Energy supply and storage optimization for mixed-type buildings

When dealing with power system emergency caused by natural disaster, information survey is the important foundation of emergency power supply and repair. The information credibility would directly inflect the credibility and timeliness of electric system emergency work. Therefore, this paper starts the study from the point of information credibility and sets minimizing total cost and loss as the objective function, taking time constraint, transportation road constraint, information credibility constraint, and the parameters of different facilities into consideration and building a location and storage optimization model for electric system emergency facility. And a corresponding facility transportation model was built considering the power loss in demand points. The simulation result shows that the models proposed by this paper could satisfy the requirement of information credibility, improve the adaptability to demand change, and cut down total cost and loss.

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What to Expect from the 2020 Gas Package

Politics and Governance ◽

10.17645/pag.v7i1.1747 ◽

2019 ◽

Vol 7 (1) ◽

pp. 165-169 ◽

Cited By ~ 1

Author(s):

Maria Olczak ◽

Andris Piebalgs

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

European Commission ◽

Energy Supply ◽

Fossil Fuels ◽

Gas Production ◽

Electricity Sector ◽

Renewable Electricity ◽

And Storage ◽

The Eu

Gas is considered an important part of the European Union’s (EU) energy mix. Making up a quarter of the energy consumed in the EU, it is widely used by both households and industry. Gas supports the penetration of intermittent renewable electricity and is considered the cleanest of the fossil fuels but its combustion emits a considerable amount of greenhouse gases. In the fight against climate change, the EU has committed itself to the near-complete decarbonisation of the energy sector well before 2050. This will have a significant impact on the gas sector, especially in the EU, which has significant gas transportation and storage assets. This commentary examines two potential pathways that could enable the gas sector to contribute to the EU’s decarbonisation efforts while continuing to play a substantial role in the EU’s energy supply. The pathways include gas and electricity sector coupling and the substantial increase of renewable gas production. Those options, which are not mutually exclusive, provide an opportunity for the gas sector to thrive in a decarbonised energy future. In some cases, it could require changes in the EU’s gas legislation announced by the European Commission to be proposed in 2020.

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Renewable energy supply and storage

10.6027/tn2016-563 ◽

2016 ◽

Author(s):

Peter Bode Nielsen ◽

Morten Hørmann ◽

Jakob Nymann Rud ◽

Frederik Møller Laugesen

Keyword(s):

Renewable Energy ◽

Energy Supply ◽

And Storage

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Integration of IoT Streaming Data With Efficient Indexing and Storage Optimization

IEEE Access ◽

10.1109/access.2020.2980006 ◽

2020 ◽

Vol 8 ◽

pp. 47456-47467 ◽

Cited By ~ 3

Author(s):

Quang-Tu Doan ◽

A. S. M. Kayes ◽

Wenny Rahayu ◽

Kinh Nguyen

Keyword(s):

Streaming Data ◽

Storage Optimization ◽

And Storage

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NetCDF: Performance and Storage Optimization of Meteorological Data

10.5194/egusphere-egu2020-21549 ◽

2020 ◽

Author(s):

Valentín Kivachuk Burdá ◽

Michaël Zamo

Keyword(s):

Neural Network ◽

Cloud Cover ◽

Deep Neural Network ◽

Meteorological Data ◽

General Assembly ◽

File Format ◽

Meteorological Field ◽

File Formats ◽

Storage Optimization ◽

And Storage

Any software relies on data, and the meteorological field is not an exception. The importance of using correct and accurate data is as important as using it efficiently. GRIB and NetCDF are the most popular file formats used in Meteorology, being able to store exactly the same data in any of them. However, they differ in how they internally treat the data, and transforming from GRIB (a simpler file format) to NetCDF is not enough to ensure the best efficiency for final applications.In this study, we improved the performance and storage of ARPEGE cloud cover forecasts post-processing with convolutional neural network and Precipitation Nowcasting using Deep Neural Network projects (proposed in other sessions for the EGU general assembly). The data treatments of both projects were studied and different NetCDF capabilities were applied in order to obtain significantly faster execution times (up to 60 times faster) and more efficient space usage.

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