EMLab - Energy Modelling Laboratory


Welcome to EMLab, the energy modelling laboratory of the TU Delft. EMLab is a node in a network of open source projects, initiated in the TU Delft. When our energy infrastructure is at stake, many efforts need to be combined in order to explore the directions in which our energy infrastructures may develop, and how we may steer the developments in desired directions. There is not a single way in which energy systems can nor should be analysed. EMLab represents a collection of approaches, tools and results that may enable new ways in which policy and design questions are addressed, and new ways in which scientific efforts are used in the energy policy process.

EMLab is a platform for open open source, multi-tool, multi-model, multi-level energy modelling.

The rest of this page introduces a number of projects that are part of EMLab. For more information, please contact dr.ir. Emile Chappin (e.j.l.chappin@tudelft.nl).


Introduction: The main purpose is to explore the long-term effects of interacting energy and climate policies by means of a simulation model of power companies investing in generation capacity. With this model, we study the influence of policy on investment in the electricity market in order to explicate possible effects of current and alternative/additional policies on the various sector goals, i.e. renewables targets, CO2 emission targets, security of supply and affordability. The methodology, agent-based modelling, allows for a different set of assumptions different as to the mainstream models for such questions: this model can explore heterogeneity of actors, consequences of imperfect expectations and investment behaviour outside of ideal conditions.

Further information: EMLab-Generation introduction, the EMLab-Generation factsheet (pdf), the EMLab-Generation project report (pdf), the doxygen report and the model source code.

Scientific publication with an overview of all modelling work: Chappin, E. J. L., L. J. de Vries, J. Richstein, P. Bhaghwat, K. Iychettira, and S. Khan. Simulating climate and energy policy with agent-based modelling: the energy modelling laboratory (EMLab). Environmental Modelling & Software, 96:421-431, 2017. doi: 10.1016/j.envsoft.2017.07.009.

Supported by: the Energy Delta Gas Research program, project A1 -- Understanding gas sector intra-market and inter-market interactions, by the Knowledge for Climate program, project INCAH -- Infrastructure Climate Adaptation in Hotspots and by the Erasmus Mundus Joint Doctorate in Sustainable Energy Technologies and Strategies Program.

Contact: dr.ir. Emile Chappin (e.j.l.chappin@tudelft.nl).


Introduction: We have developed a modelling platform called AgentSpring, which facilitates the development of agent-based models in a modular and structured manner, using state-of-the-art IT development principles and tools. An attractive web-based interface allows for the interaction with policy makers. AgentSpring is based on Java technologies and runs on all popular operating systems (Linux, Windows, and Mac). AgentSpring gets its name from and makes use of Spring Framework - a popular software development framework, that promotes the use of object oriented software patterns. One such pattern calls for separation of data, logic and user interface. Although the latter is an old concept, most modeling frameworks mix the three. This may be reasonable for creating smaller models, but for larger models it will be ineffective. Developing and using AgentSpring enabled us to build a model that is better maintainable and expandable.

Further information: EMLab-AgentSpring introduction and the source code including online documentation.

Supported by: the Energy Delta Gas Research program, project A1 -- Understanding gas sector intra-market and inter-market interactions and by the Knowledge for Climate program, project INCAH -- Infrastructure Climate Adaptation in Hotspots.

Contact: dr.ir. Emile Chappin (e.j.l.chappin@tudelft.nl).


Introduction: Enipedia is an active exploration into the applications of wikis and the semantic web for energy and industry issues. Through this we seek to create a collaborative environment for discussion, while also providing the tools that allow for data from different sources to be connected, queried, and visualized from different perspectives. A core effect is to bring together data and information on all the world's power plants, to make it available on line, for querying, visualization, for analysis, for updating and expansion. By importing and visualizing data from other open sources of energy data, enipedia serves as an alternate window, facilitating curation and maintenance of said data. Thus enipedia has allowed for the development of a rich, up-to-date, accurate picture of the state of electric power supply around the world.

Further information: Enipedia and the about page.

Supported by: the Energy Delta Gas Research program, project A1 -- Understanding gas sector intra-market and inter-market interactions and by the Knowledge for Climate program, project INCAH -- Infrastructure Climate Adaptation in Hotspots.

Contact: dr. Chris Davis (C.B.Davis@tudelft.nl).


Introduction: This participatory simulation game simulates an electricity market that is subject to congestion. Contrary to a conventional simulation model, the values of input parameters are provided by humans that participate in the simulation in the role of a power producer. All power producers have three power plants (of different fuel types) that can be used for the generation of electricity, which can be sold on the spot markets of two fictive countries, "North" and "South". Different congestion management mechanisms can be applied to deal with the limited capacity of the interconnector between these regions. The game is used to support research in the field of congestion management mechanisms, such as to analyze bidding behavior under various circumstances, but can also serve as an educational support tool as it allows one to experience the functioning of different congestion management mechanisms, which has proven to be very effective for training purposes.

Further information: EMLab-Congestion brochure (pdf).

Supported by: TenneT TSO B.V.

Contact: ir. Martti van Blijswijk (M.J.vanBlijswijk@tudelft.nl)

EMLab-Network Evolution

Introduction: This model captures the long-term development of an electricity transmission network as a consequence of the repeated decisions of a set of boundedly rational agents. The model includes two types of agents - electricity producers and a transmission system operator (TSO). The regulator and distribution system operators are excluded. Each timestep, these agents have the option to invest in various types of technical components. Electricity producers invest in generators of different types, and the TSO invests in various grid components, including power lines, substations and transformers. As a result of these repeated investment decisions, a transmission grid develops over time. We are in the process of linking this model with EMLab-generation, to allow for exploring the development of the electricity transmission grid alongside the generation portfolio under different policy regimes.

Further information: Presentation (pdf).

Supported by: Knowledge for Climate program, project INCAH -- Infrastructure Climate Adaptation in Hotspots.

Contact: Andrew Bollinger (L.A.Bollinger@tudelft.nl)


Introduction: MAIA (Modelling Agent systems Using Institutional Analysis) is a modelling framework that is used to conceptualize agent-based models of socio-technical systems.  MAIA provides a collection of  concepts and relations that are present in socio-technical systems covering the social, institutional, physical and operational aspects.  Therefore, it is a  template for data collection especially qualitative. This modelling framework comes with an online tool that can be used as an interface between modellers, programmers and problem owners to collectively build an agent-based model.  MAIA is useful for modelling energy systems as it not only puts emphasis on the stakeholders and physical artefacts, but also the policies, regulation and governance of such systems. A static description would not serve much purpose as socio-technical systems are inherently evolutionary due to changing stakeholder perceptions and goals. MAIA provides a clear structuring of agent actions which detail the interactions and outcomes necessary to simulate the evolution of an energy system.

Further information: MAIA website, PhD thesis report on MAIA, main MAIA paper

Contact: dr. Amineh Ghorbani (a.ghorbani@tudelft.nl)