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"""Processed practical 118-node distribution system from Zhejiang, China.
The dataset models two practical distribution feeders in a city in Zhejiang
Province, China. The original field measurements are not released directly;
load and impedance values in this file have been processed for security and
confidentiality.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any
import numpy as np
PROJECT_NAME = "Practical 118-Node Distribution System Dataset"
REPOSITORY = "https://github.com/your-account/practical-118-node-distribution-system"
BUS_COLUMNS = ['bus_i', 'type', 'Pd_kW', 'Qd_kVAr', 'Gs', 'Bs', 'area', 'Vm', 'Va_deg', 'baseKV', 'zone', 'Vmax', 'Vmin']
BRANCH_COLUMNS = ['fbus', 'tbus', 'r_ohm', 'x_ohm', 'b', 'rateA', 'rateB', 'rateC', 'ratio', 'angle_deg', 'status', 'angmin_deg', 'angmax_deg']
BUS = np.array([
[1, 3, 0, 0, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[2, 1, 230, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[3, 1, 200, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[4, 1, 370, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[5, 1, 110, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[6, 1, 170, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[7, 1, 150, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[8, 1, 310, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[9, 1, 130, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[10, 1, 350, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[11, 1, 50, 20, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[12, 1, 260, 130, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[13, 1, 350, 170, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[14, 1, 330, 220, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[15, 1, 120, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[16, 1, 50, 20, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[17, 1, 340, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[18, 1, 410, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[19, 1, 310, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[20, 1, 160, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[21, 1, 340, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[22, 1, 310, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[23, 1, 190, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[24, 1, 280, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[25, 1, 210, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[26, 1, 270, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[27, 1, 310, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[28, 1, 90, 30, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[29, 1, 400, 130, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[30, 1, 420, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[31, 1, 300, 130, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[32, 1, 350, 210, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[33, 1, 60, 20, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[34, 1, 110, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[35, 1, 380, 170, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[36, 1, 330, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[37, 1, 240, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[38, 1, 90, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[39, 1, 230, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[40, 1, 240, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[41, 1, 160, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[42, 1, 60, 10, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[43, 1, 210, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[44, 1, 80, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[45, 1, 60, 30, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[46, 1, 310, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[47, 1, 370, 190, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[48, 1, 90, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[49, 1, 90, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[50, 1, 220, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[51, 1, 120, 70, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[52, 1, 340, 170, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[53, 1, 280, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[54, 1, 210, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[55, 1, 420, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[56, 1, 310, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[57, 1, 130, 20, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[58, 1, 110, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[59, 1, 250, 170, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[60, 1, 160, 70, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[61, 1, 390, 260, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[62, 1, 80, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[63, 1, 160, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[64, 1, 130, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[65, 1, 320, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[66, 1, 360, 210, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[67, 1, 330, 150, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[68, 1, 110, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[69, 1, 370, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[70, 1, 50, 30, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[71, 1, 180, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[72, 1, 410, 270, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[73, 1, 400, 220, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[74, 1, 50, 30, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[75, 1, 300, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[76, 1, 110, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[77, 1, 260, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[78, 1, 140, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[79, 1, 100, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[80, 1, 240, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[81, 1, 260, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[82, 1, 220, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[83, 1, 240, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[84, 1, 170, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[85, 1, 380, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[86, 1, 140, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[87, 1, 370, 150, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[88, 1, 290, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[89, 1, 310, 100, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[90, 1, 60, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[91, 1, 290, 130, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[92, 1, 420, 210, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[93, 1, 240, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[94, 1, 400, 270, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[95, 1, 210, 140, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[96, 1, 120, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[97, 1, 350, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[98, 1, 350, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[99, 1, 70, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[100, 1, 350, 190, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[101, 1, 220, 70, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[102, 1, 370, 180, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[103, 1, 180, 80, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[104, 1, 120, 40, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[105, 1, 380, 170, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[106, 1, 270, 120, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[107, 1, 360, 160, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[108, 1, 60, 20, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[109, 1, 310, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[110, 1, 330, 110, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[111, 1, 200, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[112, 1, 120, 60, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[113, 1, 110, 50, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[114, 1, 380, 250, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[115, 1, 320, 150, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[116, 1, 380, 220, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[117, 1, 150, 90, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
[118, 1, 300, 130, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.9],
], dtype=float)
BRANCH = np.array([
[1, 2, 0.9296, 0.6679, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[2, 3, 1.279, 0.4229, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[3, 4, 0.3435, 0.2703, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[4, 5, 0.8266, 0.5939, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[5, 14, 1.8839, 1.4876, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[14, 15, 1.1214, 0.9887, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[15, 18, 1.1198, 0.5703, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[18, 19, 0.5479, 0.4287, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[19, 29, 1.2575, 1.2575, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[29, 39, 0.4578, 0.3989, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[39, 40, 1.922, 6.3531, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[40, 41, 0.6062, 0.3088, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[41, 43, 1.1971, 0.3958, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[43, 44, 1.568, 1.2337, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[44, 46, 1.4202, 1.226, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[5, 6, 1.5527, 0.7906, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[6, 7, 1.2151, 1.0949, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[7, 8, 0.3191, 0.1625, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[8, 9, 1.3694, 1.1821, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[9, 10, 1.9133, 1.8258, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[10, 11, 0.4119, 0.5446, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[10, 12, 1.6549, 1.4417, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[10, 13, 0.8923, 0.8923, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[15, 16, 1.0935, 1.46, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[16, 17, 0.7455, 0.6717, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[19, 20, 1.1845, 1.1707, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[19, 21, 0.9722, 0.9277, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[21, 22, 0.9328, 0.7314, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[21, 23, 0.9718, 0.9718, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[21, 24, 1.4811, 1.9496, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[24, 25, 1.5204, 1.5204, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[25, 26, 0.7954, 0.4051, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[26, 27, 0.2972, 0.2972, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[27, 28, 0.9323, 3.0818, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[29, 30, 0.5354, 0.3658, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[30, 36, 1.2241, 1.0664, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[36, 37, 1.8615, 1.8615, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[36, 38, 0.2791, 0.2038, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[30, 31, 0.2351, 0.2324, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[31, 35, 0.2519, 0.1982, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[31, 32, 0.8301, 0.8301, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[32, 33, 0.2293, 0.1625, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[33, 34, 0.2496, 0.1971, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[41, 42, 1.3298, 1.7754, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[44, 45, 1.2107, 1.2107, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[46, 47, 0.7999, 0.7999, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[46, 48, 1.3026, 1.3026, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[1, 49, 0.2715, 0.2129, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[49, 50, 1.2331, 0.8859, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[50, 51, 1.9574, 1.5349, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[51, 52, 1.7392, 0.8855, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[52, 53, 1.7796, 0.9064, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[53, 54, 0.5612, 1.855, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[54, 55, 1.9968, 1.0169, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[55, 56, 0.8714, 0.7592, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[52, 57, 0.3604, 0.3604, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[57, 58, 0.2532, 0.1291, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[58, 59, 1.0617, 1.0617, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[59, 60, 1.1647, 1.533, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[60, 61, 0.1567, 0.0799, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[61, 62, 1.2332, 0.89, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[62, 63, 0.2513, 0.128, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[62, 64, 0.4288, 0.3362, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[64, 65, 0.6368, 0.2106, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[57, 66, 1.224, 1.1681, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[66, 67, 1.5259, 1.0962, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[67, 68, 1.4384, 0.7327, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[68, 69, 1.3706, 0.6981, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[69, 70, 0.884, 1.0303, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[66, 71, 0.9949, 0.3289, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[71, 72, 1.2226, 1.2083, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[71, 73, 0.7605, 0.6625, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[73, 74, 1.7283, 2.6872, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[74, 75, 1.6271, 0.8284, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[75, 76, 0.6809, 0.9003, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[76, 77, 0.4654, 0.4654, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[77, 78, 1.2635, 1.2635, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[78, 79, 0.4101, 0.2088, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[77, 80, 1.9259, 1.8379, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[80, 81, 1.9828, 1.7647, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[80, 82, 0.1582, 0.124, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[82, 83, 0.2492, 0.3296, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[83, 84, 1.7101, 0.8709, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[84, 85, 0.1761, 0.1378, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[85, 86, 0.2582, 0.2582, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[84, 87, 1.1606, 0.8376, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[87, 88, 0.5156, 0.601, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[84, 89, 0.7762, 0.7762, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[89, 115, 0.7432, 0.5078, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[115, 116, 0.2086, 0.1817, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[116, 117, 0.688, 0.3503, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[117, 118, 1.314, 0.6693, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[89, 90, 1.1067, 0.5637, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[90, 91, 1.3155, 0.6701, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[91, 92, 0.8454, 1.1178, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[92, 93, 1.1147, 0.8741, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[93, 94, 0.4397, 0.224, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[91, 95, 1.1579, 0.5898, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[95, 96, 1.0978, 0.363, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[96, 97, 1.1967, 1.0425, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[97, 98, 0.9119, 0.4644, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[98, 99, 1.9484, 0.9932, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[99, 100, 0.1137, 0.1325, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[100, 101, 1.9813, 1.4043, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[101, 102, 1.0832, 0.7782, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[102, 103, 1.318, 0.4358, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[104, 105, 1.1343, 1.0824, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[105, 106, 1.0762, 1.0762, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[106, 107, 1.196, 0.6092, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[107, 108, 0.4049, 0.2064, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[108, 109, 0.5794, 0.6753, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[109, 110, 0.9534, 0.4856, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[110, 111, 0.9144, 0.9144, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[103, 104, 0.7822, 2.5855, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[104, 112, 1.229, 0.6265, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[112, 113, 0.7975, 0.7975, 0, 0, 0, 0, 0, 0, 1, -360, 360],
[113, 114, 0.4862, 0.4862, 0, 0, 0, 0, 0, 0, 1, -360, 360],
], dtype=float)
METADATA = {
"project_name": PROJECT_NAME,
"repository": REPOSITORY,
"source_description": "Practical distribution system modeled from two feeders in a city in Zhejiang Province, China.",
"confidentiality_note": "Specific load and impedance values were processed for security and confidentiality before release.",
"node_count": int(BUS.shape[0]),
"branch_count": int(BRANCH.shape[0]),
"total_Pd_kW": float(BUS[:, 2].sum()),
"total_Qd_kVAr": float(BUS[:, 3].sum()),
"root_bus": 1,
"feeder_head_buses": BRANCH[BRANCH[:, 0] == 1, 1].astype(int).tolist(),
}
@dataclass(frozen=True)
class Zhejiang118NodeSystem:
"""Container for the processed 118-node distribution-system data."""
metadata: dict[str, Any]
bus_columns: list[str]
branch_columns: list[str]
bus: np.ndarray
branch: np.ndarray
topology: np.ndarray
line_impedance_ohm: np.ndarray
load_kW_kVAr: np.ndarray
def load_case(copy: bool = True) -> Zhejiang118NodeSystem:
"""Return the processed 118-node system data.
Parameters
----------
copy:
If True, return copies of the arrays so callers can modify them without
changing module-level constants.
"""
bus = BUS.copy() if copy else BUS
branch = BRANCH.copy() if copy else BRANCH
return Zhejiang118NodeSystem(
metadata=dict(METADATA),
bus_columns=list(BUS_COLUMNS),
branch_columns=list(BRANCH_COLUMNS),
bus=bus,
branch=branch,
topology=branch[:, :2],
line_impedance_ohm=branch[:, 2:4],
load_kW_kVAr=bus[:, [0, 2, 3]],
)
if __name__ == "__main__":
case = load_case(copy=False)
print(f"{case.metadata['project_name']}")
print(f"Nodes: {case.metadata['node_count']}, branches: {case.metadata['branch_count']}")
print(f"Total load: {case.metadata['total_Pd_kW']:.2f} kW / {case.metadata['total_Qd_kVAr']:.2f} kVAr")