BESS Optimal Sizing Methodology -Degree of Impact of Several Influencing Factors

TL;DR

This paper presents a simulation-based method for optimally sizing Battery Energy Storage Systems (BESS) by analyzing the impact of various factors on performance and economic viability across different energy applications.

Contribution

It introduces a generic analytical approach for BESS sizing that considers application specifics, technical characteristics, and business models, supported by sensitivity analyses.

Findings

Control strategy significantly affects optimal size.

Forecast accuracy impacts BESS sizing outcomes.

Battery degradation influences long-term performance and sizing decisions.

Abstract

Battery Energy Storage Systems (BESS) are more and more competitive due to their increasing performances and decreasing costs. Although certain battery storage technologies may be mature and reliable from a technological perspective, with further cost reductions expected, the economic concern of battery systems is still a major barrier to be overcome before BESS can be fully utilized as a mainstream storage solution in the energy sector. Since the investment costs for deploying BESS are significant, one of the most crucial issues is to optimally size the battery system to balance the trade-off between using BESS to improve energy system performance and to achieve profitable investment. Determining the optimal BESS size for a specific application is a complex task because it relies on many factors, depending on the application itself, on the technical characteristics of the battery system and on the business model framework. This paper describes a generic simulation-based analytical method which has been developed to determine the BESS optimal size by taking into account both the application and the storage performance over its lifetime. Its implementation and the associated results are presented for two different BESS use cases: A smoothing and peak shaving application for PV injection and an off-grid hybrid microgrid case. In order to provide a better understanding of the most influencing drivers to consider during a BESS sizing procedure, several sensitivity analyses have been carried out on these two illustrative cases. The use of comparative scenarios led to quantify the degree of impact on optimal sizing results of several factors among the following topics: control strategy, forecast quality, degradation of battery performance due to ageing, precision of technical modelling.