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FINE's News Feed
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Release version 2.2.0
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FINE release (2.2.0) provides some changes in code including bug fixes for 

* plotOperationColorMap (issubclass error should not occur anymore)
* default solver (default solver is changed to None; it is searched for an available solver if no solver is specified)
* transmission components: capacityMin and opexPerOperation can be given as a pandas DataFrame
* postprocessing: no ValueError occur if components are not chosen in the optimized solution
* postprocessing: optimizationSummary is ordered correctly s.t. properties are assigned to the corresponding component.

New features were included: 
* New keyword argument linkedQuantityID: The number of different components can be forced to be the same. 
* Enable time-dependent conversion factors (e.g. for modeling heat pumps)
* Add warning for simultaneous charge and discharge of storage components; users can check if and when simultaneous charge and discharge of storage components occur
* Add operation value for considered time horizon in the optimizationSummary 
* Add new attribute objectiveValue to EnergySystemModel class

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Release version 2.1.1
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FINE release (2.1.1) provides some minor changes in code including bug fixes for 

* Missing labels (for newer pandas versions) 
* setting operationRateFix or operationRateMax for transmission components

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Release version 2.1
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In FINE release (2.1) the following functionalities were included, for example: 

* New time series aggregation method: Segmentation of time series
* Bug Fix: TAC of transmission components within the optimization summary is fixed

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Release version 2.0
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In FINE release (2.0) the following functionalities were included, for example:

* Part load behavior (using piecewise linear functions (-> MILP))
* Robust discrete pipeline design under the consideration of pressure losses (see Robinius et al. (2019) https://link.springer.com/article/10.1007/s10589-019-00085-x and Reuß et al. 2019 (https://www.sciencedirect.com/science/article/abs/pii/S0360319919338625))
* A two-stage approach to reduce computation time of MILPs with time series aggregation, while providing a lower and upper error bound (Kannengießer et al. (2020) https://www.mdpi.com/1996-1073/12/14/2825)
* The option to model nonlinear investment cost functions (via a quadratic function, Lopion et al. (2019) https://www.mdpi.com/1996-1073/12/20/4006)
* A simple approach to model myopic foresight
* Ramping behavior of conversion components
* A beta version for modeling demand side management
