edits in publication.md, some sources added to paper.bib (pareto optimization,...

edits in publication.md, some sources added to paper.bib (pareto optimization, pyomo, pv model, inverter model)

publication/paper.bib

-@article{Pearson:2017,
-  	url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P},
-  	Archiveprefix = {arXiv},
-  	Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.},
-  	Eprint = {1703.04627},
-  	Journal = {ArXiv e-prints},
-  	Keywords = {Astrophysics - Astrophysics of Galaxies},
-  	Month = mar,
-  	Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}},
-  	Year = 2017
+@misc{demandlib,
+author={{Oemof Developing Group}},
+title={Demandlib Documentation},
+year={2016},
+howpublished={Read the Docs},
+url={https://demandlib.readthedocs.io},
+urldate={2021-03-14}
+}
+
+@article{Mavrotas.2009,
+ author = {Mavrotas, George},
+ year = {2009},
+ title = {Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems},
+ keywords = {GAMS;Multi-Objective Programming;ε-Constraint method},
+ pages = {455--465},
+ volume = {213},
+ number = {2},
+ issn = {00963003},
+ journal = {Applied Mathematics and Computation},
+ doi = {10.1016/j.amc.2009.03.037}
+}
+
+@article{pyomo,
+ author = {Hart, William E. and Watson, Jean-Paul and Woodruff, David L.},
+ year = {2011},
+ title = {Pyomo: modeling and solving mathematical programs in Python},
+ url = {https://www.researchgate.net/publication/229032361_Pyomo_Modeling_and_solving_mathematical_programs_in_Python},
+ pages = {219--260},
+ volume = {3},
+ number = {3},
+ issn = {1867-2949},
+ journal = {Mathematical Programming Computation},
+ doi = {10.1007/s12532-011-0026-8}
+}
+
+@article{Riffonneau.2011,
+ abstract = {This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the ``day-ahead'' approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.},
+ author = {Riffonneau, Y. and Bacha, S. and Barruel, F. and Ploix, S.},
+ year = {2011},
+ title = {Optimal Power Flow Management for Grid Connected PV Systems With Batteries},
+ pages = {309--320},
+ volume = {2},
+ number = {3},
+ issn = {1949-3037},
+ journal = {IEEE Transactions on Sustainable Energy},
+ doi = {10.1109/TSTE.2011.2114901},
+ file = {9f117b4b-135b-4a25-85ee-ba01f26cc811:C\:\\Users\\Acer\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\fdkc0101zwa7pqlcw8slfbvllf602j27ov4o7q0nqy\\Citavi Attachments\\9f117b4b-135b-4a25-85ee-ba01f26cc811.pdf:pdf}
+}
+
+@article{Sauer,
+  year = {1996},
+  month = aug,
+  publisher = {Deutsche Gesellschaft f\"ur Sonnenenergie e.V. - International Solar Energy Society - German Section},
+  volume = {21},
+  number = {4},
+  pages = {43-47},
+  author = {H. Schmidt and D. U. Sauer},
+  title = {Wechselrichter-Wirkungsgrade - Praxisgerechte Modellierung und Abschätzung},
+  journal = {Sonnenenergie},
+ issn = {01723278},
+url={https://www.dgs.de/fileadmin/sonnenenergie/SE-4-1996-ganz/Wechselrichter-Wirkungsgrade.PDF}
 }
\ No newline at end of file

publication/publication.md

@@ -5,7 +5,7 @@ tags:
   - energy
   - optimization
   - prosumer
-authors:
+authors: # order pending
   - name: Mauricio Celi Cortés
     orcid: 0000-0002-2624-9598
     equal-contrib: # do we need this?
@@ -45,37 +45,50 @@ bibliography: paper.bib
 
 # Summary
 
+[The current idea is to do some light cleaning up of the code and libraries to push our project as open source by the end
+of August 2022. From there on, we will link this public repository in all our publications. Eventually the repository will be
+in a development state where we can submit it together with this paper to the Journal of Open Source Software (JOSS).
+For this we need full features, i.e Community (and maybe also City) fully functional, we need to trim out scripts we do not need (likely found in Tooling), 
+we need better documentation, we need to write tests for all scripts, we need an examples directory (instead of runme's everywhere). 
+Submodules GUI and Database will remain private and are not originally thought for submission although if properly 
+developed and publicly available (no accounts necessary!) they can be included. Submission to be made public will include 
+the InEEd-DC Framework repository and the Model_Library and Tooling submodules. Submission requirements: 
+https://joss.readthedocs.io/en/latest/submitting.html#the-review-process]
+
+[In theory City can be submitted as a separate JOSS publication since they also review significant contributions made to existing packages]
+
 FEN Framework [change name] is a software framework for the flexible and dynamic 
 formulation of optimization problems with a focus on multi energy sector coupling. 
-To broaden the view of multi energy systems, traditional energy sectors including AC electricity, 
-heat, cooling and gas, are extended by modern energy sectors like DC electricity and hydrogen.
-
-The resulting optimization framework is implemented as three individual stages, as it is shown 
-in Figure. At the first stage individual prosumer models are created as 
-interconnected compositions of technological components from the energy sectors of interest. 
-Besides components that are producing or consuming energy, such as [give some examples here], 
-the framework includes basic models for electrical, thermal as well as hydrogen storages. 
-This variety of different storage technologies, which are allowing long-term storage durations 
-as well as flexible withdrawal, enables studies of various use cases in the multi energy sector 
-domain.  The interconnection between the different energy sectors is achieved through the 
-introduction of energy converters models like heatpumps, heat exchangers, inverters, etc.
-
-The model building process of individual prosumers is is illustrated in Figure. In order to 
+To broaden the view of multi energy systems, traditional energy carriers including alternating current, 
+natural gas, cooling and heat grids, are extended by energy carriers such as electrochemical storages, 
+direct current and hydrogen.
+
+The resulting optimization framework is implemented as three individual stages. At the first 
+stage individual prosumer models are created as interconnected compositions of technological 
+components from the energy sectors of interest. In addition to components that produce or 
+transform energy, such as photovoltaic panels, power electronic converters and heat pumps, the framework includes energy based models for 
+electrical, thermal as well as hydrogen storages. This variety of different storage technologies, 
+which allow long-term storage as well as flexible withdrawal, enables studies 
+of various use cases in the multi energy sector domain.
+
+The model building process of individual prosumers is is illustrated in Figure [Simplified workflow of the prosumer framework]. In order to 
 generate the mathematical optimization model, a Prosumer class is instantiated with 
-certain configuration inputs, such as the regarded components and their interconnection, a 
+certain configuration inputs, such as the prosumer topology, a 
 set of time series including weather data and energy price information, as well as the 
-optimization strategy of interest and additional regulatory settings. The Prosumer 
+optimization strategy and additional regulatory settings. The Prosumer 
 then distributes the input data between the EMS class, representing an energy management 
-system, and the required Component classes implementing the required component models, 
+system, and the required Component classes which implement the required component models, 
 before orchestrating them to build the actual optimization model consisting of optimization 
 variables, constraints and one or more objective functions. Depending on the received inputs, 
-different types of optimization programs can be generated that way. Two possible use cases 
+different types of optimization programs can be generated in this way. Two possible use cases 
 are the optimization of prosumer operation and the optimization of component sizes in the 
-context of an economic feasibility study. The framework’s possibility to generate 
+context of an economic feasibility study. The framework’s ability to generate 
 multi-objective optimization problems, i.e. optimization problems with more than one 
 optimization goal, is especially useful in the second use case as it can provide decision 
 makers with a powerful tool to evaluate different criteria before deciding on specific 
 investment plans.
+[since the JOSS publication will include at least the community stage (not sure if city stage will be in the scope) maybe we should use the space to present the community briefly 
+instead of talking about the software structure in detail. A figure will be sufficient to illustrate the information flows]
 
 After creation, the optimization model is passed to one of many compatible solvers chosen by 
 the user, rendering results such as time series, optimal component sizes, investment plans and
@@ -84,33 +97,28 @@ Pareto-Set approximations in the case of multi-objective optimization scenarios.
 As the structural approach of the framework allows to feed the results from any the stages 
 into the next higher one, the prosumer models can not only be used to perform independent 
 optimization studies on a prosumer level, but the results can also be used as the basic blocks 
-for the second stage. One of the main tasks when modeling of city quarters is to establish 
+for the second stage. One of the main tasks when modeling city quarters is to establish 
 appropriate models for central supply and storage units such as converters, stationary batteries, 
 hydrogen storage, and large generation units. A further focus is on the distribution of the 
 generated or stored energy by means of DC power grids, as well as thermal and gas network, 
 in order to connect the individual prosumers as efficiently as possible. An optimization model 
 is being developed for optimal operations on the city-quarter-level with aims to use synergy 
-effects of the prosumers efficiently. The connections between city quarters forms interconnected 
+effects of the prosumers efficiently. [@jbr we have to talk a bit more about communities] The connections between city quarters forms interconnected 
 urban areas, which is the third stage of the three-stage optimization model. The power exchange 
 towards higher grid levels is investigated for networked urban areas with respect to DC power
 grid topologies as a possible alternative to gas grids and AC power grids. The optimization
 model targets on addressing the issue of providing energy and power reserves, taking into 
-account the balancing mechanisms among the city quarters.
+account the balancing mechanisms among the city quarters. [as far as I know we haven't done anything about city yet(?) are there students working on this? could this be a topic for @jbr?]
 
 In parallel to the actual development of the underlying models, complexity reduction 
 techniques like temporal aggregation of the input time series are studied and integrated
 at the framework level continuously during the project timeline. This allows ensuring feasible 
-execution times for the optimization of large scale scenarios.
-
-In addition, the fact that the EMS and the corresponding energy storage devices are included in 
-each stage of the model respectively can improve the stability of the new decentralized power system.
-The lines in the figure represent different energy pipelines, but in prosumer these are assumed to 
-be ideal pipelines, in which no energy losses are considered.
+execution times for the optimization of large scale scenarios. [how far has this been implemented?]
 
 With that, the framework is a highly flexible modeling tool, capable of generating optimization 
 models dynamically enabling holistic studies of intelligent integration approaches of available 
 energy sectors and storage technologies within a single prosumer, a city quarter or a coupled 
-district likewise. The combination of a modular topology configuration and the flexible 
+district. The combination of a modular topology configuration and the flexible 
 definition of an optimization strategy offers the possibility of modeling both technical 
 relationships and political regulations.