Model Description

The Capacity Expansion Regional Feasibility (CERF) model is written in Python and C++ and was built to determine the on-the-ground feasibility of achieving energy system expansion plans generated by integrated human-Earth systems models or regional capacity expansion models. Energy expansion projections from such models are typically limited to fairly large regions (e.g., balancing authorities or control regions) and do not account for on-the-ground barriers such as protected lands, potentially hazardous areas, highly populated areas, water availability, proximity to Class 1 airsheds, and other siting constraints that may impede the ability to achieve a planned expansion. CERF was developed to evaluate the feasibility of siting a mix of energy technologies over the contiguous United States of America (CONUS) at a resolution of 1 km² while considering current and future socioeconomic conditions, energy demands, land use, environmental regulations, and water availability. The information gleaned from determining the feasibility of technology expansion plans can be used to provide insight into factors that may create or exacerbate vulnerabilities related to energy system resilience.

CERF is unique in that it determines feasible siting locations using a combination of on-the-ground suitability constraints (e.g., protected lands) with simulated economic competition between energy technologies using an algorithm that minimizes net locational cost (NLC) to choose specific siting locations within suitable areas.¹ The NLCs are calculated for each technology and are influenced by the distance to existing transmission infrastructure, technology-specific marginal operating costs, and technology- and location-specific marginal energy values. In effect, the algorithm posits the existence of a regional planner who determines the costs and benefits of having new generation in different locations and sites power plants in order from lowest to highest NLC. Technologies that share the same suitable areas compete for siting locations based on the locational economics.

CERF was designed to receive input from other models regarding the amount and type of power plants to be sited in a particular geographic area (e.g., in each balancing authority) as well as other variables such as water availability and locational marginal energy prices. CERF also “inherits” the assumptions of these models; for example, water availability constraints can be static or based on the output from an Earth system model using a specific scenario of future Earth system changes and corresponding time-evolving water availability.

CERF can currently site 17 energy technologies.² Each technology has spatial suitability rasters and technology-specific parameters that can be set by the user. A comprehensive literature review³ was conducted to determine which suitability constraints should be applied for each technology. A suitability raster containing suitable and unsuitable areas that are common to all technologies is provided with CERF.⁴ Technology-specific suitability data are also provided.⁵ Figure 1 demonstrates the combined suitable area that is common to all technologies as mapped onto CERF’s 1 km² grid over the CONUS. This common suitability raster is then combined with technology-specific suitability rasters (Figure 2; example for nuclear power) to yield grid cells that are suitable for siting. Suitability constraints can be added by the user to specific technologies or to those that are common to all technologies, depending on research needs. The NLC of each grid cell is then used to determine economically feasible locations for siting in grid cells that are suitable for siting.⁶

Implementation and architecture

CERF is designed as a Python package that can be called in script after the user has installed the package on a Windows 7 OS. The CERF model is parameterized using an input configuration file, a required set of input XML files, and spatial data files (detailed in the Configuration section below). Ensure that local paths for the spatial data files in the technology_suitabilitymask_paths.xml have been set before a run. After these files have been prepared, CERF can be called from other Python scripts once installed as demonstrated in the example.py script⁷ which is included with the package. This can be conducted as follows:

from cerf.model import Cerf
 
if __name__ == “__main__”:
   # <pth> is the full path to the configuration file
   ini = “<pth>/config.ini”
 
   # instantiate CERF
   m = Cerf(ini)
 
   # run CERF
   m.execute()

After m.execute() is called, CERF will process each state and technology sequentially to determine the geographic coordinates, net locational costs, interconnection costs, and net operational value of each power plant. This data is saved to an XML file that is ingested by CERF’s outputs module to create summary graphics and tabular data as described in the outputs section below. A log file is also created during runtime which contains any pertinent runtime information.

The SAGA GIS [1] C++ API was used to provide spatial functionality related to ensuring consistent geographic projection use, calculating interconnection distance, and extracting masks of raster data. We extended SAGA GIS by developing a custom C++ module to calculate a local sum of a stacked group of rasters. This allows us to determine suitability for each 1 km² grid cell while maintaining the spatial integrity of the data (Figure 3). The C++ component of CERF was compiled for use in Windows 7 only and is called and parameterized through CERF’s Python model.py wrapper.

Example input datasets

CERF is designed to test the suitability and feasibility of “proposed” capacity expansion plans; therefore, it requires a range of input data both in terms of those plans as well as how the suitability and feasibility criteria might change over time. The example input dataset provided with CERF was built to demonstrate the uniqueness and functionality of CERF. A modeled energy technology expansion plan and technology-specific assumptions, in the context of a specific future scenario of environmental and sociological change (namely, the Representative Concentration Pathway, or RCP, 8.5 [2]), were generated by the US 50-state version of the Global Change Assessment Model (GCAM) [3, 4]. Gridded water availability data was derived from a large-scale Water Management (WM) model [5, 6] driven by an Earth system model using the same RCP 8.5 scenario assumptions. Locational marginal prices were provided by the PROMOD IV production cost model [7].

Publicly available datasets for transmission lines and gas pipelines were used in this demonstration as provided by the U.S. Energy Information Administration (EIA) and the Homeland Infrastructure Foundation-Level Data (HIFLD) [8, 9]. These datasets influence the interconnection cost for each site and play a role in determining NLC; therefore, we recommend obtaining more detailed representations of both transmission lines and gas pipelines if they are available. The solar (CSP) suitability raster simply adopted the common suitability criteria that applies to all thermo-electric technologies and should be enhanced to capture the idiosyncrasies of siting CSP.

Figure 4 demonstrates illustrative results for nuclear siting feasibility from the example data for the Eastern Interconnection in reference to a combination of suitability constraints that are common to all technologies and those that are specific to nuclear.

Configuration

CERF’s setup occurs in a configuration file and a set of XML files that are used to define constants, planned expansion, locational marginal pricing and carbon factors per utility zone, state zones, technology specific information, and the location of suitability rasters per technology. Examples of all input files can be found in CERF’s example directory.⁸

Figure 5 illustrates CERF’s workflow once the input files have been prepared.

The following describes the requirements and format of each input.

CERF outputs

CERF outputs are generated to help the user to interpret, summarize, and visualize CERF’s results. The main categories of CERF’s outputs can be analyzed at the site, technology, state, or project level for: siting feasibility, interconnection cost, net locational cost, and net operating value. A geographic reference (x, y coordinates) is also included for each site so the user may plot these in a geographic information system to conduct locational analysis.

The outputs may be generated using CERF’s outputs module. Usage is as follows to create a heatmap showing all states and technologies relative to one another for interconnection cost.

from cerf.outputs import Outputs
if __name__ == “__main__”:
   # <pth> is the full path to the configuration file;
   ini = “<pth>/config.ini”
   # instantiate CERF outputs
   res = Outputs(ini)
   # create a heatmap of interconnection cost
   res.heatmap(metric = ’ic’)

The Python help command may be used with any method in the class to view parameterization possibilities and a description of the method. Currently supported methods are described in Table 9.

Installation

CERF can be installed as follows:

1. CERF’s GitHub repository uses the Git Large File Storage⁹ (LFS) extension. Run the following command before cloning if you do not already have Git LFS installed: git lfs install.
2. Clone CERF into your desired location: git clone https://github.com/IMMM-SFA/cerf.git.
3. Install the Python package setuptools¹⁰ if the package is not already on your machine.
4. From the directory you cloned CERF into and your setup.py file exists run python setup.py install which will install CERF as a Python package on your machine and install of the required Python dependencies.

Quality control

Due to the complex nature of the technology expansion plan data, CERF was built to provide detailed feedback to the user upon exception when validating the configuration file and input XML files. Parameters have set limits and type requirements which are strictly enforced and the code is designed to help users navigate this as easily as possible. CERF also builds a timestamped log file and output directory structure during runtime.

A validation of CERF’s suitability constraints that are common to all technologies was conducted for the CONUS using existing operable power plant locations as provided by the EIA [8] (Table 10). The technology that was listed as the primary producer at each site was declared as the technology for that location. Locations from EIA were represented by geographic coordinates for each site and do not take into account the entire footprint of the site. For this reason, this validation quantifies how many times each plant’s geographic coordinate falls directly on an area that was suitable as well as the times the plant fell within 5 km of an area that was suitable to account for the potential variability in the site’s actual location. This validation also assumes that the sites from EIA are correctly georeferenced. Technologies were lumped into their EIA technology name (i.e., coal CC, coal IGCC, conventional coal all lumped into a category named “coal”). The validation for wind also utilized the EIA power plant locations but compared them to the technology specific suitability layer for wind since the constraints that are common to all other technologies do not always apply to wind.

Operating system

Exclusively Windows 7

Programming language

C++ and Python 2.7.13

Additional system requirements

It is recommended that a minimum of 8 GB of memory be available for runs due to the ingestion of large amounts of spatial data.

Dependencies

For C++:

• All included

For Python:

• setuptools (version 18.5)
• configobj (version 5.0.6)
• untangle (version 1.1.0)
• pandas (version 0.18.1)
• matplotlib (version 1.5.1)
• seaborn (version 0.8.1)

Software location

Language

English