Preface¶
This manual, in both PDF and HTML form, is the official documentation of Tools for Energy Model Optimization and Analysis (Temoa). It describes all functionality of the Temoa model, and explains the mathematical underpinnings of the implemented equations.
Besides this documentation, there are a couple other sources for Temoaoriented information. The most interactive is the mailing list, and we encourage any and all questions related to energy system modeling. Publications are good introductory resources, but are not guaranteed to be the most uptodate as information and implementations evolve quickly. As with many softwareoriented projects, even before this manual, the code is the most definitive resource. That said, please let us know (via the mailing list, or other avenue) of any discrepancies you find, and we will fix it as soon as possible.
What is Temoa?¶
Temoa is an energy system optimization model (ESOM). Briefly, ESOMs optimize the installation and utilization of energy technology capacity over a userdefined time horizon. Optimal decisions are driven by an objective function that minimizes the cost of energy supply. Conceptually, one may think of an ESOM as a “lefttoright” network graph, with a set of energy sources on the lefthand side of the graph that are transformed into consumable energy commodities by a set of energy technologies, which are ultimately used to meet demands on the righthand side of the network graph. 4
Key features of the core Temoa model include:
Flexible time slicing by season and timeofday
Variable length model time periods
Technology vintaging
Separate technology loan periods and lifetimes
Global and technologyspecific discount rates
Capability to perform stochastic optimization
Capability to perform modelingtogenerate alternatives (MGA)
Temoa design features include:
Source code licensed under GPLv2, available through Github 1
Open source software stack
Part of a rich Python ecosystem
Data stored in a relational database system (sqlite)
Ability to utilize multicore and compute cluster environments
The word ‘Temoa’ is actually an acronym for “Tools for Energy Model Optimization and Analysis,” currently composed of four (major) pieces of infrastructure:
The mathematical model
The implemented model (code)
Surrounding tools
An online presence
Each of these pieces is fundamental to creating a transparent and usable model with a community oriented around collaboration.
Why Temoa?¶
In short, because we believe that ESOMbased analyses should be repeatable by independent third parties. The only realistic method to make this happen is to have a freely available model, and to create an ecosystem of freely shared data and model inputs.
For a longer explanation, please see [DeCarolisHunterSreepathi13] (available from temoaproject.org). In summary, ESOMbased analyses are (1) impossible to validate, (2) complex enough as to be nonrepeatable without electronic access to exact versions of code and data input, and (3) often do a poor job addressing uncertainty. We believe that ESOMbased analyses should be completely open, independently reproducible, electronically available, and address uncertainty about the future.
Temoa Origin and Pronunciation¶
While we use ‘Temoa’ as an acronym, it is an actual word in the Nahuatl (Aztec) language, meaning “to seek something.”
One pronounces the word ‘Temoa’ as “teh”, “moe”, “uh”. Though TEMOA is an acronym for ‘Tools for Energy Model Optimization and Analysis’, we generally use ‘Temoa’ as a proper noun, and so forgo the need for allcaps.
Bug Reporting¶
Temoa strives for correctness. Unfortunately, as an energy system model and software project there are plenty of levels and avenues for error. If you spot a bug, inconsistency, or general “that could be improved”, we want to hear about it.
If you are a software developertype, feel free to open an issue on our GitHub Issue tracker. If you would rather not create a GitHub account, feel free to let us know the issue on our mailing list.
Quick Start¶
Installing Software Elements¶
Temoa is implemented in Pyomo, which is in turn written in Python. Consequently, Temoa will run on Linux, Mac, Windows, or any operating system that Pyomo supports. There are several open source software elements required to run Temoa. The easiest way to install these elements is to create a conda environment in which to run the model. Creating a customized environment ensures that the latest version of Temoa is compatible with the required software elements. To begin, you need to have conda installed either via miniconda or anaconda. Next, download the environment.yml file from our Github repo, and place it in a new directory named ‘temoapy3.’ Create this new directory in a location where you wish to store the environment. Navigate to this directory and execute the following from the command line:
$ conda env create
Then activate the environment as follows:
$ source activate temoapy3
More information on virtual environments can be found here. This new conda environment contains several elements, including Python 3, a compatible version of Pyomo, matplotlib, numpy, scipy, and two free solvers (GLPK and CBC). Windows users: the CBC solver is not available for Windows through conda. Thus, in order to install the environment properly, the last line of the ‘environment.yml’ file specifying ‘coincbc’ should be deleted. A few notes for on the choice of solvers. Different solvers have widely varying solution times. If you plan to run Temoa with large datasets and/or conduct uncertainty analysis, you may want to consider installing commercial linear solvers such as CPLEX or Gurobi. Both offer free academic licenses. Another option is to run CPLEX on the NEOS server.
There are three ways to run the model, each of which is detailed below. Note that the example commands utilize ‘temoa_utopia’, a commonly used test case for ESOMs.
Obtaining Temoa¶
Now that you have functioning environment, you need to obtain the source code for Temoa. There are a couple of options for obtaining and running Temoa from GitHub. If you want to simply run the model, you can download Temoa from GitHub as a zip file. Navigate to our GitHub repository, and click the green ‘clone or download’ button near the topright corner. Select ‘Download ZIP,’ and you can download the entire Temoa ‘energysystem’ (our main branch) to your local machine. The second option creates a local copy of the model source code in our GitHub repository. This is a two step process: first install git and then ‘clone’ the repository. Under Linux, git can be installed through the default package manager. Git for Windows and Mac can be downloaded from the Git website. To clone the Temoa repository, navigate to the directory where you want the model to reside and type the following from the prompt:
$ git clone https://github.com/TemoaProject/temoa/
Note that cloning the repository will supply the latest version of the code, and allow you to archive changes to the code and data in your own local git repository.
A few basic input data files are included in the ‘temoa/data_files’ folder. Additional Temoacompatible datasets are available in this separate GitHub repo.
The installation procedures above are meant to be generic and should work across different platforms. Nonetheless, systemspecific ambiguities and unexpected conditions inevitably arise. Please use the Temoa forum to ask for help.
Running Temoa¶
The most basic way to run Temoa is with an input data (DAT) file:
$ python temoa_model/ /path/to/dat/file
This option will simply run the model and output the results to the shell. To make sure the model is functioning correctly, try running with the ‘Utopia’ dataset:
$ python temoa_model/ data_files/utopia15.dat
To run the model with more features, use a configuration (‘config’) file. An example config file called ‘config_sample’ resides within the ‘temoa_model’ folder. Running the model with a config file allows the user to (1) use a sqlite database for storing input and output data, (2) create a formatted Excel output file, (2) specify the solver to use, (3) return the log file produced during model execution, (4) return the lp file utilized by the solver, and (5) to execute modelingtogenerate alternatives (MGA). Note that if you do not have access to a commercial solver, it may be faster run cplex on the NEOS server. To do so, simply specify cplex as the solver and uncomment the ‘–neos’ flag.
$ python temoa_model/ config=temoa_model/config_sample
For general help, use –help:
$ python temoa_model/ help usage: temoa_model [h] [path_to_logs PATH_TO_LOGS] [config CONFIG] [solver {bilevel_blp_global,bilevel_blp_local,bilevel_ld,cplex,mpec_minlp,mpec_nlp,openopt,ps} ] [dot_dat [dot_dat ...]] positional arguments: dot_dat AMPLformat data file(s) with which to create a model instance. e.g. "data.dat" optional arguments: h, help show this help message and exit path_to_logs PATH_TO_LOGS Path to where debug logs will be generated by default. See folder debug_logs in data_files. config CONFIG Path to file containing configuration information. solver {bilevel_blp_global,bilevel_blp_local,bilevel_ld,cplex,mpec_minlp,mpec_nlp,openopt,ps} Which backend solver to use. See 'pyomo help solvers' for a list of solvers with which Pyomo can interface. The list shown here is what Pyomo can currently find on this system. [Default: cplex]
Database Construction¶
Input datasets in Temoa can be constructed either as text files or relational
databases. Input text files are referred to as ‘DAT’ files and follow a specific
format. Take a look at the example DAT files in the temoa/data_files
directory.
While DAT files work fine for small datasets, relational databases are preferred for larger datasets. To first order, you can think of a database as a collection of tables, where a ‘primary key’ within each table defines a unique entry (i.e., row) within the table. In addition, a ‘foreign key’ defines a table element drawn from another table. Foreign keys enforce the defined relationships between different sets and parameters.
Temoa uses sqlite, a widely used, selfcontained database system. Building a database first requires constructing a sql file, which is simply a text file that defines the structure of different database tables and includes the input data. The snippet below is from the technology table used to define the ‘temoa_utopia’ dataset:
CREATE TABLE technologies (
tech text primary key,
flag text,
sector text,
tech_desc text,
tech_category text,
FOREIGN KEY(flag) REFERENCES technology_labels(tech_labels),
FOREIGN KEY(sector) REFERENCES sector_labels(sector));
INSERT INTO "technologies" VALUES('IMPDSL1','r','supply',' imported diesel','petroleum');
INSERT INTO "technologies" VALUES('IMPGSL1','r','supply',' imported gasoline','petroleum');
INSERT INTO "technologies" VALUES('IMPHCO1','r','supply',' imported coal','coal');
The first line creates the table. Lines 26 define the columns within this table. Note that the the technology (‘tech’) name defines the primary key. Therefore, the same technology name cannot be entered twice; each technology name must be unique. Lines 78 define foreign keys within the table. For example, each technology should be specified with a label (e.g., ‘r’ for ‘resource’). Those labels must come from the ‘technology_labels’ table. Likewise, the sector name must be defined in the ‘sector_labels’ table. This enforcement of names across tables using foreign keys helps immediately catch typos. (As you can imagine, typos happen in plain text files and Excel when defining thousands of rows of data.) Another big advantage of using databases is that the model run outputs are stored in separate database output tables. The outputs by model run are indexed by a scenario name, which makes it possible to perform thousands of runs, programatically store all the results, and execute arbitrary queries that instantaneously return the requested data.
Because some database table elements serve as foreign keys in other tables, we recommend that you populate input tables in the following order:
 Group 1: labels used for internal database processing
commodity labels: Need to identify which type of commodity. Feel free to change the abbreviations.
technology labels: Need to identify which type of technology. Feel free to change the abbreviations.
time_period_labels: Used to distinguish which time periods are simply used to specify preexisting vintages and which represent future optimization periods.
 Group 2: sets used within Temoa
commodities: list of commodities used within the database
technologies: list of technologies used within the database
time_periods: list of both past and future time periods considered in the database
time_season: seasons modeled in the database
time_of_day: time of day segments modeled in the database
 Group 3: parameters used to define processes within Temoa
GlobalDiscountRate
Demand
DemandSpecificDistribution
Efficiency
ExistingCapacity
CapacityFactor
CapacityFactorProcess (only if CF varies by vintage; overwrites CapacityFactor)
Capacity2Activity
CostFixed
CostInvest
CostVariable
EmissionsActivity
LifetimeLoanTech
LifetimeProcess
LifetimeTech
 Group 4: parameters used to define constraints within Temoa
GrowthRateSeed
GrowthRateMax
MinCapacity
MaxCapacity
MinActivity
MaxActivity
RampUp
RampDown
TechOutputSplit
TechInputSplit
For help getting started, take a look at how data_files/temoa_utopia.sql
is
constructed. Use data_files/temoa_schema.sql
(a database file with the requisite
structure but no data added) to begin building your own database file. We recommend
leaving the database structure intact, and simply adding data to the schema file.
Once the sql file is complete, you can convert it into a binary sqlite file by
installing sqlite3 and executing the following command:
$ sqlite3 my_database.sqlite < my_database.sql
Now you can specify this database as the source for both input and output data in the config file.
Visualization¶
Network Diagrams¶
From the definition of the Temoa model as “an algebraic network of linked processes,” a directed network graph is a natural visualization. Temoa utilizes an open source graphics package called Graphviz to create a series of dataspecific and interactive energysystem maps. Currently, the output graphs consist of a full energy system map as well as capacity and activity results per model time period. In addition, users can create subgraphs focused on a particular commodity or technology.
The programmatic interaction with Graphviz is entirely text based. The input files created by Temoa for Graphviz provide another means to debug the model and create an archive of visualizations for auditing purposes. In addition, we have taken care to make these intermediate files wellformatted.
To utilize graphviz, make sure it is installed on your local machine. Then
navigate to the db_io
folder, where the graphviz script and database
files reside. To review all of the graphviz options, use the help
flag:
$ python MakeGraphviz.py help
The most basic way to use graphviz is to view the full energy system map:
$ python MakeGraphviz.py i temoa_utopia.sqlite
The resultant system map will look like this:
It is also possible to create a system map showing the optimal installed capacity
and technology flows in a particular model time period. These results are associated
with a specific model run stored in the model database. To view the results, include
the scenario flag (s
) and a specific model year (y
).
$ python MakeGraphviz.py i temoa_utopia.sqlite s test_run y 1990
The output can also be finetuned to show results associated with a specific commodity or technology. For example:
$ python MakeGraphviz.py i dbs/temoa_utopia.sqlite s test_run y 2010 b E31
Output Graphs¶
Temoa can also be used to generate output graphs using matplotlib
(https://matplotlib.org/). From the command line, navigate to the db_io
folder and execute the following command:
$ python MakeOutputPlots.py help
The command above will specify all of the flags required to created a stacked bar or line plot. For example, consider the following command:
$ python MakeOutputPlots.py i dbs/temoa_utopia.sqlite s test_run p capacity c electric super
Here is the result:
The Math Behind Temoa¶
To understand this section, the reader will need at least a cursory understanding of mathematical optimization. We omit here that introduction, and instead refer the reader to various available online sources. Temoa is formulated as an algebraic model that requires information organized into sets, parameters, variables, and equation definitions.
The heart of Temoa is a technology explicit energy system optimization model. It is an algebraic network of linked processes – understood by the model as a set of engineering characteristics (e.g. capital cost, efficiency, capacity factor, emission rates) – that transform raw energy sources into enduse demands. The model objective function minimizes the presentvalue cost of energy supply by optimizing installed capacity and its utilization over time.
The most fundamental tenet of the model is the understanding of energy flow, treating all processes as black boxes that take inputs and produce outputs. Specifically, Temoa does not care about the inner workings of a process, only its global input and output characteristics. In this vein, the above graphic can be broken down into processspecific elements. For example, the coal power plant takes as input coal and produces electricity, and is subject to various costs (e.g. variable costs) and constraints (e.g. efficiency) along the way.
The modeler defines the processes and engineering characteristics through an amalgam of sets and parameters, described in the next few sections. Temoa then translates these into variables and constraints that an optimizer may then solve.
Conventions¶
In the mathematical notation, we use CAPITALIZATION to denote a container, like a set, indexed variable, or indexed parameter. Sets use only a single letter, so we use the lower case to represent an item from the set. For example, represents the set of all technologies and represents a single item from .
Variables are named V_VarName within the code to aid readability. However, in the documentation where there is benefit of italics and other font manipulations, we elide the ‘V_’ prefix.
In all equations, we bold variables to distinguish them from parameters. Take, for example, this excerpt from the Temoa default objective function:
Note that is not bold, as it is a temporary variable used for clarity while constructing the objective function. It is not a structural variable and the solver never sees it.
Where appropriate, we put the variable on the right side of the coefficient. In other words, this is not a preferred form of the previous equation:
We generally put the limiting or defining aspect of an equation on the right hand side of the relational operator, and the aspect being limited or defined on the left hand side. For example, equation (2) defines Temoa’s mathematical understanding of a process capacity () in terms of that process’ activity ():
We use the word ‘slice’ to refer to the tuple of season and time of day . For example, “winternight”.
We use the word ‘process’ to refer to the tuple of technology and vintage (), when knowing the vintage of a process is not pertinent to the context at hand.
In fact, in contrast to most other ESOMs, Temoa is “process centric.” This is a fairly large conceptual difference that we explain in detail in the rest of the documentation. However, it is a large enough point that we make it here for even the notime quickstart modelers: think in terms of “processes” while modeling, not “technologies and start times”.
Mathematical notation:
We use the symbol to represent the unit interval ([0, 1]).
We use the symbol to represent “the set of all integers.”
We use the symbol to represent natural numbers (i.e., integers greater than zero: 1, 2, 3, ).
We use the symbol to denote the set of real numbers, and to denote nonnegative real numbers.
Sets¶
Set 
Temoa Name 
Data Type 
Short Description 


string 
union of all commodity sets 


string 
enduse demand commodities 


string 
emission commodities (e.g. , ) 


string 
general energy forms (e.g. electricity, coal, uranium, oil) 


string 
physical energy carriers and enduse demands () 

string 
alias of ; used in documentation only to mean “input” 

string 
alias of ; used in documentation only to mean “output” 


model periods before optimization begins 


model time scale of interest; the last year is not optimized 


model time periods to optimize; () 


possible tech vintages; () 


string 
seasonal divisions (e.g. winter, summer) 


string 
timeofday divisions (e.g. morning) 


string 
all technologies to be modeled; () 


string 
resource extraction techs 


string 
techs producing intermediate commodities 


string 
baseload electric generators; () 


string 
storage technologies; () 


string 
technologies with curtailable output; () 
Temoa uses two different set notation styles, one for code representation and one that utilizes standard algebraic notation. For brevity, the mathematical representation uses capital glyphs to denote sets, and small glyphs to represent items within sets. For example, represents the set of all technologies and represents an item within .
The code representation is more verbose than the algebraic version, using full
words. This documentation presents them in an italicized font. The same
example of all technologies is represented in the code as tech_all
.
Note that regardless, the meanings are identical, with only minor interaction
differences inherent to “implementation details.” Table 1
lists all of the Temoa sets, with both notational schemes.
Their are four basic set “groups” within Temoa: periods, annual “slices”, technology, and energy commodities. The technological sets contain all the possible energy technologies that the model may build and the commodities sets contain all the input and output forms of energy that technologies consume and produce. The period and slice sets merit a slightly longer discussion.
Temoa’s conceptual model of time is broken up into three levels:
Periods  consecutive blocks of years, marked by the first year in the period. For example, a twoperiod model might consist of , representing the two periods of years from 2010 through 2014, and from 2015 through 2024.
Seasonal  Each year may have multiple seasons. For example, winter might demand more heating, while spring might demand more cooling and transportation.
Daily  Within a season, a day might have various times of interest. For instance, the peak electrical load might occur midday in the summer, and a secondary peak might happen in the evening.
There are two specifiable period sets: time_exist
()
and time_future
(). The time_exist
set
contains periods before time_future
. Its primary purpose is to specify
the vintages for capacity that exist prior to the model optimization. (This is
part of Temoa’s answer to what most other efforts model as “residual capacity”.)
The time_future
set contains the future periods that the model will
optimize. As this set must contain only integers, Temoa interprets the elements
to be the boundaries of each period of interest. Thus, this is an ordered set
and Temoa uses its elements to automatically calculate the length of each
optimization period; modelers may exploit this to create variable period lengths
within a model. Temoa “names” each optimization period by the first year, and
makes them easily accessible via the time_optimize
set. This final
“period” set is not userspecifiable, but is an exact duplicate of
time_future
, less the largest element. In the above example, since
, time_optimize
does not
contain 2025: .
One final note on periods: rather than optimizing each year within a period individually, Temoa makes a simplifying assumption that each period contains copies of a single, representative year. Temoa optimizes just this characteristic year, and only delineates each year within a period through a timevalue of money calculation in the objective function. Figure 3.3 gives a graphical explanation of the annual delineation.
Many modelbased analyses require subannual variations in demand as well. Temoa
allows the modeler to subdivide years into slices, comprised of a season and a
time of day (e.g. winter evening). Unlike the periods, there is no restriction
on what labels the modeler may assign to the time_season
and
time_of_day
set elements.
A Word on Index Ordering¶
The ordering of the indices is consistent throughout the model to promote an intuitive “lefttoright” description of each parameter, variable, and constraint set. For example, Temoa’s output commodity flow variable may be described as “in period () during season () at time of day (), the flow of input commodity () to technology () of vintage () generates an output commodity flow () of .” For any indexed parameter or variable within Temoa, our intent is to enable a mental model of a lefttoright arrowboxarrow as a simple mnemonic to describe the “input process output” flow of energy. And while not all variables, parameters, or constraints have 7 indices, the 7index order mentioned here (p, s, d, i, t, v, o) is the canonical ordering. If you note any case where, for example, d comes before s, that is an oversight. In general, if there is an index ordering that does not follow this rubric, we view that as a bug.
Deviations from Standard Mathematical Notation¶
Temoa deviates from standard mathematical notation and set understanding in two
ways. The first is that Temoa places a restriction on the time set elements.
Specifically, while most optimization programs treat set elements as arbitrary
labels, Temoa assumes that all elements of the time_existing
and
time_future
sets are integers. Further, these sets are assumed to be
ordered, such that the minimum element is “naught”. For example, if
, then . In
other words, the capital with the naught subscript indicates
the first element in the time_future
set. We will explain the reason
for this deviation shortly.
The second set of deviations revolves around the use of the Theta superset (). The Temoa code makes heavy use of sparse sets, for both correctness and efficient use of computational resources. For brevity, and to avoid discussion of some “implementation details,” we do not enumerate their logical creation here. Instead, we rely on the readers general understanding of the context. For example, in the sparse creation of the constraints of the Demand constraint class (explained in Network Constraints and Anatomy of a Constraint), we state simply that the constraint is instantiated “for all the tuples in ”. This means that the constraint is only defined for the exact indices for which the modeler specified enduse demands via the Demand parameter.
Summations also occur in a sparse manner. Take equation (1) as an example (described in Constraints Defining Derived Decision Variables):
It defines the Activity variable for every valid combination of as the sum over all inputs and outputs of the FlowOut variable. A naive implementation of this equation might include nonsensical items in each summation, such as an input of vehicle miles traveled and an output of sunlight for a wind powered turbine. However, in this context, summing over the inputs and outputs ( and ) implicitly includes only the valid combinations of .
Parameters¶
Parameter 
Temoa Name 
Domain 
Short Description 

CapacityFactorTech 
Technologyspecific capacity factor 

CapacityFactorProcess 
Processspecific capacity factor 

Capacity2Activity 
Converts from capacity to activity units 

CostFixed 
Fixed operations & maintenance cost 

CostInvest 
Techspecific investment cost 

CostVariable 
Variable operations & maintenance cost 

Demand 
Enduse demands, by period 

DemandDefaultDistribution 
Default demand distribution 

DemandSpecificDistribution 
Demandspecific distribution 

DiscountRate 
Techspecific interest rate on investment 

Efficiency 
Tech and commodityspecific efficiency 

EmissionsActivity 
Techspecific emissions rate 

EmissionsLimit 
Emissions limit by time period 

ExistingCapacity 
Preexisting capacity 

GlobalDiscountRate 
Global rate used to calculate present cost 

GrowthRateMax 
Global rate used to calculate present cost 

GrowthRateSeed 
Global rate used to calculate present cost 

LifetimeLoanProcess 
Tech and vintagespecific loan term 

LifetimeTech 
Tech and vintagespecific lifetime 

MaxCapacity 
maximum techspecific capacity by period 

MinCapacity 
minimum techspecific capacity by period 

ResourceBound 
Upper bound on resource use 

SegFrac 
Fraction of year represented by each (s, d) tuple 

TechInputSplit 
Technology input fuel ratio 

TechOutputSplit 
Technology output fuel ratio 

LoanAnnualize 
Loan amortization by tech and vintage; based on 

ModelLoanLife 
Smaller of model horizon or process loan life 

ModelTechLife 
Smaller of model horizon or process tech life 

PeriodLength 
Number of years in period 

PeriodRate 
Converts future annual cost to discounted period cost 

TechLifetimeFrac 
Fraction of last time period that tech is active 
Efficiency¶
We present the efficiency () parameter first as it is one of the most critical model parameters. Beyond defining the conversion efficiency of each process, Temoa also utilizes the indices to understand the valid input process output paths for energy. For instance, if a modeler does not specify an efficiency for a 2020 vintage coal power plant, then Temoa will recognize any mention of a 2020 vintage coal power plant elsewhere as an error. Generally, if a process is not specified in the efficiency table,2 Temoa assumes it is not a valid process and will provide the user a warning with pointed debugging information.
CapacityFactorTech¶
Temoa indexes the CapacityFactorTech
parameter by season, timeofday,
and technology.
CapacityFactorProcess¶
In addition to CapacityFactorTech, there may be cases where different
vintages of the same technology have different capacity factors. For example,
newer vintages of wind turbines may have higher capacity factors. So ,
CapacityFactorProcess
allows users to specify the capacity factor by
season, timeofday, technology, and vintage.
Capacity2Activity¶
Capacity and Activity are inherently two different units of measure. Capacity represents the maximum flow of energy per time (), while Activity is a measure of total energy actually produced. However, there are times when one needs to compare the two, and this parameter makes those comparisons more natural. For example, a capacity of 1 GW for one year works out to an activity of
or
When comparing one capacity to another, the comparison is easy, unit wise. However, when one needs to compare capacity and activity, how does one reconcile the units? One way to think about the utility of this parameter is in the context of the question: “How much activity would this capacity create, if used 100% of the time?”
CostFixed¶
The CostFixed
parameter specifies the fixed cost associated with any
process. Fixed costs are those that must be paid, regardless of how much the
process is utilized. For instance, if the model decides to build a nuclear
power plant, even if it decides not utilize the plant, the model must pay the
fixed costs. These costs are in addition to the capital cost, so once the
capital is paid off, these costs are still incurred every year the process
exists.
Temoa’s default objective function assumes the modeler has specified this parameter in units of currency per unit capacity ().
CostInvest¶
The CostInvest
parameter specifies the processspecific investment cost.
Unlike the CostFixed
and CostVariable
parameters,
CostInvest
only applies to vintages of technologies within the model
optimization horizon (). Like CostFixed
,
CostInvest
is specified in units of cost per unit of capacity and is
only used in the default objective function ().
CostVariable¶
The CostVariable
parameter represents the cost of a processspecific unit
of activity. Thus the incurred variable costs are proportional to the activity
of the process.
Demand¶
The Demand
parameter allows the modeler to define the total enduse
demand levels for all periods. In combination with the Efficiency
parameter, this parameter is the most important because without it, the rest of
model has no incentive to build anything. This parameter specifies the enduse
demands that appear at the far right edge of the system diagram.
To specify the distribution of demand, look to the
DemandDefaultDistribution
(DDD) and DemandSpecificDistribution
(DSD) parameters.
As a historical note, this parameter was at one time also indexed by season and time of day, allowing modelers to specify exact demands for every time slice. However, while extremely flexible, this proved too tedious to maintain for any data set of appreciable size. Thus, we implemented the DDD and DSD parameters.
DemandDefaultDistribution¶
By default, Temoa assumes that enduse demands (Demand) are evenly
distributed throughout a year. In other words, the Demand will be apportioned
by the SegFrac
parameter via:
Temoa enables this default action by automatically setting DDD equivalent to
SegFrac
for all seasons and times of day. If a modeler would like a
different default demand distribution, the modeler must specify any indices of
the DDD parameter. Like the SegFrac parameter, the sum of DDD must be 1.
DemandSpecificDistribution¶
If there is an enduse demand that varies over the course of a day or across
seasons – for example, heating or cooling in the summer or winter – the
modeler may specify the fraction of annual demand occurring in each time slice.
Like SegFrac and DemandDefaultDistribution, the sum of DSD for each must be 1.
If the modeler does not define DSD for a season, time of day, and demand
commodity, Temoa automatically populates this parameter according to DDD.
It is this parameter that is actually multiplied by the Demand
parameter
in the Demand constraint.
DiscountRate¶
In addition to the GlobalDiscountRate
, a modeler may also specify a
technologyspecific discount rate. If not specified, this rate defaults to 0.05.
EmissionActivity¶
Temoa currently has two methods for enabling a process to produce an output: the
Efficiency
parameter, and the EmissionActivity
parameter. Where
the Efficiency
parameter defines the amount of output energy a process
produces per unit of input, the EmissionActivity
parameter allows for
secondary outputs. As the name suggests, this parameter was originally intended
to account for emissions per unit activity, but it more accurately describes
parallel activity. It is restricted to emissions accounting (by the
set restriction).
EmissionLimit¶
The EmissionLimit
parameter ensures that Temoa finds a solution that
fits within the modelerspecified limit of emission in time period
.
ExistingCapacity¶
In contrast to some competing models, technologies in Temoa can have vintagespecific characteristics within the same period. Thus, Temoa treats existing technological capacity as processes, requiring all of the engineering characteristics of a standard process, with the exception of an investment cost.
GlobalDiscountRate¶
Because Temoa is a cpaacity expansion model, it must account for the time value of money. The future value (FV) of a sum of currency is related to the netpresent value (NPV) via the formula:
where is in years. This parameter is only used in Temoa’s objective function.
LifetimeLoan¶
Temoa gives the modeler the ability to separate the loan lifetime from the useful life of the technology. This parameter specifies the length of the loan associated with investing in a process, in years. If not specified, the default is 10 years.
LifetimeTech¶
Similar to LifetimeLoan, this parameter specifies the total useful life of a given technology in years. If not specified, the default is 30 years.
MaxCapacity¶
The MaxCapacity parameter enables a modeler to ensure that a certain technology is constrained to an upper bound capacity. The constraint ensures that the max total capacity (summed across vintages) of a technology class is under this maximum. That is, all active vintages are constrained. This parameter is used only in the maximum capacity constraint.
MinCapacity¶
The MinCapacity parameter is analogous to the MaxCapacity parameter, except that it specifies the minimum capacity for which Temoa must ensure installation.
ResourceBound¶
This parameter allows the modeler to specify resources to constrain per period. Note that a constraint in one period does not relate to any other periods. For instance, if the modeler specifies a limit in period 1 and does not specify a limit in period 2, then the model may use as much of that resource as it would like in period 2.
SegFrac¶
The SegFrac
parameter specifies the fraction of the year represented by
each combination of season and time of day. The sum of all combinations within
SegFrac
must be 1, representing 100% of a year.
TechInputSplit¶
Some technologies have a single output but have multiple input fuels. Some technologies require fixed shares of input. See the TechOutputSplit constraint for the implementation concept.
TechOutputSplit¶
Some technologies have a single input fuel but have multiple outputs. For the sake of modeling, certain technologies require fixed shares of output. For example, an oil refinery might have an input energy of crude oil, and the modeler wants to ensure that its output is 70% diesel and 30% gasoline. See the TechOutputSplit constraint for the implementation details.
*LoanAnnualize¶
This is a modelcalculated parameter based on the processspecific loan length
(it’s indices are the same as the LifetimeLoan
parameter), and
processspecific discount rate (the DiscountRate
parameter). It is
calculated via the formula:
*PeriodLength¶
Given that the modeler may specify arbitrary time period boundaries, this
parameter specifies the number of years contained in each period. The final year
is the largest element in time_future
which is specifically not included
in the list of periods in time_optimize
(). The length
calculation for each period then exploits the fact that the time
sets are
ordered:
The first line creates a sorted array of the period boundaries, called
boundaries. The second line defines a function I that finds the index of
period in boundaries. The third line then defines the length of period
to be the number of years between period and the next
period. For example, if ,
then boundaries would be [2015, 2020, 2030, 2045]
. For 2020, I(2020)
would return 2. Similarly, boundaries[ 3 ] = 2030. Then,
Note that LEN is only defined for elements in , and is specifically not defined for the final element in .
*PeriodRate¶
Temoa optimizes a single characteristic year within a period, and differentiates the copies of that single year solely by the appropriate discount factor. Rather than calculating the same summation for every technology and vintage within a period, we calculate it once per period and lookup the sum as necessary during the objective function generation. The formula is the sum of discount factors corresponding to each year within a period:
Note that this parameter is the implementation of the single “characteristic year” optimization per period concept discussed in the Conventions section.
*TechLifeFrac¶
The modeler may specify a useful lifetime of a process such that the process will be decommissioned part way through a period. Rather than attempt to delineate each year within that final period, Temoa makes the choice to average the total output of the process over the entire period but limit the available capacity and output of the decommissioning process by the ratio of how long through the period the process is active. This parameter is that ratio, formally defined as:
Note that this parameter is defined over the same indices as
CostVariable
– the active periods for each process . As an example, if a model has , and a process has a useful
lifetime of 5 years, then this parameter would include only the first two
activity indices for the process. Namely, as
. The values would be , and
.
In combination with the PeriodRate
parameter, this parameter is used to
implement the “single characteristic year” simplification. Specifically,
instead of trying to account for partial period decommissioning, Temoa assumes
that processes can only produce TechLifeFrac
of their installed
capacity.
Variables¶
Variable 
Temoa Name 
Domain 
Short Description 

V_FlowIn 
Commodity flow into a tech to produce a given output 

V_FlowOut 
Commodity flow out of a tech based on a given input 

V_Curtailment 
Commodity flow out of a tech that is curtailed 

V_Activity 
Total tech commodity production in each (s, d) tuple 

V_Capacity 
Required tech capacity to support associated activity 

V_CapacityAvailableByPeriodAndTech 
The Capacity of technology available in period 
The most fundamental variables in the Temoa formulation are and . They describe the commodity flows into and out of a process in a given time slice. They are related through the ProcessBalance constraint (5), which in essence, guarantees the conservation of energy for each process.
The Activity variable is defined as the sum over all inputs and outputs of a process in a given time slice (see equation (1)). At this time, one potential “gotcha” is that for a process with multiple inputs or outputs, there is no attempt to reconcile energy units: Temoa assumes all inputs are comparable, and has no understanding of units. The onus is on the modeler to ensure that all inputs and outputs have similar units.
The Capacity variable is used in the objective function as the amount of capacity of a process to build. It is indexed for each process, and Temoa constrains the Capacity variable to at least be able to meet the Activity of that process in all time slices in which it is active (2).
Finally, CapacityAvailableByPeriodAndTech is a convenience variable that is not strictly necessary, but used where the individual vintages of a technology are not warranted (e.g. in calculating the maximum or minimum total capacity allowed in a given time period).
We explain the equations governing these variables the Constraints section.
Constraints¶
There are 4 main equations that govern the flow of energy through the model network. The DemandConstraint ensures that the supply meets demand in every time slice. For each process, the ProcessBalance ensures at least as much energy enters a process as leaves it (conservation of energy at the process level). Between processes, the CommodityBalance ensures that at least as much of a commodity is generated as is demanded by other process inputs.
In combination, those three constraints ensure the flow of energy through the system. The final calculation, the objective function, is what puts a cost to the actions prescribed by the model.
The rest of this section defines each model constraint, with a rationale for
existence. We use the implementationspecific names for the constraints to
highlight the organization of the functions within the actual code. They are
listed roughly in order of importance. Note that the definitions below are
pulled directly from the docstrings embedded in temoa_rules.py
.
Constraints Defining Derived Decision Variables¶
These first two constraints elucidate the relationship among decision variables in the model. There is some overlap with the rest of the constraints, but these are unique enough to warrant special attention to a Temoa modeler.

temoa_rules.
Activity_Constraint
(M, p, s, d, t, v)[source]¶ The Activity constraint defines the Activity convenience variable. The Activity variable is mainly used in the objective function to calculate the cost associated with use of a technology. This constraint sums the over all input and output commodities.
There is one caveat to keep in mind in regards to the Activity variable: if there is more than one output, there is currently no attempt by Temoa to convert to a common unit of measurement. For example, common measurements for heat include mass of steam at a given temperature, or total BTUs, while electricity is generally measured in a variant of watthours. Reconciling these units of measurement, as for example with a cogeneration plant, is currently left as an accounting exercise for the modeler.
(1)¶

temoa_rules.
Capacity_Constraint
(M, p, s, d, t, v)[source]¶ This constraint ensures that the capacity of a given process is sufficient to support its activity across all time periods and time slices. The calculation on the left hand side of the equality is the maximum amount of energy a process can produce in the timeslice
<s
,d>
. Note that the curtailment variable shown below only applies to technologies are members of the curtailment set. Curtailment is necessary to track explicitly in scenarios that include a high renewable target. Without it, the model can generate more activity than is used to meet demand, and have all activity (including the portion curtailed) count towards the target. Tracking activity and curtailment separately prevents this possibility.(2)¶

temoa_rules.
CapacityAvailableByPeriodAndTech_Constraint
(M, p, t)[source]¶ The variable is nominally for reporting solution values, but is also used in the Max and Min constraint calculations. For any process with an endoflife (EOL) on a period boundary, all of its capacity is available for use in all periods in which it is active (the process’ TLF is 1). However, for any process with an EOL that falls between periods, Temoa makes the simplifying assumption that the available capacity from the expiring technology is available through the whole period, but only as much percentage as its lifespan through the period. For example, if a process expires 3 years into an 8 year period, then only of the installed capacity is available for use throughout the period.
(3)¶
Network Constraints¶
These three constraints define the core of the Temoa model. Together, they create the algebraic network. The Demand constraint drives the “right side” of the energy system map, the ProcessBalance constraint ensures flow through a process, and the CommodityBalance constraint ensures flow between processes.

temoa_rules.
Demand_Constraint
(M, p, s, d, dem)[source]¶ The Demand constraint drives the model. This constraint ensures that supply at least meets the demand specified by the Demand parameter in all periods and slices, by ensuring that the sum of all the demand output commodity () generated by must meet the modelerspecified demand, in each time slice.
(4)¶
Note that the validity of this constraint relies on the fact that the set is distinct from both and . In other words, an enduse demand must only be an enduse demand. Note that if an output could satisfy both an enduse and internal system demand, then the output from would be double counted.

temoa_rules.
ProcessBalance_Constraint
(M, p, s, d, i, t, v, o)[source]¶ The ProcessBalance constraint is one of the most fundamental constraints in Temoa. It defines the basic relationship between the energy entering a process () and the energy leaving a process (). This constraint sets the
FlowOut
variable, upon which all other constraints rely.This constraint requires that the output energy of a given process is equal to the product of its input energy and conversion efficiency. Note that the curtailment variable shown below only applies to technologies are members of the curtailment set. As noted in the
Capacity_Constraint
, curtailment is necessary to track explicitly in scenarios that include a high renewable target.(5)¶

temoa_rules.
CommodityBalance_Constraint
(M, p, s, d, c)[source]¶ Where the Demand constraint (4) ensures that enduse demands are met, the CommodityBalance constraint ensures that the endogenous system demands are met. This constraint requires the total production of a given commodity to equal the amount consumed, thus ensuring an energy balance at the system level.
(6)¶
Physical and Operational Constraints¶
These three constraints finetune the algebraic map created by the three previous constraints, based on various physical and operational realworld phenomena.

temoa_rules.
BaseloadDiurnal_Constraint
(M, p, s, d, t, v)[source]¶ Some electric generators cannot ramp output over a short period of time (e.g., hourly or daily). Temoa models this behavior by forcing technologies in the
tech_baseload
set to maintain a constant output across all timesofday within the same season. Note that the output of a baseload process can vary between seasons.Ideally, this constraint would not be necessary, and baseload processes would simply not have a index. However, implementing the more efficient functionality is currently on the Temoa TODO list.
(7)¶

temoa_rules.
DemandActivity_Constraint
(M, p, s, d, t, v, dem, s_0, d_0)[source]¶ For enduse demands, it is unreasonable to let the optimizer only allow use in a single time slice. For instance, if household A buys a natural gas furnace while household B buys an electric furnace, then both units should be used throughout the year. Without this constraint, the model might choose to only use the electric furnace during the day, and the natural gas furnace during the night.
This constraint ensures that the ratio of a process activity to demand is constant for all time slices. Note that if a demand is not specified in a given time slice, or is zero, then this constraint will not be considered for that slice and demand. This is transparently handled by the superset.
(8)¶

temoa_rules.
StorageEnergy_Constraint
(M, p, s, d, t, v)[source]¶ This constraint tracks the amount of storage assuming ordered time slices. The storage unit is initialized at a userspecified charge level (0100%) in the first time slice of each period, and then the charge level is updated each time slice based on the amount of energy stored or discharged. At the end of the last time slice associated with each period, the charge level must be zeroed out.

temoa_rules.
StorageEnergyUpperBound_Constraint
(M, p, s, d, t, v)[source]¶ This constraint ensures that the amount of energy stored does not exceed the upper bound set by the energy capacity of the storage device.

temoa_rules.
StorageChargeRate_Constraint
(M, p, s, d, t, v)[source]¶ This constraint ensures that the charge rate of the storage unit is limited by the power capacity (typically GW) of the storage unit.

temoa_rules.
StorageDischargeRate_Constraint
(M, p, s, d, t, v)[source]¶ This constraint ensures that the discharge rate of the storage unit is limited by the power capacity (typically GW) of the storage unit.

temoa_rules.
StorageThroughput_Constraint
(M, p, s, d, t, v)[source]¶ It is not enough to only limit the charge and discharge rate separately. We also need to ensure that the maximum throughput (charge + discharge) does not exceed the capacity (typically GW) of the storage unit.

temoa_rules.
RampUpDay_Constraint
(M, p, s, d, t, v)[source]¶ The ramp rate constraint is utilized to limit the rate of electricity generation increase and decrease between two adjacent time slices in order to account for physical limits associated with thermal power plants. Note that this constraint only applies to technologies with ramp capability, which is defined in the set . We assume for simplicity the rate limits for both ramp up and down are equal and they do not vary with technology vintage. The ramp rate limits () for technology should be expressed in percentage of its rated capacity.
Note that when is the last timeofday, , i.e., if one time slice is the last timeofday in a season and the other time slice is the first timeofday in the next season, the ramp rate limits between these two time slices can not be expressed by (9). Therefore, the ramp rate constraints between two adjacent seasons are represented in (10).
In Equation (9) and (10), we assume and .
(9)¶

temoa_rules.
RampUpSeason_Constraint
(M, p, s, t, v)[source]¶ Note that and represent the first and last timeofday, respectively.
(10)¶

temoa_rules.
RampDownDay_Constraint
(M, p, s, d, t, v)[source]¶ Similar to Equation (9), we use Equation (11) to limit ramp down rates between any two adjacent time slices.
(11)¶

temoa_rules.
RampDownSeason_Constraint
(M, p, s, t, v)[source]¶ Similar to Equation (10), we use Equation (12) to limit ramp down rates between any two adjacent seasons.
(12)¶

temoa_rules.
ReserveMargin_Constraint
(M, p, s, d)[source]¶ During each period , the sum of the available capacity of all reserve technologies , which are defined in the set , should exceed the peak load by , the regional reserve margin. Note that the reserve margin is expressed in percentage of the peak load. Generally speaking, in a database we may not know the peak demand before running the model, therefore, we write this equation for all the timeslices defined in the database in each region.
(13)¶
Objective Function¶

temoa_rules.
TotalCost_rule
(M)[source]¶ Using the
Activity
andCapacity
variables, the Temoa objective function calculates the cost of energy supply, under the assumption that capital costs are paid through loans. This implementation sums up all the costs incurred, and is defined as . Each term on the righthand side represents the cost incurred over the model time horizon and discounted to the initial year in the horizon (). The calculation of each term is given below.(14)¶
Note that capital costs () are handled in several steps. First, each capital cost is amortized using the loan rate (i.e., technologyspecific discount rate) and loan period. Second, the annual stream of payments is converted into a lump sum using the global discount rate and loan period. Third, the new lump sum is amortized at the global discount rate and technology lifetime. Fourth, loan payments beyond the model time horizon are removed and the lump sum recalculated. The terms used in Steps 34 are . The product simplifies to , where represents the active lifetime of a process before the end of the model horizon, and represents the full lifetime of a process . Fifth, the lump sum is discounted back to the beginning of the horizon () using the global discount rate. While an explicit salvage term is not included, this approach properly captures the capital costs incurred within the model time horizon, accounting for technologyspecific loan rates and periods.
(15)¶
(16)¶
UserSpecific Constraints¶
The constraints provided in this section are not required for proper system operation, but allow the modeler some further degree of system specification.

temoa_rules.
ExistingCapacity_Constraint
(M, t, v)[source]¶ Temoa treats existing capacity installed prior to the beginning of the model’s optimization horizon as regular processes that require the same parameter specification as do new vintage technologies, except for the
CostInvest
parameter. This constraint sets the capacity of processes for model periods that exist prior to the optimization horizon to userspecified values.(17)¶

temoa_rules.
EmissionLimit_Constraint
(M, p, e)[source]¶ A modeler can track emissions through use of the
commodity_emissions
set andEmissionActivity
parameter. The parameter is analogous to the efficiency table, tying emissions to a unit of activity. The EmissionLimit constraint allows the modeler to assign an upper bound per period to each emission commodity.(18)¶

temoa_rules.
GrowthRateConstraint_rule
(M, p, t)[source]¶ This constraint sets an upper bound growth rate on technologyspecific capacity.
(19)¶
where is the maximum growth rate, and should be specified as and is the growth rate seed, which has units of capacity. Without the seed, any technology with zero capacity in the first time period would be restricted to zero capacity for the remainder of the time horizon.

temoa_rules.
MaxActivity_Constraint
(M, p, t)[source]¶ The MaxActivity sets an upper bound on the activity from a specific technology. Note that the indices for these constraints are period and tech, not tech and vintage.
(20)¶

temoa_rules.
MinActivity_Constraint
(M, p, t)[source]¶ The MinActivity sets a lower bound on the activity from a specific technology. Note that the indices for these constraints are period and tech, not tech and vintage.
(21)¶

temoa_rules.
MinActivityGroup_Constraint
(M, p, g)[source]¶ The MinActivityGroup constraint sets a minimum activity limit for a userdefined technology group. Each technology within each group is multiplied by a weighting function, which determines what technology activity share can count towards the constraint.
(22)¶
where represents the assigned technology group and refers to the
MinGenGroupTarget
parameter.

temoa_rules.
MaxCapacity_Constraint
(M, p, t)[source]¶ The MaxCapacity constraint sets a limit on the maximum available capacity of a given technology. Note that the indices for these constraints are period and tech, not tech and vintage.
(23)¶

temoa_rules.
MinCapacity_Constraint
(M, p, t)[source]¶ The MinCapacity constraint sets a limit on the minimum available capacity of a given technology. Note that the indices for these constraints are period and tech, not tech and vintage.
(24)¶

temoa_rules.
ResourceExtraction_Constraint
(M, p, r)[source]¶ The ResourceExtraction constraint allows a modeler to specify an annual limit on the amount of a particular resource Temoa may use in a period.
(25)¶

temoa_rules.
TechInputSplit_Constraint
(M, p, s, d, i, t, v)[source]¶ Allows users to specify fixed or minimum shares of commodity inputs to a process producing a single output. These shares can vary by model time period. See TechOutputSplit_Constraint for an analogous explanation.

temoa_rules.
TechOutputSplit_Constraint
(M, p, s, d, t, v, o)[source]¶ Some processes take a single input and make multiple outputs, and the user would like to specify either a constant or timevarying ratio of outputs per unit input. The most canonical example is an oil refinery. Crude oil is used to produce many different refined products. In many cases, the modeler would like to specify a minimum share of each refined product produced by the refinery.
For example, a hypothetical (and highly simplified) refinery might have a crude oil input that produces 4 parts diesel, 3 parts gasoline, and 2 parts kerosene. The relative ratios to the output then are:
Note that it is possible to specify output shares that sum to less than unity. In such cases, the model optimizes the remaining share. In addition, it is possible to change the specified shares by model time period. The constraint is formulated as follows:
(26)¶
General Caveats¶
Temoa does not currently provide an easy avenue to track multiple concurrent energy flows through a process. Consider a cogeneration plant. Where a conventional power plant might simply emit excess heat as exhaust, a cogeneration plant harnesses some or all of that heat for heating purposes, either very close to the plant, or generally as hot water for district heating. Temoa’s flow variables can track both flows through a process, but each flow will have its own efficiency from the Efficiency parameter. This implies that to produce 1 unit of electricity will require units of input. At the same time, to produce 1 unit of heat will require units of input energy, and to produce both output units of heat and energy, both flows must be active, and the desired activity will be doublecounted by Temoa.
To model a parallel output device (c.f., a cogeneration plant), the modeler must
currently set up the process with the TechInputSplit
and
TechOutputSplit
parameters, appropriately adding each flow to the
Efficiency parameter and accounting for the overall process efficiency through
all flows.
The Temoa Computational Implementation¶
We have implemented Temoa within an algebraic modeling environment (AME). AMEs provide both a convenient avenue to describe mathematical optimization models for a computational context, and allow for abstract model7 formulations [Kallrath04]. In contrast to describing a model in a formal computer programming language like C or Java, AMEs generally have syntax that directly translates to standard mathematical notation. Consequently, models written in AMEs are more easily understood by a wider variety of people. Further, by allowing abstract formulations, a model written with an AME may be used with many different input data sets.
Three wellknown and popular algebraic modeling environments are the General Algebraic Modeling System (GAMS) [BrookeRosenthal03], AMPL [FourerGayKernighan87], and GNU MathProg [Makhorin00]. All three environments provide concise syntax that closely resembles standard (paper) notation. We decided to implement Temoa within a recently developed AME called Python Optimization Modeling Objects (Pyomo).
Pyomo provides similar functionality to GAMS, AMPL, and MathProg, but is open source and written in the Python scripting language. This has two general consequences of which to be aware:
Python is a scripting language; in general, scripts are an order of magnitude slower than an equivalent compiled program.
Pyomo provides similar functionality, but because of its Python heritage, is much more verbose than GAMS, AMPL, or MathProg.
It is our view that the speed penalty of Python as compared to compiled languages is inconsequential in the face of other large resource bottle necks, so we omit any discussion of it as an issue. However, the “boilerplate” code (verbosity) overhead requires some discussion. We discuss this in the Anatomy of a Constraint.
Anatomy of a Constraint¶
To help explain the Pyomo implementation, we discuss a single constraint in detail. Consider the demand constraint (4):
Implementing this with Pyomo requires two pieces, and optionally a third:
a constraint definition (in
temoa_model.py
),the constraint implementation (in
temoa_rules.py
), and(optional) sparse constraint index creation (in
temoa_initialize.py
).
We discuss first a straightforward implementation of this constraint, that specifies the sets over which the constraint is defined. We will follow it with the actual implementation which utilizes a more computationally efficient but less transparent constraint index definition (the optional step 3).
A simple definition of this constraint is:
in temoa_model.py
1 2 3 4  M.DemandConstraint = Constraint(
M.time_optimize, M.time_season, M.time_of_day, M.commodity_demand,
rule=Demand_Constraint
)

In line 1, ‘M.DemandConstraint =
’ creates a place holder in the model object
M
, called ‘DemandConstraint’. Like a variable, this is the name through
which Pyomo will reference this class of constraints. Constraint(...)
is a
Pyomospecific function that creates each individual constraint in the class.
The first arguments (line 2) are the index sets of the constraint class. Line 2
is the Pyomo method of saying “for all” (). Line 3 contains the
final, mandatory argument (rule=...
) that specifies the name of the
implementation rule for the constraint, in this case Demand_Constraint
.
Pyomo will call this rule with each tuple in the Cartesian product of the index
sets.
An associated implementation of this constraint based on the definition above is:
temoa_rules.py
…
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  def Demand_Constraint ( M, p, s, d, dem ):
if (p, s, d, dem) not in M.DemandSpecificDistribution.sparse_keys(): # If user did not specify this Demand, tell
return Constraint.Skip # Pyomo to ignore this constraint index.
# store the summation into the local variable 'supply' for later reference
supply = sum(
M.V_FlowOut[p, s, d, S_i, S_t, S_v, dem]
for S_t, S_v in M.commodityUStreamProcess[p, dem]
for S_i in M.ProcessInputsByOutput[p, S_t, S_v, dem]
)
# The '=' operator creates (in this case) a "Equal" *object*, not a
# True/False value as a Python programmer might expect; the intermediate
# variable 'expr' is thus not strictly necessary, but we leave it as reminder
# of this potentially confusing behavior
expr = (supply = M.Demand[p, s, d, dem])
# finally, return the new "Equal" object (not boolean) to Pyomo
return expr

…
The Python boilerplate code to create the rule is on line 1. It begins with
def
, followed by the rule name (matching the rule=...
argument
in the constraint definition in temoa_model
), followed by the argument list.
The argument list will always start with the model (Temoa convention shortens
this to just M
) followed by local variable names in which to store the
index set elements passed by Pyomo. Note that the ordering is the same as
specified in the constraint definition. Thus the first item after M
will be an item from time_optimize
, the second from time_season
,
the third from time_of_day
, and the fourth from
commodity_demand
. Though one could choose a
, b
,
c
, and d
(or any naming scheme), we chose p
, s
,
d
, and dem
as part of a naming scheme to aid in mnemonic understanding. Consequently, the rule
signature (Line 1) is another place to look to discover what indices define a
constraint.
Lines 2 and 3 are an indication that this constraint is implemented in a
nonsparse manner. That is, Pyomo does not inherently know the valid indices
for a given model parameter or equation. In temoa_model
, the constraint definition
listed four index sets, so Pyomo will naively call this function for every
possible combination of tuple . However, as there
may be slices for which a demand does not exist (e.g., the winter season might
have no cooling demand), there is no need to create a constraint for any tuple
involving ‘winter’ and ‘cooling’. Indeed, an attempt to access a demand for
which the modeler has not specified a value results in a Pyomo error, so it is
necessary to ignore any tuple for which no Demand exists.
Lines 6 through 12 are a single sourceline that we split over 7 lines for
clarity. These lines implement the summation of the Demand constraint, summing
over all technologies, vintages, and the inputs that generate the enduse demand
dem
. Note that the sum is performed with sparse indices, which are returned
from dictionaries created in temoa_initialize.py
.
Lines 6 through 12 also showcase a very common idiom in Python: listcomprehension. List comprehension is a concise and efficient syntax to create lists. As opposed to building a list elementbyelement with forloops, list comprehension can convert many statements into a single operation. Consider a naive approach to calculating the supply:
to_sum = list()
for S_t in M.tech_all:
for S_v in M.vintage_all:
for S_i in ProcessInputsByOutput( p, S_t, S_v, dem ):
to_sum.append( M.V_FlowOut[p, s, d, S_i, S_t, S_v, dem] )
supply = sum( to_sum )
While both implementations have the same number of lines, this last one creates
an extra list (to_sum
), then builds the list element by element with
.append()
, before finally calculating the summation. This means that
the Python interpreter must iterate through the elements of the summation, not
once, but twice.
A less naive approach would replace the .append()
call with the
+=
operator, reducing the number of iterations through the elements to
one:
supply = 0
for S_t in M.tech_all:
for S_v in M.vintage_all:
for S_i in ProcessInputsByOutput( p, S_t, S_v, dem ):
supply += M.V_FlowOut[p, s, d, S_i, S_t, S_v, dem]
Why is list comprehension necessary? Strictly speaking, it is not, especially in light of this last example, which may read more familiar to those comfortable with C, Fortran, or Java. However, due to quirks of both Python and Pyomo, listcomprehension is preferred both syntactically as “the Pythonic” way, and as the more efficient route for many list manipulations. (It also may seem slightly more familiar to those used to a more mainstream algebraic modeling language.)
With the correct model variables summed and stored in the supply
variable,
line 18 creates the actual inequality comparison. This line is superfluous, but
we leave it in the code as a reminder that inequality operators (i.e. <=
and >=
) with a Pyomo object (like supply) generate a Pyomo expression
object, not a boolean True or False as one might expect.6
It is this expression object that must be returned to Pyomo, as on line 19.
In the above implementation, the constraint is called for every tuple in the Cartesian product of the indices, and the constraint must then decide whether each tuple is valid. The below implementation differs from the one above because it only calls the constraint rule for the valid tuples within the Cartesian product, which is computationally more efficient than the simpler implementation above.
in temoa_model.py
(actual implementation)
1 2 3  M.DemandConstraint_psdc = Set( dimen=4, rule=DemandConstraintIndices )
# ...
M.DemandConstraint = Constraint( M.DemandConstraint_psdc, rule=Demand_Constraint )

As discussed above, the DemandConstraint is only valid for certain
tuples. Since the modeler can specify demand
distribution per commodity (necessary to model demands like heating, that do not
make sense in the summer), Temoa must ascertain the exact valid tuples. We have
implemented this logic in the function DemandConstraintIndices
in
temoa_initialize.py
. Thus, Line 1 tells Pyomo to instantiate
DemandConstraint_psdc
as a Set of 4length tuples indices
(dimen=4
), and populate it with what Temoa’s rule
DemandConstraintIndices
returns. We omit here an explanation of the
implementation of the DemandConstraintIndices
function, stating merely
that it returns the exact indices over which the DemandConstraint must to be
created. With the sparse set DemandConstraint_psdc
created, we can now
can use it in place of the four sets specified in the nonsparse
implementation. Pyomo will now call the constraint implementation rule the
minimum number of times.
On the choice of the _psdc
suffix for the index set name, there is no
Pyomoenforced restriction. However, use of an index set in place of the
nonsparse specification obfuscates over what indexes a constraint is defined.
While it is not impossible to deduce, either from this documentation
or from looking at the DemandConstraintIndices
or
Demand_Constraint
implementations, the Temoa convention includes
index set names that feature the onecharacter representation of each set dimension.
In this case, the name DemandConstraint_psdc
implies that this set has a
dimensionality of 4, and (following the naming scheme) the first index of each tuple will be an element of
time_optimize
, the second an element of time_season
, the third
an element of time_of_day
, and the fourth a commodity. From the
contextual information that this is the Demand constraint, one can assume that
the c
represents an element from commodity_demand
.
Over a sparseindex, the constraint implementation changes only slightly:
in temoa_rules.py
(actual implementation)
1 2 3 4 5 6 7 8 9 10 11  def Demand_Constraint ( M, p, s, d, dem ):
supply = sum(
M.V_FlowOut[p, s, d, S_i, S_t, S_v, dem]
for S_t, S_v in M.commodityUStreamProcess[p, dem]
for S_i in M.ProcessInputsByOutput[p, S_t, S_v, dem]
)
DemandConstraintErrorCheck ( supply, dem, p, s, d )
expr = (supply = M.Demand[p, dem] * M.DemandSpecificDistribution[s, d, dem])
return expr

As this constraint is guaranteed to be called only for necessary demand
constraint indices, there is no need to check for the existence of a tuple in
the Demand parameter. The only other change is the error check on line 10.
This function is defined in temoa_initialize.py
, and simply ensures that at least one
process supplies the demand dem
in time slice . If
no process supplies the demand, then it quits computation immediately (as
opposed to completing a potentially lengthy model generation and waiting for the
solver to recognize the infeasibility of the model). Further, the function
lists potential places for the modeler to look to correct the problem. This
last capability is subtle, but in practice extremely useful while building and
debugging a model.
A Word on Verbosity¶
Implementing this same constraint in AMPL, GAMS, or MathProg would require only a single sourceline (in a single file). Using MathProg as an example, it might look like:
s.t. DemandConstraint{(p, s, d, dem) in sDemand_psd_dem} :
sum{(p, s, d, Si, St, Sv, dem) in sFlowVar_psditvo}
V_FlowOut[p, s, d, Si, St, Sv, dem]
=
pDemand[p, s, d, dem];
While the syntax is not a direct translation, the indices of the constraint
(p
, s
, d
, and dem
) are clear, and by inference, so are the
indices of summation (i
, t
, v
) and operand (V_FlowOut
). This
oneline definition creates an inequality for each period, season, time of day,
and demand, ensuring that total output meets each demand in each time slice –
almost exactly as we have formulated the demand constraint (4). In
contrast, Temoa’s implementation in Pyomo takes 47 sourcelines (the code
discussed above does not include the function documentation). While some of the
verbosity is inherent to working with a general purpose scripting language, and
most of it is our formatting for clarity, the absolute minimum number of lines a
Pyomo constraint can be is 2 lines, and that likely will be even less readable.
So why use Python and Pyomo if they are so verbose? In short, for four reasons:
Temoa has the full power of Python, and has access to a rich ecosystem of tools (e.g. numpy, matplotlib) that are not as cleanly available to other AMLs. For instance, there is minimal capability in MathProg to error check a model before a solve, and providing interactive feedback like what Temoa’s DemandConstraintErrorCheck function does is difficult, if not impossible. While a subtle addition, specific and directed error messages are an effective measure to reduce the learning curve for new modelers.
Python has a vibrant community. Whereas mathematical optimization has a small community, its opensource segment even smaller, and the energy modeling segment significantly smaller than that, the Python community is huge, and encompasses many disciplines. This means that where a developer may struggle to find an answer, implementation, or workaround to a problem with a more standard AML, Python will likely enable a communitysuggested solution.
Powerful documentation tools. One of the available toolsets in the Python world is documentation generators that dynamically introspect Python code. While it is possible to inline and block comment with more traditional AMLs, the integration with Python that many documentation generators have is much more powerful. Temoa uses this capability to embed useroriented documentation literally in the code, and almost every constraint has a block comment. Having both the documentation and implementation in one place helps reduce the mental friction and discrepancies often involved in maintaining multiple sources of model authority.
AMLs are not as concise as thought.
This last point is somewhat esoteric, but consider the MathProg implementation of the Demand constraint in contrast with the last line of the Pyomo version:
expr = (supply = M.Demand[p, s, d, dem])
While the MathProg version indeed translates more directly to standard notation, consider that standard notation itself needs extensive surrounding text to explain the significance of an equation. Why does the equation compare the sum of a subset of FlowOut to Demand? In Temoa’s implementation, a highlevel understanding of what a constraint does requires only the last line of code: “Supply must meet demand.”
File Structure¶
The Temoa model code is split into 7 main files:
temoa_model.py
 contains the overall model definition, defining the various sets, parameters, variables, and equations of the Temoa model. Peruse this file for a highlevel overview of the model.
temoa_rules.py
 mainly contains the rule implementations. That is, this file implements the objective function, internal parameters, and constraint logic. Wheretemoa_model
provides the highlevel overview, this file provides the actual equation implementations.
temoa_initialize.py
 contains the code used to initialize the model, including sparse matrix indexing and checks on parameter and constraint specifications.
temoa_run.py
 contains the code required to execute the model when called with :code:’python’ rather than :code:’pyomo solve’.
temoa_stochastic.py
 contains the PySP required alterations to the deterministic model for use in a stochastic model. Specifically, Temoa only needs one additional constraint class in order to partition the calculation of the objective function per period.
temoa_mga.py
 contains the functions used to execute the modelingto generate altenatives (MGA) algorithm. Use of MGA is specified through the config file.
pformat_results.py
 formats the results returned by the model; includes outputting results to the shell, storing them in a database, and if requested, calling ‘DB_to_Excel.py’ to create the Excel file outputs.
If you are working with a Temoa Git repository, these files are in the
temoa_model/
subdirectory.
The Bleeding Edge¶
The Temoa Project uses the Git source code management system, and the services of Github.com. If you are inclined to work with the bleeding edge of the Temoa Project code base, then take a look at the Temoa repository. To acquire a copy, make sure you have Git installed on your local machine, then execute this command to clone the repository:
$ git clone git://github.com/TemoaProject/temoa.git
Cloning into 'temoa'...
remote: Counting objects: 2386, done.
remote: Compressing objects: 100% (910/910), done.
remote: Total 2386 (delta 1552), reused 2280 (delta 1446)
Receiving objects: 100% (2386/2386), 2.79 MiB  1.82 MiB/s, done.
Resolving deltas: 100% (1552/1552), done.
You will now have a new subdirectory called temoa
, that contains the entire
Temoa Project code and archive history. Note that Git is a distributed source
code management tool. This means that by cloning the Temoa repository, you have
your own copy to which you are welcome (and encouraged!) to alter and make
commits to. It will not affect the source repository.
Though this is not a Git manual, we recognize that many readers of this manual may not be software developers, so we offer a few quick pointers to using Git effectively.
If you want to see the log of commits, use the command git log:
$ git log 1
commit b5bddea7312c34c5c44fe5cce2830cbf5b9f0f3b
Date: Thu Jul 5 03:23:11 2012 0400
Update two APIs
* I had updated the internal global variables to use the _psditvo
naming scheme, and had forgotten to make the changes to _graphviz.py
* Coopr also updated their API with the new .sparse_* methods.
You can also explore the various development branches in the repository:
$ ls
data_files stochastic temoa_model create_archive.sh README.txt
$ git branch a
* energysystem
remotes/origin/HEAD > origin/energysystem
remotes/origin/energysystem
remotes/origin/exp_electric_load_duration_reorg
remotes/origin/exp_electricity_sector
remotes/origin/exp_energysystem_flow_based
remotes/origin/exp_energysystem_match_markal
remotes/origin/exp_energysystem_test_framework
remotes/origin/misc_scripts
remotes/origin/old_energysystem_coopr2
remotes/origin/temoaproject.org
$ git checkout exp_energysystem_match_markal
Branch exp_energysystem_match_markal set up to track remote branch
exp_energysystem_match_markal from origin.
Switched to a new branch 'exp_energysystem_match_markal'
$ ls
temoa_model create_archive.sh utopiamarkal20.dat
compare_with_utopia15.py README.txt
compare_with_utopia20.py utopiamarkal15.dat
To view exactly what changes you have made since the most recent commit to the
repository use the diff
command to git
:
$ git diff
diff git a/temoa_model/temoa_lib.py b/temoa_model/temoa_lib.py
index 4ff9b30..0ba15b0 100644
 a/temoa_model/temoa_lib.py
+++ b/temoa_model/temoa_lib.py
@@ 246,7 +246,7 @@ def InitializeProcessParameters ( M ):
if l_vin in M.vintage_exist:
if l_process not in l_exist_indices:
msg = ('Warning: %s has a specified Efficiency, but does not '
 'have any existing install base (ExistingCapacity)\n.')
+ 'have any existing install base (ExistingCapacity).\n')
SE.write( msg % str(l_process) )
continue
if 0 == M.ExistingCapacity[ l_process ]:
[ ... ]
For a crash course on git, here is a handy quick start guide.
Temoa Code Style Guide¶
It is an open question in programming circles whether code formatting actually matters. The Temoa Project developers believe that it does for these main reasons:
Consistentlyformatted code reduces the cognitive work required to understand the structure and intent of a code base. Specifically, we believe that before code is to be executed, it is to be understood by other humans. The fact that it makes the computer do something useful is a (happy) coincidence.
Consistentlyformatted code helps identify code smell.
Consistentlyformatted code helps one to spot code bugs and typos more easily.
Note, however, that this is a style guide, not a strict ruleset. There will also be corner cases to which a style guide does not apply, and in these cases, the judgment of what to do is left to the implementers and maintainers of the code base. To this end, the Python project has a wellwritten treatise in PEP 8:
A Foolish Consistency is the Hobgoblin of Little Minds
One of Guido’s key insights is that code is read much more often than it is written. The guidelines provided here are intended to improve the readability of code and make it consistent across the wide spectrum of Python code. As PEP 20 says, “Readability counts”.
A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important.
But most importantly: know when to be inconsistent – sometimes the style guide just doesn’t apply. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don’t hesitate to ask!
Two good reasons to break a particular rule:
When applying the rule would make the code less readable, even for someone who is used to reading code that follows the rules.
To be consistent with surrounding code that also breaks it (maybe for historic reasons) – although this is also an opportunity to clean up someone else’s mess (in true XP style).
Indentation: Tabs and Spaces¶
The indentation of a section of code should always reflect the logical structure of the code. Python enforces this at a consistency level, but we make the provision here that real tabs (specifically not spaces) should be used at the beginning of lines. This allows the most flexibility across text editors and preferences for indentation width.
Spaces (and not tabs) should be used for midline spacing and alignment.
Many editors have functionality to highlight various whitespace characters.
End of Line Whitespace¶
Remove it. Many editors have plugins or builtin functionality that will take care of this automatically when the file is saved.
Maximum Line Length¶
(Similar to PEP 8) Limit all lines to a maximum of 80 characters.
Historically, 80 characters was the width (in monospace characters) that a terminal had to display output. With the advent of graphical user interfaces with variable fontsizes, this technological limit no longer exists. However, 80 characters remains an excellent metric of what constitutes a “long line.” A long line in this sense is one that is not as transparent as to its intent as it could be. The 80character width of code also represents a good “squinttest” metric. If a codebase has many lines longer than 80 characters, it may benefit from a refactoring.
Slightly adapted from PEP 8:
The preferred way of wrapping long lines is by using Python’s implied line continuation inside parentheses, brackets and braces. Long lines can be broken over multiple lines by wrapping expressions in parentheses. These should be used in preference to using a backslash for line continuation. Make sure to indent the continued line appropriately. The preferred place to break around a binary operator is after the operator, not before it. Some examples:
class Rectangle ( Blob ): def __init__ ( self, width, height, color='black', emphasis=None, highlight=0 ): if ( width == 0 and height == 0 and color == 'red' and emphasis == 'strong' or highlight > 100 ): raise ValueError("sorry, you lose") if width == 0 and height == 0 and (color == 'red' or emphasis is None): raise ValueError("I don't think so  values are {}, {}".format( (width, height) )) Blob.__init__( self, width, height, color, emphasis, highlight )
Blank Lines¶
Separate logical sections within a single function with a single blank line.
Separate function and method definitions with two blank lines.
Separate class definitions with three blank lines.
Encodings¶
Following PEP 3120, all code files should use UTF8 encoding.
Punctuation and Spacing¶
Always put spaces after code punctuation, like equivalence tests, assignments, and index lookups.
a=b # bad
a = b # good
a==b # bad
a == b # good
a[b] = c # bad
a[ b ] = c # good
# exception: if there is more than one index
a[ b, c ] = d # acceptable, but not preferred
a[b, c] = d # good, preferred
# exception: if using a string literal, don't include a space:
a[ 'x' ] == d # bad
a['x'] == d # good
When defining a function or method, put a single space on either side of each parenthesis:
def someFunction(a, b, c): # bad
pass
def someFunction ( a, b, c ): # good
pass
Vertical Alignment¶
Where appropriate, vertically align sections of the code.
# bad
M.someVariable = Var( M.someIndex, domain=NonNegativeIntegers )
M.otherVariable = Var( M.otherIndex, domain=NonNegativeReals )
# good
M.someVariable = Var( M.someIndex, domain=NonNegativeIntegers )
M.otherVariable = Var( M.otherIndex, domain=NonNegativeReals )
Single, Double, and Triple Quotes¶
Python has four delimiters to mark a string literal in the code: "
, '
,
"""
, and '
'
'
. Use each as appropriate. One should rarely need to escape
a quote within a string literal, because one can merely alternate use of the
single, double or triple quotes:
a = "She said, \"Do not do that!\"" # bad
a = 'She said, "Do not do that!"' # good
b = "She said, \"Don't do that!\"" # bad
b = 'She said, "Don\'t do that!"' # bad
b = """She said, "Don't do that!\"""" # bad
b = '''She said, "Don't do that!"''' # good
Naming Conventions¶
All constraints attached to a model should end with Constraint
. Similarly,
the function they use to define the constraint for each index should use the
same prefix and Constraint
suffix, but separate them with an underscore
(e.g. M.somenameConstraint = Constraint( ..., rule=somename_Constraint
):
M.CapacityConstraint = Constraint( M.CapacityVar_tv, rule=Capacity_Constraint )
When providing the implementation for a constraint rule, use a consistent naming
scheme between functions and constraint definitions. For instance, we have
already chosen M
to represent the Pyomo model instance, t
to represent
technology, and v
to represent vintage:
def Capacity_Constraint ( M, t, v ):
...
The complete list we have already chosen:
to represent a period item from
to represent a season item from
to represent a time of day item from
to represent an input to a process, an item from
to represent a technology from
to represent a vintage from
to represent an output of a process, an item from
Note also the order of presentation, even in this list. In order to reduce the number mental “question marks” one might have while discovering Temoa, we attempt to rigidly reference a mental model of “left to right”. Just as the entire energy system that Temoa optimizes may be thought of as a lefttoright graph, so too are the individual processes. As mentioned above in A Word on Index Ordering:
For any indexed parameter or variable within Temoa, our intent is to enable a mental model of a lefttoright arrowboxarrow as a simple mnemonic to describe the “input process output” flow of energy. And while not all variables, parameters, or constraints have 7 indices, the 7index order mentioned here (p, s, d, i, t, v, o) is the canonical ordering. If you note any case where, for example, d comes before s, that is an oversight.
Inline Implementation Conventions¶
Wherever possible, implement the algorithm in a way that is pedagogically sound or reads like an English sentence. Consider this snippet:
if ( a > 5 and a < 10 ):
doSomething()
In English, one might translate this snippet as “If a is greater than 5 and less then 10, do something.” However, a semantically stronger implementation might be:
if ( 5 < a and a < 10 ):
doSomething()
This reads closer to the more familiar mathematical notation of 5 < a < 10
and translates to English as “If a is between 5 and 10, do something.” The
semantic meaning that a
should be between 5 and 10 is more readily
apparent from just the visual placement between 5 and 10, and is easier for the
“next person” to understand (who may very well be you in six months!).
Consider the reverse case:
if ( a < 5 or a > 10 ):
doSomething()
On the number line, this says that a must fall before 5 or beyond 10. But the intent might more easily be understood if altered as above:
if not ( 5 < a and a < 10 ):
doSomething()
This last snippet now makes clear the core question that a should not
fall
between 5 and 10.
Consider another snippet:
acounter = scounter + 1
This method of increasing or incrementing a variable is one that many
mathematiciansturnedprogrammers prefer, but is more prone to error. For
example, is that an intentional use of acounter
or scounter
? Assuming
as written that it’s incorrect, a better paradigm uses the += operator:
acounter += 1
This performs the same operation, but makes clear that the acounter
variable
is to be incremented by one, rather than be set to one greater than scounter
.
The same argument can be made for the related operators:
>>> a, b, c = 10, 3, 2
>>> a += 5; a # same as a = a + 5
15
>>> a = b; a # same as a = a  b
12
>>> a /= b; a # same as a = a / b
4
>>> a *= c; a # same as a = a * c
8
>>> a **= c; a # same as a = a ** c
64
Miscellaneous Style Conventions¶
(Same as PEP 8) Do not use spaces around the assignment operator (
=
) when used to indicate a default argument or keyword parameter:def complex ( real, imag = 0.0 ): # bad return magic(r = real, i = imag) # bad def complex ( real, imag=0.0 ): # good return magic( r=real, i=imag ) # good(Same as PEP 8) Do not use spaces immediately before the open parenthesis that starts the argument list of a function call:
a = b.calc () # bad a = b.calc ( c ) # bad a = b.calc( c ) # good(Same as PEP 8) Do not use spaces immediately before the open bracket that starts an indexing or slicing:
a = b ['key'] # bad a = b [a, b] # bad a = b['key'] # good a = b[a, b] # good
Patches and Commits to the Repository¶
In terms of code quality and maintaining a legible “audit trail,” every patch should meet a basic standard of quality:
Every commit to the repository must include an appropriate summary message about the accompanying code changes. Include enough context that one reading the patch need not also inspect the code to get a highlevel understanding of the changes. For example, “Fixed broken algorithm” does not convey much information. A more appropriate and complete summary message might be:
Fixed broken storage algorithm The previous implementation erroneously assumed that only the energy flow out of a storage device mattered. However, Temoa needs to know the energy flow in to all devices so that it can appropriately calculate the interprocess commodity balance. License: GPLv2If there is any external information that would be helpful, such as a bug report, include a “clickable” link to it, such that one reading the patch as via an email or online, can immediately view the external information.
Specifically, commit messages should follow the form:
A subject line of 50 characters or less [ an empty line ] 1. http://any.com/ 2. http://relevant.org/some/path/ 3. http://urls.edu/~some/other/path/ 4. https://github.com/blog/926shinynewcommitstyles 5. https://help.github.com/articles/githubflavoredmarkdown [ another empty line ] Any amount and format of text, such that it conforms to a linewidth of 72 characters[4]. Bonus points for being aware of the Github Markdown syntax[5]. License: GPLv2Ensure that each commit contains no more than one logical change to the code base. This is very important for later auditing. If you have not developed in a logical manner (like many of us don’t),
git add p
is a very helpful tool.If you are not a core maintainer of the project, all commits must also include a specific reference to the license under which you are giving your code to the project. Note that Temoa will not accept any patches that are not licensed under GPLv2. A line like this at the end of your commit will suffice:
... the last line of the commit message. License: GPLv2This indicates that you retain all rights to any intellectual property your (set of) commit(s) creates, but that you license it to the Temoa Project under the terms of the GNU Public License, version 2. If the Temoa Project incorporates your commit, then Temoa may not relicense your (set of) patch(es), other than to increase the version number of the GPL license. In short, the intellectual property remains yours, and the Temoa Project would be but a licensee using your code similarly under the terms of GPLv2.
Executing licensing in this manner – rather than requesting IP assignment – ensures that no one group of code contributers may unilaterally change the license of Temoa, unless all contributers agree in writing in a publicly archived forum (such as the Temoa Forum).
When you are ready to submit your (set of) patch(es) to the Temoa Project, we will utilize GitHub’s Pull Request mechanism.
Footnotes
 1
The two main goals behind Temoa are transparency and repeatability, hence the GPLv2 license. Unfortunately, there are some harsh realities in the current climate of energy modeling, so this license is not a guarantee of openness. This documentation touches on the issues involved in the final section.
 2
The efficiency parameter is often referred to as the efficiency table, due to how it looks after even only a few entries in the Pyomo input “dot dat” file.
 3
Circa 2013, GLPK uses more memory than commercial alternatives and has vastly weaker presolve capabilities.
 4
For a more indepth description of energy system optimization models (ESOMs) and guidance on how to use them, please see: DeCarolis et al. (2017) “Formalizing best practice for energy system optimization modelling”, Applied Energy, 194: 184198.
 5
SVG support in web browsers is currently hit or miss. The most recent versions of Chromium, Google Chrome, and Mozilla Firefox support SVG well enough for Temoa’s current use of SVG.
 6
A word on return expressions in Pyomo: in most contexts a relational expression is evaluated instantly. However, in Pyomo, a relational expression returns an expression object. That is, ‘M.aVar >= 5’ does not evaluate to a boolean true or false, and Pyomo will manipulate it into the final LP formulation.
 7
In contrast to a ‘concrete’ model, an abstract algebraic formulation describes the general equations of the model, but requires modelerspecified input data before it can compute any results.
 BrookeRosenthal03
Anthony Brooke and Richard E. Rosenthal. GAMS. GAMS Development, 2003.
 DeCarolisHunterSreepathi13
Joseph DeCarolis, Kevin Hunter, and Sarat Sreepathi. Modeling for Insight using tools for energy model optimization and analysis (Temoa). Energy Economics, 40:339–349, 2013.
 FourerGayKernighan87
Robert Fourer, David M. Gay, and Brian W. Kernighan. AMPL: A Mathematical Programming Language. AT&T Bell Laboratories, Murray Hill, NJ 07974, 1987.
 Kallrath04
Josef Kallrath. Modeling Languages in Mathematical Optimization. Volume 88. Springer, 2004.
 Makhorin00
Andrew Makhorin. Modeling Language GNU MathProg. Relatório Técnico, 2000.