"""
EELT 7030 — Operation and Expansion Planning of Electric Power Systems
Federal University of Paraná (UFPR)
Module: Energy Storage — Model Builder
Author
------
Augusto Mathias Adams <augusto.adams@ufpr.br>
Description
-----------
High-level builders for storage-only optimization models in Pyomo. This
module assembles the complete model structure by combining storage sets,
parameters, variables, constraints, and an optional objective function.
Functions
---------
build_storage(data, include_objective=True)
Create a new ``ConcreteModel`` with storage components and, optionally,
the balance constraint and objective.
add_storage_problem(m, data, include_objective=False)
Add storage components (sets/params/vars/constraints) to an existing
model and, optionally, attach the balance constraint and objective.
Notes
-----
- The default objective minimizes deficit cost (no explicit storage
operating costs). If you have cycling costs or degradation penalties,
replace the objective accordingly.
- For hybrid systems (hydro/thermal/renewables + storage), prefer a
combined system balance and a joint objective in a higher-level builder.
References
----------
[1] CEPEL, DESSEM. Manual de Metodologia, 2023
[2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.
"""
import copy
from pyomo.environ import ConcreteModel, Objective, minimize
from .StorageDataTypes import StorageData
from .StorageObjective import set_objective_storage
from .StorageVars import storage_add_sets_and_params, storage_add_variables
from .StorageConstraints import (
add_storage_energy_balance_constraint,
add_storage_soc_bounds_constraint,
add_storage_power_limits_constraint,
add_storage_only_balance_constraint,
add_storage_mutual_exclusion_constraint
)
from NaivePyDESSEM.Storage.StorageBuilder import build_storage, add_storage_problem
from typing import Dict
[docs]
def add_storage_subproblem(m: ConcreteModel,
data: StorageData,
stage: int) -> ConcreteModel:
"""
Add storage dispatch problem structure to an existing model.
This routine initializes storage sets and parameters, declares decision
variables, and attaches operational constraints. Optionally, it enforces
a storage-only system balance and adds a deficit-penalizing objective.
Parameters
----------
m : pyomo.environ.ConcreteModel
Target model to be updated.
data : StorageData
Input container with horizon, units, and time-step duration.
include_objective : bool, optional
If ``True``, add the storage-only balance constraint and attach the
deficit objective (default is ``False``).
Returns
-------
pyomo.environ.ConcreteModel
The updated model with storage components and, optionally, the
balance constraint and objective.
Notes
-----
- Constraints added (always):
* Energy (SoC) balance
* SoC bounds (min/max)
* Charge/discharge power limits
- If ``include_objective=True``:
* Add storage-only balance: Σ(dis − ch) + D = d
* Attach deficit objective
"""
# data copy
subproblem_data = copy.deepcopy(data)
subproblem_data.horizon = 1
subproblem_data.demand = {1: data.demand[stage+1]}
# sets & params
storage_add_sets_and_params(m, data)
# variables
storage_add_variables(m)
# constraints
add_storage_energy_balance_constraint(m)
add_storage_soc_bounds_constraint(m)
add_storage_power_limits_constraint(m)
# add_storage_mutual_exclusion_constraint(m) # descomente se usar binária
return m