Etrago

Added features
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* eTraGo is now compatible with Python 3.8
* eTraGo can now import and optimize networks that include other energy sectors such as gas, heating and mobility
* Various flexibility options from different energy sectors can be considered in the optimization:
Weather dependent capacity of transmission lines (Dynamic Line Rating), Demand Side Management, Flexible charging of electric vehicles, Heat and hydrogen stores, Power2Hydrogen, Hydrogen2Power, Methanation and Steam Methane Reforming
* eTraGo arguments can now be partially provided and updated
* eTraGo can now import datamodels from databases without using the ego.io
* Existing clustering methods were adapted to be able to reduce the complexity of not electrical sectors
* Improvement of the ehv clustering (much faster now)
* A new clustering method named "k-medoids Dijkstra Clustering" (can be called by "kmedoids-dijkstra") was implemented. This method considers the electrical distance between the buses in the network. It is also available for the methane grid.
* It is possible to select if foreign buses are considered or not during the clustering process.
* The number of CPUs used to perform the clustering can be provided by the user.
* Some more options are available to conduct a reduction in temporal dimension: segmentation, clustering to typical weeks and months
* A temporal disaggregation is available through a 2-level-approach including a dispatch optimization on the temporally full complex model. To limit the RAM usage, you can optionally divide the optimisation problem into a chosen number of slices.
* New plotting functions to visualize the optimization results from all the included energy sectors were implemented
* Functions to analyze results were updated to consider new sectors

eTraGo has now a more object-oriented programming design.

Added features
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* eTraGo uses PyPSA version 0.17.1 directly, the fork is not needed anymore. The updated pypsa version includes various features, e.g. running a lopf without using pyomo which is faster and needs less memory.
* (n-1)-security factors are set as line/transformer parameters s_max_pu instead of adding the additional argument s_nom_original
* There is now one central plotting function for all grid topology plots which also allows to combine different results (e.g. plot storage expansion and line expansion at once)
* eTraGo is now compatible to Python3.7
* A bug in setting the line_length_factor in kmeans clustering is fixed.

A minor release adding the following features.

Added features
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* for single use of eTraGo (not as a sub-module of eGo), we recommend to use the newest minor data release 'gridversion': 'v0.4.6'. This data release includes some minor bug fixes but it is not consistent with the data on the MV and LV levels. Hence, the modelling results are only adequate for the HV and EHV level applying solely the tool eTraGo.
* snapshot clustering includes now an approach to model seasonal storage as in Kotzur et al, 2018 ( https://www.sciencedirect.com/science/article/pii/S0306261918300242 ). Moreover the method may include extreme periods using an option of the tsam package.
* osm maps can now be used for background plotting
* the iterate_lopf function enables to adequately model the reactances when expanding the grid
* important bug fix for the adjustment of reactances when harmonizing the voltage level in case of applying the k-means network clustering
* multiple extra_functionalities can be called easily called now at once
* various minor changes such as specifying installation requires for flawless performance

A minor release adding new options for additional constraints, modelling assumptions and plotting.

Added features
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* Two extra functionalities were introduced in order to apply constraints concerning a minimal share of renewable energy and a global upper bound for grid expansion. You can activate these functions in the 'args' of the etrago() function.
* The branch_capacity_factor can now be defined separately for the high and extra high voltage level in order to address the (n-1) criteria more accurately.
* There are some more plotting functions e.g. plotting the state-of-charge and dispatch of storage units.
* Storage capacities in foreign countries can easily be be optimized.
* By default the maximum expansion of each line and transformer is set to four times its original capacity. Being an argument of the extendable() function it can be easily adjusted.
* k-means clustered results can now also be exported to the oedb.

eTraGo is able to produce feasible non-linear power flows based on optimization results and allows the disaggregation of clustered results to original spatial complexities.

Added features
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* The pf_post_lopf function was improved. Due to changes in the data set now the non-linear power flow (pf) creates feasible solutions. If network optimization is turned on, a second lopf which regards the updated reactances and optimizes only dispatch is performed before the pf is executed.
* The disaggregation method was included. When using a network clustering method to reduce the spatial complexity of the given network, a disaggregation method can be used afterwards to distribute the nodal results (generation and storage timeseries) to the original complexity. The method 'disaggregation': 'uniform' can be used as an interface functionality for distribution grid planning tools like eDisGo.
* For the network expansion it is now additionally possible to only optimize the German power lines or only the crossborder lines. Moreover one can choose to optimize only a predefined set of power lines which are identified by a worst-case analysis beforehand.
* Intertemporal constraints can be applied to certain power plants. For different technologies certain parameters i.e. 'start_up_cost', 'start_up_fuel', 'min_up_time' and 'min_down_time' are defined in the ramp_limits function.
* Crossborder lines can now easily be modelled as 'DC' links. Moreover the capacities of these lines can be adjusted with respect to a ACER report on thermal as well as net transfer capacities.
* Thanks to jankaeh manually the grid topology within the cities Stuttgart, Munich and Hannover was improved. Perspectively this function should be obsolete when openstreetmap and/or osmTGmod get better data coverage.
* As an alternative to the normal editing of the calcualtion settings (args) within the appl.py it is now possible to load an args.json file.

* An installation issue when installing from pypi was fixed.
* The random noise function was improved. Now you set a (reproducible) random seed.
* snapshot.weightings are used within the plotting functions
* bug fix for k-means clustering with respect to the aggregation of p_max_pu values of variable generators. They are weighted by their p_nom now.