Difference between revisions of "Energy Storage Valuation"

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DER-VET is an open-source, optimization-based planning tool to aid in the design of microgrids and distributed energy resources (DER) deployments to maximize benefit to individual customers, ratepayers, and to society. DER-VET provides a platform to model the operation and subsequent value of a set of DERs (“DER mix”), potentially configured in a microgrid, collectively providing a set of stacked services. DER-VET uses load and other site-specific data to optionally optimize the size of the DERs concurrently with its dispatch optimization. The technologies modeled in DER-VET include various types of energy storage, intermittent renewable generation, fueled generation, controllable loads/electric vehicles, and hybrid resources like combined heat and power (CHP). These energy resources can be used in any combination to improve grid reliability, improve customer resilience by providing backup to local critical loads, decrease the electricity bill incurred by the site, participate in wholesale energy or ancillary services markets, provide demand response or resource adequacy, or some allowable combination of these. DER-VET could be connected with the grid simulation tools (for example OpenDSS™, DRIVE, etc.) to allow for easy transitions between the models.
This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.
Various practical microgrid case studies are tested using DER-VET and the study results are documented in this report. Microgrid applications are broadly grouped under three usecases and they are briefly discussed. This first part of the report series will focus on the first two usecases and sub-scenarios. DER-VET results and inferences are discussed in detail for each of the two usecases and identified sub-scenarios for each usecase. To make the study realistic, actual site data and utility service territory tariffs and market structure are used in this study.

Revision as of 14:35, 21 January 2022

This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.

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