2nd Open Energy Modelling Online Lightning Talk Mini-workshop. Wednesday 15th April, 17:00 CEST lasting 2 hours made up of 10 slots for 6 minute talks + 4 minutes questions/comments.

About openmod

Aim

Aim: Open models and open data will advance knowledge and lead to better energy policies. Open up energy models improves quality, transparency, and credibility, leading to better research and policy advice.

openmod manifesto

Register here. Note if you do not have an institutional email address you must register 24 hours in advance for registration clearance, this is due to recent incidents of zoom-bombing we have decided to make the link private – you will need to register as a forum participant to get the link and to attend the meeting.

Talks:

From the openmod forum.

  1. Mihir Desu. Reducing accessibility barriers to grid planning models/tools for stakeholders in electric infrastructure proceedings. Converting opaque and black box capacity expansion and production cost models used for resource investment planning decisions into open and transparent tools.
  2. Daniel Huppmann A common nomenclature for assessing low-carbon transition pathways in Europe The Horizon 2020 project openENTRANCE aims to develop an integrated modelling platform for assessing low-carbon transition pathways in Europe – but integrating multiple frameworks across spatial scales and sectoral dimensions requires a common understanding of terms and definitions. This talk will present ongoing efforts to develop such a nomenclature in an open & transparent manner. Check out the GitHub repository for details.
  3. Zoltan Nagy or Jose Vazquez-Canteli CityLearn: An OpenAI Gym Environment for MultiAgent Reinforcement Learning and Demand Response In brief, CityLearn deals with controlling heat pump operation and battery/thermal storage charge/discharge using RL in multiple buildings (centralized & decentralized) to study building-grid interaction. We have abstracted out the building side so one can focus on algorithm development only, and hence develop benchmarks and comparisons, in particular for multi agent systems. Check out the GitHub Repository for details.
  4. Stefano Moret & Gauthier Limpens. EnergyScope: a novel open-source model for regional energy systems. EnergyScope optimises the investment and operating strategy of a multi-sector multi-vector energy system (including electricity, heating and mobility) for a target year. The linear programming model has an hourly resolution (using typical days) which makes it suitable for the integration of intermittent renewables, and its concise mathematical formulation and computational efficiency are appropriate for quick scenario assessments as well as uncertainty studies. Check out the GitHub Repository and the paper for details.
  5. (6 min total) Oleg Lugovoy. USENSYS development & Open Decarbonization updates.
    • United States Energy System (USENSYS) is capacity expansion model with primary focus on renewables and energy transition. USENSYS is an open source capacity expansion model, based on energyRt package for R. The current state of the model covers electric power sector and has 49 regions (48 lower states and Washington DC), and two time-resolution versions:
      – renewables balancing version with 1 year and 8760 hours, 49 regions;
      – electric power system transition version with 1-300 sub-annual slices and 50-100 years of horizon.
    • Open Decarbonization – an open energy modeling initiative with the purpose to develop a knowledge platform of sharable tools for reproducible decarbonization analyses, accelerate dissemination and implementation of the tools, and develop low carbon energy future scenarios available for free public use and open discussion.
      The project aims to connect modelers/researchers who has developed models and decarbonization scenarios with modelers/researchers who want to recalibrate the models for another country/region/sector, reproduce and build on the analysis, contribute low decarbonization scenarios to the public domain.
  6. Diederik Coppitters Uncertainty quantification and robust design optimization framework for hybrid renewable energy systems. The framework aims for computationally-efficient robust design optimization of hybrid renewable energy systems, including the quantification of uncertainty related to lack of data (epistemic uncertainty) and uncertainty related to natural variation (aleatory uncertainty). The framework provides designs with optimized average techno-economic performance, as well as designs which are least-sensitive to real-world uncertainties. Additionally, the dominating stochastic input parameters on the performance variation are provided through sensitivity indices, while the effect of epistemic uncertainty on these sensitivity indices indicates their confidence level.
  7. Kamaria Kuling Comparison of Two Different Equations for Modelling Energy Storage with OSeMOSYS A comparison of different methods of modelling energy storage that have been used in the open source capacity expansion modelling system OSeMOSYS. Variable renewable energy sources (VREs) such as wind and solar provide a low carbon alternative to meet our energy demands, but one drawback of such technologies is their dependence on weather cycles. Energy storage is one solution to allow energy demands to be met while using VREs. Consequently, it is paramount when making decisions regarding future energy use and incorporating more VREs into our energy infrastructure to be able to include storage in an energy model, and to have trustworthy and reliable methods to do so. This presentation will compare two storage equations formulated by Welsh et al. and Niet for OSeMOSYS and their effect on model outputs and performance.
  8. Leonard Göke. anyMOD – A framework for energy system modelling with high levels of renewables and sectoral integration. anyMOD.jl provides a framework to generate large-scale energy system models, that account for close interdependencies between sectors and the variable nature of wind and solar. Drawing on basic concepts of graph theory, this is achieved by two novel features. Frist, the level of temporal and spatial detail can be varied by energy carrier. Second, context-dependent substitution of energy carriers can be modelled. Check out the repository here.
  9. Taco NIet. Reviewing combined modelling Approaches for meeting the UN Sustainable Development Goals The integration of different models through hard- and soft-linking has both advantages and disadvantages but, to date, no review of the strengths and weaknesses of combined models has been published in the literature. SFU’s School of Sustainable Energy Engineering has recently received a grant to review the literature on these combined modelling approaches. The project aims to bring together modellers to discuss the challenges, strengths and weaknesses of combined models and to publish a review article of the findings that will provide guidance to modellers when considering combined modeling approaches.
  10. Adriaan Hilbers. Renewable test case power system models
    This repository contains a set of simple renewable power system models for benchmarking exercises for time series and optimisation methods. In many fields, standard benchmarks exist; notable examples are MNIST or CIFAR in Computer Vision and the Lorenz 63 system in Dynamical Systems. Test models used in power system research tend to differ per investigation, with each paper using a different (often not open-source) model. This repository provides a few simple test models to fill this gap. The models can be run “off-the-shelf”, containing pre-determined topologies, technologies and time series data. All that needs to be specified is the subset of time series data to use and a number of switches (e.g. integer or ramping constraints, whether to allow unmet demand) that ensure the model can contain contain most features seen in more complicated systems. These models are not modelling frameworks like OseMOSYS or Calliope (which can be used to create arbitrary power system models, but are not models themselves). The models are built and can run in Python. Documentation and examples can be found in the Github repository.