TEACOS

Somadutta Sahoo, Last update: 14 December 2023

TEACOS is a Dutch-based mathematical optimization model focusing on mid- to long-term investment analysis. Refer to https://multimodelling.readthedocs.io/en/latest/energy_models/TEACOS/index.html for more overall information regarding the model.

General model information

General model information questions were asked regarding basic information, model versions, and point of contact for questions. The TEACOS model is developed and maintained by QUOMARE.

Questions to ask	Answers/Explanation
Model name	TEACOS
Model owner	QUOMARE
Model Developer	QUOMARE
The latest model version/date
The model version used in this project
Organization	TNO-ETS
Individual	Gregor Brandt

A second set of questions was asked regarding whether the model is type or token, the intended purpose of the model, and the level of decision that the model aims to support. We understand that the model can be categorized as type and token model. The model focuses on techno-economic optimization, which is capable of long-term planning by considering investment decisions in technology options.

Questions to ask	Answers/Explanation
Is the model a token model? If so, give illustration(s).	Yes, it captures investment decisions related to specific innovative technology options, for example, hydrogen electrolyzer.
Is the model a type model? If so, give illustration(s).	Yes, it captures the core elements of an open, targeted system.
Briefly describe the intended purpose of the model	Techno-economic optimization
Strategic - long-term planning; what do we want?	Investment decisions in technology options
Tactical - medium-term; how do we approach this?
Operational - short-term; regular/ day-to-day operations?	Operational decisions of technology options

Typical questions asked of the model include future capacities and energy supplies for different technology options. One of the significant strengths of the model is its openness, i.e., user-defined system. TEACOS finds the best combination of technology options while considering optimal investment and operational decisions. One of the critical limitations of the model is the assumption of perfect foresight. The model has been used to identify the presence/absence of different

technologies in a targeted energy system analysis. Refer to the table below for further discussion on these aspects.

Questions to ask	Answers/Explanation
What are typical types of questions that can be asked to the model? provide examples of such questions	1. What are the capacity and energy supply from different technology options? 2. Whether a given technology option will be selected or not?
What are the strengths of this model? What is unique?	Open, user-defined system 2. TEACOS finds the best combination of technology options, considering optimal investment and operational decisions.
What are the important limitations of the model?	Perfect foresight assumption 2. Social interaction and impacts missing
Cases/examples where the model was used for its intended purpose	1. Used for identifying the presence/absence of different technologies in a targeted energy system analysis 2. Identifying the capacity of those technologies and investments in them.
Cases/examples where the model was not used for its intended purpose; are there any examples of model abuse or misuse?	Sometimes, for operational questions. Core applications and edge cases.

The next set of questions is related to model documentation, accessibility, and type. The model documentation is not complete. The graphical user interface (GUI) can be accessed with the owner’s permission. The model is static, deterministic, and linear programming (LP)-based.

Questions to ask	Answers/Explanation
Is the model documentation complete?	No
Is the documentation accessible? If so, how?	Some parts of it are accessible through the QUOMARE website and this project.
Is the documentation in English?	Partly English
Does the model have a GUI? If so, how to access it?	Yes, the GUI can be accessed with the whole model with the owner’s permission.
Does the model have an Application Programming Interface (API) ? If so, how to access it?	In general, the model does not have an API.
Is the model static or dynamic?	Static Additional comments/remarks: TEACOS is a multi-period model (i.e., time steps). The model uses information from the previous time step. However, this is not a prerequisite.
Is the model continuous or discrete?	Continuous Additional comments/remarks: The model has discrete system elements, but flows are continuous.
Is the model stochastic or deterministic?	Deterministic
Is it an optimization model? If so, what type of algorithms it uses?	Yes, LP

The next set of questions are regarding the modeling paradigm, implementation environment, and license. The model applies multiple formalisms, such as mathematical equations and logical expressions. The model is implemented using a modeling package called AIMMS. An AIMMS license is needed, and the owner can share the model.

Questions to ask	Answers/Explanation
What modeling paradigm or formalism does the model use?	Mathematical equations, logical expressions, energy balances, math equations, etc.
Is it implemented in a General purpose programming language?	No
Does it use a modeling/Simulation environment/package?	AIMMS
Is it implemented in a spreadsheet?
Is any license required to run the model?	AIMMS license is needed, except for educational and research purposes

Model content

A preliminary set of model content questions were related to energy system integration and scope. The model does not represent an integrated energy system. The model’s essential elements and concepts include detailed information on costs/prices, such as investment profiles or return on investments. Similarly, content-wise, the model contains important supply-related technology options and their interactions.

Questions to ask	Answers/Explanation
Does the model represent an integrated energy system?	No
What important elements and concepts are included in the model?	1. Economics - CAPEX/full NPV, investment profiles, return on investment and other standard economic KPIs 2. Content-wise coverage: Supply-related technology options, their interactions
What elements and concepts are currently not included in the model, but in your opinion, those shall be included?
Specific attention to flexibility options: What type of flexibility options are included in the model?

The next set of content-related questions included scale and resolution. There is no spatial representation but rather a topology and visual representation on a map for communication. The temporal scale is long-term (2020 - 2050).

Questions to ask	Answers/Explanation
What spatial (or geospatial) scale does the model have?	There is no spatial representation but rather a topology and visual representation on a map for communication. Additional comments/remarks: The transport sector could have cost and distance considerations.
What temporal (or time) scale does the model have?	30-year period (2020 – 2050)
Spatial resolution
Temporal resolution	Time slice of 5 years in a 30-year investment trajectory. Arbitrary applicable time periods, given a strategic focus, a month, a quarter, or a year.

The next set of questions is related to model assumptions, model inputs, parameters, and outputs, and data sources related to the model. One of the assumptions likely to be contested by others is that the model considers the time-slice approach for faster processing speed, which is problematic for analyzing peak loads for electricity. The model standard input format is MS Access, and the output format is MS Excel. Some important model inputs are technology inputs (supply options) and costs (annualized investments, fixed, variable, and operation and maintenance costs). Similarly, some important model outputs are secondary energy demand-supply balances and system costs. Data can be shared with permission from model owners.

Questions to ask	Answers/Explanation
What critical assumptions does the model have?
Which ones are likely to be contested by others? Why?	The model considers the time-slice approach for faster processing speed. This is problematic for analyzing peak loads for electricity.
What is/are the model input format(s)?	MS Access
What is/are the model output format(s)?	MS Access
What are the important model inputs?	Technology inputs (supply options), costs (annualized investments, fixed, variable, and operation and maintenance costs)
What important parameters does the model have?	Technology-related parameters (such as efficiency)
What are the important model outputs?	Secondary energy demand-supply balances, system costs, etc.
What are the data sources used by the model?
Any data that can be shared? If so, what and how to access them?	Databases (MS access format) can be accessed with permission from model owners. Databases contain most input-related data. The remaining data can be accessed by accessing the model with permission from the model owners.

Continuing with the model content, there were questions regarding verification, validation, and test, and uncertainty descriptions. The answer to test coverage of the model is that TEACOS is continuously developed using internal test sets to reference models. Internal review before the branch merges into the master branch on GitHub. Verification, validation, and testing can be done on boundary conditions and input limits/ranges.

Questions to ask	Answers/Explanation
Can you comment on the test coverage of the model?	TEACOS is continuously developed using internal test sets to reference models. Internal review before branch merges into master on GitHub.
What is being verified, validated, or tested in the model?	Verification, validation, and testing can be on the boundary conditions, inputs, limits/ranges, etc. Forcing options to look at extreme edges of the solution space.
What methods are used for the model verification, validation, and testing, if any?	1. Qualitative method: base case review through consumer, etc. 2. Quantitative method: comparison with reference cases, modeling practice, etc.
Can you comment on the uncertainty in model parameters?	Sensitivity testing, Monte Carlo on parameter values, multivariant Monte Carlo
Can you comment on the uncertainty in model input?	The model is deterministic and, therefore, does not propagate uncertainty.
Can you comment on the uncertainty in the model structure?	No structural uncertainty