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News Article | December 5, 2016
Site: www.eurekalert.org

How much electricity flows through the grid? When and where? Where are the bottlenecks? What happens when wind turbines and solar cells feed in additional energy? The answer to these questions are essential for the global energy turnaround. However, for a valid planning, one first needs a solid understanding of the infrastructure. Researchers at the Technical University of Munich (TUM) are now collecting information via an open source platform accessible to everyone. Hundreds of volunteers are already underway, and their numbers are growing every day. Armed with the OpenGridMap app on their smart phones, they meander through Munich, Berlin, Tokyo and even Teheran. Just another cell phone game? "No, we aren't chasing Pokémons," reassures Jose Rivera, director of the OpenGridMap project. "What we are interested in is the electrical infrastructure: High-voltage and low-voltage power lines, transformer sub-stations, wind turbines and solar power plants." Users of the app share photos and locations with a server housed in the Department of Computer Science at TU Munich. There, the information is analyzed, evaluated and ultimately loaded into the open source OpenStreetMap map system. The goal is a map of electric power grids worldwide. "This is a prerequisite for the energy turnaround - not only here in Germany, but in all countries around the world. You can only plan the restructuring of the energy supply if you know exactly where powerlines are located and at which locations power from high-voltage lines is transformed and fed into the low-voltage networks," explains Prof. Hans-Arno Jacobsen, director of the Department of Energy Informatics and Middleware at TUM. Building on this foundation, it is possible, for example, to simulate how feeding in renewable energy will affect the grid and where bottlenecks or surplus capacity will arise and where it might make sense to build storage facilities. What is lacking thus far is a solid pool of data, says Rivera: "Of course every power utility knows its own grids, but there are many power companies but very few open their data to the public. This is compounded in emerging markets by the fact that the information is frequently not even digitized. Contracting a company to compile the infrastructure for an entire country, or even the entire world would not be affordable for the researchers." The cost-effective alternative: crowd sourcing. The TU Munich team did not have to start at zero: A community of volunteers has been collecting data for the Wiki global map OpenStreetMap for over 10 years. This publicly accessible data set also contains information on electric power grids. "However, it they are neither complete nor verified," explains Rivera. "And that is precisely what we are now hoping to change:" Half a year ago the researcher from the Department of Energy Informatics and Middleware published his OpenGridMap app on the Google Playstore. Since then he has been looking for volunteers to map wind turbines, solar power plants, transformer sub-stations and power lines using their mobile phones. Rivera verifies the information - is a transformer sub-station indeed a transformer sub-station? - and uploads the data to the open source map. There the network of verified grids in becoming increasingly denser. Red lines traverse the map like a mesh of arteries. The denser the mesh of mapped points, the more information can be generated. In Garching, for example, where a particularly large number of volunteers are active, the researcher has successfully calculated the location of subterranean power lines leading to houses using a novel algorithm. The idea is to make data from the project available to engineers and scientists around the world. "There are many potential applications for the OpenGridMap," Professor Jacobsen emphasizes. "You could investigate the feasibility of making a state like Bavaria energy autonomous." And someone attempting to improve the infrastructure a developing or emerging country could easily recognize how far a given town is removed from the nearest powerline. It is no wonder that there is great interest in the OpenGridMap project: Siemens is a project mentor and the World Bank also supports the undertaking. The project receives further funding from the German Federal Ministry for Education and Research (BMBF) and the Alexander Humboldt Foundation. Jose Rivera, Johannes Leimhofer, and Hans-Arno Jacobsen. OpenGridMap: towards automatic power grid simulation model generation from crowdsourced data. Computer Science-Research and Development (2016): 1-11 - DOI: 10.1007/s00450-016-0317-4


Hundreds of volunteers are already underway, and their numbers are growing every day. Armed with the OpenGridMap app on their smart phones, they meander through Munich, Berlin, Tokyo and even Teheran. Just another cell phone game? "No, we aren't chasing Pokémons," reassures Jose Rivera, director of the OpenGridMap project. "What we are interested in is the electrical infrastructure: High-voltage and low-voltage power lines, transformer sub-stations, wind turbines and solar power plants." Users of the app share photos and locations with a server housed in the Department of Computer Science at TU Munich. There, the information is analyzed, evaluated and ultimately loaded into the open source OpenStreetMap map system. The goal is a map of electric power grids worldwide. "This is a prerequisite for the energy turnaround - not only here in Germany, but in all countries around the world. You can only plan the restructuring of the energy supply if you know exactly where powerlines are located and at which locations power from high-voltage lines is transformed and fed into the low-voltage networks," explains Prof. Hans-Arno Jacobsen, director of the Department of Energy Informatics and Middleware at TUM. Building on this foundation, it is possible, for example, to simulate how feeding in renewable energy will affect the grid and where bottlenecks or surplus capacity will arise and where it might make sense to build storage facilities. What is lacking thus far is a solid pool of data, says Rivera: "Of course every power utility knows its own grids, but there are many power companies but very few open their data to the public. This is compounded in emerging markets by the fact that the information is frequently not even digitized. Contracting a company to compile the infrastructure for an entire country, or even the entire world would not be affordable for the researchers." The cost-effective alternative: crowd sourcing. The TU Munich team did not have to start at zero: A community of volunteers has been collecting data for the Wiki global map OpenStreetMap for over 10 years. This publicly accessible data set also contains information on electric power grids. "However, it they are neither complete nor verified," explains Rivera. "And that is precisely what we are now hoping to change:" Half a year ago the researcher from the Department of Energy Informatics and Middleware published his OpenGridMap app on the Google Playstore. Since then he has been looking for volunteers to map wind turbines, solar power plants, transformer sub-stations and power lines using their mobile phones. Rivera verifies the information - is a transformer sub-station indeed a transformer sub-station? - and uploads the data to the open source map. There the network of verified grids in becoming increasingly denser. Red lines traverse the map like a mesh of arteries. The denser the mesh of mapped points, the more information can be generated. In Garching, for example, where a particularly large number of volunteers are active, the researcher has successfully calculated the location of subterranean power lines leading to houses using a novel algorithm. The idea is to make data from the project available to engineers and scientists around the world. "There are many potential applications for the OpenGridMap," Professor Jacobsen emphasizes. "You could investigate the feasibility of making a state like Bavaria energy autonomous." And someone attempting to improve the infrastructure a developing or emerging country could easily recognize how far a given town is removed from the nearest powerline. It is no wonder that there is great interest in the OpenGridMap project: Siemens is a project mentor and the World Bank also supports the undertaking. The project receives further funding from the German Federal Ministry for Education and Research (BMBF) and the Alexander Humboldt Foundation. More information: Jose Rivera et al, OpenGridMap: towards automatic power grid simulation model generation from crowdsourced data, Computer Science - Research and Development (2016). DOI: 10.1007/s00450-016-0317-4 Jose Rivera, Christoph Goebel, David Sardari, and Hans-Arno Jacobsen. "OpenGridMap: An Open Platform for Inferring Power Grids with Crowdsourced Data." DA-CH Conference on Energy Informatics. Springer International Publishing, 2015. DOI: 10.1007/978-3-319-25876-8_15


Taseska V.,Energy Informatics | Markovska N.,Energy Informatics | Causevski A.,Ss. Cyril and Methodius University of Skopje | Bosevski T.,Energy Informatics | Pop-Jordanov J.,Energy Informatics
Energy | Year: 2011

In this paper the GHG mitigation potential of a power system with prevailing use of lignite is assessed through the example of the Macedonian power system. The analysis is conducted using the WASP model in order to develop three different scenarios (business as usual - BAU and two mitigation scenarios) for the power system expansion over the period 2008-2025. In the first mitigation scenario two gas power plants with combined cycle are planned to replace some of the lignite-based capacities. The second mitigation scenario, besides the gas power plants, assumes electricity consumption reduction related to the large industrial consumers and an increased share of new renewable energy sources. Detailed calculations of the GHG emissions are made for all scenarios. The comparison of emissions in 2025 and in 2008 shows that the increase of 78% in the case of predominantly lignite BAU scenario is reduced to 41% by the first mitigation scenario, and to 14% by the second mitigation scenario. The mitigation costs appeared to be less then 10 $/t CO2-eq for the first mitigation scenario, and even negative for the second one. © 2010 Elsevier Ltd.


Tomovski I.,Energy Informatics | Kocarev L.,Energy Informatics | Kocarev L.,University of California at San Diego
IEEE Transactions on Circuits and Systems I: Regular Papers | Year: 2012

A simple topology-manipulative algorithm for control of virus spreading trough complex networks is suggested. The algorithm is studied and applied on an SIS model type of an infection, and the system is described with a set of nonlinear difference probabilistic equations, that represent the dynamics of infection probabilities of nodes and existence probabilities of links in the graph. The validity of the control mechanism is first proven theoretically. Then, simulations are performed and results from both the realistic (status dependent) and probabilistic (analyzed) systems are compared, proving numerically as well, that the suggested algorithm is valid tool for infection eradication from complex networks. Several strategies of control implementation were tested and efficiency of each evaluated on the probabilistic system. © 2012 IEEE.


Vlachogiannis J.G.,Energy Informatics
Journal of Power Sources | Year: 2014

This short communication introduces the first marine-current power generation model to be integrated into power flow studies of smart grids. The stochastic aspect of marine-current velocity affecting the real power output of marine-current generators is provided by a closed formula. Also, a new closed formula for power coefficient of marine-current generators versus marine-current velocities is set up. The introduced marine-current power generation model is validated on real measurements obtained in the sub-sea areas of Alderney Race (Channel Islands) in UK and Gun-barrel passage in Fiji. © 2013 Elsevier B.V. All rights reserved.


Cosic B.,University of Zagreb | Markovska N.,Energy Informatics | Krajacic G.,University of Zagreb | Taseska V.,Energy Informatics | Duic N.,University of Zagreb
Applied Thermal Engineering | Year: 2012

The energy sector in Macedonia is the main emitter of greenhouses gases (GHG) with share of about 70% in the total annual emissions. Furthermore, within the energy sector, 70e75% of emissions are associated with the electricity generation due to the predominant role of the lignite fuelled power plants. This makes the electricity sector the most significant key source and, at the same time, the main target for CO 2 emissions reduction. Recently, the government has adopted a strategy for the use of RES which identifies a target of 21% of final energy consumption from RES by 2020. The main goal of this paper is to investigate environmental and economic aspects of higher penetration of renewables into energy system of Macedonia. For this purpose a reference energy scenario for the power system expansion is developed by making use of EnergyPLAN model. The reference energy system was developed for the year 2020, and then used in the scenario analyses. The analyses of four 'RES' scenarios reveal that renewables can reduce CO 2 emissions between 0.84% and 9.54% compared to reference scenario. Increase of CO 2 price for double, compared to today s price, will lead to increase of annual operating costs over 26% in all the scenarios considered. In the case of doubling the lignite price, annual operating costs in scenarios will be increased between 6.5% and 7.6%. © 2011 Elsevier Ltd. All rights reserved.


Taseska V.,Energy Informatics | Markovska N.,Energy Informatics | Callaway J.M.,Technical University of Denmark
Energy | Year: 2012

Although previous climate change research has documented the effects of linking mitigation and adaptation in the energy sector, there is still a lack of integrated assessment, particularly at national level. This paper may contribute to fill this gap, identifying the interactions between climate change and the energy demand in Macedonia.The analyses are conducted using the MARKAL (MARKet ALlocation)-Macedonia model, with a focus on energy demand in commercial and residential sectors (mainly for heating and cooling). Three different cases are developed: 1) Base Case, which gives the optimal electricity production mix, taking into account country's development plans (without climate change); 2) Climate Change Damage Case, which introduces the climate changes by adjusting the heating and cooling degree days inputs, consistent with the existing national climate scenarios; and 3) Climate Change Adaptation Case, in which the optimal electricity generation mix is determined by allowing for endogenous capacity adjustments in the model. This modeling exercise will identify the changes in the energy demand and in electricity generation mix in the Adaptation Case, as well as climate change damages and benefits of the adaptation. © 2012 Elsevier Ltd.


Dedinec A.,Energy Informatics | Markovska N.,Energy Informatics | Taseska V.,Energy Informatics | Duic N.,University of Zagreb | Kanevce G.,Energy Informatics
Energy | Year: 2013

As forecasted by the International Energy Agency Energy Technology Perspectives baseline scenario, the largest increment in LDV (light-duty vehicles) stock, travel demand and transport sector energy consumption will take place in the developing world. In the developing countries where the import of used vehicles is allowed, a considerable portion of the LDV stock increment will be realized with used vehicles.In this paper, the analytical framework for assessment of climate change mitigation potential of transport sector in developing countries is adapted in order to incorporate the expected vehicle fleet increase with used vehicles. The evaluation of appropriate mitigation strategies is performed using the GHG Costing Model (GACMO), which compares each mitigation option with the BAU (business-as-usual) option and determines its environmental effectiveness (t CO2 reduced) and economic effectiveness (US$/t CO2 reduced).The adapted analytical framework is applied on the case of transport sector of Macedonia, evaluating appropriate options inline with five mitigation strategies: improvement of vehicle fleet, introduction of low carbon fuels, improvement of travel behaviour, advancement of vehicle equipment and improvement of driver behaviour. The resulting marginal cost curve for the year 2020 indicates a total achievable reduction of 22% with respect to BAU GHG transport sector emissions, with bulk of it at relatively high specific costs of around 90 US$/t CO2. © 2013 Elsevier Ltd.


Susmikanti M.,Energy Informatics
AIP Conference Proceedings | Year: 2013

The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life. © 2013 AIP Publishing LLC.


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