define generator parameters

src/demoses_distibuted_optimization/define_generator_parameters.py

+import numpy as np
+import pandas as pd
+from typing import Dict
+import pyomo.environ as pyo
+
+
+def define_generator_parameters(agent: str, model: pyo.AbstractModel, data: Dict, ts: pd.DataFrame) -> pyo.AbstractModel:
+    # Declare generator cost function parameters (OPEX are parametrized as a/2 * g**2 + b * g)
+    model.gen_cost_param_a = pyo.Param(name='gen_cost_param_a', initialize=data['Generators'][agent]['a'], mutable=False)
+    model.gen_cost_param_b = pyo.Param(name='gen_cost_param_b', initialize=data['Generators'][agent]['b'], mutable=False)
+    # Declare generator installed capacity parameter
+    model.gen_installed_capacity = pyo.Param(name='gen_installed_capacity', initialize=data['Generators'][agent]['C'], mutable=False)
+    # Get generator available capacity timeseries: installed capacity * availability factor
+    if 'AF' in data['Generators'][agent].keys():
+        available_capacity = model.gen_installed_capacity * ts.loc[:, data['Generators'][agent]["AF"]].values
+    else:
+        available_capacity = model.gen_installed_capacity * np.ones(data['General']["nTimesteps"])
+    # Declare available capacity parameter
+    available_capacity_dict = dict(enumerate(available_capacity))
+    model.available_capacity = pyo.Param(model.time, name='available_capacity', initialize=available_capacity_dict, domain=pyo.Reals, mutable=False)
+
+    return model
+
+
+# # Example usage
+# import yaml
+# from define_common_parameters import define_common_parameters
+
+
+# def read_config(config_file):
+#     with open(config_file, 'r') as file:
+#         config = yaml.safe_load(file)
+#     return config
+
+# data = read_config('config.yaml')
+# generator_agents = [id for id in data['Generators'].keys()]
+# ts = pd.read_csv('timeseries.csv', delimiter=';')
+
+# for agent in generator_agents:
+#     model = define_common_parameters(agent, data)
+#     model = define_generator_parameters(agent, model, data, ts)
+#     print(model.name)
+#     print()
+#     model.pprint()
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