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Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

In order to protect our health and the environment, numerous limit and guideline values have been laid down for substances and contaminants that need to be regularly monitored. In such cases, the result of an analytical measurement is compared with its corresponding limit value, and a conclusion is then drawn as to whether the given limit has been violated or not. A simple approach to this would be to simply check whether the measured value is higher than the limit value or not. However, this approach is only acceptable when the measured value is far from the limit value. In cases where the analytical result is close to the limit value, the measurement uncertainty must also be taken into account when deciding whether an observed limit value transgression is significant or not—as, for example, demanded in DIN EN 17025 [1]. Therefore, it is very important that chemical laboratories that perform routine tests for limit value transgression determine the measurement uncertainties associated with their methods by long-term validation [2, 3]. However, even in cases where a measurement result is presented together with its measurement uncertainty, clear rules should be established for comparing the measured value with a limit or guideline value [4, 5]. If such rules are misunderstood or ambiguous, feelings of insecurity can arise among the decision makers. If systematic errors are excluded, the measurement uncertainty can be reduced to random errors and dealt with statistically. Therefore, statistical concepts can be used to unobjectionably and reproducibly justify a decision made about whether a limit value has been violated or observed, and on the other hand to estimate the analytical effort required to achieve significant results. A test result leading to the statement that the limit value μ0 (in analogy to the terminology of clinical chemistry [7, 8]) has been transgressed will be called “positive,” while an inconspicious result will be termed “negative.” In this case, a test for a limit value transgression can lead to four possible outcomes, as listed in Table 1. Two types of erroneous outcome can occur, namely a false-positive (type I error) or a false-negative (type II error) result. The probability of a type I error is usually denoted α, and that of a type II error β.

Abstract An attractive path to the production of hydrogen from water is a two-step thermo chemical cycle powered by concentrated sunlight from a solar tower system. In the first process step the redox system, a ferrite coated on a monolithic honeycomb absorber, is present in its reduced form while the concentrated solar energy hits the ceramic absorber. When water vapour is fed to the honeycomb at 800 °C, oxygen is abstracted from the water molecules, bond in the redox system and hydrogen is produced. When the metal oxide system is completely oxidised it is heated up for regeneration at 1100–1200 °C in an oxygen-lean atmosphere. Under those conditions and in the second process step, oxygen is set free from the redox system, so the metal oxide is being reduced and after completion of the reaction again capable for water splitting. Since the overall process consists of two core reaction steps, which need to be carried out sequentially in a reactor unit at two different temperature steps, a special process and plant concept had to be developed enabling the continuous supply of product regardless of the alternating nature of the solar reactor operation. The challenge of the process control is to keep the two core reaction temperatures constant and to ensure regular temperature switches after completion of the individual process steps, independent of the weather conditions, like DNI fluctuation, clouds and wind speed. Also start-up, the fast switching after completion of half-cycles and the shutdown must be controlled. State of the art is the manual switching of heliostats to fulfil those control tasks. This paper describes the development and use of a system model of this process. The model consists of three main parts: the simulation of the solar flux distribution at the receiver aperture, the simulation of the temperatures in the reactor modules and the simulation of the hydrogen generation. It can be used for the analysis of the operational behaviour. The model is intended to be used in the future for the control of the whole process.

Abstract Species concentration (e.g. CO) and temperature measurements in the combustion field require fast-response technique without interfering species. In the last decade, tunable diode lasers have been established as strong technique to measure species such as CO, CO2, and H2O as well as temperature with high sensitivity. The drawback is the degree of interference that might hamper the robustness of the technique. In this work simultaneous measurements of temperature and CO concentration were carried out using an interference-free mid-infrared laser-based absorption technique behind reflected shock waves. Two transition lines of CO (P(v″ = 0, J″ = 21) and P(v″ = 1, J″ = 21)) in the fundamental vibrational band near 4.87 and 4.93 μm, respectively, were selected. Absorbance interferences from CO2 and H2O at room and high temperatures were evaluated. Spectroscopic parameters for the development of the system were measured: line strengths and collisional broadening coefficients (in Ar) of both lines were obtained at 1020–1950 K by using the scanned-wavelength direct-absorption method. The technique was demonstrated for non-reactive and reactive mixtures. For the non-reactive case, temperature and CO concentration were measured at 1030–1910 K and 1.0–3.7 bar. For the reactive case, oxidation of i-C8H18/O2/Ar and i-C8H18/C2H5OH/O2/Ar mixtures were investigated at three equivalence ratios of 2.0, 1.0, and 0.5. The two newly adopted lines exhibited good performance in the detection of CO concentration and are immune to interferences from CO2 and H2O. In addition, the simulated data from the state-of-the-art isooctane/ethanol mechanisms in literature were compared with the measured data, showing overall good agreement.

This article reviews some of the major lines of recent scientific progress relevant to the choice of global climate policy targets, focusing on changes in understanding since publication of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Developments are highlighted in the following major climate system components: ice sheets; sea ice; the Atlantic Meridional Overturning Circulation; tropical forests; and accelerated carbon release from permafrost and ocean hydrates. The most significant developments in each component are identified by synthesizing input from multiple experts from each field. Overall, while large uncertainties remain in all fields, some substantial progress in understanding is revealed.

This paper proposes an aggregated approach for the technical and economic assessment of battery energy storage systems (BESSs) in congested distribution grids (low voltage and medium voltage). For this purpose, the usage of BESS to avoid or temporally shift grid extensions is analyzed and compared with other technical measures such as grid extensions, innovative grid equipment, and feed-in curtailment. A combined optimization approach is used to identify jointly optimal economic decision of grid and storage operators. The investigations highlight the economic competitiveness of BESS, compared to other measures, when additional participation of BESS in wholesale market is allowed. It is shown that optimal decisions for grid development are strongly dependent on key grid parameters like line length and level of excess photovoltaic-feed-in. Moreover, the applied regulations, e.g., renewable curtailment rules, are found to impact considerably on the optimal decisions. The proposed methodology is, in contrast to existing approaches, suitable for the investigation of large varieties of grid configurations and grid utilizations with limited efforts. It thus allows identifying situations, in which the application of BESS in distribution grids is most effective. Moreover, it enables a thorough assessment of the impact of regulatory changes given large heterogeneity of existing distribution grids.

Predicting the variance of oil-price returns is of paramount importance for policymakers and investors. Recent research has focused on whether disaggregate measures of economic-policy uncertainty provide better forecasts. Given that the United States (U.S.) is a major player in the international oil market, we extend this line of research by exploring by means of machine-learning techniques whether accounting for U.S. state-level measures of economic-policy uncertainty results in more accurate forecasts. We find improvements in forecast accuracy, especially when we study intermediate and long forecast horizons. This finding is robust to various changes in the model configuration (realized variance vs. realized volatility, sample period, recursive vs. rolling-estimation window, loss function of forecast consumers). Understandably, our findings have important implications for oil traders and policy authorities.

This paper reports how endogenous economic growth and technological change have been introduced into a global econometric model. It explains how further technological change might be induced by mitigation policies so as to reduce greenhouse gas emissions and stabilize atmospheric concentrations. These are the first results of a structural econometric approach to modeling the global economy using the model E3MG (energy-environment-economy model of the globe), which in turn constitutes one component in the Community Integrated Assessment System (CIAS) of the UK Tyndall Centre. The model is simplified to provide a post-Keynesian view of the long-run, with an indicator of technological progress affecting each region’s exports and energy use. When technological progress is endogenous in this way, long-run growth in global GDP is partly explained by the model. Average permit prices and tax rates about $430/tC (1995) prices after 2050 are sufficient to stabilize atmospheric concentrations at 450ppm CO2 after 2100. They also lead to higher economic growth.