Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

Analysis of cogeneration in the present energy framework

In this paper, a general vision of cogeneration penetration in the European Union is shown; after this, a case study is included, evaluating as a function of two factors (electricity and emission allowance prices) the suitability of installing, for an industry with a determined thermal demand, two different options. The first one is a gas turbine cogeneration plant generating steam through a heat recovery steam generator (HRSG). The second one consists of installing a natural gas boiler for steam production covering the electricity demand from the grid. The CO2 emissions from both options are compared regarding different kinds of generation mixes from the electricity grid in the case of using the industrial boiler; taking into account the advantages of using biomass in relation to emissions, a last comparison has been carried out considering a biomass boiler instead of the natural gas boiler.

Cost-free feed-in tariffs for renewable energy deployment in Spain

Feed-in-tariff (FIT) schemes have been widely employed to promote renewable energy deployment. While FITs may be perceived by consumers as an extra cost, renewable energies cause a noticeable price reduction in wholesale electricity markets. We analyse both effects for the case of the Spanish electricity market during 2010. In particular, we examine the level of FITs that makes savings and extra costs to be similar on an hourly basis. Results are obtained for a wide range of renewable generation scenarios. It is found that FITs with null extra costs for consumers are in the range of 50–80 €/MWh. Some of the side-effects of a high penetration of renewable energy in the market are analysed in detail and discussed.