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AFM Kamal Chowdhury
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‎Model/PowNetDataSetup.py‎

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import csv
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import pandas as pd
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import numpy as np
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######=================================================########
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###### Segment A.1 ########
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######=================================================########
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SimDays = 365
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SimHours = SimDays * 24
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HorizonHours = 24 ##planning horizon (e.g., 24, 48, 72 hours etc.)
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TransLoss = 0.075 ##transmission loss as a percent of generation
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n1criterion = 0.75 ##maximum line-usage as a percent of line-capacity
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res_margin = 0.15 ##minimum reserve as a percent of system demand
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spin_margin = 0.50 ##minimum spinning reserve as a percent of total reserve
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data_name = 'pownet_data_camb_2016'
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######=================================================########
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###### Segment A.2 ########
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######=================================================########
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#read parameters for dispatchable resources (coal/gas/oil/biomass generators, imports)
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df_gen = pd.read_csv('data_camb_genparams.csv',header=0)
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##hourly ts of dispatchable hydropower at each domestic dam
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df_hydro = pd.read_csv('data_camb_hydro_2016.csv',header=0)
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##hourly ts of dispatchable hydropower at each import dam
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df_hydro_import = pd.read_csv('data_camb_hydro_import_2016.csv',header=0)
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####hourly ts of dispatchable solar-power at each plant
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##df_solar = pd.read_csv('data_solar.csv',header=0)
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##
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####hourly ts of dispatchable wind-power at each plant
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##df_wind = pd.read_csv('data_wind.csv',header=0)
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##hourly ts of load at substation-level
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df_load = pd.read_csv('data_camb_load_2016.csv',header=0)
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#capacity and susceptence of each transmission line (one direction)
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df_trans1 = pd.read_csv('data_camb_transparam.csv',header=0)
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#hourly minimum reserve as a function of load (e.g., 15% of current load)
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df_reserves = pd.DataFrame((df_load.iloc[:,4:].sum(axis=1)*res_margin).values,columns=['Reserve'])
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#capacity and susceptence of each transmission line (both directions)
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df_trans2 = pd.DataFrame([df_trans1['sink'],df_trans1['source'],df_trans1['linemva'],df_trans1['linesus']]).transpose()
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df_trans2.columns = ['source','sink','linemva','linesus']
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df_paths = pd.concat([df_trans1,df_trans2], axis=0)
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df_paths.index = np.arange(len(df_paths))
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######=================================================########
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###### Segment A.3 ########
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######=================================================########
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####======== Lists of Nodes of the Power System ========########
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h_nodes = ['TTYh','LRCh','ATYh','KIR1h','KIR3h','KMCh']
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h_imports = ['Salabam']
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##s_nodes = ['solar1','solar2']
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##w_nodes =['wind1','wind2']
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gd_nodes = ['GS1','GS2','GS3','GS5','GS7','KPCM','KPT','SHV','SRP'] ##Thermoplant nodes with demand
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gn_nodes = ['STH','Thai','Viet'] ##Thermoplants nodes without demand
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g_nodes = gd_nodes + gn_nodes
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print ('Gen_Nodes:',len(g_nodes))
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td_nodes = ['GS4','GS6','BTB','BMC','STR','TKO','KPS'] ##Transformers with demand
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tn_nodes = ['IE','KPCG','OSM','PRST'] ##Transformers without demand
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d_nodes = gd_nodes + td_nodes
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print ('Demand_Nodes:',len(d_nodes))
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all_nodes = h_nodes + h_imports + gn_nodes + gd_nodes + tn_nodes + td_nodes ## + s_nodes + w_nodes
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print ('Total_Nodes:',len(all_nodes))
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##list of types of dispatchable units
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types = ['coal_st','oil_ic','oil_st','imp_viet','imp_thai','slack'] ##,'biomass_st','gas_cc','gas_st'
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######=================================================########
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###### Segment A.4 ########
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######=================================================########
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######====== write data.dat file ======########
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with open(''+str(data_name)+'.dat', 'w') as f:
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###### generator sets by generator nodes
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for z in gd_nodes:
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# node string
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z_int = gd_nodes.index(z)
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f.write('set GD%dGens :=\n' % (z_int+1))
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'node'] == z:
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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for z in gn_nodes:
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# node string
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z_int = gn_nodes.index(z)
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f.write('set GN%dGens :=\n' % (z_int+1))
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'node'] == z:
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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####### generator sets by type
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# Coal
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f.write('set Coal_st :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'coal_st':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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# Oil_ic
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f.write('set Oil_ic :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'oil_ic':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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# Oil_st
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f.write('set Oil_st :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'oil_st':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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# Import from Vietnam
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f.write('set Imp_Viet :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'imp_viet':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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# Import from Thailand
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f.write('set Imp_Thai :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'imp_thai':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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## # Biomass
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## f.write('set Biomass_st :=\n')
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## # pull relevant generators
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## for gen in range(0,len(df_gen)):
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## if df_gen.loc[gen,'typ'] == 'biomass_st':
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## unit_name = df_gen.loc[gen,'name']
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## unit_name = unit_name.replace(' ','_')
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## f.write(unit_name + ' ')
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## f.write(';\n\n')
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##
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## # Gas_cc
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## f.write('set Gas_cc :=\n')
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## # pull relevant generators
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## for gen in range(0,len(df_gen)):
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## if df_gen.loc[gen,'typ'] == 'gas_cc':
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## unit_name = df_gen.loc[gen,'name']
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## unit_name = unit_name.replace(' ','_')
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## f.write(unit_name + ' ')
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## f.write(';\n\n')
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##
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## # Gas_st
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## f.write('set Gas_st :=\n')
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## # pull relevant generators
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## for gen in range(0,len(df_gen)):
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## if df_gen.loc[gen,'typ'] == 'gas_st':
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## unit_name = df_gen.loc[gen,'name']
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## unit_name = unit_name.replace(' ','_')
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## f.write(unit_name + ' ')
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## f.write(';\n\n')
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# Slack
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f.write('set Slack :=\n')
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# pull relevant generators
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for gen in range(0,len(df_gen)):
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if df_gen.loc[gen,'typ'] == 'slack':
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unit_name = df_gen.loc[gen,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + ' ')
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f.write(';\n\n')
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######=================================================########
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###### Segment A.5 ########
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######=================================================########
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######Set nodes, sources and sinks
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# nodes
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f.write('set nodes :=\n')
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for z in all_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# sources
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f.write('set sources :=\n')
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for z in all_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# sinks
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f.write('set sinks :=\n')
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for z in all_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# hydro_nodes
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f.write('set h_nodes :=\n')
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for z in h_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# hydro_nodes for import
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f.write('set h_imports :=\n')
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for z in h_imports:
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f.write(z + ' ')
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f.write(';\n\n')
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## # solar_nodes
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## f.write('set s_nodes :=\n')
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## for z in s_nodes:
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## f.write(z + ' ')
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## f.write(';\n\n')
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##
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## # wind_nodes
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## f.write('set w_nodes :=\n')
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## for z in w_nodes:
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## f.write(z + ' ')
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## f.write(';\n\n')
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# all demand nodes
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f.write('set d_nodes :=\n')
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for z in d_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# generator with demand nodes
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f.write('set gd_nodes :=\n')
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for z in gd_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# generator without demand nodes
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f.write('set gn_nodes :=\n')
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for z in gn_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# transformer with demand nodes
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f.write('set td_nodes :=\n')
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for z in td_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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# transformer without demand nodes
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f.write('set tn_nodes :=\n')
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for z in tn_nodes:
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f.write(z + ' ')
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f.write(';\n\n')
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######=================================================########
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###### Segment A.6 ########
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######=================================================########
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####### simulation period and horizon
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f.write('param SimHours := %d;' % SimHours)
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f.write('\n')
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f.write('param SimDays:= %d;' % SimDays)
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f.write('\n\n')
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f.write('param HorizonHours := %d;' % HorizonHours)
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f.write('\n\n')
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f.write('param TransLoss := %0.3f;' % TransLoss)
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f.write('\n\n')
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f.write('param n1criterion := %0.3f;' % n1criterion)
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f.write('\n\n')
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f.write('param spin_margin := %0.3f;' % spin_margin)
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f.write('\n\n')
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######=================================================########
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###### Segment A.7 ########
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######=================================================########
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####### create parameter matrix for generators
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f.write('param:' + '\t')
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for c in df_gen.columns:
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if c != 'name':
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f.write(c + '\t')
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f.write(':=\n\n')
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for i in range(0,len(df_gen)):
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for c in df_gen.columns:
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if c == 'name':
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unit_name = df_gen.loc[i,'name']
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unit_name = unit_name.replace(' ','_')
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f.write(unit_name + '\t')
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else:
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f.write(str((df_gen.loc[i,c])) + '\t')
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f.write('\n')
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f.write(';\n\n')
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######=================================================########
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###### Segment A.8 ########
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######=================================================########
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####### create parameter matrix for transmission paths (source and sink connections)
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f.write('param:' + '\t' + 'linemva' + '\t' +'linesus :=' + '\n')
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for z in all_nodes:
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for x in all_nodes:
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f.write(z + '\t' + x + '\t')
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match = 0
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for p in range(0,len(df_paths)):
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source = df_paths.loc[p,'source']
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sink = df_paths.loc[p,'sink']
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if source == z and sink == x:
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match = 1
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p_match = p
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if match > 0:
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f.write(str(df_paths.loc[p_match,'linemva']) + '\t' + str(df_paths.loc[p_match,'linesus']) + '\n')
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else:
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f.write('0' + '\t' + '0' + '\n')
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f.write(';\n\n')
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######=================================================########
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###### Segment A.9 ########
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######=================================================########
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####### Hourly timeseries (load, hydro, solar, wind, reserve)
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# load (hourly)
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f.write('param:' + '\t' + 'SimDemand:=' + '\n')
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for z in d_nodes:
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for h in range(0,len(df_load)):
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f.write(z + '\t' + str(h+1) + '\t' + str(df_load.loc[h,z]) + '\n')
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f.write(';\n\n')
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# hydro (hourly)
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f.write('param:' + '\t' + 'SimHydro:=' + '\n')
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for z in h_nodes:
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for h in range(0,len(df_hydro)):
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f.write(z + '\t' + str(h+1) + '\t' + str(df_hydro.loc[h,z]) + '\n')
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f.write(';\n\n')
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# hydro_import (hourly)
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f.write('param:' + '\t' + 'SimHydroImport:=' + '\n')
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for z in h_imports:
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for h in range(0,len(df_hydro_import)):
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f.write(z + '\t' + str(h+1) + '\t' + str(df_hydro_import.loc[h,z]) + '\n')
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f.write(';\n\n')
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## # solar (hourly)
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## f.write('param:' + '\t' + 'SimSolar:=' + '\n')
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## for z in s_nodes:
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## for h in range(0,len(df_solar)):
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## f.write(z + '\t' + str(h+1) + '\t' + str(df_solar.loc[h,z]) + '\n')
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## f.write(';\n\n')
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##
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## # wind (hourly)
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## f.write('param:' + '\t' + 'SimWind:=' + '\n')
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## for z in w_nodes:
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## for h in range(0,len(df_wind)):
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## f.write(z + '\t' + str(h+1) + '\t' + str(df_wind.loc[h,z]) + '\n')
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## f.write(';\n\n')
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###### System-wide hourly reserve
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f.write('param' + '\t' + 'SimReserves:=' + '\n')
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for h in range(0,len(df_load)):
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f.write(str(h+1) + '\t' + str(df_reserves.loc[h,'Reserve']) + '\n')
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f.write(';\n\n')
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print ('Complete:',data_name)

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