Can Quest Evaluate Other Battery Services?

[UGuntupalli]

@rconcep:
Ricky, Thanks a lot for compiling a great tool. I have been reading the available documentation and the project description in the Github page and it looks really promising and helpful.
Assuming this is the appropriate forum for requesting more information on the tool, I would like to request some more information:

1. From the screenshots provided, it looks like Quest Valuation supports the evaluation of a storage system, either as a frequency regulation asset or an arbitrage asset or both. Does Quest Valuation or Quest BTM have the capabilities to simulate other services like asset upgrade deferral, resource adequacy, peak shaving etc. ?
2. If the answer is no to (1), can you please provide guidance on if there are any plans to offer these features in your future road map ?
3. Is the financial performance of the energy storage asset available at hourly time steps ?
4. Is it possible to compile the anaconda environment via an yml file ? When I look through the instructions, it looks like the preferred path even after cloning the project is to download the glpk binaries and IPOPT binaries from other sources, so am curious.
   Looking forward to learning more about Quest.

Best
Uday

[rconcep]

Hi Uday,

Thanks for your comments and checking out QuESt.

1. The energy storage analytics team at Sandia has done some work looking at additional value streams/services in addition to those currently in QuESt Valuation. However, these models have not been implemented into the software.
2. Currently, there are no plans to add these features but we like to gather feedback from users to prioritize which features are most requested. We anticipate that our next major release will include tools for integrated resource planning and meeting renewable portfolio standard goals using PV, wind, and energy storage. After that, the team will evaluate what the next features to implement should be.
3. The results figures from the wizard tools are designed to succinctly summarize results at a macroscopic level. However, decision variables and derived quantities (e.g., revenue) are available at the hourly resolution through the results viewer tool and/or exporting the results.
4. When installing from the source code, the key points are to have a Python environment with the necessary Python packages as described in the setup file. When it comes to additional requirements, specifically the optimization solvers, these can be obtained in any manner so long as they are installed such that Python can recognize them. For example, the solvers GLPK and CBC can be installed through the conda package manager on certain operating systems. The installation instructions are written for the most straightforward way but there may be alternative ways to accomplish the same goals.

Best,
Ricky

[UGuntupalli]

@rconcep,
Thank you. I shall be happy to provide more feedback as I dig through the tool. I tried to compile an environment.yml file for Anaconda users to make the setup of environment smoother (I am uploading it as .txt). Feel free to look through it or enhance it based on your knowledge and kindly include it in the package if it helps.
environment.txt