When an EV requests power from a battery-buffered direct current fast charging (DCFC) station, the battery energy storage system can discharge stored energy rapidly, providing EV charging at a rate far greater than the rate at which it draws energy from the power grid.
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What is a photovoltaic charging station?
Photovoltaic charging stations are usually equipped with energy storage equipment to realize energy storage and regulation, improve photovoltaic consumption rate, and obtain economic profits through “low storage and high power generation” .
What is the optimal operation method for photovoltaic-storage charging station?
Therefore, an optimal operation method for the entire life cycle of the energy storage system of the photovoltaic-storage charging station based on intelligent reinforcement learning is proposed. Firstly, the energy storage operation efficiency model and the capacity attenuation model are finely modeled.
There have been some research results in the scheduling strategy of the energy storage system of the photovoltaic charging station. It copes with the uncertainty of electric vehicle charging load by optimizing the active and reactive power of energy storage .
Moreover, a coupled PV-energy storage-charging station (PV-ES-CS) is a key development target for energy in the future that can effectively combine the advantages of photovoltaic, energy storage and electric vehicle charging piles, and make full use of them .
Income of photovoltaic-storage charging station is up to 1759045.80 RMB in cycle of energy storage. Optimizing the energy storage charging and discharging strategy is conducive to improving the economy of the integrated operation of photovoltaic-storage charging.
How is the energy storage charging and discharging strategy optimized?
The model is trained by the actual historical data, and the energy storage charging and discharging strategy is optimized in real time based on the current period status. Finally, the proposed method and model are tested, and the proposed method is compared with the traditional model-driven method.