Lifetime Estimation of Grid-Connected Battery Storage and Power Electronics Inverter Providing Primary Frequency Regulation

Abstract

Battery Energy Storage Systems (BESSs) are a new asset for Primary Frequency Regulation (PFR), an ancillary service for improving the grid stability. The system operators determine the implementation and remuneration of PFR. However, assessing the revenue stream is not enough to define the business case, as also the components' lifetime has to be estimated. Previous studies of lifetime estimation for BESSs performing PFR considered only the electrochemical storage, disregarding the power electronics (PE). Nonetheless, researchers have shown the importance of estimating PE wear due to the operation when applied in renewable energy generation and microgrids. This paper presents a lifetime analysis of BESSs providing PFR considering IGBT modules, electrolytic capacitors and electrochemical storage degradation. The lifetime information is used to estimate BESS's Net-Present-Value (NPV), evaluating the benefits of deploying PE-based BESS in the European grid. A comparison between different countries, Germany, the Netherlands, and the UK, is performed, considering the PFR implementation and remuneration differences. The analysis shows that the BESS management strategy can extend its lifetime and that the component that exhibits the shortest lifetime is the electrochemical storage. The PE components are subject to low wear due to the low power utilization and, therefore, small thermal swings while performing PFR. In conclusion, the provision of PFR by means of BESS has been found to be profitable in all three countries. However, in the Netherlands, the potential NPV has been estimated to be 47% and 76% higher than in Germany and the UK, respectively.

Optimal Battery Energy Storage Dispatch in Energy and Frequency Regulation Markets While Peak Shaving an EV Fast Charging Station

“…Previous literature has investigated the application of BESS in energy and ancillary services markets, both in terms of its technical performance and schedule definition [5]- [7], [9], [10]. A multi-layer hierarchical approach can be applied for the joint optimization of DSO and customer services in distribution systems [11].…”

“…In (9), the State of Charge is indicated with the variable SoC and expressed as percentage of the the total battery energy E * . The limits for battery SoC are defined in (10). Since the model is not linked with specific battery size and technology, the SoC is expressed in per unit and can span between 0 and 1.…”

“…The binary variable, γ fcr , is introduced to indicate whether the TSO requires FCR regulation. It is assumed that the electricity flowing into the battery does not influence the SoC during the service and once completed, since an internal SoC management algorithm, which focuses on maintaining the SoC to a specified level, is necessary to operate in the FCR market [10], [28], [29]. An 80% factor is used to limit the P fcr and take into account the SoC management power that should be reserved for FCR.…”

“…An 80% factor is used to limit the P fcr and take into account the SoC management power that should be reserved for FCR. This choice is driven by the necessity of withholding a percentage of the BESS power for SoC management to ensure the continuous FCR delivery [10], [28].…”

Incremental Degradation Estimation Method for Online Assessment of Battery Operation Cost

“…This quantity is used to compare the degradation with other performance aspects in the economic domain. This process is suitable for the battery planning and long-term performance monitoring [2], but cannot be implemented for the real-time operation (e.g., 5 minute intervals). The main limitation is that the complete time-series stress profile is not available during real-time operation.…”