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Best Practices and Takeaways

Regardless of the specific type of distributed PV (DPV) analysis, there are several overarching best practices and takeaways.
  • Your answer is only good as your worst data source or assumption. Results depend on the quality of your input data and assumptions. 
  • Don’t let perfect get in the way of good. DPV cannot perfectly capture reality in all its complexity. And it is impossible to capture all costs and benefits.
  • Analysis questions answer the exact question you are asking. Questions are, in reality, influenced by the data, methods, tools, and approaches that one employs. Results should be presented in a qualified manner based on the limitations of each component of the analysis (e.g., data, assumptions, and methods). 
  • You cannot model every DPV customer when examining system-level impacts. It is thus a practical necessity to formulate credible DPV customers that are statistically representative of the market segment being considered. Key dimensions to consider include customer rate class, consumption patterns, system sizes, and customer location.
  • Good customer demand data are hard come by. Start collecting them now. High fidelity (e.g., hourly) demand data for individual customers are essential to most DPV analyses. Global experience strongly suggests that such data are difficult to acquire, often because of customer privacy concerns and/or a lack of data collection infrastructure. Even more difficult is considering a range of customers’ demand data to understand what a statistically representative customer’s demand patterns look like, which is an important analysis component when examining broader market impacts of certain policies and regulations. Utilities and regulators can begin systematically collecting and analyzing individual customer load data in order to inform DPV analyses (as well as various other distribution edge questions). 
  • Sensitivity analysis can be used to bound concerns and tell a compelling story. Sensitivity analysis—an approach where multiple scenarios are analyzed and compared—can help bound expectations of DPV impacts, assuage stakeholder concerns, and tell a persuasive story to utilities and policymakers. One common approach is to design a “lower-bound” scenario or “worst-case” scenario to constrain concerns (e.g., “Even under highly conservative assumptions, we expect one gigawatt of DPV deployment will result in an average retail rate increase of no more than 0.4%.”) 
  • Craft an analysis question hand-in-hand with your audience. To ensure high levels of impact with the chosen audience, solicit feedback from your intended audience before (and during) analysis activities. And, doing so will also foster stakeholder buy-in and ensure the question being asked will provide both a useful and meaningful answer.
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