def METHOD_NAME(Q_nom_CT_W, locator, technology_type):
    """
    Annualized investment costs for the Combined cycle
    :type Q_nom_CT_W : float
    :param Q_nom_CT_W: Nominal size of the cooling tower in [W]
    :rtype InvCa : float
    :returns InvCa: annualized investment costs in Dollars
    """
    Capex_a_CT_USD = 0.0
    Opex_fixed_CT_USD = 0.0
    Capex_CT_USD = 0.0
    if Q_nom_CT_W > 0:
        CT_cost_data = pd.read_excel(locator.get_database_conversion_systems(), sheet_name="CT")
        CT_cost_data = CT_cost_data[CT_cost_data['code'] == technology_type]
        max_chiller_size = max(CT_cost_data['cap_max'].values)
        # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least
        # capacity for the corresponding technology from the database
        if Q_nom_CT_W < CT_cost_data.iloc[0]['cap_min']:
            Q_nom_CT_W = CT_cost_data.iloc[0]['cap_min']
        if Q_nom_CT_W <= max_chiller_size:
            CT_cost_data = CT_cost_data[
                (CT_cost_data['cap_min'] <= Q_nom_CT_W) & (CT_cost_data['cap_max'] > Q_nom_CT_W)]
            Inv_a = CT_cost_data.iloc[0]['a']
            Inv_b = CT_cost_data.iloc[0]['b']
            Inv_c = CT_cost_data.iloc[0]['c']
            Inv_d = CT_cost_data.iloc[0]['d']
            Inv_e = CT_cost_data.iloc[0]['e']
            Inv_IR = CT_cost_data.iloc[0]['IR_%']
            Inv_LT = CT_cost_data.iloc[0]['LT_yr']
            Inv_OM = CT_cost_data.iloc[0]['O&M_%'] / 100
            InvC = Inv_a + Inv_b * (Q_nom_CT_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_CT_W) * log(Q_nom_CT_W)
            Capex_a_CT_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)
            Opex_fixed_CT_USD = InvC * Inv_OM
            Capex_CT_USD = InvC
        else:
            number_of_chillers = int(ceil(Q_nom_CT_W / max_chiller_size))
            Q_nom_each_CT = Q_nom_CT_W / number_of_chillers
            for i in range(number_of_chillers):
                CT_cost_data = CT_cost_data[
                    (CT_cost_data['cap_min'] <= Q_nom_each_CT) & (CT_cost_data['cap_max'] > Q_nom_each_CT)]
                Inv_a = CT_cost_data.iloc[0]['a']
                Inv_b = CT_cost_data.iloc[0]['b']
                Inv_c = CT_cost_data.iloc[0]['c']
                Inv_d = CT_cost_data.iloc[0]['d']
                Inv_e = CT_cost_data.iloc[0]['e']
                Inv_IR = CT_cost_data.iloc[0]['IR_%']
                Inv_LT = CT_cost_data.iloc[0]['LT_yr']
                Inv_OM = CT_cost_data.iloc[0]['O&M_%'] / 100
                InvC = Inv_a + Inv_b * (Q_nom_each_CT) ** Inv_c + (Inv_d + Inv_e * Q_nom_each_CT) * log(Q_nom_each_CT)
                Capex_a1 = calc_capex_annualized(InvC, Inv_IR, Inv_LT)
                Capex_a_CT_USD = Capex_a_CT_USD + Capex_a1
                Opex_fixed_CT_USD = Opex_fixed_CT_USD + InvC * Inv_OM
                Capex_CT_USD = Capex_CT_USD + InvC
    return Capex_a_CT_USD, Opex_fixed_CT_USD, Capex_CT_USD