Dynamic Load Management

As electric vehicle adoption surges, the power grid encounters challenges. AutoCHGR offers Dynamic Load Management solutions for homes and businesses, ensuring safe grid operation by dynamically distributing power to charging stations through software or smart OCPP gateways.


AutoCHGR offers three residential load management levels, enabling dynamic balancing of grid, solar, and battery power sources. This prevents circuit overloads, ensures smooth equipment operation, and optimizes power use for cost savings. It safeguards the grid and offers efficient EV charging.


The Dynamic Load Balancing system allocates power efficiently among EV chargers, providing priority access for regular customers and expanding infrastructure without costly hardware or grid upgrades, all while safeguarding the local electricity network.

Dynamic Load Management (DLM) FAQs

What is Dynamic Load Balancing?

Dynamic load balancing in EV charging optimizes power usage among chargers and appliances on-site, protecting the grid without expensive upgrades. It aims for efficiency and cost-effectiveness.

Why is Dynamic Load Balancing Crucial for Efficient EV Charging?

Dynamic load balancing is crucial for efficient and reliable EV charging. It optimizes resource use, enhances grid stability, speeds up charging, supports scalability, and manages costs effectively by preventing overloading and maximizing capacity.

What are the benefits of Dynamic Load Balancing?

  1. Prevent grid overloads with multiple chargers.
  2. Optimize power use efficiently.
  3. Avoid grid upgrade costs.
  4. Offer real-time energy data.
  5. Manage various charging modes (full speed, solar-assist, solar-only).
  6. Eco-friendly with the public grid, solar, and battery systems.

What is the difference between EV charging Static and Dynamic Load Balancing?

EV charging load balancing can be static or dynamic.

Static load balancing is pre-set based on factors like time or pricing, without real-time adjustments.

Dynamic load balancing adapts in real-time with complex algorithms, managing energy use and prioritizing high-demand areas.