Stochastic expansion planning of battery energy storage for the interconnected distribution and data networks

This paper presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution networks with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The proposed stochastic framework captures the interactions between the distribution network and data center operators considering limited shared information among these entities. Benders decomposition is used to capture the interactions between these autonomous operators in the electricity and data networks. The uncertainties associated with the electric demand, data center workload, solar PV generation, and the availability of the distribution branches are captured using Monte Carlo simulation. The representative scenarios are selected using a dissimilarity-based sparse subset selection algorithm. To evaluate the effectiveness of the proposed framework, numerical results are presented for a modified IEEE-34 bus distribution network with data centers and PV generation units.

Automated summary

This paper introduces a stochastic expansion planning framework to determine installation time, location, and capacity of battery energy storage systems in distribution networks with significant photovoltaic and data center penetration. The framework aims to minimize costs while ensuring energy supply security and network reliability.