> For the complete documentation index, see [llms.txt](https://docs.net2grid.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.net2grid.com/net2grid-energy-ai-edge-on-ami2/applications/ev/use-cases.md). # Use Cases ### System planning Utilities often face significant challenges due to limited visibility into behind-the-meter distributed energy resources (DERs), making it difficult to plan and manage their distribution networks accurately. This lack of granular, real-time data hampers efforts to forecast load growth, identify stressed infrastructure, and effectively prioritize upgrades. NET2GRID EnergyAI® Edge addresses this gap by delivering real-time, asset-level insights directly from smart meters. By detecting EV charging behavior at the household level, the solution empowers utilities to update feeder-level EV load models with actual usage data, identify low-voltage hotspots and overburdened transformers or feeders, and make informed, data-driven decisions. These capabilities support more efficient infrastructure planning, helping utilities prioritize investments and maintain grid reliability in the face of accelerating DER adoption. The following figures illustrate example insights derived from the edge service, which determines the precise location of EVs and their consumption patterns. ### Targeted marketing for demand response enrollment EVs are pivotal assets for effective demand response (DR) and virtual power plant (VPP) programs, offering significant flexibility to support grid stability. However, the success of such programs depends on identifying EV users who are both willing to participate and represent ideal candidates for these programs. For example, an EV user who charges their vehicle after work (i.e., typical peak hours) is a more suitable candidate for DR participation than someone who charges overnight. NET2GRID EnergyAI® Edge equips utilities with the ability to detect EV users and gain deep insights into their charging habits. By analyzing when, how often, and how much users charge, utilities can distinguish between different charging profiles, such as those who charge immediately after work versus those who charge overnight. This enables the precise targeting of the most flexible and willing participants for DR/VPP initiatives, supporting the design of tailored time-of-use (ToU) tariffs and engagement strategies that drive higher participation and greater grid impact. Below are examples of two distinct EV charging patterns: one user charges after midnight, while the other charges during work hours. These insights are invaluable for tailoring programs that align with users' consumption behaviors.
### Optimized real-time control NET2GRID EnergyAI® Edge provides near real-time insights into EV charging, empowering utilities and aggregators to manage behind-the-meter DERs with confidence. By delivering timely and accurate visibility, the solution helps prevent critical grid issues such as transformer overloads and unintended activation of protection devices. Without detailed visibility into EV charging behavior, operators often face two opposing risks. On one hand, they may act too cautiously, limiting the VPP's output or consumption to avoid transformer overloads. This conservative approach leaves valuable grid capacity underutilized. On the other hand, a lack of awareness about the additional load from EVs can lead to overly aggressive decisions, resulting in load spikes that exceed safe operating limits and jeopardize grid stability. By integrating EV charging data into real-time decision-making, NET2GRID EnergyAI® Edge eliminates this guesswork. It equips operators with the precise information needed to optimize VPP performance, maximizing energy utilization while safeguarding grid infrastructure. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.net2grid.com/net2grid-energy-ai-edge-on-ami2/applications/ev/use-cases.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.