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tdpf.py
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tdpf.py
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# fastSE
# Copyright (C) 2022 Zeyu Mao
import os
import sys
import numpy as np
from numpy import complex128
from numba import njit, config
from collections import namedtuple
from scipy.sparse import csr_matrix
from scipy.optimize import newton
from fastse._aot import *
from plum import dispatch
np.set_printoptions(threshold=sys.maxsize)
result = namedtuple('results', 'temperature diff iterations converged')
tdpf_result = namedtuple(
'results', 'temperature diff resistance V Sf loss iterations')
def run_tdpf(tas, tc0, rads, winds, V, npv, npq, r0, x, c, tap, shift, f, t, i, nl,
nb, Sbus, pv, pq, pvpq, base, line_indexes, tran_indexes, rates, tol=1e-3,
maxiter=50):
"""
add transformer temperature balance equation
tc0, rads, winds: only lines
tas, r0, x, c: all branches (lines and transformers)
"""
Cf = csr_matrix((np.ones(nl), (range(nl), f)), (nl, nb))
Ct = csr_matrix((np.ones(nl), (range(nl), t)), (nl, nb))
Ysh = csr_matrix((np.zeros(nb, dtype=complex128),
(range(nb), range(nb))), (nb, nb))
# Run conventional power flow first
r = r0.copy()
tc = tc0.copy()
tr0 = np.full(tran_indexes.sum(), 0)
V, tc, tr, r, Yf, Yt, it = tdpf_loop(V, npv, npq, Sbus, pv, pq, pvpq, base, Cf, line_indexes,
tc, r0, tas, rads, winds, rates, tran_indexes, tr0, r, x, c, tap, shift, f, t, i, nl, nb, Ct, Ysh, tol, maxiter)
# Compute system losses and remaining variables
Vf = Cf * V
Vt = Ct * V
If = Yf * V
It = Yt * V
Sf = Vf * np.conj(If)
St = Vt * np.conj(It)
# Branch losses in MVA
losses = (Sf + St) * base
t = tas.copy()
t[line_indexes] = tc
t[tran_indexes] = tas[tran_indexes] + tr
return tdpf_result(t, t-tas, r, V, Sf, losses, it)
if __name__ == "__main__":
# from scipy.sparse import csr_matrix
#
# # A 5 bus example from Prof. Overbye's textbook
# r0 = np.array([0.00150, 0.00900, 0.00450, 0.00075, 0.00225])
# x = np.array([0.02000, 0.10000, 0.05000, 0.01000, 0.02500])
# c = np.array([0.00000, 1.72000, 0.88000, 0.00000, 0.44000])
# tap = np.ones(5, dtype=float)
# shift = np.zeros(5, dtype=float)
# f = np.array([0, 3, 4, 2, 4])
# t = np.array([4, 1, 1, 3, 3])
# nb = 5 # number of buses
# nl = 5 # number of branches
# i = np.r_[range(nl), range(nl)]
# Sbus = np.array(
# [3.948 + 0.000j, -8.000 - 2.800j, 4.400 + 5.221j, 0.000 + 0.000j,
# 0.000 + 0.000j])
# V = np.ones(5, dtype=complex128) # Voltage initial guess
# pv = np.array([2])
# pq = np.array([1, 3, 4])
# pvpq = np.array([2, 1, 3, 4])
# base = np.array([100.000])
# npv = len(pv)
# npq = len(pq)
# line_indexes = np.array([False, True, True, False, True])
# # rs = np.full(3, 2.25e-4)
# # tas = np.full(3, 25) # assume all ambient temperature are 25 degree
# tas = np.array([10, 20, 25])
# tc0 = np.full(3, 25) # conductor temperature initial guess
# result = run_tdpf(tas, tc0, V, npv, npq, r0, x, c, tap, shift, f, t, i, nl,
# nb, Sbus, pv, pq, pvpq, base, line_indexes)
# print(result)
from esa import SAW
import time
np.set_printoptions(precision=4, floatmode='fixed')
sa = SAW(r"C:\Users\test\PWCase\ACTIVSg2000.pwb", CreateIfNotFound=True)
sa.pw_order = True
sa.RunScriptCommand('ResetToFlatStart(YES,NO,NO,NO);')
base = sa.GetParametersMultipleElement('Sim_Solution_Options', ['SBase']).to_numpy(
float).ravel()
key = sa.get_key_field_list('bus')
df = sa.GetParametersMultipleElement('bus', key + ['BusCat', 'BusPUVolt', 'BusRad', 'BusNetMW',
'BusNetMVR', 'BusGenMW', 'BusGenMVR',
'BusLoadMW', 'BusLoadMVR', 'GenMVRMax',
'GenMVRMin', 'BusSS', 'BusSSMW'])
df['BusNetMW'] = df['BusNetMW'].astype(float)
df['BusNetMVR'] = df['BusNetMVR'].astype(float)
df['BusGenMW'] = df['BusGenMW'].astype(float)
df['BusGenMVR'] = df['BusGenMVR'].astype(float)
df['BusLoadMW'] = df['BusLoadMW'].astype(float)
df['BusLoadMVR'] = df['BusLoadMVR'].astype(float)
df['GenMVRMax'] = df['GenMVRMax'].astype(float)
df['GenMVRMin'] = df['GenMVRMin'].astype(float)
df['BusSS'] = df['BusSS'].astype(float)
df['BusSSMW'] = df['BusSSMW'].astype(float)
df['BusNum'] = df['BusNum'].astype(int)
df.fillna(0, inplace=True)
branch = sa.GetParametersMultipleElement('branch',
sa.get_key_field_list('branch') + ['LineR', 'LineX',
'LineC', 'LineTap',
'LinePhase',
'LineLimMVA',
'BranchDeviceType'])
branch['LineR'] = branch['LineR'].astype(float)
branch['LineX'] = branch['LineX'].astype(float)
branch['LineC'] = branch['LineC'].astype(float)
branch['LineTap'] = branch['LineTap'].astype(float)
branch['LinePhase'] = branch['LinePhase'].astype(float)
branch['LineLimMVA'] = branch['LineLimMVA'].astype(float)
nb = df.shape[0]
nl = branch.shape[0]
Ys = 1 / (branch['LineR'].to_numpy() + 1j *
branch['LineX'].to_numpy()) # series admittance
Bc = branch['LineC'].to_numpy() # line charging susceptance
tap = branch['LineTap'].to_numpy()
shift = branch['LinePhase'].to_numpy()
rates = branch['LineLimMVA'].to_numpy()
tap = tap * np.exp(1j * np.pi / 180 * shift)
Ytt = Ys + 1j * Bc / 2
Yff = Ytt / (tap * np.conj(tap))
Yft = - Ys / np.conj(tap)
Ytf = - Ys / tap
Ysh = (df['BusSSMW'].to_numpy() + 1j * df['BusSS'].to_numpy()) / base
# lookup table for formatting bus numbers
def loop_translate(a, d):
n = np.ndarray(a.shape, dtype=int)
for k in d:
n[a == k] = d[k]
return n
d = dict()
for index, value in df['BusNum'].items():
d[value] = index
f = branch['BusNum'].to_numpy(dtype=int).reshape(-1)
f = loop_translate(f, d)
t = branch['BusNum:1'].to_numpy(dtype=int).reshape(-1)
t = loop_translate(t, d)
# connection matrix for line & from buses
# print(nl, nb, f, t)
Cf = csr_matrix((np.ones(nl), (range(nl), f)), (nl, nb))
# connection matrix for line & to buses
Ct = csr_matrix((np.ones(nl), (range(nl), t)), (nl, nb))
i = np.r_[range(nl), range(nl)] # double set of row indices
Yf = csr_matrix((np.hstack([Yff.reshape(-1), Yft.reshape(-1)]), (i, np.hstack([f, t]))),
(nl, nb))
Yt = csr_matrix((np.hstack([Ytf.reshape(-1), Ytt.reshape(-1)]), (i, np.hstack([f, t]))),
(nl, nb))
Ybus = Cf.T * Yf + Ct.T * Yt + \
csr_matrix((Ysh, (range(nb), range(nb))), (nb, nb))
# print(Ybus.toarray())
# Ybus = sa.get_ybus()
pv = df.index[df['BusCat'].str.contains("PV")].to_numpy(int)
pq = df.index[df['BusCat'].str.contains("PQ")].to_numpy(
int) # include PQ with Var limit
pv.sort()
pq.sort()
slack = df.index[df['BusCat'] == 'Slack'].to_numpy(int)
# set up indexing for updating v
pvpq = np.r_[pv, pq]
npv = len(pv)
npq = len(pq)
# print(npv, npq)
npvpq = npv + npq
pvpq_lookup = np.zeros(np.max(Ybus.indices) + 1, dtype=int)
pvpq_lookup[pvpq] = np.arange(npvpq)
Sbus = (df['BusNetMW'].to_numpy(dtype=float) + df['BusNetMVR'].to_numpy(
dtype=float) * 1j) / base
V = df['BusPUVolt'].to_numpy(complex)
x = branch['LineX'].to_numpy()
r = branch['LineR'].to_numpy()
c = branch['LineC'].to_numpy()
tap = branch['LineTap'].to_numpy()
shift = branch['LinePhase'].to_numpy()
f = branch['BusNum'].to_numpy(dtype=int).reshape(-1)
f = loop_translate(f, d)
t = branch['BusNum:1'].to_numpy(dtype=int).reshape(-1)
t = loop_translate(t, d)
line_indexes = (branch['BranchDeviceType'] == 'Line').to_numpy()
tran_indexes = (branch['BranchDeviceType'] == 'Transformer').to_numpy()
num_lines = line_indexes.sum()
tc0 = np.full(num_lines, 25.0, dtype=float)
# tas = np.random.uniform(20, 40, num_lines)
tas = np.random.uniform(25, 35, nl)
radiations = np.random.uniform(800, 1200, num_lines)
winds = np.random.uniform(0, 15, num_lines)
start = time.time()
result = run_tdpf(tas, tc0, radiations, winds, V, npv, npq, r, x, c, tap, shift, f, t, i, nl,
nb, Sbus, pv, pq, pvpq, base, line_indexes, tran_indexes, rates, 1e-3, 50)
# print(result.temperature)
print(result.temperature[tran_indexes][:10])
print(result.temperature[line_indexes][:10])