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- ItemCorrelated power time series of individual wind turbines: A data driven model approach(Woodbury, NY : American Inst. of Physics, 2020) Braun, Tobias; Waechter, Matthias; Peinke, Joachim; Guhr, ThomasWind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function. © 2020 Author(s).
- ItemManaging power demand from air conditioning benefits solar pv in India scenarios for 2040(Basel : MDPI, 2020) Ershad, Ahmad Murtaza; Pietzcker, Robert; Ueckerdt, Falko; Luderer, GunnarAn Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33-45% (without DSM) to ∼45-60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply. © 2020 by the authors.
- ItemSemitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks(London : Hindawi Limited, 2017) Peng, H.; Tian, Y.; Kurths, J.Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.
- ItemBio-IGCC with CCS as a long-term mitigation option in a coupled energy-system and land-use model(Amsterdam [u.a.] : Elsevier, 2011) Klein, D.; Bauer, N.; Bodirsky, B.; Dietrich, J.P.; Popp, A.This study analyses the impact of techno-economic performance of the BIGCC process and the effect of different biomass feedstocks on the technology's long term deployment in climate change mitigation scenarios. As the BIGCC technology demands high amounts of biomass raw material it also affects the land-use sector and is dependent on conditions and constraints on the land-use side. To represent the interaction of biomass demand and supply side the global energy-economy-climate model ReMIND is linked to the global land-use model MAgPIE. The link integrates biomass demand and price as well as emission prices and land-use emissions. Results indicate that BIGCC with CCS could serve as an important mitigation option and that it could even be the main bioenergy conversion technology sharing 33% of overall mitigation in 2100. The contribution of BIGCC technology to long-term climate change mitigation is much higher if grass is used as fuel instead of wood, provided that the grass-based process is highly efficient. The capture rate has to significantly exceed 60 % otherwise the technology is not applied. The overall primary energy consumption of biomass reacts much more sensitive to price changes of the biomass than to technoeconomic performance of the BIGCC process. As biomass is mainly used with CCS technologies high amounts of carbon are captured ranging from 130 GtC to 240 GtC (cumulated from 2005-2100) in different scenarios.
- ItemHow global climate policy could affect competitiveness(Amsterdam [u.a.] : Elsevier Science, 2019) Ward, Hauke; Steckel, Jan Christoph; Jakob, MichaelA global uniform carbon price would be economically efficient and at the same time avoid ‘carbon-leakage’. Still, it will affect the competitiveness of specific industries, economic activity and employment across countries. This paper assesses short-term economic shocks following the introduction of a global carbon price that would be in line with the Paris Agreement. Based on the World Input-Output Database (WIOD), we trace the carbon content of final output through global supply chains. This allows us to estimate how prices of the final output would react to the introduction of a global carbon price. We find that impacts on industrial competitiveness are highly heterogeneous across regions and economic sectors. The competitive position of Brazil, Japan, the USA and advanced economies of the EU is likely to improve, whereas industries and labor markets in newly industrializing Asian economies as well as Eastern Europe are likely to experience substantial adverse impacts. © 2019 The Author(s)
- ItemTackling long-term climate change together: The case of flexible CCS and fluctuating renewable energy(Amsterdam [u.a.] : Elsevier, 2011) Ludig, S.; Haller, M.; Bauer, N.The present study aims at shedding light into the interaction of fluctuating renewables and the operational flexibility of post-combustion capture plants in the framework of a long-term model. We developed a model of the electricity sector taking into account both long-term investment time scales to represent plant fleet development under economic and climate constraints as well as short time scales to consider fluctuations of demand and renewable energy sources. The LIMES model allows us to determine the respective roles of renewables and CCS in climate change mitigation efforts within the electricity sector. Furthermore, we assess the influence of natural gas prices on fuel choice and investigate the shares of competing CCS approaches in the technology mix. We find that the optimal technology mix includes large shares of renewables and simultaneously different competing CCS technologies, depending on emission constraints and fuel prices.
- ItemImproving power grid transient stability by plug-in electric vehicles(Bristol : Institute of Physics Publishing, 2014) Gajduk, A.; Todorovski, M.; Kurths, J.; Kocarev, L.Plug-in electric vehicles (PEVs) can serve in discharge mode as distributed energy and power resources operating as vehicle-to-grid (V2G) devices and in charge mode as loads or grid-to-vehicle devices. It has been documented that PEVs serving as V2G systems can offer possible backup for renewable power sources, can provide reactive power support, active power regulation, load balancing, peak load shaving, can reduce utility operating costs and can generate revenue. Here we show that PEVs can even improve power grid transient stability, that is, stability when the power grid is subjected to large disturbances, including bus faults, generator and branch tripping, and sudden large load changes. A control strategy that regulates the power output of a fleet of PEVs based on the speed of generator turbines is proposed and tested on the New England 10-unit 39-bus power system. By regulating the power output of the PEVs we show that (1) speed and voltage fluctuations resulting from large disturbances can be significantly reduced up to five times, and (2) the critical clearing time can be extended by 20-40%. Overall, the PEVs control strategy makes the power grid more robust.
- ItemA semantic sensor web for environmental decision support applications(Basel : MDPI AG, 2011) Gray, A.J.G.; Sadler, J.; Kit, O.; Kyzirakos, K.; Karpathiotakis, M.; Calbimonte, J.-P.; Page, K.; Garćia-Castro, R.; Frazer, A.; Galpin, I.; Fernandes, A.A.A.; Paton, N.W.; Corcho, O.; Koubarakis, M.; de Roure, D.; Martinez, K.; Gómez-Pérez, A.Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England.
- ItemMeasuring and monitoring urban impacts on climate change from space(Basel : MDPI, 2020) Milesi, Cristina; Churkina, GalinaAs urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations.
- ItemMulti-temporal analysis of forest fire probability using socio-economic and environmental variables(Basel : MDPI, 2019) Kim, Sea Jin; Lim, Chul-Hee; Kim, Gang Sun; Lee, Jongyeol; Geiger, Tobias; Rahmati, Omid; Son, Yowhan; Lee, Woo-KyunAs most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.