import numpy as np import pandas as pd import logging logger = logging.getLogger(__name__) import gc import os import pypsa from pypsa.descriptors import free_output_series_dataframes # Suppress logging of the slack bus choices pypsa.pf.logger.setLevel(logging.WARNING) from vresutils.benchmark import memory_logger def patch_pyomo_tmpdir(tmpdir): # PYOMO should write its lp files into tmp here import os if not os.path.isdir(tmpdir): os.mkdir(tmpdir) from pyutilib.services import TempfileManager TempfileManager.tempdir = tmpdir def prepare_network(n, solve_opts=None): if solve_opts is None: solve_opts = snakemake.config['solving']['options'] if 'clip_p_max_pu' in solve_opts: for df in (n.generators_t.p_max_pu, n.storage_units_t.inflow): df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True) if solve_opts.get('load_shedding'): n.add("Carrier", "Load") n.madd("Generator", n.buses.index, " load", bus=n.buses.index, carrier='load', sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW marginal_cost=1e2, # Eur/kWh # intersect between macroeconomic and surveybased # willingness to pay # http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full p_nom=1e9 # kW ) if solve_opts.get('noisy_costs'): for t in n.iterate_components(n.one_port_components): #if 'capital_cost' in t.df: # t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5) if 'marginal_cost' in t.df: t.df['marginal_cost'] += 1e-2 + 2e-3*(np.random.random(len(t.df)) - 0.5) for t in n.iterate_components(['Line', 'Link']): t.df['capital_cost'] += (1e-1 + 2e-2*(np.random.random(len(t.df)) - 0.5)) * t.df['length'] if solve_opts.get('nhours'): nhours = solve_opts['nhours'] n.set_snapshots(n.snapshots[:nhours]) n.snapshot_weightings[:] = 8760./nhours return n def add_opts_constraints(n, opts=None): if opts is None: opts = snakemake.wildcards.opts.split('-') if 'BAU' in opts: mincaps = snakemake.config['electricity']['BAU_mincapacities'] def bau_mincapacities_rule(model, carrier): gens = n.generators.index[n.generators.p_nom_extendable & (n.generators.carrier == carrier)] return sum(model.generator_p_nom[gen] for gen in gens) >= mincaps[carrier] n.model.bau_mincapacities = pypsa.opt.Constraint(list(mincaps), rule=bau_mincapacities_rule) if 'SAFE' in opts: peakdemand = (1. + snakemake.config['electricity']['SAFE_reservemargin']) * n.loads_t.p_set.sum(axis=1).max() conv_techs = snakemake.config['plotting']['conv_techs'] exist_conv_caps = n.generators.loc[n.generators.carrier.isin(conv_techs) & ~n.generators.p_nom_extendable, 'p_nom'].sum() ext_gens_i = n.generators.index[n.generators.carrier.isin(conv_techs) & n.generators.p_nom_extendable] n.model.safe_peakdemand = pypsa.opt.Constraint(expr=sum(n.model.generator_p_nom[gen] for gen in ext_gens_i) >= peakdemand - exist_conv_caps) # Add constraints on the per-carrier capacity in each country if 'CCL' in opts: agg_p_nom_limits = snakemake.config['electricity'].get('agg_p_nom_limits') try: agg_p_nom_minmax = pd.read_csv(agg_p_nom_limits, index_col=list(range(2))) except IOError: logger.exception("Need to specify the path to a .csv file containing aggregate capacity limits per country in config['electricity']['agg_p_nom_limit'].") logger.info("Adding per carrier generation capacity constraints for individual countries") gen_country = n.generators.bus.map(n.buses.country) def agg_p_nom_min_rule(model, country, carrier): min = agg_p_nom_minmax.at[(country, carrier), 'min'] return ((sum(model.generator_p_nom[gen] for gen in n.generators.index[(gen_country == country) & (n.generators.carrier == carrier)]) >= min) if np.isfinite(min) else pypsa.opt.Constraint.Skip) def agg_p_nom_max_rule(model, country, carrier): max = agg_p_nom_minmax.at[(country, carrier), 'max'] return ((sum(model.generator_p_nom[gen] for gen in n.generators.index[(gen_country == country) & (n.generators.carrier == carrier)]) <= max) if np.isfinite(max) else pypsa.opt.Constraint.Skip) n.model.agg_p_nom_min = pypsa.opt.Constraint(list(agg_p_nom_minmax.index), rule=agg_p_nom_min_rule) n.model.agg_p_nom_max = pypsa.opt.Constraint(list(agg_p_nom_minmax.index), rule=agg_p_nom_max_rule) def add_lv_constraint(n): line_volume = getattr(n, 'line_volume_limit', None) if line_volume is not None and not np.isinf(line_volume): links_dc_ext_i = n.links.index[(n.links.carrier == 'DC') & n.links.p_nom_extendable] if not n.links.empty else pd.Index([]) n.model.line_volume_constraint = pypsa.opt.Constraint( expr=((sum(n.model.passive_branch_s_nom["Line",line]*n.lines.at[line,"length"] for line in n.lines.index[n.lines.s_nom_extendable]) + sum(n.model.link_p_nom[link]*n.links.at[link,"length"] for link in links_dc_ext_i)) <= line_volume) ) def add_lc_constraint(n): line_cost = getattr(n, 'line_cost_limit', None) if line_cost is not None and not np.isinf(line_cost): links_dc_ext_i = n.links.index[(n.links.carrier == 'DC') & n.links.p_nom_extendable] if not n.links.empty else pd.Index([]) n.model.line_cost_constraint = pypsa.opt.Constraint( expr=((sum(n.model.passive_branch_s_nom["Line",line]*n.lines.at[line,"capital_cost_lc"] for line in n.lines.index[n.lines.s_nom_extendable]) + sum(n.model.link_p_nom[link]*n.links.at[link,"capital_cost_lc"] for link in links_dc_ext_i)) <= line_cost) ) def add_eps_storage_constraint(n): if not hasattr(n, 'epsilon'): n.epsilon = 1e-5 fix_sus_i = n.storage_units.index[~ n.storage_units.p_nom_extendable] n.model.objective.expr += sum(n.epsilon * n.model.state_of_charge[su, n.snapshots[0]] for su in fix_sus_i) def fix_branches(n, lines_s_nom=None, links_p_nom=None): if lines_s_nom is not None and len(lines_s_nom) > 0: for l, s_nom in lines_s_nom.iteritems(): n.model.passive_branch_s_nom["Line", l].fix(s_nom) if isinstance(n.opt, pypsa.opf.PersistentSolver): n.opt.update_var(n.model.passive_branch_s_nom) if links_p_nom is not None and len(links_p_nom) > 0: for l, p_nom in links_p_nom.iteritems(): n.model.link_p_nom[l].fix(p_nom) if isinstance(n.opt, pypsa.opf.PersistentSolver): n.opt.update_var(n.model.link_p_nom) def solve_network(n, config=None, solver_log=None, opts=None, callback=None): if config is None: config = snakemake.config['solving'] solve_opts = config['options'] solver_options = config['solver'].copy() if solver_log is None: solver_log = snakemake.log.solver solver_name = solver_options.pop('name') def extra_postprocessing(n, snapshots, duals): if hasattr(n, 'line_volume_limit') and hasattr(n.model, 'line_volume_constraint'): cdata = pd.Series(list(n.model.line_volume_constraint.values()), index=list(n.model.line_volume_constraint.keys())) n.line_volume_limit_dual = -cdata.map(duals).sum() if hasattr(n, 'line_cost_limit') and hasattr(n.model, 'line_cost_constraint'): cdata = pd.Series(list(n.model.line_cost_constraint.values()), index=list(n.model.line_cost_constraint.keys())) n.line_cost_limit_dual = -cdata.map(duals).sum() def run_lopf(n, allow_warning_status=False, fix_ext_lines=False): free_output_series_dataframes(n) pypsa.opf.network_lopf_build_model(n, formulation=solve_opts['formulation']) add_opts_constraints(n, opts) if not fix_ext_lines: add_lv_constraint(n) add_lc_constraint(n) pypsa.opf.network_lopf_prepare_solver(n, solver_name=solver_name) if fix_ext_lines: fix_branches(n, lines_s_nom=n.lines.loc[n.lines.s_nom_extendable, 's_nom_opt'], links_p_nom=n.links.loc[n.links.p_nom_extendable, 'p_nom_opt']) # Firing up solve will increase memory consumption tremendously, so # make sure we freed everything we can gc.collect() status, termination_condition = \ pypsa.opf.network_lopf_solve(n, solver_logfile=solver_log, solver_options=solver_options, formulation=solve_opts['formulation'], extra_postprocessing=extra_postprocessing #free_memory={'pypsa'} ) assert status == "ok" or allow_warning_status and status == 'warning', \ ("network_lopf did abort with status={} " "and termination_condition={}" .format(status, termination_condition)) return status, termination_condition iteration = 0 lines_ext_b = n.lines.s_nom_extendable if lines_ext_b.any(): # puh: ok, we need to iterate, since there is a relation # between s/p_nom and r, x for branches. msq_threshold = 0.01 lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']]) lines['s_nom'] = ( np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) * n.lines.bus0.map(n.buses.v_nom) ).where(n.lines.type != '', n.lines['s_nom']) lines_ext_typed_b = (n.lines.type != '') & lines_ext_b lines_ext_untyped_b = (n.lines.type == '') & lines_ext_b def update_line_parameters(n, zero_lines_below=10): if zero_lines_below > 0: n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 0. n.links.loc[n.links.p_nom_opt < zero_lines_below, 'p_nom_opt'] = 0. if lines_ext_untyped_b.any(): for attr in ('r', 'x'): n.lines.loc[lines_ext_untyped_b, attr] = ( lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt']) ) if lines_ext_typed_b.any(): n.lines.loc[lines_ext_typed_b, 'num_parallel'] = ( n.lines['s_nom_opt']/lines['s_nom'] ) logger.debug("lines.num_parallel={}".format(n.lines.loc[lines_ext_typed_b, 'num_parallel'])) iteration += 1 lines['s_nom_opt'] = lines['s_nom'] * n.lines['num_parallel'].where(n.lines.type != '', 1.) status, termination_condition = run_lopf(n, allow_warning_status=True) if callback is not None: callback(n, iteration, status) def msq_diff(n): lines_err = np.sqrt(((n.lines['s_nom_opt'] - lines['s_nom_opt'])**2).mean())/lines['s_nom_opt'].mean() logger.info("Mean square difference after iteration {} is {}".format(iteration, lines_err)) return lines_err min_iterations = solve_opts.get('min_iterations', 2) max_iterations = solve_opts.get('max_iterations', 999) while msq_diff(n) > msq_threshold or iteration < min_iterations: if iteration >= max_iterations: logger.info("Iteration {} beyond max_iterations {}. Stopping ...".format(iteration, max_iterations)) break update_line_parameters(n) lines['s_nom_opt'] = n.lines['s_nom_opt'] iteration += 1 status, termination_condition = run_lopf(n, allow_warning_status=True) if callback is not None: callback(n, iteration, status) update_line_parameters(n, zero_lines_below=100) logger.info("Starting last run with fixed extendable lines") iteration += 1 status, termination_condition = run_lopf(n, fix_ext_lines=True) if callback is not None: callback(n, iteration, status) return n if __name__ == "__main__": # Detect running outside of snakemake and mock snakemake for testing if 'snakemake' not in globals(): from vresutils.snakemake import MockSnakemake, Dict snakemake = MockSnakemake( wildcards=dict(network='elec', simpl='', clusters='45', lv='1.0', opts='Co2L-3H'), input=["networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc"], output=["results/networks/s{simpl}_{clusters}_lv{lv}_{opts}.nc"], log=dict(solver="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_solver.log", python="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_python.log") ) tmpdir = snakemake.config['solving'].get('tmpdir') if tmpdir is not None: patch_pyomo_tmpdir(tmpdir) logging.basicConfig(filename=snakemake.log.python, level=snakemake.config['logging_level']) with memory_logger(filename=getattr(snakemake.log, 'memory', None), interval=30.) as mem: n = pypsa.Network(snakemake.input[0]) n = prepare_network(n) n = solve_network(n) n.export_to_netcdf(snakemake.output[0]) logger.info("Maximum memory usage: {}".format(mem.mem_usage))