OPERA

OPERA is a Dutch-based national energy system model focusing on total system cost minimization. Refer to https://multimodelling.readthedocs.io/en/latest/energy_models/OPERA/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 OPERA model is developed and maintained by the TNO-ETS group in Amsterdam.

Questions to ask	Answers/Explanation
Model name	OPERA (Options Portfolio for Emission Reduction Assessment)
Model owner	TNO-ETS
Model Developer	TNO-ETS
The latest model version/date
The model version used in this project	2022_3
Organization	TNO-ETS
Individual	Joost van Stralen

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 long-term decision-making with different policies, targets, and measures. Even though some policy measures are incorporated, the model does not emphasize significantly in the mid-term (2025-2045). The model allows the possibility to focus on the mid-term if needed. Energy balances are maintained between the demand and supply on a regular basis, i.e., short-term.

Questions to ask	Answers/Explanation
Is the model a token model? If so, give illustration(s).	Yes. For example, the model analyses the techno- economical aspect of the energy system.
Is the model a type model? If so, give illustration(s).	Yes. For example, the model reflects universal characteristics of network infrastructure, i.e., energy flows.
Briefly describe the intended purpose of the model	Total system cost minimization at the national level (the Netherlands)
Strategic - long-term planning; what do we want?	1. Long-term national and regional policy targets related to emissions reductions, efficiency, renewable energy production 2. Long-term targets of, for example, production for industries, including subsectors, sectoral demands, energy infrastructure capacity, if any
Tactical - medium-term; how do we approach this?	1. Not much emphasis in the medium term, except in the dynamic run mode of the model 2. Model structure allows for the inclusion of medium- term policies. Most of them are already included are already in the model. 3. Certain input parameters are adjusted based on upcoming policies, for example, energy labels of offices
Operational - short-term; regular/day-to-day operations?	demand-supply energy balances, energy flows to address mismatches, short-term flexibility options

Typical questions asked of the model include future capacities of different renewable resources and energy supply options. The model has many strengths, one of them being replicability. The model can readily apply to other European national energy system modeling contexts. An additional remark is that the model can simultaneously analyze greenhouse gases (GHG), non-energy-related emissions, and air pollutants at the regional and national levels. One of the critical limitations of the model is the assumption of perfect foresight. The model has been used to formulate strategic policy advice for the Dutch government on energy decarbonization and climate change mitigation. 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 different renewable sources’ future capacities and energy supplies 2. What is the energy flow between regions, and is the network constrained to achieve that? Etc.
What are the strengths of this model? What is unique?	1. Replicability: the structure can be readily applied to other countries, particularly in Europe 2. System integration - an ideal tool for assessing the implementation of the energy transition and the establishment of a low-carbon economy 3. Linkage to the NEOMS model - this model uses data from the NEOMS model, which is used for preparing the annual Dutch national energy outlook. Additional comments/remarks: 1. The model analyzes greenhouse gases (GHG), non-energy- related emissions, and air pollutants. 2. The capacity limits (of at least essential technology options or processes) are set by expert consultation.
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 formulating strategic policy advice on energy decarbonization and climate change mitigation for the Dutch government 2. Performed exploratory studies on the role of specific low- carbon energy technologies in the energy transition of the Netherlands
Cases/examples where the model was not used for its intended purpose; are there any examples of model abuse or misuse?

The next set of questions is related to model documentation, accessibility, and type. The model content is documented in a journal paper that is open source. 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?	Content documentation is a journal paper (see reference below). There is no public documentation on the details of the model (for example, GUI, API, etc.). In addition, not every update is documented.
Is the documentation accessible? If so, how?	The journal paper is open source.
Is the documentation in English?	Yes
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: OPERA can consider 5/10-year time steps, projecting till 2050, i.e., years are optimized individually. Previous year-cycle data are not automatically fed to future years. Dynamic modeling is in progress and will not be a part of this project.
Is the model continuous or discrete?	continuous
Is the model stochastic or deterministic?	Deterministic
Is it an optimization model? If so, what type of algorithms it uses?	Yes, LP Additional comments/remarks: Due to linear structure, discrete values (say, integers) are not considered. However, limits (lower and upper) can be set as discrete values.

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 represents an integrated energy system. Essential elements and concepts the model includes are all greenhouse gas emissions in the Netherlands. Similarly, content-wise, the model contains important energy infrastructure, such as electricity, heat, and hydrogen. Some flexibility options included in the model are salt caverns (spatially dependent), batteries, or hydrogen (spatially independent).

Questions to ask	Answers/Explanation
Does the model represent an integrated energy system?	Yes
What important elements and concepts are included in the model?	1. Covers the entire energy system and all greenhouse gas emissions of the Netherlands 2. Content-wise coverage: Energy-demanding sectors (built environment, industries, agriculture, and mobility), energy supply options (for example, wind, solar, biomass, geothermal, and non-renewable sources), and energy infrastructure (electricity, heat, gas, hydrogen, and CO2)
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?	A few examples of flexibility options are salt caverns (space-specific), batteries, hydrogen storage, and a significant range of conversion techniques. Additional comments/remarks: Storage, in general, has zero costs. Only electricity and hydrogen have storage costs.

The next set of content-related questions included scale and resolution. The spatial scale of the model is the national level, and the temporal scale is long-term (till 2050). The spatial resolution is at the city or municipality level, which has only been done for Groningen province in the northern Netherlands. Temporal resolution is time slices, with a maximum possible 80 slices for a year.

Questions to ask	Answers/Explanation
What spatial (or geospatial) scale does the model have?	National
What temporal (or time) scale does the model have?	Long-term (till 2050) Per run, the calculations are done on an annual basis in the model.
Spatial resolution	town/city Additional comments/remarks: This has been done only for Groningen Province. The structure allows us to perform similar analyses in other regions within the Netherlands.
Temporal resolution	Time slices Currently, the maximum possible is 80 slices/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 critical assumptions is the state in which the energy infrastructure is considered in the model. For some, the current state is the base; for others, every investment starts from 0. The model standard input is MS Access, and the output format is MS Excel. Some important model inputs are Technology inputs (supply options), costs (annualized investments, fixed, variable, and operation and maintenance costs), and industrial processes. Similarly, some important model outputs are primary energy supply, secondary energy demand-supply balances, energy flows, and system costs. Data can be shared with permission from model owners. Most of the data are from open sources.

Questions to ask	Answers/Explanation
What critical assumptions does the model have?	1. For some infrastructure, the current state of investment is the base (or lower limit), for example, high voltage electricity network, for others, all the investments start from scratch, for instance, medium voltage electricity network 2. Cost or capacity ranges are primarily based on literature or expert suggestions.
Which ones are likely to be contested by others? Why?	1. Price includes material costs and does not include social or environmental costs 2. Every stakeholder has complete knowledge of the market Behavior. Only the system operator perspective is considered.
What is/are the model input format(s)?	MS Access Additional comments/remarks: There is a preprocessing of inputs within OPERA so that to reduce the number of activities (solving variables) that goes into the optimization process
What is/are the model output format(s)?	MS Excel Additional comments/remarks: There is postprocessing of outputs both in OPERA and in Excel.
What are the important model inputs?	Technology inputs (supply options), costs (annualized investments, fixed, variable, and operation and maintenance costs), industrial processes, emissions from industries and other activities, future targets (for example, renewable energy production, emission reduction, and efficiency improvement)
What important parameters do the model have?	technology- and process-related parameters (such as, efficiency), demand and supply profiles, limits and ranges on output, demand service units (for example, MT_steel)
What are the important model outputs?	primary energy supply, secondary energy demand-supply balances, energy flows, system costs
What are the data sources used by the model?	Open sources, such as CBS, are mostly linked to other models for specific inputs, etc.
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 there is no formal testing possibility within the modeling framework. Verification, validation, and testing can be done on boundary conditions and input limits/ranges, generally done by sensitivity analyses, expert opinions, and comparisons with other models. Inputs related to the long term are more uncertain compared to the mid-term.

Questions to ask	Answers/Explanation
Can you comment on the test coverage of the model?	There is not much formal testing possibility within the modeling framework. Input parameters can be tested by sensitivity analyses, for example. Non-optimality or model not converging conditions validate modeling outputs/results.
What is being verified, validated, or tested in the model?	Verification, validation, and testing can be on the boundary conditions, inputs, limits/ranges, etc.
What methods are used for the model verification, validation, and testing, if any?	1. Qualitative method: stakeholder and expert opinions and perspectives, literature, government reports, etc. 2. Quantitative method: comparison with other contemporary national models, scenario comparisons, etc.
Can you comment on the uncertainty in model parameters?	Important model parameters within the model operate within ranges, depending upon scenarios, to handle uncertainty
Can you comment on the uncertainty in model input?	Input is more uncertain for long-term scenarios compared to the mid-term.
Can you comment on the uncertainty in the model structure?

References:

Model Description:

• https://doi.org/10.1007/s10666-020-09741-7

Model application:

• https://doi.org/10.1016/j.apenergy.2021.118035

• https://doi.org/10.1016/j.apenergy.2022.119149