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Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

Two on-line tar measurement campaigns were carried out using an atmospheric pressure 100 “”kWth steam-O2 blown circulating fluidized bed (CFB) gasifier at the Delft University of Technology (TUD) and a 30–40kWth steam blown pressurized bubbling fluidized bed (PBFB) gasifier BabyHPR (Heatpipe Reformer) at the Technical University Munich (TUM). Agrol, willow and Dry Distiller's Grains with Solubles (DDGS) were used. An FID based on-line tar analyzer (OTA), an induced fluorescence spectroscopy (LIFS) based on-line laser instrument, and off-line solid phase adsorption (SPA) were used to quantify tar content. In general, there was a fairly good agreement between the measured results of the 10 corresponding individual tar compounds obtained from Agrol and willow CFB and PBFB atmospheric pressure tests using the SPA and LIFS methods. The measured tar concentration difference between these two methods was less than 10%. However, a higher difference (up to 30%) was observed for fluoranthene and pyrene obtained from DDGS CFB test as well as those obtained from willow PBFB under pressure test. The total tar concentration measured by the LIFS, SPA and OTA methods varied in a comparable way with changing process parameters. Both the LIFS and OTA methods can be used as indicators to observe gasifier's performance change in real time, but a regular calibration of the OTA analyzer is required to achieve good and reliable results. project:Greensyngas Advanced Cleaning Devices for Production of Green Syngas call: FP&-ENERGY-2007-1-RTD SP!-Cooperation

The concept of the Misselhorn cycle is introduced as a power cycle that aims for efficient waste heat recovery of temperature sources below 100 °C. The basic idea shows advantages over a standard Organic Rankine Cycle (ORC) in overall efficiency and utilization of the heat source. The main characteristic of this cycle is the use of at least three parallel batch evaporators instead of continuous heat exchangers. The operational phases of the evaporators are shifted so that there is always one vaporizer in discharge mode. A transient MATLAB® model (The MathWorks: Natick, MA, USA) is used to simulate the achievable performance of the Misselhorn cycle. The calculations of the thermodynamic states of the system are based on the heat flux, the equations for energy conservation and the equations of state found in the NIST Standard Reference Database 23 (Reference Fluid Thermodynamic and Transport Properties - REFPROP, National Institute of Standards and Technology: Gaithersburg, MD, USA). In the isochoric batch evaporation, the pressure and the corresponding boiling temperature rise over time. With a gradually increasing boiling temperature, no pinch point limitation occurs. Furthermore, the heat source medium is passed through the evaporators in serial order to obtain a quasi-counter flow setup. It could be shown that these features offer the possibility to gain both high thermal efficiencies and an enhanced utilization of the heat source at the same time. A basic model with a fixed estimated heat transfer coefficient promises a possible system exergy efficiency of 44.4%, which is an increase of over 60% compared to a basic ORC with a system exergy efficiency of only 26.8%.

Artificial neural networks are a popular nonlinear model structure and are known to be able to describe complex nonlinear phenomena. This article investigates the capability of artificial neural networks to serve as a basis for deducing nonlinear low-order models of the dynamics of a laminar flame from a Computational Fluid Dynamics (CFD) simulation. The methodology can be interpreted as an extension of the CFD/system identification approach: a CFD simulation of the flame is perturbed with a broadband, high-amplitude signal and the resulting fluctuations of the global heat release rate and of the reference velocity are recorded. Thereafter, an artificial neural network is identified based on the time series collected. Five data sets that differ in amplitude distribution and length were generated for the present study. Based on each of these data sets, a parameter study was conducted by varying the structure of the artificial neural network. A general fit-value criterion is applied and the 10 artificial neural networks with the highest fit values are selected. Comparing of these 10 artificial neural networks allows to obtain information on the uncertainty encountered. It is found that the methodology allows to capture the forced response of the flame reasonably well. The validation against the forced response, however, depends strongly on the forcing signal used. Therefore, an additional validation criterion is investigated. The artificial neural networks are coupled with a thermoacoustic network model. This allows to model self-excited thermoacoustic oscillations. If the training time series are sufficiently long, this coupled model allows to predict the trend of the root mean square values of fluctuations of the global heat release rate. However, the prediction of the maximal value of the fluctuation amplitude is poor. Another drawback found is that even if very long-time series are available, the behaviour of artificial neural networks cannot be guaranteed. It is concluded that more sophisticated nonlinear low-order models are necessary.

Needle segmentation is a fundamental step for needle reconstruction and image-guided surgery. Although there has been success stories in needle segmentation for non-microsurgeries, the methods cannot be directly extended to ophthalmic surgery due to the challenges bounded to required spatial resolution. As the ophthalmic surgery is performed by finer and smaller surgical instruments in micro-structural anatomies, specifically in retinal domains, difficulties are raised for delicate operation and sensitive perception. To address these challenges, in this paper we investigate needle segmentation in ophthalmic operation on 60 Optical Coherence Tomography (OCT) cubes captured during needle injection surgeries on ex-vivo pig eyes. Furthermore, we developed two different approaches, a conventional method based on morphological features (MF) and a specifically designed full convolution neural networks (FCN) method, moreover, we evaluate them on the benchmark for needle segmentation in the volumetric OCT images. The experimental results show that FCN method has a better segmentation performance based on four evaluation metrics while MF method has a short inference time, which provides valuable reference for future works.

The load-following capabilities of power plants became increasingly important in recent years as a means of ensuring a reliable operation of future power systems. In this work, we propose a generic approach, based on reachability analysis, to rigorously verify the safety of critical components that often pose limitations on the flexibility of conventional power plants to perform fast load changes. The proposed reachability algorithm makes it possible to compute the bounds of all possible trajectories for a range of operating conditions while simultaneously meeting the practical requirements of a real power plant. As an example, we consider the verification of the water level inside a drum unit. In contrast to previous work, our results are based on measurement data of a realistic configuration of a boiler system located within a 450 MW combined cycle plant in Germany. We use an abstract model which considers the modelling errors to ensure that all dynamic behaviors of the process are replicated by the abstraction. Through the implementation of our abstract model, we formally guarantee that the water level inside the drum always remains within safe limits for load changes equivalent to 40 MW which, as a result, exploits the power plant's adaptability and load-following capabilities.

The smart grid initiative has encouraged utility companies worldwide to roll-out new and smarter versions of energy meters. Before an extensive roll-out, which is both labor-intensive and incurs high capital costs, consumers need to be incentivised to reap the long-term benefits of such smart meters. Off-the-shelf energy monitors (e-monitors) can provide consumers with an insight into such potential benefits. As e-monitors are owned by the consumer, the consumer has greater control over the data, which significantly reduces the privacy and data confidentiality concerns. Because only limited online technical information is available about e-monitors, we evaluate several existing e-monitors using an online technical survey directly from the vendors. Besides automated e-monitoring, the use of different off-the-shelf e-monitors can also help to demonstrate state-of-the-art techniques such as non-intrusive load monitoring (NILM), data analytics, and the predictive maintenance of appliances. Our survey indicates a trend towards the incorporation of such state-of-the-art capabilities, particularly the appliance-level e-monitoring and load disaggregation. We have also discussed some essential requirements to implement load disaggregation in the next generation e-monitors. In future, these intelligent e-monitoring techniques will encourage effective consumer participation in the demand-side management (DSM) programs.