diff --git a/mip.py b/mip.py
index 4854e3c..a73fa3d 100644
--- a/mip.py
+++ b/mip.py
@@ -75,99 +75,6 @@ def design_with_pyomo(
         total_invest_func,
         get_max_capacity_func,
     )
-    y = pyo_env.Var(
-        max_clusters_list,
-        within=pyo_env.BinarySet,
-        initialize=False,
-        name="use_cluster",
-    )
-
-    prob.constraints += pyo_env.SummizationRule(
-        pyo_env.Summ(x[i, k] for i in n_turbines_list for k in max_clusters_list) == 1,
-        name="assign_one_turbine",
-    )
-
-    cluster_powers = [
-        pyo_env.Summ([turbines.iloc[i]["power"] * x[i, k] for i in n_turbines_list])
-        for k in max_clusters_list
-    ]
-
-    prob += pyo_env.SummizationRule(
-        pyo_env.Summ(y[k] * max_mw for k in max_clusters_list), name="cluster_capacity"
-    )
-
-    for k in max_clusters_list:
-        prob += pyo_env.SummizationRule(
-            pyo_env.Summ(x[i, k] * turbines.iloc[i]["power"] for i in n_turbines_list),
-            name=f"turbine_assign_{k}",
-        )
-
-    for k in max_clusters_list:
-        prob += y[k] <= pyo_env.summation(x[i, k] for i in n_turbines_list)
-
-    prob += pyo_env.Minimize(pyo_env.Summ([y[k] for k in max_clusters_list]))
-
-    print(
-        f"Pyomo Model: {len(prob.variables)} variables, {len(prob.constraints)} constraints"
-    )
-
-    solver = pyo_env.SolverFactory("cbc")
-    print("Pyomo: Starting to solve...")
-    result = solver.solve(prob, time_limit=time_limit)
-    print(
-        f"Pyomo: Solver status={result.solver.status}, Termination condition={result.solver.termination_condition}"
-    )
-
-    if result.solver.status == pyo_env.SolverStatus.ok:
-        cluster_assign = [-1] * n_turbines
-        active_clusters = []
-
-        for k in range(max_clusters):
-            if pyo_env.value(y[k]) > 0.5:
-                active_clusters.append(k)
-
-        for i in n_turbines_list:
-            assigned = False
-            for k in active_clusters:
-                if pyo_env.value(x[i, k]) > 0.5:
-                    cluster_assign[i] = k
-                    assigned = True
-                    break
-            if not assigned and active_clusters:
-                dists = [dist_matrix_full[0, i + 1] for k in active_clusters]
-                cluster_assign[i] = active_clusters[np.argmin(dists)]
-
-        clusters = defaultdict(list)
-        for i, c in enumerate(cluster_assign):
-            clusters[c].append(i)
-
-        connections = []
-        for c, members in clusters.items():
-            if len(members) == 0:
-                continue
-            coords = turbines.iloc[members][["x", "y"]].values
-            if len(members) > 1:
-                dm = distance_matrix(coords, coords)
-                mst = minimum_spanning_tree(dm).toarray()
-                for i in range(len(members)):
-                    for j in range(len(members)):
-                        if mst[i, j] > 0:
-                            connections.append(
-                                (
-                                    f"turbine_{members[i]}",
-                                    f"turbine_{members[j]}",
-                                    mst[i, j],
-                                )
-                            )
-            dists = [dist_matrix_full[0, m + 1] for m in members]
-            closest = members[np.argmin(dists)]
-            connections.append((f"turbine_{closest}", "substation", min(dists)))
-
-        turbines["cluster"] = cluster_assign
-        print(
-            f"Pyomo optimization completed successfully, {len(connections)} connections generated"
-        )
-        return connections, turbines
 
 
 def design_with_mip(