This manual, in both PDF and HTML form, is the official documentation of the Temoa Project. 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 Energy Economy Optimization (EEO) related questions. 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.
Temoa is an energyeconomy optimization (EEO) model. Briefly, EEO models are selfconsistent frameworks for mathematically optimizing energy flows through a userdefined energysystem.[4] One may think of an EEO model as a “rightfromleft” network graph, with a set of energy demands on the right that must be met by specific energy flows from the system, originating from energysources on the left.
Some of Temoa’s specific features are:
 technology explicit
 arbitrary model period lengths
 designed for High Performance Computing
 written in Python
 not tied to a particular solver
 user extendable
 open source (AGPL)[1]
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.
In short, because we believe that EEO model 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 [DeCarolisHunterSreepathi10] (available from temoaproject.org/). In summary, EEO modelbased analyses are impossible to verify, complex enough as to be nonrepeatable without electronic access to exact versions of code and data input, hardly address uncertainty, and yet are used to inform largescale public policy. Especially in light of the last two points, we believe that EEO model analyses should be completely open, independently reproducible, electronically available, and address uncertainty about the future.
We use the word ‘Temoa’ somewhat interchangeably to describe the project as a whole, as well as the optimization model. When the context does not make it obvious which is meant, we delineate with “Temoa Project” and “Temoa model”.
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. Regardless, either are acceptable, pursuant to the needs of the situation.
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, \(T\) represents the set of all technologies and \(t\) represents a single item from \(T\).
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:
\[\begin{split}C_{marginal} & = \sum_{p, t, v \in \Theta_{VC}} \left ( {VC}_{p, t, v} \cdot R_p \cdot \textbf{ACT}_{t, v} \right )\end{split}\]Note that \(C_{marginal}\) 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:
\[\begin{split}C_{marginal} & = \sum_{p, t, v \in \Theta_{VC}} \left ( \textbf{ACT}_{t, v} \cdot {VC}_{p, t, v} \cdot R_p \right )\end{split}\]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 (\(\textbf{CAP}\)) in terms of that process’ activity (\(\textbf{ACT}\)):
\[ \left ( {CF}_{t, v} \cdot {C2A}_{t} \cdot {SEG}_{s, d} \cdot {TLF}_{p, t, v} \right ) \cdot \textbf{CAP}_{t, v} \ge \textbf{ACT}_{p, s, d, t, v}\]\[\begin{split}\\ \forall \{p, s, d, t, v\} \in \Theta_{\text{activity}}\end{split}\]We use the word ‘slice’ to refer to the tuple of season and time of day \(\tuple{s,d}\). For example, “winternight”.
We use the word ‘process’ to refer to the tuple of technology and vintage (\(\tuple{t,v}\)), when knowing the vintage of a process is not pertinent to the context at hand.
 In fact, in contrast to most other EEO models, 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 \(\mathbb{I}\) to represent the unit interval ([0, 1]).
 We use the symbol \(\mathbb{Z}\) to represent “the set of all integers.”
 We use the symbol \(\mathbb{N}\) to represent natural numbers (i.e., integers greater than zero: 1, 2, 3, \(\ldots\)).
 We use the symbol \(\mathbb{R}\) to denote the set of real numbers, and \(\mathbb{R}^+_0\) to denote nonnegative real numbers.
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”.
Temoa strives for correctness. Unfortunately, as an EEO 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.
For those without patience, this section omits many details, giving only the bare minimum to get up and running with Temoa.
Temoa is built with Sandia National Laboratories’ COOPR project, which is in turn built with Python. Thus, one must first install these projects:
After the above 3 items are installed and tested, download both the Temoa model and the example data sets. Then run Temoa from your operating system’s command line interface. (In the example, lines beginning with the dollar symbol ‘$‘ canonically represent a Unix command line. Windows prompts will likely end with a right caret ‘>‘.)
$ coopr_python temoa.py h usage: temoa.py [h] [graph_format GRAPH_FORMAT] [show_capacity] [graph_type GRAPH_TYPE] [use_splines] dot_dat [dot_dat ...] [ ... output trimmed for brevity ... ] $ coopr_python temoa.py utopia.dat [ 0.08] Reading data files. [ 0.96] Creating Temoa model instance. [ 1.65] Solving. [ 1.77] Formatting results. Model name: Temoa Entire Energy System Economic Optimization Model Objective function value (TotalCost): 35657.0718528 Nonzero variable values: 0.578040628071 V_Activity(1990,inter,day,E01,1960) 0.1445872 V_Activity(1990,inter,day,E31,1980) [ ... output trimmed for brevity ... ]
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. The key piece to note is that Temoa eventually relies on a computer, and thus needs some concrete pieces of information to produce useful results. These pieces are generally 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 through manipulation of capacity use and installation 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 perprocess segments. 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.
Set  Temoa Name  Data Type  Short Description 

\({}^*\text{C}\)  commodity_all  string  union of all commodity sets 
\(\text{C}^d\)  commodity_demand  string  enduse demand commodities 
\(\text{C}^e\)  commodity_emissions  string  emission commodities (e.g. \(\text{CO}_\text{2}\), \(\text{NO}_\text{x}\)) 
\(\text{C}^p\)  commodity_physical  string  general energy forms (e.g. electricity, coal, uranium, oil) 
\({}^*\text{C}^c\)  commodity_carrier  string  physical energy carriers and enduse demands (\(\text{C}_p \cup \text{C}_d\)) 
\(\text{I}\)  string  alias of \(\text{C}_p\); used in documentation only to mean “input”  
\(\text{O}\)  string  alias of \(\text{C}_c\); used in documentation only to mean “output”  
\(\text{P}^e\)  time_existing  \(\mathbb{Z}\)  model periods before optimization begins 
\(\text{P}^f\)  time_future  \(\mathbb{Z}\)  model time scale of interest; the last year is not optimized 
\({}^*\text{P}^o\)  time_optimize  \(\mathbb{Z}\)  model time periods to optimize; (\(\text{P}^f  \text{max}(\text{P}^f)\)) 
\({}^*\text{V}\)  vintage_all  \(\mathbb{Z}\)  possible tech vintages; (\(\text{P}^e \cup \text{P}^o\)) 
\(\text{S}\)  time_season  string  seasonal divisions (e.g. winter, summer) 
\(\text{D}\)  time_of_day  string  timeofday divisions (e.g. morning) 
\({}^*\text{T}\)  tech_all  string  all technologies to be modeled; (\(T_r \cup T_p\)) 
\(\text{T}^r\)  tech_resource  string  resource extraction techs 
\(\text{T}^p\)  tech_production  string  techs producing intermediate commodities 
\(\text{T}^b\)  tech_baseload  string  baseload electric generators; (\(T_b \subset T\)) 
\(\text{T}^s\)  tech_storage  string  storage technologies; (\(T_s \subset T\)) 
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, \(T\) represents the set of all technologies and \(t\) represents an item within \(T\).
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 \(\text{P}^f = \setof{2010, 2015, 2025}\), 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 (\(\text{P}^e\)) and time_future (\(\text{P}^f\)). 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. (To our knowledge, this capability is unique to Temoa.) 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 \(\text{P}^f = \setof{2010, 2015, 2025}\), time_optimize does not contain 2025: \(\text{P}^o = \setof{2010, 2015}\).
One final note on periods: rather than optimizing each year within a period individually, Temoa makes a simplifying assumption that each period contains \(n\) 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 EEO efforts need to model 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. There is similarly no predescribed order, and modeling efforts should not rely on a specific ordering of annual slices.
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 \(FO_{p,s,d,i,t,v,o}\) may be described as “in period (\(p\)) during season (\(s\)) at time of day (\(d\)), the flow of input commodity (\(i\)) to technology (\(t\)) of vintage (\(v\)) generates an output commodity flow (\(o\)) of \(FO_{p,s,d,i,t,v,o}\).” 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 \(\rightarrow\) process \(\rightarrow\) 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.
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 \(\text{P}^f = \setof{2015, 2020, 2030}\), then \(P_0 = 2015\). In other words, the capital \(\text{P}\) 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 (\(\Theta\)). 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 \(\tuple{p, s, d, dem}\) tuples in \(\Theta_{\text{demand}}\)”. 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 Decision Variables):
It defines the Activity variable for every valid combination of \(\tuple{p, s, d, t, v}\) 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, like perhaps 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 (\(i\) and \(o\)) implicitly includes only the valid combinations of \(\tuple{p, s, d, i, t, v, o}\).
Parameter  Temoa Name  Domain  Short Description 

\(\text{CFD}_{s,d,t}\)  CapacityFactorDefault  \(\mathbb{I}\)  Technology default capacity factor 
\(\text{CF}_{s,d,t,v}\)  CapacityFactor  \(\mathbb{I}\)  Process specific capacity factor 
\(\text{C2A}_{t,v}\)  Capacity2Activity  \(\mathbb{R}^+_0\)  Converts from capacity to activity units 
\(\text{FC}_{p,t,v}\)  CostFixed  \(\mathbb{R}\)  Fixed operations & maintenance cost 
\(\text{IC}_{t,v}\)  CostInvest  \(\mathbb{R}\)  Techspecific investment cost 
\(\text{MC}_{p,t,v}\)  CostVariable  \(\mathbb{R}\)  Variable operations & maintenance cost 
\(\text{DEM}_{p,c}\)  Demand  \(\mathbb{R}^+_0\)  Enduse demands, by period 
\(\text{DDD}_{p,s,d}\)  DemandDefaultDistribution  \(\mathbb{I}\)  Default demand distribution 
\(\text{DSD}_{p,s,d,c}\)  DemandSpecificDistribution  \(\mathbb{I}\)  Demandspecific distribution 
\(\text{DR}_t\)  DiscountRate  \(\mathbb{R}\)  Techspecific interest rate on investment 
\(\text{EFF}_{i,t,v,o}\)  Efficiency  \(\mathbb{R}^+_0\)  Tech and commodityspecific efficiency 
\(\text{EAC}_{i,t,v,o,e}\)  EmissionsActivity  \(\mathbb{R}\)  Techspecific emissions rate 
\(\text{ELM}_{p,e}\)  EmissionsLimit  \(\mathbb{R}^+_0\)  Emissions limit by time period 
\(\text{ECAP}_{t,v}\)  ExistingCapacity  \(\mathbb{R}^+_0\)  Preexisting capacity 
\(\text{GDR}\)  GlobalDiscountRate  \(\mathbb{R}\)  Global rate used to calculate present cost 
\(\text{GRM}\)  GrowthRateMax  \(\mathbb{R}\)  Global rate used to calculate present cost 
\(\text{GRS}\)  GrowthRateSeed  \(\mathbb{R}\)  Global rate used to calculate present cost 
\(\text{LLN}_{t,v}\)  LifetimeLoan  \(\mathbb{N}\)  Tech and vintagespecific loan term 
\(\text{LTC}_{p,t,v}\)  LifetimeTech  \(\mathbb{N}\)  Tech and vintagespecific lifetime 
\(\text{MAX}_{p,t}\)  MaxCapacity  \(\mathbb{R}^+_0\)  maximum techspecific capacity by period 
\(\text{MIN}_{p,t}\)  MinCapacity  \(\mathbb{R}^+_0\)  minimum techspecific capacity by period 
\(\text{RSC}_{p,c}\)  ResourceBound  \(\mathbb{R}^+_0\)  Upper bound on resource use 
\(\text{SEG}_{s,d}\)  SegFrac  \(\mathbb{I}\)  Fraction of year represented by each (s, d) tuple 
\(\text{TIS}_{i,t}\)  TechInputSplit  \(\mathbb{I}\)  Technology input fuel ratio 
\(\text{TOS}_{t,o}\)  TechOutputSplit  \(\mathbb{I}\)  Technology output fuel ratio 
\({}^*\text{LA}_{t,v}\)  LoanAnnualize  \(\mathbb{R}^+_0\)  Loan amortization by tech and vintage; based on \(DR_t\) 
\({}^*\text{MLL}_{t,v}\)  ModelLoanLife  \(\mathbb{N}\)  Smaller of model horizon or process loan life 
\({}^*\text{MTL}_{p,t,v}\)  ModelTechLife  \(\mathbb{N}\)  Smaller of model horizon or process tech life 
\({}^*\text{LEN}_p\)  PeriodLength  \(\mathbb{N}\)  Number of years in period \(p\) 
\({}^*\text{R}_p\)  PeriodRate  \(\mathbb{R}\)  Converts future annual cost to discounted period cost 
\({}^*\text{TLF}_{p,t,v}\)  TechLifetimeFrac  \(\mathbb{I}\)  Fraction of last time period that tech is active 
\({EFF}_{i \in C_p,t \in T,v \in V,o \in C_c}\)
As it is the most influential to the rest of the Temoa model, we present the efficiency (\(EFF\)) parameter first. Beyond defining the conversion efficiency of each process, Temoa also utilizes the indices to understand the valid input \(\rightarrow\) process \(\rightarrow\) 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.
\({CFD}_{s \in S, d \in D, t \in T}\)
Where many models assign a capacity factor to a technology class only, Temoa indexes the CapacityFactor parameter by vintage and time slice as well. This enables the modeler to specify the capacity factor of a process per season and time of day, as well as recognizing any advances within a sector of technology. However, if the model calls for a capacity factor that is not 1 (the default), but is the same for all vintages of a technology, this parameter elides the need to specify all of them.
\({CF}_{s \in S, d \in D, t \in T, v \in V}\)
In addition to CapacityFactorDefault, there may be cases where different vintages have different capacity factors. This may be useful, for example, in working with a renewable portfolio, where the amount of a resource is dependent on the time of year and time of day, and the available technological skill.
\({C2A}_{t \in T}\)
Capacity and Activity are inherently two different units of measure. Capacity is a unit of energy per time (\(\frac{energy}{time}\)), while Activity is a measure of total energy actually emitted (\(energy\)). 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?”
\({FC}_{p \in P,t \in T,v \in V}\)
The CostFixed parameter specifies the fixed cost associated with any process. Fixed costs are those that must be paid, regardless of the use of a facility. 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 are in addition to the loan, so once the loan 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 (\(\tfrac{CUR}{Unit Cap}\)).
\({IC}_{t \in T,v \in P}\)
The CostInvest parameter specifies the cost of the loan. Unlike the CostFixed and CostVariable parameters, CostInvest only applies to vintages of technologies within the model optimization horizon (\(\text{P}^o\)). Like CostFixed, CostInvest is specified in units of currency per unit of capacity and is only used in the default objective function (\(\tfrac{CUR}{Unit Cap}\)).
\({MC}_{p \in P,t \in T,v \in V}\)
The CostVariable parameter is a measure of the cost of a unit of activity of an installed process. It is specified as a unit of currency per a unit of activity and is only used in the default objective function.
\({DEM}_{p \in P,c \in C^d}\)
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. In terms of the system map, this parameter specifies the far right.
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.
\({DDD}_{s \in S, d \in D}\)
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.
\({DSD}_{s \in S, d \in D, c \in C^d}\)
If there is an enduse demand that does not follow the default distribution – for example, heating or cooling in the summer or winter – the modeler may specify as much with this parameter. Like SegFrac and DemandDefaultDistribution, the sum of DSD for each \(c\) 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.
\({DR}_{t \in T}\)
In addition to the GlobalDiscountRate, a modeler may also specify a technologyspecific discount rate. If not specified, this rate defaults to the GDR to represent a social discount rate.
\({EAC}_{e \in C_e,\{i,t,v,o\} \in \Theta_{\text{efficiency}}}\)
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. For the time being, it is restricted to emissions accounting (by the \(e \in C^e\) set restriction), but an item on the Temoa TODO list is to upgrade the use of this parameter. For instance, Temoa does not currently provide an easy avenue to model a dualfunction process, such as a combined heat and power plant.
\({ELM}_{p \in P, e \in C^e}\)
The EmissionLimit parameter is fairly self explanatory, ensuring that Temoa finds a solution that fits within the modelerspecified limit of emission \(e\) in time period \(p\).
\({ECAP}_{t \in T, v \in \text{P}^e}\)
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. This is Temoa’s answer to what some call “residual capacity.”
\({GDR}\)
In financial circles, the value of money is dependent on when it was measured. There is no method to measure the absolute value of a currency, but there are generally accepted relative rates for forecasting and historical purposes. Temoa uses the same general concept, that the future value (FV) of a sum of currency is related to the net present value (NPV) via the formula:
where \(n\) is in years. This parameter is only used in Temoa’s objective function.
\({LLN}_{t \in T,v \in P}\)
Temoa differs from many EEO models by giving 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 30 years.
\({LTC}_{p \in P,t \in T,v \in V}\)
Similar to LifetimeLoan, this parameter specifies the total useful life of technology, years. If not specified, the default is 10 years.
\({MAX}_{p \in P,t \in T}\)
The MaxCapacity parameter enables a modeler to ensure that a certain technology is constrained to an upper bound. The enforcing 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.
\({MIN}_{p \in P,t \in T}\)
The MinCapacity parameter is analogous to the maximum capacity parameter, except that it specifies the minimum capacity for which Temoa must ensure installation.
\({RSC}_{p \in P,c \in C_p}\)
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.
\({SEG}_{s \in S,d \in D}\)
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.
\({SPL}_{i \in C_p, t \in T}\)
Some technologies have a single output but have multiple input fuels. For the sake of modeling, certain technologies require a fixed apportion of relative input. See the TechOutputSplit constraint for the implementation concept.
\({SPL}_{t \in T, o \in C_c}\)
Some technologies have a single input fuel but have multiple output forms of energy. For the sake of modeling, certain technologies require a fixed apportion of relative 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.
\({LA}_{t \in T,v \in P}\)
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:
\({LEN}_{p \in P}\)
Given that the modeler may specify arbitrary 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 (\(\text{P}^o\)). 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 \(p\) in boundaries. The third line then defines the length of period \(p\) to be the number of years between period \(p\) and the next period. For example, if \(\text{P}^f = \setof{2015, 2020, 2030, 2045}\), 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 \(\text{P}^o\), and is specifically not defined for the final element in \(\text{P}^f\).
\(R_{p \in P}\)
Temoa optimizes a single characteristic year within a period, and differentiates the \(n\) 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 Sets section.
\({TLF}_{p \in P,t \in T,v \in V}\)
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 \(\tuple{p, t, v}\). As an example, if a model has \(P = \setof{2010, 2012, 2020, 2030}\), and a process \(\tuple{t, v} = \tuple{car, 2010}\) has a useful lifetime of 5 years, then this parameter would include only the first two activity indices for the process. Namely, \(p \in \setof{2010, 2012}\) as \(\tuple{p, t, v} \in \setof{\tuple{2010, car, 2010}, \tuple{2012, car, 2010}}\). The values would be \({TLF}_{2010, car, 2010} = 1\), and \({TLF}_{2012, car, 2010} = \frac{3}{8}\).
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.
Variable  Temoa Name  Domain  Short Description 

\(FI_{p,s,d,i,t,v,o}\)  V_FlowIn  \(\mathbb{R}^+_0\)  Commodity flow into a tech to produce a given output 
\(FO_{p,s,d,i,t,v,o}\)  V_FlowOut  \(\mathbb{R}^+_0\)  Commodity flow out of a tech based on a given input 
\(ACT_{p,s,d,t,v}\)  V_Activity  \(\mathbb{R}^+_0\)  Total tech commodity production in each (s, d) tuple 
\(CAP_{t,v}\)  V_Capacity  \(\mathbb{R}^+_0\)  Required tech capacity to support associated activity 
\(CAPAVL_{p,t}\)  V_CapacityAvailableByPeriodAndTech  \(\mathbb{R}^+_0\)  The Capacity of technology \(t\) available in period \(p\) 
The most fundamental variables in the Temoa formulation are \(FlowIn\) and \(FlowOut\). 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 as no understanding of units. The onus is on the modeler to ensure that all inputs and outputs have similar units.[5]
The Capacity variable is used in the default 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.
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 monetary cost to the actions dictated 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 as both an artifact of our documentation generation process, and to highlight the organization of the functions within the actual code. They are listed roughly in order of importance.
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.
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. In English, this constraint states that “the activity of a process is the sum of its outputs.”
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.
Temoa’s definition of a process’ capacity is the total size of installation required to meet all of that process’ demands. The Activity convenience variable represents exactly that, so the calculation on the left hand side of the inequality is the maximum amount of energy a process can produce in the time slice <s,d>.
The \(\textbf{CAPAVL}\) 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 \(\frac{3}{8}\) of the installed capacity is available for use throughout the period.
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.
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 (\(c\)) generated by \(\textbf{FO}\) must meet the modelerspecified demand, in each time slice.
Note that the validity of this constraint relies on the fact that the \(C^d\) set is distinct from both \(C^e\) and \(C^p\). 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 \(\textbf{FO}\) would be double counted.
Note also that this constraint is an inequality, not a strict equality. “Supply must meet or exceed demand.” Like with the ProcessBalance constraint, if this constraint is not binding, it may be a clue that the model under inspection could be more tightly specified and could have at least one input data anomaly.
The ProcessBalance constraint is one of the most fundamental constraints in the Temoa model. It defines the basic relationship between the energy entering a process (\(\textbf{FI}\)) and the energy leaving a processing (\(\textbf{FO}\)). This constraint sets the FlowOut variable, upon which all other constraints rely.
Conceptually, this constraint treats every process as a “black box,” caring only about the process efficiency. In other words, the amount of energy leaving a process cannot exceed the amount coming in.
Note that this constraint is an inequality – not a strict equality. In most sane cases, the optimal solution should make this constraint and supply should exactly meet demand. If this constraint is not binding, it is likely a clue that the model under inspection could be more tightly specified and has at least one input data anomaly.
Where the Demand constraint (4) ensures that enduse demands are met, the CommodityBalance constraint ensures that the internal system demands are met. That is, this is the constraint that ties the output of one process to the input of another. At the same time, this constraint also conserves energy between process. (But it does not account for transmission loss.) In this manner, it is a corollary to both the ProcessBalance (5) and Demand (4) constraints.
These three constraints finetune the algebraic map created by the three previous constraints, based on various physical and operational realworld phenomena.
There exists within the electric sector a class of technologies whose thermodynamic properties are impossible to change over a short period of time (e.g. hourly or daily). These include coal and nuclear power plants, which take weeks to bring to an operational state, and similarly require weeks to fully shut down. Temoa models this behavior by forcing technologies in the tech_baseload set to maintain a constant output for all daily slices. Note that this allows the model to (not) use a baseload process in a season, and only applies over the time_of_day set.
Ideally, this constraint would not be necessary, and baseload processes would simply not have a \(d\) index. However, implementing the more efficient functionality is currently on the Temoa TODO list.
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 \(\Theta\) superset.
Temoa’s algorithm for storage is to ensure that the amount of energy entering and leaving a storage technology is balanced over the course of a day, accounting for the conversion efficiency of the storage process. This
constraint relies on the assumption that the total amount of storagerelated energy is small compared to the amount of energy required by the system over a season. If it were not, the algorithm would have to account for seasontoseason transitions, which would require an ordering of seasons within the model. Currently, each slice is completely independent of other slices.
Using the Activity and Capacity variables, the Temoa objective function calculates the costs associated with supplying the system with energy, under the assumption that all costs are paid for through loans (rather than with lumpsum sales). This implementation sums up all the costs incurred by the solution, and is defined as \(C_{tot} = \text{C}_\text{loans} + \text{C}_\text{fixed} + \text{C}_\text{variable}\). Similarly, each term on the righthand side is merely a summation of the costs incurred, multiplied by an annual discount factor to calculate the discounted cost in year \(\text{P}_0\).
In the last subequation, \(R_p\) is the equivalent operation to the inner summation of the other two subequations. The difference is that where the inner summations specifically account for the fixed and loan costs of partialperiod processes, the activity is constant for all years within a period. There is thus no need to calculate the timevalue of money factor for each process, and instead, \(R_p\) is calculated once for each period, as a pseudoparameter. While this amounts to little more than an efficiency of model generation, it is pedagogically significant in that it highlights the fact that Temoa optimizes only a single characteristic year within each period.
The constraints provided in this section are not required for proper system operation, but allow the modeler some further degree of system specification.
Temoa treats residual capacity from before the model’s optimization horizon as regular processes, that require the same parameter specification in the data file as do new vintage technologies (e.g. entries in the efficiency table), except the CostInvest parameter. This constraint sets the capacity of processes for model periods that exist prior to the optimization horizon to userspecified values.
A modeler can track emissions through use of the commodity_emissions set and EmissionActivity parameter. The \(EAC\) 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.
The MinCapacity and MaxCapacity constraints set limits on the what the model is allowed to (not) have available of a certain technology. Note that the indices for these constraints are period and tech_all, not tech and vintage.
The ResourceExtraction constraint allows a modeler to specify an annual limit on the amount of a particular resource Temoa may use in a period.
Some processes make a single output from multiple inputs. A subset of these processes have a constant ratio of inputs. See TechOutputSplit_Constraint for the analogous math reasoning.
Some processes take a single input and make multiple outputs. A subset of these processes have a constant ratio of outputs relative to their input. The most canonical example is that of an oil refinery. Crude oil is composed of many different types of hydrocarbons, and the refinery process exploits the fact that they each have a different boiling point. The amount of each type of product that a refinery produces is thus directly related to the makeup of the crude oil input.
The TechOutputSplit constraint assumes that the input to any process of interest has a constant ratio output. For example, a hypothetical (and highly simplified) refinery might have a crude oil input that only contains 4 parts diesel, 3 parts gasoline, and 2 parts kerosene. The relative ratios to the output then are:
In constraint in set notation is:
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 \(\frac{1}{elc eff}\) 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.
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 model[8] 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.
The Temoa model code is split into 5 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. Where temoa_model provides the highlevel overview, this file provides the actual equation implementations.
 temoa_lib.py  mainly contains meta functions for the model. For instance, Temoa makes heavy use of sparse sets, and temoa_lib contains the functions that create those sparse sets. This file also contains various error checking routines (so that their logic does not clutter the implementations in temoa_rules) as well as the logic that handles the command line interaction.
 temoa_graphviz.py  Currently Temoa’s sole visualizations are generated through Graphviz. This file contains the various Graphvizspecific graph generation routines.
 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.
If you are working with the release snapshot of the Temoa model, then you will only see one file, temoa.py. This file is actually a compressed archive (in ZIP format) containing these 5 files, and may be manipulated with standard utilities (e.g. PKZIP, WinZip, InfoZIP). (Due to implementation details, be careful uncompressing the archive as it is analogous to a “Tarbomb”.)
If you are working with a Temoa Git repository, these files are in the temoa_model/ subdirectory.
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_lib.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” (\(\forall\)). 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 21  def Demand_Constraint ( M, p, s, d, dem ):
if (p, s, d, dem) not in M.Demand: # 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 in M.tech_all
for S_v in M.vintage_all
for S_i in ProcessInputsByOutput( p, S_t, S_v, dem )
)
# The '>=' operator creates (in this case) a "Greater Than" *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 "Greater Than" 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 all of a model’s contexts. In temoa_model, the constraint definition listed four index sets, so Pyomo will naively call this function for every possible combination of tuple \(\tuple{p, s, d, dem}\). 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.
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 nave 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.[7] 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 \(\tuple{p, s, d, dem}\) 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_lib.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 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 in index set names the onecharacter version 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 12 13  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 in M.tech_all
for S_v in M.vintage_all
for S_i in 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_lib, and simply ensures that at least one process supplies the demand dem in time slice \(\tuple{p, s, d}\). 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.
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 EEO 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.”
From the definition of the Temoa model as “an algebraic network of linked processes,” a directed network graph is a natural visualization. Temoa utilizes a graphic package called Graphviz to create a series of dataspecific and interactive energysystem maps. The graphs are available in any format Graphviz provides, including scalable vector graphics (SVG), portable network graphics (PNG), portable document format (PDF), and (encapsulated) postscript (E/PS). Currently, the output graphs consist of a preresults (possible) energy map, and results per model period, including breakdowns of individual technology activity.
Here are some examples of the graphical outputs Temoa can dynamically create.
Contrary to the static nature of other image file formats (e.g. PNG), SVG is a textbased format (XML), with a much richer set of information encoding possibilities than mere image display. On top of being infinitely scalable, the SVG format allows the inclusion of links and hyperlinks. Temoa exploits this capability to make interactive system and solution graphs. Currently, this means that one can use a web browser to view and interact with the generated SVG images.[6]
Regardless of the final visual format chosen, the programmatic interaction with Graphviz is entirely text based. As RCS systems excel at handling text, the input files created by Temoa for Graphviz can simultaneously provide another vector for model debugging and a spaceefficient archive of visualizations for posterity and auditing purposes. In addition, we have taken care to make these intermediate files wellformatted so that a human eye may understand them without undue effort.
Notwithstanding the graphical output afforded by Graphviz, interaction with Temoa is currently a command line (CLI) venture. Fortunately, Temoa does not have many command line arguments so the cognitive effort required to understand the interaction is minimal.
After downloading the temoa.py file from http://temoaproject.org/, open a command line and run it with coopr_python:
$ coopr_python temoa.py
usage: temoa.py [h] [graph_format GRAPH_FORMAT] [show_capacity]
[graph_type GRAPH_TYPE] [use_splines]
dot_dat [dot_dat ...]
temoa.py: error: too few arguments
In a Unix environment, you can make the file executable, and it can be executed directly:
$ chmod +x temoa.py
$ ./temoa.py
usage: temoa.py [h] [graph_format GRAPH_FORMAT] [show_capacity]
[graph_type GRAPH_TYPE] [use_splines]
dot_dat [dot_dat ...]
temoa.py: error: too few arguments
The h command line argument provides the longer synopsis of the available arguments:
$ ./temoa.py h
usage: temoa.py [h] [graph_format GRAPH_FORMAT] [show_capacity]
[graph_type GRAPH_TYPE] [use_splines]
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
graph_format GRAPH_FORMAT
Create a systemwide visual depiction of the model.
The available options are the formats available to
Graphviz. To get a list of available formats, use the
"dot" command: dot Txxx. [Default: None]
show_capacity Choose whether or not the capacity shows up in the
subgraphs. [Default: not shown]
graph_type GRAPH_TYPE
Choose the type of subgraph depiction desired. The
available options are "explicit_vintages" and
"separate_vintages". [Default: separate_vintages]
use_splines Choose whether the subgraph edges needs to be straight
or curved. [Default: use straight lines, not splines]
Solving a model simply requires a data file. The Temoa Project supplies example data files on the website. One of those data files is called test.dat:
$ ./temoa.py test.dat
[ 0.04] Reading data files.
[ ] Creating Temoa model instance.Warning: ('t_oil_refinery', 2000) has a
Warning: ('t_elccar', 2000) has a specified Efficiency, but does not have any ex
Warning: ('t_oil_refinery', 2000) has a specified Efficiency, but does not have
[ 0.43
[ 0.80] Solving.
[ 0.85] Formatting results.
Model name: TEMOA Entire Energy System Economic Optimization Model
Objective function value (TotalCost): 398469346.331
Nonzero variable values:
395.535714285714 V_Activity(2010,summer,day,imp,coal,2000)
2.480158730159 V_Activity(2010,summer,day,imp,gsl,2000)
197.767857142857 V_Activity(2010,summer,day,t,coal,1990)
39.553571428571 V_Activity(2010,summer,day,t,elccar,2010)
0.446428571429 V_Activity(2010,summer,day,t,gascar,2000)
[ ... ]
The model world described by test.dat is incredibly simple, and so solves near instantaneously. The output is extensive, enumerating the value of all variables for the optimal configuration, so we show just the first few lines in the documentation. It is enough to get a sense of it however.
Temoa provides linebyline output as progresses through each stage of a solve. The very first line explains that Temoa took 0.04 processing seconds to read and parse the supplied model data. Processing seconds are roughly a measure of exactly how much time the program spent in the CPU, but are not a measure of how long the modeler actually waited: though the output above suggests that the whole model took about 0.85 processing seconds to complete, the time the author sat waiting for the prompt to return was closer 1.8 seconds.
The next timing slot is empty, but the same line also contains a warning message. The warning message is pointing out that the input file includes some inconsistent data that the modeler may want to address. The timing information immediately below the warnings is how long it took Coopr to create the Temoa model instance.
Temoa intelligently emits these messages. If only the results are warranted, the modeler can redirect the stderr stream to the virtual wastebin:
$ ./temoa.py test.dat 2> /dev/null
Model name: TEMOA Entire Energy System Economic Optimization Model
Objective function value (TotalCost): 398469346.331
Nonzero variable values:
395.535714285714 V_Activity(2010,summer,day,imp,coal,2000)
2.480158730159 V_Activity(2010,summer,day,imp,gsl,2000)
197.767857142857 V_Activity(2010,summer,day,t,coal,1990)
39.553571428571 V_Activity(2010,summer,day,t,elccar,2010)
0.446428571429 V_Activity(2010,summer,day,t,gascar,2000)
[ ... ]
Conversely, the modeler can redirect the stdout stream to a file, while still receiving status information about the solve. This way, the modeler can view the output later, or postprocess it as necessary.
$ ./temoa.py test.dat > test.sol
[ 0.04] Reading data files.
[ ] Creating Temoa model instance.Warning: ('t_oil_refinery', 2000) has a
Warning: ('t_elccar', 2000) has a specified Efficiency, but does not have any ex
Warning: ('t_oil_refinery', 2000) has a specified Efficiency, but does not have
[ 0.44
[ 0.81] Solving.
[ 0.86] Formatting results.
$ head test.sol
Model name: TEMOA Entire Energy System Economic Optimization Model
Objective function value (TotalCost): 398469346.331
Nonzero variable values:
395.535714285714 V_Activity(2010,summer,day,imp,coal,2000)
2.480158730159 V_Activity(2010,summer,day,imp,gsl,2000)
197.767857142857 V_Activity(2010,summer,day,t,coal,1990)
39.553571428571 V_Activity(2010,summer,day,t,elccar,2010)
0.446428571429 V_Activity(2010,summer,day,t,gascar,2000)
98.883928571429 V_Activity(2010,summer,night,imp,coal,2000)
1.240079365079 V_Activity(2010,summer,night,imp,gsl,2000)
The next command line of real interest is graph_format. If Graphviz is installed on the machine, then specifying any output format that Graphviz supports will generate set of graphs. First, test if Graphviz is available, then see what output formats it supports:
$ dot V
dot  graphviz version 2.26.3 (20100126.1600)
$ dot Txxx
Format: "xxx" not recognized. Use one of: canon cmap cmapx cmapx_np dot eps
fig gd gd2 gif gv imap imap_np ismap jpe jpeg jpg pdf plain plainext png
ps ps2 svg svgz tk vml vmlz vrml wbmp x11 xdot xlib
From the list of available outputs, one might be interested in an interactive map with SVG, so:
$ ./temoa.py test.dat graph_format svg > test.sol
[ 0.04] Reading data files.
[ ] Creating Temoa model instance.Warning: ('t_oil_refinery', 2000) has a
Warning: ('t_elccar', 2000) has a specified Efficiency, but does not have any ex
Warning: ('t_oil_refinery', 2000) has a specified Efficiency, but does not have
[ 0.43
[ 0.81] Solving.
[ 0.86] Formatting results.
[ 0.93] Creating Temoa model diagrams.
The generated image might look like Figure 4.1.
At some point, a modeler may be curious to open up the tiny file that is temoa.py to peek under the hood. Luckily, temoa.py is merely a standard ZIP file with 28 bytes of extra information to interoperate with Python. This means that a standard ZIP manipulation program can view and unpack the contents of the archive:
$ unzip l temoa.py
Archive: temoa.py
warning [temoa.py]: 28 extra bytes at beginning or within zipfile
(attempting to process anyway)
Length Date Time Name
   
19 20120622 17:21 ReferenceModel.py
4125 20120622 17:21 pformat_results.py
42167 20120705 03:17 temoa_rules.py
1698 20120622 17:21 temoa_stochastic.py
44670 20120705 03:22 temoa_graphviz.py
13910 20120705 03:18 temoa_model.py
30366 20120705 04:29 temoa_lib.py
86 20120622 17:21 __main__.py
52 20120622 17:21 __init__.py
 
137093 9 files
To work with the files, make a directory and unpack the archive:
$ mkdir temoa_model
$ cd temoa_model/
$ unzip ../temoa.py
Archive: ../temoa.py
warning [../temoa.py]: 28 extra bytes at beginning or within zipfile
(attempting to process anyway)
linking: ReferenceModel.py > temoa_stochastic.py
inflating: pformat_results.py
inflating: temoa_rules.py
inflating: temoa_stochastic.py
inflating: temoa_graphviz.py
inflating: temoa_model.py
inflating: temoa_lib.py
inflating: __main__.py
inflating: __init__.py
finishing deferred symbolic links:
ReferenceModel.py > temoa_stochastic.py
$ cd ..
If you want to make a change to Temoa, there is no need to repack it into temoa.py. Instead, Python can “execute” the directory directly:
$ coopr_python temoa_model
usage: temoa_model [h] [graph_format GRAPH_FORMAT] [show_capacity]
[graph_type GRAPH_TYPE] [use_splines]
dot_dat [dot_dat ...]
temoa_model: error: too few arguments
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/hunteke/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 ]:
[ ... ]
This manual will not discuss the file format layout, or how to explicitly write code for Temoa. For that, please direct questions and discussion to the Temoa user forum.
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).
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.
Remove it. Many editors have plugins or builtin functionality that will take care of this automatically when the file is saved.
(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 )
 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.
Following PEP 3120, all code files should use UTF8 encoding.
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
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 )
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
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:
 \(p\) to represent a period item from \(time\_optimize\)
 \(s\) to represent a season item from \(time\_season\)
 \(d\) to represent a time of day item from \(time\_of\_day\)
 \(i\) to represent an input to a process, an item from \(commodity\_physical\)
 \(t\) to represent a technology from \(tech\_all\)
 \(v\) to represent a vintage from \(vintage\_all\)
 \(o\) to represent an output of a process, an item from \(commodity\_carrier\)
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 said in section 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 \(\rightarrow\) process \(\rightarrow\) 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.
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
(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
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: AGPL3.0+If 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: AGPL3.0+Ensure 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 AGPL3.0+. A line like this at the end of your commit will suffice:
... the last line of the commit message. License: AGPL3.0+This 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 Affero Public License, version 3, or later. 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 AGPL 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 the AGPL.
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.
Though Temoa’s stature as an open source product is enforced by the AGPL, the code is only half the battle. The other half involves the exact input fed to Temoa to create an analysis, and there is no method available to require modelers to share the inner workings of their analyses.
Specifically, this means that if a modeling team makes changes to the Temoa codebase, and publishes an analysis based on them, their is no legal requirement to share either the code or the input data. To that end, all we can do is plea to modeling teams to “do the right thing”, and be transparent about all aspects of an analysis.
Footnotes
[1]  The two main goals behind Temoa are transparency and repeatability, hence the AGPL license. Unfortunately, there are some harsh realities in the current climate of EEO modeling, so this license is not a guarantee of openness. This documentation touches on the issues involved in the final section, A note on “Open Source”. 
[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 EEO models and their place in the energy modeling community, as well as references to other papers and sources, see “The TEMOA Project: Tools for Energy Model Optimization and Analysis”, by DeCarolis, J. and Hunter, K. and Sreepathi, S. (2010). (Available from temoaproject.org/.) 
[5]  There is an open ticket to address the lack of unit awareness in Temoa. See issue 5 in our issue tracker. 
[6]  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. 
[7]  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. 
[8]  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. 
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