| 000 | 02948naaaa2200313uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/75065 | ||
| 005 | 20220219200717.0 | ||
| 020 | _a4512 | ||
| 020 | _a9783832545123 | ||
| 024 | 7 |
_a10.30819/4512 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aUTR _2bicssc |
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| 100 | 1 |
_aMSP Veith, Eric _4auth |
|
| 245 | 1 | 0 | _aUniversal Smart Grid Agent for Distributed Power Generation Management |
| 260 |
_aBerlin _bLogos Verlag Berlin _c2017 |
||
| 300 | _a1 electronic resource (266 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _a``Somewhere, there is always wind blowing or the sun shining.'' This maxim could lead the global shift from fossil to renewable energy sources, suggesting that there is enough energy available to be turned into electricity. But the already impressive numbers that are available today, along with the European Union's 20-20-20 goal---to power 20% of the EU energy consumption from renewables until 2020---, might mislead us over the problem that the go-to renewables readily available rely on a primary energy source mankind cannot control: the weather. At the same time, the notion of the smart grid introduces a vast array of new data coming from sensors in the power grid, at wind farms, power plants, transformers, and consumers. The new wealth of information might seem overwhelming, but can help to manage the different actors in the power grid. This book proposes to view the problem of power generation and distribution in the face of increased volatility as a problem of information distribution and processing. It enhances the power grid by turning its nodes into agents that forecast their local power balance from historical data, using artificial neural networks and the multi-part evolutionary training algorithm described in this book. They pro-actively communicate power demand and supply, adhering to a set of behavioral rules this book defines, and finally solve the 0-1 knapsack problem of choosing offers in such a way that not only solves the disequilibrium, but also minimizes line loss, by elegant modeling in the Boolean domain. The book shows that the Divide-et-Impera approach of a distributed grid control can lead to an efficient, reliable integration of volatile renewable energy sources into the power grid. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-sa/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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| 546 | _aEnglish | ||
| 650 | 7 |
_aDistributed systems _2bicssc |
|
| 653 | _aSmart Grid | ||
| 653 | _aPower Grid Management | ||
| 653 | _aArtificial Intelligence | ||
| 653 | _aBoolean Algebra | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://www.logos-verlag.de/ebooks/OA/978-3-8325-4512-3.pdf _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/75065 _70 _zDOAB: description of the publication |
| 999 |
_c40663 _d40663 |
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