Source code for amplpower.core

import logging
from pathlib import Path

import numpy as np
import pandas as pd
from amplpy import AMPL
from matpowercaseframes import CaseFrames

from amplpower.utils import find_min_max


[docs] def compute(args): return max(args, key=len)
# TODO: Remove compute function at some point def array2dict(array): """Convert a 2D numpy array to a dictionary.""" return {(i, j): array[i, j] for i in range(array.shape[0]) for j in range(array.shape[1])}
[docs] class PowerSystem: """PowerSystem class for solving optimal power flow problems."""
[docs] def __init__(self, case_file: str, max_voltage_angle: float = np.pi / 2, min_voltage_angle: float = -np.pi / 2): """Initialize the power system with a MATPOWER case file.""" print(f"=======Initializing the power system with case file: {case_file}") self.case_file = case_file self.max_angle = max_voltage_angle self.min_angle = min_voltage_angle self.load_data() self.summary() self.compute_matrices() self.initialize() self.compute_voltage_bounds() self.compute_bigm_dc() self.compute_bigm_ac()
[docs] def load_data(self): """Load MATPOWER case data into DataFrames and convert to per unit.""" try: case = CaseFrames(self.case_file) # Load data for each component self.baseMVA = case.baseMVA self.buses = case.bus self.buses.reset_index(drop=True, inplace=True) # Create a mapping for bus numbers self.bus_mapping = {bus: idx for idx, bus in enumerate(self.buses["BUS_I"])} self.reverse_bus_mapping = {idx: bus for bus, idx in self.bus_mapping.items()} self.buses["BUS_I"] = self.buses["BUS_I"].map(self.bus_mapping) self.generators = case.gen self.generators.reset_index(drop=True, inplace=True) self.generators["GEN_BUS"] = self.generators["GEN_BUS"].map(self.bus_mapping) self.branches = case.branch self.branches.reset_index(drop=True, inplace=True) self.branches["F_BUS"] = self.branches["F_BUS"].map(self.bus_mapping) self.branches["T_BUS"] = self.branches["T_BUS"].map(self.bus_mapping) self.gencost = case.gencost self.gencost.reset_index(drop=True, inplace=True) self.nbus = len(self.buses) self.nlin = len(self.branches) self.ngen = len(self.generators) # Add default values for generator costs if not provided if "COST_2" not in self.gencost.columns: self.gencost["COST_2"] = 0 # Minimum and maximum limits for voltage angle and real/imaginary voltage variables self.buses["AMAX"] = self.max_angle self.buses["AMIN"] = self.min_angle self.buses["VRMAX"] = self.buses["VMAX"] self.buses["VRMIN"] = 0 self.buses["VIMAX"] = self.buses["VMAX"] self.buses["VIMIN"] = -self.buses["VMAX"] # Convert to per unit self.buses["PD"] /= self.baseMVA self.buses["QD"] /= self.baseMVA self.buses["GS"] /= self.baseMVA self.buses["BS"] /= self.baseMVA self.generators["PG"] /= self.baseMVA self.generators["QG"] /= self.baseMVA self.generators["PMAX"] /= self.baseMVA self.generators["PMIN"] /= self.baseMVA self.generators["QMAX"] /= self.baseMVA self.generators["QMIN"] /= self.baseMVA self.branches["RATE_A"] /= self.baseMVA self.branches["RATE_B"] /= self.baseMVA self.branches["RATE_C"] /= self.baseMVA # Set default branch limit if not provided self.default_branch_limit = np.sqrt(self.buses["PD"].sum() ** 2 + self.buses["QD"].sum() ** 2) for line_index in range(self.nlin): if self.branches.loc[line_index, "RATE_A"] == 0: self.branches.loc[line_index, "RATE_A"] = self.default_branch_limit except Exception as e: logging.error(f"Error loading data from {self.case_file}: {e}") raise
[docs] def compute_matrices(self): """Calculate the admittance matrices (yff, ytf, yft, ytt) for the network.""" # Initizalize matrices self.yff = np.zeros(self.nlin, dtype=complex) self.ytf = np.zeros(self.nlin, dtype=complex) self.yft = np.zeros(self.nlin, dtype=complex) self.ytt = np.zeros(self.nlin, dtype=complex) self.cf = np.zeros((self.nlin, self.nbus)) # Connection for F_BUS self.ct = np.zeros((self.nlin, self.nbus)) # Connection for T_BUS self.cg = np.zeros((self.ngen, self.nbus)) # Connection for generators # Compute admittance matrices for line_index in range(self.nlin): branch = self.branches.iloc[line_index] # Access branch data r = branch["BR_R"] x = branch["BR_X"] b = branch["BR_B"] tau = branch["TAP"] if branch["TAP"] != 0 else 1 # Handle TAP=0 case theta = branch["SHIFT"] # Calculate Y series and shunt admittance ys = 1 / (r + 1j * x) # Store the admittance components self.yff[line_index] = (ys + 1j * 0.5 * b) / (tau**2) self.yft[line_index] = -ys / (tau * np.exp(-1j * theta)) self.ytf[line_index] = -ys / (tau * np.exp(1j * theta)) self.ytt[line_index] = ys + 1j * 0.5 * b # Update bus connection matrices f_bus, t_bus = int(branch["F_BUS"]), int(branch["T_BUS"]) # Ensure indices are integers self.cf[line_index, f_bus] = 1 self.ct[line_index, t_bus] = 1 # Compute additional matrices self.yf = np.dot(np.diag(self.yff), self.cf) + np.dot(np.diag(self.yft), self.ct) self.yt = np.dot(np.diag(self.ytf), self.cf) + np.dot(np.diag(self.ytt), self.ct) self.ysh = self.buses["GS"].values + 1j * self.buses["BS"].values self.yb = np.dot(np.transpose(self.cf), self.yf) + np.dot(np.transpose(self.ct), self.yt) + np.diag(self.ysh) # Include admittance values in the branch DataFrame self.branches["GFF"] = np.real(self.yff) self.branches["BFF"] = np.imag(self.yff) self.branches["GFT"] = np.real(self.yft) self.branches["BFT"] = np.imag(self.yft) self.branches["GTF"] = np.real(self.ytf) self.branches["BTF"] = np.imag(self.ytf) self.branches["GTT"] = np.real(self.ytt) self.branches["BTT"] = np.imag(self.ytt) # Compute generator connection matrix for g in range(self.ngen): bus = int(self.generators.iloc[g]["GEN_BUS"]) # Ensure index is an integer self.cg[g, bus] = 1
[docs] def initialize(self, voltages=None, angles=None): """Initialize the voltage magnitudes, angles, flows, and generation levels.""" if voltages is None: voltages = np.ones(self.nbus) if angles is None: angles = np.zeros(self.nbus) self.buses["VOL0"] = voltages self.buses["ANG0"] = angles self.buses["VOLR0"] = voltages * np.cos(angles) self.buses["VOLI0"] = voltages * np.sin(angles) # Compute flows v = voltages * np.exp(1j * angles) sf = (self.cf @ v) * np.conj(self.yf @ v) st = (self.ct @ v) * np.conj(self.yt @ v) self.branches["PF0"] = np.real(sf) self.branches["QF0"] = np.imag(sf) self.branches["PT0"] = np.real(st) self.branches["QT0"] = np.imag(st) # Compute generator outputs sd = self.buses["PD"].values + 1j * self.buses["QD"].values sb = v * np.conj(self.yb @ v) sg = sb + sd self.generators["PG0"] = np.dot(np.real(sg), self.cg.T) self.generators["QG0"] = np.dot(np.imag(sg), self.cg.T)
[docs] def summary(self): """Print summary of the network.""" print(f"Number of buses: {self.nbus}") print(f"Number of lines: {self.nlin}") print(f"Number of generators: {self.ngen}") print(f"baseMVA: {self.baseMVA}") print("\nBuses:") print(self.buses.head()) print("\nGenerators:") print(self.generators.head()) print("\nBranches:") print(self.branches.head()) print("\nGenerator Costs:") print(self.gencost.head())
[docs] def compute_voltage_bounds(self): """Compute bounds for the real and imaginary parts of voltage.""" print("=======Computing voltage bounds") for bus in range(self.nbus): vmin = self.buses.loc[bus, "VMIN"] vmax = self.buses.loc[bus, "VMAX"] amin = self.buses.loc[bus, "AMIN"] amax = self.buses.loc[bus, "AMAX"] v_critical = [vmin, vmax] a_critical = [amin, amax] for ac in [0, np.pi / 2, np.pi, -np.pi / 2, -1 * np.pi]: if amin <= ac <= amax: a_critical.append(ac) vrmin = float("inf") vrmax = float("-inf") vimin = float("inf") vimax = float("-inf") for v in v_critical: for a in a_critical: vrmin = min(vrmin, v * np.cos(a)) vrmax = max(vrmax, v * np.cos(a)) vimin = min(vimin, v * np.sin(a)) vimax = max(vimax, v * np.sin(a)) self.buses.loc[bus, "VRMIN"] = vrmin self.buses.loc[bus, "VRMAX"] = vrmax self.buses.loc[bus, "VIMIN"] = vimin self.buses.loc[bus, "VIMAX"] = vimax
[docs] def compute_bigm_dc(self): """Compute Big-M values for DC power flow.""" print("=======Computing Big-M values for DC power flow") self.branches["PFUPDC"] = (1 / np.abs(self.branches["BR_X"])) * (self.cf @ self.buses["AMAX"] - self.ct @ self.buses["AMIN"]) self.branches["PFLODC"] = (1 / np.abs(self.branches["BR_X"])) * (self.cf @ self.buses["AMIN"] - self.ct @ self.buses["AMAX"]) for lin_index in range(self.nlin): branch = self.branches.iloc[lin_index] br_x = branch["BR_X"] f_bus = int(branch["F_BUS"]) t_bus = int(branch["T_BUS"]) amaxf, aminf = self.buses.loc[f_bus, "AMAX"], self.buses.loc[f_bus, "AMIN"] amaxt, amint = self.buses.loc[t_bus, "AMAX"], self.buses.loc[t_bus, "AMIN"] if br_x > 0: self.branches.loc[lin_index, "PFUPDC"] = (1 / br_x) * (amaxf - amint) self.branches.loc[lin_index, "PFLODC"] = (1 / br_x) * (aminf - amaxt) else: self.branches.loc[lin_index, "PFUPDC"] = (1 / br_x) * (aminf - amaxt) self.branches.loc[lin_index, "PFLODC"] = (1 / br_x) * (amaxf - amint)
[docs] def compute_bigm_ac(self): """Compute Big-M values for active and reactive power flows.""" print("=======Computing Big-M values for AC power flow using find_min_max") for lin_index in range(self.nlin): branch = self.branches.iloc[lin_index] f_bus = int(branch["F_BUS"]) t_bus = int(branch["T_BUS"]) vmaxf, vminf = self.buses.loc[f_bus, "VMAX"], self.buses.loc[f_bus, "VMIN"] vmaxt, vmint = self.buses.loc[t_bus, "VMAX"], self.buses.loc[t_bus, "VMIN"] amaxf, aminf = self.buses.loc[f_bus, "AMAX"], self.buses.loc[f_bus, "AMIN"] amaxt, amint = self.buses.loc[t_bus, "AMAX"], self.buses.loc[t_bus, "AMIN"] # Active power flow at "from" bus pf_min, pf_max = find_min_max( branch["GFF"], branch["GFT"], branch["BFT"], vminf, vmaxf, vmint, vmaxt, aminf - amaxt, amaxf - amint ) self.branches.loc[lin_index, "PFLOAC"] = pf_min self.branches.loc[lin_index, "PFUPAC"] = pf_max # Active power flow at "to" bus pt_min, pt_max = find_min_max( branch["GTT"], branch["GTF"], branch["BTF"], vmint, vmaxt, vminf, vmaxf, amint - amaxf, amaxt - aminf ) self.branches.loc[lin_index, "PTLOAC"] = pt_min self.branches.loc[lin_index, "PTUPAC"] = pt_max # Reactive power flow at "from" bus qf_min, qf_max = find_min_max( -branch["BFF"], -branch["BFT"], branch["GFT"], vminf, vmaxf, vmint, vmaxt, aminf - amaxt, amaxf - amint ) self.branches.loc[lin_index, "QFLOAC"] = qf_min self.branches.loc[lin_index, "QFUPAC"] = qf_max # Reactive power flow at "to" bus qt_min, qt_max = find_min_max( -branch["BTT"], -branch["BTF"], branch["GTF"], vmint, vmaxt, vminf, vmaxf, amint - amaxf, amaxt - aminf ) self.branches.loc[lin_index, "QTLOAC"] = qt_min self.branches.loc[lin_index, "QTUPAC"] = qt_max # Cosine of angle difference cos_min, cos_max = find_min_max(0, 1, 0, vminf, vmaxf, vmint, vmaxt, aminf - amaxt, amaxf - amint) self.branches.loc[lin_index, "COSFTMAX"] = cos_max self.branches.loc[lin_index, "COSFTMIN"] = cos_min # Sine of angle difference sin_min, sin_max = find_min_max(0, 0, 1, vminf, vmaxf, vmint, vmaxt, aminf - amaxt, amaxf - amint) self.branches.loc[lin_index, "SINFTMAX"] = sin_max self.branches.loc[lin_index, "SINFTMIN"] = sin_min
[docs] def solve_opf(self, opf_type="dc", switching="off", connectivity="off", solver="gurobi", options="outlev=1 timelimit=3600"): """Solve the optimal power flow problem using AMPL. Parameters: opf_type (str): Type of optimal power flow ('dc', 'acrect', 'acjabr') switching (str): Switching strategy ('off', 'nl', 'bigm') connectivity (str): Connectivity for topology solutions ('off', 'on') solver (str): Solver to use ('gurobi', 'cplex', 'cbc') options (str): Options for the solver Returns: dict: Results of the optimal power flow problem """ # set the status of the lines if isinstance(switching, np.ndarray): self.branches["BR_STATUS"] = switching elif switching == "off": self.branches["BR_STATUS"] = 1 elif switching == "nl": self.branches["BR_STATUS"] = 2 elif switching == "bigm": self.branches["BR_STATUS"] = 3 print( f"=======Solving OPF ({opf_type}) with switching {switching} and connectivity {connectivity} with solver {solver} and options {options}" ) ampl = AMPL() ampl.reset() ampl.read(Path(__file__).parent / "opf.mod") ampl.set_data(self.buses, "N") ampl.set_data(self.generators, "G") ampl.set_data(self.branches, "L") ampl.set_data(self.gencost) ampl.param["CF"] = array2dict(self.cf) ampl.param["CT"] = array2dict(self.ct) ampl.param["CG"] = array2dict(self.cg) ampl.param["OPF_TYPE"] = opf_type ampl.param["CONNECTIVITY"] = connectivity ampl.param["BASEMVA"] = self.baseMVA ampl.option["mp_options"] = options ampl.solve(solver=solver) solver_status = ampl.solve_result try: # Get the generation results Pg = ampl.get_variable("Pg").get_values().to_pandas().values.flatten() Qg = ampl.get_variable("Qg").get_values().to_pandas().values.flatten() Pg_viol = ( 100 * np.maximum(0, Pg - self.generators["PMAX"].values, self.generators["PMIN"].values - Pg) / (self.generators["PMAX"].values - self.generators["PMIN"].values) ) Qg_viol = ( 100 * np.maximum(0, Qg - self.generators["QMAX"].values, self.generators["QMIN"].values - Qg) / (self.generators["QMAX"].values - self.generators["QMIN"].values) ) gen_df = pd.DataFrame( {"Pg": Pg, "Qg": Qg, "Pg_viol": Pg_viol, "Qg_viol": Qg_viol}, index=ampl.get_variable("Pg").get_values().to_pandas().index, ) # Get the line results switching = ampl.get_variable("status").get_values().to_pandas().values.flatten() Pf = ampl.get_variable("Pf").get_values().to_pandas().values.flatten() Pt = ampl.get_variable("Pt").get_values().to_pandas().values.flatten() Qf = ampl.get_variable("Qf").get_values().to_pandas().values.flatten() Qt = ampl.get_variable("Qt").get_values().to_pandas().values.flatten() Sf = Pf + 1j * Qf St = Pt + 1j * Qt Sf_viol = ( 100 * np.maximum(0, abs(Sf) - self.branches["RATE_A"].values, -self.branches["RATE_A"].values - abs(Sf)) / (2 * self.branches["RATE_A"].values) ) St_viol = ( 100 * np.maximum(0, abs(St) - self.branches["RATE_A"].values, -self.branches["RATE_A"].values - abs(St)) / (2 * self.branches["RATE_A"].values) ) line_df = pd.DataFrame( { "switching": switching, "Pf": Pf, "Pt": Pt, "Qf": Qf, "Qt": Qt, "Sf": abs(Sf), "St": abs(St), "Sf_viol": Sf_viol, "St_viol": St_viol, }, index=ampl.get_variable("status").get_values().to_pandas().index, ) # Get the voltage results if opf_type == "acrect": volr = ampl.get_variable("Vr").get_values().to_pandas().values.flatten() voli = ampl.get_variable("Vi").get_values().to_pandas().values.flatten() Vm = np.sqrt(volr**2 + voli**2) Va = np.arctan2(voli, volr) elif opf_type == "acjabr": vol2 = ampl.get_variable("V2").get_values().to_pandas().values.flatten() Vm = np.sqrt(vol2) vfvtcosft = ampl.get_variable("cosft").get_values().to_pandas().values.flatten() vfvt = np.array([Vm[int(self.branches.loc[i, "F_BUS"])] * Vm[int(self.branches.loc[i, "T_BUS"])] for i in range(self.nlin)]) cosft = np.maximum(-1, np.minimum(1, vfvtcosft / vfvt)) # Compute angles for all buses Va = np.full(self.nbus, np.nan) # Initialize angles with NaN Va[0] = 0 # Reference bus angle is 0 # Iteratively compute angles visited = {0} # Start with the reference bus while len(visited) < self.nbus: for line_index in range(self.nlin): f_bus = int(self.branches.loc[line_index, "F_BUS"]) t_bus = int(self.branches.loc[line_index, "T_BUS"]) if f_bus in visited and np.isnan(Va[t_bus]): Va[t_bus] = Va[f_bus] + np.arccos(cosft[line_index]) visited.add(t_bus) elif t_bus in visited and np.isnan(Va[f_bus]): Va[f_bus] = Va[t_bus] - np.arccos(cosft[line_index]) visited.add(f_bus) else: Vm = ampl.get_variable("Vm").get_values().to_pandas().values.flatten() Va = ampl.get_variable("Va").get_values().to_pandas().values.flatten() Vm_viol = ( 100 * np.maximum(0, Vm - self.buses["VMAX"].values, self.buses["VMIN"].values - Vm) / (self.buses["VMAX"].values - self.buses["VMIN"].values) ) Va_viol = ( 100 * np.maximum(0, Va - self.buses["AMAX"].values, self.buses["AMIN"].values - Va) / (self.buses["AMAX"].values - self.buses["AMIN"].values) ) # Computation of power injections Sd = self.buses["PD"].values + 1j * self.buses["QD"].values Sg = Pg + 1j * Qg Ssh = self.buses["GS"].values * Vm**2 - 1j * self.buses["BS"].values * Vm**2 S_viol = Sg @ self.cg - Sd - Ssh - Sf @ self.cf - St @ self.ct P_viol = 100 * np.real(S_viol) / sum(self.buses["PD"].values) Q_viol = 100 * np.imag(S_viol) / sum(self.buses["QD"].values) bus_df = pd.DataFrame( { "Vm": Vm, "Va": Va, "Vm_viol": Vm_viol, "Va_viol": Va_viol, "P_viol": P_viol, "Q_viol": Q_viol, }, index=ampl.get_variable("Vm").get_values().to_pandas().index, ) # Reverse map bus indices to original numbers bus_df.index = bus_df.index.map(self.reverse_bus_mapping) return { "obj": ampl.get_objective("total_cost").value(), "time": ampl.get_value("_solve_time"), "gen": gen_df, "bus": bus_df, "lin": line_df, "status": solver_status, } except Exception: print("=======Error: No solution found:") return {"obj": None, "time": None, "gen": None, "bus": None, "lin": None, "status": solver_status}