generalize solve_network
* skip_iterating flag: solve network only once without updating impedances * extra_functionality parameter: add function to modify pyomo model * extra_functionality args: function arguments for extra_functionality * extra_postprocessing: add function for postprocessing steps depending on n.model
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Fabian Neumann
parent
4820588d6b
commit
dc1feadcba
@ -120,7 +120,10 @@ def fix_branches(n, lines_s_nom=None, links_p_nom=None):
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if isinstance(n.opt, pypsa.opf.PersistentSolver):
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n.opt.update_var(n.model.link_p_nom)
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def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
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def solve_network(n, config=None, solver_log=None, opts=None, callback=None,
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skip_iterating=False,
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extra_functionality=None, extra_functionality_args=None,
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extra_postprocessing=None):
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if config is None:
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config = snakemake.config['solving']
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solve_opts = config['options']
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@ -130,16 +133,20 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
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solver_log = snakemake.log.solver
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solver_name = solver_options.pop('name')
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def extra_postprocessing(n, snapshots, duals):
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if hasattr(n, 'line_volume_limit') and hasattr(n.model, 'line_volume_constraint'):
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cdata = pd.Series(list(n.model.line_volume_constraint.values()),
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index=list(n.model.line_volume_constraint.keys()))
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n.line_volume_limit_dual = -cdata.map(duals).sum()
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if extra_postprocessing is None:
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if hasattr(n, 'line_cost_limit') and hasattr(n.model, 'line_cost_constraint'):
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cdata = pd.Series(list(n.model.line_cost_constraint.values()),
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index=list(n.model.line_cost_constraint.keys()))
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n.line_cost_limit_dual = -cdata.map(duals).sum()
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def get_line_limit_duals(n, snapshots, duals):
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if hasattr(n, 'line_volume_limit') and hasattr(n.model, 'line_volume_constraint'):
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cdata = pd.Series(list(n.model.line_volume_constraint.values()),
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index=list(n.model.line_volume_constraint.keys()))
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n.line_volume_limit_dual = -cdata.map(duals).sum()
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if hasattr(n, 'line_cost_limit') and hasattr(n.model, 'line_cost_constraint'):
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cdata = pd.Series(list(n.model.line_cost_constraint.values()),
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index=list(n.model.line_cost_constraint.keys()))
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n.line_cost_limit_dual = -cdata.map(duals).sum()
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extra_postprocessing = get_line_limit_duals
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def run_lopf(n, allow_warning_status=False, fix_ext_lines=False):
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free_output_series_dataframes(n)
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@ -150,6 +157,9 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
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add_lv_constraint(n)
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add_lc_constraint(n)
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if extra_functionality is not None:
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extra_functionality(n, *extra_functionality_args)
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pypsa.opf.network_lopf_prepare_solver(n, solver_name=solver_name)
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if fix_ext_lines:
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@ -176,70 +186,73 @@ def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
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return status, termination_condition
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iteration = 0
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lines_ext_b = n.lines.s_nom_extendable
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if lines_ext_b.any():
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# puh: ok, we need to iterate, since there is a relation
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# between s/p_nom and r, x for branches.
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msq_threshold = 0.01
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lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']])
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if not skip_iterating:
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iteration = 0
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lines_ext_b = n.lines.s_nom_extendable
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if lines_ext_b.any():
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# puh: ok, we need to iterate, since there is a relation
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# between s/p_nom and r, x for branches.
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msq_threshold = 0.01
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lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']])
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lines['s_nom'] = (
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np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
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n.lines.bus0.map(n.buses.v_nom)
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).where(n.lines.type != '', n.lines['s_nom'])
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lines['s_nom'] = (
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np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
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n.lines.bus0.map(n.buses.v_nom)
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).where(n.lines.type != '', n.lines['s_nom'])
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lines_ext_typed_b = (n.lines.type != '') & lines_ext_b
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lines_ext_untyped_b = (n.lines.type == '') & lines_ext_b
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lines_ext_typed_b = (n.lines.type != '') & lines_ext_b
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lines_ext_untyped_b = (n.lines.type == '') & lines_ext_b
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def update_line_parameters(n, zero_lines_below=10):
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if zero_lines_below > 0:
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n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 0.
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n.links.loc[n.links.p_nom_opt < zero_lines_below, 'p_nom_opt'] = 0.
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def update_line_parameters(n, zero_lines_below=10):
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if zero_lines_below > 0:
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n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 0.
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n.links.loc[n.links.p_nom_opt < zero_lines_below, 'p_nom_opt'] = 0.
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if lines_ext_untyped_b.any():
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for attr in ('r', 'x'):
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n.lines.loc[lines_ext_untyped_b, attr] = (
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lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt'])
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if lines_ext_untyped_b.any():
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for attr in ('r', 'x'):
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n.lines.loc[lines_ext_untyped_b, attr] = (
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lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt'])
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)
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if lines_ext_typed_b.any():
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n.lines.loc[lines_ext_typed_b, 'num_parallel'] = (
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n.lines['s_nom_opt']/lines['s_nom']
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)
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logger.debug("lines.num_parallel={}".format(n.lines.loc[lines_ext_typed_b, 'num_parallel']))
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if lines_ext_typed_b.any():
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n.lines.loc[lines_ext_typed_b, 'num_parallel'] = (
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n.lines['s_nom_opt']/lines['s_nom']
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)
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logger.debug("lines.num_parallel={}".format(n.lines.loc[lines_ext_typed_b, 'num_parallel']))
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iteration += 1
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lines['s_nom_opt'] = lines['s_nom'] * n.lines['num_parallel'].where(n.lines.type != '', 1.)
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status, termination_condition = run_lopf(n, allow_warning_status=True)
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if callback is not None: callback(n, iteration, status)
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def msq_diff(n):
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lines_err = np.sqrt(((n.lines['s_nom_opt'] - lines['s_nom_opt'])**2).mean())/lines['s_nom_opt'].mean()
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logger.info("Mean square difference after iteration {} is {}".format(iteration, lines_err))
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return lines_err
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min_iterations = solve_opts.get('min_iterations', 2)
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max_iterations = solve_opts.get('max_iterations', 999)
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while msq_diff(n) > msq_threshold or iteration < min_iterations:
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if iteration >= max_iterations:
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logger.info("Iteration {} beyond max_iterations {}. Stopping ...".format(iteration, max_iterations))
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break
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update_line_parameters(n)
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lines['s_nom_opt'] = n.lines['s_nom_opt']
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iteration += 1
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lines['s_nom_opt'] = lines['s_nom'] * n.lines['num_parallel'].where(n.lines.type != '', 1.)
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status, termination_condition = run_lopf(n, allow_warning_status=True)
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if callback is not None: callback(n, iteration, status)
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def msq_diff(n):
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lines_err = np.sqrt(((n.lines['s_nom_opt'] - lines['s_nom_opt'])**2).mean())/lines['s_nom_opt'].mean()
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logger.info("Mean square difference after iteration {} is {}".format(iteration, lines_err))
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return lines_err
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update_line_parameters(n, zero_lines_below=100)
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min_iterations = solve_opts.get('min_iterations', 2)
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max_iterations = solve_opts.get('max_iterations', 999)
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while msq_diff(n) > msq_threshold or iteration < min_iterations:
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if iteration >= max_iterations:
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logger.info("Iteration {} beyond max_iterations {}. Stopping ...".format(iteration, max_iterations))
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break
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logger.info("Starting last run with fixed extendable lines")
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update_line_parameters(n)
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lines['s_nom_opt'] = n.lines['s_nom_opt']
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iteration += 1
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iteration += 1
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status, termination_condition = run_lopf(n, fix_ext_lines=True)
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status, termination_condition = run_lopf(n, allow_warning_status=True)
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if callback is not None: callback(n, iteration, status)
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update_line_parameters(n, zero_lines_below=100)
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logger.info("Starting last run with fixed extendable lines")
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iteration += 1
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status, termination_condition = run_lopf(n, fix_ext_lines=True)
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else:
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status, termination_condition = run_lopf(n, fix_ext_lines=False)
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if callback is not None: callback(n, iteration, status)
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return n
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