From d90fdb8e1cb8b247ada076eaffcde7b1372d7ae6 Mon Sep 17 00:00:00 2001 From: Balz Date: Tue, 20 Nov 2018 12:21:40 +0100 Subject: [PATCH 01/12] Attempt to make branch even with tum ens:master --- runme.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/runme.py b/runme.py index c50c0171..d56859b4 100644 --- a/runme.py +++ b/runme.py @@ -5,7 +5,7 @@ import urbs from datetime import datetime from pyomo.opt.base import SolverFactory - + # SCENARIOS def scenario_base(data): From 9d196777e99a8912479ff5a17bdeaa271434c334 Mon Sep 17 00:00:00 2001 From: Balz Date: Tue, 20 Nov 2018 17:27:00 +0100 Subject: [PATCH 02/12] New implementation of scenarios: Use existing base model and modify it --- input/scenario_new_timeseries_.xlsx | Bin 0 -> 1971461 bytes ...new_timeseries_example_file_extension.xlsx | Bin 0 -> 1971461 bytes runme.py | 101 ++--- urbs/__init__.py | 1 + urbs/data.py | 5 + urbs/scenario.py | 406 ++++++++++++++++++ 6 files changed, 442 insertions(+), 71 deletions(-) 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z|7F_|Cp=3hS%HI4X=46SaiA!F0{$-SZ>T>Tk?wGes!kaiL|F^}50o)atUoI5pH=oJ z%AW@y{IdlbrwIN+`SUo0KLP&qLH#Fy)8cP{e|n_;N&2V##6L-IuKq##r!~c&2>)(Y z@K1UW5W*V*kpE_6@F)47D&T(t)ZG1M;D4eJ{uA_1nf{-kO>h4b(BJ#Ze}ew|g8842 zARzRRg#Rq)e=VQ?r2b#C#0Eq7S0n%bl=JtZLtY9JI70n3UZR7DK!SjjArk^){|~Oc Bc$)wK literal 0 HcmV?d00001 diff --git a/runme.py b/runme.py index d56859b4..0dad6d47 100644 --- a/runme.py +++ b/runme.py @@ -5,57 +5,8 @@ import urbs from datetime import datetime from pyomo.opt.base import SolverFactory - - -# SCENARIOS -def scenario_base(data): - # do nothing - return data - - -def scenario_stock_prices(data): - # change stock commodity prices - co = data['commodity'] - stock_commodities_only = (co.index.get_level_values('Type') == 'Stock') - co.loc[stock_commodities_only, 'price'] *= 1.5 - return data - - -def scenario_co2_limit(data): - # change global CO2 limit - global_prop = data['global_prop'] - global_prop.loc['CO2 limit', 'value'] *= 0.05 - return data - - -def scenario_co2_tax_mid(data): - # change CO2 price in Mid - co = data['commodity'] - co.loc[('Mid', 'CO2', 'Env'), 'price'] = 50 - return data - - -def scenario_north_process_caps(data): - # change maximum installable capacity - pro = data['process'] - pro.loc[('North', 'Hydro plant'), 'cap-up'] *= 0.5 - pro.loc[('North', 'Biomass plant'), 'cap-up'] *= 0.25 - return data - - -def scenario_no_dsm(data): - # empty the DSM dataframe completely - data['dsm'] = pd.DataFrame() - return data - - -def scenario_all_together(data): - # combine all other scenarios - data = scenario_stock_prices(data) - data = scenario_co2_limit(data) - data = scenario_north_process_caps(data) - return data - +from urbs.data import timeseries_number +import pdb def prepare_result_directory(result_name): """ create a time stamped directory within the result folder """ @@ -92,13 +43,13 @@ def setup_solver(optim, logfile='solver.log'): return optim -def run_scenario(input_file, timesteps, scenario, result_dir, dt, objective, +def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, plot_tuples=None, plot_sites_name=None, plot_periods=None, report_tuples=None, report_sites_name=None): """ run an urbs model for given input, time steps and scenario Args: - input_file: filename to an Excel spreadsheet for urbs.read_excel + prob: urbs model instance initialized with base scenario timesteps: a list of timesteps, e.g. range(0,8761) scenario: a scenario function that modifies the input data dict result_dir: directory name for result spreadsheet and plots @@ -115,15 +66,17 @@ def run_scenario(input_file, timesteps, scenario, result_dir, dt, objective, # scenario name, read and modify data for scenario sce = scenario.__name__ - data = urbs.read_excel(input_file) - data = scenario(data) - urbs.validate_input(data) - # create model - prob = urbs.create_model(data, dt, timesteps, objective) + # Only needed for scenario_new_timeseries, but handed to all functions: + filename = "" + # scenario_new_timeseries needs special treatment: + if str(sce).find("scenario_new_timeseries") >= 0: + sce = sce+str(timeseries_number.pop()) + filename = os.path.join("input", "{}.xlsx").format(sce) + prob = scenario(prob, 0, filename) + # If possible: do validation of data - # refresh time stamp string and create filename for logfile - now = prob.created + # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) # solve model and read results @@ -152,6 +105,7 @@ def run_scenario(input_file, timesteps, scenario, result_dir, dt, objective, plot_sites_name=plot_sites_name, periods=plot_periods, figure_size=(24, 9)) + prob = scenario(prob, 1, filename) return prob @@ -192,9 +146,7 @@ def run_scenario(input_file, timesteps, scenario, result_dir, dt, objective, report_sites_name = {'North': 'Greenland'} # plotting timesteps - plot_periods = { - 'all': timesteps[1:] - } + plot_periods = {'all': timesteps[1:]} # add or change plot colors my_colors = { @@ -206,16 +158,23 @@ def run_scenario(input_file, timesteps, scenario, result_dir, dt, objective, # select scenarios to be run scenarios = [ - scenario_base, - scenario_stock_prices, - scenario_co2_limit, - scenario_co2_tax_mid, - scenario_no_dsm, - scenario_north_process_caps, - scenario_all_together] + urbs.scenario_base, + #urbs.scenario_stock_prices, + #urbs.scenario_co2_limit, + #urbs.scenario_co2_tax_mid, + #urbs.scenario_no_dsm, + #urbs.scenario_north_process_caps, + urbs.scenario_all_together, + urbs.scenario_new_timeseries(timeseries_number, + "example_file_extension") + ] + + # Read data from Excel Sheet and create model for use in scenarios + data = urbs.read_excel(input_file) + prob = urbs.create_model(data, dt, timesteps) for scenario in scenarios: - prob = run_scenario(input_file, timesteps, scenario, result_dir, dt, + prob = run_scenario(prob, timesteps, scenario, result_dir, dt, objective, plot_tuples=plot_tuples, plot_sites_name=plot_sites_name, diff --git a/urbs/__init__.py b/urbs/__init__.py index fd5ae5bc..fc52d752 100644 --- a/urbs/__init__.py +++ b/urbs/__init__.py @@ -19,3 +19,4 @@ from .pyomoio import get_entity, get_entities, list_entities from .report import report from .saveload import load, save +from .scenario import * \ No newline at end of file diff --git a/urbs/data.py b/urbs/data.py index 0b873f98..9996b2cd 100644 --- a/urbs/data.py +++ b/urbs/data.py @@ -28,3 +28,8 @@ 'Purchase': (0, 51, 89), 'Startup': (105, 8, 90), 'Variable': (128, 153, 172)} + + +# Helper list used for global declaration of current timeseries data sheet. +# Automatically filled by scenario_new_timeseries function +timeseries_number = [] diff --git a/urbs/scenario.py b/urbs/scenario.py new file mode 100644 index 00000000..334a5ecb --- /dev/null +++ b/urbs/scenario.py @@ -0,0 +1,406 @@ +from .model import * +from .input import split_columns +from .data import timeseries_number + +''' +These Scenarios donĀ“t built up a whole new model. Instead they save time by +using an existing base model and by manipulating the dictionaries containing +the data and updating the affected constraints afterwards. +It is important to rebuilt the base scenario once the problem is solved. +This is done by handing reverse=True to the scenario function. + +If you want to create your own scenarios either manipulate the functions below +or do the following steps: +1) Build an Excel file where the data is similar to the one the scenario + funtion should produce +2) Create a model instance of this scenario using prob = urbs.create_model() +3) Write the model to an .lp file using prob.write(filename, + io_options={"symbolic_solver_labels":True}) +4) Write a function with similar structure as the scenario functions below that + changes the data in the dictionaries as you wish +5) Do step 2 (using the standard Excel input file, not the modified version), + call your scenario function to manipulate the model, then do step 3. + This creates an .lp file of your current scenario model. +6) Compare the .lp file of your model to the .lp file you created before + (Notepad++ Plugin 'Compare' is very useful) +7) Spot the constraints that need to be updated, delete these constraints and + recreate them in your scenario function. Sometimes indices have to be + deleted, too. Do this both for the reverse=False part and the reverse=True + part +8) Repeat starting from step 5 until there are no more differences between the + two .lp files +9) Do step 2 and 3 using the standard input file, +10) Do step 2 (using the standard Excel input file), call your scenario + function to manipulate the model twice: One time with reverse=False and + afterwards with reverse=True, then do step 3. This .lp file should match + the .lp file of the base model. Compare them! +11) Congratulations! You created your own scenario function! +''' + + +def scenario_base(prob, reverse, not_used): + # do nothing + return prob + + +def scenario_stock_prices(prob, reverse, not_used): + # change stock commodity prices + if not reverse: + for x in tuple(prob.commodity_dict["price"].keys()): + if x[2] == "Stock": + prob.commodity_dict["price"][x] *= 1.5 + update_cost(prob) + return prob + if reverse: + for x in tuple(prob.commodity_dict["price"].keys()): + if x[2] == "Stock": + prob.commodity_dict["price"][x] *= 1/1.5 + update_cost(prob) + return prob + + +def scenario_co2_limit(prob, reverse, not_used): + # change global CO2 limit + if not reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 0.05 + update_co2_limit(prob) + return prob + if reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 1/0.05 + update_co2_limit(prob) + return prob + + +def scenario_co2_tax_mid(prob, reverse, not_used): + # change CO2 price in Mid + if not reverse: + prob.commodity_dict["price"][('Mid', 'CO2', 'Env')] = 50 + update_cost(prob) + return prob + if reverse: + prob.commodity_dict["price"][('Mid', 'CO2', 'Env')] = ( + prob._data["commodity"]["price"][('Mid', 'CO2', 'Env')]) + update_cost(prob) + return prob + + +def scenario_north_process_caps(prob, reverse, not_used): + # change maximum installable capacity + if not reverse: + prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 0.5 + prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 0.25 + update_process_capacity(prob) + return prob + if reverse: + prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 2 + prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 4 + update_process_capacity(prob) + return prob + + +def scenario_no_dsm(prob, reverse, not_used): + if not reverse: + del_dsm(prob) + return prob + if reverse: + recreate_dsm(prob) + return prob + + +def scenario_all_together(prob, reverse, not_used): + # combine all other scenarios + if not reverse: + prob = scenario_stock_prices(prob, 0, not_used) + prob = scenario_co2_limit(prob, 0, not_used) + prob = scenario_north_process_caps(prob, 0, not_used) + return prob + if reverse: + prob = scenario_stock_prices(prob, 1, not_used) + prob = scenario_co2_limit(prob, 1, not_used) + prob = scenario_north_process_caps(prob, 1, not_used) + return prob + + +# Usage: In main the scenarios are function handles. In order to hand further +# information to the scenario function it is necessary to define an external +# data structure to store the additional information in. This function should +# be called like this: +# scenario_new_timeseries(timeseries_number, ) +# value specifies the path/file extension in order to locate the excel file. +# value will be appended to input\\scenario_new_timeseries_ +def scenario_new_timeseries(timeseries_number, number): + timeseries_number.insert(0, number) # Is this an issue (memory not allocated)? + return scenario_new_timeseries_ + + +def scenario_new_timeseries_(prob, reverse, + filename="input\\scenario_new_timeseries.xlsx"): + if not reverse: + sheetnames = load_timeseries(prob, reverse, filename) + if "Demand" in sheetnames: + update_res_vertex(prob) + if "SupIm" in sheetnames: + update_supim(prob) + if "Buy-Sell-Price" in sheetnames: + update_cost(prob) + if 'TimeVarEff' in sheetnames: + update_TimeVarEff(prob) + if reverse: + sheetnames = load_timeseries(prob, reverse, filename) + if "Demand" in sheetnames: + update_res_vertex(prob) + if "SupIm" in sheetnames: + update_supim(prob) + if "Buy-Sell-Price" in sheetnames: + update_cost(prob) + if 'TimeVarEff' in sheetnames: + update_TimeVarEff(prob) + return prob + + +# Constraint updating funtions: +def del_dsm(prob): + # empty the DSM dataframe completely + prob.dsm_dict = pd.DataFrame().to_dict() + + prob.del_component(prob.dsm_site_tuples) + prob.del_component(prob.dsm_down_tuples) + prob.dsm_site_tuples = pyomo.Set() + prob.dsm_down_tuples = pyomo.Set() + + prob.del_component(prob.dsm_down) + prob.del_component(prob.dsm_up) + prob.dsm_down = pyomo.Var() + prob.dsm_up = pyomo.Var() + + prob.del_component(prob.def_dsm_variables) + prob.del_component(prob.res_dsm_upward) + prob.del_component(prob.res_dsm_downward) + prob.del_component(prob.res_dsm_maximum) + prob.del_component(prob.res_dsm_recovery) + + prob.def_dsm_variables = pyomo.Constraint.Skip + prob.res_dsm_upward = pyomo.Constraint.Skip + prob.res_dsm_downward = pyomo.Constraint.Skip + prob.res_dsm_maximum = pyomo.Constraint.Skip + prob.res_dsm_recovery = pyomo.Constraint.Skip + + # The following lines cause 99% of work for the formation of this scenario + update_res_vertex(prob) + + +def recreate_dsm(prob): + # dsm_variables & vertex rule + prob.dsm_dict = prob._data["dsm"].to_dict() + try: + myset = tuple(prob.dsm_dict["delay"].keys()) + except KeyError: + raise NotImplementedError("Could not rebuild base modell!") + + prob.del_component(prob.dsm_site_tuples_domain) + prob.del_component(prob.dsm_site_tuples) + prob.dsm_site_tuples = pyomo.Set( + within=prob.sit*prob.com, + initialize=myset, + doc='Combinations of possible dsm by site, e.g.(Mid, Elec)') + + prob.del_component(prob.dsm_down_tuples_domain) + prob.del_component(prob.dsm_down_tuples_domain_index_0) + prob.del_component(prob.dsm_down_tuples_domain_index_0_index_0) + prob.del_component(prob.dsm_down_tuples) + prob.dsm_down_tuples = pyomo.Set( + within=prob.tm*prob.tm*prob.sit*prob.com, + initialize=[(t, tt, site, commodity) + for (t, tt, site, commodity) + in dsm_down_time_tuples(prob.timesteps[1:], + prob.dsm_site_tuples, + prob)], + doc='Combinations of possible dsm_down combinations, e.g. ' + '(5001,5003,Mid,Elec)') + + prob.del_component(prob.dsm_up_index) + prob.del_component(prob.dsm_up) + prob.dsm_up = pyomo.Var( + prob.tm, prob.dsm_site_tuples, + within=pyomo.NonNegativeReals, + doc='DSM upshift') + + prob.del_component(prob.dsm_down) + prob.dsm_down = pyomo.Var( + prob.dsm_down_tuples, + within=pyomo.NonNegativeReals, + doc='DSM downshift') + + prob.del_component(prob.def_dsm_variables_index) + prob.del_component(prob.def_dsm_variables) + del prob.def_dsm_variables + prob.def_dsm_variables = pyomo.Constraint( + prob.tm, prob.dsm_site_tuples, + rule=def_dsm_variables_rule, + doc='DSMup * efficiency factor n == DSMdo (summed)') + + prob.del_component(prob.res_dsm_upward_index) + prob.del_component(prob.res_dsm_upward) + del prob.res_dsm_upward + prob.res_dsm_upward = pyomo.Constraint( + prob.tm, prob.dsm_site_tuples, + rule=res_dsm_upward_rule, + doc='DSMup <= Cup (threshold capacity of DSMup)') + + prob.del_component(prob.res_dsm_downward_index) + prob.del_component(prob.res_dsm_downward) + del prob.res_dsm_downward + prob.res_dsm_downward = pyomo.Constraint( + prob.tm, prob.dsm_site_tuples, + rule=res_dsm_downward_rule, + doc='DSMdo (summed) <= Cdo (threshold capacity of DSMdo)') + + prob.del_component(prob.res_dsm_maximum) + prob.del_component(prob.res_dsm_maximum_index) + del prob.res_dsm_maximum + prob.res_dsm_maximum = pyomo.Constraint( + prob.tm, prob.dsm_site_tuples, + rule=res_dsm_maximum_rule, + doc='DSMup + DSMdo (summed) <= max(Cup,Cdo)') + + prob.del_component(prob.res_dsm_recovery) + prob.del_component(prob.res_dsm_recovery_index) + del prob.res_dsm_recovery + prob.res_dsm_recovery = pyomo.Constraint( + prob.tm, prob.dsm_site_tuples, + rule=res_dsm_recovery_rule, + doc='DSMup(t, t + recovery time R) <= Cup * delay time L') + + # The following lines cause 50% of rebuilding work + update_res_vertex(prob) + + +def change_dsm(prob): + # not implemented yet + return prob + + +def upd_dsm_constraints(prob): + # not implemented yet + return prob + + +def load_timeseries(prob, reverse, filename): + with pd.ExcelFile(filename) as xls: + try: + sheetnames = xls.sheet_names + except KeyError: + print("Could not find file for new timeseries scenario") + if not reverse: + for temp in sheetnames: + temp2 = xls.parse(temp).set_index(["t"]) + temp2.columns = split_columns(temp2.columns, '.') + if str(temp) == "Demand": + prob.demand_dict = temp2.to_dict() + if str(temp) == "SupIm": + prob.supim_dict = temp2.to_dict() + if str(temp) == "Buy-Sell-Price": + prob.buy_sell_price_dict = temp2.to_dict() + if str(temp) == "TimeVarEff": + prob.eff_factor_dict = temp2.to_dict() + if reverse: + for temp in sheetnames: + if str(temp) == "demand": + prob.demand_dict = prob._data["demand"].to_dict() + if str(temp) == "SupIm": + prob.supim_dict = prob._data["supim"].to_dict() + if str(temp) == "Buy-Sell-Price": + prob.buy_sell_price_dict = (prob._data["buy_sell_price"] + .to_dict()) + if str(temp) == "TimeVarEff": + prob.eff_factor_dict = prob._data["eff_factor"].to_dict() + return sheetnames + + +def update_TimeVarEff(prob): + prob.del_component(prob.pro_timevar_output_tuples) + prob.del_component(prob.pro_timevar_output_tuples_domain) + prob.del_component(prob.pro_timevar_output_tuples_domain_index_0) + prob.pro_timevar_output_tuples = pyomo.Set( + within=prob.sit*prob.pro*prob.com, + initialize=[(site, process, commodity) + for (site, process) in tuple(prob.eff_factor_dict.keys()) + for (pro, commodity) in tuple(prob.r_out_dict.keys()) + if process == pro], + doc='Outputs of processes with time dependent efficiency') + + prob.del_component(prob.def_process_output) + prob.del_component(prob.def_process_output_index) + prob.del_component(prob.def_process_output_index_1) + prob.del_component(prob.def_process_output_index_1_index_0) + prob.def_process_output = pyomo.Constraint( + prob.tm, (prob.pro_output_tuples - prob.pro_partial_output_tuples - + prob.pro_timevar_output_tuples), + rule=def_process_output_rule, + doc='process output = process throughput * output ratio') + + prob.del_component(prob.def_process_timevar_output) + prob.del_component(prob.def_process_timevar_output_index) + prob.del_component(prob.def_process_timevar_output_index_1) + prob.del_component(prob.def_process_timevar_output_index_1_index_1) + prob.def_process_timevar_output = ( + pyomo.Constraint(prob.tm, (prob.pro_timevar_output_tuples - + (prob.pro_partial_output_tuples & + prob.pro_timevar_output_tuples)), + rule=def_pro_timevar_output_rule, + doc='e_pro_out = tau_pro * r_out * eff_factor')) + + prob.del_component(prob.def_process_partial_timevar_output) + prob.del_component(prob.def_process_partial_timevar_output_index) + prob.del_component(prob.def_process_partial_timevar_output_index_1) + prob.def_process_partial_timevar_output = pyomo.Constraint( + prob.tm, + prob.pro_partial_output_tuples & prob.pro_timevar_output_tuples, + rule=def_pro_partial_timevar_output_rule, + doc='e_pro_out = tau_pro * r_out * eff_factor') + + +def update_cost(prob): + prob.del_component(prob.def_costs) + prob.def_costs = pyomo.Constraint( + prob.cost_type, + rule=def_costs_rule, + doc='main cost function by cost type') + + +def update_supim(prob): + prob.del_component(prob.def_intermittent_supply) + prob.del_component(prob.def_intermittent_supply_index) + prob.def_intermittent_supply = pyomo.Constraint( + prob.tm, prob.pro_input_tuples, + rule=def_intermittent_supply_rule, + doc='process output = process capacity * supim timeseries') + + +def update_res_vertex(prob): + prob.del_component(prob.res_vertex) + prob.del_component(prob.res_vertex_index) + prob.res_vertex = pyomo.Constraint( + prob.tm, prob.com_tuples, + rule=res_vertex_rule, + doc='storage + transmission + process + source + buy - sell == demand') + + +def update_process_capacity(prob): + prob.del_component(prob.def_process_capacity) + prob.def_process_capacity = pyomo.Constraint( + prob.pro_tuples, + rule=def_process_capacity_rule, + doc='total process capacity = inst-cap + new capacity') + prob.del_component(prob.res_process_capacity) + prob.res_process_capacity = pyomo.Constraint( + prob.pro_tuples, + rule=res_process_capacity_rule, + doc='process.cap-lo <= total process capacity <= process.cap-up') + + +def update_co2_limit(prob): + prob.del_component(prob.res_global_co2_limit) + prob.res_global_co2_limit = pyomo.Constraint( + rule=res_global_co2_limit_rule, + doc='total co2 commodity output <= Global CO2 limit') From 893d872b37a69028f97ab0d99b4b4c1912b0b4c5 Mon Sep 17 00:00:00 2001 From: Balz Date: Tue, 27 Nov 2018 14:16:10 +0100 Subject: [PATCH 03/12] Fixed bugs at plotting and improved scenario_new_timeseries --- doc/tutorial.rst | 201 +++++++++++------- doc/workflow.rst | 94 ++++---- input/scenario_new_timeseries_1.xlsx | Bin 0 -> 1971701 bytes ...new_timeseries_example_file_extension.xlsx | Bin 1971461 -> 1971555 bytes mimo-example.xlsx | Bin 2002080 -> 2002080 bytes runme.py | 28 ++- urbs/plot.py | 9 +- urbs/pyomoio.py | 8 +- urbs/scenario.py | 2 +- 9 files changed, 207 insertions(+), 135 deletions(-) create mode 100644 input/scenario_new_timeseries_1.xlsx diff --git a/doc/tutorial.rst b/doc/tutorial.rst index 5228f380..e4516df9 100644 --- a/doc/tutorial.rst +++ b/doc/tutorial.rst @@ -10,9 +10,10 @@ expands on the steps that follow this installation. .. __: https://github.com/tum-ens/urbs/blob/master/README.md#installation -This tutorial is a commented walk-through through the script ``runme.py``, -which is a demonstration user script that can serve as a good basis for one's -own script. +This tutorial is a commented walk-through through the script ``runme.py`` and +``scenrario.py``, which is a demonstration user script that can serve as a good +basis for one's own script. ``scenrario.py`` is contained in the subolder +`urbs`. Initialisation -------------- @@ -22,13 +23,14 @@ Imports :: - import os - import pandas as pd - import pyomo.environ - import shutil - import urbs - from datetime import datetime - from pyomo.opt.base import SolverFactory + import os + import pandas as pd + import pyomo.environ + import shutil + import urbs + from datetime import datetime + from pyomo.opt.base import SolverFactory + from urbs.data import timeseries_number Several packages are included. @@ -99,63 +101,90 @@ subset to be actually run. Scenario functions ^^^^^^^^^^^^^^^^^^ -A scenario is simply a function that takes the input ``data`` and modifies it -in a certain way. with the required argument ``data``, the input -data :class:`dict`.:: - +A scenario is a function that takes an existing pyomo concrete model ``prob``, +modifies its data and updates the corresponding constraints. :: + # SCENARIOS - def scenario_base(data): + def scenario_base(prob, reverse, not_used): # do nothing - return data + return prob -The simplest scenario does not change anything in the original input file. It +The simplest scenario does not change anything in the original model. It usually is called "base" scenario for that reason. All other scenarios are -defined by 1 or 2 distinct changes in parameter values, relative to this common -foundation.:: +defined by 1 or 2 distinct changes in parameter values. In order to actually +have an effect on the model to be solved updating the corresponding constraints +is necessary. See how the changes get undone if the function is called with +reverse=True. This enables further use of the model in the following scenarios. +Cloning of the model is very expensive and for this reason not recommended. +``not_used`` is only needed for the :func:`scenario_new_timeseries` and will +contain file extensions in that case. For all other scenarios it is unused. :: - def scenario_stock_prices(data): + def scenario_stock_prices(prob, reverse, not_used): # change stock commodity prices - co = data['commodity'] - stock_commodities_only = (co.index.get_level_values('Type') == 'Stock') - co.loc[stock_commodities_only, 'price'] *= 1.5 - return data + if not reverse: + for x in tuple(prob.commodity_dict["price"].keys()): + if x[2] == "Stock": + prob.commodity_dict["price"][x] *= 1.5 + update_cost(prob) + return prob + if reverse: + for x in tuple(prob.commodity_dict["price"].keys()): + if x[2] == "Stock": + prob.commodity_dict["price"][x] *= 1/1.5 + update_cost(prob) + return prob For example, :func:`scenario_stock_prices` selects all stock commodities from -the :class:`DataFrame` ``commodity``, and increases their *price* value by 50%. -See also pandas documentation :ref:`Selection by label ` -for more information about the ``.loc`` function to access fields. Also note -the use of `Augmented assignment statements`_ (``*=``) to modify data -in-place.:: +the :class:`Dictionary` ``commodity_dict``, and increases their *price* value +by 50%. Also note the use of `Augmented assignment statements`_ (``*=``) to +modify data in-place.:: - def scenario_co2_limit(data): + def scenario_co2_limit(prob, reverse, not_used): # change global CO2 limit - hacks = data['hacks'] - hacks.loc['Global CO2 limit', 'Value'] *= 0.05 - return data + if not reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 0.05 + update_co2_limit(prob) + return prob + if reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 1/0.05 + update_co2_limit(prob) + return prob Scenario :func:`scenario_co2_limit` shows the simple case of changing a single input data value. In this case, a 95% CO2 reduction compared to the base scenario must be accomplished. This drastically limits the amount of coal and gas that may be used by all three sites.:: - def scenario_north_process_caps(data): + def scenario_north_process_caps(prob, reverse, not_used): # change maximum installable capacity - pro = data['process'] - pro.loc[('North', 'Hydro plant'), 'cap-up'] *= 0.5 - pro.loc[('North', 'Biomass plant'), 'cap-up'] *= 0.25 - return data + if not reverse: + prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 0.5 + prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 0.25 + update_process_capacity(prob) + return prob + if reverse: + prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 2 + prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 4 + update_process_capacity(prob) + return prob Scenario :func:`scenario_north_process_caps` demonstrates accessing single values in the ``process`` :class:`~pandas.DataFrame`. By reducing the amount of renewable energy conversion processes (hydropower and biomass), this scenario explores the "second best" option for this region to supply its demand.:: - def scenario_all_together(data): + def scenario_all_together(prob, reverse, not_used): # combine all other scenarios - data = scenario_stock_prices(data) - data = scenario_co2_limit(data) - data = scenario_north_process_caps(data) - return data + if not reverse: + prob = scenario_stock_prices(prob, 0, not_used) + prob = scenario_co2_limit(prob, 0, not_used) + prob = scenario_north_process_caps(prob, 0, not_used) + return prob + if reverse: + prob = scenario_stock_prices(prob, 1, not_used) + prob = scenario_co2_limit(prob, 1, not_used) + prob = scenario_north_process_caps(prob, 1, not_used) + return prob Scenario :func:`scenario_all_together` finally shows that scenarios can also be combined by chaining other scenario functions, making them dependent. This way, @@ -169,42 +198,23 @@ Scenario selection # select scenarios to be run scenarios = [ - scenario_base, - scenario_stock_prices, - scenario_co2_limit, - scenario_north_process_caps, - scenario_all_together] - scenarios = scenarios[:1] # select by slicing + urbs.scenario_base, + urbs.scenario_stock_prices, + urbs.scenario_co2_limit, + urbs.scenario_north_process_caps, + urbs.scenario_all_together] In Python, functions are objects, so they can be put into data structures just like any variable could be. In the following, the list ``scenarios`` is used to control which scenarios are being actually computed. -Run scenarios -------------- - -:: - - for scenario in scenarios: - prob = run_scenario(input_file, timesteps, scenario, result_dir, dt) - -Having prepared settings, input data and scenarios, the actual computations -happen in the function :func:`run_scenario` of the script. It is executed -``for`` each of the scenarios included in the scenario list. The following -sections describe the content of function :func:`run_scenario`. In a nutshell, -it reads the input data from its argument ``input_file``, modifies it with the -supplied ``scenario``, runs the optimisation for the given ``timesteps`` and -writes report and plots to ``result_dir``. - -Reading input -^^^^^^^^^^^^^ +Reading input & model creation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ :: - # scenario name, read and modify data for scenario - sce = scenario.__name__ data = urbs.read_excel(input_file) - data = scenario(data) + prob = urbs.create_model(data, dt, timesteps) Function :func:`read_excel` returns a dict ``data`` of six pandas DataFrames with hard-coded column names that correspond to the parameters of the @@ -216,18 +226,42 @@ convention, it must contain the six keys ``commodity``, ``process``, :class:`pandas.DataFrame`, whose index (row labels) and columns (column labels) conforms to the specification given by the example dataset in the spreadsheet :file:`mimo-example.xlsx`. +``prob`` is then modified by applying the :func:`scenario` function to it. + +Run scenarios +----------------------------- + +:: + + for scenario in scenarios: + prob = run_scenario(prob, timesteps, scenario, result_dir, dt) + + +Having prepared settings and scenarios, the actual computations happen in the +function :func:`run_scenario` of the script. It is executed ``for`` each of the +scenarios included in the scenario list. The following sections describe the +content of function :func:`run_scenario`. In a nutshell, it modifies the input +model instance ``prob`` by applying the :func:`scenario` function to it. It +then runs the optimization for the given ``timesteps`` and writes report and +plots to ``result_dir``. + +Scenario creation +^^^^^^^^^^^^^ + +:: + + # scenario name and modify data for scenario + sce = scenario.__name__ + prob = scenario(prob, 0) -``data`` is then modified by applying the :func:`scenario` function to it. +The pyomo model instance ``prob`` now contains the scenario to be solved. Solving ^^^^^^^ :: - # create model - prob = urbs.create_model(data, dt, timesteps) - # refresh time stamp string and create filename for logfile now = prob.created log_filename = os.path.join(result_dir, '{}.log').format(sce) @@ -238,10 +272,10 @@ Solving result = optim.solve(prob, tee=True) This section is the "work horse", where most computation and time is spent. The -optimization problem is first defined (:func:`create_model`), then filled with -values (``create``). The ``SolverFactory`` object is an abstract representation -of the solver used. The returned object ``optim`` has a method -:meth:`set_options` to set solver options (not used in this tutorial). +optimization problem is already well defined. The ``SolverFactory`` object is +an abstract representation of the solver used. The returned object ``optim`` +has a method :meth:`set_options` to set solver options (not used in this +tutorial). The remaining line calls the solver and reads the ``result`` object back into the ``prob`` object, which is queried to for variable values in the @@ -360,6 +394,17 @@ to save the figure as a pixel ``png`` (raster) and ``pdf`` (vector) image. The cannot be used. The filename extension or the optional ``format`` argument can be used to set the output format. Available formats depend on the used `plotting backend`_. Most backends support png, pdf, ps, eps and svg. + +Rebuilding of base scenario +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + prob = scenario(prob, 1, filename) + +This line calls the :func:`scenario` function with the codeword reverse=True. +The function is built such that it undoes all changes done to ``prob`` if +called with this codeword. Afterwards prob again contains the base scenario for +correct usage in the next scenario. .. _augmented assignment statements: http://docs.python.org/2/reference/\ diff --git a/doc/workflow.rst b/doc/workflow.rst index 936c7aa6..618d737d 100644 --- a/doc/workflow.rst +++ b/doc/workflow.rst @@ -448,18 +448,28 @@ a script can automate these. This is where a ``runme.py`` script becomes handy. Create a copy of the script file ``runme.py`` and give it a suitable name, e.g. ``runns.py``. -Modify the ``scenario_co2_limit`` function. As the base scenario now has no -limit, reducing it by 95Ā % does not make it finite. Therefore you set a fixed -hard (annual) limit of 40 million tonnes of CO2 equivalent: +Modify the ``scenario_co2_limit`` function in ``scenario.pyĀ“Ā“ in the subfolder +urbs and reduce the fixed hard (annual) limit by the factor of 20. +The scenario function modifies the model of the existing base scenario. +Therefore it is necessary to undo all changes of the model once it is solved. +This makes it possible to reuse the model without expensive cloning. +# Modify the ``scenario_co2_limit`` function. As the base scenario now has no +# limit, reducing it by 95Ā % does not make it finite. Therefore you set a fixed +# hard (annual) limit of 40 million tonnes of CO2 equivalent: .. code-block:: python :emphasize-lines: 4 - def scenario_co2_limit(data): - # change global CO2 limit - global_prop = data['global_prop'] - global_prop.loc['CO2 limit', 'value'] = 40000 - return data +def scenario_co2_limit(prob, reverse, not_used): + # change global CO2 limit + if not reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 0.05 + update_co2_limit(prob) + return prob + if reverse: + prob.global_prop_dict["value"]["CO2 limit"] *= 1/0.05 + update_co2_limit(prob) + return prob Next, set adjust the plot_tuples and report_tuples by replacing ``North``, ``Mid`` and ``South`` by the four islands of Newsealand. @@ -472,28 +482,32 @@ custom colors. So you modify the ``my_colors`` :class:`dict` like this:: 'Qlyph Archipelago': (200, 200, 230), 'Jepid Island': (215,215,215)} -Finally, you head down to the ``if __name__ == '__main__'`` section that is -executed when the script is called. There, you change the ``input_file`` to -your spreadsheet ``newsealand.xlsx`` and increase the optimisation duration to -14 days (``14*24`` time steps). For now, you don't need the other scenarios, -so you exclude them from the ``scenarios`` :class:`list`: +Finally, you head up to the beginning of the code. There you change the +``input_file`` toyour spreadsheet ``newsealand.xlsx`` and increase the +optimisation duration to 14 days (``14*24`` time steps). For now, you don't +need the other scenarios, so you exclude them from the +``scenarios`` :class:`list`: +# Finally, you head down to the ``if __name__ == '__main__'`` section that is +# executed when the script is called. There, you change the ``input_file`` to +# your spreadsheet ``newsealand.xlsx`` and increase the optimisation duration to +# 14 days (``14*24`` time steps). For now, you don't need the other scenarios, +# so you exclude them from the ``scenarios`` :class:`list`: .. code-block:: python - :emphasize-lines: 2,6,10,11,12 - - if __name__ == '__main__': - input_file = 'newsealand.xlsx' - result_name = os.path.splitext(input_file)[0] # cut away file extension - result_dir = prepare_result_directory(result_name) # name + time stamp - - (offset, length) = (3500, 14*24) # time step selection - timesteps = range(offset, offset+length+1) - dt = 1 - - # select scenarios to be run - scenarios = [ - scenario_base, - scenario_co2_limit] + :emphasize-lines: 1,5,10,11,12 + + input_file = 'newsealand.xlsx' + result_name = os.path.splitext(input_file)[0] # cut away file extension + result_dir = prepare_result_directory(result_name) # name + time stamp + + (offset, length) = (3500, 14*24) # time step selection + timesteps = range(offset, offset+length+1) + dt = 1 + + # select scenarios to be run + scenarios = [ + urbs.scenario_base, + urbs.scenario_co2_limit] # define the commodities/sites to be plotted plot_tuples = [ @@ -515,17 +529,19 @@ so you exclude them from the ``scenarios`` :class:`list`: report_sites_name = {'Vled Haven': 'Another name for Vled Haven'} # define the time periods to be plotted - plot_periods = { - 'all': timesteps[1:] - } - - for scenario in scenarios: - prob = run_scenario(input_file, timesteps, scenario, result_dir, dt, - plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - plot_periods=plot_periods, - report_tuples=report_tuples, - report_sites_name=report_sites_name) + plot_periods = {'all': timesteps[1:]} + # Read data from Excel Sheet and create model for use in scenarios + data = urbs.read_excel(input_file) + prob = urbs.create_model(data, dt, timesteps) + + for scenario in scenarios: + prob = run_scenario(prob, timesteps, scenario, result_dir, dt, + objective, + plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + plot_periods=plot_periods, + report_tuples=report_tuples, + 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timeseries_number, dt, + objective, plot_tuples=None, plot_sites_name=None, + plot_periods=None, report_tuples=None, + report_sites_name=None): """ run an urbs model for given input, time steps and scenario Args: @@ -70,18 +71,16 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, # Only needed for scenario_new_timeseries, but handed to all functions: filename = "" # scenario_new_timeseries needs special treatment: + # Add file extension to scenario name and create path to excel sheet if str(sce).find("scenario_new_timeseries") >= 0: sce = sce+str(timeseries_number.pop()) filename = os.path.join("input", "{}.xlsx").format(sce) + # model instance, undo scenario changes?, path to excel sheet prob = scenario(prob, False, filename) - # If possible: do validation of data # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) - #Write model to lp File - model_filename = os.path.join(result_dir, '{}.lp').format(sce) - prob.write(model_filename, io_options={"symbolic_solver_labels":True}) - + # solve model and read results optim = SolverFactory('glpk') # cplex, glpk, gurobi, ... optim = setup_solver(optim, logfile=log_filename) @@ -109,11 +108,6 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, periods=plot_periods, figure_size=(24, 9)) prob = scenario(prob, True, filename) - - #Write model to lp File - model_filename = os.path.join(result_dir, '{}_base.lp').format(sce) - prob.write(model_filename, io_options={"symbolic_solver_labels":True}) - return prob @@ -174,7 +168,7 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, urbs.scenario_north_process_caps, urbs.scenario_all_together, urbs.scenario_new_timeseries(timeseries_number, - "1") + "example_file_extension") ] # Read data from Excel Sheet and create model for use in scenarios @@ -182,8 +176,8 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, prob = urbs.create_model(data, dt, timesteps) for scenario in scenarios: - prob = run_scenario(prob, timesteps, scenario, result_dir, dt, - objective, + prob = run_scenario(prob, timesteps, scenario, result_dir, + timeseries_number, dt, objective, plot_tuples=plot_tuples, plot_sites_name=plot_sites_name, plot_periods=plot_periods, diff --git a/urbs/plot.py b/urbs/plot.py index 821f1c38..eca5c142 100644 --- a/urbs/plot.py +++ b/urbs/plot.py @@ -126,7 +126,7 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, # if so, show DSM subplot (even if delta == 0 for the whole time) df_dsm = get_input(prob, 'dsm_dict') plot_dsm=0 - for s in sit: #someone meant to cast sit to a list... + for s in sit: plot_dsm += (df_dsm["cap-max-do"][s,com] + df_dsm["cap-max-up"] [s,com]) plot_dsm=plot_dsm>0 diff --git a/urbs/scenario.py b/urbs/scenario.py index e6cd0b73..f6624d58 100644 --- a/urbs/scenario.py +++ b/urbs/scenario.py @@ -129,7 +129,7 @@ def scenario_all_together(prob, reverse, not_used): # value specifies the path/file extension in order to locate the excel file. # value will be appended to input\\scenario_new_timeseries_ def scenario_new_timeseries(timeseries_number, number): - timeseries_number.insert(0, number) # Is this an issue (memory not allocated)? + timeseries_number.insert(0, number) return scenario_new_timeseries_ @@ -305,7 +305,7 @@ def load_timeseries(prob, reverse, filename): prob.eff_factor_dict = temp2.to_dict() if reverse: for temp in sheetnames: - if str(temp) == "demand": + if str(temp) == "Demand": prob.demand_dict = prob._data["demand"].to_dict() if str(temp) == "SupIm": prob.supim_dict = prob._data["supim"].to_dict() From 518333bd157dd74000f4f89f0c1f87bdae2ce197 Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 5 Dec 2018 12:51:38 +0100 Subject: [PATCH 05/12] Fixed bug for saving .h5 file with old input data. process_commodity is still split up into r_in, r_out, r_in_min_fraction and r_out_min_fraction, as used in the model. --- urbs/saveload.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/urbs/saveload.py b/urbs/saveload.py index 9cebf738..660d5990 100644 --- a/urbs/saveload.py +++ b/urbs/saveload.py @@ -35,8 +35,12 @@ def save(prob, filename): prob._result = create_result_cache(prob) with pd.HDFStore(filename, mode='w') as store: - for name in prob._data.keys(): - store['data/'+name] = prob._data[name] + # Search all attributes of prob for those containing the input data + for name in prob.__dict__: + if str(name).find("_dict") > 0: + name_no_dict = name.split("_dict")[0] # remove _dict + store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, + name), index=[0])) for name in prob._result.keys(): store['result/'+name] = prob._result[name] From 67ed45ab6fcb5a1297b6f7f05beed13874fa8dbf Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 5 Dec 2018 14:09:24 +0100 Subject: [PATCH 06/12] Repaired indexing and pep8 conformity --- urbs/saveload.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/urbs/saveload.py b/urbs/saveload.py index 660d5990..02dd9cfa 100644 --- a/urbs/saveload.py +++ b/urbs/saveload.py @@ -39,8 +39,13 @@ def save(prob, filename): for name in prob.__dict__: if str(name).find("_dict") > 0: name_no_dict = name.split("_dict")[0] # remove _dict - store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, - name), index=[0])) + try: + store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, + name))) + # 1D dictionaries need an index: + except ValueError: + store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, + name), index=[0])) for name in prob._result.keys(): store['result/'+name] = prob._result[name] From 675dc02280146245c65bdaf51ae432e8dd7dd84f Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 5 Dec 2018 16:16:08 +0100 Subject: [PATCH 07/12] Merged changes of tum ens master manually into branch and reduced library dependencies. --- runme.py | 255 +++++++++-------------------- urbs/__init__.py | 11 +- urbs/runfunctions.py | 113 +++++++++++++ urbs/{scenario.py => scenarios.py} | 2 +- 4 files changed, 193 insertions(+), 188 deletions(-) create mode 100644 urbs/runfunctions.py rename urbs/{scenario.py => scenarios.py} (99%) diff --git a/runme.py b/runme.py index a0e4990b..712216e5 100644 --- a/runme.py +++ b/runme.py @@ -1,185 +1,82 @@ import os -import pandas as pd -import pyomo.environ import shutil import urbs -from datetime import datetime -from pyomo.opt.base import SolverFactory from urbs.data import timeseries_number -def prepare_result_directory(result_name): - """ create a time stamped directory within the result folder """ - # timestamp for result directory - now = datetime.now().strftime('%Y%m%dT%H%M') - - # create result directory if not existent - result_dir = os.path.join('result', '{}-{}'.format(result_name, now)) - if not os.path.exists(result_dir): - os.makedirs(result_dir) - - return result_dir - - -def setup_solver(optim, logfile='solver.log'): - """ """ - if optim.name == 'gurobi': - # reference with list of option names - # http://www.gurobi.com/documentation/5.6/reference-manual/parameters - optim.set_options("logfile={}".format(logfile)) - # optim.set_options("timelimit=7200") # seconds - # optim.set_options("mipgap=5e-4") # default = 1e-4 - elif optim.name == 'glpk': - # reference with list of options - # execute 'glpsol --help' - optim.set_options("log={}".format(logfile)) - # optim.set_options("tmlim=7200") # seconds - # optim.set_options("mipgap=.0005") - elif optim.name == 'cplex': - optim.set_options("log={}".format(logfile)) - else: - print("Warning from setup_solver: no options set for solver " - "'{}'!".format(optim.name)) - return optim - - -def run_scenario(prob, timesteps, scenario, result_dir, timeseries_number, dt, - objective, plot_tuples=None, plot_sites_name=None, - plot_periods=None, report_tuples=None, - report_sites_name=None): - """ run an urbs model for given input, time steps and scenario - - Args: - prob: urbs model instance initialized with base scenario - timesteps: a list of timesteps, e.g. range(0,8761) - scenario: a scenario function that modifies the input data dict - result_dir: directory name for result spreadsheet and plots - dt: length of each time step (unit: hours) - plot_tuples: (optional) list of plot tuples (c.f. urbs.result_figures) - plot_sites_name: (optional) dict of names for sites in plot_tuples - plot_periods: (optional) dict of plot periods(c.f. urbs.result_figures) - report_tuples: (optional) list of (sit, com) tuples (c.f. urbs.report) - report_sites_name: (optional) dict of names for sites in report_tuples - - Returns: - the urbs model instance - """ - - # scenario name, read and modify data for scenario - sce = scenario.__name__ - - # Only needed for scenario_new_timeseries, but handed to all functions: - filename = "" - # scenario_new_timeseries needs special treatment: - # Add file extension to scenario name and create path to excel sheet - if str(sce).find("scenario_new_timeseries") >= 0: - sce = sce+str(timeseries_number.pop()) - filename = os.path.join("input", "{}.xlsx").format(sce) - # model instance, undo scenario changes?, path to excel sheet - prob = scenario(prob, False, filename) - - # create filename for logfile - log_filename = os.path.join(result_dir, '{}.log').format(sce) - - # solve model and read results - optim = SolverFactory('glpk') # cplex, glpk, gurobi, ... - optim = setup_solver(optim, logfile=log_filename) - result = optim.solve(prob, tee=True) - assert str(result.solver.termination_condition) == 'optimal' - - # save problem solution (and input data) to HDF5 file - urbs.save(prob, os.path.join(result_dir, '{}.h5'.format(sce))) - - # write report to spreadsheet - urbs.report( - prob, - os.path.join(result_dir, '{}.xlsx').format(sce), - report_tuples=report_tuples, - report_sites_name=report_sites_name) - - # result plots - urbs.result_figures( - prob, - os.path.join(result_dir, '{}'.format(sce)), - timesteps, - plot_title_prefix=sce.replace('_', ' '), - plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - periods=plot_periods, - figure_size=(24, 9)) - prob = scenario(prob, True, filename) - return prob - - -if __name__ == '__main__': - input_file = 'mimo-example.xlsx' - result_name = os.path.splitext(input_file)[0] # cut away file extension - result_dir = prepare_result_directory(result_name) # name + time stamp - - # copy input file to result directory - shutil.copyfile(input_file, os.path.join(result_dir, input_file)) - # copy runme.py to result directory - shutil.copy(__file__, result_dir) - - # objective function - objective = 'cost' # set either 'cost' or 'CO2' as objective - - # simulation timesteps - (offset, length) = (3500, 168) # time step selection - timesteps = range(offset, offset+length+1) - dt = 1 # length of each time step (unit: hours) - - # plotting commodities/sites - plot_tuples = [ - ('North', 'Elec'), - ('Mid', 'Elec'), - ('South', 'Elec'), - (['North', 'Mid', 'South'], 'Elec')] - - # optional: define names for sites in plot_tuples - plot_sites_name = {('North', 'Mid', 'South'): 'All'} - - # detailed reporting commodity/sites - report_tuples = [ - ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), - ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] - - # optional: define names for sites in report_tuples - report_sites_name = {'North': 'Greenland'} - - # plotting timesteps - plot_periods = {'all': timesteps[1:]} - - # add or change plot colors - my_colors = { - 'South': (230, 200, 200), - 'Mid': (200, 230, 200), - 'North': (200, 200, 230)} - for country, color in my_colors.items(): - urbs.COLORS[country] = color - - # select scenarios to be run - scenarios = [ - urbs.scenario_base, - urbs.scenario_stock_prices, - urbs.scenario_co2_limit, - urbs.scenario_co2_tax_mid, - urbs.scenario_no_dsm, - urbs.scenario_north_process_caps, - urbs.scenario_all_together, - urbs.scenario_new_timeseries(timeseries_number, - "example_file_extension") - ] - - # Read data from Excel Sheet and create model for use in scenarios - data = urbs.read_excel(input_file) - prob = urbs.create_model(data, dt, timesteps) - - for scenario in scenarios: - prob = run_scenario(prob, timesteps, scenario, result_dir, - timeseries_number, dt, objective, - plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - plot_periods=plot_periods, - report_tuples=report_tuples, - report_sites_name=report_sites_name) +input_file = 'mimo-example.xlsx' +result_name = os.path.splitext(input_file)[0] # cut away file extension +result_dir = urbs.prepare_result_directory(result_name) # name + time stamp + +# copy input file to result directory +shutil.copyfile(input_file, os.path.join(result_dir, input_file)) +# copy runme.py to result directory +shutil.copy(__file__, result_dir) +# copy current version of scenario functions +shutil.copy('urbs/scenarios.py', result_dir) + +# choose solver (cplex, glpk, gurobi, ...) +solver = 'glpk' + +# objective function +objective = 'cost' # set either 'cost' or 'CO2' as objective + +# simulation timesteps +(offset, length) = (3500, 168) # time step selection +timesteps = range(offset, offset+length+1) +dt = 1 # length of each time step (unit: hours) + +# plotting commodities/sites +plot_tuples = [ + ('North', 'Elec'), + ('Mid', 'Elec'), + ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec')] + +# optional: define names for sites in plot_tuples +plot_sites_name = {('North', 'Mid', 'South'): 'All'} + +# detailed reporting commodity/sites +report_tuples = [ + ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec'), + ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] + +# optional: define names for sites in report_tuples +report_sites_name = {('North', 'Mid', 'South'): 'Greenland'} + +# plotting timesteps +plot_periods = {'all': timesteps[1:]} + +# add or change plot colors +my_colors = { + 'South': (230, 200, 200), + 'Mid': (200, 230, 200), + 'North': (200, 200, 230)} +for country, color in my_colors.items(): + urbs.COLORS[country] = color + +# select scenarios to be run +scenarios = [ + urbs.scenario_base, + urbs.scenario_stock_prices, + urbs.scenario_co2_limit, + urbs.scenario_co2_tax_mid, + urbs.scenario_no_dsm, + urbs.scenario_north_process_caps, + urbs.scenario_all_together, + urbs.scenario_new_timeseries(timeseries_number, + "example_file_extension") + ] + +# Read data from Excel Sheet and create model for use in scenarios +data = urbs.read_excel(input_file) +prob = urbs.create_model(data, dt, timesteps) + +for scenario in scenarios: + prob = urbs.run_scenario(prob, solver, timesteps, scenario, result_dir, dt, + objective, plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + plot_periods=plot_periods, + report_tuples=report_tuples, + report_sites_name=report_sites_name) diff --git a/urbs/__init__.py b/urbs/__init__.py index fc52d752..e3d2c6d7 100644 --- a/urbs/__init__.py +++ b/urbs/__init__.py @@ -11,12 +11,7 @@ """ from .data import COLORS -from .model import create_model -from .input import read_excel, get_input +from .input import read_excel from .validation import validate_input -from .output import get_constants, get_timeseries -from .plot import plot, result_figures, to_color -from .pyomoio import get_entity, get_entities, list_entities -from .report import report -from .saveload import load, save -from .scenario import * \ No newline at end of file +from .runfunctions import * +from .scenarios import * diff --git a/urbs/runfunctions.py b/urbs/runfunctions.py new file mode 100644 index 00000000..5d341256 --- /dev/null +++ b/urbs/runfunctions.py @@ -0,0 +1,113 @@ +import os +import pyomo.environ +from pyomo.opt.base import SolverFactory +from datetime import datetime +from .model import * +from .report import * +from .plot import * +from .input import * +from .validation import * +from .saveload import * +from .data import timeseries_number + +def prepare_result_directory(result_name): + """ create a time stamped directory within the result folder """ + # timestamp for result directory + now = datetime.now().strftime('%Y%m%dT%H%M') + + # create result directory if not existent + result_dir = os.path.join('result', '{}-{}'.format(result_name, now)) + if not os.path.exists(result_dir): + os.makedirs(result_dir) + + return result_dir + + +def setup_solver(optim, logfile='solver.log'): + """ """ + if optim.name == 'gurobi': + # reference with list of option names + # http://www.gurobi.com/documentation/5.6/reference-manual/parameters + optim.set_options("logfile={}".format(logfile)) + # optim.set_options("timelimit=7200") # seconds + # optim.set_options("mipgap=5e-4") # default = 1e-4 + elif optim.name == 'glpk': + # reference with list of options + # execute 'glpsol --help' + optim.set_options("log={}".format(logfile)) + # optim.set_options("tmlim=7200") # seconds + # optim.set_options("mipgap=.0005") + elif optim.name == 'cplex': + optim.set_options("log={}".format(logfile)) + else: + print("Warning from setup_solver: no options set for solver " + "'{}'!".format(optim.name)) + return optim + + +def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, + objective, + plot_tuples=None, plot_sites_name=None, plot_periods=None, + report_tuples=None, report_sites_name=None): + """ run an urbs model for given input, time steps and scenario + + Args: + prob: urbs model instance initialized with base scenario + solver: name of the solver to be used + timesteps: a list of timesteps, e.g. range(0,8761) + scenario: a scenario function that modifies the input data dict + result_dir: directory name for result spreadsheet and plots + dt: length of each time step (unit: hours) + plot_tuples: (optional) list of plot tuples (c.f. urbs.result_figures) + plot_sites_name: (optional) dict of names for sites in plot_tuples + plot_periods: (optional) dict of plot periods(c.f. urbs.result_figures) + report_tuples: (optional) list of (sit, com) tuples (c.f. urbs.report) + report_sites_name: (optional) dict of names for sites in report_tuples + + Returns: + the urbs model instance + """ + + # scenario name, read and modify data for scenario + sce = scenario.__name__ + + # Only needed for scenario_new_timeseries, but handed to all functions: + filename = "" + # scenario_new_timeseries needs special treatment: + # Add file extension to scenario name and create path to excel sheet + if str(sce).find("scenario_new_timeseries") >= 0: + sce = sce+str(timeseries_number.pop()) + filename = os.path.join("input", "{}.xlsx").format(sce) + # model instance, undo scenario changes?, path to excel sheet + prob = scenario(prob, False, filename) + + # create filename for logfile + log_filename = os.path.join(result_dir, '{}.log').format(sce) + + # solve model and read results + optim = SolverFactory(solver) # cplex, glpk, gurobi, ... + optim = setup_solver(optim, logfile=log_filename) + result = optim.solve(prob, tee=True) + assert str(result.solver.termination_condition) == 'optimal' + + # save problem solution (and input data) to HDF5 file + save(prob, os.path.join(result_dir, '{}.h5'.format(sce))) + + # write report to spreadsheet + report( + prob, + os.path.join(result_dir, '{}.xlsx').format(sce), + report_tuples=report_tuples, + report_sites_name=report_sites_name) + + # result plots + result_figures( + prob, + os.path.join(result_dir, '{}'.format(sce)), + timesteps, + plot_title_prefix=sce.replace('_', ' '), + plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + periods=plot_periods, + figure_size=(24, 9)) + return prob diff --git a/urbs/scenario.py b/urbs/scenarios.py similarity index 99% rename from urbs/scenario.py rename to urbs/scenarios.py index f6624d58..db737389 100644 --- a/urbs/scenario.py +++ b/urbs/scenarios.py @@ -1,4 +1,4 @@ -ļ»æfrom .model import * +from .model import * from .input import split_columns from .data import timeseries_number From 949cfcae2fb4adc0f198d4af5b1edbce7077d761 Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 5 Dec 2018 17:30:41 +0100 Subject: [PATCH 08/12] Resolving differences in documentation --- doc/tutorial.rst | 395 +++++++++++++++++++++++++---------------------- 1 file changed, 213 insertions(+), 182 deletions(-) diff --git a/doc/tutorial.rst b/doc/tutorial.rst index e4516df9..92edf768 100644 --- a/doc/tutorial.rst +++ b/doc/tutorial.rst @@ -12,24 +12,17 @@ expands on the steps that follow this installation. This tutorial is a commented walk-through through the script ``runme.py`` and ``scenrario.py``, which is a demonstration user script that can serve as a good -basis for one's own script. ``scenrario.py`` is contained in the subolder +basis for one's own script. ``scenrario.py`` is contained in the subfolder `urbs`. -Initialisation --------------- - Imports -^^^^^^^ +------- :: import os - import pandas as pd - import pyomo.environ import shutil import urbs - from datetime import datetime - from pyomo.opt.base import SolverFactory from urbs.data import timeseries_number @@ -46,42 +39,64 @@ Several packages are included. if not os.path.exists(result_dir): os.makedirs(result_dir) -* `urbs`_ is the directory which includes the modules, whose functions are used mainly in this script. These - are :func:`read_excel`, :func:`create_model`, :func:`report` and - :func:`result_figures`. More functions can be found in the document :ref:`API`. - -* `pyomo.opt.base`_ is a utility package by `pyomo`_ and provides the function - ``SolverFactory`` that allows creating a ``solver`` object. This objects - hides the differences in input/output formats among solvers from the user. - More on that in section `Solving`_ below. - -* `datetime` is used to append the current date and time to the result - directory name (used in :func:`prepare_result_directory`) +* `urbs`_ is the directory which includes the modules, whose functions are used + mainly in this script. These are :func:`read_excel`, :func:`runs_scenario`, + :func:`create_model`, :func:`report`, :func:`result_figures` and all scenario + functions. More functions can be found in the document :ref:`API`. -Settings -^^^^^^^^ -From here on, the script is best read from the back.:: +In the following sections the user definded input, output and scenario settings +are described. - if __name__ == '__main__': - input_file = 'mimo-example.xlsx' - result_name = os.path.splitext(input_file)[0] # cut away file extension - result_dir = prepare_result_directory(result_name) # name + time stamp - - (offset, length) = (3500, 168) # time step selection - timesteps = range(offset, offset+length+1) - dt = 1 +Input Settings +-------------- +The script starts with the specification of the input file, which is to be +located in the same folder as script ``runme.py``:: + + input_file = 'mimo-example.xlsx' + result_name = os.path.splitext(input_file)[0] # cut away file extension + result_dir = urbs.prepare_result_directory(result_name) # name+time stamp + + # copy input file to result directory + shutil.copyfile(input_file, os.path.join(result_dir, input_file)) + # copy runme.py to result directory + shutil.copy(__file__, result_dir) + # copy current version of scenario functions + shutil.copy('urbs/scenarios.py', result_dir) Variable ``input_file`` defines the input spreadsheet, from which the -optimization problem will draw all its set/parameter data. - +optimization problem will draw all its set/parameter data. The input file and +the script ``runme.py`` are automatically copied into the result folder. + +Next variables specifying the desired solver and objective function are set:: + + # choose solver (cplex, glpk, gurobi, ...) + solver = 'glpk' + + # objective function + objective = 'cost' # set either 'cost' or 'CO2' as objective + +The solver has to be licensed for the specific user, where the open source +solver "glpk2 is used as the standard. For the objective function urbs +currently allows for two options: "cost" and "CO2" (case sensitive). In the +former case the total system cost and in the latter case the total +CO2-emissions are minimized. + +The model parameters are finalized with a specification of timestep length and +modeled time horizon:: + + # simulation timesteps + (offset, length) = (3500, 168) # time step selection + timesteps = range(offset, offset+length+1) + dt = 1 # length of each time step (unit: hours) + Variable ``timesteps`` is the list of timesteps to be simulated. Its members must be a subset of the labels used in ``input_file``'s sheets "Demand" and "SupIm". It is one of the function arguments to :func:`create_model` and accessible directly, so that one can quickly reduce the problem size by reducing the simulation ``length``, i.e. the number of timesteps to be -optimised. Variable ``dt`` is the duration of each timestep in the list -(unit: hours). +optimised. Variable ``dt`` is the duration of each timestep in the list in +hours, where any positiv real value is allowed. :func:`range` is used to create a list of consecutive integers. The argument ``+1`` is needed, because ``range(a,b)`` only includes integers from ``a`` to @@ -90,13 +105,147 @@ optimised. Variable ``dt`` is the duration of each timestep in the list >>> range(1,11) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] -The following section deals with the definition of different scenarios. -Starting from the same base scenarios, defined by the data in ``input_file``, -they serve as a short way of defining the difference in input data. If needed, -completely separate input data files could be loaded as well. +Output Settings +--------------- +The desired output is also specified by the user in script ``runme.py``. It is +split into two parts: reporting and plotting. The former is used to generate +an excel output file and the latter for standard graphs. + +Reporting +^^^^^^^^^ + +:: + + # write report to spreadsheet + urbs.report( + prob, + os.path.join(result_dir, '{}-{}.xlsx').format(sce, now), + report_tuples=report_tuples, report_sites_name=report_sites_name) + +The :func:`urbs.report` function creates a spreadsheet from the main results. +Summaries of costs, emissions, capacities (process, transmissions, storage) are +saved to one sheet each. For timeseries, each combination of the given +``sites`` and ``commodities`` are summarised both in sum (in sheet "Energy +sums") and as individual timeseries (in sheet "... timeseries"). +:: + + # detailed reporting commodity/sites + report_tuples = [ + ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec') + ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] + + # optional: define names for sites in report_tuples + report_sites_name = {['North', 'Mid', 'South']: 'Greenland'} + +Optional it is possible to define ``report_tuples`` to control what should be +reported. And with ``report_sites_name`` it is possible to define, if the sites +inside the report tuples should be named differently. If they are empty, the +default value will be taken. See also :ref:`report-function` for a detailed +explanation of the implementation of this function. + +Plotting +^^^^^^^^ + +:: + + # add or change plot colors + my_colors = { + 'South': (230, 200, 200), + 'Mid': (200, 230, 200), + 'North': (200, 200, 230)} + for country, color in my_colors.items(): + urbs.COLORS[country] = color + +First, the use of the module constant :data:`COLORS` for customising plot +colors is demonstrated. All plot colors are user-defineable by adding color +:func:`tuple` ``(r, g, b)`` or modifying existing tuples for commodities and +plot decoration elements. Here, new colors for displaying import/export are +added. Without these, pseudo-random colors are generated in +:func:`to_color`. +:: + + # create timeseries plot for each demand (site, commodity) timeseries + for sit, com in prob.demand.columns: + +Finally, for each demand commodity (only ``Elec`` in this case), a plot over +the whole optimisation period is created. If ``timesteps`` were longer, a +shorter plotting period could be defined and given as an optional argument to +:func:`plot`. +:: + + # plotting commodities/sites + plot_tuples = [ + ('North', 'Elec'), + ('Mid', 'Elec'), + ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec')] + + # optional: define names for plot_tuples + plot_sites_name = { + ('North', 'Mid', 'South') : 'NMS plot'} + +As in the reporting section mentioned, also for plotting the output of plots +can optionally changed with ``plot_tuples``. The sites in title and name of +each plot can be adapted with ``plot_sites_name``. +:: + + # change the figure title + # if no custom title prefix is specified, use the figure_basename + ax0 = fig.get_axes()[0] + if not plot_title_prefix: + plot_title_prefix = os.path.basename(figure_basename) + new_figure_title = '{}: {} in {}'.format( + plot_title_prefix, com, plot_sites_name[sit]) + ax0.set_title(new_figure_title) + + # save plot to files + for ext in extensions: + fig_filename = '{}-{}-{}-{}.{}'.format( + figure_basename, com, ''.join( + plot_sites_name[sit]), period, ext) + fig.savefig(fig_filename, bbox_inches='tight') + plt.close(fig) + +The paragraph "change figure title" shows exemplarily how to use matplotlib +manually to modify some aspects of a plot without having to recreate the +plotting function from scratch. For more ideas for adaptations, look into +:func:`plot`'s code or the `matplotlib documentation`_. -In addition to defining scenarios, the ``scenarios`` list allows to select a -subset to be actually run. +The second paragraph uses the :meth:`~matplotlib.figure.Figure.savefig` method +to save the figure as a pixel ``png`` (raster) and ``pdf`` (vector) image. The +``bbox_inches='tight'`` argument eliminates whitespace around the plot. + +.. note:: :meth:`~matplotlib.figure.Figure.savefig` has some more interesting + arguments. For example ``dpi=600`` can be used to create higher resolution + raster output for use with printing, in case the preferable vector images + cannot be used. The filename extension or the optional ``format`` argument + can be used to set the output format. Available formats depend on the used + `plotting backend`_. Most backends support png, pdf, ps, eps and svg. + +Scenarios +--------- +This section deals with the definition of different scenarios. Starting from +the same base scenarios, defined by the data in ``input_file``, they serve as a +short way of defining the difference in input data. If needed, completely +separate input data files could be loaded as well. + +The ``scenarios`` list in the end of the script ``runme.py`` allows then to +select the scenarios to be actually run. +In Python, functions are objects, so they can be put into data structures just +like any variable could be. :: + + scenarios = [ + urbs.scenario_base, + urbs.scenario_stock_prices, + urbs.scenario_co2_limit, + urbs.scenario_co2_tax_mid, + urbs.scenario_no_dsm, + urbs.scenario_north_process_caps, + urbs.scenario_all_together] + +The following scenario functions are specified in the subfolder ``urbs`` in +script ``scenarios.py``. Scenario functions ^^^^^^^^^^^^^^^^^^ @@ -191,34 +340,20 @@ combined by chaining other scenario functions, making them dependent. This way, complex scenario trees can written with any single input change coded at a single place and then building complex composite scenarios from those. -Scenario selection -^^^^^^^^^^^^^^^^^^ - -:: - - # select scenarios to be run - scenarios = [ - urbs.scenario_base, - urbs.scenario_stock_prices, - urbs.scenario_co2_limit, - urbs.scenario_north_process_caps, - urbs.scenario_all_together] -In Python, functions are objects, so they can be put into data structures just -like any variable could be. In the following, the list ``scenarios`` is used to -control which scenarios are being actually computed. - + + Reading input & model creation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ :: - + # Read data from Excel Sheet and create model for use in scenarios data = urbs.read_excel(input_file) prob = urbs.create_model(data, dt, timesteps) -Function :func:`read_excel` returns a dict ``data`` of six pandas DataFrames -with hard-coded column names that correspond to the parameters of the -optimization problem (like ``eff`` for efficiency or ``inv-cost-c`` for +Function :func:`read_excel` returns a dict ``data`` of up to 12 pandas +DataFrames with hard-coded column names that correspond to the parameters of +the optimization problem (like ``eff`` for efficiency or ``inv-cost-c`` for capacity investment costs). The row labels on the other hand may be freely chosen (like site names, process identifiers or commodity names). By convention, it must contain the six keys ``commodity``, ``process``, @@ -229,18 +364,24 @@ conforms to the specification given by the example dataset in the spreadsheet ``prob`` is then modified by applying the :func:`scenario` function to it. Run scenarios ------------------------------ +------------- :: for scenario in scenarios: - prob = run_scenario(prob, timesteps, scenario, result_dir, dt) - - -Having prepared settings and scenarios, the actual computations happen in the -function :func:`run_scenario` of the script. It is executed ``for`` each of the -scenarios included in the scenario list. The following sections describe the -content of function :func:`run_scenario`. In a nutshell, it modifies the input + prob = urbs.run_scenario(input_file, solver, timesteps, scenario, + result_dir, dt, objective, + plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + plot_periods=plot_periods, + report_tuples=report_tuples, + report_sites_name=report_sites_name) + +Having prepared settings, input data and scenarios, the actual computations +happen in the function :func:`run_scenario` of the script ``runfunctions.py`` +in subfolder ``urbs``. It is executed for each of the scenarios included in the +scenario list. The following sections describe the content of function +:func:`run_scenario`. In a nutshell, it modifies the input model instance ``prob`` by applying the :func:`scenario` function to it. It then runs the optimization for the given ``timesteps`` and writes report and plots to ``result_dir``. @@ -282,118 +423,6 @@ into the ``prob`` object, which is queried to for variable values in the remaining script file. Argument ``tee=True`` enables the realtime console output for the solver. If you want less verbose output, simply set it to ``False`` or remove it. - -Reporting -^^^^^^^^^ - -:: - - # write report to spreadsheet - urbs.report( - prob, - os.path.join(result_dir, '{}-{}.xlsx').format(sce, now), - report_tuples=report_tuples, report_sites_name=report_sites_name) - -The :func:`urbs.report` function creates a spreadsheet from the main results. -Summaries of costs, emissions, capacities (process, transmissions, storage) are -saved to one sheet each. For timeseries, each combination of the given -``sites`` and ``commodities`` are summarised both in sum (in sheet "Energy -sums") and as individual timeseries (in sheet "... timeseries"). -:: - - # detailed reporting commodity/sites - report_tuples = [ - ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), - ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] - - # optional: define names for sites in report_tuples - report_sites_name = { - ('North') : 'Greenland', - ('North', 'South') : 'Not Mid'} - -Optional it is possible to define ``report_tuples`` to control what shell be reported. And -with ``report_sites_name`` you can define, if the sites inside the report tuples should -be named differently. If they are empty, the default value will be taken. -See also :ref:`report-function` for a detailed explanation of this function's implementation. - -Plotting -^^^^^^^^ - -:: - - # add or change plot colors - my_colors = { - 'South': (230, 200, 200), - 'Mid': (200, 230, 200), - 'North': (200, 200, 230)} - for country, color in my_colors.items(): - urbs.COLORS[country] = color - -First, the use of the module constant :data:`COLORS` for customising plot -colors is demonstrated. All plot colors are user-defineable by adding color -:func:`tuple` ``(r, g, b)`` or modifying existing tuples for commodities and -plot decoration elements. Here, new colors for displaying import/export are -added. Without these, pseudo-random colors are generated in -:func:`to_color`. -:: - - # create timeseries plot for each demand (site, commodity) timeseries - for sit, com in prob.demand.columns: - -Finally, for each demand commodity (only ``Elec`` in this case), a plot over -the whole optimisation period is created. If ``timesteps`` were longer, a -shorter plotting period could be defined and given as an optional argument to -:func:`plot`. -:: - - # plotting commodities/sites - plot_tuples = [ - ('North', 'Elec'), - ('Mid', 'Elec'), - ('South', 'Elec'), - (['North', 'Mid', 'South'], 'Elec')] - - # optional: define names for plot_tuples - plot_sites_name = { - ('North', 'Mid', 'South') : 'NMS plot'} - -As in the reporting section mentioned, also for plotting the output of plots -can optionally changed with ``plot_tuples``. The sites in title and name of -each plot can be adapted with ``plot_sites_name``. -:: - - # change the figure title - # if no custom title prefix is specified, use the figure_basename - ax0 = fig.get_axes()[0] - if not plot_title_prefix: - plot_title_prefix = os.path.basename(figure_basename) - new_figure_title = '{}: {} in {}'.format( - plot_title_prefix, com, plot_sites_name[sit]) - ax0.set_title(new_figure_title) - - # save plot to files - for ext in extensions: - fig_filename = '{}-{}-{}-{}.{}'.format( - figure_basename, com, ''.join( - plot_sites_name[sit]), period, ext) - fig.savefig(fig_filename, bbox_inches='tight') - plt.close(fig) - -The paragraph "change figure title" shows exemplarily how to use matplotlib -manually to modify some aspects of a plot without having to recreate the -plotting function from scratch. For more ideas for adaptations, look into -:func:`plot`'s code or the `matplotlib documentation`_. - -The last paragraph uses the :meth:`~matplotlib.figure.Figure.savefig` method -to save the figure as a pixel ``png`` (raster) and ``pdf`` (vector) image. The -``bbox_inches='tight'`` argument eliminates whitespace around the plot. - -.. note:: :meth:`~matplotlib.figure.Figure.savefig` has some more interesting - arguments. For example ``dpi=600`` can be used to create higher resolution - raster output for use with printing, in case the preferable vector images - cannot be used. The filename extension or the optional ``format`` argument - can be used to set the output format. Available formats depend on the used - `plotting backend`_. Most backends support png, pdf, ps, eps and svg. Rebuilding of base scenario ^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -419,6 +448,8 @@ correct usage in the next scenario. .. _pandas: https://pandas.pydata.org .. _plotting backend: http://matplotlib.org/faq/usage_faq.html#what-is-a-backend -.. _pyomo: https://software.sandia.gov/trac/coopr/wiki/Pyomo +.. _pyomo: http://www.pyomo.org/ +.. _pyomo.opt.base: + https://pyomo.readthedocs.io/en/latest/_modules/pyomo/opt/base/solvers.html .. _urbs: https://github.com/tum-ens/urbs .. _urbs.py: https://github.com/tum-ens/urbs/blob/master/urbs.py From 47a8fb6d36031e01335b97c4d21956b82cb0f36f Mon Sep 17 00:00:00 2001 From: Leonhard Balz Date: Fri, 14 Dec 2018 16:19:01 +0100 Subject: [PATCH 09/12] Serialized plotting from .h5 file using multiprocessing --- runme.py | 128 +++++++++++++++++++++---------------------- urbs/input.py | 2 +- urbs/output.py | 64 +++++++++------------- urbs/plot.py | 39 ++++++------- urbs/pyomoio.py | 4 +- urbs/runfunctions.py | 56 ++++++++++++------- urbs/saveload.py | 4 +- 7 files changed, 146 insertions(+), 151 deletions(-) diff --git a/runme.py b/runme.py index 712216e5..2ad9585f 100644 --- a/runme.py +++ b/runme.py @@ -4,79 +4,79 @@ from urbs.data import timeseries_number -input_file = 'mimo-example.xlsx' -result_name = os.path.splitext(input_file)[0] # cut away file extension -result_dir = urbs.prepare_result_directory(result_name) # name + time stamp +if __name__ == "__main__": + input_file = 'mimo-example.xlsx' + result_name = os.path.splitext(input_file)[0] # cut away file extension + result_dir = urbs.prepare_result_directory(result_name) # name + timestamp -# copy input file to result directory -shutil.copyfile(input_file, os.path.join(result_dir, input_file)) -# copy runme.py to result directory -shutil.copy(__file__, result_dir) -# copy current version of scenario functions -shutil.copy('urbs/scenarios.py', result_dir) + # copy input file to result directory + shutil.copyfile(input_file, os.path.join(result_dir, input_file)) + # copy runme.py to result directory + shutil.copy(__file__, result_dir) + # copy current version of scenario functions + shutil.copy('urbs/scenarios.py', result_dir) -# choose solver (cplex, glpk, gurobi, ...) -solver = 'glpk' + # choose solver (cplex, glpk, gurobi, ...) + solver = 'glpk' -# objective function -objective = 'cost' # set either 'cost' or 'CO2' as objective + # objective function + objective = 'cost' # set either 'cost' or 'CO2' as objective -# simulation timesteps -(offset, length) = (3500, 168) # time step selection -timesteps = range(offset, offset+length+1) -dt = 1 # length of each time step (unit: hours) + # simulation timesteps + (offset, length) = (3500, 168) # time step selection + timesteps = range(offset, offset+length+1) + dt = 1 # length of each time step (unit: hours) -# plotting commodities/sites -plot_tuples = [ - ('North', 'Elec'), - ('Mid', 'Elec'), - ('South', 'Elec'), - (['North', 'Mid', 'South'], 'Elec')] + # plotting commodities/sites + plot_tuples = [ + ('North', 'Elec'), + ('Mid', 'Elec'), + ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec')] -# optional: define names for sites in plot_tuples -plot_sites_name = {('North', 'Mid', 'South'): 'All'} + # optional: define names for sites in plot_tuples + plot_sites_name = {('North', 'Mid', 'South'): 'All'} -# detailed reporting commodity/sites -report_tuples = [ - ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), - (['North', 'Mid', 'South'], 'Elec'), - ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] + # detailed reporting commodity/sites + report_tuples = [ + ('North', 'Elec'), ('Mid', 'Elec'), ('South', 'Elec'), + (['North', 'Mid', 'South'], 'Elec'), + ('North', 'CO2'), ('Mid', 'CO2'), ('South', 'CO2')] -# optional: define names for sites in report_tuples -report_sites_name = {('North', 'Mid', 'South'): 'Greenland'} + # optional: define names for sites in report_tuples + report_sites_name = {('North', 'Mid', 'South'): 'Greenland'} -# plotting timesteps -plot_periods = {'all': timesteps[1:]} + # plotting timesteps + plot_periods = {'all': timesteps[1:]} -# add or change plot colors -my_colors = { - 'South': (230, 200, 200), - 'Mid': (200, 230, 200), - 'North': (200, 200, 230)} -for country, color in my_colors.items(): - urbs.COLORS[country] = color + # add or change plot colors + my_colors = { + 'South': (230, 200, 200), + 'Mid': (200, 230, 200), + 'North': (200, 200, 230)} + for country, color in my_colors.items(): + urbs.COLORS[country] = color -# select scenarios to be run -scenarios = [ - urbs.scenario_base, - urbs.scenario_stock_prices, - urbs.scenario_co2_limit, - urbs.scenario_co2_tax_mid, - urbs.scenario_no_dsm, - urbs.scenario_north_process_caps, - urbs.scenario_all_together, - urbs.scenario_new_timeseries(timeseries_number, - "example_file_extension") - ] + # select scenarios to be run + scenarios = [ + urbs.scenario_base, + urbs.scenario_stock_prices, + urbs.scenario_co2_limit, + urbs.scenario_co2_tax_mid, + urbs.scenario_no_dsm, + urbs.scenario_north_process_caps, + urbs.scenario_all_together, + urbs.scenario_new_timeseries(timeseries_number, + "example_file_extension") + ] -# Read data from Excel Sheet and create model for use in scenarios -data = urbs.read_excel(input_file) -prob = urbs.create_model(data, dt, timesteps) - -for scenario in scenarios: - prob = urbs.run_scenario(prob, solver, timesteps, scenario, result_dir, dt, - objective, plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - plot_periods=plot_periods, - report_tuples=report_tuples, - report_sites_name=report_sites_name) + # Read data from Excel Sheet and create model for use in scenarios + data = urbs.read_excel(input_file) + prob = urbs.create_model(data, dt, timesteps) + for scenario in scenarios: + prob = urbs.run_scenario(prob, solver, timesteps, scenario, result_dir, + dt, objective, plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + plot_periods=plot_periods, + report_tuples=report_tuples, + report_sites_name=report_sites_name) diff --git a/urbs/input.py b/urbs/input.py index 0e5435d0..f0b1d1af 100644 --- a/urbs/input.py +++ b/urbs/input.py @@ -210,7 +210,7 @@ def get_input(prob, name): separation, e.g. 'process_commodity'. Args: - prob: a urbs model instance + prob: a urbs model instance or a model instance loaded from an h5 file name: an input DataFrame name ('commodity', 'process', ...) Returns: diff --git a/urbs/output.py b/urbs/output.py index 4c13ccba..fc10bcfd 100644 --- a/urbs/output.py +++ b/urbs/output.py @@ -4,7 +4,7 @@ from .util import is_string -def get_constants(instance): +def get_constants(h5): """Return summary DataFrames for important variables Usage: @@ -15,27 +15,12 @@ def get_constants(instance): Returns: (costs, cpro, ctra, csto) tuple - - Example: - >>> import pyomo.environ - >>> from pyomo.opt.base import SolverFactory - >>> data = read_excel('mimo-example.xlsx') - >>> prob = create_model(data, range(1,25)) - >>> optim = SolverFactory('glpk') - >>> result = optim.solve(prob) - >>> cap_pro = get_constants(prob)[1]['Total'] - >>> cap_pro.xs('Wind park', level='Process').apply(int) - Site - Mid 13000 - North 23258 - South 0 - Name: Total, dtype: int64 """ - costs = get_entity(instance, 'costs') - cpro = get_entities(instance, ['cap_pro', 'cap_pro_new']) - ctra = get_entities(instance, ['cap_tra', 'cap_tra_new']) - csto = get_entities(instance, ['cap_sto_c', 'cap_sto_c_new', - 'cap_sto_p', 'cap_sto_p_new']) + costs = h5._result["costs"].copy() + cpro = get_entities(h5._result, ['cap_pro', 'cap_pro_new']).copy() + ctra = get_entities(h5._result, ['cap_tra', 'cap_tra_new']).copy() + csto = get_entities(h5._result, ['cap_sto_c', 'cap_sto_c_new', + 'cap_sto_p', 'cap_sto_p_new']).copy() # better labels and index names and return sorted if not cpro.empty: @@ -53,7 +38,7 @@ def get_constants(instance): return costs, cpro, ctra, csto -def get_timeseries(instance, com, sites, timesteps=None): +def get_timeseries(h5, com, sites, timesteps=None): """Return DataFrames of all timeseries referring to given commodity Usage: @@ -61,7 +46,7 @@ def get_timeseries(instance, com, sites, timesteps=None): dsm) = get_timeseries(instance, commodity, sites, timesteps) Args: - instance: a urbs model instance + h5: loaded h5 file in form of data frame containing model com: a commodity name sites: a site name or list of site names timesteps: optional list of timesteps, default: all modelled timesteps @@ -79,7 +64,7 @@ def get_timeseries(instance, com, sites, timesteps=None): """ if timesteps is None: # default to all simulated timesteps - timesteps = sorted(get_entity(instance, 'tm').index) + timesteps = sorted(h5._result['tm'].index) else: timesteps = sorted(timesteps) # implicit: convert range to list @@ -92,15 +77,16 @@ def get_timeseries(instance, com, sites, timesteps=None): try: # select relevant timesteps (=rows) # select commodity (xs), then the sites from remaining simple columns - # and sum all together to form a Series - demand = (pd.DataFrame.from_dict(get_input(instance, 'demand_dict')) - .loc[timesteps].xs(com, axis=1, level=1)[sites].sum(axis=1)) + # and sum all together to form a Series. + # get_input will use h5._data["demand"] + demand = (get_input(h5, 'demand').loc[timesteps] + .xs(com, axis=1, level=1)[sites].sum(axis=1)) except KeyError: demand = pd.Series(0, index=timesteps) demand.name = 'Demand' # STOCK - eco = get_entity(instance, 'e_co_stock') + eco = h5._result['e_co_stock'] eco = eco.xs([com, 'Stock'], level=['com', 'com_type']) try: stock = eco.unstack()[sites].sum(axis=1) @@ -109,7 +95,7 @@ def get_timeseries(instance, com, sites, timesteps=None): stock.name = 'Stock' # PROCESS - created = get_entity(instance, 'e_pro_out') + created = h5._result['e_pro_out'] created = created.xs(com, level='com').loc[timesteps] try: created = created.unstack(level='sit')[sites].fillna(0).sum(axis=1) @@ -118,7 +104,7 @@ def get_timeseries(instance, com, sites, timesteps=None): except KeyError: created = pd.DataFrame(index=timesteps) - consumed = get_entity(instance, 'e_pro_in') + consumed = h5._result['e_pro_in'] consumed = consumed.xs(com, level='com').loc[timesteps] try: consumed = consumed.unstack(level='sit')[sites].fillna(0).sum(axis=1) @@ -128,12 +114,12 @@ def get_timeseries(instance, com, sites, timesteps=None): consumed = pd.DataFrame(index=timesteps) # TRANSMISSION - other_sites = get_input(instance, 'site').index.difference(sites) + other_sites = get_input(h5, 'site').index.difference(sites) # if commodity is transportable - df_transmission = get_input(instance, 'transmission') - if com in set(df_transmission.index.get_level_values('Commodity')): - imported = get_entity(instance, 'e_tra_out') + df_transmission = get_input(h5, 'transmission') + if com in tuple(df_transmission.index)[0]: + imported = h5._result['e_tra_out'] imported = imported.loc[timesteps].xs(com, level='com') imported = imported.unstack(level='tra').sum(axis=1) imported = imported.unstack(level='sit_')[sites].fillna(0).sum(axis=1) @@ -144,7 +130,7 @@ def get_timeseries(instance, com, sites, timesteps=None): imported = imported[other_sites_im] # ...from other_sites imported = drop_all_zero_columns(imported) - exported = get_entity(instance, 'e_tra_in') + exported = h5._result['e_tra_in'] exported = exported.loc[timesteps].xs(com, level='com') exported = exported.unstack(level='tra').sum(axis=1) exported = exported.unstack(level='sit')[sites].fillna(0).sum(axis=1) @@ -167,7 +153,9 @@ def get_timeseries(instance, com, sites, timesteps=None): # STORAGE # group storage energies by commodity # select all entries with desired commodity co - stored = get_entities(instance, ['e_sto_con', 'e_sto_in', 'e_sto_out']) + stored = pd.DataFrame(h5._result['e_sto_con']).join(pd.DataFrame( + h5._result['e_sto_in']).join(pd.DataFrame( + h5._result['e_sto_out']), how='outer'), how='outer') try: stored = stored.loc[timesteps].xs(com, level='com') stored = stored.groupby(level=['t', 'sit']).sum() @@ -178,8 +166,8 @@ def get_timeseries(instance, com, sites, timesteps=None): columns=['Level', 'Stored', 'Retrieved']) # DEMAND SIDE MANAGEMENT (load shifting) - dsmup = get_entity(instance, 'dsm_up') - dsmdo = get_entity(instance, 'dsm_down') + dsmup = h5._result['dsm_up'] + dsmdo = h5._result['dsm_down'] if dsmup.empty: # if no DSM happened, the demand is not modified (delta = 0) diff --git a/urbs/plot.py b/urbs/plot.py index eca5c142..4f16fed0 100644 --- a/urbs/plot.py +++ b/urbs/plot.py @@ -1,4 +1,5 @@ import matplotlib.pyplot as plt +import matplotlib as mpl import numpy as np import os import pandas as pd @@ -56,7 +57,7 @@ def sort_plot_elements(elements): return elements_sorted -def plot(prob, com, sit, dt, timesteps, timesteps_plot, +def plot(h5, com, sit, dt, timesteps, timesteps_plot, power_name='Power', energy_name='Energy', power_unit='MW', energy_unit='MWh', time_unit='h', figure_size=(16, 12)): @@ -66,7 +67,7 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, with stored energy in a second subplot. Args: - prob: urbs model instance + h5: loaded h5 file in form of data frame containing model com: commodity name to plot sit: site name to plot dt: length of each time step (unit: hours) @@ -83,25 +84,20 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, Returns: fig: figure handle """ - import matplotlib.pyplot as plt - import matplotlib as mpl if timesteps is None: # default to all simulated timesteps - timesteps = sorted(get_entity(prob, 'tm').index) + timesteps = sorted(h5._result['tm'].index) # convert timesteps to hour series for the plots hoursteps = timesteps * dt[0] hoursteps_plot = timesteps_plot * dt[0] - if is_string(sit): # wrap single site in 1-element list for consistent behaviour sit = [sit] - (created, consumed, stored, imported, exported, - dsm) = get_timeseries(prob, com, sit, timesteps) - - costs, cpro, ctra, csto = get_constants(prob) + dsm) = get_timeseries(h5, com, sit, timesteps) + costs, cpro, ctra, csto = get_constants(h5) # move retrieved/stored storage timeseries to created/consumed and # rename storage columns back to 'storage' for color mapping @@ -124,12 +120,12 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, try: # detect whether DSM could be used in this plot # if so, show DSM subplot (even if delta == 0 for the whole time) - df_dsm = get_input(prob, 'dsm_dict') - plot_dsm=0 + df_dsm = get_input(h5, 'dsm') # get_input will use h5._data["dsm"] + plot_dsm = 0 for s in sit: - plot_dsm += (df_dsm["cap-max-do"][s,com] + df_dsm["cap-max-up"] - [s,com]) - plot_dsm=plot_dsm>0 + plot_dsm += (df_dsm["cap-max-do"][s, com] + df_dsm["cap-max-up"] + [s, com]) + plot_dsm = plot_dsm > 0 except (KeyError, TypeError): plot_dsm = False @@ -312,17 +308,16 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, skip_lowest = mpl.ticker.FuncFormatter( lambda y, pos: '' if pos == 0 else y) ax.yaxis.set_major_formatter(skip_lowest) - return fig -def result_figures(prob, figure_basename, timesteps, plot_title_prefix=None, +def result_figures(h5, figure_basename, timesteps, plot_title_prefix=None, plot_tuples=None, plot_sites_name={}, periods=None, extensions=None, **kwds): """Create plots for multiple periods and sites and save them to files. Args: - prob: urbs model instance + h5: loaded h5 file in form of data frame containing model figure_basename: relative filename prefix that is shared plot_title_prefix: (optional) plot title identifier plot_tuples: (optional) list of (sit, com) tuples to plot @@ -336,15 +331,15 @@ def result_figures(prob, figure_basename, timesteps, plot_title_prefix=None, **kwds: (optional) keyword arguments are forwarded to urbs.plot() """ # retrieve parameter 'dt' from the model - dt = get_entity(prob, 'dt') + dt = h5._result['dt'] # default to all demand (sit, com) tuples if none are specified if plot_tuples is None: - plot_tuples = get_input(prob, 'demand').columns + plot_tuples = get_input(h5, 'demand').columns # default to all timesteps if no periods are given if periods is None: - periods = {'all': sorted(get_entity(prob, 'tm').index)} + periods = {'all': sorted(h5._result['tm'].index)} # default to PNG and PDF plots if no filetypes are specified if extensions is None: @@ -366,7 +361,7 @@ def result_figures(prob, figure_basename, timesteps, plot_title_prefix=None, for period, periodrange in periods.items(): # do the plotting - fig = plot(prob, com, help_sit, dt, timesteps, periodrange, **kwds) + fig = plot(h5, com, help_sit, dt, timesteps, periodrange, **kwds) # change the figure title ax0 = fig.get_axes()[0] diff --git a/urbs/pyomoio.py b/urbs/pyomoio.py index d30b3399..95bdf5a8 100644 --- a/urbs/pyomoio.py +++ b/urbs/pyomoio.py @@ -113,10 +113,10 @@ def get_entities(instance, names): df = pd.DataFrame() for name in names: - other = get_entity(instance, name) + other = pd.DataFrame(instance[name]) if df.empty: - df = other.to_frame() + df = other else: index_names_before = df.index.names diff --git a/urbs/runfunctions.py b/urbs/runfunctions.py index 3313ff1b..5a8110e4 100644 --- a/urbs/runfunctions.py +++ b/urbs/runfunctions.py @@ -9,6 +9,9 @@ from .validation import * from .saveload import * from .data import timeseries_number +from .saveload import load +# multiprocessing for serialized plotting +import multiprocessing as mp def prepare_result_directory(result_name): @@ -46,6 +49,26 @@ def setup_solver(optim, logfile='solver.log'): return optim +def serialized_plotting_reporting(result_dir, sce, report_tuples, + report_sites_name, timesteps, plot_tuples, + plot_sites_name, plot_periods): + + # load the model/h5 file corresponding to 'sce' + urbs_path = os.path.join(result_dir, "{}.h5").format(sce) + h5 = load(urbs_path) + + # write report to spreadsheet + report(h5, os.path.join(result_dir, '{}.xlsx').format(sce), + report_tuples=report_tuples, + report_sites_name=report_sites_name) + + # result plots + result_figures(h5, os.path.join(result_dir, '{}'.format(sce)), timesteps, + plot_title_prefix=sce.replace('_', ' '), + plot_tuples=plot_tuples, plot_sites_name=plot_sites_name, + periods=plot_periods, figure_size=(24, 9)) + + def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, objective, plot_tuples=None, plot_sites_name=None, plot_periods=None, @@ -53,12 +76,8 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, """ run an urbs model for given input, time steps and scenario Args: -<<<<<<< HEAD prob: urbs model instance initialized with base scenario solver: name of the solver to be used -======= - input_file: filename to an Excel spreadsheet for urbs.read_excel ->>>>>>> d2a86f73ce34ce553598fec145b6b39ee22a88e1 timesteps: a list of timesteps, e.g. range(0,8761) scenario: a scenario function that modifies the input data dict result_dir: directory name for result spreadsheet and plots @@ -72,7 +91,6 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, Returns: the urbs model instance """ - # scenario name, read and modify data for scenario sce = scenario.__name__ @@ -86,6 +104,10 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, # model instance, undo scenario changes?, path to excel sheet prob = scenario(prob, False, filename) + # Write model to lp File + model_filename = os.path.join(result_dir, '{}.lp').format(sce) + prob.write(model_filename, io_options={"symbolic_solver_labels": True}) + # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) @@ -98,21 +120,13 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, # save problem solution (and input data) to HDF5 file save(prob, os.path.join(result_dir, '{}.h5'.format(sce))) - # write report to spreadsheet - report( - prob, - os.path.join(result_dir, '{}.xlsx').format(sce), - report_tuples=report_tuples, - report_sites_name=report_sites_name) + # start a new process and plot the results using the h5 file + plot_process = mp.Process(target=serialized_plotting_reporting, + args=(result_dir, sce, report_tuples, + report_sites_name, timesteps, plot_tuples, + plot_sites_name, plot_periods)) + plot_process.start() - # result plots - result_figures( - prob, - os.path.join(result_dir, '{}'.format(sce)), - timesteps, - plot_title_prefix=sce.replace('_', ' '), - plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - periods=plot_periods, - figure_size=(24, 9)) + # Undo all changes to model instance to retrieve base scenario model + prob = scenario(prob, True, filename) return prob diff --git a/urbs/saveload.py b/urbs/saveload.py index 02dd9cfa..76a02ed1 100644 --- a/urbs/saveload.py +++ b/urbs/saveload.py @@ -31,9 +31,7 @@ def save(prob, filename): warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning) - if not hasattr(prob, '_result'): - prob._result = create_result_cache(prob) - + prob._result = create_result_cache(prob) with pd.HDFStore(filename, mode='w') as store: # Search all attributes of prob for those containing the input data for name in prob.__dict__: From 9f82478be9104cc10a163da93be0a03d946b2c64 Mon Sep 17 00:00:00 2001 From: Balz Date: Tue, 8 Jan 2019 09:22:48 +0100 Subject: [PATCH 10/12] Removed writing of lp file --- urbs/runfunctions.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/urbs/runfunctions.py b/urbs/runfunctions.py index 5a8110e4..98862a0a 100644 --- a/urbs/runfunctions.py +++ b/urbs/runfunctions.py @@ -104,10 +104,6 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, # model instance, undo scenario changes?, path to excel sheet prob = scenario(prob, False, filename) - # Write model to lp File - model_filename = os.path.join(result_dir, '{}.lp').format(sce) - prob.write(model_filename, io_options={"symbolic_solver_labels": True}) - # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) From 1446c7775fdc778c7aa44c2f18f85228ad72b006 Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 9 Jan 2019 16:15:22 +0100 Subject: [PATCH 11/12] Reactivated use of result_cache in get_entity() --- input/scenario_new_timeseries_1.xlsx | Bin 1971701 -> 0 bytes urbs/pyomoio.py | 9 +++++---- urbs/runfunctions.py | 4 ---- urbs/saveload.py | 2 +- 4 files changed, 6 insertions(+), 9 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z8-*T;5nc}!)`hS$*hR~rnR2LB3@zXgL56M$`T+=t7+7{m7Gw(gaR}%JpzlFN7?5%k zYye_MD!Nwm^%DrKk3T`QqOYbvHvx570YX0m1K$@}28=}p=-SbzfDr~P{04RaF%!b* zhN8FW5r!`RM~tE1@nv*FQ9H~CBN-TE+2jx&g?69Ob>nuDDyJMMk_q?|-02ALW(Af| Spz;!Uh!iUW!)yULAP)eM>O;{0 diff --git a/urbs/pyomoio.py b/urbs/pyomoio.py index 95bdf5a8..fb31cb73 100644 --- a/urbs/pyomoio.py +++ b/urbs/pyomoio.py @@ -2,7 +2,7 @@ import pyomo.core as pyomo -def get_entity(instance, name): +def get_entity(instance, name, skip_result_cache=False): """ Retrieve values (or duals) for an entity in a model instance. Args: @@ -13,10 +13,11 @@ def get_entity(instance, name): a Pandas Series with domain as index and values (or 1's, for sets) of entity name. For constraints, it retrieves the dual values """ - # The following would destroy many plots when model instances are reused. + # magic: short-circuit if problem contains a result cache - # if hasattr(instance, '_result') and name in instance._result: - # return instance._result[name].copy(deep=True) + if (not skip_result_cache and hasattr(instance, '_result') and name in + instance._result): + return instance._result[name].copy(deep=True) # retrieve entity, its type and its onset names entity = instance.__getattribute__(name) diff --git a/urbs/runfunctions.py b/urbs/runfunctions.py index 5a8110e4..98862a0a 100644 --- a/urbs/runfunctions.py +++ b/urbs/runfunctions.py @@ -104,10 +104,6 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, # model instance, undo scenario changes?, path to excel sheet prob = scenario(prob, False, filename) - # Write model to lp File - model_filename = os.path.join(result_dir, '{}.lp').format(sce) - prob.write(model_filename, io_options={"symbolic_solver_labels": True}) - # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) diff --git a/urbs/saveload.py b/urbs/saveload.py index 76a02ed1..6986477f 100644 --- a/urbs/saveload.py +++ b/urbs/saveload.py @@ -13,7 +13,7 @@ def create_result_cache(prob): result_cache = {} for entity in entities: - result_cache[entity] = get_entity(prob, entity) + result_cache[entity] = get_entity(prob, entity, skip_result_cache=True) return result_cache From f97f3a8629b2961e9c1bf0d07a0c5354d9d95116 Mon Sep 17 00:00:00 2001 From: Leon Date: Sun, 3 Feb 2019 20:21:30 +0100 Subject: [PATCH 12/12] Untangle changes from alternative Scenarios --- input/scenario_new_timeseries_0.xlsx | Bin 0 -> 1971701 bytes input/scenario_new_timeseries_1.xlsx | Bin 0 -> 530516 bytes input/scenario_new_timeseries_2.xlsx | Bin 0 -> 1213702 bytes input/scenario_new_timeseries_3.xlsx | Bin 0 -> 152693 bytes input/scenario_new_timeseries_4.xlsx | Bin 0 -> 262416 bytes 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z8-*T;5nc}!)`hS$*hR~rnR2LB3@zXgL56M$`T+=t7+7{m7Gw(gaR}%JpzlFN7?5%k zYye_MD!Nwm^%DrKk3T`QqOYbvHvx570YX0m1K$@}28=}p=-SbzfDr~P{04RaF%!b* zhN8FW5r!`RM~tE1@nv*FQ9H~CBN-TE+2jx&g?69Ob>nuDDyJMMk_q?|-02ALW(Af| Spz;!Uh!iUW!)yULAP)eM>O;{0 literal 0 HcmV?d00001 diff --git a/runme.py b/runme.py index 2ad9585f..cfefcfdd 100644 --- a/runme.py +++ b/runme.py @@ -1,13 +1,15 @@ import os +import pandas as pd +import pyomo.environ import shutil import urbs -from urbs.data import timeseries_number +from datetime import datetime +from pyomo.opt.base import SolverFactory - -if __name__ == "__main__": +if __name__ == '__main__': input_file = 'mimo-example.xlsx' result_name = os.path.splitext(input_file)[0] # cut away file extension - result_dir = urbs.prepare_result_directory(result_name) # name + timestamp + result_dir = urbs.prepare_result_directory(result_name) # name+time stamp # copy input file to result directory shutil.copyfile(input_file, os.path.join(result_dir, input_file)) @@ -47,7 +49,9 @@ report_sites_name = {('North', 'Mid', 'South'): 'Greenland'} # plotting timesteps - plot_periods = {'all': timesteps[1:]} + plot_periods = { + 'all': timesteps[1:] + } # add or change plot colors my_colors = { @@ -65,18 +69,13 @@ urbs.scenario_co2_tax_mid, urbs.scenario_no_dsm, urbs.scenario_north_process_caps, - urbs.scenario_all_together, - urbs.scenario_new_timeseries(timeseries_number, - "example_file_extension") - ] + urbs.scenario_all_together] - # Read data from Excel Sheet and create model for use in scenarios - data = urbs.read_excel(input_file) - prob = urbs.create_model(data, dt, timesteps) for scenario in scenarios: - prob = urbs.run_scenario(prob, solver, timesteps, scenario, result_dir, - dt, objective, plot_tuples=plot_tuples, - plot_sites_name=plot_sites_name, - plot_periods=plot_periods, - report_tuples=report_tuples, - report_sites_name=report_sites_name) + prob = urbs.run_scenario(input_file, solver, timesteps, scenario, + result_dir, dt, objective, + plot_tuples=plot_tuples, + plot_sites_name=plot_sites_name, + plot_periods=plot_periods, + report_tuples=report_tuples, + report_sites_name=report_sites_name) \ No newline at end of file diff --git a/urbs/runfunctions.py b/urbs/runfunctions.py index 98862a0a..85a39fd6 100644 --- a/urbs/runfunctions.py +++ b/urbs/runfunctions.py @@ -8,7 +8,6 @@ from .input import * from .validation import * from .saveload import * -from .data import timeseries_number from .saveload import load # multiprocessing for serialized plotting import multiprocessing as mp @@ -69,14 +68,14 @@ def serialized_plotting_reporting(result_dir, sce, report_tuples, periods=plot_periods, figure_size=(24, 9)) -def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, +def run_scenario(input_file, solver, timesteps, scenario, result_dir, dt, objective, plot_tuples=None, plot_sites_name=None, plot_periods=None, report_tuples=None, report_sites_name=None): """ run an urbs model for given input, time steps and scenario Args: - prob: urbs model instance initialized with base scenario + input_file: filename to an Excel spreadsheet for urbs.read_excel solver: name of the solver to be used timesteps: a list of timesteps, e.g. range(0,8761) scenario: a scenario function that modifies the input data dict @@ -93,16 +92,12 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, """ # scenario name, read and modify data for scenario sce = scenario.__name__ + data = read_excel(input_file) + data = scenario(data) + validate_input(data) - # Only needed for scenario_new_timeseries, but handed to all functions: - filename = "" - # scenario_new_timeseries needs special treatment: - # Add file extension to scenario name and create path to excel sheet - if str(sce).find("scenario_new_timeseries") >= 0: - sce = sce+str(timeseries_number.pop()) - filename = os.path.join("input", "{}.xlsx").format(sce) - # model instance, undo scenario changes?, path to excel sheet - prob = scenario(prob, False, filename) + # create model + prob = create_model(data, dt, timesteps, objective) # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) @@ -122,7 +117,4 @@ def run_scenario(prob, solver, timesteps, scenario, result_dir, dt, report_sites_name, timesteps, plot_tuples, plot_sites_name, plot_periods)) plot_process.start() - - # Undo all changes to model instance to retrieve base scenario model - prob = scenario(prob, True, filename) return prob diff --git a/urbs/saveload.py b/urbs/saveload.py index 6986477f..d7086111 100644 --- a/urbs/saveload.py +++ b/urbs/saveload.py @@ -32,18 +32,10 @@ def save(prob, filename): category=pd.io.pytables.PerformanceWarning) prob._result = create_result_cache(prob) + with pd.HDFStore(filename, mode='w') as store: - # Search all attributes of prob for those containing the input data - for name in prob.__dict__: - if str(name).find("_dict") > 0: - name_no_dict = name.split("_dict")[0] # remove _dict - try: - store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, - name))) - # 1D dictionaries need an index: - except ValueError: - store['data/'+name_no_dict] = (pd.DataFrame(getattr(prob, - name), index=[0])) + for name in prob._data.keys(): + store['data/'+name] = prob._data[name] for name in prob._result.keys(): store['result/'+name] = prob._result[name] diff --git a/urbs/scenarios.py b/urbs/scenarios.py index db737389..1a8a1946 100644 --- a/urbs/scenarios.py +++ b/urbs/scenarios.py @@ -1,406 +1,54 @@ -from .model import * -from .input import split_columns -from .data import timeseries_number +import pandas as pd -''' -These Scenarios donĀ“t built up a whole new model. Instead they save time by -using an existing base model and by manipulating the dictionaries containing -the data and updating the affected constraints afterwards. -It is important to rebuilt the base scenario once the problem is solved. -This is done by handing reverse=True to the scenario function. +# SCENARIO GENERATORS +# In this script a variety of scenario generator functions are defined to +# facilitate scenario definitions. -If you want to create your own scenarios either manipulate the functions below -or do the following steps: -1) Build an Excel file where the data is similar to the one the scenario - funtion should produce -2) Create a model instance of this scenario using prob = urbs.create_model() -3) Write the model to an .lp file using prob.write(filename, - io_options={"symbolic_solver_labels":True}) -4) Write a function with similar structure as the scenario functions below that - changes the data in the dictionaries as you wish -5) Do step 2 (using the standard Excel input file, not the modified version), - call your scenario function to manipulate the model, then do step 3. - This creates an .lp file of your current scenario model. -6) Compare the .lp file of your model to the .lp file you created before - (Notepad++ Plugin 'Compare' is very useful) -7) Spot the constraints that need to be updated, delete these constraints and - recreate them in your scenario function. Sometimes indices have to be - deleted, too. Do this both for the reverse=False part and the reverse=True - part -8) Repeat starting from step 5 until there are no more differences between the - two .lp files -9) Do step 2 and 3 using the standard input file, -10) Do step 2 (using the standard Excel input file), call your scenario - function to manipulate the model twice: One time with reverse=False and - afterwards with reverse=True, then do step 3. This .lp file should match - the .lp file of the base model. Compare them! -11) Congratulations! You created your own scenario function! -''' - -def scenario_base(prob, reverse, not_used): +def scenario_base(data): # do nothing - return prob + return data -def scenario_stock_prices(prob, reverse, not_used): +def scenario_stock_prices(data): # change stock commodity prices - if not reverse: - for x in tuple(prob.commodity_dict["price"].keys()): - if x[2] == "Stock": - prob.commodity_dict["price"][x] *= 1.5 - update_cost(prob) - return prob - if reverse: - for x in tuple(prob.commodity_dict["price"].keys()): - if x[2] == "Stock": - prob.commodity_dict["price"][x] *= 1/1.5 - update_cost(prob) - return prob + co = data['commodity'] + stock_commodities_only = (co.index.get_level_values('Type') == 'Stock') + co.loc[stock_commodities_only, 'price'] *= 1.5 + return data -def scenario_co2_limit(prob, reverse, not_used): +def scenario_co2_limit(data): # change global CO2 limit - if not reverse: - prob.global_prop_dict["value"]["CO2 limit"] *= 0.05 - update_co2_limit(prob) - return prob - if reverse: - prob.global_prop_dict["value"]["CO2 limit"] *= 1/0.05 - update_co2_limit(prob) - return prob + global_prop = data['global_prop'] + global_prop.loc['CO2 limit', 'value'] *= 0.05 + return data -def scenario_co2_tax_mid(prob, reverse, not_used): +def scenario_co2_tax_mid(data): # change CO2 price in Mid - if not reverse: - prob.commodity_dict["price"][('Mid', 'CO2', 'Env')] = 50 - update_cost(prob) - return prob - if reverse: - prob.commodity_dict["price"][('Mid', 'CO2', 'Env')] = ( - prob._data["commodity"]["price"][('Mid', 'CO2', 'Env')]) - update_cost(prob) - return prob + co = data['commodity'] + co.loc[('Mid', 'CO2', 'Env'), 'price'] = 50 + return data -def scenario_north_process_caps(prob, reverse, not_used): +def scenario_north_process_caps(data): # change maximum installable capacity - if not reverse: - prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 0.5 - prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 0.25 - update_process_capacity(prob) - return prob - if reverse: - prob.process_dict["cap-up"][('North', 'Hydro plant')] *= 2 - prob.process_dict["cap-up"][('North', 'Biomass plant')] *= 4 - update_process_capacity(prob) - return prob - - -def scenario_no_dsm(prob, reverse, not_used): - if not reverse: - del_dsm(prob) - return prob - if reverse: - recreate_dsm(prob) - return prob - - -def scenario_all_together(prob, reverse, not_used): - # combine all other scenarios - if not reverse: - prob = scenario_stock_prices(prob, 0, not_used) - prob = scenario_co2_limit(prob, 0, not_used) - prob = scenario_north_process_caps(prob, 0, not_used) - return prob - if reverse: - prob = scenario_stock_prices(prob, 1, not_used) - prob = scenario_co2_limit(prob, 1, not_used) - prob = scenario_north_process_caps(prob, 1, not_used) - return prob + pro = data['process'] + pro.loc[('North', 'Hydro plant'), 'cap-up'] *= 0.5 + pro.loc[('North', 'Biomass plant'), 'cap-up'] *= 0.25 + return data -# Usage: In main the scenarios are function handles. In order to hand further -# information to the scenario function it is necessary to define an external -# data structure to store the additional information in. This function should -# be called like this: -# scenario_new_timeseries(timeseries_number, ) -# value specifies the path/file extension in order to locate the excel file. -# value will be appended to input\\scenario_new_timeseries_ -def scenario_new_timeseries(timeseries_number, number): - timeseries_number.insert(0, number) - return scenario_new_timeseries_ - - -def scenario_new_timeseries_(prob, reverse, - filename="input\\scenario_new_timeseries.xlsx"): - if not reverse: - sheetnames = load_timeseries(prob, reverse, filename) - if "Demand" in sheetnames: - update_res_vertex(prob) - if "SupIm" in sheetnames: - update_supim(prob) - if "Buy-Sell-Price" in sheetnames: - update_cost(prob) - if 'TimeVarEff' in sheetnames: - update_TimeVarEff(prob) - if reverse: - sheetnames = load_timeseries(prob, reverse, filename) - if "Demand" in sheetnames: - update_res_vertex(prob) - if "SupIm" in sheetnames: - update_supim(prob) - if "Buy-Sell-Price" in sheetnames: - update_cost(prob) - if 'TimeVarEff' in sheetnames: - update_TimeVarEff(prob) - return prob - - -# Constraint updating funtions: -def del_dsm(prob): +def scenario_no_dsm(data): # empty the DSM dataframe completely - prob.dsm_dict = pd.DataFrame().to_dict() - - prob.del_component(prob.dsm_site_tuples) - prob.del_component(prob.dsm_down_tuples) - prob.dsm_site_tuples = pyomo.Set() - prob.dsm_down_tuples = pyomo.Set() - - prob.del_component(prob.dsm_down) - prob.del_component(prob.dsm_up) - prob.dsm_down = pyomo.Var() - prob.dsm_up = pyomo.Var() - - prob.del_component(prob.def_dsm_variables) - prob.del_component(prob.res_dsm_upward) - prob.del_component(prob.res_dsm_downward) - prob.del_component(prob.res_dsm_maximum) - prob.del_component(prob.res_dsm_recovery) - - prob.def_dsm_variables = pyomo.Constraint.Skip - prob.res_dsm_upward = pyomo.Constraint.Skip - prob.res_dsm_downward = pyomo.Constraint.Skip - prob.res_dsm_maximum = pyomo.Constraint.Skip - prob.res_dsm_recovery = pyomo.Constraint.Skip - - # The following lines cause 99% of work for the formation of this scenario - update_res_vertex(prob) - - -def recreate_dsm(prob): - # dsm_variables & vertex rule - prob.dsm_dict = prob._data["dsm"].to_dict() - try: - myset = tuple(prob.dsm_dict["delay"].keys()) - except KeyError: - raise NotImplementedError("Could not rebuild base modell!") - - prob.del_component(prob.dsm_site_tuples_domain) - prob.del_component(prob.dsm_site_tuples) - prob.dsm_site_tuples = pyomo.Set( - within=prob.sit*prob.com, - initialize=myset, - doc='Combinations of possible dsm by site, e.g.(Mid, Elec)') - - prob.del_component(prob.dsm_down_tuples_domain) - prob.del_component(prob.dsm_down_tuples_domain_index_0) - prob.del_component(prob.dsm_down_tuples_domain_index_0_index_0) - prob.del_component(prob.dsm_down_tuples) - prob.dsm_down_tuples = pyomo.Set( - within=prob.tm*prob.tm*prob.sit*prob.com, - initialize=[(t, tt, site, commodity) - for (t, tt, site, commodity) - in dsm_down_time_tuples(prob.timesteps[1:], - prob.dsm_site_tuples, - prob)], - doc='Combinations of possible dsm_down combinations, e.g. ' - '(5001,5003,Mid,Elec)') - - prob.del_component(prob.dsm_up_index) - prob.del_component(prob.dsm_up) - prob.dsm_up = pyomo.Var( - prob.tm, prob.dsm_site_tuples, - within=pyomo.NonNegativeReals, - doc='DSM upshift') - - prob.del_component(prob.dsm_down) - prob.dsm_down = pyomo.Var( - prob.dsm_down_tuples, - within=pyomo.NonNegativeReals, - doc='DSM downshift') - - prob.del_component(prob.def_dsm_variables_index) - prob.del_component(prob.def_dsm_variables) - del prob.def_dsm_variables - prob.def_dsm_variables = pyomo.Constraint( - prob.tm, prob.dsm_site_tuples, - rule=def_dsm_variables_rule, - doc='DSMup * efficiency factor n == DSMdo (summed)') - - prob.del_component(prob.res_dsm_upward_index) - prob.del_component(prob.res_dsm_upward) - del prob.res_dsm_upward - prob.res_dsm_upward = pyomo.Constraint( - prob.tm, prob.dsm_site_tuples, - rule=res_dsm_upward_rule, - doc='DSMup <= Cup (threshold capacity of DSMup)') - - prob.del_component(prob.res_dsm_downward_index) - prob.del_component(prob.res_dsm_downward) - del prob.res_dsm_downward - prob.res_dsm_downward = pyomo.Constraint( - prob.tm, prob.dsm_site_tuples, - rule=res_dsm_downward_rule, - doc='DSMdo (summed) <= Cdo (threshold capacity of DSMdo)') + data['dsm'] = pd.DataFrame() + return data - prob.del_component(prob.res_dsm_maximum) - prob.del_component(prob.res_dsm_maximum_index) - del prob.res_dsm_maximum - prob.res_dsm_maximum = pyomo.Constraint( - prob.tm, prob.dsm_site_tuples, - rule=res_dsm_maximum_rule, - doc='DSMup + DSMdo (summed) <= max(Cup,Cdo)') - prob.del_component(prob.res_dsm_recovery) - prob.del_component(prob.res_dsm_recovery_index) - del prob.res_dsm_recovery - prob.res_dsm_recovery = pyomo.Constraint( - prob.tm, prob.dsm_site_tuples, - rule=res_dsm_recovery_rule, - doc='DSMup(t, t + recovery time R) <= Cup * delay time L') - - # The following lines cause 50% of rebuilding work - update_res_vertex(prob) - - -def change_dsm(prob): - # not implemented yet - return prob - - -def upd_dsm_constraints(prob): - # not implemented yet - return prob - - -def load_timeseries(prob, reverse, filename): - with pd.ExcelFile(filename) as xls: - try: - sheetnames = xls.sheet_names - except KeyError: - print("Could not find file for new timeseries scenario") - if not reverse: - for temp in sheetnames: - temp2 = xls.parse(temp).set_index(["t"]) - temp2.columns = split_columns(temp2.columns, '.') - if str(temp) == "Demand": - prob.demand_dict = temp2.to_dict() - if str(temp) == "SupIm": - prob.supim_dict = temp2.to_dict() - if str(temp) == "Buy-Sell-Price": - prob.buy_sell_price_dict = temp2.to_dict() - if str(temp) == "TimeVarEff": - prob.eff_factor_dict = temp2.to_dict() - if reverse: - for temp in sheetnames: - if str(temp) == "Demand": - prob.demand_dict = prob._data["demand"].to_dict() - if str(temp) == "SupIm": - prob.supim_dict = prob._data["supim"].to_dict() - if str(temp) == "Buy-Sell-Price": - prob.buy_sell_price_dict = (prob._data["buy_sell_price"] - .to_dict()) - if str(temp) == "TimeVarEff": - prob.eff_factor_dict = prob._data["eff_factor"].to_dict() - return sheetnames - - -def update_TimeVarEff(prob): - prob.del_component(prob.pro_timevar_output_tuples) - prob.del_component(prob.pro_timevar_output_tuples_domain) - prob.del_component(prob.pro_timevar_output_tuples_domain_index_0) - prob.pro_timevar_output_tuples = pyomo.Set( - within=prob.sit*prob.pro*prob.com, - initialize=[(site, process, commodity) - for (site, process) in tuple(prob.eff_factor_dict.keys()) - for (pro, commodity) in tuple(prob.r_out_dict.keys()) - if process == pro], - doc='Outputs of processes with time dependent efficiency') - - prob.del_component(prob.def_process_output) - prob.del_component(prob.def_process_output_index) - prob.del_component(prob.def_process_output_index_1) - prob.del_component(prob.def_process_output_index_1_index_0) - prob.def_process_output = pyomo.Constraint( - prob.tm, (prob.pro_output_tuples - prob.pro_partial_output_tuples - - prob.pro_timevar_output_tuples), - rule=def_process_output_rule, - doc='process output = process throughput * output ratio') - - prob.del_component(prob.def_process_timevar_output) - prob.del_component(prob.def_process_timevar_output_index) - prob.del_component(prob.def_process_timevar_output_index_1) - prob.del_component(prob.def_process_timevar_output_index_1_index_1) - prob.def_process_timevar_output = ( - pyomo.Constraint(prob.tm, (prob.pro_timevar_output_tuples - - (prob.pro_partial_output_tuples & - prob.pro_timevar_output_tuples)), - rule=def_pro_timevar_output_rule, - doc='e_pro_out = tau_pro * r_out * eff_factor')) - - prob.del_component(prob.def_process_partial_timevar_output) - prob.del_component(prob.def_process_partial_timevar_output_index) - prob.del_component(prob.def_process_partial_timevar_output_index_1) - prob.def_process_partial_timevar_output = pyomo.Constraint( - prob.tm, - prob.pro_partial_output_tuples & prob.pro_timevar_output_tuples, - rule=def_pro_partial_timevar_output_rule, - doc='e_pro_out = tau_pro * r_out * eff_factor') - - -def update_cost(prob): - prob.del_component(prob.def_costs) - prob.def_costs = pyomo.Constraint( - prob.cost_type, - rule=def_costs_rule, - doc='main cost function by cost type') - - -def update_supim(prob): - prob.del_component(prob.def_intermittent_supply) - prob.del_component(prob.def_intermittent_supply_index) - prob.def_intermittent_supply = pyomo.Constraint( - prob.tm, prob.pro_input_tuples, - rule=def_intermittent_supply_rule, - doc='process output = process capacity * supim timeseries') - - -def update_res_vertex(prob): - prob.del_component(prob.res_vertex) - prob.del_component(prob.res_vertex_index) - prob.res_vertex = pyomo.Constraint( - prob.tm, prob.com_tuples, - rule=res_vertex_rule, - doc='storage + transmission + process + source + buy - sell == demand') - - -def update_process_capacity(prob): - prob.del_component(prob.def_process_capacity) - prob.def_process_capacity = pyomo.Constraint( - prob.pro_tuples, - rule=def_process_capacity_rule, - doc='total process capacity = inst-cap + new capacity') - prob.del_component(prob.res_process_capacity) - prob.res_process_capacity = pyomo.Constraint( - prob.pro_tuples, - rule=res_process_capacity_rule, - doc='process.cap-lo <= total process capacity <= process.cap-up') - - -def update_co2_limit(prob): - prob.del_component(prob.res_global_co2_limit) - prob.res_global_co2_limit = pyomo.Constraint( - rule=res_global_co2_limit_rule, - doc='total co2 commodity output <= Global CO2 limit') +def scenario_all_together(data): + # combine all other scenarios + data = scenario_stock_prices(data) + data = scenario_co2_limit(data) + data = scenario_north_process_caps(data) + return data \ No newline at end of file

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possible: do validation of data # create filename for logfile log_filename = os.path.join(result_dir, '{}.log').format(sce) - + #Write model to lp File + model_filename = os.path.join(result_dir, '{}.lp').format(sce) + prob.write(model_filename, io_options={"symbolic_solver_labels":True}) + # solve model and read results optim = SolverFactory('glpk') # cplex, glpk, gurobi, ... optim = setup_solver(optim, logfile=log_filename) @@ -105,7 +108,12 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, plot_sites_name=plot_sites_name, periods=plot_periods, figure_size=(24, 9)) - prob = scenario(prob, 1, filename) + prob = scenario(prob, True, filename) + + #Write model to lp File + model_filename = os.path.join(result_dir, '{}_base.lp').format(sce) + prob.write(model_filename, io_options={"symbolic_solver_labels":True}) + return prob @@ -159,14 +167,14 @@ def run_scenario(prob, timesteps, scenario, result_dir, dt, objective, # select scenarios to be run scenarios = [ urbs.scenario_base, - #urbs.scenario_stock_prices, - #urbs.scenario_co2_limit, - #urbs.scenario_co2_tax_mid, - #urbs.scenario_no_dsm, - #urbs.scenario_north_process_caps, + urbs.scenario_stock_prices, + urbs.scenario_co2_limit, + urbs.scenario_co2_tax_mid, + urbs.scenario_no_dsm, + urbs.scenario_north_process_caps, urbs.scenario_all_together, urbs.scenario_new_timeseries(timeseries_number, - "example_file_extension") + "1") ] # Read data from Excel Sheet and create model for use in scenarios diff --git a/urbs/plot.py b/urbs/plot.py index cc1ee78b..821f1c38 100644 --- a/urbs/plot.py +++ b/urbs/plot.py @@ -124,9 +124,12 @@ def plot(prob, com, sit, dt, timesteps, timesteps_plot, try: # detect whether DSM could be used in this plot # if so, show DSM subplot (even if delta == 0 for the whole time) - df_dsm = get_input(prob, 'dsm') - plot_dsm = df_dsm.loc[(sit, com), - ['cap-max-do', 'cap-max-up']].sum().sum() > 0 + df_dsm = get_input(prob, 'dsm_dict') + plot_dsm=0 + for s in sit: #someone meant to cast sit to a list... + plot_dsm += (df_dsm["cap-max-do"][s,com] + df_dsm["cap-max-up"] + [s,com]) + plot_dsm=plot_dsm>0 except (KeyError, TypeError): plot_dsm = False diff --git a/urbs/pyomoio.py b/urbs/pyomoio.py index bf91f750..d30b3399 100644 --- a/urbs/pyomoio.py +++ b/urbs/pyomoio.py @@ -13,9 +13,10 @@ def get_entity(instance, name): a Pandas Series with domain as index and values (or 1's, for sets) of entity name. For constraints, it retrieves the dual values """ + # The following would destroy many plots when model instances are reused. # magic: short-circuit if problem contains a result cache - if hasattr(instance, '_result') and name in instance._result: - return instance._result[name].copy(deep=True) + # if hasattr(instance, '_result') and name in instance._result: + # return instance._result[name].copy(deep=True) # retrieve entity, its type and its onset names entity = instance.__getattribute__(name) @@ -74,8 +75,7 @@ def get_entity(instance, name): [(v[0], v[1].value) for v in entity.iteritems()]) else: # assert(entity.dim() == 0) - results = pd.DataFrame( - [(v[0], v[1].value) for v in entity.iteritems()]) + results = pd.DataFrame() labels = ['None'] # check for duplicate onset names and append one to several "_" to make diff --git a/urbs/scenario.py b/urbs/scenario.py index 334a5ecb..e6cd0b73 100644 --- a/urbs/scenario.py +++ b/urbs/scenario.py @@ -1,4 +1,4 @@ -from .model import * +ļ»æfrom .model import * from .input import split_columns from .data import timeseries_number From 0854007e94e772d181ce0d9ba2890d91759aa7c1 Mon Sep 17 00:00:00 2001 From: Balz Date: Wed, 28 Nov 2018 15:14:48 +0100 Subject: [PATCH 04/12] Fixed scenario_new_timeseries reverse function; timeseries_number no more declared global. --- ...new_timeseries_example_file_extension.xlsx | Bin 1971555 -> 1971701 bytes runme.py | 26 +++++++----------- urbs/plot.py | 2 +- urbs/scenario.py | 4 +-- 4 files changed, 13 insertions(+), 19 deletions(-) diff --git a/input/scenario_new_timeseries_example_file_extension.xlsx b/input/scenario_new_timeseries_example_file_extension.xlsx index 4a5d6cc39fec7b53e48c5db684ff64d9997b8b2b..e755e99d333d97bc80d4e65f1600749e2491c53e 100644 GIT binary patch delta 1784244 zcmbTed0dV8`#(-pM1?HLX~B>bLPlwyF}6}h-pW|okZhG`k-Alsv`x}R+ssgmsDz?O zofJwTRN7Q3ON)y3<#%1L`@T;nGw=8J^Z5Pa@i?7(dA+XXc|EV^^*ZYQcKUYBDQU&{ z@d8H`6SfMB8g(yNYrG_tf4E$EQpSuA+T$|HZ%$R;D4MU^@ssGS(?P0Fe$h$#bL(_- 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