Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training methods, which rely on role-playing by experienced personnel, are labor-intensive, time-consuming, and often neglect the specific needs of underserved communities. To address these challenges, we introduce Sim911 (More details: meiyima.github.io/angie.html), the first training simulation for 9-1-1 dispatchers powered by Large Language Models (LLMs). Sim911 enhances training through three key technical innovations: (1) knowledge construction, which utilizes archived 9-1-1 call data to generate simulations that closely mirror real-world scenarios; (2) context-aware controlled generation, which employs dynamic prompts and vector bases to ensure that LLM behavior aligns with training objectives; and (3) validation with looped correction, which filters out low-quality responses and refines the system performance. Beyond its technical advancements, Sim911 delivers significant social impacts. Successfully deployed in the Metro Nashville Department of Emergency Communications (MNDEC), Sim911 has been integrated into multiple training sessions, saving time for dispatchers. By supporting a diverse range of incident types and caller tags, Sim911 provides more realistic and inclusive training experiences. In a conducted user study, 90.00% of participants found Sim911 to be as effective or even superior to traditional human-led training, making it a valuable tool for emergency communications centers nationwide, particularly those facing staffing challenges.