Life cycle assessment of lithium-ion batteries and vanadium redox flow batteries-based renewable energy storage systems

Renewable energy has become an important alternative to fossil energy, as it is associated with lower greenhouse gas emissions. However, the intermittent characteristic of renewables urges for energy storage systems, which play an important role in matching the supply and demand of renewable-based electricity. The life cycle of these storage systems results in environmental burdens, which are investigated in this study, focusing on lithium-ion and vanadium flow batteries for renewable energy (solar and wind) storage for grid applications. The impacts are assessed through a life cycle assessment covering the batteries supply phase, their use and end-of-life, with experimental data from test set-ups. The battery composition is investigated in detail as a factor for the final impacts, by comparing two types of cathodes for the lithium-ion battery and the use of recycled electrolyte for the vanadium flow battery. Results indicate that the vanadium-based storage system results in overall lower impacts when manufactured with 100% fresh raw materials, but the impacts are significantly lowered if 50% recycled electrolyte is used, with up to 45.2% lower acidification and 11.1% lower global warming potential. The new lithium-ion battery cathode chemistry results in overall higher impacts, with 41.7% more particulate matter and 52.2% more acidification

A comprehensive and time efficient characterisation of redox flow batteries through Design of Experiments

As the need for a sustainable economy rightly drives the share of renewable energy, electric grids and supporting infrastructure must flexibly adapt. As valuable building blocks in integrated systems, battery energy storage systems (BESSs) can provide the required flexibility for energy and power applications. Redox flow batteries (RFBs) are emerging as promising alternatives to lithium-ion batteries to meet this growing demand. As end-users, RFB operators must characterise the batteries to learn more about the battery's behaviour and performance and better integrate such RFB technology into energy systems. Characterisation experiments yield this information, which is essential to successfully operate and integrate redox flow battery systems. However, conducting classical characterisation protocols can take more than two weeks for large RFB modules (capacities >30 kWh), which is too long for an efficient RFB roll-out. Better characterisation methods are required to efficiently scale up, integrate and operate RFBs in an appropriate manner. Ideally, characterisation experiments would yield a more comprehensive understanding about the battery performance and behaviour in a shorter amount of time. In order to achieve this, statistical design of experiments (DoE) is explored as an RFB characterisation tool. DoE is a statistical method that makes optimal use of the available time and resources and increases the efficiency of experiments in a statistically sound manner. Designed experiments result in empirical models for the studied system, which can predict system outputs for a vast amount of operating points. This will enable optimal operation of the battery in terms of remaining capacity management and overall electrical efficiency. Through a number of such designed experiments, dominant RFB system variables could be identified, which allow reliable modelling of the RFB performance for different charge-discharge cycles. This facilitated the design of an optimised characterisation experiment. A 50% reduction of the required RFB characterisation time is achieved and the optimal experiment yields comprehensive information about the battery performance and behaviour. As such, a shorter and better RFB characterisation procedure is realised through DoE

Predictive emissions monitoring using a continuously updating neural network

In the European Union, power plants of more than 50 MW (thermal energy) need to comply with the Large Combustion Plant Directive (LCPD, 2001) implying that flue gas emissions need to be measured continuously. Traditionally, emissions from power plants are measured using Automated Measuring Systems (AMS). The LCPD states that no more than 10 days of emission data may be lost within one year including days needed for maintenance. This is the reason why more and more power plants are currently installing a second, back-up AMS since they have problems with the availability of their AMS. Since early 1990’s, Predictive Emissions Monitoring Systems (PEMS) are being developed and accepted by some local authorities within Europe and the United States. PEMS are in contrast to AMS based on the prediction of gaseous emissions (most commonly NOx and CO) using plant operational data (eg. fuel properties, pressure, temperature, excess air, …) rather than the actual measurement of these emissions. The goal of this study is to develop a robust PEMS that can accurately predict the NOx and CO emissions across the entire normal working range of a gas turbine. Furthermore, the PEMS should require as little maintenance as possible. The study does not intend to replace the AMS by a PEMS but rather to use the PEMS as a backup for the AMS. Operational data of a gas turbine, acquired over a long period, was used to identify inputs with a high influence on the NOx and CO formation. Consequently, simulations were done testing different model structures and calibration methodologies. The study shows that a static model failed to predict the emissions accurately over long time periods. In contrast, a dynamic or self-adapting algorithm proved to be most efficient in predicting the emissions over a long time period with a minimum of required intervention and maintenance. The self-adapting algorithm uses measured AMS data to continuously update the neural network. Since the PEMS is developed as a backup for the AMS, these data are readily available. The study shows that in case of a failing AMS, the developed model could accurately predict the NOx emissions for a duration of several weeks. Although not discussed in detail in this study, a quality assurance system of the PEMS is also developed since the PEMS needs to comply to the EN14181 (as does any AMS). The PEMS as a backup of the AMS instead of a second AMS is cost and time saving. Not only is the purchase of a second AMS avoided (between 40 and 100 k€) but equally important and of the same order of magnitude are the cost and time savings with respect to the Quality Assurance of the second AMS

Importing renewable energy to EU via hydrogen vector: Levelized cost of energy assessment

European Green Deal sets the EU’s target towards becoming the world’s first climate-neutral continent by 2050. To achieve the 2050 Green Deal target, multi-combined actions are required, such as increasing renewable energy (RE) production in the EU, enhancing efficiency, and importing RE. The limited area, high population density, and geographical position constrain the EU’s RE self-sufficiency; in fact, the energy import dependency of the European Union (EU-27) reached 58.4% and 60.7% in 2018 and 2019, respectively. Interestingly, the final energy consumption by fuel comprises 23% of electricity and 77% of molecules. Consequently, a sustainable energy system requires not only green electricity but green molecules as well to move from fossil to electrified chemical industry (chemistree). In this context, the work analyses the LCOE of importing RE from Morocco, Algeria, Egypt, and Saudi Arabia to selected locations in the EU namely Rome, Madrid, and Cologne, since they have both a well-established energy importing/exporting network with the EU and a high potential of RE sources. A promising LCOE of H2 is found in all importing scenarios with an average of 5.20 €/kgH2. Hydrogen transport via pipelines (0.14 €/kg/1000 km) is found to be the optimal solution for the studied cases. Further investigation is required for importing RE via other types of molecules and e-fuels such as ammonia, methanol, and methane from the Middle East and North Africa (MENA) to the EU