NaivePyDECOMP.Storage package

Module contents

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Package: Energy Storage Modeling (Storage)

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

The Storage package provides a modular framework for incorporating battery energy storage systems (BESS) into Pyomo-based dispatch and unit commitment models. It defines the necessary data structures, sets, parameters, variables, constraints, objectives, and builder functions to assemble storage-only or hybrid optimization models.

Submodules

StorageDataTypes

Dataclasses for storage units and system-level configuration.

StorageVars

Initialization of Pyomo sets, parameters, and variables.

StorageConstraints

Constraint builders (SoC balance, bounds, power limits, etc.).

StorageObjectives

Objective function definitions for storage-only models.

StorageBuilder

High-level routines to assemble complete storage models.

Notes

  • The package is designed to be interoperable with the Hydro, Thermal, and Renewable packages, enabling the construction of hybrid models.

  • Naming conventions are consistent across subsystems for clarity and maintainability.

  • Extensions (e.g., cycling cost, degradation models) can be added within this package without altering the external interface.

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.

Submodules

NaivePyDECOMP.Storage.StorageBuilder module

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.

NaivePyDECOMP.Storage.StorageBuilder.add_storage_subproblem(m: ConcreteModel, data: StorageData, stage: int) → ConcreteModel[source]

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:

The updated model with storage components and, optionally, the balance constraint and objective.

Return type:

pyomo.environ.ConcreteModel

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

NaivePyDECOMP.Storage.StorageConstraints module

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Energy Storage — Constraints

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

This module defines the operational constraints for battery energy storage systems (BESS) within Pyomo-based dispatch and unit commitment models. The formulation is consistent with the hydraulic and renewable modules, ensuring interoperability in hybrid optimization frameworks.

Functions

add_storage_energy_balance_constraint(m)

Enforce intertemporal state-of-charge balance with charge/discharge efficiencies and time-step duration.

add_storage_soc_bounds_constraint(m)

Impose minimum and maximum state-of-charge limits.

add_storage_power_limits_constraint(m)

Impose per-period charging and discharging power limits.

add_storage_mutual_exclusion_constraint(m)

(Optional) Prohibit simultaneous charging and discharging through a big-M formulation with binary mode variables.

add_storage_only_balance_constraint(m)

Enforce system-wide balance for storage-only models, equating net injection plus deficit to the demand at each period.

Notes

  • The efficiencies (eta_c, eta_d) can be specified per stage or derived from a round-trip value split by the user.

  • For hybrid models (hydro, thermal, renewable, storage), the global balance should be implemented at a higher builder level.

  • The formulation assumes a fixed period duration delta_t in hours, used to convert power (MW) to energy (MWh).

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.

NaivePyDECOMP.Storage.StorageDataTypes module

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Module: Energy Storage Bank — Data Structures

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

Lightweight data classes for battery energy storage systems (BESS). These structures provide standardized inputs for Pyomo-based unit commitment and dispatch models.

Classes

StorageUnit

Parameters of a single storage unit (SoC bounds, power limits, efficiencies, initial state).

StorageData

System-level container that aggregates storage units and common parameters such as time-step duration.

Notes

  • The efficiencies can be specified per stage (charge/discharge) or derived from a round-trip value split across both stages by the user before instantiation.

  • The time-step duration delta_t (hours) converts power (MW) to energy (MWh) in the state-of-charge balance.

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.

NaivePyDECOMP.Storage.StorageEquations module

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Storage Model Expression Utilities for Pyomo Optimization

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

This module provides helper functions to construct symbolic expressions related to energy storage systems in Pyomo-based optimization models. These expressions can be incrementally assembled and integrated into constraints (e.g., state-of-charge evolution) or cost functions (e.g., charging/discharging costs).

The functions are designed to support modular model construction and hybrid system integration. They can be used in conjunction with other technology modules (e.g., thermal, hydro, renewable) to build power balance constraints and system-wide cost objectives.

All expressions are symbolic and compatible with Pyomo’s modeling framework. Each function includes safeguards to ensure that required model components exist before attempting to generate expressions.

Intended Use

  • To append storage-related cost and energy balance expressions to lists that contribute to the overall objective function and constraint set.

  • To modularize and standardize storage modeling across different hybrid energy system configurations.

Examples

>>> cost_terms = []
>>> add_storage_cost_expression(model, cost_terms)
>>> model.TotalCost = Objective(expr=sum(cost_terms), sense=minimize)

>>> balance_terms = []
>>> add_storage_balance_expression(model, t=5, balance_array=balance_terms)
>>> model.StorageBalance.add(balance_terms[0])

Notes

  • This module assumes that storage behavior is modeled using variables such as: storage_E, storage_ch, storage_dis, storage_Eini, and parameters like storage_eta_c, storage_eta_d, and storage_delta_t.

  • The structure is compatible with Pyomo’s ConstraintList and indexed Constraint(T).

  • Expressions are constructed incrementally and can be combined with other sources (e.g., thermal, hydro) in hybrid dispatch models.

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023..

NaivePyDECOMP.Storage.StorageObjective module

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Module: Energy Storage — Objective Function

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

Defines the objective function for storage-only dispatch models in Pyomo. The formulation minimizes the total penalty cost associated with unmet demand (deficit) over the planning horizon, using the parameter Cdef.

Functions

set_objective_storage(m)

Attach a minimization objective to the model that penalizes unmet demand through Cdef * D[t].

Notes

  • This objective is tailored for storage-only systems without explicit operating or cycling costs.

  • The penalty coefficient Cdef should be chosen sufficiently high to discourage deficits under normal operating conditions.

  • For hybrid models (hydro, thermal, renewable, storage), a combined system-wide objective should be defined at a higher level.

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.

NaivePyDECOMP.Storage.StorageVars module

EELT 7030 — Operation and Expansion Planning of Electric Power Systems Federal University of Paraná (UFPR)

Module: Energy Storage — Sets, Parameters, and Variables

Author

Augusto Mathias Adams <augusto.adams@ufpr.br>

Description

Provides initialization routines for adding storage-related sets, parameters, and decision variables to Pyomo-based dispatch and unit commitment models. This module ensures a consistent interface for modeling battery energy storage systems (BESS) across different formulations.

Functions

storage_add_sets_and_params(m, data)

Initialize sets and Pyomo parameters for storage units, including state-of-charge bounds, initial conditions, power limits, efficiencies, and time-step duration.

storage_add_variables(m)

Declare continuous decision variables for state-of-charge, charging power, and discharging power. If absent, also define a deficit variable for feasibility.

Notes

  • Parameters are namespaced with the prefix storage_ for clarity and compatibility with other subsystems (hydro, thermal, renewable).

  • The time-step duration (delta_t) converts power (MW) to energy (MWh) within the state-of-charge balance equation.

  • The variables are declared as non-negative and continuous. Binary variables may be added in other modules if mutual exclusion between charge and discharge is required.

References

[1] CEPEL, DESSEM. Manual de Metodologia, 2023 [2] Unsihuay Vila, C. Introdução aos Sistemas de Energia Elétrica, Lecture Notes, EELT7030/UFPR, 2023.