Storage Sizing and Placement through Operational and Uncertainty-Aware Simulations

TL;DR

This paper presents a simulation-based heuristic method for optimal energy storage placement and sizing in power systems with high renewable penetration, considering operational uncertainties and time-dependent factors.

Contribution

It introduces a novel heuristic approach that uses operational simulations and statistics to improve storage placement and sizing decisions in renewable-rich power grids.

Findings

Heuristic effectively identifies minimal storage node sets.

Simulation results outperform naive placement strategies.

Method adapts to various network topologies and renewable profiles.

Abstract

As the penetration level of transmission-scale time-intermittent renewable generation resources increases, control of flexible resources will become important to mitigating the fluctuations due to these new renewable resources. Flexible resources may include new or existing synchronous generators as well as new energy storage devices. Optimal placement and sizing of energy storage to minimize costs of integrating renewable resources is a difficult optimization problem. Further,optimal planning procedures typically do not consider the effect of the time dependence of operations and may lead to unsatisfactory results. Here, we use an optimal energy storage control algorithm to develop a heuristic procedure for energy storage placement and sizing. We perform operational simulation under various time profiles of intermittent generation, loads and interchanges (artificially generated or from historical data) and accumulate statistics of the usage of storage at each node under the optimal dispatch. We develop a greedy heuristic based on the accumulated statistics to obtain a minimal set of nodes for storage placement. The quality of the heuristic is explored by comparing our results to the obvious heuristic of placing storage at the renewables for IEEE benchmarks and real-world network topologies.