Future of Energy

What is Hosting Capacity Analysis and why understanding it well is important

This post discusses the benefits of hosting capacity analysis for utilities, regulators and stakeholders, exploring use cases such as accelerating PV deployment, enhancing DG/DER application processes, and planning for future infrastructure improvements. Hosting capacity can be used to identify areas of cost-effectiveness and to create reliable technical screens while also reducing hindrances to integrating DERs.

Sayonsom Chanda, Ph.D.
January 27, 2024

A detailed hosting capacity analysis can provide utilities, policymakers, and solar developers with the necessary insight into how introducing more distributed photovoltaic (DPV) installations will affect the electrical distribution network.

The advanced hosting capacity analysis can determine when other DPV systems will require upgrades or modifications to the electrical distribution system. It also determines the cost of various options for expanding our existing hosting capabilities.  These maps are also often called "solar accommodation", "solar power suitability", "Interconnection Analysis" maps, etc.. depending on the Utility and state.

Equipping stakeholders with a deeper insight into the distribution system costs that come with integrating new DPV systems at different penetration levels is essential, and making them aware of available hosting capacity will make it possible to improve their analytical processes being rate-case planning that's necessary for system upgrades.

But, first lets see what is Hosting Capacity


You must have heard of it - before any upgrades or alterations are made to the distribution system, "hosting capacity" must be considered.


In simple terms, hosting capacity refers to the amount of DPV added without compromising safety and reliability measures. Hosting capacity does not indicate an absolute upper limit regarding the amount of DPV that can be incorporated into the distribution system. By implementing upgrades, the system's hosting capacity is amplified. The analysis of these sequential increases in hosting capacity and related costs is at the core of a modern approach.

Many aspects influence the hosting capacity, such as:

  • The DPV system's effectiveness is primarily determined by several factors, such as if the advanced inverter settings are used, its size, and placement on the circuit.
  • The precise whereabouts and varying performance of distributed energy resources, including distributed storage systems, along the circuit.
  • As investments are made by both utilities and DPV owners or developers, the current equipment mounted onto a circuit is continually adapting.
  • The utility's methods for distribution planning, particularly how they decide when it is necessary to perform upgrades or other forms of mitigation.

Three Approaches to Hosting Capacity Analysis

Snapshot Hosting Capacity

Snapshot (or static) hosting capacity is the classic notion of hosting that:

This means that it only looks at a few moments in time, using settings and behaviors that do not change, i.e., it is static.

This model cannot factor in load and distributed energy resource changes over time or accurately capture grid device activity.

Reflect upon circumstances that are unlikely to take place (e.g., all DPV systems achieving their highest possible output in tandem with the least amount of load).

An abundance of techniques exists to determine static hosting capacity.


You can find a list of hosting capacity maps for utilities in 24 states (39 utilitie) online with a quick google search. Here's a list of few hosting capacity maps. But to save you the trouble of searching through it, here's the full list of what's available in 2024:

Arizona:

California:

  • Los Angeles Department of Water and Power Load Map
  • Pacific Gas & Electric DG and Load (account required)
  • San Diego Gas & Electric DER Map (account required)
  • Southern California Edison DG and Load
  • California Energy Commission, EVSE Deployment and Grid Evaluation (EDGE) Tool EV

Colorado: Xcel Energy. Public Service Company of Colorado DG

Connecticut:

  • Eversource EV
  • United Illuminating (Avangrid) EV

Delaware:

  • Delmarva Power (Pepco Holdings, Exelon) EV

District of Columbia:

  • Potomac Electric Power Co. (Pepco Holdings, Exelon) EV

Hawaii:

  • Hawaiian Electric DG

Idaho:

Illinois:

  • Commonwealth Edison (COMED), Exelon EV
  • Ameren EV

Maine:

Maryland: Pepco Holdings, Exelon EV

Massachusetts:

Michigan:

  • Consumers Energy DG
  • DTE DER

Minnesota:

Nevada:

New Hampshire:

  • Eversource DG
  • New Hampshire Electric Co-op DG

New Jersey:

  • Orange & Rockland Electric Company DER, Storage and EV
  • Jersey Central Power & Light (JCP&L) (FirstEnergy) EV
  • Public Service Electric & Gas Company (PSE&G) EV
  • Atlantic City Electric (Pepco Holdings, Exelon) EV

New York:

Oregon:

  • Pacific Power DG
  • Portland General Electric DG

Rhode Island: Rhode Island Energy DER and EV

Utah:

Vermont:

  • Burlington Electric DG
  • Green Mountain Power DG

Virginia: Dominion Energy EV

Washington:

  • Puget Sound Energy DER
  • Avista

Dynamic Hosting Capacity


Hosting capacity is the maximum amount of energy from distributed energy resources or photovoltaic (PV) systems that can be fed into a utility's electrical distribution system without compromising safety or reliability. There are three approaches to analyzing hosting capacity: snapshot, dynamic, and advanced. Snapshot hosting capacity only looks at a specific moment in timeThe other commonly used concept for hosting capacity analysis is based on a novel concept of dynamic hosting capacity, which utilizes quasi-static time-series simulation to:

Examine the operation of DPV, loads, and grid devices throughout time.

Consider that short-term and occasional over-voltages or thermal overloads are permissible for specific periods during the year.

The power flows for dynamic hosting capacity are not dictated by worst-case scenarios. Instead, computationally accurate screens must consider the unpredictable nature of time-series input variables, such as hourly PV productions and building loads. As a result, this pioneering concept of dynamic hosting capacity is constantly being perfected, as distributed energy resources increase their footprint across the utility's jurisdiction.

Depending on the configuration of both utility-owned grid devices and individual DPVs, two different types of dynamic hosting capacity must be employed.

Uncoordinated dynamic hosting capacity

When local and self-governing control operations are utilized for DERs (Distributed Energy Resources) and grid devices without communication between them.

Coordinated dynamic hosting capacity

When a communication-focused, standard control method is applied to modify the productivity of PV systems and  other distributed energy resources. Coordination can be facilitated at various levels, for example, by using distributed controls within a segment of the feeder or across the entire distribution system. Additionally, coordination could also take place on an individual substation level. This could also mean optimizing the output or simply adjusting it to adhere to a pre-defined set of rules and principles regarding accessibility.

Various control architectures can be used to coordinate the case. This organized and interactive system of coordinated connectivity has been colloquially referred to as "flexible interconnection." DPV integration strategies executed collaboratively can also be referred to as Active Network Management or a subset of distributed energy management systems capabilities.

Where's the value in hosting capacity analysis?

Countless utility providers across the nation are actively examining their systems' hosting abilities. An increasing number of people are starting to consider it. What advantages can utilities, stakeholders, and customers enjoy from this? It's a valid query, given that it can be challenging to identify what hosting capacity enables and for which parties.

Teams and individuals should set their sights on the use case, making it the primary focus.

Grasping the value of hosting capacity necessitates an in-depth examination of its objectives and value proposition to stakeholders. No methodology or approach can cover every value proposition or stakeholder. With multiple methods to evaluate this type of analysis, each having its respective strengths and weaknesses, it is important not to begin with the methodology or data. To get the optimal value equation, starting points should be something other than these two factors; that would be akin to putting the cart before the horse.

To understand precisely which methodology, tools, and data are required for a successful hosting capacity analysis, it is essential to consider the value this process should bring and who will benefit from its outcomes. Only when you have determined these elements can you determine the intended output and methodology necessary to produce the desired result.

The creation of this use case is unique to the context and heavily shaped by elements such as power grid design, policy ideas, rates of renewable energy growth, regulatory landscape, utility planning criteria, and market structure. To help you better understand their impact on implementation, here is a sample of use cases from three commonly discussed applications:

Enabling DER Development: Hosting capacity is not merely a utility tool but an external-facing resource for distributed energy resource developers (DERs). With hosting capacity, DER developers can pinpoint the areas within a utility's service area, offering lower interconnection costs to maximize their investments. To maximize the value of their systems, utilities have released heat maps showing hosting capacity values. For example, New York State recently released a portal to demonstrate this information. A comprehensive analysis of the entire utility service area should be conducted to inform DER development accurately. Although this guidance tool does not attempt to quantify interconnection costs precisely, it can still take into account intelligent approximations. By implementing streamlined methods, developers can decrease the calculation complexity of ascertaining hosting capacity values throughout the system. This strategy also allows for regular refreshing of the analysis to give developers a more up-to-date perspective on where within their systems they can add additional DERs.

Enhancing DG Application Processes: The interconnection processes of DG, such as Rule 21 in California and the Standardized Interconnection Requirements in New York, can be challenging to navigate. Their technical screens exist; they identify applications requiring more thorough research. Nevertheless, these screens have been using inaccurate assumptions which fail to reflect the actual constraints of the system. Regarding voltage, thermal, or protection criteria violations caused by applications, hosting capacity analysis can be used to predict when such issues may occur. Unlike the DER development use case scenario, which is designed for developers and requires an online mapping interface as part of its implementation process, utilizing hosting capacity analysis does not necessitate this same feature to fulfill its purpose.

During a technical assessment, the hosting capacity analysis offers important utility insights into how in-depth and precise an analysis must be to handle a new DG request. In addition, the preferred strategy needs to account for the locational and temporal impacts of the DG on the distribution network — revealing that further examination is essential.

Instead of carrying out a full-fledged study with every DER location and size combination, the analysis can be simplified. Nevertheless, benchmarking against an in-depth evaluation will become more critical for this particular project. Additionally, technical accuracy is paramount as California evaluates incorporating hosting capacity into Rule 21 as part of the recently introduced Order Instituting Rulemaking. Therefore, the interconnection screening process must be conducted meticulously to ensure that correct and reliable data can adequately serve this particular use case.

Advancing Distribution Planning Analytics: Hosting capacity applications in the realm of distribution system planning can help utilities to predict when their hosting capacity could become hindered. Areas that have already encountered challenges due to high distributed generation penetration, like California and Hawaii, are leading this exploration. In California, utilities are examining the influence of power grid investments on hosting capacity. For instance, Southern California Edison clarifies such considerations in its 4kV Programs. Meanwhile, Hawaiian planners recognize hosting capacity "to better forecast and plan for the integration of DG-PV" by pinpointing circuits that predict risk limitations being exceeded and measuring expenses to alleviate any predicted constraints.

Utilities can leverage innovative solutions, such as flexible interconnection, to boost their hosting capacity beyond what is usually required. Moreover, implementing hosting capacity in planning will help ensure accurate long-term load and DER forecasting since these forecasts' outputs will serve as an entry requirement for this particular use case. Here, long-term forecasts must consider temporal and geospatial granularity to evaluate hosting capacity under future loads. This calls for a detailed load and distributed energy resources (DER) forecast to understand how system loading curves will evolve. The result could shape how utilities ascertain their needs if they have access to suitable cost recovery mechanisms.

Who uses Hosting Capacity Analysis and How

1. State

Enabling DER Development

Objective

Accelerate DER deployment

Means

Invest development resources into more cost-effective areas.

Challenges

Regularly performing analyses on the entire system and creating data visualizations to make it simple for external individuals.

2. State

Enhancing DG Application Processes

Objective

Accelerate the comprehensive DG application process by expediting it.

Means

Hosting capacity is a far more reliable tool in technical screens of interconnection than traditional and often imprecise rules of thumb.

Challenges

It must be particular, ensuring the model is valid and comparing results to other detailed studies.


3. State

Advancing Distribution Planning Analytics

Objective

Minimize any hindrances to integrating Distributed Energy Resources (DER) in the future.

Means

Proactively anticipating infrastructure improvements to optimize hosting capacity.

Challenges

Increasing data input needs.


We are on a mission to make sure different utility professionals (at any management level), regulators, and stakeholders understand the process. This way, everyone knows what to expect, agrees on what investments are needed, and can use the developed analyses correctly. Developing circuit models, gathering data, and ensuring the highest quality outcomes demands significant resources; however, by investing in value propositions upfront, we can ensure that our efforts are focused on achieving optimal results. Establishing a detailed use case is essential to achieving the perfect balance of hosting capacity, allowing utilities, developers, and policymakers alike to accomplish their desired goals while extracting optimal value for customers.

Not to mention, the three mentioned use cases could be combined for a larger purpose. Furthermore, there are numerous other scenarios in which hosting capacity comes into play. This example doesn't consider the "click and claim" interconnection scenarios or extra interior utility cases. However, it does highlight how imperative it is to comprehend who might capitalize from hosting capacity and where. If done correctly, your analysis will be tailored precisely toward achieving its desired outcome - so you can get the most out of what you have!

We want to know your methods to comprehend better and assess hosting capacity. Have we overlooked any other use cases, concepts, or difficulties? Reach out via Twitter, Email, or LinkedIn, and let us know!


ABOUT THE AUTHOR
Sayonsom Chanda, Ph.D.

Sayonsom Chanda is a senior scientist at National Renewable Energy Laboratory (NREL) in Boulder, Colorado. He works in the intersection of advanced computing technologies and the electric power grid. For last seven years, he has worked extensively in implementation of AI technology for electric utilities in North America. Recently, his work on interfacing quantum computers and power grid simulators for developing industrial applications of quantum computing for solving the complex, challenges of our times - including energy insecurity and climate change. Prior to joining NREL, Dr. Chanda was a Senior Data Scientist at National Grid in New York and an electrical engineer at Idaho National Laboratory. He is also the founder of two tech start-up companies where he helped them raise venture capital and develop commercial solutions for the utility industry. Over a dozen prominent conferences in the United States and abroad have invited him to speak on AI applications in the Energy industry, including a TEDX talk in 2021. He holds a Ph.D. in Electrical Engineering from Washington State University, has published more than 18 articles in journals with a high impact factor and holds three patents in cloud computing for power systems. He is also the author of a book “Resiliency of Electricity Distribution Systems," published by Wiley in the United Kingdom.

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