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How about HJ s hybrid energy for communication base stations

How about HJ s hybrid energy for communication base stations

This solution utilizes HuiJue’s self-developed intelligent hybrid energy control system, integrating photovoltaic power generation, lithium-ion battery storage, and emergency diesel generator backup power, helping operators transition from “heavy oil dependency” to “solar-storage-based power supply,” achieving multiple objectives of cost reduction, efficiency improvement, and green upgrading. [pdf]

Patented design of wind-solar hybrid energy storage for communication base stations

Patented design of wind-solar hybrid energy storage for communication base stations

The invention relates to a wind and solar hybrid generation system for a communication base station based on dual direct-current bus control, comprising photovoltaic arrays, a wind-power generator, storage battery sets, unloading devices, an intelligent controller, a charging side direct-current bus, a discharging side direct-current bus, a storage battery set switching circuit, a photovoltaic array switching circuit, an unloading device switching circuit, an overload protecting circuit, a load distributing circuit, an AC / DC converter and a DC / AC inverter. [pdf]

Wind power energy storage integrated machine

Wind power energy storage integrated machine

This innovative product deeply integrates intelligent wind turbines, efficient energy storage systems, and the "Yuanjing Tianshu" energy model, marking the official entry of the new energy field into a new stage of "physical artificial intelligence", providing the industry with an integrated solution that is grid friendly, smart trading, and extremely secure. [pdf]

Composite energy storage interconnected microgrid optimization

Composite energy storage interconnected microgrid optimization

Abstract: In order to optimise the coordinated control of micro-grid complex energy storage including photovoltaic and wind power, improve the absorption ability of distributed energy generation and reduce the cost, this paper proposes a Double Deep Q-Network reinforcement learning algorithm to train agents to interact with the microgrid environment and learn the optimal scheduling control mechanism. [pdf]

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