• Energy Research
• 7. Clean energy close
• 15. Life on land close
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AbstractThe future European energy supply system will have a high share of renewable energy sources (RES) to meet the greenhouse gas emission policy of the European Commission. Such a system is characterized by the need for a strongly interconnected energy transport grid as well as a high demand of energy storage capacities to compensate the time fluctuating characteristic of most RE generation technologies. With the RE generators at the location of high harvest potential, the appropriate dimension of storage and transmission system between different regions, a cost efficient system can be achieved. To find the preferred target system, the optimization tool GENESYS (Genetic Optimization of a European Energy System) was developed. The example calculations under the assumption of 100% self-supply, show a need of about 2,500 GW RES in total, a storage capacity of about 240,000 GWh, corresponding to 6% of the annual energy demand, and a HVDC transmission grid of 375,000 GWkm. The combined cost for generation, storage and transmission excluding distribution, was estimated to be 6.87 ct/kWh.

Abstract A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

The Grid 4 Vehicles project (G4V) has analyzed the effects of a mass integration of electric and plug-in hybrid electric vehicles (EV and PHEV) into some distribution grids in Europe. A consortium consisting of six European research institutions and six European utilities has developed a methodology to investigate the effects of EV and PHEV in distribution grids and has proposed different options for intelligent and “grid-friendly” EV charging. This paper introduces the algorithms used for grid calculation, describes intelligent charging methodologies and demonstrates the most significant results of these calculations.

Due to climate change and current efforts to reduce emissions in the construction sector, this study evaluates and discusses the results of a comparative cradle-to-grave Life Cycle Assessment (LCA), with a main focus on Global Warming Potential for functionally equivalent carbon-reinforced concrete (CRC) and steel-reinforced concrete (SRC) façade panels for the first time. The novelty of this study is the focus on construction waste and, in particular, the worst-case application of non-recycled construction waste. The use of CRC requires a lower concrete thickness than SRC because the carbon fiber reinforcement does not corrode, in contrast to steel reinforcement. Façade panels of the same geometrical dimensions and structural performance were defined as functional units (FU). Assuming an End-of-Life (EoL) scenario of 50% landfill and 50% recycling, the Global Warming Potential (GWP, given in CO2 equivalent (CO2e)) of the CRC façade (411–496 kg CO2e) is shown to perform better than or equal to the SRC façade (492 kg CO2e). Changing the assumption of CRC to a worst-case scenario, going fully to landfill and not being recycled (single life cycle), turns the GWP results in favor of the SRC façade. Assuming a 50-year service life for the SRC façade panel and relativizing the emissions to the years, the more durable CRC façade performs much better. Finally, depending on the system boundary, the assumed EoL and lifetime, CRC can represent a lower-emission alternative to a functionally equivalent component made of SRC. The most important and “novel” result in this study, which also leads to future research opportunities, is that delicate adjustments (especially concerning EoL scenarios and expected service life) can lead to completely different recommendations for decision-makers. Only by combining the knowledge of LCA experts, structural engineers, and builders optimal decisions can be made regarding sustainable materials and building components.

This paper describes the methodology developed and the calculation steps used to evaluate the energy efficiency potential of office buildings. The methodology enables a detailed analysis of retrofit options for the building envelope and its energy supply system. Different simplification measures accelerate the data acquisition process for office building stock owners and allow a data handling according to the existing building information, thus enabling office building structures to be prompted to design typical building constructions. We implement solutions enabling both a time-saving accelerated data input for office buildings and the handling of incomplete data. An automated calculation of the most common refurbishment measures allows a comparison of up to 64 combinations of measures, the illustration of energy and CO2 savings, and an economic evaluation. The latter takes into account the time value of money, the uncertainty of future energy prices, and the possibility of delaying an investment. To this end, a net present value analysis and a real options analysis are implemented, enabling a comparison of retrofit alternatives with different initial and future cash flows both for buildings occupied by the investor (owner-occupier perspective) and for rented buildings (tenant perspective). Energy price scenarios as well as a Monte Carlo simulation account for the uncertainty in energy price trends. For a university building used as a test case, the simplified and time-saving data input methods were successfully tested and an automated evaluation of 64 typical retrofit combinations carried out. The results of the energy, ecological and economic efficiency evaluation shows that a generally preferred retrofit option cannot always be identified. Specifically, for the test case, the best-rated economic refurbishment possibility leads to the largest increase in final energy demand amongst all options considered, which points out the necessity of a multi-criteria evaluation.

Biotechnology can greatly improve the efficiency of forest tree breeding for the production of biomass, energy, and materials. However, EU regulations impede the market introduction of genetically modified (GM) trees so their socioeconomic and environmental benefits are not realized. European policy makers should concentrate on a science-based regulatory process.

Abstract This study simulated the effect of time on bioenergy production from dairy manure and associated variation in energy demand and GHG emission in Ohio and Hawaii. Increasing the residence-time (RT) decreases the bioenergy-production in a nonlinear-fashion, for both the states (Ohio and Hawaii). Using the main scenario in Hawaii, the highest bioenergy production for 30 days RT was 11.2 × 104 GJ. Life-cycle-assessment of produced bioenergy showed that energy requirement and GHG emission of the produced-bioenergy (GJ) varied from 0.65 to 0.67 and 28–35 kg CO2/GJ bioenergy respectively. Year-round bioenergy production through main scenario was more advantageous with respect to bioenergy production and GHG emission. Decreasing nitrogen concentration for algal biomass production increased the bioenergy production significantly with a reduced energy demand and marginally increased GHG emission. Hence, the LCA model predicted that running a biorefinery for short residence time, and using diluted waste, could provide bioenergy with reduced energy requirement and GHG-emission.

Abstract A thermogravimetric analyzer (TGA), a fluidized bed reactor (FBR) and a drop tube reactor (DTR) are used to study the effect of reactor type, heating rate and temperature on the pyrolysis of pulverized walnut shell particles in N2 and in CO2. These setups cover a temperature range of 400–1300 K with heating rates of 10−1 to 105 K s−1. The single first-order model in combination with an Arrhenius approach is used to describe the pyrolysis reaction. Derived activation energies for all setups show similar values ( E a , TGA = 71.96 kJ mol−1, E a , FBR = 68.60 kJ mol−1 and E a , DTR = 60.83 kJ mol−1), while an increase in the reactor temperature tend to lower the activation energy. Pyrolysis gas compositions in FBR and DTR reveal consistent trends towards lower H2O and higher CO contents with increasing reactor temperature. To evaluate the impact of CO2 on the solid conversion, TGA measurements in CO2 are used to determine gasification kinetics ( E a , g = 214.1 kJ mol−1, A g = 71.96 s−1). CFD simulations using these kinetics in CO2 drop tube experiments let assume that the changed thermophysical properties of the gas and not the gasification reaction lead to the observed stronger conversion in CO2 compared to N2.

Abstract The integration of renewable energy sources into building energy systems and the progressive coupling between the thermal and electrical domains makes the analysis of these systems increasingly complex. At the same time, however, more and more building monitoring data is being collected. The manual evaluation of this data is time-consuming and requires expert knowledge. Hence, there is a strong need for tools that enable the automatic knowledge extraction from these huge data sets to support system integrators and favor the development of smart energy services, e.g., predictive maintenance. One crucial step in knowledge extraction is the detection of change points and hidden states in measurements. In this work, we present a tool for automated detection of hidden operation modes based on multivariate time series data deploying motif-aware state assignment (MASA). The tool is evaluated utilizing measurements of a heat pump and compared to two baseline algorithms, namely k-Means and k-Medoids. MASA performs particularly well on noisy data, where it shows only a small deviation in the number of detected change points compared to the ground truth with up to 77% accuracy. Furthermore, it almost always outperforms the baseline algorithms, which in turn require extensive preprocessing.