Energy Storage System Sizing for Peak Hour Utility Applications

TL;DR

This paper introduces a stochastic analytical framework for optimally sizing energy storage systems in smart grids, aiming to reduce costs and emissions by balancing demand with grid and storage capacity.

Contribution

It presents a novel stochastic modeling approach and analytical solution for ESS sizing, improving cost efficiency and utilization in peak hour utility applications.

Findings

Significant savings in ESS size are achievable.

The framework effectively balances demand and storage capacity.

Analytical solutions provide practical sizing guidelines.

Abstract

In future smart grids, energy storage systems (ESSs) are expected to play a key role in reducing peak hour electricity generation cost and the associated level of carbon emissions. Considering their high acquisition, operation, and maintenance costs, ESSs are likely to serve a large number of users. Hence, optimal sizing of energy ESSs plays a critical role as over-provisioning ESS size leads to under-utilizing costly assets and under-provisioning it taxes operation lifetime. This paper proposes a stochastic framework for analyzing the optimal size of energy storage systems. In this framework the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceed grid capacity. In this framework an analytical method is developed, which provides tractable solution to the ESS sizing problem of interest. The results indicate that significant savings in terms of ESS size can be achieved.