Where's the value in hosting capacity analysis?

Many utilities across the country are actively analyzing the hosting capacity of their systems.  Many more are just beginning to think about it. But what value does this deliver for utilities, stakeholders and customers? It’s a good question, because it isn’t always clear what outcomes hosting capacity enables and for whom.

These teams and individuals should start with a focus on one thing: use case.

What is Hosting Capacity Analysis?

Hosting capacity analysis provides an estimate of the amount of distributed energy resources (DER) that can be accommodated without significant upgrades. Understanding the real value of hosting capacity requires a closer look at the intended objectives and the value proposition to stakeholders. In particular, it’s important to note that we aren’t talking about one methodology or one approach intended for a single value proposition or stakeholder, because there are many ways to approach this type of analysis, each with its own benefits and drawbacks. But getting the value equation right means that the methodology and the data should not be the starting point of the discussion. That’s putting the cart before the horse. 

Rather, the choice of methodology and the associated data and tool requirements should be the end product of a careful consideration of what value the hosting capacity analysis is intended to enable and who those use cases are intended to serve. Only by understanding the intended output of the methodology and the value proposition can one arrive at the right methodology, tools and data needed.

The development of that use case will be context-specific and depend on factors such as utility structure, policy objectives, DER growth rates, regulatory environment, utility planning criteria, and market structure. To help illustrate how these play out, let’s look at a sample of use cases drawn from three oft-discussed applications:

Enabling DER Development: The most widespread use of hosting capacity is not as a tool for utilities, but as an external-facing tool for DER developers. In this case, hosting capacity enables DER developers to identify locations in a utility’s service territory where interconnection costs are likely to be lower and to direct their investments. To enable this value, utilities have published heat maps to provide information about the range of hosting capacity values across the system. The utilities in New York State, for instance, recently published portals along these lines. To inform DER development, the analysis should include coverage across the full utility service territory, but since it is meant to be a guidance tool rather than attempting to quantify interconnection costs, the analysis can rely on careful approximations. Developers can use more streamlined methods to ease the computation complexity of calculating hosting capacity values across the full system. This approach also facilitates refreshing the analysis on a regular basis to give developers a more current view of where the system can accommodate additional DER.

Enhancing DG Application Processes: DG interconnection processes like Rule 21 in California or the Standardized Interconnection Requirements in New York often include a number of technical screens that help utilities identify which applications need more detailed study. Historically, these technical screens have used assumptions that don’t adequately reflect the constraints on the system. In these cases, we can use hosting capacity analysis to determine when an application is likely to cause a violation related to voltage, thermal, or protection criteria. Unlike the DER development use case, this is not intended to be a proactive guide for developers, so implementing hosting capacity analysis for this purpose alone would not necessarily require an online mapping interface.

In the context of a technical screen, the hosting capacity analysis now provides utility insights as to the needed depth and analytical rigor necessary to process a new DG application. As such, the chosen methodology should reflect the locational and temporal impacts of the DG to the distribution system — and expose the need for a more detailed study. 

This doesn’t necessarily mean that the analysis needs to be a full iterative power flow analysis of every permutation of DER location and size. It does mean, though, that the importance of benchmarking against the results of a detailed study will be much more important for this application. As California begins to look at how Rule 21 can incorporate hosting capacity as part of the recent Order Instituting Rulemaking, this will be an important consideration. The incorporation of hosting capacity into the interconnection screening process requires a higher level of technical rigor to ensure the analysis provides technically sound information that can appropriately serve this type of use case.

Advancing Distribution Planning Analytics: The application of hosting capacity in the context of distribution system planning could enable utilities to identify when hosting capacity will become constrained. This has been most directly explored in the contexts of California and Hawaii where distributed generation penetration has already begun to create specific system constraints. In California, utilities are starting to look at the impact of grid investments on hosting capacity, such as the DER integration considerations that Southern California Edison enunciates in the context of its 4kV Programs. In Hawaii, planners are using hosting capacity “to more appropriately predict and plan for the integration of DG-PV” by identifying circuits where they forecast hosting capacity limits being exceeded and evaluating the costs to mitigate any anticipated constraints.

Utilities can also identify approaches, like flexible interconnection, that allow them to exceed nominal hosting capacity limits. The application of hosting capacity in the planning context creates a touchpoint with long-term load and DER forecasting as well, since the outputs from the forecast will be an input for this hosting capacity use case. Here, the temporal and geospatial granularity of long-term forecasting need to be able to evaluate hosting capacity under future loads. This requires planners to develop a long-term granular forecast for load and DER so that the evolution of system load curves can inform projected hosting capacity. The outcome could impact the way utilities ultimately identify system needs if adequate cost recovery mechanisms are in place.