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The results in this work show the influence of long-term operation on the decomposition of working fluids in eight different organic rankine cycle (ORC) power plants (both heat-led and electricity-led) in a range of 900 kW el to 2 MW el . All case study plants are using octamethyltrisiloxane (MDM) as a working fluid; the facilities are between six to 12 years old. Detailed analyses, including the fluid distribution throughout the cycle, are conducted on one system. All presented fluid samples are analyzed via head space gas chromatography mass spectrometry (HS-GC-MS). Besides the siloxane composition, the influence of contaminants, such as mineral oil-based lubricants (and their components), is examined. In most cases, the original working fluid degrades to fractions of siloxanes with a lower boiling point (low boilers) and fractions with a higher boiling point (high boilers). As a consequence of the analyses, a new fluid recycling and management system was designed and tested in one case study plant (Case Study #8). Pre-post comparisons of fluid samples prove the effectiveness of the applied methods. The results show that the recovery of used working fluid offers an alternative to the purchase of fresh fluid, since operating costs can be significantly reduced. For large facilities, the prices for new fluid range from € 15 per liter (in 2006) to € 22 per liter (in 2013), which is a large reinvestment, especially in light of filling volumes of 4000 liters to 7000 liters per unit. Using the aforementioned method, a price of € 8 per liter of recovered MDM can be achieved.

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets.

In this research, twenty-four high capacity (1360 mAh) NMC622/Si-alloy Li-ion full pouch cells with high silicon-alloy content (55%) are cycle aged under seven different cycling conditions to study the effect of different stressors on the cycle life of Si-anode full cells, among which are the effect of ambient temperature, Depth of Discharge (DoD) and the discharge current. The cells are volumetrically constrained at an optimal initial pressure to improve their cycle life, energy and power capabilities. Furthermore, the innovative test setup allows measuring the developed pressure as a result of repeated (de-)lithiation during battery cycling. This uniquely vast testing campaign on Si-anode full cells allows us to study and quantify independently the influence of different stress factors on their cycle life for the first time, as well as to develop a new capacity fade model based on an observed linear relationship between capacity retention and total discharge capacity throughput.

The development of collective and industrial energy systems, based on wood biomass, knows a significant increase since the end of the 90’s in France, with more than 6000 power plants and heating plants developed currently. Because these systems are built for a minimal duration of 30 years, it is relevant to assess the availability of wood resources according to the potential impacts of global warming on five tree species mainly used in such a supply chain. The assessment of the potential spatial distribution of the suitable areas of these trees in 2050, by using the IPCC (Intergovernmental Panel on Climate Change) RCP6.0 scenario (Representative Concentration Pathway), shows an average decrease of 22% of the plots in comparison with the current situation. The results also point out that mountain areas would maintain a high probability of the development of four tree species. The assessment of the Net Primary Productivity (NPP) underlines a potential decrease for 93% of the plots in 2050, and an increase of this parameter in mountain areas. According to these assumptions, the proposed ecosystem based methodology can be considered as a prospective approach to support stakeholders’ decisions for the development of the wood energy supply chain.

With the increasing shares of intermittent renewable sources in the grid, it becomes increasingly essential to quantify the requirements of the power systems flexibility. In this article, an adjusted weight flexibility metric (AWFM) is developed to quantify the available flexibility within individual generators as well as within the overall system. The developed metric is useful for power system operators who require a fast, simple, and offline metric. This provides a more realistic and accurate quantification of the available technical flexibility without performing time-consuming multi-temporal simulations. Another interesting feature is that it can be used to facilitate scenario comparisons. This is achieved by developing a new framework to assure the consistency of the metric and by proposing a new adjusted weighting mechanism based on correlation analysis and analytic hierarchy process (AHP). A new ranking approach based on flexibility was also proposed to increase the share of the renewable energy sources (RESs). The proposed framework was tested on the IEEE RTS-96 test-system. The results demonstrate the consistency of the AWFM. Moreover, the results show that the proposed metric is adaptive as it automatically adjusts the flexibility index with the addition or removal of generators. The new ranking approach proved its ability to increase the wind share from 28% to 37.2% within the test system. The AWFM can be a valuable contribution to the field of flexibility for its ability to provide systematic formulation for the precise analysis and accurate assessment of inherent technical flexibility for a low carbon power system.

Efficient high speed propulsion requires exploiting the cooling capability of the cryogenic fuel in the propulsion cycle. This paper presents the numerical model of a combined cycle engine while in air turbo-rocket configuration. Specific models of the various heat exchanger modules and the turbomachinery elements were developed to represent the physical behavior at off-design operation. The dynamic nature of the model allows the introduction of the engine control logic that limits the operation of certain subcomponents and extends the overall engine operational envelope. The specific impulse and uninstalled thrust are detailed while flying a determined trajectory between Mach 2.5 and 5 for varying throttling levels throughout the operational envelope.