Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach

TL;DR

This paper presents a combinatorial optimization framework for optimal placement and sizing of energy storage units in power grids to minimize frequency deviations during transient events, considering spatial effects and network topology.

Contribution

It introduces a comprehensive modeling framework and numeric approaches, including brute-force and cross-entropy methods, for optimal storage placement under frequency stability constraints.

Findings

Storage should be placed near disturbance areas and high inertia sites.

The proposed methods effectively identify optimal storage distributions.

Case study on the Israeli grid demonstrates practical applicability.

Abstract

As the share of renewable sources is increasing the need for multiple storage units appropriately sized and located is essential to achieve better inertial response. This work focuses on the question of "how to distribute constant number of storage units in the gird under transient events such that the inertial response of the maximum frequency deviation is minimized?". To answer this question, we provide a comprehensive modeling framework for energy storage units placement and size for frequency stability under spatial effects. The distributed storage units are modeled as grid supporting inverters and the total storage capacity in the grid is bounded based on the allowed steady-state frequency deviation after disturbances. The problem of finding the optimal distributions can be considered as combinatorial problem which consists of high dimensional solutions. In this light, we develop two numeric approaches based on Brute-force search and adaptation of the Cross-entropy method for finding the best distribution and examined it on a case study of the future Israeli grid. The results on the case study provide a new insight---the storage units should be placed around the area of the disturbances, including in sites with high inertia in accordance to the network topology.