Allocation method of coupled PV-energy storage-charging station in hybrid AC/DC distribution networks balanced with economics and resilience

The hybrid AC/DC distribution network has become a research hotspot because of the wide access to multiple sources and loads. Meanwhile, extreme disasters in the planning period cause huge losses to the hybrid AC/DC distribution networks. A coupled PV-energy storage-charging station (PV-ES-CS) is an efficient use form of local DC energy sources that can provide significant power restoration during recovery periods. However, over investment will happen if too many PV-ES-CSs are installed. Therefore, it is important to determine the optimal numbers and locations of PV-ES-CS in hybrid AC/DC distribution networks balanced with economics and resilience. Firstly, the advantages of PV-ES-CS in normal operation and extreme disasters are analysed and the payment function is quantified accurately. Secondly, a bi-level optimal allocation model of PV-ES-CS in hybrid AC/DC distribution networks is established. In this model, the payment function using Nash equilibrium to balance economics and resilience is addressed at the upper-level, and the typical scenarios are simulated, and the optimal results are obtained using the genetic algorithm in lower level. Finally, a series of examples are analysed, which demonstrate the necessity of balancing economics and resilience, and advantages of DC lines in network restoration after disasters. An optimal planning strategy for PV-energy storage-charging station (PV-ES-CS) in hybrid AC/DC distribution networks considering normal operation conditions and resilience under extreme events is proposed. The bi-level planning model based on balanced variables accurately depicts the payment function under normal operation and extreme disasters, and the optimal configuration scheme of PV-ES-CS in a hybrid AC/DC distribution network is calculated by the genetic algorithm.image

Automated summary

The study analyzes the advantages of PV-ES-CS in hybrid AC/DC distribution networks, establishes a bi-level optimal allocation model, and uses Nash equilibrium to balance economics and resilience. By simulating typical scenarios and using genetic algorithms, optimal results for PV-ES-CS configuration in the network are obtained.