pypsa-eur/scripts/solve_network.py

"""
Solve networks iteratively linear optimal power flow, while updating reactances
"""

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)

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))