From e0032a6d5173f538ca84c79b4a13b05ea34f151f Mon Sep 17 00:00:00 2001 From: Thomas Zipperle Date: Tue, 26 May 2015 09:42:44 +0200 Subject: [PATCH 1/4] proposal #3: Time-dependent commodity prices --- mimo-example.xlsx | Bin 1772948 -> 2001467 bytes urbs.py | 214 ++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 207 insertions(+), 7 deletions(-) diff --git a/mimo-example.xlsx b/mimo-example.xlsx index 6b0c5af0923f8f6d72dc30055194ddfc0b52204e..c112713a0706ae6b247eb23528a0b631b1f540c2 100644 GIT binary patch delta 242535 zcmXuKV|1iV*RUPiwr$(CZQGbQ9Zqc9wrwYqiS0~m8(*&b`QAUhs?W`J>|M30tLse8 zvg=Cr##E981w#XZ0D=Mn0wM;INhHrG00sgYZoni10Zb_jF(HXROMQqQ(A#X@(Unuf z637aH2=uQmj8>ZJ<^r$xStg2vDp{i|N7$i|KW+NdlA2q%ao6cRK|tHzgkl}|B?PVv zXbI8}-!IrZQn8R7#<&cK5@Q7HyL9()dh3YwM_2J<6p}?^suVI5sHBHMt z%bTlt14>JY5Z^ylD}pI+7K?O5qa|0ElV61%k+8>cy}xNI*#oO@W_}9_b0AWo_E><8Z%0z@a5a z9UR1l7J^ApZ`<;pVj*_Y#6{wz&9zUP{l&gY9 z&W0zt6x2-FF%Qq#x(IIH!F2~>#g;Xq2KZhgI*qWXpFt^n&Uh4bt8xD_)IIbf-&Kxi zgDB=U6&6euFo8Czrdk0{4}qgE{+!@x9uCP3Gj`2m3tHJPI7q>>{+${%NIQJbOSDH$;Qr}#A z;DNB0V`dGjyX-$_ANz6-h3OLqdCdN%%}9Kyl?I8=Fwv`3y|ZMtevmezONoXdCnon{ zIxv2CoN}sq*iflx0|J#wO59v`K@z8!D#$-lNFlm4D{&L1&-BL6PysckfUFM<2P9fF z;i4zYx`Z>$exr!iXz&F$Omcur`9Kv4D@C1VWvBg){!PoU`>LcYG}sak-u+=P12C zjo8;jPI@p>OvWhrL-+o$>$zbk|J=p&wf%*q+x4Py3W@@9TC}W@gZWI2cVitd zaa7j|G$zJFx=v>?1S&F3k%>PZy8_X1bYRzvnH$c${0r7-uoFt_zVSie-}3LF0Tw=m z9#Qr~%hetPFy3=v2iwW9Gv2M`e&=+yI?-YDoAAe{t&knPdko`ip7>?#;XVxZQ_a<0 zqF2_T!~O`%7hM-bkG_Au%Cgijd3nAvB+~|laWI;0U?ZSlD!yjcpWuLi3SdK(=W6bns$0i`-FEm-X0L&l9hmerTlUOT>^&)$0tU_gy?J>GI&@I*9_C!Rw))Km zHTjQ2hk4&_Ul*@C!7=8ca2T7rg)PuSTiSp!)O1{1TQGY^%Z~VJet>Pr%L3p6jCY4c zL(lr$&3o&ryoueejhrs~HEi`}*>3#{SJauIRw}seR@Ub7|fD8EhBJ5OyoC^W#&8^uq-B^d! 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'supim': supim} + 'supim': supim, + 'buy_sell_price': buy_sell_price} if hacks is not None: data['hacks'] = hacks @@ -153,6 +158,7 @@ def create_model(data, timesteps=None, dt=1): m.storage = data['storage'] m.demand = data['demand'] m.supim = data['supim'] + m.buy_sell_price = data['buy_sell_price'] m.timesteps = timesteps # process input/output ratios @@ -211,7 +217,7 @@ def create_model(data, timesteps=None, dt=1): # cost_type m.cost_type = pyomo.Set( - initialize=['Inv', 'Fix', 'Var', 'Fuel'], + initialize=['Inv', 'Fix', 'Var', 'Fuel','Revenue','Purchase'], doc='Set of cost types (hard-coded)') # tuple sets @@ -257,6 +263,14 @@ def create_model(data, timesteps=None, dt=1): within=m.com, initialize=commodity_subset(m.com_tuples, 'Stock'), doc='Commodities that can be purchased at some site(s)') + m.com_sell = pyomo.Set( + within=m.com, + initialize=commodity_subset(m.com_tuples, 'Sell'), + doc='Commodities that can be sold') + m.com_buy = pyomo.Set( + within=m.com, + initialize=commodity_subset(m.com_tuples, 'Buy'), + doc='Commodities that can be purchased') m.com_demand = pyomo.Set( within=m.com, initialize=commodity_subset(m.com_tuples, 'Demand'), @@ -289,7 +303,7 @@ def create_model(data, timesteps=None, dt=1): # costs m.costs = pyomo.Var( m.cost_type, - within=pyomo.NonNegativeReals, + within=pyomo.Reals, doc='Costs by type (EUR/a)') # commodity @@ -297,6 +311,14 @@ def create_model(data, timesteps=None, dt=1): m.tm, m.com_tuples, within=pyomo.NonNegativeReals, doc='Use of stock commodity source (MW) per timestep') + m.e_co_sell = pyomo.Var( + m.tm, m.com_tuples, + within=pyomo.NonNegativeReals, + doc='Use of sell commodity source (kW) per timestep') + m.e_co_buy = pyomo.Var( + m.tm, m.com_tuples, + within=pyomo.NonNegativeReals, + doc='Use of buy commodity source (kW) per timestep') # process m.cap_pro = pyomo.Var( @@ -376,7 +398,7 @@ def create_model(data, timesteps=None, dt=1): m.res_vertex = pyomo.Constraint( m.tm, m.com_tuples, rule=res_vertex_rule, - doc='storage + transmission + process + source >= demand') + doc='storage + transmission + process + source + buy - sell == demand') m.res_stock_step = pyomo.Constraint( m.tm, m.com_tuples, rule=res_stock_step_rule, @@ -385,6 +407,22 @@ def create_model(data, timesteps=None, dt=1): m.com_tuples, rule=res_stock_total_rule, doc='total stock commodity input <= commodity.max') + m.res_sell_step = pyomo.Constraint( + m.tm, m.com_tuples, + rule=res_sell_step_rule, + doc='sell commodity output per step <= commodity.maxperstep') + m.res_sell_total = pyomo.Constraint( + m.com_tuples, + rule=res_sell_total_rule, + doc='total sell commodity output <= commodity.max') + m.res_buy_step = pyomo.Constraint( + m.tm, m.com_tuples, + rule=res_buy_step_rule, + doc='buy commodity output per step <= commodity.maxperstep') + m.res_buy_total = pyomo.Constraint( + m.com_tuples, + rule=res_buy_total_rule, + doc='total buy commodity output <= commodity.max') m.res_env_step = pyomo.Constraint( m.tm, m.com_tuples, rule=res_env_step_rule, @@ -419,6 +457,10 @@ def create_model(data, timesteps=None, dt=1): m.pro_tuples, rule=res_process_capacity_rule, doc='process.cap-lo <= total process capacity <= process.cap-up') + m.res_sell_buy_symmetry = pyomo.Constraint( + m.pro_input_tuples, + rule=res_sell_buy_symmetry_rule, + doc='total power connection capacity must be symmetric in both directions') # transmission m.def_transmission_capacity = pyomo.Constraint( @@ -526,6 +568,16 @@ def res_vertex_rule(m, tm, sit, com, com_type): if com in m.com_stock: power_surplus += m.e_co_stock[tm, sit, com, com_type] + # if com is a sell commodity, the commodity source term e_co_sell + # can supply a possibly positive power_surplus + if com in m.com_sell: + power_surplus -= m.e_co_sell[tm, sit, com, com_type] + + # if com is a buy commodity, the commodity source term e_co_buy + # can supply a possibly negative power_surplus + if com in m.com_buy: + power_surplus += m.e_co_buy[tm, sit, com, com_type] + # if com is a demand commodity, the power_surplus is reduced by the # demand value; no scaling by m.dt or m.weight is needed here, as this # constraint is about power (MW), not energy (MWh) @@ -534,7 +586,7 @@ def res_vertex_rule(m, tm, sit, com, com_type): power_surplus -= m.demand.loc[tm][sit, com] except KeyError: pass - return power_surplus >= 0 + return power_surplus == 0 # stock commodity purchase == commodity consumption, according to # commodity_balance of current (time step, site, commodity); @@ -561,6 +613,52 @@ def res_stock_total_rule(m, sit, com, com_type): return (total_consumption <= m.commodity.loc[sit, com, com_type]['max']) +# limit sell commodity use per time step +def res_sell_step_rule(m, tm, sit, com, com_type): + if com not in m.com_sell: + return pyomo.Constraint.Skip + else: + return (m.e_co_sell[tm, sit, com, com_type] <= + m.commodity.loc[sit, com, com_type]['maxperstep']) + +# limit sell commodity use in total (scaled to annual consumption, thanks +# to m.weight) +def res_sell_total_rule(m, sit, com, com_type): + if com not in m.com_sell: + return pyomo.Constraint.Skip + else: + # calculate total sale of commodity com + total_consumption = 0 + for tm in m.tm: + total_consumption += ( + m.e_co_sell[tm, sit, com, com_type] * m.dt) + total_consumption *= m.weight + return (total_consumption <= + m.commodity.loc[sit, com, com_type]['max']) + +# limit buy commodity use per time step +def res_buy_step_rule(m, tm, sit, com, com_type): + if com not in m.com_buy: + return pyomo.Constraint.Skip + else: + return (m.e_co_buy[tm, sit, com, com_type] <= + m.commodity.loc[sit, com, com_type]['maxperstep']) + +# limit buy commodity use in total (scaled to annual consumption, thanks +# to m.weight) +def res_buy_total_rule(m, sit, com, com_type): + if com not in m.com_buy: + return pyomo.Constraint.Skip + else: + # calculate total sale of commodity com + total_consumption = 0 + for tm in m.tm: + total_consumption += ( + m.e_co_buy[tm, sit, com, com_type] * m.dt) + total_consumption *= m.weight + return (total_consumption <= + m.commodity.loc[sit, com, com_type]['max']) + # environmental commodity creation == - commodity_balance of that commodity # used for modelling emissions (e.g. CO2) or other end-of-pipe results of # any process activity; @@ -622,6 +720,29 @@ def res_process_capacity_rule(m, sit, pro): m.cap_pro[sit, pro], m.process.loc[sit, pro]['cap-up']) +# power connection capacity: Sell == Buy +def res_sell_buy_symmetry_rule(m, sit_in, pro_in, coin): +# constraint only for sell and buy processes +# and the processes musst be in the same site + if coin in m.com_buy: + pro_output_tuples = list(m.pro_output_tuples.value) + pro_input_tuples = list(m.pro_input_tuples.value) + # search the output commodities for the "buy" process + # out = (site,output_commodity) + out = set([(x[0],x[2]) for x in pro_output_tuples if x[1] == pro_in]) + # search the sell process for the output_commodity from the buy process + inp_p = ([x for x in pro_output_tuples if x[2] in m.com_sell]) + for k in range(len(inp_p)): + pro_out = inp_p[k][1] + inp = set([(x[0],x[2]) for x in pro_input_tuples if x[1] == pro_out]) + # check: buy - commodity == commodity - sell; for a site + if not(inp.isdisjoint(out)): + return (m.cap_pro[sit_in, pro_in] == + m.cap_pro[sit_in, pro_out]) + return pyomo.Constraint.Skip + else: + return pyomo.Constraint.Skip + # transmission # transmission capacity == new capacity + existing capacity @@ -786,6 +907,43 @@ def def_costs_rule(m, cost_type): for tm in m.tm for c in m.com_tuples if c[1] in m.com_stock) + elif cost_type == 'Revenue': + revenue = 0 + sell_tuples = commodity_subset_list(m.com_tuples, m.com_sell) + for c in sell_tuples: + # check com price fix or timeseries + if not isinstance(m.commodity.loc[c]['price'], float): + # factor, to realize a different commodity price for each site + factor = extract_number_str(m.commodity.loc[c]['price']) + # calculate the purchase with the hourly commodity price + revenue += sum(m.e_co_sell[(tm,) + c] * factor * + m.buy_sell_price.loc[(tm,) + (c[1],)] * m.weight * m.dt + for tm in m.tm) + else: + # commodity price is the same in each hour + revenue += sum(m.e_co_sell[(tm,) + c] * m.dt * + m.commodity.loc[c]['price'] * m.weight + for tm in m.tm) + return m.costs['Revenue'] == -revenue + + elif cost_type == 'Purchase': + purchase = 0 + buy_tuples = commodity_subset_list(m.com_tuples, m.com_buy) + for c in buy_tuples: + # check com price fix or timeseries + if not isinstance(m.commodity.loc[c]['price'], float): + # factor, to realize a different commodity price for each site + factor = extract_number_str(m.commodity.loc[c]['price']) + # calculate the purchase with the hourly commodity price + purchase += sum(m.e_co_buy[(tm,) + c] * factor * + m.buy_sell_price.loc[(tm,) + (c[1],)] * m.weight * m.dt + for tm in m.tm) + else: + # commodity price is the same in each hour + purchase += sum(m.e_co_buy[(tm,) + c] * m.dt * + m.commodity.loc[c]['price'] * m.weight + for tm in m.tm) + return m.costs['Purchase'] == purchase else: raise NotImplementedError("Unknown cost type.") @@ -927,17 +1085,59 @@ def split_columns(columns, sep='.'): def commodity_subset(com_tuples, type_name): """ Unique list of commodity names for given type. - + Args: com_tuples: a list of (site, commodity, commodity type) tuples type_name: a commodity type ('Stock', 'SupIm', 'Env' or 'Demand') - + Returns: The set (unique elements) of commodity names of the desired type """ return set(com for sit, com, com_type in com_tuples if com_type == type_name) +def commodity_subset_list(com_tuples, type_list): + """ Unique list of commodity names for given list (type). + + Args: + com_tuples: a list of (site, commodity, commodity type) tuples + type_list: list of a commodity type ('Buy')=>('Elec buy', 'Heat buy') + + Returns: + The set (unique list of tuples) of tuples of the desired type + """ + return set((sit, com, com_type) for sit, com, com_type in com_tuples + if com in type_list) + +def extract_number_str(str_in): + """ Extract the first number from a given string and + and convert to the float type + + Excel sheet: Commodity - price - for Buy and Sell: + + - fix price: (0.12) [input type: float] + the same price for each timestep + - timeseries: (1.25BUY) [input type: string] + 1.) extract the number 1.25 + 2.) select the com price for the given timestep + 3.) calculate the price with the factor (1.25) + => 1.25 x Buy-Sell-Price(h) + + Args: + str_in: a string ('1,20BUY') + + Returns: + A float number (1.20) + """ + import re + # replace all commas with points + str_in = str_in.replace(',','.') + # find the first number and convert to float + factor_str = re.search('\d+[\.]?\d*',str_in) + if factor_str is None: + return 1 + else: + return float(factor_str.group(0)) def get_entity(instance, name): """ Return a DataFrame for an entity in model instance. 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z|FsE2VD>!oU)w4KKu-DJf7|~(k|h?{NXWsBApl&8|E^d6TV)5{2m!EB{I_u7;9mVV z8<-^pW(ozoMfk1)HV*}eBY125nP2jY7BaX!6u=6v4c-d{NFuE3|DCo(7=R1j2<#LF zkVUj?!Az#bA_R}XVu(o4e;xdASYED$z><*w2C(%X=SAXQ2lB^3SNzw(i-5I50G5mZ zXdzJP{Tpk8#SpSg|8;O9VFEI+L?l29o*q_A1<+IcT>$WKaJI0TENlz<{~mepbR<9w aVag3m5CsrHc=7~mL;-|RPy+xk(f Date: Wed, 10 Jun 2015 11:17:32 +0200 Subject: [PATCH 3/4] Code refactoring: time-dependent commodity prices #3 --- mimo-example.xlsx | Bin 2001552 -> 2001891 bytes urbs.py | 193 +++++++++++++++++++++++++++------------------- 2 files changed, 114 insertions(+), 79 deletions(-) diff --git a/mimo-example.xlsx b/mimo-example.xlsx index 949eb88532be50c9306f3545a2695dc58462580c..5158b00e80aab7b9f929892b3f103acb786ba658 100644 GIT binary patch delta 220936 zcmXteV|ZTA7j0}?jcr>^W81dXSSPmK*mlymvC}lRZQD1$|GoFydES{lv(8#;_MS8I z#;17grg_!n!U8_kuAK^CARz0IARx#fARr#LOzsZOHpUJPHjEy2wpGb`4lB$Up_f$W zLPhFD`qS~jwWJ~(Llvx6DioUDT5Z<>n%r)tnP8uO!z7A9UiDIcq;0zo`47hl*UzO< 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the "buy" process - # out = (site,output_commodity) - out = set([(x[0],x[2]) for x in pro_output_tuples if x[1] == pro_in]) - # search the sell process for the output_commodity from the buy process - inp_p = ([x for x in pro_output_tuples if x[2] in m.com_sell]) - for k in range(len(inp_p)): - pro_out = inp_p[k][1] - inp = set([(x[0],x[2]) for x in pro_input_tuples if x[1] == pro_out]) - # check: buy - commodity == commodity - sell; for a site - if not(inp.isdisjoint(out)): - return (m.cap_pro[sit_in, pro_in] == - m.cap_pro[sit_in, pro_out]) - return pyomo.Constraint.Skip + sell_pro = search_sell_buy_tuple(m, sit_in, pro_in, coin) + if sell_pro is None: + return pyomo.Constraint.Skip + else: + return (m.cap_pro[sit_in, pro_in] == + m.cap_pro[sit_in, sell_pro]) else: return pyomo.Constraint.Skip @@ -908,42 +899,21 @@ def def_costs_rule(m, cost_type): if c[1] in m.com_stock) elif cost_type == 'Revenue': - revenue = 0 - sell_tuples = commodity_subset_list(m.com_tuples, m.com_sell) - for c in sell_tuples: - # check com price fix or timeseries - if not isinstance(m.commodity.loc[c]['price'], float): - # factor, to realize a different commodity price for each site - factor = extract_number_str(m.commodity.loc[c]['price']) - # calculate the purchase with the hourly commodity price - revenue += sum(m.e_co_sell[(tm,) + c] * factor * - m.buy_sell_price.loc[(tm,) + (c[1],)] * m.weight * m.dt - for tm in m.tm) - else: - # commodity price is the same in each hour - revenue += sum(m.e_co_sell[(tm,) + c] * m.dt * - m.commodity.loc[c]['price'] * m.weight - for tm in m.tm) - return m.costs['Revenue'] == -revenue + sell_tuples = commodity_subset(m.com_tuples, m.com_sell) + com_prices = get_com_price(m, sell_tuples) + + return m.costs['Revenue'] == -sum( + m.e_co_sell[(tm,) + c] * com_prices[c].loc[tm] * m.weight * m.dt + for tm in m.tm for c in sell_tuples) elif cost_type == 'Purchase': - purchase = 0 - buy_tuples = commodity_subset_list(m.com_tuples, m.com_buy) - for c in buy_tuples: - # check com price fix or timeseries - if not isinstance(m.commodity.loc[c]['price'], float): - # factor, to realize a different commodity price for each site - factor = extract_number_str(m.commodity.loc[c]['price']) - # calculate the purchase with the hourly commodity price - purchase += sum(m.e_co_buy[(tm,) + c] * factor * - m.buy_sell_price.loc[(tm,) + (c[1],)] * m.weight * m.dt - for tm in m.tm) - else: - # commodity price is the same in each hour - purchase += sum(m.e_co_buy[(tm,) + c] * m.dt * - m.commodity.loc[c]['price'] * m.weight - for tm in m.tm) - return m.costs['Purchase'] == purchase + buy_tuples = commodity_subset(m.com_tuples, m.com_buy) + com_prices = get_com_price(m, buy_tuples) + + return m.costs['Purchase'] == sum( + m.e_co_buy[(tm,) + c] * com_prices[c].loc[tm] * m.weight * m.dt + for tm in m.tm for c in buy_tuples) + else: raise NotImplementedError("Unknown cost type.") @@ -1088,40 +1058,54 @@ def commodity_subset(com_tuples, type_name): Args: com_tuples: a list of (site, commodity, commodity type) tuples - type_name: a commodity type ('Stock', 'SupIm', 'Env' or 'Demand') + type_name: a commodity type or a ist of a commodity types Returns: - The set (unique elements) of commodity names of the desired type + The set (unique elements/list) of commodity names of the desired type """ - return set(com for sit, com, com_type in com_tuples - if com_type == type_name) + if type(type_name) is str: + # type_name: ('Stock', 'SupIm', 'Env' or 'Demand') + return set(com for sit, com, com_type in com_tuples + if com_type == type_name) + else: + # type(type_name) is a class 'coopr.pyomo.base.sets.SimpleSet' + # type_name: ('Buy')=>('Elec buy', 'Heat buy') + return set((sit, com, com_type) for sit, com, com_type in com_tuples + if com in type_name) -def commodity_subset_list(com_tuples, type_list): - """ Unique list of commodity names for given list (type). +def get_com_price(instance, tuples): + """ Calculate commodity prices for each modelled timestep. Args: - com_tuples: a list of (site, commodity, commodity type) tuples - type_list: list of a commodity type ('Buy')=>('Elec buy', 'Heat buy') + instance: a Pyomo ConcreteModel instance + tuples: a list of (site, commodity, commodity type) tuples Returns: - The set (unique list of tuples) of tuples of the desired type + a Pandas DataFrame with entities as columns and timesteps as index """ - return set((sit, com, com_type) for sit, com, com_type in com_tuples - if com in type_list) + com_price = pd.DataFrame(index=instance.tm) + for c in tuples: + # check commodity price: fix or has a timeseries + # type(instance.commodity.loc[c]['price']): + # float => fix: com price = 0.15 + # string => var: com price = '1.25xBuy' (Buy: refers to timeseries) + if not isinstance(instance.commodity.loc[c]['price'], float): + # a different commodity price for each hour + # factor, to realize a different commodity price for each site + factor = extract_number_str(instance.commodity.loc[c]['price']) + price = factor * instance.buy_sell_price.loc[(instance.tm,) + (c[1],)] + com_price[c] = pd.Series(price, index=com_price.index) + else: + # same commdoity price for each hour + price = instance.commodity.loc[c]['price'] + com_price[c] = pd.Series(price, index=com_price.index) + return com_price def extract_number_str(str_in): - """ Extract the first number from a given string and - and convert to the float type - - Excel sheet: Commodity - price - for Buy and Sell: + """ Extract first number from a given string and convert to a float number. - - fix price: (0.12) [input type: float] - the same price for each timestep - - timeseries: (1.25BUY) [input type: string] - 1.) extract the number 1.25 - 2.) select the com price for the given timestep - 3.) calculate the price with the factor (1.25) - => 1.25 x Buy-Sell-Price(h) + The function works with the following formats (,25), (.25), (2), (2,5), (2.5), + (1,000.25), (1.000,25) and doesn't with (1e3), (1.5-0.4j) and negative numbers. Args: str_in: a string ('1,20BUY') @@ -1130,14 +1114,65 @@ def extract_number_str(str_in): A float number (1.20) """ import re - # replace all commas with points - str_in = str_in.replace(',','.') - # find the first number and convert to float - factor_str = re.search('\d+[\.]?\d*',str_in) - if factor_str is None: - return 1 - else: - return float(factor_str.group(0)) + # deletes all char starting after the number + start_char = re.search('[*:!%$&?a-zA-Z]',str_in).start() + str_num = str_in[:start_char] + + if re.search('\d+',str_num) is None: + # no number in str_num + return 1.0 + elif re.search('^(\d+|\d{1,3}(,\d{3})*)(\.\d+)?$',str_num) is not None: + #Commas required between powers of 1,000 + #Can't start with "." + #Pass: (1,000,000), (0.001) + #Fail: (1000000), (1,00,00,00), (.001) + str_num = str_num.replace(',','') + return float(str_num) + elif re.search('^(\d+|\d{1,3}(.\d{3})*)(\,\d+)?$',str_num) is not None: + #Dots required between powers of 1.000 + #Can't start with "," + #Pass: (1.000.000), (0,001) + #Fail: (1000000), (1.00.00,00), (,001) + str_num = str_num.replace('.','') + return float(str_num.replace(',','.')) + elif re.search('^\d*\.?\d+$',str_num) is not None: + #No commas allowed + #Pass: (1000.0), (001), (.001) + #Fail: (1,000.0 + return float(str_num) + elif re.search('^\d*\,?\d+$',str_num) is not None: + #No dots allowed + #Pass: (1000,0), (001), (,001) + #Fail: (1.000,0 + str_num = str_num.replace(',','.') + return float(str_num) + +def search_sell_buy_tuple(instance, sit_in, pro_in, coin): + """ Return the equivalent sell-process for a given buy-process. + + Args: + instance: a Pyomo ConcreteModel instance + sit_in: a site + pro_in: a process + co_in: a commodity + + Returns: + a process + """ + pro_output_tuples = list(instance.pro_output_tuples.value) + pro_input_tuples = list(instance.pro_input_tuples.value) + # search the output commodities for the "buy" process + # buy_out = (site,output_commodity) + buy_out = set([(x[0],x[2]) for x in pro_output_tuples if x[1] == pro_in]) + # search the sell process for the output_commodity from the buy process + sell_output_tuple = ([x for x in pro_output_tuples if x[2] in instance.com_sell]) + for k in range(len(sell_output_tuple)): + sell_pro = sell_output_tuple[k][1] + sell_in = set([(x[0],x[2]) for x in pro_input_tuples if x[1] == sell_pro]) + # check: buy - commodity == commodity - sell; for a site + if not(sell_in.isdisjoint(buy_out)): + return sell_pro + return None def get_entity(instance, name): """ Return a DataFrame for an entity in model instance. 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