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Abstract This paper discusses the minimization of the fuel consumption of a gasoline engine through dynamic optimization. The minimization uses a mean value model of the powertrain and vehicle. This model has two state variables: the pressure in the engine intake manifold and the engine speed. The control input is the throttle valve angle. The model is identified on a universal engine dynamometer. Optimal state and control trajectories are calculated using Bock’s direct multiple shooting method, implemented in the software MUSCOD-II. The developed approach is illustrated both in simulation and experimentally for a generic test case where a vehicle accelerates from 1100 rpm to 3700 rpm in 30 s . The optimized trajectories yield minimal fuel consumption. The experiments show that a linear engine speed trajectory yields an extra fuel consumption of 13 % when compared to the optimal trajectory. It is shown that, with a simple model, a significant amount of fuel can be saved without loss of the fun-to-drive.

This paper describes the needs an challenges in the sector of Life Cycle Assessment of Algae Biofuels

Countries are pushing for the use of local, renewable energy sources in order to reduce the dependence on fossil fuels for energy supply. One of the main problems with several renewable energy sources is their variability, which can be solved with energy storage. With buildings representing an important share of energy consumption, and given the growing capacity of distributed generation, distributed energy storage in buildings is expected to become increasingly present. In this context, the optimal dimensioning of home installations of photovoltaics and lithium-ion batteries, and the impact of such installations on the grid, is of the utmost importance. While there have been developments on this field, some important handicaps remain, notably the independent treatment of installation optimisation and grid impact and the substantial result differences between studies. In this paper, photovoltaics and lithium-ion storage installations are optimised through the use of real, high-resolution data from several individual households, based on realistic cost figures, and through well-defined metrics that correctly grasp the problem at hand. The impact on the grid as well as possible mitigation measures are also analysed. Results show that up to about 30% of electricity self-sufficiency can be obtained using only PV and close to grid parity. Above 40% self-sufficiency, energy storage must be used, strongly increasing the cost of such installations. Economies of scale play an important role suggesting a preferential implementation for larger users or at a community-scale. Feed-in limits seem to be a good solution to attenuate grid impact. On the other hand, a higher share of a capacity-based component on the grid tariff strongly affects the economic viability of such installations for the average household. These results are important for studies on distributed photovoltaics and energy storage as well as for energy policy. Also, the large range of results made available, calculated on a free market perspective using a simple control mechanism, provide a much-needed benchmark for further comparable studies.

Abstract The large employment of renewable energies requires flexible, efficient and low-carbon production from fossil fuels. From all non-renewable power production routes, the electricity produced with micro Gas Turbines (mGTs) running on natural gas has a very high load flexibility and the lowest CO2 emissions. However, if we want to move towards full carbon clean power production, then CO2 in the exhaust must be captured. In this scenario, mGTs coupled with a Carbon Capture (CC) plant could be a suitable option, but only a few numerical and experimental analyses are available which assess their real potential. The low concentration of CO2 in the mGT exhaust gas is disadvantageous from a CC point of view, however the concentration can be increased by performing Exhaust Gas Recirculation (EGR). Furthermore, the efficiency loss introduced by the CC plant can also be reduced by using the concept of micro Humid Air Turbine (mHAT). In this study, both the dry and the wet operations of the Turbec T100 coupled with a chemical-absorption plant are simulated and compared using the software Aspen Plus®. The goal of these simulations was to investigate state-of-the-art measures which could be applied to small-scale generation to assess what the energy impact of a carbon clean production would be. Simulation results show that applying EGR to a mGT or mHAT can reduce the exhaust gas mass flow rate by 50% and it can increase the concentration of CO2 by three times the traditional cycle. The humidification of the mGT can entirely compensate for the efficiency losses of the EGR application. Both cycle performances are strongly affected by the thermal input from the stripping process, decreasing the global electric efficiency by around 7.9 absolute percentage points in the mGT case and by 8.3 absolute percentage points in the mHAT case in full load conditions. The results of this paper can be used as a starting point for new cycle concepts between the mGT and CC plant.

Abstract The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism – an air-source heat pump (ASHP) in this case – at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort level. This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kW h). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

Abstract Co-utilization of fossil fuels and biomass is a successful way to make efficient use of biomass for power production. When replacing only a limited amount of fossil fuel by biomass, measurements of net output power and input fuel rates will however not suffice to accurately determine the marginal efficiency of the newly introduced alternative fuel. The present paper therefore proposes a technique to determine the marginal biomass efficiency with more accuracy. The process simulation model for co-utilization of natural gas and a small perturbing fraction of biomass in an existing combined cycle plant (500 MW th Drogenbos, Belgium) is taken as case study. In this particular plant, biomass is introduced into the cycle as fuel for a primary steam reforming process of the input natural gas. This paper proposes a perturbation analysis that has been developed to allow for an accurate assessment of the marginal efficiency of biomass by using only accurately measurable variables. To achieve this, effects of co-utilization were studied in each component of the gas turbine down to its steam bottom cycle to identify the components most affected by the limited perturbing amount of biomass. The procedure is validated through process simulation, where accurate marginal efficiencies can be compared with the efficiency obtained from the perturbation analysis. A full off-design simulation is required to achieve this result. Through the use of process simulation, the accuracy of the mathematical model could be verified for each formula and each assumption. Compared to process simulation data, the model was found to accurately predict marginal efficiencies of the introduced biomass for biomass shares as low as 0.1%.

Electric vehicles (EVs) have been widely introduced into the bus fleet while the short driving range and long charging time for battery electric buses (BEBs) are the two main barriers. Thus, bus operators are considering alternatives. Hydrogen buses (HBs) could be a promising option because of their longer driving range and shorter refueling time compared to BEBs. However, introducing HBs would be costly and thus it remains unclear which fuel option (hydrogen or electricity) is more feasible for an electrified public transit system. In response, this study proposed a data-driven micro-simulation approach to compare the system cost and level of service (i.e., the delay time of deviating from the timetable caused by charging events) with different fuel options (electricity or hydrogen) for electrifying public transit, using real-world bus operation information extracted from a GPS bus trajectory dataset in Shenzhen, China. The results suggested that the charging demands of BEBs tended to be concentrated in the central and northwest areas of the city while the refueling demands of HBs were more evenly distributed in not only the center but also the southwest and northeast areas. These resulted in different layouts of charging/hydrogen stations accordingly. Furthermore, given almost the same level of service to maintain, the system cost of the HB scenario could be 48.2% higher than that of the BEB scenario. Therefore, BEBs tended to be more economically feasible in Shenzhen.

Abstract Low-grade thermal energy recovery has attained a renewed relevance, driven by the desire to improve system efficiency and reduce the carbon footprint of power generation. Various technologies have been suggested to exploit low-temperature thermal energy sources, otherwise difficult to access using conventional power generation systems. In this paper, the authors review the most recent advances and challenges for the exploitation of low grade thermal energy resources, with particular emphasis on ORC systems, based on information gathered from the technical literature. An outline of the issues related to ORC system modeling is also presented, and some guidelines drawn to develop an effective and powerful simulation tool. As a summary conclusion of the revised models, a simulation tool of an ORC system suitable for the exploitation of low grade thermal energy is introduced.