QuESt Planning: A Long-term Power System Capacity Expansion Planning Tool Focused on Energy Storage Systems

Current release version: 1.0.0

Table of Contents

• Introduction
• Key Features of QuESt Planning
• Getting Started
• Workflow of QuESt Planning Tool
  • Data Preparation
  • Graphical User Interface
  • Advanced Simulations
• Examples
• Tips for Running the QuESt Planning Tool
• Feedback
• Development Status
• Acknowledgements

Introduction

QuESt Planning is a capacity expansion planning model that identifies cost-optimal energy storage, resource, and transmission investments. This tool is part of QuESt 2.0: Open-source Platform for Energy Storage Analytics. Below is a high-level overview of the inputs and outputs of the QuESt Planning tool.

Long-term capacity expansion planning models are complex optimization models that require careful consideration of modeling assumptions and input data. Model build and solve times can vary significantly, from minutes to days, based on assumptions made while configuring the model inputs and the selection of solver. For more guidance, please refer to the Tips section.

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Key Features of QuESt Planning

Key features of the QuESt Planning tool include:

• Optimization of Grid Investments: Leverages a Pyomo-based optimization model to find the cost-optimal mix of generation, transmission, and storage.

• Energy Storage System Evaluation: Designed to evaluate a broad range of energy storage technologies and their role in the optimal mix of generation required to support system operations. Users are able to define energy storage technologies based on power and energy capacity cost, asset lifetime, round-trip efficiency, and other operational characteristics.

• Model Flexibility: Supports various scenarios and sensitivity analyses to explore different investment portfolios and pathways.

• Scenario-based Planning: Allows users to develop multiple scenarios to evaluate planning sensitivities and scenarios.

• User-Friendly Interface: Simplifies the process of input data upload, planning model setup, scenario construction, model execution, and results interpretation.

• Enhanced Visualizations: The QuESt Planning tool provides several visualizations of the optimization model results, including optimal resource expansion plots, costs breakdowns, and interactive maps.

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Getting started

Prerequisites

• Python 3.11

Solver Installation

Ensure an optimization solver is installed on your machine. For best performance, use a commercial solver such as Gurobi and Cplex. Solvers to consider include:

Commercial Solvers

• Gurobi
• Cplex

Open-source Solvers

• GLPK
• Clp
• HiGHs

Setting Up a Virtual Environment

1. Install virtualenv (if not already installed):

   python -m pip install virtualenv

2. Create a virtual environment (named venv):

   python -m virtualenv venv

3. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

Cloning the Repository and Installing Dependencies

1. Clone the repository:

   git clone <repository_url>

   Replace <repository_url> with the URL of the QuESt Planning repository.

2. Navigate to the QuESt Planning Directory:

   cd path/to/quest_planning

   Replace path/to/quest_planning with the name of the directory where QuESt Planning was cloned.

3. Install Dependencies:

   python -m pip install -e .

Run the QuESt Planning Tool

1. Once the dependencies are installed the QuESt Planning package is installed in the virtual environment is activated. You can run tool using the following command from any directory:

   python -m quest_planning

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Workflow of QuESt Planning Tool

The figure below provides the basic structure of the QuESt Planning tool. The input data are in the form of CSV files and more information can be found in the Data Preparation Section. There are two options for running the QuESt Planning tool:

• Option A: Graphical User Interface: This application is designed for smaller problems and allows for enhanced visualizations.

• Option B: Advanced Simulations: This command-line script is designed for larger problems and enables the use of remote computing for advanced simulations.

Data Preparation

The input data is constructed via several CSV files. The QuESt Planning tool require the following CSV files with correct format in the data_explan folder within the quest_planning directory:

Power System Data

• bus.csv
• branch.csv
• gen.csv
• gen_viz.csv
• tech.csv
• storage.csv
• load.csv

Economic Data

• capex_tech.csv
• capex_es.csv
• fuel.csv

Renewable Profile Data

• solar.csv
• solar_cand.csv
• wind.csv
• wind_cand.csv

Other Files

• policy,csv
• scalars.csv

The CSV files and the associated data are structured as follows:

bus.csv

Column	Description
Number	Bus number
Bus_name	Bus name
Bus_number	Bus number
Load_share	Share of system peak load (%)
LAT	Latitude
LON	Longitude

branch.csv

Column	Description
Line_Number	Unique branch ID
From_Bus	From Bus Number
From_Bus_Name	From Bus Name
To_Bus	To Bus Number
To_Bus_Name	To Bus Name
Rating_F	Forward Rating (MW)
Rating_B	Backward Rating (MW)
R	Branch resistance p.u.
X	Branch reactance p.u.
B	Branch charging susceptance p.u.
Length	Line length (miles)
Tx_Cost	Transmission Cost
Tx_limit	Transmission investment limit (MW)
Lead_Time	Transmission investment lead time (years)

gen.csv

Column	Description
Gen_num	Generator number
Gen_name	Generator name
Bus_num	Bus number where generator is located
Bus	Bus name where generator is located
Tech	Generator technology name
Tech_Num	Generator technology number
Cap	Generator installed capacity (MW)
MinCap	Generator minimum stable level (MW)
CandCap	Generator maximum annual investible capacity (MW)
SystCap	Generator maximum total investible capacity (MW)
PlannedYr	Planned year for operation
RetCap	Generator planned retirement capacity (MW)
RetYr	Generator planned retirement year
CapCred	Capacity credit (%)
LeadTime	Lead time required from investment to operation (not used)
YearAvail	Eligible year for investment
HR	Generator heat rate at maximum output (MMBTU/MWhr)
FOM	Fixed Operating & Maintenance Cost ($/MW)
VOM	Variable Operating & Maintenance Cost ($/MWh)
PTC	Production tax credit ($/MWh)
ITC	Investment tax credit ($/MW)
Lifetime	Generator lifetime (years)
Ramp	Generator ramp rate (%)
FOR	Forced Outage Rate (%)
CO2	CO2 emission rate (MMTons/MWhr))
SO2	SO2 emission rate (MMTons/MWhr))
NO2	NCO2 emission rate (MMTons/MWhr))
TransAdder	Transmission cost adder($/MW))

gen_viz.csv

Column	Description
Gen_num	Generator number
Gen_name	Generator name
Bus_num	Bus number where generator is located
Bus	Bus name where generator is located
Tech	Generator technology name
Tech_Num	Generator technology number
LAT	Latitude
LON	Longitude

tech.csv

Column	Description
Num	Technology number
Tech	Name of the technology (e.g., solar, wind)
Tech_Name	Technology name
Tech ID	Unique technology ID
Tech_Num	Technology number
LeadTime	Lead time required from investment to operation (not used)
CandCap	Generator maximum annual investible capacity (MW)
SystCap	Generator maximum total investible capacity (MW)
RampRate	Generator ramp rate (%)
FOM	Fixed Operating & Maintenance Cost ($/MW)
VOM	Variable Operating & Maintenance Cost ($/MWh)
PTC	Production tax credit ($/MWh)
ITC	Investment tax credit ($/MW)
CapCred	Capacity credit (%)
Lifetime	Generator lifetime (years)
YearAvail	Eligible year for investment

storage.csv

Column	Description
Gen_num	Generator number
Gen_name	Generator name
Tech	Generator technology name
Tech_Num	Generator technology number
RTE	Round-trip efficiency (%)
Min_Duration	Minimum duration (hours)
Max_Duration	Maximum duration (hours)

load.csv

Column	Description
datetime	mm/dd/yy hh:mm
year	yy
day	mm/dd/yy
hour	hh
load_forecast #1	Load forecast #1
load_forecast #2	Load forecast #2
...	Add additional load forecasts as desired

capex_tech.csv

Column	Description
Tech_Num	Generator technology number
Tech_Name	Generator technology name
Year 1	Year 1 capital costs ($/MW)
Year 2	Year 2 capital costs ($/MW)
...	Add up to year N capital costs ($/MW)

capex_es.csv

Column	Description
Tech_Num	Generator technology number
Tech_Name	Generator technology name
Cost	Cost type (i.e. power or energy)
Year 1	Year 1 capital costs ($/MW or $/MWh)
Year 2	Year 2 capital costs ($/MW or $/MWh)
...	Add up to year N capital costs ($/MW or $/MWh)

High and low ES cost trajectories can be created from capex_es.csv and name capex_h_es.csv and capex_l_es.csv, respectively.

fuel.csv

Column	Description
Gen_Num	Generator number
Gen_Name	Generator name
Bus	Bus where generator is located
Tech	Generator technology name
Year 1	Year 1 fuel costs ($/MMBTU)
Year 2	Year 2 fuel costs ($/MMBTU)
...	Add up to year N fuel costs ($/MMBTU)

solar.csv

Column	Description
datetime	mm/dd/yy hh:mm
year	yy
day	mm/dd/yy
solar gen #1	Solar plant #1
solar gen #2	Solar plant #2
...	Add additional solar profiles to match solar generators

solar_cand.csv

Column	Description
datetime	mm/dd/yy hh:mm
year	yy
day	mm/dd/yy
solar gen #1	Candidate solar plant #1
solar gen #2	Candidate solar plant #2
...	Add additional candidate solar profiles to match solar generators

wind.csv

Column	Description
datetime	mm/dd/yy hh:mm
year	yy
day	mm/dd/yy
wind gen #1	wind plant #1
wind gen #2	wind plant #2
...	Add additional wind profiles to match solar generators

wind_cand.csv

Column	Description
datetime	mm/dd/yy hh:mm
year	yy
day	mm/dd/yy
wind gen #1	Candidate wind plant #1
wind gen #2	Candidate wind plant #2
...	Add additional candidate wind profiles to match solar generators

policy.csv

Column	Description
Policy ID	Unique policy ID
Name	Name of the policy
Type	Type of policy (e.g., subsidy, tax)
Impact	Expected impact on the energy system

scalars.csv

Column	Description
Scalar	Scalar name
Value	Scalar value
Unit	Unit

Back to Workflow Options

User-Interface Workflow

The following section provides details on the workflow of the QuESt Planning user interface.

1. Start

Upon executing the quest_planning package, a landing page will appear. The Documentation button will provide access to this README.md file in a separate dialog window. To start using the tool press the Start button.

2. Power System Data

In the Power System Data page, the corresponding CSV data will be uploaded and processed via the data handler in the backend of the tool. To begin, the user must navigate to the correct directory where all CSV files are located. Use the Browse button to open File Explorer. The user should also fill in the System Name text box with the desired name. The System name will be used to name saved files, plots, and scenarios.

Once the correct directory is selected, click Open. Upon clicking a breakdown of the system information will appear as follows:

Once the data is collected, click Next.

3. Planning Model Setup

In the Planning Model Setup page, the user can define the modeling assumptions to be fed to the optimizer. Select the Begin Year and the End Year for the simulation. Then click the Select Simulation Years button and a separate dialog button will open.

Select the years desired for the planning model simulation or select All Years. Click OK to save the years.

Select the desired Transmission Model. Currently the tool supports the transportation ("pipes & bubbles") modeling of transmission systems. Future releases will support copper sheet and DC power flow capabilities.

Select the desired Temporal Resolution. For quicker simulations, select Seasonal Blocks. More advanced features include Representative Weeks and 8760 Analysis, which will be upcoming in a future release.

Select a desired Annual Discount Rate, which will be used for the net-present value calculations, and the Base Currency Year, which is used to scale costs based on the input data provided in the CSV files.

Once complete, the planning model setup will be populated in tab window as follows:

When the planning model is setup, click Next.

4. Scenario Builder

In the Scenario Builder page, users are able to design and modify scenarios based on several planning uncertainties. To begin, specify a Scenario Name which will be used for file identification when printing the results.

Select a capital cost trend used for the energy storage capital costs. These capital cost trends are specified in the input CSV files.

Select a Load Forecast.

Select a Renewable Portfolio Standard or create a new policy.

Transmission Expansion will allow for the co-optimization of the generation and transmission expansion. This feature is in testing will be released in a later version.

To select the candidate technologies click the Candidate Technologies buttons. The below window will appear.

Select the default technologies, combination of the candidate technologies, or a Custom technology. If a Custom technology is selected, the following window will appear:

When completed selecting candidate technologies, press OK.

Once the scenario is built the window should look like the below:

If desired, click View Scenario. This will generate a planning model and scenario information breakdown page. Click 'Save Scenario` to save this page to a text file. This is useful to keep track of the scenarios if evaluating several scenarios.

When completed building the scenario, click Next.

5. Execute Model

In the Execute Model page, the optimization model will be built and solved. First, select a location to save the results. If a results folder is not selected, a folder in the main directory will be created.

Specify the solver to be used for the optimization. For best performance, select Gurobi. Ensure that you have the solver installed correctly.

Click 'Build' to build the optimization model.

Progress will appear in the window as shown below. Once the Pyomo model has been built, click Solve.

Once, the model has solved. The user will be notified and prompted to go to the Results Viewer page by clicking Next.

6. Results Viewer

The Results Viewer page is designed to provide high-level results including a breakdown of total costs, optimal installed resource capacities, and energy storage power and energy capacities throughout the planning horizon.

To begin, click Collect Results. This will populate the cost results in a table and save all plots to the desired results file.

Click Generate Plots to display the stacked bar charts for the optimal installed resource capacities and energy storage power and energy capacities throughout the planning horizon.

Click Open Results Folder to open the results folder in File Explorer.

Click Open Maps to open the subfolder containing several maps with generation and energy storage siting visualizations.

Click Save Results to save the raw results of the optimization model to an Excel file. Further analysis of results can be performed with the results via the Excel files.

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Back to Workflow Options

Advanced Simulations

For users seeking to analyze more complex scenarios or larger power systems, QuESt Planning offers the capability to run simulations through script-based execution without the use of the GUI. This approach is particularly beneficial for users leveraging the computational power of remote servers or High-Performance Computing (HPC) systems, which provide significantly more memory and processing capabilities than typical desktop environments.

Prerequisites

Ensure you have SSH access to the remote server if you wish to execute the script in and that Python is installed on the system.

Running Command-line Advanced Simulations

To initiate an advanced simulation, users can execute the explan_simulation.py script from the command line. This script is designed to process large datasets and complex simulation parameters efficiently.

1. Create and Activate a Virtual Environment:

1. Install virtualenv (if not already installed):

   pip install virtualenv

2. Create a virtual environment (named venv):

   virtualenv venv

3. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

2. Navigate to the QuESt Planning Directory:

1. Navigate to the QuESt Planning Directory:

   cd path/to/quest_planning

   Replace path/to/quest_planning with the name of the directory where QuESt Planning was cloned.

   Ensure the explan_simulation.py script is located in the same home directory.

2. Install Dependencies:

   pip install -e .

3. Configure the Input File:

Before running the simulation, configure an input.yaml file with the specific simulation parameters. Open the file in a text editor and adjust the settings according to your scenario and planning model requirements. The required parameters are detailed in the test case provided. The configuration files are located in the config folder.

4. Run the simulation:

Once the configuration file is complete, run the explan_simulation.py file to initiate the simulation.

python -m quest_planning.explan_simulation /path/to/input.yaml

Ensure that the /path/to/input.yaml is the correct location of the configuration file.

5. Monitor the simulation:

The progress of the model build and the optimization solve will be provided in the command terminal. It is suggested to periodically monitor the progress throughout the simulation.

6. Access Results:

Once the model has solved, navigate to the Results directory to access results and visualizations.

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Examples

A test case is included with the initial release of QuESt Planning. The test case includes the IEEE RTS-GMLC synthetic grid which is a publicly available test system that is derived from IEEE RTS-96 test system. Figure 1 displays the nodal model of the RTS-GMLC test case that can be used for advanced simulations. Figure 2 provides a highly aggregated zonal RTS GMLC system that can be used for simple and quick simulations.

Figure 1: IEEE RTS-GMLC Test Case nodal model

Figure 2: IEEE RTS-GMLC Test Case zonal model

The data_explan folder contains the RTS-GMLC test cases in the required format to run the QuESt Planning simulations. The nodal system is in the rts_csv_data folder and the zonal model is in the rts_csv_data_zonal. For Option A, which deploys the graphical user interface, follow the instructions detailed in the User-Interface Workflow section. For Option B, the advanced simulation option, follow the instructions detailed in the Advanced Simulations section. The configuration files for a base case simulation of the nodal and zonal models are called input_rts_nodal_base.yaml and input_rts_zonal_base.yaml, respectively. These files are located in the config folder.

Data Sources & Data Preparation Tools

The QuESt Planning tool requires several data to run simulations. Listed below are common data sources that can be used to develop test cases for QuESt Planning:

Economic Data

• Annual Technology Baseline: provides cost and performance data for new and existing resources (developed by the National Renewable Energy Laboratory)
• Energy Storage Cost and Performance Database: provides cost and performance data for a variety of energy storage technologies (developed by Pacific Northwest National Laboratory)
• Energy Storage Pricing Survey: provides a standardized reference system prices various energy storage technologies with different power and energy ratings. (Sandia National Laboratories)
• Annual Energy Outlook: provides regional projections of energy supply, demand, and fuel prices out to 2050 (developed by the Energy Information Administration)

Renewable Data

• National Solar Radiation Database: hourly and half-hourly timeseries of solar irradiance in the U.S. and select countries (developed by the National Renewable Energy Laboratory)

• pvlib: python-based package that includes a set of functions and classes used for simulating the performance of PV systems (developed by Sandia National Laboratories)

• Wind Integration National Dataset (WIND) Toolkit: provides computer model-based meteorological conditions and calculated turbine power for the continental U.S. (developed by the National Renewable Energy Laboratory)

Generator Performance Data

• Form EIA-860: provides generator-level specific information about existing and planned generators (1 MW or greater)(developed by the Energy Information Administration)

Demand Data

• Form No. 714: provides balancing authority and planning area generation, actual and scheduled power transfers, and load. (Provided by the Federal Energy Regulatory Commission)

Renewable & Energy Storage Policies

• Database of State Incentives for Renewables & Efficiency: comprehensive set of renewable policies and incentives in the United States (developed by North Carolina State University)

Additional test cases are under further development and will be included in future releases.

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Tips for Running the QuESt Planning Tool

The advanced simulations could be exceptionally difficult to solve based on the configuration of the model. There are several tips to consider when facing difficulties with solving:

• Simplify the Model: Too increase tractability and decrease complexity, try limiting the number of variables or constraints. Examples include reducing the number of candidate technologies, reducing simulation years, altering the transmission model constraints, or selecting a less-complex temporal resolution.

• Adjust Solver Settings: Adjust the solver parameters to explore different methods to improve the solver's performance. Alternatively, explore different solvers with varying capabilities based on the model formulation. Please refer to the corresponding solver documentation for more information and guidance.

• Review Model Formulation: Carefully review the model formulation to identify logical errors or constraints that are too restrictive.

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Feedback

Please submit feedback, issues, and suggestions, through the Issues page. For more information, please reach out to the project developer Cody Newlun (cjnewlu@sandia.gov).

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Development Status & Future Updates

The QuESt Planning tool is under active development and more features will be included in future releases. the QuESt Planning tool will be integrated into the QuESt 2.0, an open-source platform for energy storage analytics, where it will be available to be installed and integrated with other tools available in the QuESt platform.

Future updates to QuESt Planning that are being considered include:

• Improved reliability and resilience constraints
• Technology-specific energy storage models
• Enhanced temporal resolution
• Improved transmission models & investment options
• Improved renewable energy resource modeling
• Improved GUI & scenario viewer

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Acknowledgment

The QuESt Planning tool is developed and maintained by the Energy Storage Analytics Group at Sandia National Laboratories.

Project team:

• Cody Newlun (cjnewlu@sandia.gov)
• Atri Bera
• Walker Olis
• Andres Lopez Ramirez
• Yung-Jai Pomeroy
• Tu Nguyen

This material is based upon work supported by the U.S. Department of Energy, Office of Electricity (OE), Energy Storage Division.

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License

Please see the LICENSE file.

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