diff --git a/README.md b/README.md
index c593b616..e2524441 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,49 @@
 This is a repository for WaterTAP development and analysis for the NAWI 3.25 project.
 
-To install this environment, open command prompt or terminal and from the location of this repo execute the following command:
+## Installation
 
-`conda env create -f flex_desal_analysis_setup.yml`
+### 1. Create the conda environment
+
+Open command prompt or terminal and from the location of this repo execute:
+
+```bash
+conda env create -f flex_desal_analysis_setup.yml
+conda activate flex_desal_analysis
+```
+
+### 2. Install WaterTAP (development version)
+
+This project requires the development version of WaterTAP installed in editable mode:
+
+```bash
+# Clone watertap repository (if not already done)
+git clone https://github.com/watertap-org/watertap.git
+cd watertap
+
+# Install in editable mode without dependencies to avoid version conflicts
+pip install --no-deps -e .
+cd ..
+```
+
+### 3. Install idaes-pse 2.10
+
+Force install the required version:
+
+```bash
+pip install --upgrade --force-reinstall idaes-pse==2.10.0
+```
+
+### 4. Install this package
+
+Return to the flex_desal directory and install in editable mode:
+
+```bash
+pip install --no-deps -e .
+```
+
+### Notes
+
+- This project requires Python 3.11
+- WaterTAP 1.5.dev0+ is required for compatibility
+- idaes-pse 2.10.0 is required (WaterTAP may try to downgrade this)
+- After installation, you may see dependency warnings from pip - these can typically be ignored
diff --git a/dummy_result.csv b/dummy_result.csv
new file mode 100644
index 00000000..72d92f26
--- /dev/null
+++ b/dummy_result.csv
@@ -0,0 +1,11 @@
+,Day,Time,LMP,bypass_pretreatment_flow,power_from_grid,intake.op_mode,intake.startup,intake.shutdown,intake.energy_intensity,intake.power_consumption,intake.recovery,intake.feed_flowrate,intake.product_flowrate,intake.reject_flowrate,pretreatment.op_mode,pretreatment.startup,pretreatment.shutdown,pretreatment.energy_intensity,pretreatment.power_consumption,pretreatment.recovery,pretreatment.feed_flowrate,pretreatment.product_flowrate,pretreatment.reject_flowrate,reverse_osmosis.op_mode,reverse_osmosis.startup,reverse_osmosis.shutdown,reverse_osmosis.power_consumption,reverse_osmosis.feed_flowrate,reverse_osmosis.product_flowrate,reverse_osmosis.reject_flowrate,reverse_osmosis.inlet_flowrate,reverse_osmosis.leftover_flow,reverse_osmosis.ro_skid[1].op_mode,reverse_osmosis.ro_skid[1].startup,reverse_osmosis.ro_skid[1].shutdown,reverse_osmosis.ro_skid[1].energy_intensity,reverse_osmosis.ro_skid[1].power_consumption,reverse_osmosis.ro_skid[1].recovery,reverse_osmosis.ro_skid[1].feed_flowrate,reverse_osmosis.ro_skid[1].product_flowrate,reverse_osmosis.ro_skid[1].reject_flowrate,reverse_osmosis.ro_skid[2].op_mode,reverse_osmosis.ro_skid[2].startup,reverse_osmosis.ro_skid[2].shutdown,reverse_osmosis.ro_skid[2].energy_intensity,reverse_osmosis.ro_skid[2].power_consumption,reverse_osmosis.ro_skid[2].recovery,reverse_osmosis.ro_skid[2].feed_flowrate,reverse_osmosis.ro_skid[2].product_flowrate,reverse_osmosis.ro_skid[2].reject_flowrate,reverse_osmosis.ro_skid[3].op_mode,reverse_osmosis.ro_skid[3].startup,reverse_osmosis.ro_skid[3].shutdown,reverse_osmosis.ro_skid[3].energy_intensity,reverse_osmosis.ro_skid[3].power_consumption,reverse_osmosis.ro_skid[3].recovery,reverse_osmosis.ro_skid[3].feed_flowrate,reverse_osmosis.ro_skid[3].product_flowrate,reverse_osmosis.ro_skid[3].reject_flowrate,posttreatment.op_mode,posttreatment.startup,posttreatment.shutdown,posttreatment.energy_intensity,posttreatment.power_consumption,posttreatment.feed_flowrate,posttreatment.product_flowrate,posttreatment.reject_flowrate,brine_discharge.op_mode,brine_discharge.startup,brine_discharge.shutdown,brine_discharge.feed_flowrate,brine_discharge.power_consumption,num_skids_online,net_power_consumption,water_revenue,demand_response_revenue,emissions_cost,energy_cost
+0,1,1,0.12955,7.154440539027692e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,0.0,,,,1441.9791216291196,1063.499999999993,431.6029166298917,581.4070833701084,1013.01,50.489999999992875,1.0,0.0,0.0,3.3409855820451884,480.6597072097066,0.4260598776220292,337.67,143.8676388766306,193.80236112336942,1.0,0.0,0.0,3.3409855820451893,480.65970720970665,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045188,480.65970720970654,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.9571958182556,431.6029166298917,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589163e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+1,1,2,0.12955,7.154440539028204e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,8.829685626331071e-44,,,,1441.9791216291196,1063.499999999993,431.6029166298917,581.4070833701084,1013.01,50.4899999999929,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.95719581825557,431.6029166298917,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589161e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+2,1,3,0.12955,7.154440539026487e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,4.3941107118622926e-44,,,,1441.9791216291196,1063.499999999993,431.60291662989175,581.4070833701084,1013.01,50.489999999992904,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.65970720970665,0.4260598776220291,337.67,143.8676388766306,193.80236112336942,1.0,0.0,0.0,3.340985582045188,480.6597072097065,0.4260598776220292,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.9571958182556,431.60291662989175,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589163e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+3,1,4,0.12955,7.1544405390276086e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,4.495102730091952e-44,,,,1441.9791216291196,1063.499999999993,431.6029166298917,581.4070833701084,1013.01,50.489999999992875,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.3409855820451884,480.65970720970654,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.9571958182556,431.6029166298917,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589161e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+4,1,5,0.12955,7.154440539026553e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,0.0,,,,1441.9791216291196,1063.499999999993,431.60291662989175,581.4070833701084,1013.01,50.489999999992875,1.0,0.0,0.0,3.340985582045189,480.65970720970665,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.65970720970665,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045188,480.6597072097065,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.9571958182556,431.60291662989175,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589163e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+5,1,6,0.12955,7.154440539027364e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,6.7601703259419886e-46,,,,1441.9791216291196,1063.499999999993,431.6029166298917,581.4070833701084,1013.01,50.489999999992875,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.3409855820451884,480.6597072097066,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.95719581825557,431.6029166298917,431.6029166324336,0.0,0.0,,,631.8970833675664,5.98866549358916e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+6,1,7,0.12955,7.15444053902837e-12,1796.670281616262,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999929,1,1063.499999999993,1063.499999999993,0.0,,,,1441.9791216291196,1063.499999999993,431.6029166298917,581.4070833701084,1013.01,50.489999999992875,1.0,0.0,0.0,3.3409855820451884,480.65970720970654,0.4260598776220291,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.340985582045189,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,0.0,0.0,3.3409855820451884,480.6597072097066,0.42605987762202907,337.67,143.86763887663056,193.80236112336945,1.0,,,0.41,176.9571958182556,431.6029166298917,431.6029166324336,0.0,0.0,,,631.8970833675664,5.988665493589161e-08,3.0,1796.6702816162617,0.0,-0.0,0.0,58.189658745846685
+7,1,8,0.12955,6.462239583664445e-12,1351.4388464356916,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999936,1,1063.4999999999936,1063.4999999999936,0.0,,,,1045.373418782598,1063.4999999999936,313.00356945541756,362.33643054458247,675.34,388.1599999999935,1.0,0.0,0.0,3.339813090954207,522.686709391299,0.4634755374410187,337.67,156.50178472770878,181.16821527229123,1.0,0.0,0.0,3.339813090954207,522.686709391299,0.4634755374410187,337.67,156.50178472770878,181.16821527229123,0.0,0.0,1.0,3.3580698795641535,7.531979245745892e-44,0.48957107422033336,0.0,2.2420775429197073e-44,0.0,1.0,,,0.41,128.33146347672118,313.00356945541756,313.0035694579595,0.0,0.0,,,750.4964305420405,6.737255316331029e-08,2.0,1351.4388464356919,0.0,-0.0,0.0,43.76972563893596
+8,1,9,0.12955,6.462239583663725e-12,1351.4388464356916,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999936,1,1063.4999999999936,1063.4999999999936,3.1389085600875902e-43,,,,1045.373418782598,1063.4999999999936,313.00356945541756,362.33643054458247,675.34,388.1599999999935,1.0,0.0,0.0,3.3398130909542076,522.686709391299,0.4634755374410186,337.67,156.50178472770875,181.16821527229126,1.0,0.0,0.0,3.339813090954207,522.6867093912991,0.46347553744101866,337.67,156.50178472770878,181.16821527229123,0.0,0.0,0.0,3.358069879564154,0.0,0.48957107422033236,2.5817282058546874e-44,0.0,7.066156982800415e-44,1.0,,,0.41,128.33146347672118,313.00356945541756,313.0035694579595,0.0,0.0,,,750.4964305420406,6.73725531633103e-08,2.0,1351.4388464356919,0.0,-0.0,0.0,43.76972563893596
+9,1,10,0.12955,6.46223958366386e-12,1351.4388464356916,,,,0.157121734,167.098964109,1,1063.5,1063.5,0.0,1,,,0.01,10.634999999999936,1,1063.4999999999936,1063.4999999999936,0.0,,,,1045.373418782598,1063.4999999999936,313.0035694554175,362.3364305445825,675.34,388.1599999999935,1.0,0.0,0.0,3.3398130909542076,522.686709391299,0.4634755374410186,337.67,156.50178472770875,181.16821527229126,1.0,0.0,0.0,3.3398130909542076,522.6867093912989,0.46347553744101855,337.67,156.50178472770872,181.1682152722913,0.0,0.0,0.0,3.3580698795641535,3.0121348396432044e-43,0.4895710742203331,1.793662034335766e-43,8.96831017167883e-44,8.96831017167883e-44,1.0,,,0.41,128.33146347672115,313.0035694554175,313.00356945795943,0.0,0.0,,,750.4964305420406,6.737255316331029e-08,2.0,1351.4388464356919,0.0,-0.0,0.0,43.76972563893596
diff --git a/flex_desal_analysis_setup.yml b/flex_desal_analysis_setup.yml
index 3eecc124..06bc7910 100644
--- a/flex_desal_analysis_setup.yml
+++ b/flex_desal_analysis_setup.yml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python=3.10
+  - python=3.11
   - git
   - pytest
   - pip
diff --git a/operation_profile.png b/operation_profile.png
new file mode 100644
index 00000000..2066099c
Binary files /dev/null and b/operation_profile.png differ
diff --git a/pyproject.toml b/pyproject.toml
index 61407b98..c7a5cf11 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,9 +7,16 @@ name = "flex_desal_analysis"
 readme = "README.md"
 version = "0.1.0"
 description = "Python package for conducting analysis for NAWI 3.25"
-requires-python = ">=3.10,<3.11"
+requires-python = ">=3.11,<3.12"
 dependencies = [
-    "watertap-reflo>=0.2"
+    "idaes-pse>=2.10",
+    "pyomo>=6.9.3",
+    "pint>=0.24",
+    "pyyaml",
+    "pandas",
+    "numpy",
+    "matplotlib",
+    "scipy"
 ]
 
 [tool.setuptools]
diff --git a/src/pricetaker/__init__.py b/src/pricetaker/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/pricetaker/flowsheets/__init__.py b/src/pricetaker/flowsheets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/pricetaker/flowsheets/flowsheet.py b/src/pricetaker/flowsheets/flowsheet.py
new file mode 100644
index 00000000..ac4e48f0
--- /dev/null
+++ b/src/pricetaker/flowsheets/flowsheet.py
@@ -0,0 +1,324 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+"""
+This module contains the functions needed for the construction of flexible
+desalination flowsheet
+"""
+
+from idaes.apps.grid_integration import OperationModel
+from pyomo.environ import (
+    Constraint,
+    Expression,
+    NonNegativeReals,
+    Param,
+    Var,
+    units as pyunits,
+)
+from watertap.flowsheets.flex_desal import params as um_params
+from watertap.flowsheets.flex_desal import unit_models as um
+
+
+def add_operational_cost_expressions(blk, params: um_params.FlexDesalParams):
+    """
+    Adds cost expressions to the flowsheet
+    """
+    # Water revenue
+    blk.water_revenue = Expression(
+        expr=(
+            params.product_water_price
+            * blk.posttreatment.product_flowrate
+            * params.timestep_hours
+        ),
+        doc="Revenue generated from product water",
+    )
+
+    # Customer cost
+    blk.customer_cost = Param(
+        initialize=0,
+        mutable=True,
+        doc="Fixed customer cost",
+    )
+
+    # Demand response revenue
+    blk.demand_response_price = Param(
+        initialize=0, mutable=True, doc="Demand-response prices"
+    )
+    blk.baseline_power = Param(
+        initialize=100, mutable=True, doc="Baseline power requirement"
+    )
+    blk.demand_response_revenue = Expression(
+        expr=blk.demand_response_price
+        * (blk.baseline_power - blk.power_from_grid)
+        * params.timestep_hours,
+        doc="Revenue generated from demand response",
+    )
+
+    # Cost of emissions
+    blk.emissions_intensity = Param(
+        initialize=0, mutable=True, units=pyunits.kg / pyunits.kWh
+    )
+    blk.emissions_cost = Expression(
+        expr=(
+            blk.emissions_intensity
+            * blk.power_from_grid
+            * params.timestep_hours
+            * params.emissions_cost
+            / 907.185  # Conversion factor: $/ton to $/kg
+        ),
+        doc="Cost associated with carbon emissions",
+    )
+
+    # Cost of energy
+    blk.LMP = Param(
+        initialize=0,
+        mutable=True,
+        doc="Locational marginal price of electricity [$/kWh]",
+    )
+    blk.energy_cost = Expression(
+        expr=blk.LMP * blk.power_from_grid * params.timestep_hours,
+        doc="Cost of electricity purchased from the grid",
+    )
+
+    # Demand cost parameters
+    blk.fixed_demand_rate = Param(
+        initialize=0,
+        mutable=True,
+        doc="Constant demand tariff",
+    )
+    blk.variable_demand_rate = Param(
+        initialize=0,
+        mutable=True,
+        doc="Variable demand tariff",
+    )
+
+
+def build_desal_flowsheet(blk, params: um_params.FlexDesalParams):
+    """
+    Builds a flowsheet instance of the entire desalination process
+
+    Parameters
+    ----------
+    blk : Block
+        Pyomo Block instance
+
+    params : object
+        Object containing model parameters
+    """
+    # Build units
+    blk.intake = OperationModel(
+        model_func=um.intake_operation_model,
+        model_args={"params": params.intake},
+    )
+    blk.bypass_pretreatment_flow = Var(
+        within=NonNegativeReals,
+        doc="Flowrate bypassed to brine discharge due to pretreatment shutdown",
+    )
+    blk.pretreatment = OperationModel(
+        model_func=um.pretreatment_operation_model,
+        model_args={"params": params.pretreatment},
+    )
+    blk.reverse_osmosis = OperationModel(
+        model_func=um.reverse_osmosis_operation_model,
+        model_args={"params": params.ro},
+    )
+    blk.posttreatment = OperationModel(
+        model_func=um.posttreatment_operation_model,
+        model_args={"params": params},
+    )
+    blk.brine_discharge = OperationModel(
+        model_func=um.brine_discharge_operation_model,
+        model_args={"params": params},
+    )
+
+    # Flowsheet connections
+    blk.arc_intake_pretreatment = Constraint(
+        expr=blk.intake.product_flowrate
+        == blk.pretreatment.feed_flowrate + blk.bypass_pretreatment_flow,
+        doc="intake-pretreatment mass balance",
+    )
+    blk.suppress_pretreatment_bypass = Constraint(
+        expr=blk.bypass_pretreatment_flow
+        <= (1 - blk.pretreatment.op_mode) * params.intake.nominal_flowrate
+    )
+    blk.arc_pretreatment_ro = Constraint(
+        expr=blk.pretreatment.product_flowrate == blk.reverse_osmosis.feed_flowrate,
+        doc="pretreatment-reverse_osmosis mass balance",
+    )
+    blk.arc_ro_posttreatment = Constraint(
+        expr=blk.reverse_osmosis.product_flowrate == blk.posttreatment.feed_flowrate,
+        doc="reverse_osmosis-posttreatment mass balance",
+    )
+    blk.calculate_brine_discharge = Constraint(
+        expr=blk.brine_discharge.feed_flowrate
+        == (
+            blk.intake.reject_flowrate
+            + blk.bypass_pretreatment_flow
+            + blk.pretreatment.reject_flowrate
+            + blk.reverse_osmosis.reject_flowrate
+            + blk.reverse_osmosis.leftover_flow
+            + blk.posttreatment.reject_flowrate
+        ),
+        doc="Computes the total inflow to brine discharge",
+    )
+
+    blk.num_skids_online = Expression(
+        expr=sum(blk.reverse_osmosis.ro_skid[:].op_mode),
+        doc="Calculates the number of skids operating at time t",
+    )
+
+    blk.net_power_consumption = Expression(
+        expr=blk.intake.power_consumption
+        + blk.pretreatment.power_consumption
+        + blk.reverse_osmosis.power_consumption
+        + blk.posttreatment.power_consumption
+        + blk.brine_discharge.power_consumption,
+        doc="Net power consumed from the grid",
+    )
+
+    if params.include_onsite_solar:
+        blk.power_generation = OperationModel(
+            model_func=um.power_generation_operation_model,
+            model_args={"params": params},
+        )
+        blk.net_power_consumption += -blk.power_generation.power_utilized
+
+    if params.include_battery:
+        blk.battery = OperationModel()
+        blk.net_power_consumption += blk.battery.power_charge - blk.battery.discharge
+
+    # Power purchased from the grid
+    blk.power_from_grid = Var(
+        within=NonNegativeReals,
+        units=pyunits.kW,
+        doc="Total power purchased from the grid",
+    )
+    blk.overall_power_balance = Constraint(
+        expr=blk.power_from_grid == blk.net_power_consumption,
+        doc="Computes the total power purchased from the grid",
+    )
+
+    # Add cost expressions
+    add_operational_cost_expressions(blk, params)
+
+
+def add_delayed_startup_constraints(m):
+    """Adds the delayed startup constraints to the model"""
+    params: um_params.FlexDesalParams = m.params
+
+    # "Shutdown" post-treatment unit if RO startup is initiated
+    @m.Constraint(m.period.index_set())
+    def posttreatment_unit_commitment(blk, d, t):
+        indices = [(d, t - i) for i in range(params.ro.startup_delay) if t - i > 0]
+        return (1 - blk.period[d, t].posttreatment.op_mode) == sum(
+            blk.period[p].reverse_osmosis.ro_skid[1].startup for p in indices
+        )
+
+    # Brine pump must operate if RO startup is initiated
+    @m.Constraint(m.period.index_set())
+    def brine_pump_unit_commitment(blk, d, t):
+        indices = [(d, t - i) for i in range(params.ro.startup_delay) if t - i > 0]
+        return blk.period[d, t].brine_discharge.op_mode == sum(
+            blk.period[p].reverse_osmosis.ro_skid[1].startup for p in indices
+        )
+
+
+def add_demand_and_fixed_costs(m):
+    """Adds variables and expressions/constraints for demand and fixed costs"""
+
+    params: um_params.FlexDesalParams = m.params
+    m.fixed_demand_cost = Var(
+        within=NonNegativeReals,
+        doc="Total fixed demand charge value for the entire horizon",
+    )
+    m.variable_demand_cost = Var(
+        within=NonNegativeReals,
+        doc="Total variable demand charge value for the entire time horizon",
+    )
+    m.fixed_monthly_cost = Var(
+        within=NonNegativeReals,
+        doc="Total customer cost for the entire time horizon",
+    )
+
+    @m.Constraint(m.period.index_set())
+    def calculate_fixed_demand_cost(blk, d, t):
+        return (
+            blk.fixed_demand_cost
+            >= blk.period[d, t].fixed_demand_rate
+            * blk.period[d, t].power_from_grid
+            * params.num_months
+        )
+
+    @m.Constraint(m.period.index_set())
+    def calculate_variable_demand_cost(blk, d, t):
+        return (
+            blk.variable_demand_cost
+            >= blk.period[d, t].variable_demand_rate
+            * blk.period[d, t].power_from_grid
+            * params.num_months
+        )
+
+    m.calculate_fixed_monthly_cost = Constraint(
+        expr=m.fixed_monthly_cost == params.fixed_monthly_cost * params.num_months
+    )
+
+
+def add_useful_expressions(m):
+    """Defines useful expressions for custom objective functions"""
+
+    m.total_water_revenue = Expression(expr=sum(m.period[:, :].water_revenue))
+    m.total_demand_response_revenue = Expression(
+        expr=sum(m.period[:, :].demand_response_revenue)
+    )
+    m.total_emissions_cost = Expression(expr=sum(m.period[:, :].emissions_cost))
+
+
+def constrain_water_production(m, baseline_production: float = None):
+    """Constrains the total water production rate"""
+
+    params: um_params.FlexDesalParams = m.params
+    if baseline_production is not None:
+        m.curtailment_fraction = Param(
+            initialize=params.curtailment_fraction,
+            mutable=True,
+            units=pyunits.dimensionless,
+            doc="Fraction of water production that is curtailed",
+        )
+
+        m.baseline_production = Param(
+            initialize=baseline_production,
+            mutable=True,
+            units=pyunits.m**3,
+            doc="Baseline water production",
+        )
+
+        m.water_production_target = Constraint(
+            expr=m.total_water_production
+            >= m.baseline_production * (1 - m.curtailment_fraction)
+        )
+
+    elif params.annual_production_AF is not None:
+        # Convert production rate from acre-ft/year to m^3/year
+        annual_production_m3 = params.annual_production_AF * 1233.48
+        m.production_target_abs = Param(
+            initialize=annual_production_m3 / 365 * params.num_days,
+            mutable=True,
+            units=pyunits.m**3,
+            doc="Absolute water production target",
+        )
+
+        m.water_production_target = Constraint(
+            expr=m.total_water_production >= m.production_target_abs
+        )
+
+    else:
+        raise ValueError("Water production targets not specified in params")
diff --git a/src/pricetaker/flowsheets/params.py b/src/pricetaker/flowsheets/params.py
new file mode 100644
index 00000000..3366e3dd
--- /dev/null
+++ b/src/pricetaker/flowsheets/params.py
@@ -0,0 +1,432 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+"""
+This module contains the default values of all the required
+parameters.
+"""
+
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from typing import Optional
+
+import numpy as np
+from pyomo.environ import exp
+from scipy import optimize
+
+
+@dataclass
+class UnitParams:
+    """Abstract dataclass for parameters of all units"""
+
+    energy_intensity: Optional[float] = None
+    allow_shutdown: bool = False
+    leakage_fraction: Optional[float] = None
+    minimum_flowrate: Optional[float] = None
+    nominal_flowrate: Optional[float] = None
+    maximum_flowrate: Optional[float] = None
+    nominal_recovery: Optional[float] = None
+    minimum_recovery: Optional[float] = None
+    maximum_recovery: Optional[float] = None
+    minimum_uptime: Optional[int] = None
+    minimum_downtime: Optional[int] = None
+    startup_delay: Optional[int] = None
+
+    @property
+    def get_leakage_fraction(self):
+        """Returns the leakage fraction value"""
+        if self.leakage_fraction is not None:
+            return self.leakage_fraction
+
+        if self.recovery is not None:
+            return 1 - self.recovery
+
+        raise ValueError("leakage_fraction is not specified")
+
+    @property
+    def get_recovery(self):
+        """Returns the recovery value"""
+        if self.nominal_recovery is not None:
+            return self.nominal_recovery
+
+        if self.leakage_fraction is not None:
+            return 1 - self.leakage_fraction
+
+        raise ValueError("recovery is not specified")
+
+    def update(self, value_map: dict):
+        """Updates the values of the specified attributes"""
+        # Raise an error if an unrecognized attribute is provided
+        new_values = value_map.copy()
+        for key in value_map:
+            if key not in self.__dict__:
+                if "_" + key in self.__dict__:
+                    # This is a property
+                    setattr(self, key, value_map[key])
+                    # Remove this element
+                    new_values.pop(key)
+
+                else:
+                    raise KeyError(f"Unrecognized attribute {key}")
+
+        self.__dict__.update(new_values)
+
+
+@dataclass
+class IntakeParams(UnitParams):
+    """Parameters for the intake unit"""
+
+    energy_intensity: float = 0.157121734
+    leakage_fraction: float = 0
+    minimum_flowrate: float = 1063.5
+    nominal_flowrate: float = 1063.5
+    maximum_flowrate: float = 1063.5
+
+
+@dataclass
+class PretreatmentParams(UnitParams):
+    """Parameters for the pretreatment unit"""
+
+    allow_shutdown: bool = False
+    energy_intensity: float = 0.01
+    leakage_fraction: float = 0
+    minimum_downtime: int = 0
+    startup_delay: int = 0
+
+
+@dataclass
+class ROParams(UnitParams):
+    """Parameters for the RO unit"""
+
+    num_ro_skids: int = 3
+    minimum_operating_skids: int = 2
+    allow_shutdown: bool = True
+    nominal_flowrate: float = 337.670
+    minimum_recovery: float = 0.4
+    nominal_recovery: float = 0.465
+    maximum_recovery: float = 0.55
+    minimum_uptime: int = 1
+    minimum_downtime: int = 4
+    startup_delay: int = 8
+    allow_variable_recovery: bool = False
+
+    def __post_init__(self):
+        self._surrogate_type = "exponential_quadratic"
+        self.surrogate_a = 6.180228375549232
+        self.surrogate_b = 2.3684824891480476
+        self.surrogate_c = 6.474944185881354
+        self.surrogate_d = 1.9065595663669615e-05
+
+    @property
+    def surrogate_type(self):
+        """Returns the surrogate type for RO energy intensity"""
+        return self._surrogate_type
+
+    @surrogate_type.setter
+    def surrogate_type(self, value: str):
+        if value in ["exponential_quadratic", "quadratic_surrogate"]:
+            self._surrogate_type = value
+
+        else:
+            raise ValueError("Unrecognized surrogate type")
+
+    @property
+    def surrogate_coeffs(self):
+        """Returs the coefficients of the surrogate model as a dictionary"""
+        return {
+            "a": self.surrogate_a,
+            "b": self.surrogate_b,
+            "c": self.surrogate_c,
+            "d": self.surrogate_d,
+        }
+
+    def get_energy_intensity(self, recovery):
+        """Returns the energy intensity for a given recovery"""
+        coeffs = self.surrogate_coeffs
+        if self.surrogate_type == "exponential_quadratic":
+            return (
+                coeffs["a"] * exp(-coeffs["b"] * recovery)
+                + coeffs["c"] * recovery**2
+                + coeffs["d"]
+            )
+
+        if self.surrogate_type == "quadratic_surrogate":
+            return coeffs["a"] * recovery**2 + coeffs["b"] * recovery + coeffs["c"]
+
+        return None
+
+    def get_optimum_energy_intensity(self, recovery_lb, recovery_ub):
+        """
+        Returns the optimum energy intensity if it exists inside the
+        interval. Returns None is the optimum is at the bounds.
+        """
+        # Optimum exists inside the interval, and it is unique.
+        coeffs = self.surrogate_coeffs
+        if self.surrogate_type == "exponenetial_quadratic":
+
+            def _first_der(rec):
+                return (
+                    -coeffs["a"] * coeffs["b"] * exp(-coeffs["b"] * rec)
+                    + 2 * coeffs["c"] * rec
+                )
+
+            # First derivative is a monotonically increasing function.
+            # Therefore, if the first derivative has the same sign at both
+            # ends of the interval, then there is no point in the interval
+            # at which the derivative vanishes. So, the optimum is at its bounds
+            if _first_der(recovery_lb) * _first_der(recovery_ub) > 0:
+                # Optimum does not exist, so return
+                return None
+
+            root = optimize.bisect(_first_der, recovery_lb, recovery_ub, maxiter=1000)
+
+        else:
+            # This is "quadratic_surrogate":
+            root = -coeffs["b"] / (2 * coeffs["a"])
+
+        return self.get_energy_intensity(root)
+
+    def get_energy_intensity_bounds(self, recovery_lb=None, recovery_ub=None):
+        """
+        Returns the bounds on energy intensity based on the bounds of
+        recovery
+        """
+        if recovery_lb is None:
+            recovery_lb = self.minimum_recovery
+        if recovery_ub is None:
+            recovery_ub = self.maximum_recovery
+
+        ei_values = [
+            self.get_energy_intensity(recovery=self.minimum_recovery),
+            self.get_energy_intensity(recovery=self.maximum_recovery),
+            self.get_optimum_energy_intensity(recovery_lb, recovery_ub),
+        ]
+
+        ei_values = list(filter(None, ei_values))  # remove None, if it exists
+        return min(ei_values), max(ei_values)
+
+
+@dataclass
+class PosttreatmentParams(UnitParams):
+    """Parameters for the posttreatment unit"""
+
+    energy_intensity: float = 0.41
+    leakage_fraction: float = 0
+
+
+@dataclass
+class BrineDischargeParams(UnitParams):
+    """Parameters for the brine discharge unit"""
+
+    energy_intensity: float = 0.1
+    leakage_fraction: float = 0
+
+
+@dataclass
+class Battery:
+    """Parameters for the battery"""
+
+    energy_capacity: float = 0
+    power_capacity: float = 50
+    efficiency: float = 0.86
+    initial_soc: float = 0.5
+    minimum_soc: float = 0.2
+    maximum_soc: float = 0.95
+
+
+@dataclass
+class FlexDesalParams:
+    """Parameters for fleible desalination"""
+
+    start_date: str = "2022-07-05 00:00:00"
+    end_date: str = "2022-07-06 00:00:00"
+    timestep_hours: float = 0.25
+
+    product_water_price: float = 0
+    fixed_monthly_cost: float = 766000
+    customer_rate: float = 100
+    constrain_to_baseline_production: bool = False
+    curtailment_fraction: float = 0.0
+    annual_production_AF: float = 3125  # in acre-ft / year
+    production_constraint_to_objective: bool = False
+    production_constraint_penalty: float = 0.6
+    emissions_cost: float = 0  # Cost of emissions in $/kg
+
+    include_demand_response: bool = False
+    include_battery: bool = False
+    include_onsite_solar: bool = False
+    onsite_capacity: float = 0
+
+    def __post_init__(self):
+        self.intake = IntakeParams()
+        self.pretreatment = PretreatmentParams()
+        self.wrd_uf = WRD_UFParams()  ### ADDED
+        self.wrd_ro = WRD_ROParams()  ### ADDED
+        self.ro = ROParams()
+        self.posttreatment = PosttreatmentParams()
+        self.brinedischarge = BrineDischargeParams()
+        self.battery = Battery()
+
+        # datetime array
+        t = np.arange(
+            datetime.fromisoformat(self.start_date),
+            datetime.fromisoformat(self.end_date),
+            timedelta(hours=self.timestep_hours),
+        ).astype(datetime)
+
+        # length of time step in seconds
+        dt_seconds = self.timestep_hours * 3600
+        total_num_seconds = (t[-1] - t[0]).total_seconds() + dt_seconds
+
+        self.num_hours = total_num_seconds / 3600
+        self.num_days = self.num_hours / 24
+        self.num_months = self.num_days / 31
+
+
+@dataclass
+class WRD_ROParams(UnitParams):
+    """Parameters for the RO unit"""
+
+    num_ro_skids: int = 4
+    minimum_operating_skids: int = 2
+    allow_shutdown: bool = True
+    minimum_flowrate: float = 0
+    nominal_flowrate: float = 337.670
+    maximum_flowrate: float = 400
+    minimum_recovery: float = 0.4  # Not used
+    nominal_recovery: float = 0.465
+    maximum_recovery: float = 0.55  # Not used
+    minimum_uptime: int = 1
+    minimum_downtime: int = 4
+    startup_delay: int = 8
+    allow_variable_recovery: bool = False
+
+    def __post_init__(self):
+        # self._surrogate = # load the surrogate model here.
+        self.surrogate_type: str = "constant_energy_intensity"
+        self.surrogate_a = (
+            1  # Not sure this input is being passed from the main script...
+        )
+        self.surrogate_b = 1
+
+    @property
+    def surrogate_coeffs(self):
+        """Returs the coefficients of the surrogate model as a dictionary"""
+        return {
+            "a": self.surrogate_a,
+            "b": self.surrogate_b,
+        }
+
+    def get_energy_intensity(self, flowrate):
+        """Returns the energy intensity for a given flowrate"""
+        # Placeholder implementation
+        coeffs = self.surrogate_coeffs
+        if self.surrogate_type == "constant_energy_intensity":
+            return coeffs["a"] + coeffs["b"] * flowrate
+
+        return None
+
+    def get_optimum_energy_intensity(self, flowrate_lb, flowrate_ub):
+        """
+        Returns the optimum energy intensity if it exists inside the
+        interval. Returns None is the optimum is at the bounds.
+        """
+        # No optimum exists inside the interval, because energy is constant.
+        return None
+
+    def get_energy_intensity_bounds(self, flowrate_lb=None, flowrate_ub=None):
+        """
+        Returns the bounds on energy intensity based on the bounds of
+        flowrate
+        """
+        if flowrate_lb is None:
+            flowrate_lb = self.minimum_flowrate
+        if flowrate_ub is None:
+            flowrate_ub = self.maximum_flowrate
+
+        ei_values = [
+            self.get_energy_intensity(flowrate=self.minimum_flowrate),
+            self.get_energy_intensity(flowrate=self.maximum_flowrate),
+            self.get_optimum_energy_intensity(flowrate_lb, flowrate_ub),
+        ]
+
+        ei_values = list(filter(None, ei_values))  # remove None, if it exists
+        return min(ei_values), max(ei_values)
+
+
+@dataclass
+class WRD_UFParams(UnitParams):
+    """Parameters for the UF unit"""
+
+    num_uf_pumps: int = 4
+    minimum_operating_pumps: int = 1
+    allow_shutdown: bool = True
+    minimum_flowrate: float = 0
+    nominal_flowrate: float = 337.670
+    maximum_flowrate: float = 1000
+    nominal_recovery: float = 1
+    minimum_uptime: int = 1
+    minimum_downtime: int = 4
+    startup_delay: int = 8
+    allow_variable_recovery: bool = False
+
+    def __post_init__(self):
+        # self._surrogate = # load the surrogate model here.
+        self.surrogate_type: str = "constant_energy_intensity"
+        self.surrogate_a = (
+            1  # Not sure this input is being passed from the main script...
+        )
+        self.surrogate_b = 1
+
+    @property
+    def surrogate_coeffs(self):
+        """Returs the coefficients of the surrogate model as a dictionary"""
+        return {
+            "a": self.surrogate_a,
+            "b": self.surrogate_b,
+        }
+
+    def get_energy_intensity(self, flowrate):
+        """Returns the energy intensity for a given flowrate"""
+        # Placeholder implementation
+        coeffs = self.surrogate_coeffs
+        if self.surrogate_type == "constant_energy_intensity":
+            return coeffs["a"] + coeffs["b"] * flowrate
+
+        return None
+
+    def get_optimum_energy_intensity(self, flowrate_lb, flowrate_ub):
+        """
+        Returns the optimum energy intensity if it exists inside the
+        interval. Returns None is the optimum is at the bounds.
+        """
+        # No optimum exists inside the interval, because energy is constant.
+        return None
+
+    def get_energy_intensity_bounds(self, flowrate_lb=None, flowrate_ub=None):
+        """
+        Returns the bounds on energy intensity based on the bounds of
+        flowrate
+        """
+        if flowrate_lb is None:
+            flowrate_lb = self.minimum_flowrate
+        if flowrate_ub is None:
+            flowrate_ub = self.maximum_flowrate
+
+        ei_values = [
+            self.get_energy_intensity(flowrate=self.minimum_flowrate),
+            self.get_energy_intensity(flowrate=self.maximum_flowrate),
+            self.get_optimum_energy_intensity(flowrate_lb, flowrate_ub),
+        ]
+
+        ei_values = list(filter(None, ei_values))  # remove None, if it exists
+        return min(ei_values), max(ei_values)
diff --git a/src/pricetaker/flowsheets/sbce_pricesignal.csv b/src/pricetaker/flowsheets/sbce_pricesignal.csv
new file mode 100644
index 00000000..6b655ef5
--- /dev/null
+++ b/src/pricetaker/flowsheets/sbce_pricesignal.csv
@@ -0,0 +1,961 @@
+DateTime,electric_customer_0_2022-07-05_2022-07-14_0,electric_energy_0_2022-07-05_2022-07-14_0,electric_energy_1_2022-07-05_2022-07-14_0,electric_energy_2_2022-07-05_2022-07-14_0,electric_energy_3_2022-07-05_2022-07-14_0,electric_demand_peak-summer_2022-07-05_2022-07-14_0,electric_demand_maximum_2022-07-05_2022-07-14_0
+2022-07-05 00:00:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 00:15:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 00:30:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 00:45:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 01:00:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 01:15:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 01:30:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
+2022-07-05 01:45:00,3.8018333333333336,0.0,0.12955,0.0,0.0,0.0,8.03
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diff --git a/src/pricetaker/flowsheets/tests/__init__.py b/src/pricetaker/flowsheets/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/pricetaker/flowsheets/tests/test_pricetaker_workflow.py b/src/pricetaker/flowsheets/tests/test_pricetaker_workflow.py
new file mode 100644
index 00000000..b9cb7191
--- /dev/null
+++ b/src/pricetaker/flowsheets/tests/test_pricetaker_workflow.py
@@ -0,0 +1,257 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+import os
+
+import pyomo.environ as pyo
+import pytest
+import pandas as pd
+
+from idaes.apps.grid_integration import PriceTakerModel
+
+from watertap.flowsheets.flex_desal import flowsheet as fs
+from watertap.flowsheets.flex_desal import utils
+from watertap.flowsheets.flex_desal.params import FlexDesalParams
+from watertap.core.solvers import get_solver
+
+solver = get_solver()
+
+
+@pytest.mark.requires_idaes_solver
+class TestPriceTakerWorkflow:
+    @pytest.fixture(scope="class")
+    def system_frame(self):
+        price_data_path = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)),
+            "..",
+            "sbce_pricesignal.csv",
+        )
+        price_data = pd.read_csv(price_data_path)
+        price_data["Energy Rate"] = (
+            price_data["electric_energy_0_2022-07-05_2022-07-14_0"]
+            + price_data["electric_energy_1_2022-07-05_2022-07-14_0"]
+            + price_data["electric_energy_2_2022-07-05_2022-07-14_0"]
+            + price_data["electric_energy_3_2022-07-05_2022-07-14_0"]
+        )
+        price_data["Fixed Demand Rate"] = price_data[
+            "electric_demand_maximum_2022-07-05_2022-07-14_0"
+        ]
+        price_data["Var Demand Rate"] = price_data[
+            "electric_demand_peak-summer_2022-07-05_2022-07-14_0"
+        ]
+        price_data["Emissions Intensity"] = 0
+        price_data["Customer Cost"] = price_data[
+            "electric_customer_0_2022-07-05_2022-07-14_0"
+        ]
+
+        m = PriceTakerModel()
+
+        m.params = FlexDesalParams(
+            start_date="2022-07-05 00:00:00",
+            end_date="2022-07-15 00:00:00",
+            annual_production_AF=3125,  # acrft/yr
+        )
+        m.params.intake.nominal_flowrate = 1063.5  # m3/hr
+        m.params.ro.update(
+            {
+                "startup_delay": 8,  # hours
+                "minimum_downtime": 4,  # hours
+                "nominal_flowrate": 337.670,  # m3/hr
+                "surrogate_type": "quadratic_surrogate",
+                "surrogate_a": 11.509,
+                "surrogate_b": -10.269,
+                "surrogate_c": 5.627,
+                "surrogate_d": 0,
+                "minimum_recovery": 0.4,
+                "nominal_recovery": 0.465,
+                "maximum_recovery": 0.52,
+                "allow_variable_recovery": True,
+            }
+        )
+
+        # Append LMP data to the model
+        m.append_lmp_data(lmp_data=price_data["Energy Rate"])
+
+        m.build_multiperiod_model(
+            flowsheet_func=fs.build_desal_flowsheet,
+            flowsheet_options={"params": m.params},
+        )
+
+        # Update the time-varying parameters other than the LMP, such as
+        # demand costs and emissions intensity. LMP value is updated by default
+        m.update_operation_params(
+            {
+                "fixed_demand_rate": price_data["Fixed Demand Rate"],
+                "variable_demand_rate": price_data["Var Demand Rate"],
+                "emissions_intensity": price_data["Emissions Intensity"],
+                "customer_cost": price_data["Customer Cost"],
+            }
+        )
+
+        return m, price_data
+
+    @pytest.mark.unit
+    def test_price_data_structure(self, system_frame):
+        m, price_data = system_frame
+
+        assert "Energy Rate" in price_data.columns
+        assert "Fixed Demand Rate" in price_data.columns
+        assert "Var Demand Rate" in price_data.columns
+        assert "Emissions Intensity" in price_data.columns
+        assert "Customer Cost" in price_data.columns
+        assert "electric_energy_0_2022-07-05_2022-07-14_0" in price_data.columns
+        assert "electric_energy_1_2022-07-05_2022-07-14_0" in price_data.columns
+        assert "electric_energy_2_2022-07-05_2022-07-14_0" in price_data.columns
+        assert "electric_energy_3_2022-07-05_2022-07-14_0" in price_data.columns
+        assert "electric_demand_maximum_2022-07-05_2022-07-14_0" in price_data.columns
+        assert (
+            "electric_demand_peak-summer_2022-07-05_2022-07-14_0" in price_data.columns
+        )
+        assert "electric_customer_0_2022-07-05_2022-07-14_0" in price_data.columns
+
+        assert hasattr(m.params.intake, "nominal_flowrate")
+        assert hasattr(m.params.ro, "startup_delay")
+        assert hasattr(m.params.ro, "minimum_downtime")
+        assert hasattr(m.params.ro, "nominal_flowrate")
+        assert hasattr(m.params.ro, "surrogate_type")
+        assert hasattr(m.params.ro, "surrogate_a")
+        assert hasattr(m.params.ro, "surrogate_b")
+        assert hasattr(m.params.ro, "surrogate_c")
+        assert hasattr(m.params.ro, "surrogate_d")
+        assert hasattr(m.params.ro, "minimum_recovery")
+        assert hasattr(m.params.ro, "nominal_recovery")
+        assert hasattr(m.params.ro, "maximum_recovery")
+        assert hasattr(m.params.ro, "allow_variable_recovery")
+
+    @pytest.mark.unit
+    def test_add_constraints(self, system_frame):
+        m, price_data = system_frame
+
+        # Add demand cost and fixed cost calculation constraints
+        fs.add_demand_and_fixed_costs(m)
+
+        assert isinstance(m.fixed_demand_cost, pyo.Var)
+        assert isinstance(m.variable_demand_cost, pyo.Var)
+        assert isinstance(m.fixed_monthly_cost, pyo.Var)
+        assert isinstance(m.calculate_fixed_demand_cost, pyo.Constraint)
+        assert isinstance(m.calculate_variable_demand_cost, pyo.Constraint)
+        assert isinstance(m.calculate_fixed_monthly_cost, pyo.Constraint)
+
+        # Add the startup delay constraints
+        fs.add_delayed_startup_constraints(m)
+        assert isinstance(m.posttreatment_unit_commitment, pyo.Constraint)
+        assert isinstance(m.brine_pump_unit_commitment, pyo.Constraint)
+
+    @pytest.mark.unit
+    def test_add_useful_expressions(self, system_frame):
+        m, price_data = system_frame
+
+        fs.add_useful_expressions(m)
+
+        assert isinstance(m.total_water_revenue, pyo.Expression)
+        assert isinstance(m.total_demand_response_revenue, pyo.Expression)
+        assert isinstance(m.total_emissions_cost, pyo.Expression)
+
+    @pytest.mark.unit
+    def test_add_capacity_limits(self, system_frame):
+        m, price_data = system_frame
+
+        for skid in range(1, m.params.ro.num_ro_skids + 1):
+            m.add_capacity_limits(
+                # Name of the operation model block
+                op_block_name=f"reverse_osmosis.ro_skid[{skid}]",
+                # Name of the commodity on the operation model that capacity constraints will be applied to
+                commodity="feed_flowrate",
+                # Maximum capacity on the commodity
+                capacity=m.params.ro.nominal_flowrate,
+                # Ratio of the capacity at minimum stable operation to the maximum capacity. Must be between [0, 1]
+                op_range_lb=1,
+            )
+
+    @pytest.mark.unit
+    def test_constrain_water_production(self, system_frame):
+        m, price_data = system_frame
+
+        m.total_water_production = pyo.Expression(
+            expr=m.params.timestep_hours
+            * sum(m.period[:, :].posttreatment.product_flowrate)
+        )
+        m.total_energy_cost = pyo.Expression(expr=sum(m.period[:, :].energy_cost))
+        m.total_demand_cost = pyo.Expression(
+            expr=m.fixed_demand_cost + m.variable_demand_cost
+        )
+        m.total_customer_cost = pyo.Expression(
+            expr=sum(m.period[:, :].customer_cost) * m.params.num_months
+        )
+        m.total_electricity_cost = pyo.Expression(
+            expr=m.total_energy_cost + m.total_demand_cost + m.total_customer_cost
+        )
+
+        # Feed flow to the intake does not vary with time
+        m.fix_operation_var("intake.feed_flowrate", m.params.intake.nominal_flowrate)
+        # Pretreatment is either active (1) or inactive (0) for the entire run
+        m.fix_operation_var("pretreatment.op_mode", 1)
+
+        fs.constrain_water_production(m)
+
+        # If water recovery is static, it must be fixed
+        if not m.params.ro.allow_variable_recovery:
+            utils.fix_recovery(m, recovery=m.params.ro.nominal_recovery)
+
+    @pytest.mark.unit
+    def test_update_recovery_bounds(self, system_frame):
+        m, price_data = system_frame
+        utils.update_recovery_bounds(m, lb=0.4, ub=0.5)
+
+    # @pytest.mark.unit
+    # def test_get_baseline_model(self, system_frame):
+    #     m, price_data = system_frame
+    #     utils.get_baseline_model(m)
+
+    # @pytest.mark.component
+    # @pytest.mark.xfail
+    # # This test will fail if the user does not have a Gurobi license
+    # def test_gurobi_solve(self, system_frame):
+    #     m, price_data = system_frame
+    #
+    #     solver = pyo.SolverFactory("gurobi")
+    #     solver.options["MIPGap"] = 0.03
+    #     solver.solve(m)
+    #
+    # @pytest.mark.component
+    # @pytest.mark.xfail
+    # # This test will fail if the user does not have a Gurobi license
+    # def test_gurobi_util_solve(self, system_frame):
+    #     m, price_data = system_frame
+    #
+    #     solver = utils.get_gurobi_solver_model(m)
+    #     solver.solve(m)
+
+    # @pytest.mark.component
+    # # Took 6 hours to solve locally
+    # def test_baron_solve(self, system_frame):
+    #     m, price_data = system_frame
+    #
+    #     solver = pyo.SolverFactory("gams")
+    #     solver.solve(m, solver="baron", add_options=[f"options optcr={0.03};"])
+    #
+    #     assert pyo.value(m.fixed_demand_cost) == pytest.approx(5657.94712, rel=1e-3)
+    #     assert pyo.value(m.variable_demand_cost) == pytest.approx(1195.44895, rel=1e-3)
+    #     assert pyo.value(m.fixed_monthly_cost) == pytest.approx(247096.77419, rel=1e-3)
+    #     assert pyo.value(m.total_water_production) == pytest.approx(
+    #         105606.16438, rel=1e-3
+    #     )
+    #     assert pyo.value(m.total_energy_cost) == pytest.approx(62361.435709, rel=1e-3)
+    #     assert pyo.value(m.total_demand_cost) == pytest.approx(6853.39607, rel=1e-3)
+    #     assert pyo.value(m.total_customer_cost) == pytest.approx(1177.34194, rel=1e-3)
+    #     assert pyo.value(m.total_electricity_cost) == pytest.approx(
+    #         70392.17371, rel=1e-3
+    #     )
diff --git a/src/pricetaker/flowsheets/unit_models.py b/src/pricetaker/flowsheets/unit_models.py
new file mode 100644
index 00000000..648db1f0
--- /dev/null
+++ b/src/pricetaker/flowsheets/unit_models.py
@@ -0,0 +1,346 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+"""
+This module contains unit models needed for flexible desalination
+analysis.
+"""
+
+from idaes.apps.grid_integration import OperationModel
+from pyomo.environ import (
+    Constraint,
+    NonNegativeReals,
+    Param,
+    RangeSet,
+    Var,
+    exp,
+    units as pyunits,
+)
+from pricetaker.flowsheets import params as um_params
+
+
+# NOTE: OperationModel class automatically adds startup, shutdown,
+# and op_mode binary variables. So, no need to define these variables
+# explicitly.
+def _add_required_variables(blk):
+    """Function for defining common variables and constraints"""
+    # Declare variables
+    blk.energy_intensity = Var(
+        within=NonNegativeReals, units=pyunits.kWh / pyunits.m**3
+    )
+    blk.power_consumption = Var(within=NonNegativeReals, units=pyunits.kW)
+
+    blk.recovery = Var(
+        within=NonNegativeReals, bounds=(0, 1), units=pyunits.dimensionless
+    )
+    blk.feed_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    blk.product_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    blk.reject_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+
+    # Declare model constraints
+    blk.mass_balance = Constraint(
+        expr=blk.feed_flowrate == blk.product_flowrate + blk.reject_flowrate,
+        doc="Overall mass balance",
+    )
+    blk.calculate_product_flowrate = Constraint(
+        expr=blk.product_flowrate == blk.feed_flowrate * blk.recovery,
+        doc="Computes product flowrate",
+    )
+    blk.calculate_power_consumption = Constraint(
+        expr=blk.power_consumption == blk.energy_intensity * blk.product_flowrate,
+        doc="Power requirement for the unit",
+    )
+
+
+def intake_operation_model(blk, params: um_params.IntakeParams):
+    """
+    Builds operation model for the intake
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    _add_required_variables(blk)
+    blk.recovery.fix(params.get_recovery)
+    blk.energy_intensity.fix(params.energy_intensity)
+
+
+def pretreatment_operation_model(blk, params: um_params.PretreatmentParams):
+    """
+    Builds operation model for the pretreatment unit
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    _add_required_variables(blk)
+    blk.recovery.fix(params.get_recovery)
+    blk.energy_intensity.fix(params.energy_intensity)
+
+
+def ro_skid_operation_model(blk, params: um_params.ROParams):
+    """
+    Builds operation model for an RO skid
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    # # Remove the constant if its magnitude is too small
+    # if abs(params.surrogate_d) < 1e-3:
+    #     params.surrogate_d = 0
+
+    _add_required_variables(blk)
+    blk.coeffs = Param(["a", "b", "c", "d"], initialize=params.surrogate_coeffs)
+
+    blk.operational_limits = Constraint(
+        expr=blk.feed_flowrate == blk.op_mode * params.nominal_flowrate
+    )
+
+    if params.surrogate_type == "exponential_quadratic":
+        blk.calculate_energy_intensity = Constraint(
+            expr=blk.energy_intensity
+            == (
+                blk.coeffs["a"] * exp(-blk.coeffs["b"] * blk.recovery)
+                + blk.coeffs["c"] * blk.recovery**2
+                + blk.coeffs["d"]
+            ),
+            doc="Calculates the specific energy requirement",
+        )
+
+    elif params.surrogate_type == "quadratic_surrogate":
+        blk.calculate_energy_intensity = Constraint(
+            expr=blk.energy_intensity
+            == (
+                blk.coeffs["a"] * blk.recovery**2
+                + blk.coeffs["b"] * blk.recovery
+                + blk.coeffs["c"]
+            )
+        )
+
+
+def reverse_osmosis_operation_model(blk, params: um_params.ROParams):
+    """
+    Builds operation model for the reverse osmosis unit
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    # Declare required variables
+    _add_required_variables(blk)
+    blk.inlet_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    # Defining a slack variable for flowrate that is not accounted
+    # for by the sum of RO intake pumps
+    blk.leftover_flow = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+
+    # Build RO skid models
+    blk.set_ro_skids = RangeSet(params.num_ro_skids)
+    blk.ro_skid = OperationModel(
+        blk.set_ro_skids,
+        model_func=ro_skid_operation_model,
+        model_args={"params": params},
+        minimum_up_time=params.minimum_uptime,
+        minimum_down_time=params.minimum_downtime,
+    )
+
+    # Remove overall mass balance and power consumption calculation
+    blk.del_component(blk.recovery)
+    blk.del_component(blk.energy_intensity)
+    blk.del_component(blk.mass_balance)
+    blk.del_component(blk.calculate_product_flowrate)
+    blk.del_component(blk.calculate_power_consumption)
+
+    # Declare required constraints
+    blk.calculate_leftover_flow = Constraint(
+        expr=blk.feed_flowrate == blk.inlet_flowrate + blk.leftover_flow,
+        doc="Calculates leftover flowrate",
+    )
+    blk.feed_mass_balance = Constraint(
+        expr=blk.inlet_flowrate
+        == sum(blk.ro_skid[i].feed_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance at the feed",
+    )
+    blk.product_mass_balance = Constraint(
+        expr=blk.product_flowrate
+        == sum(blk.ro_skid[i].product_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance on permeate side",
+    )
+    blk.reject_mass_balance = Constraint(
+        expr=blk.reject_flowrate
+        == sum(blk.ro_skid[i].reject_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance on brine side",
+    )
+    blk.calculate_power_consumption = Constraint(
+        expr=blk.power_consumption
+        == sum(blk.ro_skid[i].power_consumption for i in blk.set_ro_skids),
+        doc="Calculates the total power requirement for RO",
+    )
+
+    # symmetry breaking for >1 skid. Skids can only operate if the previous skid is on
+    @blk.Constraint(blk.set_ro_skids)
+    def symmetry_breaking_cuts(b, index):
+        if index == 1:
+            return Constraint.Skip
+        return b.ro_skid[index].op_mode <= b.ro_skid[index - 1].op_mode
+
+    # Ensure that the operation of minimum number of skids is identical
+    blk.set_min_operating_skids = RangeSet(2, params.minimum_operating_skids)
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_startup(b, index):
+        return b.ro_skid[index].startup == b.ro_skid[1].startup
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_op_mode(b, index):
+        return b.ro_skid[index].op_mode == b.ro_skid[1].op_mode
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_shutdown(b, index):
+        return b.ro_skid[index].shutdown == b.ro_skid[1].shutdown
+
+    # Update bounds on recovery and energy intensity for all skids
+    ei_lb, ei_ub = params.get_energy_intensity_bounds()
+    for skid in blk.set_ro_skids:
+        blk.ro_skid[skid].recovery.setlb(params.minimum_recovery)
+        blk.ro_skid[skid].recovery.setub(params.maximum_recovery)
+        blk.ro_skid[skid].energy_intensity.setlb(ei_lb)
+        blk.ro_skid[skid].energy_intensity.setub(ei_ub)
+
+
+def posttreatment_operation_model(blk, params: um_params.FlexDesalParams):
+    """
+    Builds operation model for the posttreatment unit
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    _add_required_variables(blk)
+
+    blk.energy_intensity.fix(params.posttreatment.energy_intensity)
+    blk.del_component(blk.recovery)
+    blk.del_component(blk.calculate_product_flowrate)
+
+    # If the posttreatment unit is not operating, then set product flowrate to zero
+    # Connect post-treatment operation with the RO startup variables
+    blk.suppress_product_flowrate = Constraint(
+        expr=blk.product_flowrate <= params.intake.nominal_flowrate * blk.op_mode
+    )
+    blk.suppress_reject_flowrate = Constraint(
+        expr=blk.reject_flowrate <= params.intake.nominal_flowrate * (1 - blk.op_mode)
+    )
+
+    # Update the power consumption calculation constraint
+    blk.calculate_power_consumption.set_value(
+        blk.power_consumption == blk.energy_intensity * blk.feed_flowrate,
+    )
+
+
+def brine_discharge_operation_model(blk, params: um_params.FlexDesalParams):
+    """
+    Builds operation model for the brine discharge unit
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    # Declare model parameters
+    blk.energy_intensity = Param(
+        initialize=params.brinedischarge.energy_intensity,
+        units=pyunits.kWh / pyunits.m**3,
+        mutable=True,
+    )
+
+    # Declare essential variables
+    blk.feed_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    blk.power_consumption = Var(within=NonNegativeReals, units=pyunits.kW)
+
+    # the brine sump only consumes power if the RO is off,
+    # otherwise brine is pushed out by the leftover RO pressure
+    blk.calculate_power_consumption = Constraint(
+        expr=blk.power_consumption
+        >= blk.energy_intensity
+        * (blk.feed_flowrate + params.intake.nominal_flowrate * (blk.op_mode - 1)),
+        doc="Power requirement for brine discharge",
+    )
+
+
+def power_generation_operation_model(blk, params: um_params.FlexDesalParams):
+    """
+    Builds the operation model for onsite power generation
+
+    Parameters
+    ----------
+    blk : OperationModel
+        Instance of IDAES OperationModel
+
+    design_blk : DesignModel
+        Design model containing information on the peak capacity
+    """
+    # Declare model parameters
+    blk.capacity_factor = Param(
+        initialize=0,
+        mutable=True,
+        units=pyunits.dimensionless,
+        doc="capacity factor of onsite power generation",
+    )
+
+    # Declare essential variables
+    # The installed capacity could exceed the power requirement
+    # We will assume that the excess power is curtailed.
+    blk.power_utilized = Var(
+        initialize=0,
+        within=NonNegativeReals,
+        units=pyunits.kW,
+        doc="Power utilized by the system",
+    )
+    blk.power_curtailed = Var(
+        initialize=0,
+        within=NonNegativeReals,
+        units=pyunits.kW,
+        doc="Total power curtailed by the system",
+    )
+
+    # Energy balance: Sum of power utilized and the power
+    # curtailed must be equal to the total power generated.
+    blk.calculate_power_generation = Constraint(
+        expr=(
+            blk.power_utilized + blk.power_curtailed
+            == params.onsite_capacity * blk.capacity_factor
+        ),
+        doc="Computes the total power generated onsite",
+    )
diff --git a/src/pricetaker/flowsheets/utils.py b/src/pricetaker/flowsheets/utils.py
new file mode 100644
index 00000000..6b3787ee
--- /dev/null
+++ b/src/pricetaker/flowsheets/utils.py
@@ -0,0 +1,146 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+"""
+This module contains some utility functions
+"""
+from pyomo.environ import SolverFactory
+from pyomo.common.dependencies import attempt_import
+
+# pylint: disable = import-error
+gurobipy, gurobipy_available = attempt_import("gurobipy", defer_import=False)
+if gurobipy_available:
+    from gurobipy import nlfunc
+
+
+def get_gurobi_solver_model(m, mip_gap=0.01, time_limit=3600, tee=True):
+    """
+    Returns a Pyomo SolverFactory object that is compatible with Gurobi.
+    This function is needed only when the RO recovery is a variable.
+    """
+    if not gurobipy_available:
+        pass
+    else:
+        solver = SolverFactory("gurobi_persistent")
+        solver.options["MIPGap"] = mip_gap
+        solver.options["TimeLimit"] = time_limit
+        solver.options["OutputFlag"] = int(tee)
+
+        if (
+            not m.period[1, 1]
+            .reverse_osmosis.ro_skid[1]
+            .calculate_energy_intensity.active
+        ):
+            # If the nonlinear constraint is not active, then return the solver
+            # object directly
+            solver.set_instance(m)
+            return solver
+
+        if m.params.surrogate_type == "quadratic_surrogate":
+            # Model is quadratic, so Pyomo's writer can handle it.
+            solver.set_instance(m)
+            return solver
+
+        # Nonlinear constraint is present.
+        # Step 1: Deactivate the nonlinear constraint
+        for p in m.period:
+            for skid in m.period[p].reverse_osmosis.set_ro_skids:
+                m.period[p].reverse_osmosis.ro_skid[
+                    skid
+                ].calculate_energy_intensity.deactivate()
+
+        # pylint: disable = protected-access
+        # Step 2: Build the gurobipy model
+        solver.set_instance(m)
+        gm = solver._solver_model  # Gurobipy model
+        pm_to_gm = solver._pyomo_var_to_solver_var_map
+
+        # Step 3: Add the nonlinear constraint
+        coeffs = m.period[1, 1].reverse_osmosis.ro_skid[1].coeffs
+        for p in m.period:
+            for skid in m.period[p].reverse_osmosis.set_ro_skids:
+                ro_skid = m.period[p].reverse_osmosis.ro_skid[skid]
+                recovery_var = pm_to_gm[ro_skid.recovery]
+                gm.addConstr(
+                    (
+                        pm_to_gm[ro_skid.energy_intensity]
+                        == coeffs["a"] * nlfunc.exp(-coeffs["b"] * recovery_var)
+                        + coeffs["c"] * recovery_var * recovery_var
+                        + coeffs["d"]
+                    ),
+                    name=f"ro_energy_intensity_{p[0]}_{p[1]}_{skid}",
+                )
+
+    return solver
+
+
+def fix_recovery(m, recovery):
+    """Modifies the model for the fixed recovery case"""
+    # Compute the energy intensity
+    ro_skid = m.period[1, 1].reverse_osmosis.ro_skid[1]
+    energy_intensity = m.params.ro.get_energy_intensity(recovery)
+
+    for p in m.period:
+        for skid in m.period[p].reverse_osmosis.set_ro_skids:
+            ro_skid = m.period[p].reverse_osmosis.ro_skid[skid]
+            ro_skid.recovery.fix(recovery)
+            ro_skid.energy_intensity.fix(energy_intensity)
+            ro_skid.calculate_energy_intensity.deactivate()
+
+
+def wrd_fix_recovery(m, recovery):
+    """Modifies the model for the fixed recovery case"""
+    # Compute the energy intensity
+    ro_skid = m.period[1, 1].reverse_osmosis.ro_skid[1]
+    energy_intensity = m.params.wrd_ro.get_energy_intensity(recovery)
+
+    for p in m.period:
+        for skid in m.period[p].reverse_osmosis.set_ro_skids:
+            ro_skid = m.period[p].reverse_osmosis.ro_skid[skid]
+            ro_skid.recovery.fix(recovery)
+            ro_skid.energy_intensity.fix(energy_intensity)
+            ro_skid.calculate_energy_intensity.deactivate()
+
+
+def update_recovery_bounds(m, lb, ub):
+    """Updates the bounds on the recovery variable"""
+    ro_skid = m.period[1, 1].reverse_osmosis.ro_skid[1]
+    ei_lb, ei_ub = m.params.ro.get_energy_intensity_bounds(lb, ub)
+
+    for p in m.period:
+        for skid in m.period[p].reverse_osmosis.set_ro_skids:
+            ro_skid = m.period[p].reverse_osmosis.ro_skid[skid]
+            ro_skid.recovery.setlb(lb)
+            ro_skid.recovery.setub(ub)
+            ro_skid.energy_intensity.setlb(ei_lb)
+            ro_skid.energy_intensity.setub(ei_ub)
+
+
+def get_baseline_model(m):
+    """Returns a baseline model from the given model"""
+    bm = m.clone()
+
+    # Ensure that the pretreatment unit is always on, and no leakage from it
+    bm.fix_operation_var("pretreatment.op_mode", 1)
+    bm.fix_operation_var("pretreatment.recovery", 1)
+
+    # Ensure that the first three skids all always on
+    for skid in [1, 2, 3]:
+        bm.fix_operation_var(f"reverse_osmosis.ro_skid[{skid}].op_mode", 1)
+
+    if hasattr(m.period[1, 1], "battery"):
+        # If the battery model exists, ensure that there is no charging
+        # and discharging
+        bm.fix_operation_var("battery.power_charge", 0)
+        bm.fix_operation_var("battery.power_discharge", 0)
+
+    return bm
diff --git a/src/pricetaker/flowsheets/wrd_ro_flowsheet.py b/src/pricetaker/flowsheets/wrd_ro_flowsheet.py
new file mode 100644
index 00000000..04735f28
--- /dev/null
+++ b/src/pricetaker/flowsheets/wrd_ro_flowsheet.py
@@ -0,0 +1,328 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+"""
+Copied from the flexible desalination flowsheet, but altered ro surrogate.
+"""
+
+from idaes.apps.grid_integration import OperationModel
+from pyomo.environ import (
+    Constraint,
+    Expression,
+    NonNegativeReals,
+    Param,
+    Var,
+    units as pyunits,
+)
+from pricetaker.flowsheets import params as um_params
+from pricetaker.flowsheets import unit_models as um
+from pricetaker.flowsheets.wrd_unit_models import (
+    wrd_reverse_osmosis_operation_model,
+    wrd_uf_operation_model,
+)
+
+
+def add_operational_cost_expressions(blk, params: um_params.FlexDesalParams):
+    """
+    Adds cost expressions to the flowsheet
+    """
+    # Water revenue
+    blk.water_revenue = Expression(
+        expr=(
+            params.product_water_price
+            * blk.posttreatment.product_flowrate
+            * params.timestep_hours
+        ),
+        doc="Revenue generated from product water",
+    )
+
+    # Customer cost
+    blk.customer_cost = Param(
+        initialize=0,
+        mutable=True,
+        doc="Fixed customer cost",
+    )
+
+    # Demand response revenue
+    blk.demand_response_price = Param(
+        initialize=0, mutable=True, doc="Demand-response prices"
+    )
+    blk.baseline_power = Param(
+        initialize=100, mutable=True, doc="Baseline power requirement"
+    )
+    blk.demand_response_revenue = Expression(
+        expr=blk.demand_response_price
+        * (blk.baseline_power - blk.power_from_grid)
+        * params.timestep_hours,
+        doc="Revenue generated from demand response",
+    )
+
+    # Cost of emissions
+    blk.emissions_intensity = Param(
+        initialize=0, mutable=True, units=pyunits.kg / pyunits.kWh
+    )
+    blk.emissions_cost = Expression(
+        expr=(
+            blk.emissions_intensity
+            * blk.power_from_grid
+            * params.timestep_hours
+            * params.emissions_cost
+            / 907.185  # Conversion factor: $/ton to $/kg
+        ),
+        doc="Cost associated with carbon emissions",
+    )
+
+    # Cost of energy
+    blk.LMP = Param(
+        initialize=0,
+        mutable=True,
+        doc="Locational marginal price of electricity [$/kWh]",
+    )
+    blk.energy_cost = Expression(
+        expr=blk.LMP * blk.power_from_grid * params.timestep_hours,
+        doc="Cost of electricity purchased from the grid",
+    )
+
+    # Demand cost parameters
+    blk.fixed_demand_rate = Param(
+        initialize=0,
+        mutable=True,
+        doc="Constant demand tariff",
+    )
+    blk.variable_demand_rate = Param(
+        initialize=0,
+        mutable=True,
+        doc="Variable demand tariff",
+    )
+
+
+def build_desal_flowsheet(blk, params: um_params.FlexDesalParams):
+    """
+    Builds a flowsheet instance of the entire desalination process
+
+    Parameters
+    ----------
+    blk : Block
+        Pyomo Block instance
+
+    params : object
+        Object containing model parameters
+    """
+
+    # Build units
+    blk.intake = OperationModel(
+        model_func=um.intake_operation_model,
+        model_args={"params": params.intake},
+    )
+    blk.bypass_pretreatment_flow = Var(
+        within=NonNegativeReals,
+        doc="Flowrate bypassed to brine discharge due to pretreatment shutdown",
+    )
+    blk.pretreatment = OperationModel(
+        model_func=um.pretreatment_operation_model,
+        model_args={"params": params.wrd_uf},
+    )
+    blk.reverse_osmosis = OperationModel(
+        model_func=wrd_reverse_osmosis_operation_model,
+        model_args={"params": params.wrd_ro},
+    )
+    blk.posttreatment = OperationModel(
+        model_func=um.posttreatment_operation_model,
+        model_args={"params": params},
+    )
+    blk.brine_discharge = OperationModel(
+        model_func=um.brine_discharge_operation_model,
+        model_args={"params": params},
+    )
+
+    # Flowsheet connections
+    blk.arc_intake_pretreatment = Constraint(
+        expr=blk.intake.product_flowrate
+        == blk.pretreatment.feed_flowrate + blk.bypass_pretreatment_flow,
+        doc="intake-pretreatment mass balance",
+    )
+    blk.suppress_pretreatment_bypass = Constraint(
+        expr=blk.bypass_pretreatment_flow
+        <= (1 - blk.pretreatment.op_mode) * params.intake.nominal_flowrate
+    )
+    blk.arc_pretreatment_ro = Constraint(
+        expr=blk.pretreatment.product_flowrate == blk.reverse_osmosis.feed_flowrate,
+        doc="pretreatment-reverse_osmosis mass balance",
+    )
+    blk.arc_ro_posttreatment = Constraint(
+        expr=blk.reverse_osmosis.product_flowrate == blk.posttreatment.feed_flowrate,
+        doc="reverse_osmosis-posttreatment mass balance",
+    )
+    blk.calculate_brine_discharge = Constraint(
+        expr=blk.brine_discharge.feed_flowrate
+        == (
+            blk.intake.reject_flowrate
+            + blk.bypass_pretreatment_flow
+            + blk.pretreatment.reject_flowrate
+            + blk.reverse_osmosis.reject_flowrate
+            + blk.reverse_osmosis.leftover_flow
+            + blk.posttreatment.reject_flowrate
+        ),
+        doc="Computes the total inflow to brine discharge",
+    )
+
+    blk.num_skids_online = Expression(
+        expr=sum(blk.reverse_osmosis.ro_skid[:].op_mode),
+        doc="Calculates the number of skids operating at time t",
+    )
+
+    blk.net_power_consumption = Expression(
+        expr=blk.intake.power_consumption
+        + blk.pretreatment.power_consumption
+        + blk.reverse_osmosis.power_consumption
+        + blk.posttreatment.power_consumption
+        + blk.brine_discharge.power_consumption,
+        doc="Net power consumed from the grid",
+    )
+
+    if params.include_onsite_solar:
+        blk.power_generation = OperationModel(
+            model_func=um.power_generation_operation_model,
+            model_args={"params": params},
+        )
+        blk.net_power_consumption += -blk.power_generation.power_utilized
+
+    if params.include_battery:
+        blk.battery = OperationModel()
+        blk.net_power_consumption += blk.battery.power_charge - blk.battery.discharge
+
+    # Power purchased from the grid
+    blk.power_from_grid = Var(
+        within=NonNegativeReals,
+        units=pyunits.kW,
+        doc="Total power purchased from the grid",
+    )
+    blk.overall_power_balance = Constraint(
+        expr=blk.power_from_grid == blk.net_power_consumption,
+        doc="Computes the total power purchased from the grid",
+    )
+
+    # Add cost expressions
+    add_operational_cost_expressions(blk, params)
+
+
+def add_delayed_startup_constraints(m):
+    """Adds the delayed startup constraints to the model"""
+    params: um_params.FlexDesalParams = m.params
+
+    # "Shutdown" post-treatment unit if RO startup is initiated
+    @m.Constraint(m.period.index_set())
+    def posttreatment_unit_commitment(blk, d, t):
+        indices = [(d, t - i) for i in range(params.wrd_ro.startup_delay) if t - i > 0]
+        return (1 - blk.period[d, t].posttreatment.op_mode) == sum(
+            blk.period[p].reverse_osmosis.ro_skid[1].startup for p in indices
+        )
+
+    # Brine pump must operate if RO startup is initiated
+    @m.Constraint(m.period.index_set())
+    def brine_pump_unit_commitment(blk, d, t):
+        indices = [(d, t - i) for i in range(params.wrd_ro.startup_delay) if t - i > 0]
+        return blk.period[d, t].brine_discharge.op_mode == sum(
+            blk.period[p].reverse_osmosis.ro_skid[1].startup for p in indices
+        )
+
+
+def add_demand_and_fixed_costs(m):
+    """Adds variables and expressions/constraints for demand and fixed costs"""
+
+    params: um_params.FlexDesalParams = m.params
+    m.fixed_demand_cost = Var(
+        within=NonNegativeReals,
+        doc="Total fixed demand charge value for the entire horizon",
+    )
+    m.variable_demand_cost = Var(
+        within=NonNegativeReals,
+        doc="Total variable demand charge value for the entire time horizon",
+    )
+    m.fixed_monthly_cost = Var(
+        within=NonNegativeReals,
+        doc="Total customer cost for the entire time horizon",
+    )
+
+    @m.Constraint(m.period.index_set())
+    def calculate_fixed_demand_cost(blk, d, t):
+        return (
+            blk.fixed_demand_cost
+            >= blk.period[d, t].fixed_demand_rate
+            * blk.period[d, t].power_from_grid
+            * params.num_months
+        )
+
+    @m.Constraint(m.period.index_set())
+    def calculate_variable_demand_cost(blk, d, t):
+        return (
+            blk.variable_demand_cost
+            >= blk.period[d, t].variable_demand_rate
+            * blk.period[d, t].power_from_grid
+            * params.num_months
+        )
+
+    m.calculate_fixed_monthly_cost = Constraint(
+        expr=m.fixed_monthly_cost == params.fixed_monthly_cost * params.num_months
+    )
+
+
+def add_useful_expressions(m):
+    """Defines useful expressions for custom objective functions"""
+
+    m.total_water_revenue = Expression(expr=sum(m.period[:, :].water_revenue))
+    m.total_demand_response_revenue = Expression(
+        expr=sum(m.period[:, :].demand_response_revenue)
+    )
+    m.total_emissions_cost = Expression(expr=sum(m.period[:, :].emissions_cost))
+
+
+def constrain_water_production(m, baseline_production: float = None):
+    """Constrains the total water production rate"""
+
+    params: um_params.FlexDesalParams = m.params
+    if baseline_production is not None:
+        m.curtailment_fraction = Param(
+            initialize=params.curtailment_fraction,
+            mutable=True,
+            units=pyunits.dimensionless,
+            doc="Fraction of water production that is curtailed",
+        )
+
+        m.baseline_production = Param(
+            initialize=baseline_production,
+            mutable=True,
+            units=pyunits.m**3,
+            doc="Baseline water production",
+        )
+
+        m.water_production_target = Constraint(
+            expr=m.total_water_production
+            >= m.baseline_production * (1 - m.curtailment_fraction)
+        )
+
+    elif params.annual_production_AF is not None:
+        # Convert production rate from acre-ft/year to m^3/year
+        annual_production_m3 = params.annual_production_AF * 1233.48
+        m.production_target_abs = Param(
+            initialize=annual_production_m3 / 365 * params.num_days,
+            mutable=True,
+            units=pyunits.m**3,
+            doc="Absolute water production target",
+        )
+
+        m.water_production_target = Constraint(
+            expr=m.total_water_production >= m.production_target_abs
+        )
+
+    else:
+        raise ValueError("Water production targets not specified in params")
diff --git a/src/pricetaker/flowsheets/wrd_unit_models.py b/src/pricetaker/flowsheets/wrd_unit_models.py
new file mode 100644
index 00000000..06224d42
--- /dev/null
+++ b/src/pricetaker/flowsheets/wrd_unit_models.py
@@ -0,0 +1,261 @@
+from idaes.apps.grid_integration import OperationModel
+from pyomo.environ import (
+    Constraint,
+    NonNegativeReals,
+    Param,
+    RangeSet,
+    Var,
+    exp,
+    units as pyunits,
+)
+from pricetaker.flowsheets import params as um_params
+from pricetaker.flowsheets.unit_models import _add_required_variables
+
+
+def ro_skid_operation_model(blk, params: um_params.WRD_ROParams):
+    """
+    Builds operation model for an RO skid
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    _add_required_variables(blk)
+    blk.coeffs = Param(["a", "b"], initialize=params.surrogate_coeffs)
+
+    blk.operational_limits = Constraint(
+        expr=blk.feed_flowrate == blk.op_mode * params.nominal_flowrate
+    )
+
+    if params.surrogate_type == "constant_energy_intensity":
+        blk.calculate_energy_intensity = Constraint(
+            expr=blk.energy_intensity
+            == (blk.coeffs["a"] + blk.coeffs["b"] * blk.feed_flowrate),
+            doc="Calculates the specific energy requirement",
+        )
+    else:
+        raise ValueError("Unrecognized surrogate type")
+
+
+def wrd_reverse_osmosis_operation_model(blk, params: um_params.WRD_ROParams):
+    """
+    Builds operation model for the reverse osmosis unit
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    # Declare required variables
+    _add_required_variables(blk)
+    blk.inlet_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    # Defining a slack variable for flowrate that is not accounted
+    # for by the sum of RO intake pumps
+    blk.leftover_flow = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+
+    # Build RO skid models
+    blk.set_ro_skids = RangeSet(params.num_ro_skids)
+    blk.ro_skid = OperationModel(
+        blk.set_ro_skids,
+        model_func=ro_skid_operation_model,
+        model_args={"params": params},
+        minimum_up_time=params.minimum_uptime,
+        minimum_down_time=params.minimum_downtime,
+    )
+
+    # Remove overall mass balance and power consumption calculation
+    blk.del_component(blk.recovery)
+    blk.del_component(blk.energy_intensity)
+    blk.del_component(blk.mass_balance)
+    blk.del_component(blk.calculate_product_flowrate)
+    blk.del_component(blk.calculate_power_consumption)
+
+    # Declare required constraints
+    blk.calculate_leftover_flow = Constraint(
+        expr=blk.feed_flowrate == blk.inlet_flowrate + blk.leftover_flow,
+        doc="Calculates leftover flowrate",
+    )
+    blk.feed_mass_balance = Constraint(
+        expr=blk.inlet_flowrate
+        == sum(blk.ro_skid[i].feed_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance at the feed",
+    )
+    blk.product_mass_balance = Constraint(
+        expr=blk.product_flowrate
+        == sum(blk.ro_skid[i].product_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance on permeate side",
+    )
+    blk.reject_mass_balance = Constraint(
+        expr=blk.reject_flowrate
+        == sum(blk.ro_skid[i].reject_flowrate for i in blk.set_ro_skids),
+        doc="Mass balance on brine side",
+    )
+    blk.calculate_power_consumption = Constraint(
+        expr=blk.power_consumption
+        == sum(blk.ro_skid[i].power_consumption for i in blk.set_ro_skids),
+        doc="Calculates the total power requirement for RO",
+    )
+
+    # symmetry breaking for >1 skid. Skids can only operate if the previous skid is on
+    @blk.Constraint(blk.set_ro_skids)
+    def symmetry_breaking_cuts(b, index):
+        if index == 1:
+            return Constraint.Skip
+        return b.ro_skid[index].op_mode <= b.ro_skid[index - 1].op_mode
+
+    # Ensure that the operation of minimum number of skids is identical
+    blk.set_min_operating_skids = RangeSet(2, params.minimum_operating_skids)
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_startup(b, index):
+        return b.ro_skid[index].startup == b.ro_skid[1].startup
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_op_mode(b, index):
+        return b.ro_skid[index].op_mode == b.ro_skid[1].op_mode
+
+    @blk.Constraint(blk.set_min_operating_skids)
+    def minimum_ro_skids_shutdown(b, index):
+        return b.ro_skid[index].shutdown == b.ro_skid[1].shutdown
+
+    # Update bounds on recovery and energy intensity for all skids
+    ei_lb, ei_ub = params.get_energy_intensity_bounds()
+    for skid in blk.set_ro_skids:
+        blk.ro_skid[skid].feed_flowrate.setlb(params.minimum_flowrate)
+        blk.ro_skid[skid].feed_flowrate.setub(params.maximum_flowrate)
+        blk.ro_skid[skid].energy_intensity.setlb(ei_lb)
+        blk.ro_skid[skid].energy_intensity.setub(ei_ub)
+
+
+# Currently implementing UF same way as RO.
+# However, this will increase the decision variables significantly, increasing solve time.
+
+
+def uf_pump_operation_model(blk, params: um_params.WRD_UFParams):
+    """
+    Builds operation model for a UF pump
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    _add_required_variables(blk)
+    blk.coeffs = Param(["a", "b"], initialize=params.surrogate_coeffs)
+
+    blk.operational_limits = Constraint(
+        expr=blk.feed_flowrate == blk.op_mode * params.nominal_flowrate
+    )
+
+    if params.surrogate_type == "constant_energy_intensity":
+        blk.calculate_energy_intensity = Constraint(
+            expr=blk.energy_intensity
+            == (blk.coeffs["a"] + blk.coeffs["b"] * blk.feed_flowrate),
+            doc="Calculates the specific energy requirement",
+        )
+    else:
+        raise ValueError("Unrecognized surrogate type")
+
+
+def wrd_uf_operation_model(blk, params: um_params.WRD_UFParams):
+    """
+    Builds operation model for UF unit in WRD case
+
+    Parameters
+    ----------
+    blk : OperationModel
+        IDAES OperationModel instance
+
+    params : object
+        Input parameters needed for the model
+    """
+    # Declare required variables
+    _add_required_variables(blk)
+    blk.inlet_flowrate = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+    # Defining a slack variable for flowrate that is not accounted
+    # for by the sum of RO intake pumps
+    blk.leftover_flow = Var(within=NonNegativeReals, units=pyunits.m**3 / pyunits.hr)
+
+    # Build RO skid models
+    blk.set_uf_pumps = RangeSet(params.num_uf_pumps)
+    blk.uf_pumps = OperationModel(
+        blk.set_uf_pumps,
+        model_func=uf_pump_operation_model,
+        model_args={"params": params},
+        minimum_up_time=params.minimum_uptime,
+        minimum_down_time=params.minimum_downtime,
+    )
+
+    # Remove overall mass balance and power consumption calculation
+    blk.del_component(blk.recovery)
+    blk.del_component(blk.energy_intensity)
+    blk.del_component(blk.mass_balance)
+    blk.del_component(blk.calculate_product_flowrate)
+    blk.del_component(blk.calculate_power_consumption)
+
+    # Declare required constraints
+    blk.calculate_leftover_flow = Constraint(
+        expr=blk.feed_flowrate == blk.inlet_flowrate + blk.leftover_flow,
+        doc="Calculates leftover flowrate",
+    )
+    blk.feed_mass_balance = Constraint(
+        expr=blk.inlet_flowrate
+        == sum(blk.uf_pumps[i].feed_flowrate for i in blk.set_uf_pumps),
+        doc="Mass balance at the feed",
+    )
+    blk.product_mass_balance = Constraint(
+        expr=blk.product_flowrate
+        == sum(blk.uf_pumps[i].product_flowrate for i in blk.set_uf_pumps),
+        doc="Mass balance on permeate side",
+    )
+    blk.reject_mass_balance = Constraint(
+        expr=blk.reject_flowrate
+        == sum(blk.uf_pumps[i].reject_flowrate for i in blk.set_uf_pumps),
+        doc="Mass balance on brine side",
+    )
+    blk.calculate_power_consumption = Constraint(
+        expr=blk.power_consumption
+        == sum(blk.uf_pumps[i].power_consumption for i in blk.set_uf_pumps),
+        doc="Calculates the total power requirement for RO",
+    )
+
+    # symmetry breaking for >1 skid. Skids can only operate if the previous skid is on
+    # This is not true in practice, but which exact pump is on shouldn't matter
+    @blk.Constraint(blk.set_uf_pumps)
+    def symmetry_breaking_cuts(b, index):
+        if index == 1:
+            return Constraint.Skip
+        return b.uf_pumps[index].op_mode <= b.uf_pumps[index - 1].op_mode
+
+    # Ensure that the operation of minimum number of skids is identical
+    blk.set_min_operating_pumps = RangeSet(2, params.minimum_operating_pumps)
+
+    @blk.Constraint(blk.set_min_operating_pumps)
+    def minimum_uf_pumps_startup(b, index):
+        return b.uf_pumps[index].startup == b.uf_pumps[1].startup
+
+    @blk.Constraint(blk.set_min_operating_pumps)
+    def minimum_uf_pumps_op_mode(b, index):
+        return b.uf_pumps[index].op_mode == b.uf_pumps[1].op_mode
+
+    @blk.Constraint(blk.set_min_operating_pumps)
+    def minimum_uf_pumps_shutdown(b, index):
+        return b.uf_pumps[index].shutdown == b.uf_pumps[1].shutdown
+
+    # Update bounds on recovery and energy intensity for all skids
+    ei_lb, ei_ub = params.get_energy_intensity_bounds()
+    for pump in blk.set_uf_pumps:
+        blk.uf_pumps[pump].feed_flowrate.setlb(params.minimum_flowrate)
+        blk.uf_pumps[pump].feed_flowrate.setub(params.maximum_flowrate)
+        blk.uf_pumps[pump].energy_intensity.setlb(ei_lb)
+        blk.uf_pumps[pump].energy_intensity.setub(ei_ub)
diff --git a/src/pricetaker/tutorials/__init__.py b/src/pricetaker/tutorials/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/pricetaker/tutorials/mp_model.ipynb b/src/pricetaker/tutorials/mp_model.ipynb
new file mode 100644
index 00000000..16c8aac9
--- /dev/null
+++ b/src/pricetaker/tutorials/mp_model.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Flexible RO Multiperiod/Pricetaker Workflow\n",
+    "\n",
+    "This tutorial will show how to optimize the cost of a pricetaker model given a dataset of price signals and the system's operating conditions (start date, end date, flow rate, minimum recovery, etc.). See the [IDAES Pricetaker Documentation](https://github.com/IDAES/idaes-pse/blob/main/docs/reference_guides/apps/grid_integration/multiperiod/Price_Taker.rst) for more details.\n",
+    "\n",
+    "### Important files\n",
+    " - **flowsheet**: contains the functions needed for the construction of a flexible desalination flowsheet with cost expressions\n",
+    " - **utils**: contains utility functions to streamline the workflow\n",
+    " - **params**: contains the default values of all the required parameters\n",
+    " - **PriceTakerModel**: contains the pricetaker model, where pricetakers are entities that must accept market prices since they lack the market share to directly influence the market price. When coupled with the multi-period modeling, this model is able to synthesize grid-centric modeling with steady-state, process-centric modeling:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ max_{d,u, \\delta } = \\sum_{s \\in  S} \\sum_{t \\in  T} w_{s}[R(d,u_{s,t},\\delta _{s,t},\\pi _{s,t}) - C(d,u_{s,t},\\delta _{s,t})] $\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ g(d,u_{s,t},\\delta _{s,t}) = 0; \\forall _{s} \\in  S, t \\in  T $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ h(d,u_{s,t},\\delta _{s,t},u_{s,t+1},\\delta _{s,t+1}) = 0; \\forall _{s} \\in  S, t \\in  T $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Import libraries from pandas, pyomo, and IDAES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from idaes.apps.grid_integration import PriceTakerModel\n",
+    "import pandas as pd\n",
+    "import pyomo.environ as pyo\n",
+    "from pricetaker.flowsheets import flowsheet as fs\n",
+    "from pricetaker.flowsheets import utils\n",
+    "from pricetaker.flowsheets.params import FlexDesalParams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Interpret price signal data from the .csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "price_data = pd.read_csv(\"sbce_pricesignal.csv\")\n",
+    "price_data[\"Energy Rate\"] = (\n",
+    "    price_data[\"electric_energy_0_2022-07-05_2022-07-14_0\"]\n",
+    "    + price_data[\"electric_energy_1_2022-07-05_2022-07-14_0\"]\n",
+    "    + price_data[\"electric_energy_2_2022-07-05_2022-07-14_0\"]\n",
+    "    + price_data[\"electric_energy_3_2022-07-05_2022-07-14_0\"]\n",
+    ")\n",
+    "price_data[\"Fixed Demand Rate\"] = price_data[\n",
+    "    \"electric_demand_maximum_2022-07-05_2022-07-14_0\"\n",
+    "]\n",
+    "price_data[\"Var Demand Rate\"] = price_data[\n",
+    "    \"electric_demand_peak-summer_2022-07-05_2022-07-14_0\"\n",
+    "]\n",
+    "# units? + Don't really understand the Var demand rate... it's only applied at peak hours, but it's billed monthly? But so is the fixed demand rate, so is it in addition to that?\n",
+    "price_data[\"Emissions Intensity\"] = 0\n",
+    "price_data[\"Customer Cost\"] = price_data[\"electric_customer_0_2022-07-05_2022-07-14_0\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Create an instance of the PriceTakerModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = PriceTakerModel()\n",
+    "\n",
+    "# Instantiate an object containing the model parameters\n",
+    "m.params = FlexDesalParams(\n",
+    "    start_date=\"2022-07-05 00:00:00\",\n",
+    "    end_date=\"2022-07-15 00:00:00\",\n",
+    "    annual_production_AF=3125,  # acrft/yr\n",
+    ")\n",
+    "m.params.intake.nominal_flowrate = 1063.5  # m3/hr\n",
+    "m.params.ro.update(\n",
+    "    {\n",
+    "        \"startup_delay\": 8,  # hours\n",
+    "        \"minimum_downtime\": 4,  # hours\n",
+    "        \"nominal_flowrate\": 337.670,  # m3/hr\n",
+    "        \"surrogate_type\": \"quadratic_surrogate\",\n",
+    "        \"surrogate_a\": 11.509,\n",
+    "        \"surrogate_b\": -10.269,\n",
+    "        \"surrogate_c\": 5.627,\n",
+    "        \"surrogate_d\": 0,\n",
+    "        \"minimum_recovery\": 0.4,\n",
+    "        \"nominal_recovery\": 0.465,\n",
+    "        \"maximum_recovery\": 0.52,\n",
+    "        \"allow_variable_recovery\": True,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Append LMP data to the model\n",
+    "m.append_lmp_data(lmp_data=price_data[\"Energy Rate\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Build the multiperiod model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Found minimum uptime/downtime data for reverse_osmosis.ro_skid[1] - Adding minimum uptime/downtime constraints.\n",
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Created startup/shutdown constraints for operation model  reverse_osmosis.ro_skid[1] at ro_skid_1_startup_shutdown.\n",
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Found minimum uptime/downtime data for reverse_osmosis.ro_skid[2] - Adding minimum uptime/downtime constraints.\n",
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Created startup/shutdown constraints for operation model  reverse_osmosis.ro_skid[2] at ro_skid_2_startup_shutdown.\n",
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Found minimum uptime/downtime data for reverse_osmosis.ro_skid[3] - Adding minimum uptime/downtime constraints.\n",
+      "2026-02-02 15:52:19 [INFO] idaes.apps.grid_integration.pricetaker.price_taker_model: Created startup/shutdown constraints for operation model  reverse_osmosis.ro_skid[3] at ro_skid_3_startup_shutdown.\n"
+     ]
+    }
+   ],
+   "source": [
+    "m.build_multiperiod_model(\n",
+    "    flowsheet_func=fs.build_desal_flowsheet,\n",
+    "    flowsheet_options={\"params\": m.params},\n",
+    ")\n",
+    "\n",
+    "# Update the time-varying parameters other than the LMP, such as\n",
+    "# demand costs and emissions intensity. LMP value is updated by default\n",
+    "m.update_operation_params(\n",
+    "    {\n",
+    "        \"fixed_demand_rate\": price_data[\"Fixed Demand Rate\"],\n",
+    "        \"variable_demand_rate\": price_data[\"Var Demand Rate\"],\n",
+    "        \"emissions_intensity\": price_data[\"Emissions Intensity\"],\n",
+    "        \"customer_cost\": price_data[\"Customer Cost\"],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Add constraints from the flowsheet (fs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add demand cost and fixed cost calculation constraints\n",
+    "fs.add_demand_and_fixed_costs(m)\n",
+    "\n",
+    "# Add the startup delay constraints\n",
+    "fs.add_delayed_startup_constraints(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Construct useful expressions or model-level constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.total_water_production = pyo.Expression(\n",
+    "    expr=m.params.timestep_hours * sum(m.period[:, :].posttreatment.product_flowrate)\n",
+    ")\n",
+    "m.total_energy_cost = pyo.Expression(expr=sum(m.period[:, :].energy_cost))\n",
+    "m.total_demand_cost = pyo.Expression(expr=m.fixed_demand_cost + m.variable_demand_cost)\n",
+    "m.total_customer_cost = pyo.Expression(\n",
+    "    expr=sum(m.period[:, :].customer_cost) * m.params.num_months\n",
+    ")\n",
+    "m.total_electricity_cost = pyo.Expression(\n",
+    "    expr=m.total_energy_cost + m.total_demand_cost + m.total_customer_cost\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Fix operation variables that do not vary with time "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feed flow to the intake does not vary with time\n",
+    "m.fix_operation_var(\"intake.feed_flowrate\", m.params.intake.nominal_flowrate)\n",
+    "# Pretreatment is either active (1) or inactive (0) for the entire run\n",
+    "m.fix_operation_var(\"pretreatment.op_mode\", 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Call the flowsheet function that constrains water production based on the water production target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs.constrain_water_production(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Call the objective function to minimize operating costs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If water recovery is static, it must be fixed\n",
+    "if not m.params.ro.allow_variable_recovery:\n",
+    "    utils.fix_recovery(m, recovery=m.params.ro.nominal_recovery)\n",
+    "\n",
+    "m.obj = pyo.Objective(\n",
+    "    expr=m.total_energy_cost + m.total_demand_cost,\n",
+    "    sense=pyo.minimize,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Use the following code to optimize net profit\n",
+    "# m.add_hourly_cashflows(\n",
+    "#     revenue_streams=[\"water_revenue\", \"demand_response_revenue\"],\n",
+    "#     operational_costs=[\"emissions_cost\", \"energy_cost\"],\n",
+    "# )\n",
+    "# m.add_overall_cashflows(\n",
+    "#     other_costs=[m.fixed_demand_cost, m.variable_demand_cost, m.fixed_monthly_cost]\n",
+    "# )\n",
+    "\n",
+    "# m.add_objective_function(objective_type=\"net_profit\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 10: Call Gurobi to solve the optimization problem. If a Gurobi license is not available, try resorting to a different solver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solver_name = \"gurobi\"\n",
+    "# solver_name = \"baron\"\n",
+    "# solver_name = \"scip\"\n",
+    "# solver_name = \"cplex\"\n",
+    "mip_gap = 0.03\n",
+    "\n",
+    "if solver_name == \"gurobi\":\n",
+    "    # solver = utils.get_gurobi_solver_model(m, mip_gap=0.005)\n",
+    "    solver = pyo.SolverFactory(\"gurobi\")\n",
+    "    solver.options[\"MIPGap\"] = mip_gap\n",
+    "    solver.solve(m, tee=True)\n",
+    "\n",
+    "elif solver_name == \"scip\":\n",
+    "    solver = pyo.SolverFactory(\"scip\", validate=False)\n",
+    "    solver.solve(m, tee=True)\n",
+    "\n",
+    "elif solver_name in [\"baron\", \"cplex\"]:\n",
+    "    solver = pyo.SolverFactory(\"gams\")\n",
+    "    solver.solve(\n",
+    "        m, tee=True, solver=solver_name, add_options=[f\"options optcr={mip_gap};\"]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 11: Compile results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write optimal values of all operational variables to a csv file\n",
+    "m.get_operation_var_values().to_csv(\"dummy_result.csv\")\n",
+    "\n",
+    "# Plot operational variables\n",
+    "fig, axs = m.plot_operation_profile(\n",
+    "    [\n",
+    "        \"fixed_demand_rate\",\n",
+    "        \"variable_demand_rate\",\n",
+    "        \"posttreatment.product_flowrate\",\n",
+    "        \"num_skids_online\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "# Return the values of all variables and expressions that do not vary with time\n",
+    "m.get_design_var_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "watertap-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/pricetaker/tutorials/notebook_as_script.py b/src/pricetaker/tutorials/notebook_as_script.py
new file mode 100644
index 00000000..230dfe39
--- /dev/null
+++ b/src/pricetaker/tutorials/notebook_as_script.py
@@ -0,0 +1,162 @@
+import os
+from idaes.apps.grid_integration import PriceTakerModel
+import pandas as pd
+import pyomo.environ as pyo
+from pathlib import Path
+from pricetaker.flowsheets import flowsheet as fs
+from pricetaker.flowsheets import utils
+from pricetaker.flowsheets.params import FlexDesalParams
+from watertap.core.solvers import get_solver
+from idaes.core.util.model_diagnostics import DiagnosticsToolbox
+
+
+if __name__ == "__main__":
+    # Get the directory where this script is located
+    script_dir = Path(__file__).parent
+    price_data = pd.read_csv(script_dir / "sbce_pricesignal_short.csv")
+    price_data["Energy Rate"] = (
+        price_data["electric_energy_0_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_1_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_2_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_3_2022-07-05_2022-07-14_0"]
+    )
+    price_data["Fixed Demand Rate"] = price_data[
+        "electric_demand_maximum_2022-07-05_2022-07-14_0"
+    ]
+    price_data["Var Demand Rate"] = price_data[
+        "electric_demand_peak-summer_2022-07-05_2022-07-14_0"
+    ]
+    # units? + Don't really understand the Var demand rate... it's only applied at peak hours, but it's billed monthly? But so is the fixed demand rate, so is it in addition to that?
+    price_data["Emissions Intensity"] = 0
+    price_data["Customer Cost"] = price_data[
+        "electric_customer_0_2022-07-05_2022-07-14_0"
+    ]
+
+    m = PriceTakerModel()
+
+    # Instantiate an object containing the model parameters
+    m.params = FlexDesalParams(
+        start_date="2022-07-05 00:00:00",
+        end_date="2022-07-05 02:15:00",
+        annual_production_AF=3125,
+        # fixed_monthly_cost = 10000,
+        # customer_rate=price_data["Customer Cost"][1],  # acrft/yr
+    )
+    m.params.intake.nominal_flowrate = 1063.5  # m3/hr
+    m.params.ro.update(
+        {
+            "startup_delay": 8,  # hours
+            "minimum_downtime": 4,  # hours
+            "nominal_flowrate": 337.670,  # m3/hr
+            "surrogate_type": "quadratic_surrogate",
+            "surrogate_a": 11.509,
+            "surrogate_b": -10.269,
+            "surrogate_c": 5.627,
+            "surrogate_d": 0,
+            "minimum_recovery": 0.4,
+            "nominal_recovery": 0.465,
+            "maximum_recovery": 0.52,
+            "allow_variable_recovery": True,
+        }
+    )
+
+    # Append LMP data to the model
+    m.append_lmp_data(lmp_data=price_data["Energy Rate"])
+
+    m.build_multiperiod_model(
+        flowsheet_func=fs.build_desal_flowsheet,
+        flowsheet_options={"params": m.params},
+    )
+
+    # Update the time-varying parameters other than the LMP, such as
+    # demand costs and emissions intensity. LMP value is updated by default
+
+    # First, discover what blocks exist in the model
+    print("Discovering blocks in period[1,1]:")
+    for component in m.period[1, 1].component_objects(pyo.Block, descend_into=False):
+        print(f"  - {component.name}")
+
+    # Skipping this b/c it doesn't work and instead setting fixed rate in the FlexDesalParams
+    m.update_operation_params(
+        {
+            "fixed_demand_rate": price_data["Fixed Demand Rate"],
+            "variable_demand_rate": price_data["Var Demand Rate"],
+            "emissions_intensity": price_data["Emissions Intensity"],
+            "customer_cost": price_data["Customer Cost"],
+        }
+    )
+
+    # Add demand cost and fixed cost calculation constraints
+    fs.add_demand_and_fixed_costs(m)
+
+    # Add the startup delay constraints
+    fs.add_delayed_startup_constraints(m)
+
+    m.total_water_production = pyo.Expression(
+        expr=m.params.timestep_hours
+        * sum(m.period[:, :].posttreatment.product_flowrate)
+    )
+    m.total_energy_cost = pyo.Expression(expr=sum(m.period[:, :].energy_cost))
+    m.total_demand_cost = pyo.Expression(
+        expr=m.fixed_demand_cost + m.variable_demand_cost
+    )
+    m.total_customer_cost = pyo.Expression(
+        expr=sum(m.period[:, :].customer_cost) * m.params.num_months
+    )
+    m.total_electricity_cost = pyo.Expression(
+        expr=m.total_energy_cost + m.total_demand_cost + m.total_customer_cost
+    )
+
+    # Feed flow to the intake does not vary with time
+    m.fix_operation_var("intake.feed_flowrate", m.params.intake.nominal_flowrate)
+    # Pretreatment is either active (1) or inactive (0) for the entire run
+    m.fix_operation_var("pretreatment.op_mode", 1)
+
+    fs.constrain_water_production(m)
+
+    # If water recovery is static, it must be fixed
+    if not m.params.ro.allow_variable_recovery:
+        utils.fix_recovery(m, recovery=m.params.ro.nominal_recovery)
+
+    m.obj = pyo.Objective(
+        expr=m.total_energy_cost + m.total_demand_cost,
+        sense=pyo.minimize,
+    )
+
+    # Can't use gurobi because it requires a liciense for integer variables
+    # So going to use ipopt, but may need to look into this further
+    dt = DiagnosticsToolbox(m)
+    # solver = get_solver()
+    # results = solver.solve(m)
+    
+    # Configure GLPK executable path
+    os.environ['PATH'] = r'C:\Users\rchurchi\AppData\Local\anaconda3\pkgs\glpk-4.65-h17947e8_4\Library\bin' + os.pathsep + os.environ.get('PATH', '')
+    
+    solver = pyo.SolverFactory("mindtpy")
+    results = solver.solve(
+        m,
+        mip_solver="glpk",
+        nlp_solver="ipopt",
+        tee=True,
+    )
+    
+    pyo.assert_optimal_termination(results)
+
+    # Write optimal values of all operational variables to a csv file
+    # This required adding a function to pricetakermodel
+    m.get_operation_var_values().to_csv("dummy_result.csv")
+
+    # Plot operational variables
+    fig, axs = m.plot_operation_profile(
+        [
+            "fixed_demand_rate",
+            "variable_demand_rate",
+            "posttreatment.product_flowrate",
+            "num_skids_online",
+        ],
+    )
+    fig.savefig("operation_profile.png")
+    # Return the values of all variables and expressions that do not vary with time
+    print(m.get_design_var_values())
+
+    # OK this runs and solves at least
diff --git a/src/pricetaker/tutorials/ro_const_SEC.py b/src/pricetaker/tutorials/ro_const_SEC.py
new file mode 100644
index 00000000..02dc774c
--- /dev/null
+++ b/src/pricetaker/tutorials/ro_const_SEC.py
@@ -0,0 +1,162 @@
+from idaes.apps.grid_integration import PriceTakerModel
+import pandas as pd
+import pyomo.environ as pyo
+from pathlib import Path
+from pricetaker.flowsheets import wrd_ro_flowsheet as fs
+from pricetaker.flowsheets import utils
+from pricetaker.flowsheets.params import FlexDesalParams
+from watertap.core.solvers import get_solver
+from idaes.core.util.model_diagnostics import DiagnosticsToolbox
+
+
+if __name__ == "__main__":
+    # Get the directory where this script is located
+    script_dir = Path(__file__).parent
+    price_data = pd.read_csv(script_dir / "sbce_pricesignal_short.csv")
+    # price_data["Energy Rate"] = (
+    #     price_data["electric_energy_on_peak"]
+    #     + price_data["electric_energy_mid_peak"]
+    #     + price_data["electric_energy_off_peak"]
+    #     + price_data["electric_energy_super_off_peak"]
+    # )
+    # price_data["Fixed Demand Rate"] = price_data["electric_demand_fixed_summer"]
+    # price_data["Var Demand Rate"] = price_data["electric_demand_peak_adder_summer"]
+    price_data["Energy Rate"] = (
+        price_data["electric_energy_0_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_1_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_2_2022-07-05_2022-07-14_0"]
+        + price_data["electric_energy_3_2022-07-05_2022-07-14_0"]
+    )
+    price_data["Fixed Demand Rate"] = price_data[
+        "electric_demand_maximum_2022-07-05_2022-07-14_0"
+    ]
+    price_data["Var Demand Rate"] = price_data[
+        "electric_demand_peak-summer_2022-07-05_2022-07-14_0"
+    ]
+    # units? + Don't really understand the Var demand rate... it's only applied at peak hours, but it's billed monthly? But so is the fixed demand rate, so is it in addition to that?
+    price_data["Emissions Intensity"] = 0
+    # price_data["Customer Cost"] = price_data["electric_customer_fixed_charge"]
+    price_data["Customer Cost"] = price_data[
+        "electric_customer_0_2022-07-05_2022-07-14_0"
+    ]
+    m = PriceTakerModel()
+
+    # Instantiate an object containing the model parameters
+    m.params = FlexDesalParams(
+        start_date="2022-07-05 00:00:00",
+        end_date="2022-07-05 02:15:00",
+        annual_production_AF=1000,
+        # fixed_monthly_cost = 10000,
+        # customer_rate=price_data["Customer Cost"][1],  # acrft/yr
+    )
+    m.params.intake.nominal_flowrate = 1063.5  # m3/hr
+    m.params.wrd_uf.update(
+        {
+            "surrogate_type": "constant_energy_intensity",
+            "surrogate_a": 1.0,
+            "surrogate_b": 0.0,
+            "nominal_recovery": 1,
+        }
+    )
+    m.params.wrd_ro.update(
+        {
+            "startup_delay": 8,  # hours
+            "minimum_downtime": 4,  # hours
+            "nominal_flowrate": 363.4,  # m3/hr #per skid
+            "surrogate_type": "constant_energy_intensity",
+            "surrogate_a": 1.0,
+            "surrogate_b": 0.0,
+            "nominal_recovery": 0.465,
+            "num_ro_skids": 4,
+        }
+    )
+
+    # Append LMP data to the model
+    m.append_lmp_data(lmp_data=price_data["Energy Rate"])
+
+    m.build_multiperiod_model(
+        flowsheet_func=fs.build_desal_flowsheet,
+        flowsheet_options={"params": m.params},
+    )
+
+    # Update the time-varying parameters other than the LMP, such as
+    # demand costs and emissions intensity. LMP value is updated by default
+
+    # First, discover what blocks exist in the model
+
+    print("Discovering blocks in period[1,1]:")
+    for component in m.period[1, 1].component_objects(pyo.Block, descend_into=False):
+        print(f"  - {component.name}")
+
+    m.update_operation_params(
+        {
+            "fixed_demand_rate": price_data["Fixed Demand Rate"],
+            "variable_demand_rate": price_data["Var Demand Rate"],
+            "emissions_intensity": price_data["Emissions Intensity"],
+            "customer_cost": price_data["Customer Cost"],
+        }
+    )
+
+    # Add demand cost and fixed cost calculation constraints
+    fs.add_demand_and_fixed_costs(m)
+
+    # Add the startup delay constraints
+    fs.add_delayed_startup_constraints(m)
+
+    m.total_water_production = pyo.Expression(
+        expr=m.params.timestep_hours
+        * sum(m.period[:, :].posttreatment.product_flowrate)
+    )
+    m.total_energy_cost = pyo.Expression(expr=sum(m.period[:, :].energy_cost))
+    m.total_demand_cost = pyo.Expression(
+        expr=m.fixed_demand_cost + m.variable_demand_cost
+    )
+    m.total_customer_cost = pyo.Expression(
+        expr=sum(m.period[:, :].customer_cost) * m.params.num_months
+    )
+    m.total_electricity_cost = pyo.Expression(
+        expr=m.total_energy_cost + m.total_demand_cost + m.total_customer_cost
+    )
+
+    # Feed flow to the intake does not vary with time
+    m.fix_operation_var("intake.feed_flowrate", m.params.intake.nominal_flowrate)
+
+    # Pretreatment is either active (1) or inactive (0) for the entire run
+    # m.fix_operation_var("pretreatment.op_mode", 1)
+
+    fs.constrain_water_production(m)
+
+    # If water recovery is static, it must be fixed
+    if not m.params.wrd_ro.allow_variable_recovery:
+        utils.wrd_fix_recovery(m, recovery=m.params.wrd_ro.nominal_recovery)
+
+    m.obj = pyo.Objective(
+        expr=m.total_energy_cost + m.total_demand_cost,
+        sense=pyo.minimize,
+    )
+
+    # Can't use gurobi because it requires a liciense for integer variables
+    # So going to use ipopt, but may need to look into this further
+    dt = DiagnosticsToolbox(m)
+    solver = get_solver()
+    results = solver.solve(m)
+
+    pyo.assert_optimal_termination(results)
+
+    # Write optimal values of all operational variables to a csv file
+    m.get_operation_var_values().to_csv("dummy_wrd_result.csv")
+
+    # Plot operational variables
+    fig, axs = m.plot_operation_profile(
+        operation_vars=[
+            "fixed_demand_rate",
+            "variable_demand_rate",
+            "posttreatment.product_flowrate",
+            "num_skids_online",
+        ],
+    )
+    fig.savefig("operation_profile.png")
+    # Return the values of all variables and expressions that do not vary with time
+    print(m.get_design_var_values())
+
+    # OK this runs and solves at least
diff --git a/src/pricetaker/tutorials/sbce_pricesignal.csv b/src/pricetaker/tutorials/sbce_pricesignal.csv
new file mode 100644
index 00000000..6b655ef5
--- /dev/null
+++ b/src/pricetaker/tutorials/sbce_pricesignal.csv
@@ -0,0 +1,961 @@
+DateTime,electric_customer_0_2022-07-05_2022-07-14_0,electric_energy_0_2022-07-05_2022-07-14_0,electric_energy_1_2022-07-05_2022-07-14_0,electric_energy_2_2022-07-05_2022-07-14_0,electric_energy_3_2022-07-05_2022-07-14_0,electric_demand_peak-summer_2022-07-05_2022-07-14_0,electric_demand_maximum_2022-07-05_2022-07-14_0
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diff --git a/src/pricetaker/tutorials/sbce_pricesignal_day.csv b/src/pricetaker/tutorials/sbce_pricesignal_day.csv
new file mode 100644
index 00000000..fca677e5
--- /dev/null
+++ b/src/pricetaker/tutorials/sbce_pricesignal_day.csv
@@ -0,0 +1,97 @@
+DateTime,electric_customer_0_2022-07-05_2022-07-14_0,electric_energy_0_2022-07-05_2022-07-14_0,electric_energy_1_2022-07-05_2022-07-14_0,electric_energy_2_2022-07-05_2022-07-14_0,electric_energy_3_2022-07-05_2022-07-14_0,electric_demand_peak-summer_2022-07-05_2022-07-14_0,electric_demand_maximum_2022-07-05_2022-07-14_0
+7/5/2022 0:00,3.801833333,0,0.12955,0,0,0,8.03
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diff --git a/src/pricetaker/tutorials/sbce_pricesignal_short.csv b/src/pricetaker/tutorials/sbce_pricesignal_short.csv
new file mode 100644
index 00000000..55d101f3
--- /dev/null
+++ b/src/pricetaker/tutorials/sbce_pricesignal_short.csv
@@ -0,0 +1,11 @@
+DateTime,electric_customer_0_2022-07-05_2022-07-14_0,electric_energy_0_2022-07-05_2022-07-14_0,electric_energy_1_2022-07-05_2022-07-14_0,electric_energy_2_2022-07-05_2022-07-14_0,electric_energy_3_2022-07-05_2022-07-14_0,electric_demand_peak-summer_2022-07-05_2022-07-14_0,electric_demand_maximum_2022-07-05_2022-07-14_0
+7/5/2022 0:00,3.801833333,0,0.12955,0,0,0,8.03
+7/5/2022 0:15,3.801833333,0,0.12955,0,0,0,8.03
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+7/5/2022 1:00,3.801833333,0,0.12955,0,0,0,8.03
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+7/5/2022 2:15,3.801833333,0,0.12955,0,0,0,8.03
diff --git a/src/pricetaker/tutorials/wrd_pricesignal_august.csv b/src/pricetaker/tutorials/wrd_pricesignal_august.csv
new file mode 100644
index 00000000..8f298848
--- /dev/null
+++ b/src/pricetaker/tutorials/wrd_pricesignal_august.csv
@@ -0,0 +1,97 @@
+DateTime,electric_customer_fixed_charge,electric_energy_on_peak,electric_energy_mid_peak,electric_energy_off_peak,electric_energy_super_off_peak,electric_demand_fixed_summer,electric_demand_peak_adder_summer
+8/19/2021 0:00,894.1,0,0,0.06402,0,37.62,0
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diff --git a/src/pricetaker/tutorials/wrd_pricesignal_summer.csv b/src/pricetaker/tutorials/wrd_pricesignal_summer.csv
new file mode 100644
index 00000000..430e3ad7
--- /dev/null
+++ b/src/pricetaker/tutorials/wrd_pricesignal_summer.csv
@@ -0,0 +1,257 @@
+DateTime,electric_customer_fixed_charge,electric_energy_on_peak,electric_energy_mid_peak,electric_energy_off_peak,electric_energy_super_off_peak,electric_demand_fixed_summer,electric_demand_peak_adder_summer
+8/19/2021 0:00,,0,0,0.06402,0,37.62,0
+8/19/2021 0:15,,0,0,0.06402,0,37.62,0
+8/19/2021 0:30,,0,0,0.06402,0,37.62,0
+8/19/2021 0:45,,0,0,0.06402,0,37.62,0
+8/19/2021 1:00,,0,0,0.06402,0,37.62,0
+8/19/2021 1:15,,0,0,0.06402,0,37.62,0
+8/19/2021 1:30,,0,0,0.06402,0,37.62,0
+8/19/2021 1:45,,0,0,0.06402,0,37.62,0
+8/19/2021 2:00,,0,0,0.06402,0,37.62,0
+8/19/2021 2:15,,0,0,0.06402,0,37.62,0
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diff --git a/src/pricetaker/tutorials/wrd_pricesignal_summer.xlsx b/src/pricetaker/tutorials/wrd_pricesignal_summer.xlsx
new file mode 100644
index 00000000..b204859f
Binary files /dev/null and b/src/pricetaker/tutorials/wrd_pricesignal_summer.xlsx differ