NaivePyDESSEM.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

NaivePyDESSEM.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.

NaivePyDESSEM.RenewableGenerator.RenewableConstraints.add_renewable_balance_constraint(m)[source]

Add the system-wide energy balance constraint for renewable-only models.

Enforces, for each period t, that the total renewable generation plus the deficit variable equals the system demand:

sum_r renewable_gen[r,t] + D[t] == d[t]

Parameters:

m (pyomo.environ.ConcreteModel) – Model containing: - Sets: m.RU (renewable units), m.T (time periods) - Variables: m.renewable_gen[r,t] (MW), m.D[t] (MW, deficit) - Parameters: m.d[t] (MW, demand)

Returns:

The same model with constraint block m.renewable_balance_constraint.

Return type:

pyomo.environ.ConcreteModel

Notes

  • This balance assumes a system composed solely of renewable units and deficit. For hybrid systems (hydro, thermal, renewables), a combined balance constraint should be implemented.

  • The deficit variable m.D[t] allows feasibility in case renewable generation is insufficient to meet demand.

NaivePyDESSEM.RenewableGenerator.RenewableConstraints.add_renewable_capacity_constraints(m)[source]

Add renewable capacity (availability) constraints.

For each renewable unit r and time period t, the dispatched renewable generation cannot exceed its available potential (exogenous profile gbar):

renewable_gen[r,t] <= renewable_gbar[r,t]

Parameters:

m (pyomo.environ.ConcreteModel) – Model containing: - Sets: m.RU (renewable units), m.T (time periods) - Variables: m.renewable_gen[r,t] (MW) - Parameters: m.renewable_gbar[r,t] (MW, availability profile)

Returns:

The same model with constraint block m.renewable_cap_constraint.

Return type:

pyomo.environ.ConcreteModel

Notes

  • Ensures that renewable generation respects exogenous availability.

  • The availability parameter m.renewable_gbar is typically initialized from forecast profiles or scenario data.

NaivePyDESSEM.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.

class NaivePyDESSEM.RenewableGenerator.RenewableDataTypes.RenewableData(horizon: int, demand: Dict[int, float], units: Dict[str, RenewableUnit], Cdef: float = 1000.0)[source]

Bases: object

System-wide data structure for renewable generation modeling.

Parameters:

  • horizon (int) – Number of periods in the planning horizon.

  • demand (Dict[int, float]) – Mapping of each period t (1-based) to system demand (MW).

  • units (Dict[str, RenewableUnit]) – Dictionary mapping unit identifiers to their respective RenewableUnit objects.

  • Cdef (float, optional) – Penalty cost for unmet demand (R$/MWh), default is 1000.0.

Notes

  • units can include heterogeneous renewable technologies such as wind turbines, photovoltaic plants, or run-of-river hydro, provided they are represented by RenewableUnit.

  • Cdef defines the cost of deficit used in the optimization objective when renewables are integrated into dispatch problems.

  • This class is typically consumed by Pyomo-based optimization models for renewable-aware unit commitment or economic dispatch.

Cdef: float = 1000.0
demand: Dict[int, float]
horizon: int
units: Dict[str, RenewableUnit]
class NaivePyDESSEM.RenewableGenerator.RenewableDataTypes.RenewableUnit(name: str, gbar: List[float])[source]

Bases: object

Data container for a renewable generating unit (e.g., wind or solar).

Parameters:

  • name (str) – Unique identifier for the renewable unit (e.g., 'PV1', 'WTG3').

  • gbar (List[float]) – Time series of expected available generation (MW) for each planning period. Typically derived from forecasts or historical profiles.

Notes

  • The list gbar is assumed to have length equal to the system planning horizon. Access is usually aligned as gbar[t-1] for period t (1-based index).

  • Variability and uncertainty of renewables are represented through this exogenous availability profile rather than decision variables.

gbar: List[float]
name: str

NaivePyDESSEM.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.

NaivePyDESSEM.RenewableGenerator.RenewableEquations.add_renewable_balance_expression(m: ConcreteModel, t: Any, balance_array: List[Any]) → List[Any][source]

Append renewable generation terms at time t to the power balance expression list.

This function constructs a symbolic expression representing the total power injected by renewable sources at a given time step t, and appends it to the power balance equation. The function is compatible with modular power balance construction in hybrid energy system models.

Parameters:

  • m (ConcreteModel) – Pyomo model instance containing renewable generation variables.

  • t (int) – Time index for which the renewable generation is evaluated.

  • balance_array (list of expressions) – List of symbolic expressions contributing to the power balance equation.

Returns:

The updated list including the total renewable generation at time t.

Return type:

list of expressions

Notes

  • The model is expected to contain the following components: ‘RU’ (renewable unit set), ‘T’ (time set), and ‘renewable_gen’ (generation variable).

  • This function is designed for use within a larger balance constraint rule such as Constraint(model.T, rule=…) or within a ConstraintList.

NaivePyDESSEM.RenewableGenerator.RenewableEquations.add_renewable_cost_expression(m: ConcreteModel, cost_array: List[Any]) → List[Any][source]

Append renewable generation cost terms to the total cost expression list.

This function is a placeholder or extension point for incorporating renewable energy cost terms into the objective function. While many renewable sources (e.g., wind, solar) have negligible marginal costs, this function allows the inclusion of opportunity costs, curtailment penalties, or tariffs if applicable.

Parameters:

  • m (ConcreteModel) – Pyomo model instance containing renewable variables and parameters.

  • cost_array (list of expressions) – List of symbolic expressions that contribute to the total cost.

Returns:

The input list, optionally extended with renewable cost expressions.

Return type:

list of expressions

Notes

  • If no cost expression is required for renewables, the function simply returns the list unchanged.

  • This function can be expanded to include curtailment penalties or contracted delivery costs based on renewable_gen variables.

NaivePyDESSEM.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.

NaivePyDESSEM.RenewableGenerator.RenewableGeneratorBuilder.add_renewable_problem(m: ConcreteModel, data: RenewableData, include_objective: bool = False) → 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.

NaivePyDESSEM.RenewableGenerator.RenewableGeneratorBuilder.build_renewables(data: RenewableData, include_objective: bool = True) → ConcreteModel[source]

Construct a complete Pyomo model for renewable-only dispatch.

This high-level builder creates a new ConcreteModel instance and populates it with sets, parameters, variables, constraints, and optionally the objective for renewable generation systems. It calls add_renewable_problem() internally.

Parameters:

  • data (RenewableData) – Input data structure with horizon, demand profile, deficit penalty, and renewable units with their availability profiles.

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

Returns:

A new Pyomo model fully configured for renewable-only dispatch.

Return type:

pyomo.environ.ConcreteModel

Notes

  • The model contains:

    • Sets: time periods, renewable units

    • Parameters: demand, availability (gbar), deficit penalty

    • Variables: renewable generation, deficit

    • Constraints: capacity limits, and optionally balance

    • Objective: deficit-penalizing minimization (if enabled)

  • For integration with hydro/thermal systems, use combined builders.

NaivePyDESSEM.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.

NaivePyDESSEM.RenewableGenerator.RenewableObjectives.set_objective_renewable(m)[source]

Define the objective function for renewable-only dispatch models.

The objective minimizes the total penalty associated with unmet demand (deficit) across the planning horizon. This is expressed as:

minimize sum_t Cdef * D[t]

where: - D[t] is the deficit variable (MW) at time period t. - Cdef is the penalty coefficient for unserved energy (e.g., $/MWh).

Parameters:

m (pyomo.environ.ConcreteModel) – Pyomo model with: - Set: m.T (time periods) - Variable: m.D[t] (deficit at time t) - Parameter: m.Cdef (deficit penalty cost)

Returns:

The same model with objective function m.OBJ attached, set for minimization.

Return type:

pyomo.environ.ConcreteModel

Notes

  • This formulation is intended for renewable-only systems, where generation costs are absent and the only economic penalty comes from unmet demand.

  • Ensure that the penalty Cdef is set to a sufficiently high value so that the optimizer prioritizes meeting demand whenever possible.

  • The objective does not account for renewable curtailment or spill penalties. If such penalties are desired, they must be added explicitly.

Examples

>>> from pyomo.environ import ConcreteModel
>>> m = ConcreteModel()
>>> m.T = [1, 2, 3]
>>> m.D = {t: 0 for t in m.T}  # deficit variables (mocked here)
>>> m.Cdef = 1000.0
>>> _ = set_objective_renewable(m)
>>> m.OBJ.pprint()

NaivePyDESSEM.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.

NaivePyDESSEM.RenewableGenerator.RenewableVars.renewable_add_sets_and_params(m: ConcreteModel, data: RenewableData) → ConcreteModel[source]

Initialize sets and parameters for renewable units in a Pyomo model.

Adds the renewable unit set, the renewable availability parameter renewable_gbar[r,t], and ensures that the time set m.T, demand m.d, and deficit penalty m.Cdef exist. If those components are already present in the model, they are not overwritten.

Parameters:

  • m (pyomo.environ.ConcreteModel) – Pyomo model to which renewable sets and parameters will be added.

  • data (RenewableData) – Input data structure containing the planning horizon, demand profile, deficit penalty, and renewable units with their availability series.

Returns:

The same model with renewable sets and parameters attached.

Return type:

pyomo.environ.ConcreteModel

Notes

  • The set m.RU indexes renewable units.

  • The parameter m.renewable_gbar[r,t] stores the available generation (MW) of unit r at period t.

  • The list gbar in each RenewableUnit is assumed to be 0-based, so initialization uses t-1 when accessing it.

  • If the model already contains m.T, m.d, or m.Cdef, they are left unchanged to allow integration with thermal/hydro subsystems.

NaivePyDESSEM.RenewableGenerator.RenewableVars.renewable_add_variables(m: ConcreteModel) → ConcreteModel[source]

Declare renewable generation and deficit decision variables.

Adds the continuous non-negative variable m.renewable_gen[r,t] for renewable generation and, if not already present, the deficit variable m.D[t].

Parameters:

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

Returns:

The same model with renewable generation and deficit variables.

Return type:

pyomo.environ.ConcreteModel

Notes

  • m.renewable_gen[r,t] represents the actual dispatched renewable generation (MW) for unit r at time t.

  • m.D[t] represents unmet demand (MW) at time t. It is created only if not already defined in the model, ensuring compatibility with other subsystems (thermal, hydro, batteries).

  • Both variables are continuous and non-negative.