def _powerflow(net, **kwargs): """ Gets called by runpp or rundcpp with different arguments. """ # get infos from options init_results = net["_options"]["init_results"] ac = net["_options"]["ac"] recycle = net["_options"]["recycle"] mode = net["_options"]["mode"] algorithm = net["_options"]["algorithm"] max_iteration = net["_options"]["max_iteration"] net["converged"] = False net["OPF_converged"] = False _add_auxiliary_elements(net) if not ac or init_results: verify_results(net) else: reset_results(net) # TODO remove this when zip loads are integrated for all PF algorithms if algorithm not in ['nr', 'bfsw']: net["_options"]["voltage_depend_loads"] = False if recycle["ppc"] and "_ppc" in net and net[ "_ppc"] is not None and "_pd2ppc_lookups" in net: # update the ppc from last cycle ppc, ppci = _update_ppc(net) else: # convert pandapower net to ppc ppc, ppci = _pd2ppc(net) # store variables net["_ppc"] = ppc if not "VERBOSE" in kwargs: kwargs["VERBOSE"] = 0 # ----- run the powerflow ----- result = _run_pf_algorithm(ppci, net["_options"], **kwargs) # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly result = _copy_results_ppci_to_ppc(result, ppc, mode) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: _clean_up(net, res=False) raise LoadflowNotConverged("Power Flow {0} did not converge after " "{1} iterations!".format( algorithm, max_iteration)) else: net["_ppc"] = result net["converged"] = True _extract_results(net, result) _clean_up(net)
def _runpppf_dd(net, init, ac, calculate_voltage_angles, tolerance_kva, trafo_model, trafo_loading, enforce_q_lims, numba, recycle, **kwargs): """ Gets called by runpp or rundcpp with different arguments. """ net["converged"] = False if (ac and not init == "results") or not ac: reset_results(net) # select elements in service (time consuming, so we do it once) is_elems = _select_is_elements(net, recycle) if recycle["ppc"] and "_ppc" in net and net[ "_ppc"] is not None and "_bus_lookup" in net: # update the ppc from last cycle ppc, ppci, bus_lookup = _update_ppc(net, is_elems, recycle, calculate_voltage_angles, enforce_q_lims, trafo_model) else: # convert pandapower net to ppc ppc, ppci, bus_lookup = _pd2ppc(net, is_elems, calculate_voltage_angles, enforce_q_lims, trafo_model, init_results=(init == "results")) # store variables net["_ppc"] = ppc net["_bus_lookup"] = bus_lookup net["_is_elems"] = is_elems if not "VERBOSE" in kwargs: kwargs["VERBOSE"] = 0 # run the powerflow result = _run_fbsw(ppci, ppopt=ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3, **kwargs))[0] # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly result = _copy_results_ppci_to_ppc(result, ppc, bus_lookup) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: raise LoadflowNotConverged("Loadflow did not converge!") else: net["_ppc"] = result net["converged"] = True _extract_results(net, result, is_elems, bus_lookup, trafo_loading, ac) _clean_up(net)
def _powerflow(net, **kwargs): """ Gets called by runpp or rundcpp with different arguments. """ # get infos from options init = net["_options"]["init"] ac = net["_options"]["ac"] recycle = net["_options"]["recycle"] mode = net["_options"]["mode"] net["converged"] = False _add_auxiliary_elements(net) if (ac and not init == "results") or not ac: reset_results(net) if recycle["ppc"] and "_ppc" in net and net[ "_ppc"] is not None and "_pd2ppc_lookups" in net: # update the ppc from last cycle ppc, ppci = _update_ppc(net) else: # convert pandapower net to ppc ppc, ppci = _pd2ppc(net) # store variables net["_ppc"] = ppc if not "VERBOSE" in kwargs: kwargs["VERBOSE"] = 0 # ----- run the powerflow ----- result = _run_pf_algorithm(ppci, net["_options"], **kwargs) # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly result = _copy_results_ppci_to_ppc(result, ppc, mode) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: raise LoadflowNotConverged("Power Flow did not converge!") else: net["_ppc"] = result net["converged"] = True _extract_results(net, result) _clean_up(net)
def read_pm_results_to_net(net, ppc, ppci, result_pm): """ reads power models results from result_pm to ppc / ppci and then to pandapower net """ # read power models results from result_pm to result (== ppc with results) result, multinetwork = pm_results_to_ppc_results(net, ppc, ppci, result_pm) net._pm_result = result_pm success = ppc["success"] if success: if not multinetwork: # results are extracted from a single time step to pandapower dataframes _extract_results(net, result) _clean_up(net) net["OPF_converged"] = True else: _clean_up(net, res=False) logger.warning("OPF did not converge!")
def _runpppf(net, init, ac, calculate_voltage_angles, tolerance_kva, trafo_model, trafo_loading, enforce_q_lims, suppress_warnings, Numba, **kwargs): """ Gets called by runpp or rundcpp with different arguments. """ net["converged"] = False if (ac and not init == "results") or not ac: reset_results(net) # select elements in service (time consuming, so we do it once) is_elems = _select_is_elements(net) # convert pandapower net to ppc ppc, ppci, bus_lookup = _pd2ppc(net, is_elems, calculate_voltage_angles, enforce_q_lims, trafo_model, init_results=(init == "results")) net["_ppc"] = ppc if not "VERBOSE" in kwargs: kwargs["VERBOSE"] = 0 # run the powerflow with or without warnings. If init='dc', AC PF will be # initialized with DC voltages if suppress_warnings: with warnings.catch_warnings(): warnings.simplefilter("ignore") result = _runpf(ppci, init, ac, Numba, ppopt=ppopt.ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3, **kwargs))[0] else: result = _runpf(ppci, init, ac, Numba, ppopt=ppopt.ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3, **kwargs))[0] # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly result = _copy_results_ppci_to_ppc(result, ppc) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: raise LoadflowNotConverged("Loadflow did not converge!") else: net["_ppc"] = result net["converged"] = True _extract_results(net, result, is_elems, bus_lookup, trafo_loading, ac) _clean_up(net)
def _ppci_to_net(result, net): # reads the results from result (== ppci with results) to pandapower net mode = net["_options"]["mode"] # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly ppc = net["_ppc"] result = _copy_results_ppci_to_ppc(result, ppc, mode) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: _clean_up(net, res=False) algorithm = net["_options"]["algorithm"] max_iteration = net["_options"]["max_iteration"] raise LoadflowNotConverged("Power Flow {0} did not converge after " "{1} iterations!".format(algorithm, max_iteration)) else: net["_ppc"] = result net["converged"] = True _extract_results(net, result) _clean_up(net)
def read_pm_results_to_net(net, ppc, ppci, result_pm): """ reads power models results from result_pm to ppc / ppci and then to pandapower net """ # read power models results from result_pm to result (== ppc with results) net._pm_result_orig = result_pm result_pm = _deep_copy_pm_results(result_pm) result_pm = _convert_pm_units_to_pp_units(result_pm, net.sn_mva) net._pm_result = result_pm result, multinetwork = pm_results_to_ppc_results(net, ppc, ppci, result_pm) success = ppc["success"] if success: if not multinetwork: # results are extracted from a single time step to pandapower dataframes _extract_results(net, result) _clean_up(net) net["OPF_converged"] = True else: _clean_up(net, res=False) logger.warning("OPF did not converge!") raise OPFNotConverged("PowerModels.jl OPF not converged")
def _runpm(net): #pragma: no cover net["OPF_converged"] = False net["converged"] = False _add_auxiliary_elements(net) reset_results(net) ppc, ppci = _pd2ppc(net) net["_ppc_opf"] = ppci pm = ppc_to_pm(net, ppci) net._pm = pm if net._options["pp_to_pm_callback"] is not None: net._options["pp_to_pm_callback"](net, ppci, pm) result_pm = _call_powermodels(pm, net._options["julia_file"]) net._pm_res = result_pm result = pm_results_to_ppc_results(net, ppc, ppci, result_pm) net._pm_result = result_pm success = ppc["success"] if success: _extract_results(net, result) _clean_up(net) net["OPF_converged"] = True else: _clean_up(net, res=False) logger.warning("OPF did not converge!")
def _optimal_powerflow(net, verbose, suppress_warnings, **kwargs): ac = net["_options"]["ac"] init = net["_options"]["init"] ppopt = ppoption(VERBOSE=verbose, OPF_FLOW_LIM=2, PF_DC=not ac, INIT=init, **kwargs) net["OPF_converged"] = False net["converged"] = False _add_auxiliary_elements(net) reset_results(net, all_empty=False) ppc, ppci = _pd2ppc(net) if not ac: ppci["bus"][:, VM] = 1.0 net["_ppc_opf"] = ppci if len(net.dcline) > 0: ppci = add_userfcn(ppci, 'formulation', _add_dcline_constraints, args=net) if init == "pf": ppci = _run_pf_before_opf(net, ppci) if suppress_warnings: with warnings.catch_warnings(): warnings.simplefilter("ignore") result = opf(ppci, ppopt) else: result = opf(ppci, ppopt) # net["_ppc_opf"] = result if verbose: ppopt['OUT_ALL'] = 1 printpf(baseMVA=result["baseMVA"], bus=result["bus"], gen=result["gen"], fd=stdout, branch=result["branch"], success=result["success"], et=result["et"], ppopt=ppopt) if verbose: ppopt['OUT_ALL'] = 1 printpf(baseMVA=result["baseMVA"], bus=result["bus"], gen=result["gen"], fd=stdout, branch=result["branch"], success=result["success"], et=result["et"], ppopt=ppopt) if not result["success"]: raise OPFNotConverged("Optimal Power Flow did not converge!") # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly mode = net["_options"]["mode"] result = _copy_results_ppci_to_ppc(result, ppc, mode=mode) # net["_ppc_opf"] = result net["OPF_converged"] = True _extract_results(net, result) _clean_up(net)
def _optimal_powerflow(net, verbose, suppress_warnings, **kwargs): ac = net["_options"]["ac"] init = net["_options"]["init"] if "OPF_FLOW_LIM" not in kwargs: kwargs["OPF_FLOW_LIM"] = 2 if net["_options"]["voltage_depend_loads"] and not ( allclose(net.load.const_z_percent.values, 0) and allclose(net.load.const_i_percent.values, 0)): logger.error( "pandapower optimal_powerflow does not support voltage depend loads." ) ppopt = ppoption(VERBOSE=verbose, PF_DC=not ac, INIT=init, **kwargs) net["OPF_converged"] = False net["converged"] = False _add_auxiliary_elements(net) if not ac or net["_options"]["init_results"]: verify_results(net) else: init_results(net, "opf") ppc, ppci = _pd2ppc(net) if not ac: ppci["bus"][:, VM] = 1.0 net["_ppc_opf"] = ppci if len(net.dcline) > 0: ppci = add_userfcn(ppci, 'formulation', _add_dcline_constraints, args=net) if init == "pf": ppci = _run_pf_before_opf(net, ppci) if suppress_warnings: with warnings.catch_warnings(): warnings.simplefilter("ignore") result = opf(ppci, ppopt) else: result = opf(ppci, ppopt) # net["_ppc_opf"] = result if verbose: ppopt['OUT_ALL'] = 1 printpf(baseMVA=result["baseMVA"], bus=result["bus"], gen=result["gen"], branch=result["branch"], f=result["f"], success=result["success"], et=result["et"], fd=stdout, ppopt=ppopt) if not result["success"]: raise OPFNotConverged("Optimal Power Flow did not converge!") # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly mode = net["_options"]["mode"] result = _copy_results_ppci_to_ppc(result, ppc, mode=mode) # net["_ppc_opf"] = result net["OPF_converged"] = True _extract_results(net, result) _clean_up(net)
def estimate(self, v_start=None, delta_start=None, calculate_voltage_angles=True): """ The function estimate is the main function of the module. It takes up to three input arguments: v_start, delta_start and calculate_voltage_angles. The first two are the initial state variables for the estimation process. Usually they can be initialized in a "flat-start" condition: All voltages being 1.0 pu and all voltage angles being 0 degrees. In this case, the parameters can be left at their default values (None). If the estimation is applied continuously, using the results from the last estimation as the starting condition for the current estimation can decrease the amount of iterations needed to estimate the current state. The third parameter defines whether all voltage angles are calculated absolutely, including phase shifts from transformers. If only the relative differences between buses are required, this parameter can be set to False. Returned is a boolean value, which is true after a successful estimation and false otherwise. The resulting complex voltage will be written into the pandapower network. The result fields are found res_bus_est of the pandapower network. INPUT: **net** - The net within this line should be created **v_start** (np.array, shape=(1,), optional) - Vector with initial values for all voltage magnitudes in p.u. (sorted by bus index) **delta_start** (np.array, shape=(1,), optional) - Vector with initial values for all voltage angles in degrees (sorted by bus index) OPTIONAL: **calculate_voltage_angles** - (bool) - Take into account absolute voltage angles and phase shifts in transformers Default is True. OUTPUT: **successful** (boolean) - True if the estimation process was successful Optional estimation variables: The bus power injections can be accessed with *se.s_node_powers* and the estimated values corresponding to the (noisy) measurement values with *se.hx*. (*hx* denotes h(x)) EXAMPLE: success = estimate(np.array([1.0, 1.0, 1.0]), np.array([0.0, 0.0, 0.0])) """ if self.net is None: raise UserWarning("Component was not initialized with a network.") # add initial values for V and delta # node voltages # V<delta if v_start is None: v_start = np.ones(self.net.bus.shape[0]) if delta_start is None: delta_start = np.zeros(self.net.bus.shape[0]) # initialize result tables if not existant _copy_power_flow_results(self.net) # initialize ppc ppc, ppci = _init_ppc(self.net, v_start, delta_start, calculate_voltage_angles) mapping_table = self.net["_pd2ppc_lookups"]["bus"] # add measurements to ppci structure ppci = _add_measurements_to_ppc(self.net, mapping_table, ppci, self.s_ref) # calculate relevant vectors from ppci measurements z, self.pp_meas_indices, r_cov = _build_measurement_vectors(ppci) # number of nodes n_active = len(np.where(ppci["bus"][:, 1] != 4)[0]) slack_buses = np.where(ppci["bus"][:, 1] == 3)[0] # Check if observability criterion is fulfilled and the state estimation is possible if len(z) < 2 * n_active - 1: self.logger.error("System is not observable (cancelling)") self.logger.error( "Measurements available: %d. Measurements required: %d" % (len(z), 2 * n_active - 1)) return False # set the starting values for all active buses v_m = ppci["bus"][:, 7] delta = ppci["bus"][:, 8] * np.pi / 180 # convert to rad delta_masked = np.ma.array(delta, mask=False) delta_masked.mask[slack_buses] = True non_slack_buses = np.arange(len(delta))[~delta_masked.mask] # matrix calculation object sem = wls_matrix_ops(ppci, slack_buses, non_slack_buses, self.s_ref, bus_cols, branch_cols) # state vector E = np.concatenate((delta_masked.compressed(), v_m)) # invert covariance matrix r_inv = csr_matrix(np.linalg.inv(np.diagflat(r_cov)**2)) current_error = 100. current_iterations = 0 while current_error > self.tolerance and current_iterations < self.max_iterations: self.logger.debug(" Starting iteration %d" % (1 + current_iterations)) try: # create h(x) for the current iteration h_x = sem.create_hx(v_m, delta) # residual r r = csr_matrix(z - h_x).T # jacobian matrix H H = csr_matrix(sem.create_jacobian(v_m, delta)) # gain matrix G_m # G_m = H^t * R^-1 * H G_m = H.T * (r_inv * H) # state vector difference d_E # d_E = G_m^-1 * (H' * R^-1 * r) d_E = spsolve(G_m, H.T * (r_inv * r)) E += d_E # update V/delta delta[non_slack_buses] = E[:len(non_slack_buses)] v_m = np.squeeze(E[len(non_slack_buses):]) # prepare next iteration current_iterations += 1 current_error = np.max(np.abs(d_E)) self.logger.debug("Current error: %.4f" % current_error) except np.linalg.linalg.LinAlgError: self.logger.error( "A problem appeared while using the linear algebra methods." "Check and change the measurement set.") return False # print output for results if current_error <= self.tolerance: successful = True self.logger.info( "WLS State Estimation successful (%d iterations)" % current_iterations) else: successful = False self.logger.info( "WLS State Estimation not successful (%d/%d iterations)" % (current_iterations, self.max_iterations)) # store results for all elements # write voltage into ppc ppci["bus"][:, 7] = v_m ppci["bus"][:, 8] = delta * 180 / np.pi # convert to degree # calculate bus power injections v_cpx = v_m * np.exp(1j * delta) bus_powers_conj = np.zeros(len(v_cpx), dtype=np.complex128) for i in range(len(v_cpx)): bus_powers_conj[i] = np.dot(sem.Y_bus[i, :], v_cpx) * np.conjugate( v_cpx[i]) ppci["bus"][:, 2] = bus_powers_conj.real # saved in per unit ppci["bus"][:, 3] = -bus_powers_conj.imag # saved in per unit # calculate line results (in ppc_i) s_ref, bus, gen, branch = _get_pf_variables_from_ppci(ppci)[0:4] out = np.flatnonzero(branch[:, BR_STATUS] == 0) # out-of-service branches br = np.flatnonzero(branch[:, BR_STATUS]).astype( int) # in-service branches # complex power at "from" bus Sf = v_cpx[np.real(branch[br, F_BUS]).astype(int)] * np.conj( sem.Yf[br, :] * v_cpx) * s_ref # complex power injected at "to" bus St = v_cpx[np.real(branch[br, T_BUS]).astype(int)] * np.conj( sem.Yt[br, :] * v_cpx) * s_ref branch[np.ix_(br, [PF, QF, PT, QT])] = np.c_[Sf.real, Sf.imag, St.real, St.imag] branch[np.ix_(out, [PF, QF, PT, QT])] = np.zeros((len(out), 4)) ppci = _store_results_from_pf_in_ppci(ppci, bus, gen, branch) # convert to pandapower indices ppc = _copy_results_ppci_to_ppc(ppci, ppc, mode="se") # extract results from ppc _add_pf_options(self.net, tolerance_kva=1e-5, trafo_loading="current", numba=True, ac=True, algorithm='nr', max_iteration="auto") _extract_results(self.net, ppc) # restore backup of previous results _rename_results(self.net) # additionally, write bus results (these are not written in _extract_results) self.net.res_bus_est.p_kw = -get_values( ppc["bus"][:, 2], self.net.bus.index, mapping_table) * self.s_ref / 1e3 self.net.res_bus_est.q_kvar = -get_values( ppc["bus"][:, 3], self.net.bus.index, mapping_table) * self.s_ref / 1e3 # store variables required for chi^2 and r_N_max test: self.R_inv = r_inv.toarray() self.Gm = G_m.toarray() self.r = r.toarray() self.H = H.toarray() self.Ht = self.H.T self.hx = h_x self.V = v_m self.delta = delta return successful
def ts_newtonpf(self, net): options = net["_options"] bus = self.ppci["bus"] branch = self.ppci["branch"] gen = self.ppci["gen"] # compute complex bus power injections [generation - load] # self.Cg = _get_Cg(gen_on, bus) # Sbus = _get_Sbus(self.baseMVA, bus, gen, self.Cg) Sbus = makeSbus(self.baseMVA, bus, gen) # run the newton power flow V, success, _, _, _, _ = nr_pf.newtonpf(self.Ybus, Sbus, self.V, self.pv, self.pq, self.ppci, options) if not success: logger.warning("Loadflow not converged") logger.info("Lines of of service:") logger.info(net.line[~net.line.in_service]) raise LoadflowNotConverged("Power Flow did not converge after") if self.ppci["gen"].shape[ 0] == 1 and not options["voltage_depend_loads"]: pfsoln = pf_solution_single_slack else: pfsoln = pfsoln_full bus, gen, branch = pfsoln(self.baseMVA, bus, gen, branch, self.Ybus, self.Yf, self.Yt, V, self.ref, self.ref_gens, Ibus=self.Ibus) self.ppci["bus"] = bus self.ppci["branch"] = branch self.ppci["gen"] = gen self.ppci["success"] = success self.ppci["et"] = None # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly self.ppc = _copy_results_ppci_to_ppc(self.ppci, self.ppc, options["mode"]) # raise if PF was not successful. If DC -> success is always 1 if self.ppc["success"] != 1: _clean_up(net, res=False) else: net["_ppc"] = self.ppc net["converged"] = True self.V = V _extract_results(net, self.ppc) return net
def _runpppf(net, **kwargs): """ Gets called by runpp or rundcpp with different arguments. """ # get infos from options init = net["_options"]["init"] ac = net["_options"]["ac"] recycle = net["_options"]["recycle"] numba = net["_options"]["numba"] enforce_q_lims = net["_options"]["enforce_q_lims"] tolerance_kva = net["_options"]["tolerance_kva"] mode = net["_options"]["mode"] algorithm = net["_options"]["algorithm"] max_iteration = net["_options"]["max_iteration"] net["converged"] = False _add_auxiliary_elements(net) if (ac and not init == "results") or not ac: reset_results(net) # select elements in service (time consuming, so we do it once) net["_is_elems"] = _select_is_elements(net, recycle) if recycle["ppc"] and "_ppc" in net and net[ "_ppc"] is not None and "_pd2ppc_lookups" in net: # update the ppc from last cycle ppc, ppci = _update_ppc(net, recycle) else: # convert pandapower net to ppc ppc, ppci = _pd2ppc(net) # store variables net["_ppc"] = ppc if not "VERBOSE" in kwargs: kwargs["VERBOSE"] = 0 # run the powerflow # algorithms implemented within pypower algorithm_pypower_dict = {'nr': 1, 'fdBX': 2, 'fdXB': 3, 'gs': 4} if algorithm == 'fbsw': # foreward/backward sweep power flow algorithm result = _run_fbsw_ppc(ppci, ppopt=ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3, PF_MAX_IT_GS=max_iteration, **kwargs))[0] elif algorithm in algorithm_pypower_dict: ppopt = ppoption(**kwargs) ppopt['PF_ALG'] = algorithm_pypower_dict[algorithm] ppopt['ENFORCE_Q_LIMS'] = enforce_q_lims ppopt['PF_TOL'] = tolerance_kva if max_iteration is not None: if algorithm == 'nr': ppopt['PF_MAX_IT'] = max_iteration elif algorithm == 'gs': ppopt['PF_MAX_IT_GS'] = max_iteration else: ppopt['PF_MAX_IT_FD'] = max_iteration result = _runpf(ppci, init, ac, numba, recycle, ppopt=ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3, **kwargs))[0] else: AlgorithmUnknown("Algorithm {0} is unknown!".format(algorithm)) # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly result = _copy_results_ppci_to_ppc(result, ppc, mode) # raise if PF was not successful. If DC -> success is always 1 if result["success"] != 1: raise LoadflowNotConverged("Loadflow did not converge!") else: net["_ppc"] = result net["converged"] = True _extract_results(net, result) _clean_up(net)