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 .
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” .
The energy management strategy for residential PV-BES systems is also developed considering the matching of thermostatically controlled demand and battery charging. The case study shows that the system energy consumption is reduced by 30% while maintaining the power supply quality and extending the battery lifecycle .
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.
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.
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Optimizing the energy storage charging and discharging strategy is conducive to improving the economy of the integrated operation of photovoltaic-storage charging. The
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