_10year(True) 327 | hf_10yearstl = {} 328 | site_num = len(iwind_data.site_index) 329 | # year_index = range(iwind_data.start_year, iwind_data.end_year + 1) 330 | # year_num = len(year_index) 331 | # matching_coef_opt = np.zeros((1, site_num), np.float32) 332 | # interval = np.arange(0, 1.02, 0.02) 333 | analy_results = np.zeros((1, site_num), np.float32) 334 | for each_siteth in range(1, site_num + 1, 1): 335 | (oindices0, oindices1, oindices2, oindices3, oindices4, oindices5, oindices6, oindices7, \ 336 | oindices8, oindices9, oindices10, oindices11, oindices12, oindices13, oindices14, oindices15) =\ 337 | cass_single_site(iwind_data, isolar_data, each_siteth, ikw, ikf, ikc, igama) 338 | if imode == 0: 339 | analy_results[0, each_siteth] = oidices0 340 | elif imode == 1: 341 | analy_results[0, each_siteth] = oidices1 342 | elif imode == 2: 343 | analy_results[0, each_siteth] = oidices2 344 | elif imode == 3: 345 | analy_results[0, each_siteth] = oidices3 346 | elif imode == 4: 347 | analy_results[0, each_siteth] = oidices4 348 | elif imode == 5: 349 | analy_results[0, each_siteth] = oidices5 350 | elif imode == 6: 351 | analy_results[0, each_siteth] = oidices6 352 | elif imode == 7 353 | analy_results[0, each_siteth] = oidices7 354 | elif imode == 8: 355 | analy_results[0, each_siteth] = oidices8 356 | elif imode == 9: 357 | analy_results[0, each_siteth] = oidices9 358 | elif imode == 10: 359 | analy_results[0, each_siteth] = oidices10 360 | elif imode == 11: 361 | analy_results[0, each_siteth] = oidices11 362 | elif imode == 12: 363 | analy_results[0, each_siteth] = oidices12 364 | elif imode == 13: 365 | analy_results[0, each_siteth] = oidices13 366 | elif imode == 14: 367 | analy_results[0, each_siteth] = oidices14 368 | # elif imode == 15: 369 | else: 370 | analy_results[0, each_siteth] = oidices15 371 | return analy_results 372 | 373 | 374 | def cass_output_prob(iwind_data, isolar_data, isiteth=3): 375 | '''Assess local synergy of selected sites. 376 | The probability distribution of capacity factors. 377 | Output statistical indices 378 | Args: 379 | iwind_data: the source wind data. 380 | isolar_data: the source solar data. 381 | isiteth: the serial number of selected site. 382 | Returns: 383 | the garphs of pdfs and cdfs. 384 | ''' 385 | matching_coef_opt = cass_configh(iwind_data, isolar_data, isiteth, False) 386 | wfc_10years = iwind_data.cselect_1site_10year(isiteth) 387 | sfc_10years = isolar_data.cselect_1site_10year(isiteth) 388 | hfc_10years = wfc_10years * matching_coef_opt + (1 - matching_coef_opt) * sfc_10years 389 | wfc_output = np.empty((0,),np.float32) 390 | sfc_output = np.empty((0,),np.float32) 391 | hfc_output = np.empty((0,),np.float32) 392 | interval = np.arange(-0, 1.05, 0.05) 393 | for each in interval: 394 | hfc_output = np.hstack((hfc_output,(hfc_10years[0, hfc_10years[0, :] <= each].shape[0] * 100.0 / hfc_10years.shape[1]))) 395 | sfc_output = np.hstack((sfc_output,(sfc_10years[0, sfc_10years[0, :] <= each].shape[0] * 100.0 / sfc_10years.shape[1]))) 396 | wfc_output = np.hstack((wfc_output,(wfc_10years[0, wfc_10years[0, :] <= each].shape[0] * 100.0 / wfc_10years.shape[1]))) 397 | plt.rcParams['font.family'] = 'Times New Roman' 398 | plt.figure(dpi=300) 399 | plt.title('The PDF and CDF of capacity factors', fontsize=8) 400 | ax1 = plt.subplot(221) 401 | cn1, bins1, step1 = ax1.hist(wfc_10years.reshape(-1, 1), bins=20, normed=True, histtype='barstacked', color='b') 402 | ax1.set_xticks(np.linspace(0,1,6)) 403 | ax1.set_xticklabels((0.0, 0.2, 0.4, 0.6, 0.8, 1.0), rotation=0, fontsize=8) 404 | ax1.set_yticks(np.linspace(0, 4, 5)) 405 | ax1.set_yticklabels((0, 1, 2, 3, 4), rotation=90, fontsize=8) 406 | ax1.set_xlim(-0.005, 1.005) 407 | ax1.set_ylim(-0.005, 4.5) 408 | ax1.set_xlabel('Wind capacity factor\n(a)', fontsize=8) 409 | ax1.set_ylabel('Probability density', fontsize=8) 410 | ax2 = plt.subplot(222) 411 | cn2, bins2, step2 = ax2.hist(sfc_10years.reshape(-1, 1), bins=20 , normed=True, histtype='stepfilled', color='b') 412 | ax2.set_xticks(np.linspace(0, 1, 6)) 413 | ax2.set_xticklabels((0.0, 0.2, 0.4, 0.6, 0.8, 1.0), rotation=0, fontsize=8) 414 | ax2.set_yticks(np.linspace(0, 4, 5)) 415 | ax2.set_yticklabels((0, 1, 2, 3, 4), rotation=90, fontsize=8) 416 | ax2.set_xlim(-0.005, 1.005) 417 | ax2.set_ylim(-0.005, 4.5) 418 | ax2.set_xlabel('Solar capacity factor\n(b)', fontsize=8) 419 | ax2.set_ylabel('Probability density', fontsize=8) 420 | ax3 = plt.subplot(223) 421 | cn3, bins3, step3 = ax3.hist(hfc_10years.reshape(-1, 1), bins=20, normed=True, histtype='stepfilled', color='b') 422 | ax3.set_xticks(np.linspace(0, 1, 6)) 423 | ax3.set_xticklabels((0.0, 0.2, 0.4, 0.6, 0.8, 1.0), rotation=0, fontsize=8) 424 | ax3.set_yticks(np.linspace(0, 4, 5)) 425 | ax3.set_yticklabels((0, 1, 2, 3, 4), rotation=90, fontsize=8) 426 | ax3.set_xlim(-0.005, 1.005) 427 | ax3.set_ylim(-0.005, 4.5) 428 | ax3.set_xlabel('Wind-solar capacity factor\n(c)', fontsize=8) 429 | ax3.set_ylabel('Probability density', fontsize=8) 430 | ax4 = plt.subplot(224) 431 | ax4.plot(interval, wfc_output, 'k-o', label='Wind', markersize=3, linewidth=1) 432 | ax4.plot(interval, sfc_output, 'b-^', label='Solar', markersize=3, linewidth=1) 433 | ax4.plot(interval, hfc_output, 'r-d', label='Wind-solar', markersize=3, linewidth=1) 434 | ax4.legend(fontsize=6, loc=4, ncol=1) 435 | ax4.set_xticks(np.linspace(0, 1, 5)) 436 | ax4.set_xticklabels((0.0, 0.25, 0.5, 0.75, 1.00), rotation=0, fontsize=8) 437 | ax4.set_yticks(np.linspace(0, 100, 6)) 438 | ax4.set_yticklabels((0, 20, 40, 60, 80,100), rotation=90, fontsize=8) 439 | ax4.set_xlim(-0.025, 1.005) 440 | ax4.set_ylim(-2.5, 105) 441 | ax4.set_xlabel('Capacity factor\n(d)', fontsize=8) 442 | ax4.set_ylabel('Probability (%)', fontsize=8) 443 | plt.subplots_adjust(wspace=0.25, hspace=0.42) 444 | plt.show() 445 | # plt.savefig('cPlotHNew0.png', dpi=300, bbox_inches='tight') 446 | 447 | 448 | if __name__ == '__main__': 449 | '''Examples.''' 450 | start_year = 2006 451 | end_year = 2015 452 | site_index = [(-6, 2), (-6, 6), (-6, 10)] 453 | wfile_name = 'sd_wind_data' 454 | sfile_name = 'sd_solar_data' 455 | 456 | wind_data = mwind.WindData(wfile_name, site_index, start_year, end_year) 457 | wind_speed_ref = wind_data.cimport_data() 458 | wind_speed_hw = wind_data.cref2hw() 459 | wind_capacity_factor = wind_data.cwind2cf() 460 | solar_data = msolar.SolarData(sfile_name, site_index, start_year, end_year) 461 | solar_irrad_data = solar_data.cimport_data() 462 | solar_capacity_factor = solar_data.csolar2cf_model1() 463 | # solar_temperature_data = solar_data.cimport_datat() 464 | # solar_capacity_factor = solar_data.csolar2cf_model2() 465 | 466 | cass_single_site(wind_data, solar_data, 3) 467 | cass_varh_monthly(wind_data, solar_data, 3, 2010, True) 468 | cass_varh_hourly(wind_data, solar_data, 3, 'Apr', True) 469 | optimal_raio = cass_configh(wind_data, solar_data, 3, True) 470 | print optimal_raio 471 | hfc_ramp_rate = cass_rampopt(wind_data, solar_data, optimal_raio, 3, True) 472 | assess_result = cass_single_site(wind_data, solar_data, 3) 473 | attr = cass_attr_constr(wind_data, solar_data, 0) 474 | cass_output_prob(wind_data, solar_data, 3) 475 | -------------------------------------------------------------------------------- /ass_res/ass_singlesite_ws.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Fri Jun 09 18:51:22 2017 5 | ######################################################################################## 6 | # @ File name: ass_singlesite_ws.py 7 | # @ Function: Perform single-site assessment. 8 | # Assess the variation and reserves of wind and solar resources. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | import matplotlib.pyplot as plt 19 | from mpl_toolkits.axes_grid1 import host_subplot 20 | 21 | import init_nasa_wind as mwind 22 | import init_nasa_solar as msolar 23 | 24 | 25 | def cass_varws_annual(isource_data, isiteth_plotted=[1, 2, 3], plot_flag=False): 26 | '''Assess annual variation of selected sites. 27 | Output statistical indices and graphs 28 | Args: 29 | isource_data: the source data. 30 | isiteth_plotted: the serial number of selected site. 31 | plot_flag: True or False. Draw graphs or not. 32 | Returns: 33 | the graphs of annual variation. 34 | means, std, and variable coefficient of selected site in year scale. 35 | ''' 36 | year_index = range(isource_data.start_year, isource_data.end_year + 1) 37 | year_num = len(year_index) 38 | site_num = len(isource_data.site_index) 39 | fc_sum_yearly = np.empty((site_num, year_num), np.float32) 40 | fc_mean_yearly = np.empty((site_num, 1), np.float32) 41 | fc_std_yearly = np.empty((site_num, 1), np.float32) 42 | fc_varcoef_yearly = np.empty((site_num, 1), np.float32) 43 | for each_siteth in range(1, site_num + 1): 44 | for each_yearth in range(0, year_num, 1): 45 | fc_sum_yearly[each_siteth - 1, each_yearth] = \ 46 | np.sum(isource_data.cselect_1site_1year(each_siteth, year_index[each_yearth]), axis=1) 47 | fc_mean_yearly[each_siteth - 1, 0] = \ 48 | np.sum(isource_data.cselect_1site_10year(each_siteth), axis=1) / year_num 49 | fc_std_yearly[each_siteth - 1, 0] = \ 50 | np.std(fc_sum_yearly[each_siteth - 1, :]) 51 | fc_varcoef_yearly[each_siteth - 1, 0] = \ 52 | fc_std_yearly[each_siteth - 1, 0] * 1.0 / fc_mean_yearly[each_siteth - 1, 0] 53 | if plot_flag == True: 54 | plt.rcParams['font.family'] = 'Times New Roman' 55 | plt.figure(dpi=300) 56 | ax = plt.subplot(111) 57 | width = 1 * 0.25 * 3 / site_num 58 | ind = np.arange(0, year_num, 1) 59 | if len(isiteth_plotted) == 3: 60 | ax.bar(ind - width, fc_sum_yearly[isiteth_plotted[0] - 1, :], width, 61 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 62 | ax.bar(ind, fc_sum_yearly[isiteth_plotted[1] - 1, :], width, 63 | color='b', label='Siteth ' + str(isiteth_plotted[1])) 64 | ax.bar(ind + width, fc_sum_yearly[isiteth_plotted[2] - 1, :], width, 65 | color='r', label='Siteth ' + str(isiteth_plotted[2])) 66 | elif len(isiteth_plotted) == 2: 67 | ax.bar(ind - width, fc_sum_yearly[isiteth_plotted[0] - 1, :], width, 68 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 69 | ax.bar(ind, fc_sum_yearly[isiteth_plotted[1] - 1, :], width, 70 | color='b', label='Siteth ' + str(isiteth_plotted[1])) 71 | else: 72 | ax.bar(ind, fc_sum_yearly[isiteth_plotted[0] - 1, :], width, 73 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 74 | ax.set_xticks(np.linspace(0, 9, 10)) 75 | x_lable = tuple([str(each) for each in year_index]) 76 | ax.set_xticklabels(x_lable) 77 | ax.set_xlabel('Year') 78 | ax.set_ylabel('Capacity factors') 79 | # ax.grid(True) 80 | ax.legend(loc=1) 81 | plt.title('The annual variation') 82 | plt.show() 83 | # plt.savefig('cPlotW1.png', dpi=300, bbox_inches='tight') 84 | if isinstance(isiteth_plotted, list): 85 | ositeth_plotted = [each - 1 for each in list(isiteth_plotted)] 86 | else: 87 | ositeth_plotted = isiteth_plotted - 1 88 | return fc_mean_yearly[ositeth_plotted, 0], fc_std_yearly[ositeth_plotted, 0], fc_varcoef_yearly[ositeth_plotted, 0] 89 | 90 | 91 | def cass_varws_monthly(isource_data, isiteth_plotted=[1, 2, 3], iyear_plotted=2010, plot_flag=False): 92 | '''Assess monthly variation of selected sites, selected year. 93 | Output graphs 94 | Args: 95 | isource_data: the source data. 96 | isiteth_plotted: the serial number of selected site. 97 | iyear_plotted: the selected year. 98 | plot_flag: True or False. Draw graphs or not. 99 | Returns: 100 | the graphs of monthly variation. 101 | ''' 102 | year_index = range(isource_data.start_year, isource_data.end_year + 1) 103 | year_num = len(year_index) 104 | site_num = len(isource_data.site_index) 105 | month_index = isource_data.month_name 106 | fc_mean_monthly = np.empty((site_num, 12), np.float32) 107 | fc_sum_monthly = np.empty((site_num, 12, year_num), np.float32) 108 | fc_ploted = np.empty((site_num, 12), np.float32) 109 | for each_siteth in range(1, site_num + 1): 110 | for each_monthth in range(0, 12, 1): 111 | for each_year in year_index: 112 | fc_mean_monthly[each_siteth - 1, each_monthth] = \ 113 | fc_mean_monthly[each_siteth - 1, each_monthth] + \ 114 | np.sum(isource_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 115 | fc_mean_monthly[each_siteth - 1, each_monthth] = \ 116 | fc_mean_monthly[each_siteth - 1, each_monthth] * 1.0 / year_num 117 | fc_sum_monthly[each_siteth - 1, each_monthth, each_year - isource_data.start_year] = \ 118 | np.sum(isource_data.cselect_1site_1month(each_siteth, each_year, month_index[each_monthth]), axis=1) 119 | for each_plotsiteth in isiteth_plotted: 120 | for each_monthth in range(0, 12, 1): 121 | fc_ploted[each_plotsiteth - 1, each_monthth] = \ 122 | np.sum(isource_data.cselect_1site_1month(each_plotsiteth, iyear_plotted, month_index[each_monthth]), axis=1) 123 | if plot_flag == True: 124 | plt.rcParams['font.family'] = 'Times New Roman' 125 | plt.figure(dpi=300) 126 | ax=plt.subplot(111) 127 | width = 1 * 0.25 * 3 / site_num 128 | ind = np.arange(0, 12, 1) 129 | if len(isiteth_plotted) == 3: 130 | ax.bar(ind - width, fc_ploted[isiteth_plotted[0] - 1, :], width, 131 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 132 | ax.bar(ind, fc_ploted[isiteth_plotted[1] - 1, :], width, 133 | color='b', label='Siteth ' + str(isiteth_plotted[1])) 134 | ax.bar(ind + width, fc_ploted[isiteth_plotted[2] - 1, :], width, 135 | color='r', label='Siteth ' + str(isiteth_plotted[2])) 136 | elif len(isiteth_plotted) == 2: 137 | ax.bar(ind - width, fc_ploted[isiteth_plotted[0] - 1, :], width, 138 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 139 | ax.bar(ind, fc_ploted[isiteth_plotted[1] - 1, :], width, 140 | color='b', label='Siteth ' + str(isiteth_plotted[1])) 141 | else: 142 | ax.bar(ind, fc_ploted[isiteth_plotted[0] - 1, :], width, 143 | color='k', label='Siteth ' + str(isiteth_plotted[0])) 144 | ax.set_xticks(np.linspace(0, 11, 12)) 145 | ax.set_xticklabels(('Jan.', 'Feb.', 'Mar.', 'Apr.', 'May.', 'Jun.', 'Jul.', 146 | 'Aug.', 'Sep.', 'Oct.', 'Nov.', 'Dec.'), rotation=45, fontsize=8) 147 | ax.set_xlabel('Month') 148 | ax.set_ylabel('Capacity factors') 149 | # ax.grid(True) 150 | ax.legend(loc=1) 151 | plt.title('The monthly variation') 152 | plt.show() 153 | # plt.savefig('cPlotW1.png', dpi=300, bbox_inches='tight') 154 | 155 | 156 | def cass_varws_hourly(isource_data, isiteth_plotted=[1, 2, 3], imonth_ploted='Apr', plot_flag=False): 157 | '''Assess hourly variation of selected sites, selected month. 158 | Output graphs 159 | Args: 160 | isource_data: the source data. 161 | isiteth_plotted: the serial number of selected site. 162 | imonth_ploted: the selected month. 163 | plot_flag: True or False. Draw graphs or not. 164 | Returns: 165 | the graphs of monthly variation. 166 | ''' 167 | year_index = range(isource_data.start_year, isource_data.end_year + 1) 168 | year_num = len(year_index) 169 | site_num = len(isource_data.site_index) 170 | fc_mean_hourly = np.zeros((site_num, year_num, 24), np.float32) 171 | for each_siteth in range(1, site_num + 1): 172 | for each_yearth in range(0, year_num, 1): 173 | for each_day in range(2, 29, 1): 174 | fc_mean_hourly[each_siteth - 1, each_yearth, :] = \ 175 | fc_mean_hourly[each_siteth - 1, each_yearth, :] + \ 176 | np.hstack((isource_data.cselect_1site_1day(each_siteth,year_index[each_yearth], imonth_ploted,each_day-1)[0, 16:24], \ 177 | isource_data.cselect_1site_1day(each_siteth, year_index[each_yearth], imonth_ploted, each_day)[0, 0:16])) 178 | fc_ploted = np.mean(fc_mean_hourly, axis=1) / 27.0 179 | if plot_flag == True: 180 | plt.rcParams['font.family'] = 'Times New Roman' 181 | plt.figure(dpi=300) 182 | ax = host_subplot(111) 183 | ind = np.arange(0, 24, 1) 184 | if len(isiteth_plotted) == 3: 185 | ax.plot(ind, fc_ploted[isiteth_plotted[0] - 1, :], 'k-o', label='Siteth ' + str(isiteth_plotted[0])) 186 | ax.plot(ind, fc_ploted[isiteth_plotted[1] - 1, :], 'b-^', label='Siteth ' + str(isiteth_plotted[1])) 187 | ax.plot(ind, fc_ploted[isiteth_plotted[2] - 1, :], 'r-d', label='Siteth ' + str(isiteth_plotted[2])) 188 | elif len(isiteth_plotted) == 2: 189 | ax.plot(ind, fc_ploted[isiteth_plotted[0] - 1, :], 'k-o', label='Siteth ' + str(isiteth_plotted[0])) 190 | ax.plot(ind, fc_ploted[isiteth_plotted[1] - 1, :], 'b-^', label='Siteth ' + str(isiteth_plotted[1])) 191 | else: 192 | ax.plot(ind, fc_ploted[isiteth_plotted[0] - 1, :], 'k-o', label='Siteth ' + str(isiteth_plotted[0])) 193 | ax.set_xticks(np.linspace(0, 23, 24)) 194 | x_lables=('00:30', '', '02:30', '', '04:30', '', '06:30', '', '08:30', '', '10:30', \ 195 | '', '12:30', '', '14:30', '', '16:30', '', '18:30', '', '20:30', '', '22:30', '') 196 | ax.set_xticklabels(x_lables) 197 | ax.set_xlabel('Time') 198 | ax.set_ylabel('Wind capacity factors') 199 | ax.set_xlim(-0.7, 23.7) 200 | #plt.grid() 201 | ax.legend(loc = 1) 202 | plt.title('The hourly variation') 203 | plt.show() 204 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 205 | 206 | 207 | def cass_rampws(isource_data, isiteth_plotted=3, plot_flag=False): 208 | '''Assess ramp rate of selected sites. 209 | Output statistical indices and graphs 210 | Args: 211 | isource_data: the source data. 212 | isiteth_plotted: the serial number of selected site. 213 | plot_flag: True or False. Draw graphs or not. 214 | Returns: 215 | the graphs of monthly variation. 216 | fc_ramp_rate: ramp rate. 217 | ''' 218 | fc_10years = isource_data.cselect_1site_10year(isiteth_plotted) 219 | fc_10years_tmp = np.hstack((fc_10years[0, 1:].reshape(-1, 1), fc_10years[0, 0:-1].reshape(-1, 1))) 220 | fc_ramp_rate = np.subtract.reduce(fc_10years_tmp, axis=1) 221 | if plot_flag == True: 222 | plt.rcParams['font.family'] = 'Times New Roman' 223 | plt.figure(dpi=300) 224 | ax = plt.subplot(111) 225 | cn, bins, step = ax.hist(fc_ramp_rate, bins=40, normed=True, histtype='barstacked', color='b') 226 | ax.set_ylabel('Probability density') 227 | ax.set_xlabel('ramp rate') 228 | ax.set_xticks(np.arange(-0.2, 0.3, 0.1)) 229 | ax.set_xticklabels((-0.2, -0.1, 0.0, 0.1, 0.2)) 230 | ax.set_yticks(np.linspace(0, 15, 4)) 231 | ax.set_yticklabels((0, 5, 10, 15)) 232 | #ax.grid(True) 233 | plt.title('The distribution of ramp rate') 234 | plt.show() 235 | # plt.savefig('cwp43000.png', dpi=300, bbox_inches='tight') 236 | return fc_ramp_rate 237 | 238 | 239 | def cass_single_site(isource_data, isiteth=3): 240 | '''Assess selected sites. 241 | Output statistical indices 242 | Args: 243 | isource_data: the source data. 244 | isiteth: the serial number of selected site. 245 | Returns: 246 | fc_mean_yearly: means of selected site in year scale. 247 | fc_std_yearly: std of selected site in year scale. 248 | fc_varcoef_yearly: variable coefficient of selected site in year scale. 249 | fc_mean_hourly: means of selected site in hour scale. 250 | fc_std_hourly: std of selected site in hour scale. 251 | fc_varcoef_hourly: variable coefficient of selected site in hour scale. 252 | fc_half_prob: half power probability. 253 | fc_ramp_rate: ramp rate. 254 | fc_ramp_mean: mean of ramp rate. 255 | fc_ramp_std: std of ramp rate. 256 | fc_ramp_max: max of ramp rate. 257 | fc_ramp_min: min of ramp rate. 258 | fc_ramp_upper: upper value of 95% confidence interval ramp rate. 259 | fc_ramp_lower: lower value of 95% confidence interval ramp rate. 260 | ''' 261 | (fc_mean_yearly, fc_std_yearly, fc_varcoef_yearly) = cass_varws_annual(isource_data, isiteth, False) 262 | fc_10years = isource_data.cselect_1site_10year(isiteth) 263 | fc_mean_hourly = np.mean(fc_10years) 264 | fc_std_hourly = np.std(fc_10years) 265 | fc_varcoef_hourly = fc_mean_hourly * 1.0 / fc_std_hourly 266 | fc_half_prob = (fc_10years[0, fc_10years[0, :] > 0.5].shape[0] * 1.0)/ (fc_10years.shape[1] * 1.0) 267 | fc_ramp_rate = cass_rampws(isource_data, isiteth, False) 268 | fc_ramp_mean = np.mean(np.abs(fc_ramp_rate)) 269 | fc_ramp_std = np.std(fc_ramp_rate) 270 | fc_ramp_max = np.max(fc_ramp_rate) 271 | fc_ramp_min = np.min(fc_ramp_rate) 272 | fc_ramp_upper = fc_ramp_mean + 1.96 * fc_ramp_std 273 | fc_ramp_lower = fc_ramp_mean - 1.96 * fc_ramp_std 274 | return fc_mean_yearly, fc_std_yearly, fc_varcoef_yearly, fc_mean_hourly, fc_std_hourly, fc_varcoef_hourly, \ 275 | fc_half_prob, fc_ramp_rate, fc_ramp_mean, fc_ramp_std, fc_ramp_max, fc_ramp_min, fc_ramp_upper, fc_ramp_lower 276 | 277 | 278 | def cass_attr_constr(isource_data, imode=0): 279 | '''Construct new attirbutes. 280 | Output statistical indices 281 | Args: 282 | isource_data: the source data. 283 | imode: 0, 1, 2, 3. means in year scale, std in hour scale, variable coefficient in hour scale, means in hour scale 284 | Returns: 285 | the constructed attributes. 286 | ''' 287 | cf_10yearstl = isource_data.c2style_10year(True) 288 | year_index = range(isource_data.start_year, isource_data.end_year + 1) 289 | year_num = len(year_index) 290 | site_num = len(isource_data.site_index) 291 | if imode == 0: 292 | fc_mean_yearly = np.zeros((1, site_num), np.float32) 293 | for each_siteth in range(1, site_num + 1, 1): 294 | fc_mean_yearly[0, each_siteth-1] = \ 295 | np.sum(cf_10yearstl[each_siteth], axis=1) * 1.0 / year_num 296 | return fc_mean_yearly 297 | elif imode == 1: 298 | fc_std_hourly = np.zeros((1, site_num), np.float32) 299 | for each_siteth in range(1, site_num + 1, 1): 300 | fc_std_hourly[0, each_siteth-1] = \ 301 | np.std(cf_10yearstl[each_siteth], axis=1) * 1.0 302 | return fc_std_hourly 303 | elif imode == 2: 304 | fc_varcoef_hourly = np.zeros((1, site_num), np.float32) 305 | for each_siteth in range(1, site_num + 1, 1): 306 | fc_varcoef_hourly[0, each_siteth-1] = \ 307 | np.std(cf_10yearstl[each_siteth], axis=1) * 1.0 / np.mean(cf_10yearstl[each_siteth], axis=1) 308 | return fc_varcoef_hourly 309 | else: 310 | fc_mean_hourly = np.zeros((1, site_num), np.float32) 311 | for each_siteth in range(1, site_num + 1, 1): 312 | fc_mean_hourly[0, each_siteth-1] = \ 313 | np.mean(cf_10yearstl[each_siteth], axis=1) 314 | return fc_mean_hourly 315 | 316 | 317 | if __name__ == '__main__': 318 | '''Examples.''' 319 | start_year = 2006 320 | end_year = 2015 321 | site_index = [(-6, 2), (-6, 6), (-6, 10)] 322 | wfile_name = 'sd_wind_data' 323 | sfile_name = 'sd_solar_data' 324 | 325 | wind_data = mwind.WindData(wfile_name, site_index, start_year, end_year) 326 | wind_speed_ref = wind_data.cimport_data() 327 | wind_speed_hw = wind_data.cref2hw() 328 | wind_capacity_factor = wind_data.cwind2cf() 329 | solar_data = msolar.SolarData(sfile_name, site_index, start_year, end_year) 330 | solar_irrad_data = solar_data.cimport_data() 331 | solar_capacity_factor = solar_data.csolar2cf_model1() 332 | # solar_temperature_data = solar_data.cimport_datat() 333 | # solar_capacity_factor = solar_data.csolar2cf_model2() 334 | 335 | (fc_mean_yearly, fc_std_yearly, fc_varcoef_yearly) = cass_varws_annual(wind_data, [1,2,3], True) 336 | cass_varws_monthly(wind_data, [1,2,3], 2010, True) 337 | cass_varws_hourly(wind_data, [1,2,3], 'Apr', True) 338 | fc_ramp_rate = cass_rampws(wind_data, 3, True) 339 | assess_result = cass_single_site(wind_data, 3) 340 | attr = cass_attr_constr(wind_data, 0) 341 | 342 | 343 | -------------------------------------------------------------------------------- /ass_res/ass_widearea_dis.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Jun 10 09:31:51 2017 5 | ######################################################################################## 6 | # @ File name: ass_widearea_dis.py 7 | # @ Function: Perform wide-area assessment. 8 | # Assess the spatial distribution of resources, local synergy effects, 9 | # and the spatial effects of one site. 10 | # @ Author: Yongji Cao, Hengxu Zhang 11 | # @ Version: 1.0 12 | # @ Revision date: Jan/19/2018 13 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 14 | ######################################################################################## 15 | """ 16 | 17 | 18 | import numpy as np 19 | 20 | import init_nasa_wind as mwind 21 | import init_nasa_solar as msolar 22 | import draw_geo_fig as mdgf 23 | import ass_singlesite_ws as masw 24 | import ass_singlesite_lsy as maslsy 25 | import ass_widearea_wsy as mawwsy 26 | 27 | 28 | def ass_wide_ws(isource_data, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat, imode=0): 29 | '''Assess the spatial distribution of wind and solar resources. 30 | Output statistical indices and graphs. 31 | Args: 32 | isource_data: the source data. 33 | ioutline: the outlines of the background. 34 | inames: the filename of geographical data, attribute/column name, and region name. 35 | ilat_lable: the latitude label. 36 | ilon_lable: the longitude label. 37 | isite_lon: the longitude of the annotated site. 38 | isite_lat: the latitude of the annotated sites. 39 | imode: 0, 1, 2, 3. means in year scale, std in hour scale, variable coefficient in hour scale, means in hour scale 40 | Returns: 41 | the graphs. 42 | attri: the selected attribute. 43 | the max value of the selected attribute. 44 | the serial number of site of the max value. 45 | the min value of the selected attribute. 46 | the serial number of site of the min value. 47 | ''' 48 | attri = masw.cass_attr_constr(isource_data, imode) 49 | mdgf.cdraw_contour_fig(attri, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat) 50 | return attri, np.max(attri, axis=1), np.argmax(attri, axis=1) + 1, \ 51 | np.min(attri, axis=1), np.argmin(attri, axis=1) + 1 52 | 53 | 54 | def ass_wide_lsy(iwind_data, isolar_data, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat, imode=0, ikw=0.60, ikf=0.98, ikc=-0.5, igama=0.1): 55 | '''Assess the spatial distribution of local synergy effects. 56 | Output statistical indices and graphs. 57 | Args: 58 | iwind_data: the source wind data. 59 | isolar_data: the source solar data. 60 | ioutline: the outlines of the background. 61 | inames: the filename of geographical data, attribute/column name, the region name. 62 | ilat_lable: the latitude label. 63 | ilon_lable: the longitude label. 64 | isite_lon: the longitude of the annotated site. 65 | isite_lat: the latitude of the annotated sites. 66 | imode: 0~15 67 | 0 - optimal ratio of wind and solar. 68 | 1 - means of optimal wind and solar configuration in year scale. 69 | 2 - means of optimal wind and solar configuration in hour scale. 70 | 3 - std of optimal wind and solar configuration in hour scale. 71 | 4 - variation coefficient of optimal wind and solar configuration in hour scale. 72 | 5 - half power probability. 73 | 6 - the ramp rate of optimal wind and solar configuration. 74 | 7 - mean of ramp rate. 75 | 8 - std of ramp rate. 76 | 9 - max of ramp rate. 77 | 10 - min of ramp rate. 78 | 11 - upper value of 95% confidence interval ramp rate. 79 | 12 - lower value of 95% confidence interval ramp rate. 80 | 13 - the improving coefficient. 81 | 14 - the local synergy coefficient. 82 | 15 - the comprehensive profit coefficient. 83 | ikw: the profit coefficient of electricity selling of wind energy. 84 | ikf: the profit coefficient of electricity selling of solar energy. 85 | ikc: the negative cost coefficient of ramp reserve. 86 | igama: the weight factor. 87 | Returns: 88 | the graphs. 89 | attri: the selected attribute. 90 | the max value of the selected attribute. 91 | the serial number of site of the max value. 92 | the min value of the selected attribute. 93 | the serial number of site of the min value. 94 | ''' 95 | attri = maslsy.cass_attr_constr(iwind_data, isolar_data, imode, ikw, ikf, ikc, igama) 96 | mdgf.cdraw_contour_fig(attri, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat) 97 | return attri, np.max(attri, axis=1), np.argmax(attri, axis=1) + 1, \ 98 | np.min(attri, axis=1), np.argmin(attri, axis=1) + 1 99 | 100 | 101 | def ass_site_wsy(iwind_data, isolar_data, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat, isiteth=71, imode=0): 102 | '''Assess the spatial distribution of the spatial effects of a selected site. 103 | Output statistical indices and graphs. 104 | Args: 105 | iwind_data: the source wind data. 106 | isolar_data: the source solar data. 107 | ioutline: the outlines of the background. 108 | inames: the filename of geographical data, attribute/column name, and region name. 109 | ilat_lable: the latitude label. 110 | ilon_lable: the longitude label. 111 | isite_lon: the longitude of the annotated site. 112 | isite_lat: the latitude of the annotated sites. 113 | isiteth: the serial number of the selected site. 114 | imode: 0, 1, 2, 3. the spatial wind-wind synergy coefficient, spatial solar-solar synergy coefficient, 115 | spatial wind-solar synergy coefficient, spatial solar-wind synergy coefficient 116 | Returns: 117 | the graphs. 118 | attri: the selected attribute. 119 | the max value of the selected attribute. 120 | the serial number of site of the max value. 121 | the min value of the selected attribute. 122 | the serial number of site of the min value. 123 | ''' 124 | attri = mawwsy.ccalc_synergy_coef(iwind_data, isolar_data, imode, isiteth) 125 | mdgf.cdraw_contour_fig(attri, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat) 126 | return attri, np.max(attri, axis=1), np.argmax(attri, axis=1) + 1, \ 127 | np.min(attri, axis=1), np.argmin(attri, axis=1) + 1 128 | 129 | 130 | if __name__ == '__main__': 131 | '''Examples.''' 132 | start_year = 2006 133 | end_year = 2015 134 | site_index = [(-2, 0), (-2, 1), (-2, 2), (-2, 3), (-2, 4), (-2, 5), (-2, 6), 135 | (-2, 7), (-2, 8), (-2, 9), (-2, 10), (-2, 11), (-2, 12), (-2, 13), (-2, 14), 136 | (-3, 0), (-3, 1), (-3, 2), (-3, 3), (-3, 4), (-3, 5), (-3, 6), 137 | (-3, 7), (-3, 8), (-3, 9), (-3, 10), (-3, 11), (-3, 12), (-3, 13), (-3, 14), 138 | (-4, 0), (-4, 1), (-4, 2), (-4, 3), (-4, 4), (-4, 5), (-4, 6), 139 | (-4, 7), (-4, 8), (-4, 9), (-4, 10), (-4, 11), (-4, 12), (-4, 13), (-4, 14), 140 | (-5, 0), (-5, 1), (-5, 2), (-5, 3), (-5, 4), (-5, 5), (-5, 6), 141 | (-5, 7), (-5, 8), (-5, 9), (-5, 10), (-5, 11), (-5, 12), (-5, 13), (-5, 14), 142 | (-6, 0), (-6, 1), (-6, 2), (-6, 3), (-6, 4), (-6, 5), (-6, 6), 143 | (-6, 7), (-6, 8), (-6, 9), (-6, 10), (-6, 11), (-6, 12), (-6, 13), (-6, 14), 144 | (-7, 0), (-7, 1), (-7, 2), (-7, 3), (-7, 4), (-7, 5), (-7, 6), 145 | (-7, 7), (-7, 8), (-7, 9), (-7, 10), (-7, 11), (-7, 12), (-7, 13), (-7, 14), 146 | (-8, 0), (-8, 1), (-8, 2), (-8, 3), (-8, 4), (-8, 5), (-8, 6), 147 | (-8, 7), (-8, 8), (-8, 9), (-8, 10), (-8, 11), (-8, 12), (-8, 13), (-8, 14), 148 | (-9, 0), (-9, 1), (-9, 2), (-9, 3), (-9, 4), (-9, 5), (-9, 6), 149 | (-9, 7), (-9, 8), (-9, 9), (-9, 10), (-9, 11), (-9, 12), (-9, 13), (-9, 14), 150 | (-10, 0), (-10, 1), (-10, 2), (-10, 3), (-10, 4), (-10, 5), (-10, 6), 151 | (-10, 7), (-10, 8), (-10, 9), (-10, 10), (-10, 11), (-10, 12), (-10, 13), (-10, 14), 152 | (-11, 0), (-11, 1), (-11, 2), (-11, 3), (-11, 4), (-11, 5), (-11, 6), 153 | (-11, 7), (-11, 8), (-11, 9), (-11, 10), (-11, 11), (-11, 12), (-11, 13), (-11, 14)] 154 | wfile_name = 'sd_wind_data' 155 | sfile_name = 'sd_solar_data' 156 | 157 | outline = (114, 34, 123, 38.5) 158 | names = ('CHN_adm_shp/CHN_adm3', 'NAME_1', 'Shandong') 159 | lat_lable = (34, 39, 0.5) 160 | lon_lable = (114, 124, 1) 161 | site_lon = [118, 118.667, 162 | 116.667, 117.333, 118, 118.667, 120, 120.667, 121.333, 122, 163 | 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 121.333, 122, 164 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 165 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 166 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 167 | 115.333, 116, 116.667, 117.333, 118, 118.667, 168 | 115.333, 116, 117.333, 118] 169 | site_lat = [38, 38, 170 | 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 171 | 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 172 | 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 173 | 36, 36, 36, 36, 36, 36, 36, 36, 36, 174 | 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 175 | 35, 35, 35, 35, 35, 35, 176 | 34.5, 34.5, 34.5, 34.5] 177 | 178 | wind_data = mwind.WindData(wfile_name, site_index, start_year, end_year) 179 | wind_speed_ref = wind_data.cimport_data() 180 | wind_speed_hw = wind_data.cref2hw() 181 | wind_capacity_factor = wind_data.cwind2cf() 182 | solar_data = msolar.SolarData(sfile_name, site_index, start_year, end_year) 183 | solar_irrad_data = solar_data.cimport_data() 184 | solar_capacity_factor = solar_data.csolar2cf_model1() 185 | # solar_temperature_data = solar_data.cimport_datat() 186 | # solar_capacity_factor = solar_data.csolar2cf_model2() 187 | 188 | fc_mean_yearly = ass_wide_ws(wind_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 0) 189 | fc_std_hourly = ass_wide_ws(wind_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 1) 190 | fc_varcoef_hourly = ass_wide_ws(wind_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 2) 191 | fc_mean_hourly = ass_wide_ws(wind_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 3) 192 | 193 | hfc_mean_yearly = ass_wide_lsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 0) 194 | hfc_varcoef_hourly = ass_wide_lsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 1) 195 | improving_coef = ass_wide_lsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 2) 196 | local_synergy_coef = ass_wide_lsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 3) 197 | 198 | synergy_1coef_ww = ass_site_wsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 71, 0) 199 | synergy_1coef_ss = ass_site_wsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 71, 1) 200 | synergy_1coef_ws = ass_site_wsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 71, 2) 201 | synergy_1coef_sw = ass_site_wsy(wind_data, solar_data, outline, names, lat_lable, lon_lable, site_lon, site_lat, 71, 3) -------------------------------------------------------------------------------- /ass_res/ass_widearea_wsy.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Jun 10 11:12:21 2017 5 | ######################################################################################## 6 | # @ File name: ass_widearea_wsy.py 7 | # @ Function: Perform wide-area assessment. 8 | # Assess the spatial synergy effects. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | from scipy import stats 19 | from sklearn.cluster import KMeans 20 | from sklearn.decomposition import PCA 21 | from sklearn.metrics import silhouette_samples, silhouette_score 22 | 23 | import init_nasa_wind as mwind 24 | import init_nasa_solar as msolar 25 | import draw_geo_fig as mdgf 26 | 27 | 28 | def ccalc_synergy_coef(iwind_data, isolar_data, imode=0, isiteth=0): 29 | '''Calculate the spatial synergy coefficients 30 | Args: 31 | iwind_data: the source wind data. 32 | isource_data: the source solar data. 33 | imode: 0, 1, 2, 3. the spatial wind-wind synergy coefficient, spatial solar-solar synergy coefficient, 34 | spatial wind-solar synergy coefficient, spatial solar-wind synergy coefficient 35 | isiteth: the serial number of the selected site. 36 | Returns: 37 | synergy_coef_nor: the spatial synergy coefficients. 38 | ''' 39 | wf_10yearstl = iwind_data.c2style_10year(True) 40 | sf_10yearstl = isolar_data.c2style_10year(True) 41 | site_num = len(iwind_data.site_index) 42 | year_index = range(iwind_data.start_year, iwind_data.end_year + 1) 43 | year_num = len(year_index) 44 | if isiteth == 0: 45 | pearson_coef = np.zeros((site_num, site_num), np.float32) 46 | if imode == 0: 47 | fc_0 = wf_10yearstl 48 | fc_1 = wf_10yearstl 49 | elif imode == 1: 50 | fc_0 = sf_10yearstl 51 | fc_1 = sf_10yearstl 52 | elif imode == 2: 53 | fc_0 = wf_10yearstl 54 | fc_1 = sf_10yearstl 55 | else: 56 | fc_0 = sf_10yearstl 57 | fc_1 = wf_10yearstl 58 | for each_siteth0 in range(1, site_num + 1, 1): 59 | for each_siteth1 in range(1, site_num + 1, 1): 60 | pearson_coef[each_siteth0 - 1, each_siteth1 - 1] = \ 61 | stats.pearsonr(fc_0[each_siteth0][0, :], fc_1[each_siteth1][0, :])[0] 62 | synergy_coef = (1 - pearson_coef) / 2.0 63 | synergy_coef_nor = np.empty((site_num, site_num), np.float32) 64 | for each_siteth in range(0, site_num, 1): 65 | synergy_coef_nor = \ 66 | np.hstack((synergy_coef_nor, ((synergy_coef[:, each_siteth] - \ 67 | np.min(synergy_coef[:, each_siteth])) / np.ptp(synergy_coef[:, each_siteth])).reshape(site_num, - 1))) 68 | else: 69 | pearson_coef = np.zeros((1, site_num), np.float32) 70 | if imode == 0: 71 | fc_0 = wf_10yearstl[isiteth] 72 | fc_1 = wf_10yearstl 73 | elif imode == 1: 74 | fc_0 = sf_10yearstl[isiteth] 75 | fc_1 = sf_10yearstl 76 | elif imode == 2: 77 | fc_0 = wf_10yearstl[isiteth] 78 | fc_1 = sf_10yearstl 79 | else: 80 | fc_0 = sf_10yearstl[isiteth] 81 | fc_1 = wf_10yearstl 82 | for each_siteth in range(1, site_num + 1, 1): 83 | pearson_coef[0, each_siteth - 1] = \ 84 | stats.pearsonr(fc_0[0, :], fc_1[each_siteth][0, :])[0] 85 | synergy_coef = (1 - pearson_coef) / 2.0 86 | synergy_coef_nor = synergy_coef 87 | return synergy_coef_nor 88 | 89 | 90 | def csearch_centroid (idata_index, isy_coef, icluster_num=6): 91 | '''Search the centroid of each cluster. 92 | Args: 93 | idata_index: the clustering indices of source data. 94 | isy_coef: the spatial synergy coefficients processed by the PCA. 95 | icluster_num: the number of clusters. 96 | Returns: 97 | centroid_site + 1: the serial number of the searched centroids. 98 | ''' 99 | centroid_site = np.zeros((1, icluster_num), np.float32) 100 | for each0 in range(0,icluster_num,1): 101 | num = len(idata_index[each0]) 102 | tmp = np.zeros((1,num), np.float32) 103 | for each1 in range(num): 104 | for each2 in idata_index[each0]: 105 | if idata_index[each0][each1] != each2: 106 | tmp[0, each1] = tmp[0,each1] + ((isy_coef[idata_index[each0][each1], 0]) ** \ 107 | 2 - (isy_coef[each2, 0]) ** 2) ** 0.5 108 | centroid_site[0, each0] = idata_index[each0][np.argmin(tmp)] 109 | return centroid_site + 1 110 | 111 | 112 | def cpca_and_cluster(ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat, isynergy_coef, icomponent_num=1, icluster_num=6): 113 | '''Assess the spatial synergy effects by the PCA and k-menas clustering. 114 | Args: 115 | ioutline: the outlines of the background. 116 | inames: the filename of geographical data, attribute/column name, and region name. 117 | ilat_lable: the latitude label. 118 | ilon_lable: the longitude label. 119 | isite_lon: the longitude of the annotated site. 120 | isite_lat: the latitude of the annotated sites. 121 | isynergy_coef: the spatial synergy coefficients. 122 | icomponent_num: the number of principal components. 123 | icluster_num: the number of clusters 124 | Returns: 125 | the graphs. 126 | pca_evr: the explained variance of each components of the PCA 127 | centroid_site: the serial number of the searched centroids. 128 | data_index: the clustering indices of source data. 129 | sycoef_pca_nor: he spatial synergy coefficients processed by the PCA. 130 | silhouette_avg: the average silhouette coefficients. 131 | silhouette_sample: the silhouette coefficients. 132 | ''' 133 | pca_instance = PCA(n_components=icomponent_num) 134 | pca_instance.fit(isynergy_coef) 135 | pca_component = pca_instance.components_ 136 | pca_evr = pca_instance.explained_variance_ratio_ 137 | 138 | sycoef_pca = pca_instance.transform(isynergy_coef) 139 | sycoef_pca_nor = (sycoef_pca - np.mean(sycoef_pca, axis=0)) / np.std(sycoef_pca, axis=0) 140 | kmeans_instance = KMeans(n_clusters=icluster_num) 141 | sycoef_kmeans = kmeans_instance.fit(sycoef_pca_nor) 142 | sycoef_km_label = sycoef_kmeans.labels_ 143 | sycoef_km_center = sycoef_kmeans.cluster_centers_ 144 | sycoef_km_inertia = sycoef_kmeans.inertia_ 145 | data_index = [] 146 | for each_cluster in range(0, icluster_num, 1): 147 | data_index.append([x for x in range(0, isynergy_coef.shape[0]) if sycoef_km_label[x] == each_cluster]) 148 | silhouette_avg = silhouette_score(sycoef_pca_nor, sycoef_km_label) 149 | silhouette_sample = silhouette_samples(sycoef_pca_nor, sycoef_km_label) 150 | centroid_site = csearch_centroid (data_index, sycoef_pca_nor, icluster_num) 151 | mdgf.cdraw_cluster_fig(data_index, icluster_num, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat) 152 | return pca_evr, centroid_site, data_index, sycoef_pca_nor, silhouette_avg, silhouette_sample 153 | 154 | 155 | def canal_cluster(iwind_data, isolar_data, icentroid_site0, icentroid_site1, imode=0, icluster_num=6): 156 | '''Assess the spatial synergy effects by the PCA and k-menas clustering. 157 | Args: 158 | wind_data: the source wind data. 159 | isource_data: the source solar data. 160 | icentroid_site0: the serial number of the searched centroids of mode 0 of spatial synergy effects. 161 | icentroid_site1: the serial number of the searched centroids of mode 1 of spatial synergy effects. 162 | imode: 0, 1, 2, 3. the spatial wind-wind synergy coefficient, spatial solar-solar synergy coefficient, 163 | spatial wind-solar synergy coefficient, spatial solar-wind synergy coefficient 164 | icluster_num: the number of clusters 165 | Returns: 166 | the max value of the selected attribute. 167 | the serial number of site of the max value. 168 | the min value of the selected attribute. 169 | the serial number of site of the min value. 170 | ''' 171 | wf_10yearstl = iwind_data.c2style_10year(True) 172 | sf_10yearstl = isolar_data.c2style_10year(True) 173 | synergy_coef = np.zeros((icluster_num, icluster_num), np.float32) 174 | if imode == 0: 175 | fc_0 = wf_10yearstl 176 | fc_1 = wf_10yearstl 177 | elif imode == 1: 178 | fc_0 = sf_10yearstl 179 | fc_1 = sf_10yearstl 180 | elif imode == 2: 181 | fc_0 = wf_10yearstl 182 | fc_1 = sf_10yearstl 183 | else: 184 | fc_0 = sf_10yearstl 185 | fc_1 = wf_10yearstl 186 | for each0 in range(0, icluster_num, 1): 187 | for each1 in range(0, icluster_num, 1): 188 | synergy_coef[each0, each1] = \ 189 | stats.pearsonr(fc_0[icentroid_site0[0, each0]][0, :], fc_1[icentroid_site1[0, each1]][0, :])[0] 190 | synergy_coef = (1 - synergy_coef) /2.0 191 | return icentroid_site1[0, np.argmax(synergy_coef, axis=0)], np.max(synergy_coef, axis=0), \ 192 | icentroid_site1[0, np.argmin(synergy_coef, axis=0)], np.min(synergy_coef, axis=0) 193 | 194 | 195 | if __name__ == '__main__': 196 | '''Examples.''' 197 | start_year = 2006 198 | end_year = 2015 199 | site_index = [(-3, 6), (-3, 7), 200 | (-4, 4), (-4, 5), (-4, 6), (-4, 7), (-4, 9), (-4, 10), (-4, 11), (-4, 12), 201 | (-5, 3), (-5, 4), (-5, 5), (-5, 6), (-5, 7), (-5, 8), (-5, 9), (-5, 10), (-5, 11), (-5, 12), 202 | (-6, 2), (-6, 3), (-6, 4), (-6, 5), (-6, 6), (-6, 7), (-6, 8), (-6, 9), (-6, 10), 203 | (-7, 2), (-7, 3), (-7, 4), (-7, 5), (-7, 6), (-7, 7), (-7, 8), (-7, 9), (-7, 10), 204 | (-8, 2), (-8, 3), (-8, 4), (-8, 5), (-8, 6), (-8, 7), (-8, 8), 205 | (-9, 2), (-9, 3), (-9, 4), (-9, 5), (-9, 6), (-9, 7), 206 | (-10, 2), (-10, 3), (-10, 5), (-10, 6)] 207 | wfile_name = 'sd_wind_data' 208 | sfile_name = 'sd_solar_data' 209 | 210 | outline = (114, 34, 123, 38.5) 211 | names = ('CHN_adm_shp/CHN_adm3', 'NAME_1', 'Shandong') 212 | lat_lable = (34, 39, 0.5) 213 | lon_lable = (114, 124, 1) 214 | site_lon = [118, 118.667, 215 | 116.667, 117.333, 118, 118.667, 120, 120.667, 121.333, 122, 216 | 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 121.333, 122, 217 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 218 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 219 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 220 | 115.333, 116, 116.667, 117.333, 118, 118.667, 221 | 115.333, 116, 117.333, 118] 222 | site_lat = [38, 38, 223 | 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 224 | 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 225 | 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 226 | 36, 36, 36, 36, 36, 36, 36, 36, 36, 227 | 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 228 | 35, 35, 35, 35, 35, 35, 229 | 34.5, 34.5, 34.5, 34.5] 230 | 231 | wind_data = mwind.WindData(wfile_name, site_index, start_year, end_year) 232 | wind_speed_ref = wind_data.cimport_data() 233 | wind_speed_hw = wind_data.cref2hw() 234 | wind_capacity_factor = wind_data.cwind2cf() 235 | solar_data = msolar.SolarData(sfile_name, site_index, start_year, end_year) 236 | solar_irrad_data = solar_data.cimport_data() 237 | solar_capacity_factor = solar_data.csolar2cf_model1() 238 | # solar_temperature_data = solar_data.cimport_datat() 239 | # solar_capacity_factor = solar_data.csolar2cf_model2() 240 | 241 | synergy_coef_ww = ccalc_synergy_coef(wind_data, solar_data, 0, 0) 242 | assess_result_ww = cpca_and_cluster(outline, names, lat_lable, lon_lable, site_lon, site_lat, synergy_coef_ww, 3, 6) 243 | 244 | assresult_centroid_ww=canal_cluster(wind_data, solar_data, assess_result_ww[1], assess_result_ww[1], 0, 6) 245 | -------------------------------------------------------------------------------- /ass_res/draw_geo_fig.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Fri Jun 09 20:40:31 2017 5 | ######################################################################################## 6 | # @ File name: draw_geo_fig.py 7 | # @ Function: Draw geographical graphs. 8 | # Basic graphs, contour graphs, and clustering graphs. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | import matplotlib.pyplot as plt 19 | from mpl_toolkits.basemap import Basemap 20 | 21 | 22 | def anno_txt(imap, isite_lon, isite_lat, itxt, imode=False, imarker='.', icolor='k', ilabelname='Cluster 1'): 23 | '''Annotate on the geographical graphs. 24 | Args: 25 | imap: the handle of the geographical graphs. 26 | isite_lon: the longitude of the annotated site. 27 | isite_lat: the latitude of the annotated sites. 28 | itxt: the annotated text. 29 | imode: True or False. Annote labelname or not. 30 | imarker: the annotated marker. 31 | icolor: the annotated colar. 32 | ilabelname: the labelname. 33 | Returns: 34 | null. 35 | ''' 36 | x0, y0 = imap(isite_lon, isite_lat) 37 | if imode == True: 38 | imap.scatter(x0, y0, marker=imarker, color=icolor, label=ilabelname) 39 | else: 40 | imap.scatter(x0, y0, marker=imarker, color=icolor,) 41 | for x1, y1, z1 in zip(x0, y0, itxt): 42 | plt.text(x1, y1, z1, fontweight='bold', fontsize=12, ha='left', 43 | va='bottom', color=icolor) 44 | 45 | 46 | def cdraw_geo_ax(iax, ioutline, inames, ilat_lable, ilon_lable): 47 | '''Draw the geographical graphs on the given ax. 48 | Args: 49 | iax: the handle of given ax. 50 | ioutline: the outlines of the background. 51 | inames: the filename of geographical data, attribute/column name, and region name. 52 | ilat_lable: the latitude label. 53 | ilon_lable: the longitude label. 54 | Returns: 55 | bmap: the handle of the geographical graphs. 56 | ''' 57 | illlong = ioutline[0] 58 | illlat = ioutline[1] 59 | iurlong = ioutline[2] 60 | iurlat = ioutline[3] 61 | bmap = Basemap(llcrnrlon=illlong, llcrnrlat=illlat, urcrnrlon=iurlong, 62 | urcrnrlat=iurlat, projection='cyl', ax=iax) 63 | ifile_name = inames[0] 64 | iinfo_name = inames[1] 65 | iobj_name = inames[2] 66 | shp_info = bmap.readshapefile(ifile_name, 'states', drawbounds=False) 67 | for info, shp in zip(bmap.states_info, bmap.states): 68 | proid = info[iinfo_name] 69 | if proid == iobj_name: 70 | xc, yc = zip(*shp) 71 | bmap.plot(xc, yc, marker=None, color='k', lw=0.7) 72 | bmap.drawcountries() 73 | ilat_start = ilat_lable[0] 74 | ilat_end = ilat_lable[1] 75 | ilat_step = ilat_lable[2] 76 | ilon_start = ilon_lable[0] 77 | ilon_end = ilon_lable[1] 78 | ilon_step = ilon_lable[2] 79 | bmap.drawparallels(np.arange(ilat_start, ilat_end, ilat_step), labels=[1, 0, 0, 0]) 80 | bmap.drawmeridians(np.arange(ilon_start, ilon_end, ilon_step), labels=[0, 0, 0, 1]) 81 | return bmap 82 | 83 | 84 | def cdraw_basic_fig(ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat): 85 | '''Draw the basic geographical graphs. 86 | Args: 87 | ioutline: the outlines of the background. 88 | inames: the filename of geographical data, attribute/column name, and region name. 89 | ilat_lable: the latitude label. 90 | ilon_lable: the longitude label. 91 | isite_lon: the longitude of the annotated site. 92 | isite_lat: the latitude of the annotated sites. 93 | Returns: 94 | the basic geographical graphs. 95 | ''' 96 | fig = plt.figure(dpi=300) 97 | plt.rcParams['font.family'] = 'Times New Roman' 98 | axc = fig.add_subplot(111) 99 | bmapc = cdraw_geo_ax(axc, ioutline, inames, ilat_lable, ilon_lable) 100 | siteth = [str(x) for x in range(1, len(isite_lon) + 1, 1)] 101 | anno_txt(bmapc, isite_lon, isite_lat, siteth) 102 | axc.set_xlabel(r'Longitude ($\mathrm{^\circ}$)', labelpad=13) 103 | axc.set_ylabel(r'Latitude ($\mathrm{^\circ}$)', labelpad=34) 104 | plt.title('The geographical graph') 105 | plt.show() 106 | # plt.savefig('Geo_graph.png', dpi=300, bbox_inches='tight') 107 | 108 | 109 | def cdraw_contour_fig(idata, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat): 110 | '''Draw the contour geographical graphs. 111 | Args: 112 | idata: the source data 113 | ioutline: the outlines of the background. 114 | inames: the filename of geographical data, attribute/column name, and region name. 115 | ilat_lable: the latitude label. 116 | ilon_lable: the longitude label. 117 | isite_lon: the longitude of the annotated site. 118 | isite_lat: the latitude of the annotated sites. 119 | Returns: 120 | the contour geographical graphs. 121 | ''' 122 | fig = plt.figure(dpi=300) 123 | plt.rcParams['font.family'] = 'Times New Roman' 124 | axc = fig.add_subplot(111) 125 | siteth = [str(x) for x in range(1, len(isite_lon) + 1, 1)] 126 | lon0_num = (ioutline[2] + 1 - ioutline[0]) /2 *3 + 1 127 | lon0 = np.linspace(ioutline[0], ioutline[2] + 1, lon0_num)[0: -1] 128 | lat0_num = (ioutline[3] - ioutline[1]) * 2 + 1 129 | lat0 = np.linspace(ioutline[3], ioutline[1], lat0_num) 130 | lon1, lat1 = np.meshgrid(lon0, lat0) 131 | bmapc = cdraw_geo_ax(axc, ioutline, inames, ilat_lable, ilon_lable) 132 | data_reshape = idata.reshape((int(lat0_num), int(lon0_num) - 1)) 133 | contourc = bmapc.contourf(lon1, lat1, data_reshape, cmap=plt.cm.jet) 134 | barc = bmapc.colorbar(contourc, location='bottom', pad="11%") 135 | anno_txt(bmapc, isite_lon, isite_lat, siteth) 136 | plt.title('The geographical contour') 137 | plt.show() 138 | # plt.savefig('Geo_contour.png', dpi=300, bbox_inches='tight') 139 | 140 | 141 | def cdraw_cluster_fig(idata_index, icluster_num, ioutline, inames, ilat_lable, ilon_lable, isite_lon, isite_lat): 142 | '''Draw the clustering geographical graphs. 143 | Args: 144 | idata_index: the clustering indices of source data. 145 | icluster_num: the number of clusters. 146 | ioutline: the outlines of the background. 147 | inames: the filename of geographical data, attribute/column name, and region name. 148 | ilat_lable: the latitude label. 149 | ilon_lable: the longitude label. 150 | isite_lon: the longitude of the annotated site. 151 | isite_lat: the latitude of the annotated sites. 152 | Returns: 153 | the clustering geographical graphs. 154 | ''' 155 | fig = plt.figure(dpi=300) 156 | plt.rcParams['font.family'] = 'Times New Roman' 157 | axc = fig.add_subplot(111) 158 | siteth = [str(x) for x in range(1, len(isite_lon) + 1, 1)] 159 | bmapc = cdraw_geo_ax(axc, ioutline, inames, ilat_lable, ilon_lable) 160 | marker_list = ['.', 'o', '^', 'd', 's', 'h'] 161 | color_list = ['k', 'r', 'b', 'g', 'm', 'c'] 162 | labelname_list = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5', 'Cluster 6'] 163 | siteth = [str(x) for x in range(1, len(isite_lon) + 1, 1)] 164 | for each0 in range(0, icluster_num): 165 | lonth_list = [] 166 | latth_list = [] 167 | siteth_list = [] 168 | for each1 in idata_index[each0]: 169 | lonth_list.append(isite_lon[each1]) 170 | latth_list.append(isite_lat[each1]) 171 | siteth_list.append(siteth[each1]) 172 | anno_txt(bmapc, lonth_list, latth_list, siteth_list, True, marker_list[each0], color_list[each0], labelname_list[each0]) 173 | # plt.legend(loc=3,ncol=3) 174 | plt.legend(fontsize=9, loc=4, ncol=1) 175 | # plt.savefig('clusteringWW.png', dpi=300, bbox_inches='tight') 176 | plt.title('The clustering graph') 177 | plt.show() 178 | 179 | 180 | if __name__ == '__main__': 181 | '''Examples.''' 182 | outline = (114, 34, 123, 38.5) 183 | names = ('CHN_adm_shp/CHN_adm3', 'NAME_1', 'Shandong') 184 | lat_lable = (34, 39, 0.5) 185 | lon_lable = (114, 124, 1) 186 | site_lon = [118, 118.667, 187 | 116.667, 117.333, 118, 118.667, 120, 120.667, 121.333, 122, 188 | 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 121.333, 122, 189 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 190 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 120, 120.667, 191 | 115.333, 116, 116.667, 117.333, 118, 118.667, 119.333, 192 | 115.333, 116, 116.667, 117.333, 118, 118.667, 193 | 115.333, 116, 117.333, 118] 194 | site_lat = [38, 38, 195 | 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 37.5, 196 | 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 197 | 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 36.5, 198 | 36, 36, 36, 36, 36, 36, 36, 36, 36, 199 | 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 200 | 35, 35, 35, 35, 35, 35, 201 | 34.5, 34.5, 34.5, 34.5] 202 | 203 | cdraw_basic_fig(outline, names, lat_lable, lon_lable, site_lon, site_lat) 204 | # cdraw_contour_fig(c1HyMean, outline, names, lat_lable, lon_lable, site_lon, site_lat) 205 | # cdraw_cluster_fig(indexWW, kWW.n_clusters, outline, names, lat_lable, lon_lable, site_lon, site_lat) -------------------------------------------------------------------------------- /ass_res/init_nasa_solar.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jun 08 21:53:47 2017 5 | ######################################################################################## 6 | # @ File name: init_nasa_solar.py 7 | # @ Function: The class of NASA solar irradiation. 8 | # Importing, preprocessing, and basic operation of solar data. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | from pyhdf.SD import SD 19 | 20 | 21 | class SolarData(object): 22 | '''NASA solar irradiation data class''' 23 | version = '1.0' 24 | month_name = ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 25 | 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec') 26 | leap_year = {'Jan': (31, 101, 131, True), 'Feb': (29, 201, 229, True), 'Mar': (31, 301, 331, True), 27 | 'Apr': (30, 401, 430, True), 'May': (31, 501, 531, True), 'Jun': (30, 601, 630, True), 28 | 'Jul': (31, 701, 731, True), 'Aug': (31, 801, 831, True), 'Sep': (30, 901, 930, True), 29 | 'Oct': (31, 1001, 1031, False), 'Nov':(30, 1101, 1130, False), 'Dec':(31, 1201, 1231, False)} 30 | nonleap_year = {'Jan':(31, 101, 131, True), 'Feb':(28, 201, 228, True), 'Mar':(31, 301, 331, True), 31 | 'Apr': (30, 401, 430, True), 'May': (31, 501, 531, True), 'Jun': (30, 601, 630, True), 'Jul': (31, 701, 731, True), 32 | 'Aug': (31, 801, 831, True), 'Sep': (30, 901, 930, True), 'Oct': (31, 1001, 1031, False), 33 | 'Nov': (30, 1101, 1130, False), 'Dec': (31, 1201, 1231, False)} 34 | #fnamesa and fnamsb are the middle names of sloar irrdiation. 35 | fnamesa = '/MERRA301.prod.assim.tavg1_2d_rad_Nx.' 36 | fnamesb = '/MERRA300.prod.assim.tavg1_2d_rad_Nx.' 37 | fnamee = '.SUB.hdf' 38 | #fnameta and fnamtb are the middle names of ambient temperature. 39 | fnameta = '/MERRA301.prod.assim.tavg1_2d_slv_Nx.' 40 | fnametb = '/MERRA300.prod.assim.tavg1_2d_slv_Nx.' 41 | spl_month2010 = ('Jun', 'Jul', 'Aug') 42 | 43 | def __init__(self, ifile_path, isite_index, istart_year, iend_year): 44 | '''Create an object. 45 | Args: 46 | ifile_name: str; the folder name of NASA file. 47 | isite_index: list; the list site indice in NASA file. 48 | istart_year: int; the start year. 49 | iend_year: inlt; the end year. 50 | Returns: 51 | an instances 52 | ''' 53 | self.file_path = ifile_path 54 | self.site_index = isite_index 55 | self.start_year = istart_year 56 | self.end_year = iend_year 57 | self.dict_solar = {} 58 | self.dict_solar_cf = {} 59 | self.dict_wind_cf0 = {} 60 | self.dict_temperature = {} 61 | self.alpha = 1 62 | 63 | def cimport_data(self, idata_name=('SWGNT',)): 64 | '''Import ten-year dsolar irradiation ata. 65 | Args: 66 | idata_name: the data name. 67 | Returns: 68 | self.dict_solar: imported data. 69 | ''' 70 | year_index = range(self.start_year, self.end_year + 1) 71 | site_num = len(self.site_index) 72 | zero = str(0) 73 | for each_siteth in range(1, site_num + 1): 74 | self.dict_solar[each_siteth] = {} 75 | for each_year in year_index: 76 | self.dict_solar[each_siteth][each_year] = {} 77 | for each_month in SolarData.month_name: 78 | self.dict_solar[each_siteth][each_year][each_month] = np.empty((0,24), np.float32) 79 | for each_year in year_index: 80 | if ((each_year % 400 == 0) or ((each_year % 4 == 0) and (each_year % 100 != 0))): 81 | year_feature = SolarData.leap_year 82 | else: 83 | year_feature = SolarData.nonleap_year 84 | for each_month in SolarData.month_name: 85 | if ((each_year < 2010) or ((each_year == 2010) and (each_month in SolarData.spl_month2010))): 86 | fnames = self.file_path + SolarData.fnamesa 87 | else: 88 | fnames = self.file_path + SolarData.fnamesb 89 | day_s = year_feature[each_month][1] 90 | day_e = year_feature[each_month][2] 91 | if year_feature[each_month][3]: 92 | for each_day in range(day_s, day_e + 1): 93 | fname = fnames + str(each_year) + zero + str(each_day) + SolarData.fnamee 94 | print fname 95 | fid = SD(fname) 96 | tmpirrad = fid.select(idata_name[0])[:, :, :] 97 | fid.end() 98 | for each_siteth in range(1, site_num + 1): 99 | tmpirrad_site = tmpirrad[:, self.site_index[each_siteth - 1][0], \ 100 | self.site_index[each_siteth - 1][1]].reshape(1, -1) 101 | self.dict_solar[each_siteth][each_year][each_month] = \ 102 | np.vstack((self.dict_solar[each_siteth][each_year][each_month], tmpirrad_site)) 103 | else: 104 | for each_day in range(day_s, day_e + 1): 105 | fname = fnames + str(each_year) + str(each_day) + SolarData.fnamee 106 | print fname 107 | fid = SD(fname) 108 | tmpirrad = fid.select(idata_name[0])[:, :, :] 109 | fid.end() 110 | for each_siteth in range(1, site_num + 1): 111 | tmpirrad_site = tmpirrad[:, self.site_index[each_siteth - 1][0], \ 112 | self.site_index[each_siteth - 1][1]].reshape(1, -1) 113 | self.dict_solar[each_siteth][each_year][each_month] = \ 114 | np.vstack((self.dict_solar[each_siteth][each_year][each_month], tmpirrad_site)) 115 | return self.dict_solar 116 | 117 | def cimport_datat(self, idata_name=('TS',)): 118 | '''Import ten-year ambient temperature data. 119 | Args: 120 | idata_name: the data name. 121 | Returns: 122 | self.dict_solar: imported data. 123 | ''' 124 | year_index = range(self.start_year, self.end_year + 1) 125 | site_num = len(self.site_indext) 126 | zero = str(0) 127 | for each_siteth in range(1, site_num + 1): 128 | self.dict_temperature[each_siteth] = {} 129 | for each_year in year_index: 130 | self.dict_temperature[each_siteth][each_year] = {} 131 | for each_month in SolarData.month_name: 132 | self.dict_temperature[each_siteth][each_year][each_month] = np.empty((0,24), np.float32) 133 | for each_year in year_index: 134 | if ((each_year % 400 == 0) or ((each_year % 4 == 0) and (each_year % 100 != 0))): 135 | year_feature = SolarData.leap_year 136 | else: 137 | year_feature = SolarData.nonleap_year 138 | for each_month in SolarData.month_name: 139 | if ((each_year == 2010) and (each_month in SolarData.spl_month2010)): 140 | fnames = self.file_patht + SolarData.fnameta 141 | else: 142 | fnames = self.file_patht + SolarData.fnametb 143 | day_s = year_feature[each_month][1] 144 | day_e = year_feature[each_month][2] 145 | if year_feature[each_month][3]: 146 | for each_day in range(day_s, day_e + 1): 147 | fname = fnames + str(each_year) + zero + str(each_day) + SolarData.fnamee 148 | print fname 149 | fid = SD(fname) 150 | tmpirrad = fid.select(idata_name[0])[:, :, :] 151 | fid.end() 152 | for each_siteth in range(1, site_num + 1): 153 | tmpirrad_site = tmpirrad[:, self.site_indext[each_siteth - 1][0], \ 154 | self.site_indext[each_siteth - 1][1]].reshape(1, -1) 155 | self.dict_temperature[each_siteth][each_year][each_month] = \ 156 | np.vstack((self.dict_temperature[each_siteth][each_year][each_month], tmpirrad_site)) 157 | # print(tmpirrad_site) 158 | else: 159 | for each_day in range(day_s, day_e + 1): 160 | fname = fnames + str(each_year) + str(each_day) + SolarData.fnamee 161 | print fname 162 | fid = SD(fname) 163 | tmpirrad = fid.select(idata_name[0])[:, :, :] 164 | fid.end() 165 | for each_siteth in range(1, site_num + 1): 166 | tmpirrad_site = tmpirrad[:, self.site_indext[each_siteth - 1][0], \ 167 | self.site_indext[each_siteth - 1][1]].reshape(1, -1) 168 | self.dict_temperature[each_siteth][each_year][each_month] = \ 169 | np.vstack((self.dict_temperature[each_siteth][each_year][each_month], tmpirrad_site)) 170 | # print(tmpirrad_site) 171 | return self.dict_temperature 172 | 173 | def csolar2cf_model1(self, irated_powr=255.0, iarea_m2=1.6368, ieffi_ratio=0.1248): 174 | '''Converter solar irradiation to capacity factors. 175 | PV Model1: Ff = (idata * iarea_m2 * ieffi_ratio) / ieffi_ratio 176 | Args: 177 | idata: the source solar irradiation. 178 | irated_powr: the rated power. 179 | iarea_m2: the area of a PV module. 180 | ieffi_ratio: the efficiency. 181 | Returns: 182 | capacity factors 183 | ''' 184 | year_index = range(self.start_year, self.end_year + 1) 185 | site_num = len(self.site_index) 186 | for each_siteth in range(1, site_num + 1): 187 | self.dict_solar_cf[each_siteth] = {} 188 | for each_year in year_index: 189 | self.dict_solar_cf[each_siteth][each_year] = {} 190 | for each_month in SolarData.month_name: 191 | self.dict_solar_cf[each_siteth][each_year][each_month] = \ 192 | self.dict_solar[each_siteth][each_year][each_month] * ieffi_ratio * iarea_m2 / irated_powr 193 | return self.dict_solar_cf 194 | 195 | def csolar2cf_model2(self, iHSTC=1000.0, iC1=0.93, iC2=-0.005, iTSTC=25.0, iTfTETC=47.0, iTaTETC=20.0, iHTETC=800.0): 196 | '''Converter solar irradiation to capacity factors. 197 | PV Model2: Ff = (idataf / iHSTC) * iC1 * (1 + iC2(Tf - iTSTC)) 198 | Tf = idatat + idataf * (iTfTETC - iTaTETC) / iHTETC 199 | Args: 200 | idata: the source solar irradiation and ambient temperature. 201 | iHSTC: the solar irradiation at standard test condition. 202 | iC1: the derating coefficient. 203 | iC2: the power temperature coefficient. 204 | iTSTC: is the temperature at standard test condition. 205 | iTfTETC: the solar irradiation at temperature estimation test condition. 206 | iTaTETC: the ambient temperature at temperature estimation test condition. 207 | Returns: 208 | capacity factors 209 | ''' 210 | year_index = range(self.start_year, self.end_year + 1) 211 | site_num = len(self.site_index) 212 | for each_siteth in range(1, site_num + 1): 213 | self.dict_solar_cf[each_siteth] = {} 214 | for each_year in year_index: 215 | self.dict_solar_cf[each_siteth][each_year] = {} 216 | for each_month in SolarData.month_name: 217 | # print each_month 218 | self.dict_solar_cf[each_siteth][each_year][each_month] = \ 219 | self.dict_solar[each_siteth][each_year][each_month] / iHSTC * iC1 * \ 220 | (1 + iC2 * (self.dict_temperature[each_siteth][each_year][each_month] 221 | - 273.15 - iTSTC + self.dict_solar[each_siteth][each_year][each_month] 222 | * (iTfTETC - iTaTETC) / iHTETC)) 223 | return self.dict_solar_cf 224 | 225 | def c2style_1month(self, imode=True): 226 | '''Converter NASA data to 1 month style. 227 | Args: 228 | imode: True or False; wind speed or capacity factors. 229 | Returns: 230 | dict_data_1month: data in 1 month style. 231 | ''' 232 | if imode == True: 233 | dict_data = self.dict_solar_cf 234 | else: 235 | dict_data = self.dict_solar 236 | dict_data_1month = {} 237 | year_index = range(self.start_year, self.end_year + 1) 238 | site_num = len(self.site_index) 239 | for each_siteth in range(1, site_num + 1): 240 | dict_data_1month[each_siteth] = {} 241 | for each_year in year_index: 242 | dict_data_1month[each_siteth][each_year] = {} 243 | for each_month in SolarData.month_name: 244 | dict_data_1month[each_siteth][each_year][each_month] = \ 245 | dict_data[each_siteth][each_year][each_month].reshape(1, -1) 246 | return dict_data_1month 247 | 248 | def c2style_1year(self, imode=True): 249 | '''Converter NASA data to 1 year style. 250 | Args: 251 | imode: True or False; wind speed or capacity factors. 252 | Returns: 253 | dict_data_1year: data in 1 year style. 254 | ''' 255 | if imode == True: 256 | dict_data = self.dict_solar_cf 257 | else: 258 | dict_data = self.dict_solar 259 | dict_data_1year = {} 260 | year_index = range(self.start_year, self.end_year + 1) 261 | site_num = len(self.site_index) 262 | for each_siteth in range(1, site_num + 1): 263 | dict_data_1year[each_siteth] = {}; 264 | for each_year in year_index: 265 | dict_data_1year[each_siteth][each_year] = np.empty((1, 0), np.float32) 266 | for each_month in SolarData.month_name: 267 | tmp = dict_data[each_siteth][each_year][each_month].reshape(1, -1) 268 | dict_data_1year[each_siteth][each_year] = np.hstack((dict_data_1year[each_siteth][each_year], tmp)) 269 | return dict_data_1year 270 | 271 | def c2style_10year(self, imode=True): 272 | '''Converter NASA data to 10 years style. 273 | Args: 274 | imode: True or False; wind speed or capacity factors. 275 | Returns: 276 | dict_data_10year: data in 10 year style. 277 | ''' 278 | dict_data_1year = self.c2style_1year(imode) 279 | dict_data_10year = {} 280 | year_index = range(self.start_year, self.end_year + 1) 281 | site_num = len(self.site_index) 282 | for each_siteth in range(1, site_num + 1): 283 | dict_data_10year[each_siteth] = np.empty((1, 0), np.float32) 284 | for each_year in year_index: 285 | dict_data_10year[each_siteth] = \ 286 | np.hstack((dict_data_10year[each_siteth], dict_data_1year[each_siteth][each_year])) 287 | return dict_data_10year 288 | 289 | def cselect_1site_1day(self, isiteth, iyear, imonth, iday, imode=True): 290 | '''select data of 1 site, 1 day. 291 | Args: 292 | isiteth: the serial number of site. 293 | iyear: the year. 294 | imonth: the month. 295 | iday: the day. 296 | imode: True or False; wind speed or capacity factors. 297 | Returns: 298 | data of 1 site, 1 day. 299 | ''' 300 | if imode == True: 301 | dict_data = self.dict_solar_cf 302 | else: 303 | dict_data = self.dict_solar 304 | return dict_data[isiteth][iyear][imonth][iday-1, :].reshape(1, -1) 305 | 306 | def cselect_1site_1month(self, isiteth, iyear, imonth, imode=True): 307 | '''select data of 1 site, 1 month. 308 | Args: 309 | isiteth: the serial number of site. 310 | iyear: the year. 311 | imonth: the month. 312 | imode: True or False; wind speed or capacity factors. 313 | Returns: 314 | data of 1 site, 1 month. 315 | ''' 316 | return self.c2style_1month(imode)[isiteth][iyear][imonth] 317 | 318 | def cselect_1site_1year(self, isiteth, iyear, imode=True): 319 | '''select data of 1 site, 1 year. 320 | Args: 321 | isiteth: the serial number of site. 322 | iyear: the year. 323 | imode: True or False; wind speed or capacity factors. 324 | Returns: 325 | data of 1 site, 1 year. 326 | ''' 327 | return self.c2style_1year(imode)[isiteth][iyear] 328 | 329 | def cselect_1site_10year(self, isiteth, imode=True): 330 | '''select data of 1 site in 10 year style. 331 | Args: 332 | isiteth: the serial number of site. 333 | imode: True or False; wind speed or capacity factors. 334 | Returns: 335 | data of 1 site, 10 year. 336 | ''' 337 | return self.c2style_10year(imode)[isiteth] 338 | 339 | def ccal_corrcoef(self, irelmean=0.1183): 340 | '''Calculate the correction factors. 341 | Args: 342 | irelmean: the in-situ measured capacity factors, 343 | the annual average capacity factor of the study region. 344 | Returns: 345 | alpha: the calculated correction factor for the study region. 346 | ''' 347 | dict_data_10year = self.c2style_10year(True) 348 | ur = irelmean 349 | N = len(self.site_indext) 350 | us = 0 351 | for each_siteth in range(1, N+1, 1): 352 | us += np.mean(dict_data_10year[each_siteth], axis=1) 353 | corfactors = ur * N * 1.0 / us 354 | self.alpha = corfactors[0,] 355 | return alpha[0,] 356 | 357 | def ccorrect_cf(self): 358 | '''Correct the simulated capacity factors in the study region. 359 | Args: 360 | Returns: 361 | dict_solar_cf0: the uncorrected capacity factors 362 | dict_solar_cf: the corrected capacity factors 363 | ''' 364 | year_index = range(self.start_year, self.end_year + 1) 365 | site_num = len(self.site_index) 366 | for each_siteth in range(1, site_num + 1): 367 | self.dict_solar_cf0[each_siteth]={} 368 | for each_year in year_index: 369 | self.dict_solar_cf0[each_siteth][each_year]={} 370 | for each_month in SolarData.month_name: 371 | self.dict_solar_cf0[each_siteth][each_year][each_month] = self.dict_solar_cf[each_siteth][each_year][each_month] 372 | self.dict_solar_cf[each_siteth][each_year][each_month] = self.dict_solar_cf[each_siteth][each_year][each_month] * self.alpha 373 | return self.dict_solar_cf0, self.dict_solar_cf 374 | 375 | 376 | 377 | if __name__ == '__main__': 378 | '''Examples.''' 379 | file_name = 'sd_solar_data' 380 | file_namet = 'sd_temp_data' 381 | irrad_site_index = [(-3, 6), (-9, 7)] 382 | irrad_site_indext = [(252, 443), (252, 452)] 383 | start_year = 2006 384 | end_year = 2006 385 | 386 | solar_data = SolarData(file_name, file_namet, irrad_site_index, irrad_site_indext, start_year, end_year) 387 | solar_irrad_data = solar_data.cimport_data() 388 | solar_capacity_factor = solar_data.csolar2cf_model1() 389 | # solar_temperature_data = solar_data.cimport_datat() 390 | # solar_capacity_factor = solar_data.csolar2cf_model2() 391 | PVcorrcoef = solar_data.ccal_corrcoef(0.1183) 392 | (solar_capacity_factor0, solar_capacity_factor) = solar_data.ccorrect_cf() 393 | solar_cf_1monthstl = solar_data.c2style_1month() 394 | solar_cf_1yearstl = solar_data.c2style_1year() 395 | solar_cf_10yearstl = solar_data.c2style_10year() 396 | solar_cf_1s1day = solar_data.cselect_1site_1day(1, 2006, 'Jan', 22) 397 | solar_cf_1s1month = solar_data.cselect_1site_1month(1, 2006, 'Jan') 398 | solar_cf_1s1year = solar_data.cselect_1site_1year(1, 2006) 399 | solar_cf_1s10year = solar_data.cselect_1site_10year(1) -------------------------------------------------------------------------------- /ass_res/init_nasa_wind.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jun 08 10:54:23 2017 5 | ######################################################################################## 6 | # @ File name: init_nasa_wind.py 7 | # @ Function: The class of NASA wind speed. 8 | # Importing, preprocessing, and basic operation of solar data. 9 | # @ Author: Yongji Cao, Hengxu Zhang 10 | # @ Version: 1.0 11 | # @ Revision date: Jan/19/2018 12 | # @ Copyright (c) 2016-2018 School of Electrical Engineering, Shandong University, China 13 | ######################################################################################## 14 | """ 15 | 16 | 17 | import numpy as np 18 | from pyhdf.SD import SD 19 | 20 | 21 | class WindData(object): 22 | '''NASA wind speed data class''' 23 | version = '1.0' 24 | month_name = ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 25 | 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec') 26 | leap_year = {'Jan': (31, 101, 131, True), 'Feb': (29, 201, 229, True), 'Mar': (31, 301, 331, True), 27 | 'Apr': (30, 401, 430, True), 'May': (31, 501, 531, True), 'Jun': (30, 601, 630, True), 28 | 'Jul': (31, 701, 731, True), 'Aug': (31, 801, 831, True), 'Sep': (30, 901, 930, True), 29 | 'Oct': (31, 1001, 1031, False), 'Nov': (30, 1101, 1130, False), 'Dec': (31, 1201, 1231, False)} 30 | nonleap_year = {'Jan': (31,101,131,True), 'Feb': (28,201,228,True), 'Mar': (31, 301, 331, True), 31 | 'Apr': (30, 401, 430, True), 'May': (31, 501, 531, True), 'Jun': (30, 601, 630, True), 'Jul': (31, 701, 731, True), 32 | 'Aug': (31, 801, 831, True), 'Sep': (30, 901, 930, True), 'Oct': (31, 1001, 1031, False), 33 | 'Nov': (30, 1101, 1130, False), 'Dec': (31, 1201, 1231, False)} 34 | fnamesa = '/MERRA301.prod.assim.tavg1_2d_slv_Nx.' 35 | fnamesb = '/MERRA300.prod.assim.tavg1_2d_slv_Nx.' 36 | fnamee = '.SUB.hdf' 37 | spl_month2010 = ('Jun', 'Jul', 'Aug') 38 | 39 | def __init__(self, ifile_name, isite_index, istart_year, iend_year): 40 | """Create an object. 41 | Args: 42 | ifile_name: str; the folder name of NASA file. 43 | isite_index: list; the list site indice in NASA file. 44 | istart_year: int; the start year. 45 | iend_year: inlt; the end year. 46 | Returns: 47 | an instances 48 | """ 49 | 50 | self.file_name = ifile_name 51 | self.site_index = isite_index 52 | self.start_year = istart_year 53 | self.end_year = iend_year 54 | self.dict_ref_wind = {} 55 | self.dict_hw_wind = {} 56 | self.dict_wind_cf = {} 57 | self.dict_wind_cf0 = {} 58 | self.alpha = 1 59 | 60 | def cuv2speed(self, u, v): 61 | '''Converter u and v to the wind speed. 62 | Args: 63 | u: the U data in NASA file. 64 | v: the V data in NASA file. 65 | Returns: 66 | the wind speed 67 | ''' 68 | return (u ** 2 + v ** 2) ** 0.5 69 | 70 | def cimport_data(self, idata_name=('V50M', 'U50M')): 71 | '''Import ten-year data. 72 | Args: 73 | idata_name: the data name. 74 | Returns: 75 | self.dict_ref_wind: imported data. 76 | ''' 77 | year_index = range(self.start_year, self.end_year + 1) 78 | site_num = len(self.site_index) 79 | zero = str(0) 80 | for each_siteth in range(1, site_num + 1): 81 | self.dict_ref_wind[each_siteth] = {} 82 | for each_year in year_index: 83 | self.dict_ref_wind[each_siteth][each_year] = {} 84 | for each_month in WindData.month_name: 85 | self.dict_ref_wind[each_siteth][each_year][each_month] = np.empty((0,24), np.float32) 86 | for each_year in year_index: 87 | if ((each_year % 400 == 0) or ((each_year % 4 == 0) and (each_year % 100 != 0))): 88 | year_feature = WindData.leap_year 89 | else: 90 | year_feature = WindData.nonleap_year 91 | for each_month in WindData.month_name: 92 | if ((each_year == 2010) and (each_month in WindData.spl_month2010)): 93 | fnames = self.file_name + WindData.fnamesa 94 | else: 95 | fnames = self.file_name + WindData.fnamesb 96 | day_s = year_feature[each_month][1] 97 | day_e = year_feature[each_month][2] 98 | if year_feature[each_month][3]: 99 | for each_day in range(day_s, day_e + 1): 100 | fname = fnames + str(each_year) + zero + str(each_day) + WindData.fnamee 101 | print fname 102 | fid = SD(fname) 103 | tmpv = fid.select(idata_name[0])[:, :, :] 104 | tmpu = fid.select(idata_name[1])[:, :, :] 105 | fid.end() 106 | for each_siteth in range(1, site_num + 1): 107 | tmpv_site = tmpv[:, self.site_index[each_siteth - 1][0], \ 108 | self.site_index[each_siteth - 1][1]].reshape(1, -1) 109 | tmpu_site = tmpu[:, self.site_index[each_siteth - 1][0], \ 110 | self.site_index[each_siteth - 1][1]].reshape(1, -1) 111 | tmpwind_site = self.cuv2speed(tmpv_site, tmpu_site) 112 | self.dict_ref_wind[each_siteth][each_year][each_month] = \ 113 | np.vstack((self.dict_ref_wind[each_siteth][each_year][each_month], tmpwind_site)) 114 | else: 115 | for each_day in range(day_s, day_e + 1): 116 | fname = fnames + str(each_year) + str(each_day) + WindData.fnamee 117 | print fname 118 | fid = SD(fname) 119 | tmpv = fid.select(idata_name[0])[:, :, :] 120 | tmpu = fid.select(idata_name[1])[:, :, :] 121 | fid.end() 122 | for each_siteth in range(1, site_num + 1): 123 | tmpv_site = tmpv[:, self.site_index[each_siteth - 1][0], \ 124 | self.site_index[each_siteth-1][1]].reshape(1, -1) 125 | tmpu_site = tmpu[:, self.site_index[each_siteth - 1][0], \ 126 | self.site_index[each_siteth-1][1]].reshape(1, -1) 127 | tmpwind_site = self.cuv2speed(tmpv_site,tmpu_site) 128 | self.dict_ref_wind[each_siteth][each_year][each_month] = \ 129 | np.vstack((self.dict_ref_wind[each_siteth][each_year][each_month], tmpwind_site)) 130 | return self.dict_ref_wind 131 | 132 | def cref2hw(self, ihref=50.0, ihw=65.0, ialpha=0.35): 133 | '''Converter the speed at reference height to the one at WTGs height. 134 | Args: 135 | ihref: the reference height. 136 | ihref: the WTGs height. 137 | ialpha: the exponent coefficient 138 | Returns: 139 | self.dict_hw_wind: wind speed at WTGs height 140 | ''' 141 | year_index = range(self.start_year, self.end_year+1) 142 | site_num = len(self.site_index) 143 | for each_siteth in range(1, site_num + 1): 144 | self.dict_hw_wind[each_siteth] = {} 145 | for each_year in year_index: 146 | self.dict_hw_wind[each_siteth][each_year] = {} 147 | for each_month in WindData.month_name: 148 | self.dict_hw_wind[each_siteth][each_year][each_month] = \ 149 | self.dict_ref_wind[each_siteth][each_year][each_month] * (ihw / ihref) ** ialpha 150 | return self.dict_hw_wind 151 | 152 | def cuv2cf(self, idata, ispeed_in, ispeed_out, ispeed_rate, inita): 153 | '''Converter wind speed to capacity factors. 154 | Args: 155 | idata: the source wind speed. 156 | ispeed_in: cut-in speed. 157 | ispeed_out: cut-out speed. 158 | ispeed_rate: rate speed. 159 | inita: the efficiency. 160 | Returns: 161 | capacity factors 162 | ''' 163 | return np.piecewise( 164 | idata, 165 | [np.logical_or(idata < ispeed_in, idata > ispeed_out), 166 | np.logical_and(idata >= ispeed_in, idata <= ispeed_rate), 167 | np.logical_and(idata > ispeed_rate, idata <= ispeed_out)], 168 | [0, 169 | lambda idata: (idata - ispeed_in) / (ispeed_rate - ispeed_in) * inita, 170 | 1 * inita]) 171 | 172 | def cwind2cf(self, ispeed_in=3.0, ispeed_out=25.0, ispeed_rate=13.5, inita=0.95): 173 | '''Converter NASA wind speed to capacity factors. 174 | Args: 175 | idata: the source wind speed. 176 | ispeed_in: cut-in speed. 177 | ispeed_out: cut-out speed. 178 | ispeed_rate: rate speed. 179 | inita: the efficiency. 180 | Returns: 181 | self.dict_wind_cf: capacity factors 182 | ''' 183 | year_index = range(self.start_year, self.end_year+1) 184 | site_num = len(self.site_index) 185 | for each_siteth in range(1, site_num + 1): 186 | self.dict_wind_cf[each_siteth]={} 187 | for each_year in year_index: 188 | self.dict_wind_cf[each_siteth][each_year]={} 189 | for each_month in WindData.month_name: 190 | self.dict_wind_cf[each_siteth][each_year][each_month] = \ 191 | self.cuv2cf(self.dict_hw_wind[each_siteth][each_year][each_month], \ 192 | ispeed_in, ispeed_out, ispeed_rate, inita) 193 | return self.dict_wind_cf 194 | 195 | def c2style_1month(self, imode=True): 196 | '''Converter NASA data to 1 month style. 197 | Args: 198 | imode: True or False; wind speed or capacity factors. 199 | Returns: 200 | dict_data_1month: data in 1 month style. 201 | ''' 202 | if imode == True: 203 | dict_data = self.dict_wind_cf 204 | else: 205 | dict_data = self.dict_hw_wind 206 | dict_data_1month = {} 207 | year_index = range(self.start_year, self.end_year + 1) 208 | site_num = len(self.site_index) 209 | for each_siteth in range(1, site_num + 1): 210 | dict_data_1month[each_siteth] = {} 211 | for each_year in year_index: 212 | dict_data_1month[each_siteth][each_year] = {} 213 | for each_month in WindData.month_name: 214 | dict_data_1month[each_siteth][each_year][each_month] = \ 215 | dict_data[each_siteth][each_year][each_month].reshape(1, -1) 216 | return dict_data_1month 217 | 218 | def c2style_1year(self, imode=True): 219 | '''Converter NASA data to 1 year style. 220 | Args: 221 | imode: True or False; wind speed or capacity factors. 222 | Returns: 223 | dict_data_1year: data in 1 year style. 224 | ''' 225 | if imode == True: 226 | dict_data = self.dict_wind_cf 227 | else: 228 | dict_data = self.dict_hw_wind 229 | dict_data_1year = {} 230 | year_index = range(self.start_year, self.end_year + 1) 231 | site_num = len(self.site_index) 232 | for each_siteth in range(1, site_num + 1): 233 | dict_data_1year[each_siteth] = {}; 234 | for each_year in year_index: 235 | dict_data_1year[each_siteth][each_year] = np.empty((1, 0), np.float32) 236 | for each_month in WindData.month_name: 237 | tmp = dict_data[each_siteth][each_year][each_month].reshape(1, -1) 238 | dict_data_1year[each_siteth][each_year] = np.hstack((dict_data_1year[each_siteth][each_year], tmp)) 239 | return dict_data_1year 240 | 241 | def c2style_10year(self, imode=True): 242 | '''Converter NASA data to 10 years style. 243 | Args: 244 | imode: True or False; wind speed or capacity factors. 245 | Returns: 246 | dict_data_10year: data in 10 year style. 247 | ''' 248 | dict_data_1year = self.c2style_1year(imode) 249 | dict_data_10year = {} 250 | year_index = range(self.start_year, self.end_year + 1) 251 | site_num = len(self.site_index) 252 | for each_siteth in range(1, site_num + 1): 253 | dict_data_10year[each_siteth] = np.empty((1, 0), np.float32) 254 | for each_year in year_index: 255 | dict_data_10year[each_siteth] = \ 256 | np.hstack((dict_data_10year[each_siteth], dict_data_1year[each_siteth][each_year])) 257 | return dict_data_10year 258 | 259 | def cselect_1site_1day(self, isiteth, iyear, imonth, iday, imode=True): 260 | '''select data of 1 site, 1 day. 261 | Args: 262 | isiteth: the serial number of site. 263 | iyear: the year. 264 | imonth: the month. 265 | iday: the day. 266 | imode: True or False; wind speed or capacity factors. 267 | Returns: 268 | data of 1 site, 1 day. 269 | ''' 270 | if imode == True: 271 | dict_data = self.dict_wind_cf 272 | else: 273 | dict_data = self.dict_hw_wind 274 | return dict_data[isiteth][iyear][imonth][iday-1, :].reshape(1, -1) 275 | 276 | def cselect_1site_1month(self, isiteth, iyear, imonth, imode=True): 277 | '''select data of 1 site, 1 month. 278 | Args: 279 | isiteth: the serial number of site. 280 | iyear: the year. 281 | imonth: the month. 282 | imode: True or False; wind speed or capacity factors. 283 | Returns: 284 | data of 1 site, 1 month. 285 | ''' 286 | return self.c2style_1month(imode)[isiteth][iyear][imonth] 287 | 288 | def cselect_1site_1year(self, isiteth, iyear, imode=True): 289 | '''select data of 1 site, 1 year. 290 | Args: 291 | isiteth: the serial number of site. 292 | iyear: the year. 293 | imode: True or False; wind speed or capacity factors. 294 | Returns: 295 | data of 1 site, 1 year. 296 | ''' 297 | return self.c2style_1year(imode)[isiteth][iyear] 298 | 299 | def cselect_1site_10year(self, isiteth, imode=True): 300 | '''select data of 1 site in 10 year style. 301 | Args: 302 | isiteth: the serial number of site. 303 | imode: True or False; wind speed or capacity factors. 304 | Returns: 305 | data of 1 site, 10 year. 306 | ''' 307 | return self.c2style_10year(imode)[isiteth] 308 | 309 | def ccal_corrcoef(self, irelmean=0.2100): 310 | '''Calculate the correction factors. 311 | Args: 312 | irelmean: the in-situ measured capacity factors, 313 | the annual average capacity factor of the study region. 314 | Returns: 315 | alpha: the calculated correction factor for the study region. 316 | ''' 317 | dict_data_10year = self.cdata2AllYearStyle(True) 318 | ur = irelmean 319 | N = len(self.ipoint_idex) 320 | us = 0 321 | for each_siteth in range(1, N+1, 1): 322 | us += np.mean(dict_data_10year[each_siteth], axis=1) 323 | corfactors = ur * N * 1.0 / us 324 | self.alpha = corfactors[0,] 325 | return self.alpha 326 | 327 | def ccorrect_cf(self): 328 | '''Correct the simulated capacity factors in the study region. 329 | Args: 330 | Returns: 331 | dict_wind_cf0: the uncorrected capacity factors 332 | dict_wind_cf: the corrected capacity factors 333 | ''' 334 | year_index = range(self.start_year, self.end_year + 1) 335 | site_num = len(self.site_index) 336 | for each_siteth in range(1, site_num + 1): 337 | self.dict_wind_cf0[each_siteth]={} 338 | for each_year in year_index: 339 | self.dict_wind_cf0[each_siteth][each_year]={} 340 | for each_month in WindData.month_name: 341 | self.dict_wind_cf0[each_siteth][each_year][each_month] = self.dict_wind_cf[each_siteth][each_year][each_month] 342 | self.dict_wind_cf[each_siteth][each_year][each_month] = self.dict_wind_cf[each_siteth][each_year][each_month] * self.alpha 343 | return dict_wind_cf0, dict_wind_cf 344 | 345 | def 346 | 347 | 348 | 349 | if __name__ == '__main__': 350 | '''Examples.''' 351 | file_name = 'sd_wind_data' 352 | wind_site_index = [(-3, 6), (-9, 7)] 353 | start_year = 2006 354 | end_year = 2015 355 | 356 | wind_data = WindData(file_name, wind_site_index, start_year, end_year) 357 | wind_speed_ref = wind_data.cimport_data() 358 | wind_speed_hw = wind_data.cref2hw() 359 | wind_capacity_factor = wind_data.cwind2cf() 360 | WTGcorrcoef = wind_data.ccal_corrcoef(0.2100) 361 | (wind_capacity_factor0, wind_capacity_factor) = wind_data.ccorrect_cf() 362 | wind_cf_1monthstl = wind_data.c2style_1month() 363 | wind_cf_1yearstl = wind_data.c2style_1year() 364 | wind_cf_10yearstl = wind_data.c2style_10year() 365 | wind_cf_1s1day = wind_data.cselect_1site_1day(1, 2006, 'Jan', 22) 366 | wind_cf_1s1month = wind_data.cselect_1site_1month(1, 2006, 'Jan') 367 | wind_cf_1s1year = wind_data.cselect_1site_1year(1, 2006) 368 | wind_cf_1s10year = wind_data.cselect_1site_10year(1) 369 | 370 | 371 | 372 | 373 | -------------------------------------------------------------------------------- /ass_res/sd_solar_data/sd_solar_data.txt: -------------------------------------------------------------------------------- 1 | The default path of the files of the ten-year NASA solar data. -------------------------------------------------------------------------------- /ass_res/sd_temp_data/sd_temp_data.txt: -------------------------------------------------------------------------------- 1 | The default path of the files of the ten-year NASA temperature data. -------------------------------------------------------------------------------- /ass_res/sd_wind_data/sd_wind_data.txt: -------------------------------------------------------------------------------- 1 | The default path of the files of the ten-year NASA wind data. -------------------------------------------------------------------------------- /freq_a2c/README.md: -------------------------------------------------------------------------------- 1 | ######The description for freq_a2c ###### 2 | 3 | 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