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Merge pull request #51 from fneum/solve_network

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Add `extra_functionality` and `extra_postprocessing` to solve_network()
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Fabian Neumann 2019-11-06 11:38:15 +01:00 committed by GitHub
commit bf12d25879
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1 changed files with 74 additions and 61 deletions
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135
scripts/solve_network.py
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@ -229,7 +229,10 @@ def fix_branches(n, lines_s_nom=None, links_p_nom=None):
if isinstance(n.opt, pypsa.opf.PersistentSolver): if isinstance(n.opt, pypsa.opf.PersistentSolver):
n.opt.update_var(n.model.link_p_nom) n.opt.update_var(n.model.link_p_nom)
def solve_network(n, config=None, solver_log=None, opts=None, callback=None): def solve_network(n, config=None, solver_log=None, opts=None, callback=None,
skip_iterating=False,
extra_functionality=None, extra_functionality_args=None,
extra_postprocessing=None):
if config is None: if config is None:
config = snakemake.config['solving'] config = snakemake.config['solving']
solve_opts = config['options'] solve_opts = config['options']
@ -239,16 +242,20 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
solver_log = snakemake.log.solver solver_log = snakemake.log.solver
solver_name = solver_options.pop('name') solver_name = solver_options.pop('name')
def extra_postprocessing(n, snapshots, duals): if extra_postprocessing is None:
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'): def get_line_limit_duals(n, snapshots, duals):
cdata = pd.Series(list(n.model.line_cost_constraint.values()), if hasattr(n, 'line_volume_limit') and hasattr(n.model, 'line_volume_constraint'):
index=list(n.model.line_cost_constraint.keys())) cdata = pd.Series(list(n.model.line_volume_constraint.values()),
n.line_cost_limit_dual = -cdata.map(duals).sum() 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()
extra_postprocessing = get_line_limit_duals
def run_lopf(n, allow_warning_status=False, fix_ext_lines=False): def run_lopf(n, allow_warning_status=False, fix_ext_lines=False):
free_output_series_dataframes(n) free_output_series_dataframes(n)
@ -261,6 +268,9 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
add_lv_constraint(n) add_lv_constraint(n)
add_lc_constraint(n) add_lc_constraint(n)
if extra_functionality is not None:
extra_functionality(n, n.snapshots, *extra_functionality_args)
pypsa.opf.network_lopf_prepare_solver(n, solver_name=solver_name) pypsa.opf.network_lopf_prepare_solver(n, solver_name=solver_name)
if fix_ext_lines: if fix_ext_lines:
@ -287,70 +297,73 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
return status, termination_condition return status, termination_condition
iteration = 0 if not skip_iterating:
lines_ext_b = n.lines.s_nom_extendable iteration = 0
if lines_ext_b.any(): lines_ext_b = n.lines.s_nom_extendable
# puh: ok, we need to iterate, since there is a relation if lines_ext_b.any():
# between s/p_nom and r, x for branches. # puh: ok, we need to iterate, since there is a relation
msq_threshold = 0.01 # between s/p_nom and r, x for branches.
lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']]) msq_threshold = 0.01
lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']])
lines['s_nom'] = ( lines['s_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) * np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom) n.lines.bus0.map(n.buses.v_nom)
).where(n.lines.type != '', n.lines['s_nom']) ).where(n.lines.type != '', n.lines['s_nom'])
lines_ext_typed_b = (n.lines.type != '') & lines_ext_b lines_ext_typed_b = (n.lines.type != '') & lines_ext_b
lines_ext_untyped_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): def update_line_parameters(n, zero_lines_below=10):
if zero_lines_below > 0: if zero_lines_below > 0:
n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 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. n.links.loc[n.links.p_nom_opt < zero_lines_below, 'p_nom_opt'] = 0.
if lines_ext_untyped_b.any(): if lines_ext_untyped_b.any():
for attr in ('r', 'x'): for attr in ('r', 'x'):
n.lines.loc[lines_ext_untyped_b, attr] = ( n.lines.loc[lines_ext_untyped_b, attr] = (
lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt']) 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']))
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 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) status, termination_condition = run_lopf(n, allow_warning_status=True)
if callback is not None: callback(n, iteration, status) 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
update_line_parameters(n, zero_lines_below=100) 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
logger.info("Starting last run with fixed extendable lines") update_line_parameters(n)
lines['s_nom_opt'] = n.lines['s_nom_opt']
iteration += 1
iteration += 1 status, termination_condition = run_lopf(n, allow_warning_status=True)
status, termination_condition = run_lopf(n, fix_ext_lines=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)
else:
status, termination_condition = run_lopf(n, fix_ext_lines=False)
if callback is not None: callback(n, iteration, status) if callback is not None: callback(n, iteration, status)
return n return n