Other Practical Considerations

Analytical Transparency—Are the underlying methods and data of the analysis available and accessible to the audience? Or, does the analysis rely on proprietary or “black box” tools? Transparency may be of more value when analyses are influencing public policy decisions. Contrarily, building new open-source analysis tools when existing proprietary tools are available may add additional time and cost to the analysis.

Feasibility of Execution—Is it possible to acquire sufficient input data (e.g., of the proper time resolution) to answer the analysis question being proposed? If not, how can the analysis question be adjusted to reflect something more feasible to answer?

Accuracy of Analysis—How strong are the underlying assumptions and methods of the analysis? What is the quality of the input data? How statistically representative are the DPV customers being modeled? Barring input data limitations, greater accuracy can often be pursued, but almost always at greater financial cost.

Acceptance of Analysis—Analyses of DPV costs and benefits can be contentious. Has the analysis process been designed in such a way that the analysis itself will be accepted by its target audience? Was the analysis question formulated in an inclusive manner? To what degree has stakeholder input been solicited and incorporated? Have the results of the analysis been appropriately caveated and qualified?

Scalability of Analysis—It is well-established that the value (and impacts) of DPV change as market penetration increases. If an analysis examines the financial impact of 100 MW of DPV to a utility, would that same analysis yield a 2x impact for 200 MW of DPV? If so, the analysis can be considered linearly “scalable,” which may help policymakers consider impacts at a greater or lesser scale.