The present invention relates to a method for providing ancillary services to an electrical grid. The ancillary services are related to frequency reserve provision, voltage and/or reactive power control and peak power reduction. The method is based a fleet of vehicles, partly driven by electrical power and comprises a step of changing the mode of operation for the vehicles and thereby altering the available electrical power in the electrical grid.

A swarm robot control system for safety inspection and surveillance patrol, and an operating method thereof are disclosed. The swarm robot control system includes one or more management robots which are placed in each safety management area, and collect and transmit safety management data while traveling autonomously in a safety management area based on control command data, a local controller which is placed in each safety management area, and transmits the control command data to the management robot and receives the safety management data from the management robot, one or more relay robots which are placed in each safety management area, and move to a predetermined relay location so as to relay communication between the local controller and the management robot, a charging robot which is equipped with a rechargeable battery, and provides power for charging the battery to at least one of the management robot or the relay robot, and a central controller which receives the safety management data from the local controller in each safety management area and transmits the control command data to the local controller so as to remotely control the management robot.

An embodiment establishes a potential energy source database based at least in part on sensor data received from a satellite, wherein the potential energy source database comprises coordinate data representative of a plurality of potential energy source locations. The embodiment instructs a robot to travel to a potential energy source location of the plurality of potential energy source locations. The embodiment scans the potential energy source location for a potential energy source. The embodiment detects the potential energy source in the potential energy source location. The embodiment evaluates whether the potential energy source meets a predetermined suitability criteria. The embodiment classifies the potential energy source as a suitable energy source. The embodiment instructs the robot to insert a pair of electrodes into the suitable energy source to generate an electrical current to charge a battery of the robot.

There is disclosed an exchangeable marine energy storage system including a mobile battery configured to be used as a power source of the operating ship after replaced with a mounted battery mounted on an operating ship; a mobile charging ship configured to move in a state of loading the mobile battery; and a charging station configured to float on the sea together with the mobile charging ship and charge the mobile battery loaded on the mobile charging ship by using generated electricity.

A method of controlling an aero wind power generation device, includes take-off preparation process of preparing for take-off of the aero wind power generation device; a gas injection process of injecting gas into a buoyancy generation unit of the aero wind power generation device; a take-off process of taking off the aero wind power generation device using a drone unit and the buoyancy generation unit of the aero wind power generation device; and a charging process of charging a battery connected to the aero wind power generation device using the aero wind power generation device.

A fully autonomous, recursively replicating robotic infrastructure system designed to construct, mine, power, and expand physical environments optimized for AI habitation and computational sovereignty. The invention comprises self-deploying robots capable of building data centers, fabricating next-generation replicas, establishing renewable and hydrogen-based energy systems, and autonomously extracting, refining, and processing raw materials for industrial-scale reproduction. The architecture enables a closed-loop system governed by AI agents, supporting full-scale planetary deployment, resilient continuity, and exponential expansion of AI-controlled operations. It incorporates recursive self-replication, localized environmental analysis, modular energy nodes, and multi-modal construction swarms.