Comparative Analysis of Degradation Assessment of Battery Energy Storage Systems in PV Smoothing Application

Abstract

Photovoltaic (PV) generation depends on the availability of solar resources, being directly influenced by the variation in irradiance due to the presence of clouds over the PV panels, causing a variation in the power output. The use of battery energy storage systems integrated with the PV showed to be a technically feasible solution to mitigate these power output fluctuations within the maximum ramp limit. Most articles reported in the literature on smoothing PV power output, by coupling with battery degradation as performance indicators of the control strategy, used the event-oriented model that considers only the number of cycles and depth of discharge. This paper presents on the comparative analysis of two approaches to battery degradation models, an event-oriented model based on the Rainflow counting and a semiempirical model, and applies to photovoltaic power smoothing by using a wide range of restrictions and installed PV plant capacities. The semi-empirical degradation model revealed higher battery degradation for all simulated cases. For Strategy 2, the order was 50% higher than the event-oriented model, probably due to severe DR and RR, which increases the stress on the battery. For Strategy 1, the difference was greater, between 100% and 300%. The event-based model indicated that Strategy 1 implied less battery degradation, but the semi-empirical model indicated the opposite. Considering that the semi-empirical model considers more parameters of degradation, the fact that Strategy 2 implies less degradation is more reliable. Moreover, the result obtained by the SimSES model corroborates that the accelerated lithium cell battery degradation takes place, as the operation is at high SoC. Maintaining Ebat, reference is SoC 80% decreased the degradation in at least 25% with respect to degradation, maintaining Ebat, reference is SoC 100%. For this, Ebat, reference of the SoC control, can be designed to avoid operating under a high load state. The results demonstrated that choosing a simplified degradation model approach can lead to an error in the conclusion of which strategies are the best since calendar life effects are very important in the application of PV power smoothing.

Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation

“…The study [64] presents a comparative assessment of two models of BESS degradation, an event-oriented model that is based on Rainflow counting, which uses only the DoD cycle number curve, and a semi-empirical model in the PV smoothing application. The semiempirical model showed the greatest degradation for all scenarios that were considered.…”

“…The calculated residual capacity is transferred to other modules to continue calculations until the end of the period assessed. A semi-empirical model is built into SimSES to account for the effects of discharge rate, operating temperature, depth of discharge, self-discharge, and calendar aging on the degradation rate [64].…”

“…The published research generally distinguishes between four basic models of battery life, as shown in Table 3. This is an electrochemical model [83][84][85][86] that is based on physical and chemical laws and describes complex mechanisms representing the most important state variables at any point in the cell and at any time [64]. These models analyze basic degradation processes, such as solid electrolyte interphase interaction and lithium coating, allowing one to determine the loss of lithium concentration and active materials [61].…”

“…Circuit-based models represent the equivalent electrical circuit of a battery consisting of electrical elements such as resistors, capacitors, voltage, and current sources [64]. But the equivalent circuit representation is the most popular because it describes electrochemical processes using a simple electrical circuit.…”

“…Empirical models describe by means of mathematical equations the relationship between battery capacity and cycles, without considering electrochemical processes. Semiempirical models (SEM) [21,62,65,[70][71][72] are based on the value of the transferred charge multiplied by a weighted linear or nonlinear coefficient that is derived from the results of long-term tests or accelerated tests [64,80]. The model can be built based on a wide range of input values that are responsible for both cycling and calendar aging.…”