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Longer chain alcohols, such as butanol, possess major physiochemical advantages over ethanol as bio-components for gasoline, including higher energy content, better engine compatibility, and less water solubility. In this study, two butanol isomers (n-butanol and isobutanol) are investigated as potential fuels for Homogeneous Charge Compression Ignition (HCCI) engines. Wide ranges of intake pressure and equivalence ratio are investigated and the results are presented in comparison to ethanol and gasoline as reference fuels. Under all tested conditions, the butanol isomers require lower intake temperatures for a fixed combustion phasing, indicating higher HCCI reactivity. Both isomers show single-stage ignition behavior at all test points and behave similarly in regard to the combustion stability. Engine operation using n-butanol is slightly more stable under all conditions and misfiring occurs slightly later under very lean and naturally aspirated conditions. Similar to gasoline, n-butanol shows a higher heat release rate (HRR) at the beginning of combustion. The intermediate temperature heat release (ITHR) lowers the coefficient of variation (CoV) of IMEPg (gross indicated mean effective pressure), especially at retarded combustion timing and lean mixtures. However, the knock resistance of n-butanol is lower compared to isobutanol and the other tested fuels. The exhaust emissions of the two butanol isomers are in the same range as the two reference fuels. Overall, the results indicate that butanol is suited for use as a fuel in HCCI engines, either in neat form or in blend with gasoline. Applied Energy, 165 ISSN:0306-2619 ISSN:1872-9118

Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directly from measurement data. Machine learning methods have been widely applied to this problem and validated mostly in simulation; there are, however, few studies on a direct comparison of different models or validation in real buildings to be found in the literature. Methods that are indeed validated in application often lead to computationally complex non-convex optimization problems. Here we compare physics-informed Autoregressive-Moving-Average with Exogenous Inputs (ARMAX) models to Machine Learning models based on Random Forests and Input Convex Neural Networks and the resulting convex MPC schemes in experiments on a practical building application with the goal of minimizing energy consumption while maintaining occupant comfort, and in a numerical case study. We demonstrate that Predictive Control in general leads to savings between 26% and 49% of heating and cooling energy, compared to the building's baseline hysteresis controller. Moreover, we show that all model types lead to satisfactory control performance in terms of constraint satisfaction and energy reduction. However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models. Moreover, even if abundant training data is available, the ARMAX models have a significantly lower prediction error than the Machine Learning models, which indicates that the encoded physics-based prior of the former cannot independently be found by the latter. 17 pages, 11 Figures, submitted to Applied Energy

Abstract Latent storages utilising phase change materials (PCM) to store thermal energy offer a considerably higher energy density at a nearly constant temperature level in comparison to sensible storage systems. Despite this advantage, only a few latent storage technologies have been integrated successfully to the market. This may be due several engineering challenges and in particular the lack of a computationally fast and accurate mathematical model to facilitate the optimal incorporation of latent heat storages into an energy system. The presented study fills this gap and proposes a new, fast and experimentally validated mathematical modelling approach for latent heat storage units. The numerical model proposed combines high accuracy, low computational effort and numerical stability. The validation was performed with two different commercial latent storage units supplied by Sunamp Ltd. with a nominal phase change temperatures of 34 °C and of 58 °C. Both units use a salt hydrate based phase change material in combination with a fin-tube heat exchanger. The proposed model may be used for both fast system level performance investigations as well as latent storage design for a given application. It may therefore be implemented in commercial software packages such as TRNSYS [1] or Simulink [2].

Development of battery technology is making battery electric heavy duty trucks technically and commercially viable and several manufacturers have introduced battery electric trucks recently. However, the national and sectoral differences in freight transport operations affect the viability of electric trucks. The aim of this paper is to develop a methodology for estimating the potential of electric trucks and demonstrate the results in Switzerland and Finland. Commodity-level analysis of the continuous road freight survey data were carried out in both countries. As much as 71% of Swiss road freight transport tonne-kilometers may be electrified using battery electric trucks but Finland has very limited potential of 35%, due to the use of long and heavy truck-trailer combinations. Within both countries the electrification potential varies considerably between commodities, although in Finland more so than in Switzerland. Commodities which are constrained by payload volume rather than weight and are to large extent carried using medium duty or <26t rigid trucks trucks seem to provide high potential for electrification even with the current technology. Electric trucks increase the annual electricity consumption by only 1–3%, but truck charging is likely to have a large impact on local grids near logistics centres and rest stations along major roads. A spatial analysis by routing the trips reported in the datasets used in this study should be carried out. Future research should also include comparison between the alternate ways of electrifying road freight transport, i.e. batteries with charging, batteries with battery swapping and electrified road systems. Applied Energy, 236 ISSN:0306-2619 ISSN:1872-9118

Abstract Water-energy nexus refers to the interdependence between water resources and energy conversion, and it encompasses the multiple phases of electric power generation and water processing and distribution. Current policies for the utilization of freshwater resources in electric power generation regulate the thermal discharges and their effect on the aquatic life. Water withdrawals and consumption polices are mainly prescribed at the regional level instead. This paper focuses on the effects of water policy constraints on electric power generation in changing climate conditions. A river basin is simulated, which hosts two hydraulically linked power generating stations, namely an upstream hydropower plant with reservoir and a downstream thermal power plant. Two alternative cooling designs are tested for the thermal power plant, i.e. once-through and wet tower cooling. Severe drought conditions leading to small river flows and high water temperatures are analyzed, and the limitations to the energy conversion at the thermal plant stemming from the water policies are quantified. The results show that some small flexibility in the water policy constraints during extreme droughts can secure a significant amount of energy to the power system, which would have been curtailed otherwise. Remarkably, the relaxation of 1.5 °C in the water policy constraints prevents the curtailment of 42% of the generation capacity of a 1000 MW e thermal plant during the analyzed 24 h drought scenario. In general, the type and the required amount of constraint relaxation depend on the environmental conditions and are to be judged case-by-case. Furthermore, the smart scheduling of water resources grants a 7% increase of the energy converted during droughts in the hydraulically linked hydro and thermal power plants. Finally, the analysis shows that once-through cooling systems are extremely sensitive to changes in water flow and temperature opening space for less sensitive technologies, i.e. wet cooling towers.

Abstract According to its ‘Energy Strategy 2050’ (case ‘new energy policy’) Switzerland aims to reduce its industrial electricity demand by 25% and 35% in 2035 and 2050 respectively compared to 2010. Electric motor driven systems in Swiss industry, which currently account for approximately 69% of the sector’s total electricity demand, are expected to contribute significantly to this strategy. This study assesses the potential of electricity savings for electric motor driven systems in industry and its associated specific costs and presents the results in the form of energy efficiency cost curves. For the short term, the economic potential for electricity savings in Swiss industrial electric motor systems is estimated at approximately 17%. The importance of accounting for additionality by using energy-relevant investment instead of total investment for the cost-benefit analysis in order to avoid underestimation of the economic electricity savings potential is demonstrated. The results of this analysis can serve as basis for formulating more effective policies and may also be applicable to other countries with similarly ambitious targets.

Retrofit measures are an effective means to improve both the heating energy and carbon footprint of a building. On one hand, reducing the losses through the envelope reduces the energy consumption. On the other hand, updating the heating from a fossil-fuel based system to an emission-free one bears the potential for CO2-emission free operation. The latter can be achieved if the supply temperature of the heating system can be sufficiently reduced, such that the operation of a heat pump with a high coefficient of performance becomes feasible. For this, typically the heating area is increased to facilitate the heat transfer. Qualitatively, it is understood that increasing the heating area and improving the insulation of the envelope allows one to lower the supply temperature. However, it is unclear how these improvements relate to each other, or what their individual or combined effect is. In this research, we present a steady-state model to illustrate the impact of retrofit measures on the supply temperature. The model requires the determination of two dimensionless parameters, as well as an estimate for the thermal transmittance (U-value) of the envelope. For this, we developed a flexible, low-cost sensor network. We apply our model to a real retrofit scenario of a historically listed building in Zurich, Switzerland, and show that the current state of the building is already suitable for a low temperature heating system. The findings of our model are confirmed by a calibrated dynamic building simulation. The proposed model provides a means to relate energy savings to reduction of green house gases, and, thus to reduce the CO2 footprint of the building stock.