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RING is an automated reaction network generation and analysis tool. RING takes in as inputs initial reactants and reaction rules of a chemical system to generate the reaction network of that system exhaustively. Further, RING also takes in as input post-processing instruction to analyze, topologically, the generated network. Post-processing instructions include: (a) reaction pathway queries to get pathways from reactants to specified products, (b) lumping of isomers in the network that are functionally equivalent so as to reduce the size of the network, and (c) mechanism queries to get the overall transformation of the initial reactants to form a final product. The inputs are written in a reaction language that translates the instructions into internal representations that allow for exhaustive enumeration of all possible reactions. The outputs are a list of species and reactions that constitute the generated network, as well as analysis results carried out as a post-processing step. The figure below shows the structure, inputs, and outputs of RING.
RING has been applied in two broad classes of problems:
1. Analysis of complex reaction systems - For systems that are complex, we can use RING to analyze the transformations that exist in the network by using either experimental or computational data.
2. Identification of synthetic routes to desired products - RING can be used to find out all possible synthetic routes to desired compounds from available chemicals using a known set of chemistry rules.
Keep checking the website for latest updates, and
feel free to contact Udit Gupta (firstname.lastname@example.org), Srinivas Rangarajan (email@example.com), Prof.
Prodromos Daoutidis (firstname.lastname@example.org) or Prof. Aditya Bhan
(email@example.com) with questions, suggestions, bugs, etc.
We appreciate your interest in this work and hope you find RING valuable in your research.
Financial support from Financial support from the Initiative for Renewable Energy (Large Grant: RL-0004-09) at the University of Minnesota, the National Science Foundation Emerging Frontiers in Research and Innovation program, grant # 0937706 is gratefully acknowledged. Aditya Bhan was supported as part of the Catalysis Center for Energy Innovation, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under Award number DE-SC0001004. The reaction language interface of RING was developed in collaboration with Prof. Eric Van Wyk and Ted Kaminski, Department of Computer Science & Engineering at the University of Minnesota, using the tools SILVER and COPPER developed in the MELT group.
This Simulink model offers a basic grid-tied microgrid coupled with a building thermal model for use in dynamic simulation. The model is suitable for studies related to the scheduling and dispatch of microgrid power systems, particularly with integrated flexible operation of building HVAC systems. In particular, we seek to offer a good starting point for researchers seeking to validate/investigate supervisory control strategies using a continuous-time dynamic simulation.
The model consists of:
1. A solar panel array
2. A battery bank
3. Three natural gas-fired microturbines
4. A controllable ventilation and A/C system
5. A building thermal model based on a 3 story, 5,000 sq. m. office building
A supervisory control dispatches setpoint decisions to each unit within the system (including the controllable HVAC system), but it is left to the user to design block. Each unit within the microgrid has some local control designed to track setpoint requests from this supervisory level. Any local power imbalance is rectified by import/export of power with the external power grid (i.e. macrogrid).
The Simulink model takes in a set of exogenous inputs, in particular:
1. Incident solar radiation (insolation) on building surfaces and solar photovoltaic panels
2. Ambient (i.e. outdoor air) temperature
3. Local inflexible power demand
4. Internal heat generation within zones of the building
5. Forecasts for insolation, temperature, and power demand
It outputs a set of results in the form of timeseries variables in Matlab data files (.mat files) to the active path. The reported results are:
1. Power exchange with the external power grid, and the cumulative energy exchange
2. Zone temperatures within the building
3. Power generation from the various microgrid units
4. Ventilation flowrates to the building zones
5. Power consumption by the HVAC system
6. Fuel consumption by the microturbines
The model can easily be modified to report additional results or include additional/different distributed energy units as desired by the user.
Note that some of the very fast dynamics related to the power converters/inverters are neglected. Instead, these conversions are approximated by a static efficiency. In addition, reactive power is not modeled, topological constraints in local power distribution are not considered, and perfect state estimation is used. Of course, the user is free to relax these assumptions by modifying the file.
The following references were used in developing the microgrid model:
 F. A. Mohamed, Microgrid Modelling and Online Management. PhD thesis, Helsinki University of Technology, 2008.
 S. Liu and R. Dougal, "Dynamic multiphysics model for solar array," IEEE Transactions on Energy Conversion, vol. 17, no. 2, pp. 285-294, 2002.
 M. Ceraolo, "New dynamical models of lead-acid batteries," IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1184-1190, 2000.
Download OfficeMicrogrid.zip (Release data 02/24/2017). This .zip contains the Simulink file, a Matlab .m file used to initialize parameter values, and Matlab .mat data files which contain exogenous inputs such as ambient temperature and solar intensity. Note that a valid installation and license for Matlab/Simulink are required to use this model.
Feel free to contact Michael Zachar (firstname.lastname@example.org) or Prof. Prodromos Daoutidis (email@example.com) with questions, suggestions, bugs, etc. We appreciate your interest in this work and hope you find this model valuable in your research.
Financial support from Financial support from the Initiative for Renewable Energy (Large Grant: RL-0010-13) at the University of Minnesota is gratefully acknowledged. Michael Zachar is also supported under the National Science Foundation Graduate Research Fellowship program under grant number 00039202. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.