Package: pommesdispatch
pommesdispatchA bottom-up fundamental power market model for the German electricity sectorThis is thedispatch variantof the fundamental power market modelPOMMES(POwerMarketModel ofEnergy and reSources).
Please navigate to the section of interest to find out more.ContentsIntroductionDocumentationInstallationSetting up pommesdispatchInstalling a solverContributingCitingLicenseIntroductionPOMMESitself is a cosmos consisting of adispatch model(stored in this repository and described here), adata preparation routineand aninvestment modelfor the German wholesale power market. The model was originally developed by a group of researchers and students at thechair of Energy and Resources Management of TU Berlinand is now maintained by a group of alumni and open for other contributions.If you are interested in the data preparation routines used or investment modeling, please find more information here:pommesdata: A full-featured transparent data preparation routine from raw data to POMMES model inputspommesinvest: A multi-period integrated investment and dispatch model for the German power sector (upcoming).Purpose and model characterizationThedispatch variantof the power market modelPOMMESpommesdispatchenables the user to simulate thedispatch of backup power plants, storages as well as demand response units for the Federal Republic of Germanyfor an arbitrary year or timeframe between 2017 and 2030. The dispatch of renewable power plants is exogeneously determined by normalized infeed time series and capacity values. The models' overall goal is to minimize power system costs occuring from wholesale markets whereby no network constraints are considered except for the existing bidding zone configuration used for modeling electricity exchange. Thus, the model purpose is to simulatedispatch decisionsand the resultingday-ahed market prices. A brief categorization of the model is given in the following table. An extensive categorization can be found in themodel documentation.criterionmanifestationPurpose- simulation of power plant dispatch and day-ahead prices for DE (scenario analysis)Spatial coverage- Germany (DE-LU) + electrical neighbours (NTC approach)Time horizon- usually 1 year in hourly resolutionTechnologies- conventional power plants, storages, demand response (optimized)- renewable generators (fixed)- demand: exogenous time seriesData sources- input data not shipped out, but can be obtained frompommesdata; OPSD, BNetzA, ENTSO-E, othersImplementation- graph representation & linear optimization:oemof.solph/pyomo- data management: python / .csvMathematical and technical implementationThe models' underlying mathematical method is alinear programmingapproach, seeking to minimize overall
power system costs under constraints such as satisfying power demand at all times and not violating power generation
capacity or storage limits. Thus, binary variables such as units' status, startups and shutdowns are not accounted for.The model builds on the frameworkoemof.solphwhich allows modeling
energy systems in a graph-based representation with the underlying mathematical constraints and objective function
terms implemented inpyomo. Some of the required oemof.solph featuresm - such as demand response modeling - have been provided by thePOMMESmain developers which are also active in the
oemof community. Users not familiar with oemof.solph may find further information in theoemof.solph documentation.DocumentationAn extensivedocumentation of pommesdispatchcan be found on readthedocs. It contains a user's guide, a model categorization, some energy economic and technical background information, a complete model formulation as well as documentation of the model functions and classes.InstallationTo set uppommesdispatch, set up a virtual environment (e.g. using conda) or add the required packages to your python installation. Additionally, you have to install a solver in order to solve the mathematical optimization problem.Setting up pommesdispatchpommesdispatchis hosted onPyPI.
To install it, please use the following commandpip install pommesdispatchIf you want to contribute as a developer, you fist have toforkit and then clone the repository, in order to copy the files locally by typinggit clone https://github.com/your-github-username/pommesdispatch.gitAfter cloning the repository, you have to install the required dependencies.
Make sure you have conda installed as a package manager.
If not, you can download ithere.
Open a command shell and navigate to the folder
where you copied the environment to.Use the following command to install dependenciesconda env create -f environment.ymlActivate your environment by typingconda activate pommes_dispatchInstalling a solverIn order to solve apommesdispatchmodel instance, you need a solver installed. Please seeoemof.solph's information on solvers. As a default, gurobi is used forpommesdispatchmodels. It is a commercial solver, but provides academic licenses, though, if this applies to you. Elsewhise, we recommend to use CBC as the solver oemof recommends. To test your solver and oemof.solph installation, again see information fromoemof.solph.ContributingEvery kind of contribution or feedback is warmly welcome.We use theGitHub issue managementas well aspull requestsfor collaboration. We try to stick to the PEP8 coding standards.The following people have contributed in the following manner topommesdispatch:NameContributionStatusJohannes Kochemsmajor development & conceptualizationconceptualization, core functionality (esp. dispatch, power prices, demand response, rolling horizon modeling), architecture, publishing processcoordinator & maintainer,developer & corresponding authorYannick Wernermajor development & conceptualizationconceptualization, core functionality (esp. exchange, RES, CHP modeling), interface to pommesdatadeveloper & corresponding authorJohannes Giehldevelopmentearly-stage core functionalitydeveloperBenjamin Grossedevelopmentsupport for conceptualization, early-stage contributions at the interface to pommesdatadeveloperSophie Westphaldevelopmentearly-stage contributions at the interface to pommesdataformer developer (student assistant)Flora von Mikulicz-Radeckitestingearly-stage comprehensive testingformer tester (student assistant)Carla Spillerdevelopmentearly-stage rolling horizon and cross-border exchange integrationformer developer (student assistant)Fabian Büllesbachdevelopmentearly-stage rolling horizon implementationformer developer (master's student)Timona Ghoshdevelopmentearly-stage cross-border exchange implementationformer developer (master's student)Paul Verwiebesupportsupport of early-stage core functionality developmentformer supporter (research associate)Leticia Encinas Rosasupportsupport of early-stage core functionality developmentformer supporter (research associate)Joachim Müller-Kirchenbauersupport & conceptualizationearly-stage conceptualization, fundingsupporter (university professor)Note: Not every single contribution is reflected in the current version ofpommesdispatch. This is especially true for those marked as early-stage
contributions that may have been extended, altered or sometimes discarded.
Nonetheless, all people listed have made valuable contributions. The ones
discarded might be re-integrated at some point in time.
Dedicated contributions topommesdataandpommesinvestare not included
in the list, but listed individually for these projects.CitingA publication using and introducingpommesdispatchis currently in preparation.If you are usingpommesdispatchfor your own analyses, we recommend citing as:Kochems, J.; Werner, Y.; Giehl, J.; Grosse, B. et al. (2021): pommesdispatch. A bottom-up fundamental power market model for the German electricity sector.https://github.com/pommes-public/pommesdispatch, accessed YYYY-MM-DD.We furthermore recommend naming the version tag or the commit hash used for the sake of transparency and reproducibility.Also see theCITATION.cfffile for citation information.LicenseThis software is licensed under MIT License.Copyright 2021 pommes developer groupPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.