Sensitivity Analysis in Economic Load Dispatch

EDsensitivity.py

"""
## GAMSSOURCE: https://www.gams.com/latest/psoptlib_ml/libhtml/psoptlib_EDsensitivity.html
## LICENSETYPE: Demo
## MODELTYPE: QCP


Sensitivity Analysis in Economic Load Dispatch

For more details please refer to Chapter 3 (Gcode3.2), of the following book:
Soroudi, Alireza. Power System Optimization Modeling in GAMS. Springer, 2017.
--------------------------------------------------------------------------------
Model type: QCP
--------------------------------------------------------------------------------
Contributed by
Dr. Alireza Soroudi
IEEE Senior Member
Email: alireza.soroudi@gmail.com
We do request that publications derived from the use of the developed GAMS code
explicitly acknowledge that fact by citing
Soroudi, Alireza. Power System Optimization Modeling in GAMS. Springer, 2017.
DOI: doi.org/10.1007/978-3-319-62350-4
"""

from __future__ import annotations

import os

import pandas as pd
from gamspy import (
    Card,
    Container,
    Equation,
    Model,
    Parameter,
    Set,
    Sum,
    Variable,
)


def reformat_df(dataframe):
    return dataframe.reset_index().melt(
        id_vars="index", var_name="Category", value_name="Value"
    )


def data_records():
    # data records table
    cols = ["a", "b", "c", "Pmin", "Pmax"]
    inds = [f"g{i}" for i in range(1, 6)]
    data = [
        [3.0, 20.0, 100.0, 28, 206],
        [4.05, 18.07, 98.87, 90, 284],
        [4.05, 15.55, 104.26, 68, 189],
        [3.99, 19.21, 107.21, 76, 266],
        [3.88, 26.18, 95.31, 19, 53],
    ]
    data_recs = reformat_df(pd.DataFrame(data, columns=cols, index=inds))

    return data_recs


def main():
    m = Container(
        system_directory=os.getenv("SYSTEM_DIRECTORY", None),
    )

    # SETS #
    gen = Set(m, name="gen", records=[f"g{i}" for i in range(1, 6)])
    counter = Set(m, name="counter", records=[f"c{i}" for i in range(1, 12)])

    # PARAMETERS #
    report = Parameter(m, name="report", domain=[counter, "*"])
    repGen = Parameter(m, name="repGen", domain=[counter, gen])
    load = Parameter(m, name="load", records=400)
    data = Parameter(m, name="data", domain=[gen, "*"], records=data_records())

    # VARIABLES #
    P = Variable(m, name="P", domain=gen)

    # EQUATIONS #
    eq1 = Sum(
        gen,
        data[gen, "a"] * P[gen] * P[gen]
        + data[gen, "b"] * P[gen]
        + data[gen, "c"],
    )

    eq2 = Equation(m, name="eq2", type="regular")
    eq2[...] = Sum(gen, P[gen]) >= load

    P.lo[gen] = data[gen, "Pmin"]
    P.up[gen] = data[gen, "Pmax"]

    ECD = Model(
        m,
        name="ECD",
        equations=[eq2],
        problem="qcp",
        sense="min",
        objective=eq1,
    )

    for idx, cc in enumerate(counter.toList()):
        load[...] = Sum(gen, data[gen, "Pmin"]) + (
            (idx) / (Card(counter) - 1)
        ) * Sum(gen, data[gen, "Pmax"] - data[gen, "Pmin"])
        ECD.solve()
        repGen[cc, gen] = P.l[gen]
        report[cc, "OF"] = ECD.objective_value
        report[cc, "load"] = load

    import math

    assert math.isclose(ECD.objective_value, 911044.0900, rel_tol=0.001)

    print("repgen:  \n", repGen.pivot().round(3))
    print("report:  \n", report.pivot().round(3))


if __name__ == "__main__":
    main()