Strategic Utilization of Cellular Operator Energy Storages for Smart Grid Frequency Regulation

TL;DR

This paper explores how cellular operator energy storages can be used for smart grid frequency regulation, balancing communication reliability and energy market participation to enhance grid stability and economic benefits.

Contribution

It introduces a joint resource allocation framework that enables cellular base station storages to support grid services while maintaining URLLC requirements, optimizing revenue and battery lifespan.

Findings

Increasing power vacancy compensation from 31% to 46% by adjusting reliability levels.

A network of 1500 BSs can provide up to 13.5 MW for frequency regulation.

The approach exceeds wind turbine capacity for certain power vacancy scenarios.

Abstract

The innovative use of cellular operator energy storage enhances smart grid resilience and efficiency. Traditionally used to ensure uninterrupted operation of cellular base stations (BSs) during grid outages, these storages can now dynamically participate in the energy flexibility market. This dual utilization enhances the economic viability of BS storage systems and supports sustainable energy management. In this paper, we explore the potential of BS storages for supporting grid ancillary services by allocating a portion of their capacity while ensuring Ultra Reliable Low Latency (URLLC) requirements, such as meeting delay and reliability requirements. This includes feeding BS stored energy back into the grid during high-demand periods or powering BSs to regulate grid frequency. We investigate the impacts of URLLC requirements on grid frequency regulation, formulating a joint resource allocation problem. This problem maximizes total revenues of cellular networks, considering both the total sum rate in the communication network and BS storages participation in frequency regulation, while considering battery aging and cycling constraints. Simulation results show that a network with 1500 BSs can increase power vacancy compensation from 31% to 46% by reducing reliability from 10^(-8) to 10^(-3). For a power vacancy of -30 MW, this varies from 9.3 MW to 13.5 MW, exceeding a wind turbines capacity.