EMLab - Energy Modelling Laboratory

Introduction

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).

EMLab-Generation

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).

The Y-factor for Climate Abatement

Introduction: The Y-factor visualizes the complexity of climate abatement. There is a wide variety of technologies to reduce greenhouse gas emissions, spread throughout all sectors of the economy. Abatement options are typically presented in terms of the abatement costs and abatement potential, in a so-called marginal abatement cost curve (MACC). The abatement costs are the costs needed to reduce reduce greenhouse gas emissions (Euro/ton CO2). The abatement potential are the emissions that can be avoided (ton CO2). But there is more to it than Euro's and tons. Choosing between abatement measures is difficult: low-cost options are not necessarily easy to realize. This tool, the Y-factor visualizes a variety of factors influencing why abatement options are or are not realized. It provides a complementary view on climate mitigation: carbon abatement options are scored on 12 factors, spread across 4 categories: 1) costs and financials, 2) multi-actor complexity, 3) physical interdependencies, and 4) behaviour.

Further information: interactive Y factor and the scientific paper describing the method and the global Y curve.

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

Optimal Network Layout

Introduction: Networked infrastructures (gas pipes, water pipes, electricity cables, glass fibre, (rail) roads) form the backbone of our society as they provide essential utilities and services. In a densely built and highly urbanised environment, such as the Netherlands, the roll-out of new networks encounters physical or legal boundaries that make the planning of such networks a difficult task. Furthermore, if several independent organisations are to be connected to these networks, the actual commitment of these parties and the capacities they require from the network can remain uncertain for a long time. This makes the planning process cumbersome and requires tools that can deal with this uncertainty. The need for planning tools is becoming larger as for example in the energy sector several initiatives are being developed that require new networks to be built. The method discussed here can be used to find a minimal cost network that connects multiple sources with multiple sinks taking into account the demand and supply patterns over multiple time steps. Depending on the time, nodes can be suppliers at some time steps and consumers at other times. Routing can (if needed) be limited to specific connections or around obstacles in the area. Part of the network can exist and new connections can be chosen to use existing connections if sufficient capacity remains.

Further information: Optimal Network Layout, source code and documentation.

Contact: dr. ir. Petra Heijnen (P.W.Heijnen@tudelft.nl).

MAIA

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)

EMLab-Congestion

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)

Enipedia

Introduction: Enipedia, now discontinued, 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. Enipedia is no longer actively maintained.

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).