Micro use case report

1. Introduction

1.1. Use case description

In the micro use case, the MMvIB approach has been applied on a local scale, for a business park.

In recent years, the energy transition on business parks has accelerated. Increased energy prices and changing markets stimulate entrepreneurs to invest in energy efficiency and renewable energy, and policy makers act as they realise business parks play a key role in reaching climate targets. A collective (business park) approach is beneficial in this transition.

The micro use case is intended to support decision making on business parks by using a multi-model approach to calculate long-term optimized investment paths towards a sustainable business park.

As a case study, the business parks Welgelegen and Slabbecoornpolder in the municipality of Tholen have been used. Business on these business parks are united in the Regional Energy Community (REC) Tholen, which has the mission to collectively invest in renewable energy measures towards a CO2 neutral or energy positive business park. As for a lot of other business parks in the Netherlands, current grid congestion is a barrier for electrification and renewable electricity measures for Welgelegen and Slabbecoornpolder.

The energy system for the case study is simplified to provide a technical proof of concept for a business park MMvIB multi-model chain. The multi-model chain has not been tested in a decision making process, as the scope of the model chain is not detailed enough yet.

1.2. Models used

To cover all aspects of the problem scope given by the Tholen case study, 6 models/tools have been coupled in this use case:

  1. The ESDL MapEditor is a map-based scenario editor, using the Energy System Description Language (ESDL) to describe the energy system.

  2. The Energy Potential Scan for business parks (EPS) is a calculation tool which estimates the energy use for each business on a business parks, and the potential for energy saving and PV measures.

  3. The Energy Transition Model (ETM) is an interactive energy scenario tool, which can be used for countries, regions and municipalities.

  4. A new agent-based model (ABM) has been developed in this use case to simulate human investment behaviour.

  5. Techno-Economic Analysis Of Complex Option Spaces (TEACOS) is a long-term optimisation tool that calculates the optimal investment paths for an energy system.

  6. The Energy System Simulator (ESSIM) is a tool that simulates network balancing and the effects thereof, in an interconnected hybrid energy system (described in ESDL) over a period of time.

1.3. Multi-model aspects showcased

A number of multi-model challenges are addressed in the micro use case, both on a conceptual and a technical level.


There are three key conceptual aspects that are challenging in the micro use case:

  1. Convergence of energy flows between models (ESSIM – TEACOS)

  2. Agent-based versus global investment optimum

  3. Multi-period

First of all, the convergence. When using multiple models there is always a chance that several models calculate similar results but in a different way. This occurred between ESSIM and TEACOS, where TEACOS, in the configured setup, determines how many PV panels need to be installed and calculates an electricity flow from a group of PV panels to users/companies that have a demand for electricity, on a yearly basis. In ESSIM the electricity flow is calculated based on a given PV panel capacity, on hourly basis, using a profile of sun intensity for a historic year. With a provided demand profile of the user/company, ESSIM can take into account the disconnect in timing between supply and demand, where TEACOS on a yearly basis cannot. On the other hand, TEACOS can determine the best investment size given the properties of the rest of the energy system and set the PV panel capacity for ESSIM to work with. In this way there is added value to combine the two models. It is not trivial that the two models end up with the same electricity flow between PV panels and users/companies.

The way this has been solved is the following:

  1. TEACOS runs and determines the optimal investment in PV panels. This is based on 100% of the capacity going to users, nothing to grid.

  2. ESSIM uses the TEACOS result as the given capacity in PV panels and calculates how much of this electricity (in %) can actually be used by the users and how much will be absorbed by the grid. This % is then passed back to TEACOS

  3. TEACOS recalculates the optimum investment with the given addition of the useful % electricity and recalculates the optimum investment.

  4. Steps 2 and 3 are repeated until either the % doesn’t change anymore or the optimum investment remains the same. Convergence has then been reached.

Theoretically there is no guarantee that it will converge, but in realistic cases it is expected that it will.

Secondly, the agent-based versus global investment optimum. The thesis work of Menghua Prisse 1 covers this topic extensively, so we will here suffice with a short notion. TEACOS will determine the mathematical optimal solution for the energy system that is presented. In the micro use case this is determining the optimum investment size and resulting electricity flows for the entire business park. In this case the objective value for TEACOS to optimise on, is the total cost for the entire business park lumped together. This means that it is possible that the result is not optimal or even beneficial for some of the individual businesses in the business park. The agent-based approach tries to mimic real live, where there is no full transparency between all the businesses in the business park and each business will take decisions that are most beneficial for their own situation. The struggle for the agent-based approach is when there are general constraints for the entire business park e.g. a grid-limitation for all businesses combined. TEACOS can deal with that when it is optimising the total business park, agent-based modelling will have to find a way around that. It is possible to run TEACOS for individual businesses in order to make the optimal decision for that business and as such use it as a part of the agent-based model.

Thirdly, the multi-period aspect. There are several aspects with regard to the handling of time. Both ESSIM and ETM use historic hourly profiles for solar and wind in their calculations. With these hourly profiles insight can be gained on the expected balancing in the system over an entire year. Although TEACOS can handle hourly time periods as well, it is in the micro casus setup in a configuration where a time bucket is the size of a year, meaning that there is no insight of differentiation of what happens within that year. This leads already an interesting situation within MMvIB on how to handle differences in time. ETM calculated results for a given situation over an entire year. ESSIM can theoretically handle a longer time period. In the beginning the project team started out with a scope of a single year. TEACOS would have 1 time bucket, ESSIM an hourly profile for 1 year and ETM could do a regular time slice of 1 year. However, in real life decisions are not made based on data or expected results of a single year. Investments are often spread out in time and result in a transition path towards the future in order to achieve future goals. It made sense to see how this notion of multiple years would fit in this micro use case multi model environment. The idea would be that the optimisation would still be an optimisation but not over a single time bucket of a year but over multiple time buckets of a year. Initially this proved to be a challenge because up till then all information was based on a single year ESDL file. TEACOS would now need information of multiple years and there was a choice to be made. Either extend the ESDL to contain multiple years or go for multiple ESDL’s of a single year. It turned out that a single ESDL for multiple years would require too much effort on various sides in order to get it running so the decision was made to go for multiple ESDL files that would each contain information of a single year. The interface with TEACOS needed to be adapted because TEACOS would now use multiple ESDL’s instead of 1 in order to get a complete multi period model run going and output multiple ESDL’s as a result. The runs for ESSIM and ETM would still be based on single ESDL files but would be run multiple times for each of the individual years.

1.3.2.Technical problem description

The MMvIB platform seeks to automate complex multi-model workflows in order to support decision making. However, models used in the micro use case such as ESSIM, TEACOS and ETM do not inherently work together. In addition, the location that these models are hosted on may vary across experiment and deployment. In order to provide a robust platform, a large range of circumstances must be supported, and models must fit together like building bricks.

To address these challenges, the platform employs a modular architecture that facilitates seamless integration of a wide range of models. By using a standardized interface and data format, the platform enables smooth communication between the individual models. Models are treated as modular components that can be easily assembled and reconfigured as needed. Furthermore, the platform incorporates a flexible hosting infrastructure, allowing infrastructure and models to be deployed across various locations and environments.

This versatility ensures that the platform can adapt to a wide array of circumstances, providing decision-makers with a reliable toolset to navigate complex scenarios efficiently.

2. Approach

In this chapter, the model chain workflow, the individual model developments, the infrastructure aspects and the orchestrator configuration for the micro use case multi-model are described.

2.1. Model chain

The micro use case model chain is depicted in a flow diagram in figure 1. The steps are as follows:

  1. The EPS calculates an ESDL representation of the energy system of the business park, based on available data for all buildings and businesses, and standard energy demand profiles.

  2. In the ESDL MapEditor, potential energy measures can be added as optional assets.

  3. The ETM provides energy prices to the ESDL, based on energy scenario data.

  4. TEACOS loads the ESDL with optional assets and energy price scenarios, and calculates the optimal investments (in time) from a business park perspective, using economic parameters as an input.

  5. As TEACOS bases its decisions on yearly energy demand, and energy production and demand profiles vary over time, ESSIM is used the simulate the resulting (ESDL) energy system with an hourly resolution, optimizing dispatch and including flexibility.

  6. The import and export electricity flows (between the business park and its connection with the grid) and potential grid congestion are sent back to TEACOS for an adjusted optimization run.

  7. The TEACOS optimization and ESSIM simulation are iterative. When the energy flows between TEACOS and ESSIM have converged, the results are sent to ETM and ABM.

  8. ETM calculates the impact of the investments on the system KPI’s on a municipality level.

  9. ABM uses the TEACOS (business park) investment optimum as an input for agent-based decision making. The agent-based decision for the businesses are compared with the TEACOS business park optimum.


Figure 1. Micro use case multi-model chain.

The results for the optimal investment path(s) for the business park are:

  • Local energy production (in MWh)

  • Investments (in EUR)

  • Energy costs (in EUR/year)

  • Direct and indirect CO2 emissions (in kton CO2)

Steps 3-8 are part of the MMvIB orchestrator, the other steps are still manual.

2.2. Individual model developments


The Agent-Based Model is a relatively very simple simulation model developed in Python using the Mesa and Mesa Geo packages to simulate investment behaviour in the optional assets based on the ESDL-file. The key outcome this model aims to represent is the number and distribution of solar panels that are purchased by agents in a single simulation run. The Mesa Model aims to replicate the real-life decision-making processes that influence the acquisition of optional assets in an abstract manner, considering financial (i.e., costs and ROI) and social factors (i.e., how agents are influenced by each other). The decisions made by the agents are written back into the ESDL-file. The results of the ABM are presented in the thesis Coupling for multi-models 2.

The Energy System Description Language (ESDL) is a standardized language used to describe the structure, components, and characteristics of energy systems. It is designed to facilitate the exchange of information between different energy models and tools, promoting interoperability and collaboration in the field of energy system analysis and planning.

During the development of the MMvIB project, it was identified that some models needed access to energy profiles but there was no standardised way in providing this. Instead, ESDL was updated by the team at TNO to be able to embed and link to energy profiles directly in the ESDL file. This ensured that models within the multi-model chain could have access to the same set of energy profiles.

The Energy System Simulator (ESSIM) models the dynamics of network balancing within energy systems over specific timeframes. Using the energy system defined as ESDL as its input, ESSIM computes the ideal schedule for flexible producers, analyzing its impact on emissions, costs, network load, and other relevant factors.

The primary adaptations for ESSIM during the MMvIB project were the development of the ESSIM adapter based on a REST API interface, the ability to read and utilise profiles embedded within an ESDL file and the inclusion of calculated KPIs directly into the ESDL file during the operation of multi-model workflows.


The Energy transition model (‘ETM’) works with a separate app specifically built to enable the translation of ESDL-files to ETM-scenario’s and vice versa. For this use case three features have been added to the app:

  1. Electricity price (curve) additions to the ESDL based on an ETM scenario

  2. Creating a context ETM scenario based on two ESDL files with more local information

  3. Adding KPI’s to an ESDL-file based on ETM scenario results

The first feature enables the addition of a future average electricity price based on the hourly electricity price in a given scenario with a given end-year. For example, for 2030 the ‘Klimaat en Energieverkenning’ (KEV) can be used to provide an average electricity price. The second feature enables users to understand and project the impact of certain choices made by the business park owners on a larger scale, such as the municipality. If the business park makes certain choices with regard to energy production or heating this can be aggregated and projected onto the amount of local energy production or mix of heating technologies in the municipality (or province, country, etc.). This enables efficient and fast communication between stakeholders on multiple levels of scale. Lastly, the KPI feature can quickly showcase the differences and results of energy plans in the business park.


The TEACOS developments that were specifically done within the MMvIB project are the following:

  • Creating code for reading in ESDL files and converting the information to an SQL database

  • Creating code for reading in the SQL database information and storing the information in local TEACOS parameters in memory

  • Creating and implementing logic for interpreting the data and turning it into a consistent TEACOS model that could be optimised

  • Optimisation procedures were already in place so those did not need to be created

  • Code was created for writing back results out of the optimisation to the SQL database format

  • Code was created for writing the combination of input/output information back to a new ESDL format file

  • The whole sequence was wrapped in an API that could be called externally

The work that was involved in these previous bullets was initially done for the single time period scenario. Later on, all the steps were revisited and extended in order to be able to handle both single and multiple time period scenarios.

General TEACOS information

TEACOS is a mathematical optimization tool for mid- to long-term strategic investment analysis. The tool is designed to assist in the investment decision making process. It aims to answer the following questions:

  • In which (decarbonization) opportunities to invest?

  • What is the optimal investment timing?

  • How much to invest?

By answering these questions, TEACOS provides credible, affordable and competitive transition pathways towards a low carbon energy system. TEACOS is completely data driven. Because of this, it can be applied in any industrial sector and on any scale.

TEACOS models the supply chain as a network. In the network, nodes represent locations or (production) units, and the connections between the nodes (arcs) represent transport of commodities between the nodes. Additionally, possible adaptations to the network infrastructure can be modelled as investments.

The model selects the best combination of investments and calculates the corresponding product flow such that either the Net Present Value is as high as possible, or the costs are minimized.

One of the major strengths of TEACOS lies in answering ‘what-if’ questions: i.e. ‘what if CO2 emission costs rise?’, by defining several scenarios in which certain assumptions are altered: i.e. a scenario with fixed CO2 emission costs and one where CO2 emission costs change over time.

TEACOS needs input on five different aspects:

  • Supply: resource availability and cost, utility availability and cost.

  • Conversion Infrastructure: yields and capacities, CAPEX and OPEX.

  • Transport Infrastructure: capacities, CAPEX and OPEX.

  • Demand: product/service demand and sales prices.

  • Strategic input: investment opportunities and their impact, outlook on prices and costs, environmental constraints, learning curves, supply and demand scenario’s, other constraints, other scenario’s.

The input is usually read from an Excel file or from a database. Specially for MMvIB the data is obtained by reading and interpreting ESDL format files.

2.3. Multi-model infrastructure

In order to achieve this, first and foremost models need a common way to exchange and parse data. For this ESDL was used as a common language for models, which saw a good fit as ESDL supports inclusion of custom KPIs with relevant metadata.

Next, a common communication methodology is required so that models can communicate results with each other. For this the Handler – Adapter protocol was designed. Each task is linked with a handler that specifies a generic protocol such as REST or MQTT, and each model-specific Adapter is able to interpret such requests and communicate these to the model in a standardised way.

In order to configure such workflows, the researcher executing the experiment needs to provide a configuration for the experiment. This configuration includes what types (and versions) of models each step requires and their configuration. The system dynamically allocates requested models via the model registry, to which model adapters are registered. This method allows for registration of secure external models, local models and even models running on different clusters or operating systems within VMs. This results in a very wide range of support for model applications across operating systems and networks.

Finally, intermediate and final results are stored in an inter-model storage solution. For this the standardised S3 protocol was used, which allows for storing large amounts of varied and unstructured data. This allows models to not only retrieve and store ESDL files, but also store any other files such as separate KPIs, logs, and more.


The multi-model infrastructure used for the micro use case consists of the following components:

Core Infrastructure

  • Airflow

    • Airflow Webserver

    • Airflow Infrastructure

    • Kubernetes/Celery Cluster

  • Model Registry

  • Inter-Model Storage

    • MinIO

Model Infrastructure


    • TEACOS REST Adapter

    • TEACOS Infrastructure

    • TEACOS Model (Proprietary Cloud-Native)


    • ESSIM REST Adapter

    • ESSIM KPI Modules

    • ESSIM Infrastructure

    • ESSIM Model (Open-Source)

  • ETM

    • ETM REST Adapter

    • ETM Model (Open-Source Cloud-Native)

2.4. Orchestrator configuration

Experiments within the MMvIB platform require two components:

  • Workflow Specification

  • Experiment Configuration

The workflow is a static definition of what the experiment is about. For the micro use case, this means that it specifies the looping behaviour between TEACOS and ESSIM, as well as calculating the KPIs in the final step.

The configuration on the other hand defines how the experiment should be conducted. For example, which exact model version or end-point to use, how that model should be configured and where the experimental results should be stored.

This division allows for large scale and parallel experimentation by running the same workflow horizontally or vertically over different configurations. Using the Airflow API, parameter spaces can be searched to find optimal solutions to complex multi-model problems by providing robust configurations for the workflow that is being studied.

A graphic representation of the micro use case orchestrator configuration is depicted in Figure 2.


Figure 2. Directed A-cyclic Graph in AirFlow for a 2 iteration micro use case configuration.

3. Results

The micro use case multi-model workflow works on a functional level without iterations between TEACOS and ESSIM, but with multi-period aspects. The corresponding multi-model Apache AirFlow sequence worked in a TNO controlled environment, but unfortunately still has issues in the TU Delft environment. Therefore, the ESSIM-TEACOS convergence couldn’t be further studied within the scope of this project. These key results are further detailed in this chapter.

Successful workflow on a functional level

The micro use case multi-model workflow works on a functional level – for a stylised representation of the Tholen business park and without iterating between TEACOS and ESSIM:

  • We were able to create a scenario in the MapEditor that (albeit stylised, see Figure 3) represented the Tholen business park case that we were trying to model, including optional assets.

  • This scenario was exported from MapEditor in an ESDL file format

  • ETM added information to the ESDL file on electricity pricing

  • The resulting ESDL file could be read and optimised by TEACOS and a resulting file could be written back to ESDL format including the decision to be either “ENABLED” or “DISABLED” for all the optional assets

  • This ESDL file could be picked up by ESSIM and assessed by ESSIM on what realistic generation and usage of electricity was, the result written back to an ESDL including a KPI parameter on the percentage of electricity that was effectively used and what percentage would flow back to the grid

  • The results could be integrated in an ETM scenario for the municipality of Tholen


Figure 3. Stylised representation of the Tholen business parks for the micro use case technical proof of concept.

Successful Apache AirFlow sequence in TNO controlled environment

Now in principle this all worked in an automated sequence via the Apache AirFlow orchestrating software in February 2023. All parties involved participated in providing adapters that made the communication possible. TNO performed tests in a TNO controlled environment and reported that the sequence worked.

Positive side there is the starting of the individual models and the communication between the models via ESDL was proven and working. This in itself is a major result!

No convergence between TEACOS and ESSIM

Negative side was that after the initial tests and due to the limited access to the orchestrator software and output files the results took a long time to check on completeness and correctness. Eventually it became clear that the recursive loop between TEACOS and ESSIM, although being run multiple times, was not resulting in the expected behaviour and therefore not delivering the expected result.

No reproduction in TUDelft environment

In the period from August to November 2023 there has been significant effort to reproduce the initial runs, identify the problem(s), fix them, and do a complete and correct sequence. Due to several issues this work could unfortunately not be completed before the agreed deadline of beginning of November 2023. Below there is a list of issues that came up in this process, to give an idea of what happened in those three months.

Problems that were encountered when trying to get the full sequence running in Apache AirFlow by QuoMare on the TUDelft environment:

  • When calling the Directed A-cyclic Graph (DAG) in AirFlow, this immediately resulted in an error. After getting access, the logfile indicated that the TEACOS adapter was not present in the model registry on the TUDelft environment. TNO added the needed information in the environment

  • The needed input files in the TUDelft environment were not in the correct Minio directory. TNO added the correct file at the correct location.

  • It turned out that TNO did not have sufficient accessing rights in their SQL credentials. TNO switched to a different account with more accessing rights.

  • When all the information was finally there QuoMare could investigate the problem and it turned out that there was a function missing in the TEACOS adapter for writing back to ESDL while in connection with Minio. This function was added.

  • It still didn’t work and the next issue was found: the ‘Configuration JSON’ was incorrect with the result that the DAG was called with the wrong configuration.

  • Now a new issue arose: TEACOS constraint information was not correctly read in from the ESDL file. A predefined maximum capacity of the solar panels was not taken into account and the resulting optimised capacity was over the maximum capacity. This issue was fixed and as a result it worked locally in the QuoMare environment but not when called via AirFlow, then errors occur.

  • In general, there have been errors generated both by the adapters of ETM and ESSIM dure to configuration errors in the adapters. TNO eventually managed to solve the problems.

  • It turned out that ESSIM has a default value of 0, meaning that if something has the value 0, that no value is written back in the ESDL file. For TEACOS the interpretation is different: 0 means that there is a value (e.g. for a minimum or maximum capacity) and that value is 0, if there is no value then there is no limit. TEACOS needed also the 0 values for interpreting the scenario correctly. This was fixed by adding very specific logic in the TEACOS code.

  • A general issue was that access to all the different systems and environments was difficult to get and took a long time. It also was not clear to which systems access was needed in order to get something running in the orchestrator. For looking at results again a different access was needed, and it was just hurdle after hurdle. The access to Minio was in the end the problem that took long to arrange, and which led to insufficient time left to solve all the remaining practical issues.

  • A changing in personnel on both Quo Mare and TNO sides in August made it extra challenging.

TEACOS-ESSIM results integrated in ETM scenarios

The goal of the Energy Transition Model (‘ETM’) in the micro-case was to simulate the context. In this case this consists of the municipality Tholen and the Netherlands. Two results were produced using the ETM:

  1. The yearly average electricity price based on the (future) installed capacities in the Netherlands

  2. The effect of energy plans on the municipal energy plans (or province or RES-region)

For the first result the ETM calculates the yearly average electricity price based on the hourly electricity price of a given scenario. This can be done based on the KEV (‘Klimaat en Energieverkenning’) for 2030 or the II3050 for 2030 or 2050. However, using the ETM transition path tool these scenario’s can be backcasted to any given year. These results are used by TEACOS to calculate the optimal energy system configuration.

The second set of results are based on the technology decisions made by TEACOS and ESSIM. These technology decisions (such as the amount of solar-pv per building) are aggregated and projected onto the municipal energy system. For solar and wind this means they are simply added to the current solar and wind in municipalities which are set in MW in the ETM. Other technologies, such as a heat pump in buildings, are set with a percentage slider based on the energy demand in buildings and the total energy demand of buildings in the municipality (see image below for example). This enables users of the multi-model chain to understand how plans and decisions made by business parks such as Tholen have an impact on municipal plans. It is also possible to use this function for regional or national plans if necessary.


The amount of heat pumps has increased based on the EPS results. If we translate this to the municipality we can see the use of ambient heat increase and the use of natural gas decrease in the future. In this way it is possible to see what effect plans in business parks have on the energy transition plans within a municipality.

Multi-period integrated in a local TEACOS setting

The initial setup for the micro use case was a single time. It was recognised that there would be value in multiple period aspects as described in an earlier paragraph. The general setup was made by getting multiple ESDL files each reflecting a certain time slice but which combined would deliver a multi period approach. The ETM and ESSIM adapter could basically still run from single period perspective, only reading in a single file and doing the calculation. On the TEACOS side these multiple time period would have to be taken into account into a single optimisation run. Although TEACOS is multi period in itself, the reading of the multiple ESDL files and the conversion into a multi period model needed to be created.

This was all implemented in the TEACOS adapter and TEACOS code and it worked in a local setting. Unfortunately, we were not able to test it in the orchestrator environment due to the earlier mentioned problem we already had with getting the single time period model running there.

4. Conclusions and lessons learned


The micro use case multi model works!

We can conclude that even though multi-modelling is complex, a major step forward towards a multi-model ecosystem was taken in the micro use case:

  • The micro use case multi-model workflow works on a functional level with 6 (!) different energy models.

  • Multi-period functionality was implemented on TEACOS side of the multi-model.

  • The multi-model orchestrator worked in a TNO controlled environment.

Unfortunately, the convergence between TEACOS and ESSIM in the micro use case multi-model could not be further studied within the scope of this project, due to several issues in getting the multi-model to run in the TU Delft IT environment.

Next step: supporting a decision making process

The micro use case multi-model works as a technical proof of concept for a stylized representation of the Tholen business park energy system and scenarios.

After fixing the current IT infrastructure issues, the energy model representation in the multi-model can be extended step by step towards a full representation of the business park energy system and all relevant scenario/technology options.

When this multi-model orchestrator works correctly, its results can be validated in an integral decision process on a long-term investment path towards a sustainable business park. In this way, the end-user value of the multi-model approach is tested in practice.

In the longer term, if the multi-model approach provides end-user value, management and maintenance for the orchestrator should be set up, and its usability should be matched with (potential) user requirements.

Lessons Learned

Pioneering on building a (micro use case) multi-model provided us with valuable lessons learned, which can be used in helping follow-up projects. These lessons, coming from different partners working on the micro use case multi-model, are summed up in this chapter. Both from a technical and an organisational perspective.


Understanding and harmonising different model languages takes time It is vital to know and understand which information a model needs to operate in order to exchange information in a coherent manner. For example, at first hand the EPS provided results that could not be interpreted by the ETM as it regards the energy system differently. This was the case in the built environment. The EPS views a building simply as a building whilst the ETM needs to know whether a building is a household, utility or industrial building to allocate energy demand and technologies correctly according to the EPS results. It takes time to learn to understand each other. Different modellers use a slightly different language and are sometimes not aware of that.

ESDL is a key enabler in multi-modelling ESDL is a good medium for transporting information between different models when talking about energy systems.

However, ESDL is not the solution for all challenges, so additional agreements are required. Understanding what additional agreements also takes time (you need to understand the core ESDL concepts and the reasoning behind ESDL).

Generic multi-modelling is complex Although we are able to make the multi-model work, it is almost certain that if we use a slightly different energy system with a different topology, that we might run into problems pretty soon. Nothing that is not fixable but up to now, that is the case. Making things really generic is very complex.

Multi-modelling is IT-complex The project is quite “IT-complex” and “IT-intense”, maybe more than we realized in advance.

IT environment barriers can provide major delays There was a lot of time lost with working in the TUDelft environment. All the access rights needed to be arranged from there while at the same time all the technical knowledge was within TNO. Even people from TNO did often not have the correct access rights to get something working.

QuoMare wanted to get some experience on working with Apache AirFlow but it seemed almost impossible to:

  • Get access to Apache AirFlow

  • Get rights to see input files

  • Get rights to see the correct DAGs

  • Change the DAGs

  • Upload the changed DAGs to the correct folder

  • Run the DAGs

  • See the correct output files

Individual steps seem small and could eventually be done but it was up to the end not possible to go through the total sequence without needing external help because we didn’t have access ourselves.

When getting a multi model going it need to be properly logged what you need to do in order to get access to ALL the relevant systems and to get it running.

Define rules for UoM and default values Most problems we encountered (and time we lost fixing it) with TEACOS had to do with units of measurement (UoM). There are multiple ways in ESDL to specify an UoM with a flow, but all of them are depending on text interpretation and often these UoM’s are not specified because some sort of default is assumed. For example, if an investment cost for a PV panel is specified, it can be that it says “100” with a certain max size, say 15 MW. For the interpretation in TEACOS it is not immediately clear whether this 100 is 100 Euro, 100 kEuro, 100 MEuro, or even 100 Euro/MW, 100 kEuro/MW, or 100 MEuro/MW. Even if the UoM’s are specified there are still different ways to interpret the numbers, e.g. 100 MWh can be 100 Mwh per day but also 100 MWh per year. A common set of rules of behaviour around the use of UoM’s would be valuable.

Similar to the UoM issues, there were problems with default values. If a value is 0, ESSIM will conclude that it is a default. A not included value of 0 will in TEACOS have a different interpretation than an included value of 0.


Align expectations Even within the case team there were different expectations of what the result would be of this exercise. This was ranging from “Can we get this multi model to work?” to “What are the results that I can show to my customer?”. Part of this is caused by the desire to start with “real” use-cases, that raises the expectation level for the people who provide the use case. Quo Mare would be in favour in getting the principle working first and then expand to real-life cases.

Split the project in a conceptual phase for showing the possibility, and an operational phase where this is expanded to an actual real live case.

Align available capacities When you are working on a multi model, there is often interaction with other parties. Other parties have other priorities and availabilities. What is important for us at this moment might not be important at this time for them, if they are available in the first place. As a result, there is often quite some delay over the total scope of work to get it working. Because of the exploratory nature of this project this is understandable. If this were an operational project, it would put pressure on the timeline if this is not aligned and formalised upfront.

Creating sessions where people are physically together helps. The time that was spent waiting on other parties was enormous. Even with the best intentions from all parties involved.

Experienced developers required Since coupling models is tech-heavy, you need (relatively) experienced developers at the table in order to create sustainable ESDL conversion models and adapters. The process has a significant technical footprint, for some companies who are less “IT-minded” this can be a hurdle.

Having the right expertise at the table (and thinking about this beforehand) is vital for a successful and efficient project.

Start doing “Just start doing it, with vallen en opstaan”, seems to be a good approach. Once we stopped talking and started doing it, the relevant questions started popping up. “Don’t wait for something to happen.”

Arrange back-up for key project members The personal aspect played a part in getting delay in the process. It would be advised to get at least 2 people involved with a similar knowledge level from all sides such that one person changing roles, being on holiday, sick, whatever, does have a less significant impact for other parties to continue.

Positive energy helps to stay motived Having a positive energy in the group, helps a lot to move things forward.


Coupling for multi-models | TU Delft Repositories


Coupling for multi-models | TU Delft Repositories