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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Open energy system models | 3/16 | https://en.wikipedia.org/wiki/Open_energy_system_models | reference | science, encyclopedia | 2026-05-05T03:49:30.157219+00:00 | kb-cron |
Under development at the European Commission's Joint Research Centre (JRC), Petten, the Netherlands, Dispa-SET is a unit commitment and dispatch model intended primarily for Europe. It is written in Python (with Pyomo) and GAMS and uses Python for data processing. A valid GAMS license is required. The model is formulated as a mixed integer problem and JRC uses the proprietary CPLEX sover although open source libraries may also be deployed. Technical descriptions are available for versions 2.0 and 2.1. Dispa-SET is hosted on GitHub, together with a trial dataset, and third-party contributions are encouraged. The codebase has been tested on Windows, macOS, and Linux. Online documentation is available. The SET in the project name refers to the European Strategic Energy Technology Plan (SET-Plan), which seeks to make Europe a leader in energy technologies that can fulfill future (2020 and 2050) energy and climate targets. Energy system modeling, in various forms, is central to this European Commission initiative.
The model power system is managed by a single operator with full knowledge of the economic and technical characteristics of the generation units, the loads at each node, and the heavily simplified transmission network. Demand is deemed fully inelastic. The system is subject to intra-period and inter-period unit commitment constraints (the latter covering nuclear and thermal generation for the most part) and operated under economic dispatch. Hourly data is used and the simulation horizon is normally one year. But to ensure the model remains tractable, two day rolling horizon optimization is employed. The model advances in steps of one day, optimizing the next 48 hours ahead but retaining results for just the first 24 hours. Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of variable renewable energy (VRE). These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly pumped-storage hydro), cross-border interconnectors, demand side management, renewables curtailment, last resort load shedding, and nascent power-to-X solutions (with X being gas, heat, or mobility). The modeler can set a target for renewables and place caps on CO2 and other pollutants. Planned extensions to the software include support for simplified AC power flow (transmission is currently treated as a transportation problem), new constraints (like cooling water supply), stochastic scenarios, and the inclusion of markets for ancillary services. Dispa-SET has been or is being applied to case studies in Belgium, Bolivia, Greece, Ireland, and the Netherlands. A 2014 Belgium study investigates what if scenarios for different mixes of nuclear generation, combined cycle gas turbine (CCGT) plant, and VRE and finds that the CCGT plants are subject to more aggressive cycling as renewable generation penetrates. A 2020 study investigated the collective impact of future climatic conditions on 34 European power systems, including potential variations in solar, wind, and hydro‑power output and electricity demand under various projected meteorological scenarios for the European continent. Dispa-SET has been applied in Africa with soft linking to the LISFLOOD model to examine water‑energy nexus problems in the context of a changing climate.
=== E4ST === See e4st.org or www.rff.org/topics/data-and-decision-tools/e4st/.
=== EMLab-Generation ===
EMLab-Generation is an agent-based model covering two interconnected electricity markets – be they two adjoining countries or two groups of countries. The software is being developed at the Energy Modelling Lab, Delft University of Technology, Delft, the Netherlands. A factsheet is available. And software documentation is available. EMLab-Generation is written in Java. EMLab-Generation simulates the actions of power companies investing in generation capacity and uses this to explore the long-term effects of various energy and climate protection policies. These policies may target renewable generation, CO2 emissions, security of supply, and/or energy affordability. The power companies are the main agents: they bid into power markets and they invest based on the net present value (NPV) of prospective power plant projects. They can adopt a variety of technologies, using scenarios from the 2011 IEA World Energy Outlook. The agent-based methodology enables different sets of assumptions to be tested, such as the heterogeneity of actors, the consequences of imperfect expectations, and the behavior of investors outside of ideal conditions. EMLab-Generation offers a new way of modeling the effects of public policy on electricity markets. It can provide insights into actor and system behaviors over time – including such things as investment cycles, abatement cycles, delayed responses, and the effects of uncertainty and risk on investment decisions. A 2014 study using EMLab-Generation investigates the effects of introducing floor and ceiling prices for CO2 under the EU ETS. And in particular, their influence on the dynamic investment pathway of two interlinked electricity markets (loosely Great Britain and Central Western Europe). The study finds a common, moderate CO2 auction reserve price results in a more continuous decarbonisation pathway and reduces CO2 price volatility. Adding a ceiling price can shield consumers from extreme price shocks. Such price restrictions should not lead to an overshoot of emissions targets in the long-run.
=== EMMA ===