Energy storage is one of the key elements within the actual stage of the energy transition, as it is probably one of the most important factors to allow high penetration of fluctuating renewable energies, such as wind or solar, in the existing power systems. Intensive research is being conducted to assess the economic aspects and technical performance of renewable energy-based systems supported by batteries by evaluating different services that batteries can provide to the electric grid or to the end-consumers. In Germany, where the majority of the currently installed 43 GW of PV capacity corresponds to small residential, commercial, or industrial facilities, an interesting market for batteries to enhance local self-consumption and autarky is already booming, with more than 80 000 storage system installations in 2017. In this context, this study presents a comprehensive analysis of the photovoltaic battery model by analyzing the technical and economic consequences that variations on the most relevant system parameters induce. The presented results are based on high resolution data obtained from a representative residential district with an autarky of above 95%. The employed battery model is based on the results obtained through an extensive test campaign and includes electrical and thermal sub-models. The analysis predicts that grid parity of residential PV battery systems can be reached in the upcoming years, with especially great potential of the retrofitting market for those PV installations which run out of the feed-in tariff policy.
Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests - annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. There are different strategies for such data-driven diagnostics. Some approaches, referred to as cumulative damage models, require full operational history of the batteries in order to predict state of health, and this may be impractical due to several reason. Thus, snapshot methods that are able to give reliable estimation of state of health based on only snapshots of the data streams are attractive candidates for data-driven diagnostics of battery systems on board ships. In this paper, data-driven snapshot methods are explored and applied to a novel set of degradation data from battery cells cycled in laboratory tests. The paper presents the laboratory tests, the resulting battery data, shows how relevant features can be extracted from snapshots of the data and presents data-driven models for state of health prediction. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for 40% of the tested cells. This is greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. Nevertheless, improved accuracy is required for such snapshot methods to be recommended for ships in actual operation.
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