A Comparative Analysis of Power Smoothing Metrics: Unveiling Limitations Through Observational Data

Algorithms such as Moving Average and Ramp Rate have been widely used in reference generation for Energy Storage Systems (ESSs) in the context of power smoothing techniques. In these applications, the excessive power variations, typically caused by the intermittency of renewable energy sources (RESs), can lead to frequency and voltage fluctuations that may ultimately jeopardize the stability and reliability of the electrical system. In this scenario, this paper analyzes typical metrics used for evaluating power smoothing techniques and evaluates their limitations. Photovoltaic generation data from the National Renewable Energy Laboratory (NREL) is used to validate the comparison. The results highlight the need to develop new metrics for a more equitable evaluation of smoothing techniques.

I. Introduction

Human-induced climate change, primarily caused by green-house gas emissions, poses a significant threat to the sustainability of life on Earth. To mitigate this problem, renewable energy sources (RESs), such as photovoltaic (PV) and wind generation, have gained substantial relevance as economically viable alternatives. Recent price drops in these technologies have accelerated their adoption as significant contributors to countries' power grids. Although relatively clean, these alternatives are not free of negative impacts, particularly concerning their integration into power grids. Literature reports highlight issues such as voltage flickers, frequency deviations, and the need for larger power reserves, all stemming from the hard-to-anticipate nature of power variability [1].