Core Optimization Functions#

Main functions for running SDOM optimizations.

Model Initialization#

sdom.optimization_main.initialize_model(data, n_hours=8760, with_resilience_constraints=False, model_name='SDOM_Model')[source]#

Initializes and configures a Pyomo optimization model for the SDOM framework. This function sets up the model structure, including sets, parameters, variables, objective function, and constraints for power system optimization. It supports optional resilience constraints and allows customization of the model name and simulation horizon.

Profiling is always enabled: time and memory usage are measured for each initialization step and a summary table is printed at the end. The profiler is attached to the model as model.profiler for programmatic access.

Parameters:

data (dict) – Input data required for model initialization, including system parameters, time series, and technology characteristics.
n_hours (int, optional) – Number of hours to simulate (default is 8760, representing a full year).
with_resilience_constraints (bool, optional) – If True, adds resilience-related constraints to the model (default is False).
model_name (str, optional) – Name to assign to the Pyomo model instance (default is “SDOM_Model”).

Returns:

A fully initialized Pyomo ConcreteModel object ready for: optimization. The model includes a ‘profiler’ attribute containing the ModelInitProfiler instance with detailed timing and memory data.

Return type:

ConcreteModel

Solver Configuration#

sdom.optimization_main.get_default_solver_config_dict(solver_name='cbc', executable_path='.\\Solver\\bin\\cbc.exe')[source]#

Generate a default solver configuration dictionary with standard SDOM settings.

Creates a pre-configured dictionary for solver initialization with recommended settings for SDOM optimization problems. Includes solver options and solve keywords for controlling optimization behavior.

Parameters:

solver_name (str, optional) – Solver to use. Supported values: - ‘cbc’: COIN-OR CBC open-source MILP solver (requires executable_path) - ‘highs’: HiGHS open-source MILP solver (uses appsi interface) - ‘xpress’: FICO Xpress commercial solver (uses direct interface) Defaults to ‘cbc’.
executable_path (str, optional) – Path to solver executable file. Required for CBC solver. Defaults to ‘.Solverbincbc.exe’.

Returns:

Configuration dictionary with keys:

’solver_name’ (str): Solver identifier for SolverFactory
’executable_path’ (str): Path to executable (CBC only)
’options’ (dict): Solver options (mip_rel_gap, etc.)
’solve_keywords’ (dict): Arguments for solver.solve() call (tee, load_solutions, logfile, timelimit, etc.)

Return type:

dict

Notes

Default MIP relative gap is 0.002 (0.2%). Log output is written to ‘solver_log.txt’. Solution loading and timing reports are enabled by default. HiGHS uses ‘appsi_highs’ interface for better performance.

sdom.optimization_main.configure_solver(solver_config_dict: dict)[source]#

Configure and instantiate a Pyomo solver based on configuration dictionary.

Creates a SolverFactory instance with the specified solver and applies any provided options. Handles solver-specific initialization (e.g., executable paths for CBC).

Parameters:

solver_config_dict (dict) –

Configuration dictionary containing: - ‘solver_name’ (str): Solver identifier (e.g., ‘cbc’, ‘appsi_highs’,

’xpress_direct’, ‘gurobi’)

’executable_path’ (str): Path to solver executable (required for CBC, optional for others)
’options’ (dict): Solver-specific options to apply (e.g., mip_rel_gap, loglevel)

Returns:

Configured solver instance ready to: solve optimization models.

Return type:

Solver instance

Raises:

RuntimeError – If the specified solver is not available on the system.

Notes

CBC solver requires explicit executable_path. Other solvers use system PATH. Solver availability is checked before returning the instance.

Running Optimization#

sdom.optimization_main.run_solver(model, solver_config_dict: dict, case_name: str = 'run') → OptimizationResults[source]#

Solve the optimization model and return structured results.

Solves the given optimization model using the configured solver and collects all results into an OptimizationResults dataclass.

Parameters:

model (pyomo.core.base.PyomoModel.ConcreteModel) – The Pyomo optimization model to be solved. The model must have an attribute ‘GenMix_Target’ that can be set.
solver_config_dict (dict) – Solver configuration dictionary from get_default_solver_config_dict().
case_name (str, optional) – Case identifier for labeling results. Defaults to “run”.

Returns:

A dataclass containing all optimization results including: - termination_condition: Solver termination status - total_cost: Objective value - generation_df: Hourly generation dispatch DataFrame - storage_df: Hourly storage operation DataFrame - summary_df: Summary metrics DataFrame - capacity: Installed capacities by technology - storage_capacity: Storage capacities (charge, discharge, energy) - cost_breakdown: Detailed cost breakdown - problem_info: Solver problem information

Return type:

OptimizationResults

Raises:

RuntimeError – If the solver is not available on the system.

Notes

If the solver does not find an optimal solution, the returned OptimizationResults will have is_optimal=False and minimal data populated.

Configuration#

sdom.config_sdom.configure_logging(level=20, log_file=None)[source]#

Configure the logging system with colored console output and optional file logging.

Sets up logging handlers with color-coded formatting for terminal output. Optionally writes logs to a file as well. Should be called once at the start of SDOM execution.

Parameters:

level (int, optional) – Logging level threshold (e.g., logging.INFO, logging.DEBUG). Defaults to logging.INFO.
log_file (str, optional) – Path to a file where logs should be written. If None, logs only to console. Defaults to None.

Returns:

None

Notes

The format includes timestamp and log level: ‘YYYY-MM-DD HH:MM:SS-new line-LEVEL - message’ Color formatting is applied via the ColorFormatter class using ANSI codes. Multiple calls will reconfigure the logging system.

class sdom.config_sdom.ColorFormatter(fmt=None, datefmt=None, style='%', validate=True, *, defaults=None)[source]#

Custom logging formatter that adds color codes to log level names.

This formatter applies ANSI color codes to different log levels for improved readability in terminal output. Colors are defined in the LOG_COLORS constant.

COLORS#

Mapping of log level names to ANSI color codes.

Type:: dict

RESET#

ANSI reset code to restore default terminal colors.

Type:: str

COLORS = {'CRITICAL': '\x1b[91m', 'DEBUG': '\x1b[94m', 'ERROR': '\x1b[91m', 'INFO': '\x1b[92m', 'WARNING': '\x1b[93m'}#

RESET = '\x1b[0m'#

format(record)[source]#

Apply color formatting to log record level names.

Parameters:: record (logging.LogRecord) – The log record to format.
Returns:: The formatted log message with colored level name.
Return type:: str

Example Usage#

from sdom import (
    load_data,
    initialize_model, 
    get_default_solver_config_dict,
    run_solver,
    export_results
)

# Load data
data = load_data('./Data/scenario/')

# Initialize model
model = initialize_model(
    data=data,
    n_hours=8760,
    with_resilience_constraints=False
)

# Configure solver
solver_config = get_default_solver_config_dict(
    solver_name="cbc",
    executable_path="./Solver/bin/cbc.exe"
)

# Run optimization - returns OptimizationResults object
results = run_solver(model, solver_config)

# Check if solution is optimal
if results.is_optimal:
    print(f"Total Cost: ${results.total_cost:,.2f}")
    print(f"Wind Capacity: {results.total_cap_wind:.2f} MW")
    print(f"Solar Capacity: {results.total_cap_pv:.2f} MW")
    
    # Access storage capacities
    for tech, cap in results.total_cap_storage_charge.items():
        print(f"{tech} Capacity: {cap:.2f} MW")
    
    # Export results to CSV files
    export_results(results, case="scenario_1")
else:
    print(f"Optimization failed: {results.termination_condition}")

Core Optimization Functions#

Model Initialization#

Solver Configuration#

Running Optimization#

Configuration#

Example Usage#

See Also#

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