build consumer agent's optimization problem

src/demoses_distibuted_optimization/build_consumer_agent.py

+import pyomo.environ as pyo
+
+
+def build_consumer_agent(model: pyo.AbstractModel) -> pyo.AbstractModel:
+    # Declare optimization variables
+    model.var_g = pyo.Var(model.time, name='demand', domain=pyo.Reals, initialize=0) # positive if consumer feeds power to the system
+    # Declare objective function
+    def consumption_cost_rule(model):
+        return (
+            -1 * sum(model.λ_EOM[t] * model.var_g[t] for t in model.time) +
+            sum(model.ρ_EOM / 2 * (model.var_g[t] - model.g_bar[t])**2 for t in model.time)
+        )
+    model.objective_function = pyo.Objective(rule=consumption_cost_rule, sense=pyo.minimize)
+    # Declare energy balance constraint
+    def energy_balance_rule(model, t):
+        return (model.var_g[t] <= model.pv_profile[t] - model.demand_profile[t])
+    model.energy_balance = pyo.Constraint(model.time, rule=energy_balance_rule)
+
+    return model
+
+
+# # Example usage
+# import yaml
+# import pandas as pd
+# from define_common_parameters import define_common_parameters
+# from define_consumer_parameters import define_consumer_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')
+# ts = pd.read_csv('timeseries.csv', delimiter=';')
+# consumer_agents = [id for id in data['Consumers'].keys()]
+# mdict = {m: define_common_parameters(m, data) for m in consumer_agents}
+
+
+# for agent, model in mdict.items():
+#     model = define_consumer_parameters(agent, model, data, ts)
+#     model = build_consumer_agent(model)
+#     model_instance = model.create_instance()
+#     print(model_instance.name)
+#     print()
+#     model_instance.pprint()
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