"""
EELT 7030 — Operation and Expansion Planning of Electric Power Systems
Federal University of Paraná (UFPR)
Hydropower Model Initialization: Sets, Parameters, and Variables
Description
-----------
This module provides initialization routines for setting up the basic
sets, parameters, and continuous variables required by hydropower
optimization models in Pyomo. It accommodates multiple hydropower
generation modes, natural-inflow handling, and head-dependent power
relationships supplied elsewhere in the modeling stack.
Functions
---------
hydraulyc_add_sets_and_params(m, data)
Initialize sets and model-level parameters/containers for hydropower units
and system-wide demand.
hydralic_add_variables_g(m)
Declare continuous decision variables for hydropower dispatch modeling.
Modeling Conventions and Units
------------------------------
- Time periods are indexed as integers ``t = 1, …, horizon``.
- Turbined/Spill discharges (Q, S): hm^3.
- Storage volume (V): hm^3.
- Demand and power (d, G): MWh.
Notes
-----
- The argument ``data`` is expected to be an instance of ``HydraulicData``,
where each unit is a ``HydraulicUnit``.
- Some attributes attached to the model are plain Python containers
(e.g., dictionaries) rather than Pyomo ``Param`` objects, by design.
- This module targets short-term planning models and is suitable for
integration into MILP/MIQP hydropower (or hydrothermal) formulations.
References
----------
[1] CEPEL, DESSEM. Manual de Metodologia, 2023
[2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.
"""
from pyomo.environ import (RangeSet, Set, Param, Var,
NonNegativeReals)
from NaivePyDESSEM.HydraulicGenerator.HydraulicVars import (
hydralic_add_variables_g
)
[docs]
def hydraulyc_add_sets_and_params(m, data):
"""
Initialize hydropower sets and model-level parameters/containers.
Configures the Pyomo model ``m`` with time and hydropower unit sets,
system-level demand, and per-unit attributes such as flow limits,
storage bounds, initial/terminal storages, natural inflows, cascade
topology, and flags used by inflow aggregation. Values are sourced
from the ``HydraulicData``/``HydraulicUnit`` objects.
Parameters
----------
m : pyomo.core.base.PyomoModel.ConcreteModel
Target Pyomo model to be populated with sets and parameters.
data : HydraulicData
Input data object with planning horizon, demand mapping, and the
dictionary of hydropower units.
Returns
-------
pyomo.core.base.PyomoModel.ConcreteModel
The same model ``m`` with initialized sets and parameters/containers.
Notes
-----
- All plant-specific containers are indexed by the hydropower unit set
``m.HG``.
- Time-varying arrays (e.g., ``afluencia``) are assumed to be external,
0-based Python lists that are accessed as ``[t-1]`` inside constraints.
- Some attributes (e.g., ``m.hydro_Qmin``, ``m.hydro_Vmax``) are plain
Python dictionaries attached to the model for convenience; others
(e.g., ``m.d``) are Pyomo ``Param`` objects.
- The attribute ``m.horizon`` stores the number of periods for quick access.
"""
T = data.horizon
H = list(data.units.keys())
m.HG = Set(initialize=H)
if not hasattr(m, "T"):
m.T = RangeSet(1, T)
if not hasattr(m, "d"):
m.d = Param(m.T, initialize=data.demand, within=NonNegativeReals)
if not hasattr(m, "Cdef"):
m.Cdef = data.Cdef
m.horizon = T
m.hydro_Qmin = {h: data.units[h].Qmin for h in H}
m.hydro_Qmax = {h: data.units[h].Qmax for h in H}
m.hydro_Vmin = {h: data.units[h].Vmin for h in H}
m.hydro_Vmax = {h: data.units[h].Vmax for h in H}
m.hydro_Vini = {h: data.units[h].Vini for h in H}
m.hydro_afluencia = {h: data.units[h].afluencia for h in H}
m.hydro_upstreams = {h: data.units[h].upstreams for h in H}
m.hydro_compute_total_inflow = {h: data.units[h].compute_total_inflow for h in H}
return m