NaivePyDECOMP.RenewableGenerator package

Module contents

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

Package: Renewable Generation Modeling (RenewableGenerator)

Author

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

Description

The RenewableGenerator package provides a modular framework for modeling renewable energy sources (e.g., wind and solar) in Pyomo-based dispatch and unit commitment models. It includes standardized data types, sets, parameters, variables, constraints, objectives, and builder functions.

Submodules

RenewableDataTypes

Dataclasses defining renewable units (e.g., expected generation profiles).

RenewableVars

Initialization routines for Pyomo sets, parameters, and variables.

RenewableConstraints

Constraint builders enforcing availability limits and system balance.

RenewableObjectives

Objective function definitions for renewable-only systems.

RenewableBuilder

High-level routines to assemble complete renewable generation models.

Notes

  • The renewable availability profiles (gbar) are treated as exogenous, typically coming from forecasts.

  • The package is interoperable with the Hydro, Thermal, and Storage packages, enabling hybrid model construction.

  • Consistent naming and structure ensure clarity and maintainability across subsystems.

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.RenewableGenerator.RenewableConstraints module

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

Module: Renewable Unit Commitment — Constraints

Author

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

Description

This module defines the operational constraints for renewable generation within Pyomo-based unit commitment and dispatch models. It enforces both the per-unit generation availability limits and the system-wide energy balance when only renewable units and deficit are considered.

Functions

add_renewable_capacity_constraints(m)

Enforce that renewable generation for each unit and period does not exceed the exogenous availability profile gbar.

add_renewable_balance_constraint(m)

Enforce the energy balance by equating total renewable generation plus deficit to the system demand at each time period.

Notes

  • The capacity constraints guarantee that renewable_gen[r,t] is bounded above by renewable_gbar[r,t], which typically comes from forecasts.

  • The balance constraint is suitable for renewable-only models. In hybrid models (thermal + hydro + renewables), a combined balance formulation must be applied instead.

  • The deficit variable D[t] provides feasibility when renewable generation cannot meet demand.

References

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

NaivePyDECOMP.RenewableGenerator.RenewableDataTypes module

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

Module: Renewable Generation Data Structures

Author

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

Description

This module defines lightweight data classes for representing renewable generation units (e.g., wind and solar) and their aggregated system-level data. These structures serve as standardized inputs for optimization models of unit commitment and dispatch in Pyomo.

Classes

RenewableUnit

Encapsulates the characteristics of a single renewable unit, including identifier and time-varying availability profile.

RenewableData

Aggregates renewable units with system-wide parameters such as horizon, demand profile, and deficit penalty cost.

Notes

  • The exogenous availability profiles are represented as deterministic time series (MW) and aligned with the planning horizon.

  • The classes are designed for seamless integration into Pyomo-based optimization formulations, ensuring consistency with similar structures for thermal and hydro subsystems.

  • Extend these classes as needed to incorporate stochastic scenarios or uncertainty modeling for renewable availability.

References

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

NaivePyDECOMP.RenewableGenerator.RenewableEquations module

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

Renewable 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 renewable energy generation in Pyomo-based optimization models. The expressions generated here can be used to assemble power balance constraints and, when applicable, cost functions that include renewable generation impacts such as curtailment penalties or contractual delivery.

The functions are designed to operate modularly, enabling integration into hybrid models that combine thermal, hydro, storage, and renewable sources in a unified dispatch or expansion framework.

Each function verifies the availability of required model components before contributing expressions, allowing for flexible integration without error propagation in partially defined models.

Intended Use

  • To support the incremental construction of power balance constraints by including generation terms from renewable units.

  • To optionally incorporate renewable-related cost components, including penalties or market mechanisms.

Examples

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

>>> balance_terms = []
>>> add_renewable_balance_expression(model, t=5, balance_array=balance_terms)
>>> model.PowerBalance.add(balance_terms[0])

Notes

  • This module assumes the existence of model components such as: RU (set of renewable units), T (time set), and renewable_gen (generation variable).

  • Cost expressions are optional and can be enabled based on use case (e.g., curtailment penalties).

  • The symbolic expressions returned can be used with ConstraintList or Constraint(model.T).

References

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

NaivePyDECOMP.RenewableGenerator.RenewableGeneratorBuilder module

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

Module: Renewable Unit Commitment — Model Builder

Author

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

Description

This module provides high-level builders for renewable-only optimization models in Pyomo. It assembles the complete model structure by combining sets, parameters, variables, constraints, and optionally the objective function for renewable generation systems.

Functions

build_renewables(data, include_objective=True)

Create a new ConcreteModel pre-populated with renewable unit commitment and dispatch components.

add_renewable_problem(m, data, include_objective=False)

Add renewable-related sets, parameters, variables, constraints, and optionally the objective function to an existing Pyomo model.

Notes

  • Suitable for renewable-only systems (e.g., wind and solar). For hybrid hydrothermal-renewable systems, the balance constraint and objective must be adapted accordingly.

  • The availability profiles are deterministic and exogenous, provided by the RenewableData structure.

  • The optional objective minimizes deficit costs and enforces demand balance when include_objective=True.

References

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

NaivePyDECOMP.RenewableGenerator.RenewableGeneratorBuilder.add_renewable_subproblem(m: ConcreteModel, data: RenewableData, stage: int) → ConcreteModel[source]

Add renewable dispatch problem structure to a Pyomo model.

This routine attaches all renewable-related components to an existing ConcreteModel. It initializes sets, parameters, variables, and the renewable capacity constraints. Optionally, it also includes the balance constraint and renewable objective.

Parameters:

  • m (pyomo.environ.ConcreteModel) – Pyomo model to which renewable problem components will be added.

  • data (RenewableData) – Input data structure containing horizon, demand, units, and availability profiles.

  • include_objective (bool, optional) – If True, add the balance constraint and renewable objective function (default is False).

Returns:

The updated model with renewable sets, parameters, variables, constraints, and optionally the objective.

Return type:

pyomo.environ.ConcreteModel

Notes

  • Constraints added:

    • Renewable generation capacity limits

    • Renewable balance (if include_objective=True)

  • The objective minimizes deficit cost and optionally penalizes renewable spill (depending on implementation of set_objective_renewable()).

  • This builder is intended for renewable-only systems. For mixed hydro/thermal/renewable dispatch, a combined balance formulation is required.

NaivePyDECOMP.RenewableGenerator.RenewableObjectives module

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

Module: Renewable Unit Commitment — Objective Function

Author

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

Description

Defines the objective function for renewable-only dispatch models in Pyomo. The objective minimizes the total cost of unmet demand (deficit) over the planning horizon. It uses the deficit penalty parameter Cdef to quantify the economic impact of unserved energy.

Functions

set_objective_renewable(m)

Attach a minimization objective to the model, penalizing unmet demand.

Notes

  • This objective is designed for renewable-only systems, where generation is fully determined by availability and no explicit fuel costs exist.

  • The deficit variable D[t] ensures feasibility when renewable generation cannot meet the demand d[t].

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

References

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

NaivePyDECOMP.RenewableGenerator.RenewableVars module

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

Module: Renewable Unit Commitment — Sets, Parameters, and Variables

Author

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

Description

This module provides initialization routines for renewable generation within unit-commitment and dispatch models in Pyomo. It defines the sets, parameters, and decision variables required to incorporate renewable units (e.g., wind or solar) into short-term planning formulations.

Functions

renewable_add_sets_and_params(m, data)

Initialize sets and parameters for renewable units, including the renewable unit set and availability profiles gbar.

renewable_add_variables(m)

Declare renewable generation decision variables and, if not present, the deficit variable.

Notes

  • Availability profiles gbar are assumed to be exogenous, deterministic, and aligned with the planning horizon.

  • The module is designed for integration with hydro and thermal subsystems, ensuring that common components such as time set m.T, demand m.d, and deficit penalty m.Cdef are not redefined if already present.

  • Renewable generation is always bounded above by its availability; the actual capacity constraint should be added separately.

References

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