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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.

Nature-based solutions may actively reduce hydro-meteorological risks in urban areas as a part of climate change adaptation. However, the main reason for the increasing uptake of this type of solution is their many benefits for the local inhabitants, including recreational value. Previous studies on recreational value focus on studies of existing nature sites that are often much larger than what is considered as new NBS for flood adaptation studies in urban areas. We thus prioritized studies with smaller areas and nature types suitable for urban flood adaptation and divided them into four common nature types for urban flood adaptation: sustainable urban drainage systems, city parks, nature areas and rivers. We identified 23 primary valuation studies, including both stated and revealed preference studies, and derived two value transfer functions based on meta-regression analysis on existing areas. We investigated trends between values and variables and found that for the purpose of planning of new NBS the size of NBS and population density were determining factors of recreational value. For existing NBS the maximum travelling distance may be included as well. We find that existing state-of-the-art studies overestimate the recreational with more than a factor of 4 for NBS sizes below 5 ha. Our results are valid in a European context for nature-based solutions below 250 ha and can be applied across different NBS types and sizes.

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.

This paper presents a selective survey of papers, books, and reports that articulate recent trends of Security Constrained Economic Dispatch (SCED) of integrated renewable energy systems (IRES). The time-period under consideration is 2008 through 2020. This is done to provide an up-to-date review of the recent, major advancements in the SCED, and state-of-the-art since 2008. This helps identify further challenges needed in adopting smarter grids, and indicate ways to address these challenges. The study was conducted in three areas of interest that are relevant for articulating the recent trends of SCED. These areas are (i) SCED of power systems with IRES, (ii) SCED mathematical formulation and solution methods, and (iii) SCED challenges. The review results and research directions deduce that the state of the art research is not enough and needs special attention on following the path of artificial intelligence-based optimization.

This work deals with the simultaneous coordinated tuning of the FACTS (flexible AC transmission systems) POD power oscillation damping) controller and the conventional PSS (power system stabilizer) controllers in multimachine power systems. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among FACTS controller and PSS controllers are considered. Furthermore, their parameters are optimized simultaneously. Simulation results of multimachine power system validate the efficiency of this new approach. The proposed algorithm is an effective method for the tuning of multicontrollers in large power systems.

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.

Pressure ratios of modern high pressure radial compressors tend to increase along with pressure fluctuations and the excitation potential on the impellers. The vibrational interactions between side cavities, filled with high pressure fluid, and the impeller structure play an important role in designing a machine for reliable operation. However, they are not yet fully understood. Vibrations at frequencies that have been uncritical at lower pressure levels could become critical at a higher pressure level. Additionally, coupling effects between fluid and structure are becoming stronger at higher fluid densities. For a safe and reliable design, the excitation and the damping mechanism of coupled modes has to be better understood. To understand the interaction, especially regarding the damping behavior, of coupled structure and acoustic modes, a comprehension of the behavior of the uncoupled or weakly coupled modes is required. The structural damping ratio is very small and it has been analyzed in existing literature extensively. The damping behavior of uncoupled acoustic modes, however, is not yet well investigated. This paper focuses on the damping behavior of acoustic modes that are weakly coupled to structure modes. Measurement results gathered at the aeroacoustic test rig at the University of Duisburg-Essen are presented. The results show the influence of fluid pressure variations on the damping behavior of acoustic modes. Therefore, the response functions of some selected acoustic modes are evaluated with the Peak-to-Peak method. In general, the damping decreases with increasing fluid pressure. Furthermore, a relationship of the damping ratio, the kinematic viscosity, and the natural frequency of the acoustic modes has been detected.

Abstract Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.