Energy storage and charging project operation plan

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.

What is the scheduling strategy of photovoltaic charging station?

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 .

How to optimize the energy storage system?

The uncertainty of photovoltaic power generation output, electric vehicle charging load, and electricity price are considered to construct the IRL model for the optimal operation of the energy storage system. A double-delay deep deterministic policy gradient algorithm are utilized to solve the system optimization operation problems.

What is the income of photovoltaic-storage charging station?

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.

What is a photovoltaic-storage charging station?

The photovoltaic-storage charging station consists of photovoltaic power generation, energy storage and electric vehicle charging piles, and the operation mode of which is shown in Fig. 1. The energy of the system is provided by photovoltaic power generation devices to meet the charging needs of electric vehicles.

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.