000			04911cam a2200577Mu 4500
001			on1028234286
003			OCoLC
005			20230405100223.0
006			m d
007			cr |n|---|||||
008			180310s2017 gw o 000 0 eng d
040			_aEBLCP _beng _epn _cEBLCP _dYDX _dEZ9 _dOCLCQ _dUKKNU _dOCLCF _dOCLCO _dUAB _dOCLCQ _dN$T
019			_a1028592037 _a1028655432
020			_a9783832592233
020			_a3832592237
020			_a3832545123
020			_a9783832545123 _q(electronic bk.)
024	3		_a9783832545123
035			_a1727772 _b(N$T)
035			_a(OCoLC)1028234286 _z(OCoLC)1028592037 _z(OCoLC)1028655432
050		4	_aInternet Access _bAEU
072		7	_aTK _2lcco
082	0	4	_a006.32 _223
049			_aMAIN
100	1		_aVeith, Eric Msp.
245	1	0	_aUniversal Smart Grid Agent for Distributed Power Generation Management.
260			_aBerlin : _bLogos Verlag Berlin, _c2017.
300			_a1 online resource (268 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aPrint version record.
505	0		_aIntro; 1 Introduction; 1.1 Motivation; 1.2 Contribution and Constraints; 1.3 Overview; 2 Fundamentals and Related Work; 2.1 The Electric Power Grid; 2.2 Simulation and Modeling; 2.3 Computer Networks; 2.4 Artificial Intelligence; 2.5 Boolean Algebra; 3 Approaching the Smart Grid by Modeling and Simulation; 3.1 Models of the Power Grid; 3.2 Reference Situation; 3.3 Smart Grid Simulation Environment; 3.4 Data Quality Assessment and its Influence on Simulation Runs; 4 The Universal Grid Agent; 4.1 Modular Design Principle; 4.2 Interfaces; 4.3 Agent Behavior; 5 Forecasting Power Demand and Supply.
505	8		_a5.1 Design of the Forecaster Universal Smart Grid Agent Module5.2 The Multipart Evolutionary Training Algorithm for Artificial Neural Networks; 5.3 Forecasting Accuracy and Efficiency; 6 Social Component: Inter-Agent Communication; 6.1 Motivation; 6.2 Design Principles; 6.3 Data Encoding; 6.4 Analysis; 7 Modeling and Calculating Demand and Supply for Agents; 7.1 Agent-Local Power Balance; 7.2 The Combinatorial Demand-Supply Problem; 7.3 A Boolean Model of Demand and Supply; 7.4 Evaluation of Efficiency; 8 Conclusion.
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.
590			_aAdded to collection customer.56279.3
650		0	_aTechnology.
650		2	_aTechnology
650		6	_aTechnologie.
650		7	_aComputers / Computer Science. _2bisacsh
650		7	_aTechnology & Engineering / Construction. _2bisacsh
650		7	_aTechnology. _2fast _0(OCoLC)fst01145078
776	0	8	_iPrint version: _aVeith, Eric Msp. _tUniversal Smart Grid Agent for Distributed Power Generation Management. _dBerlin : Logos Verlag Berlin, �2017 _z9783832545123
856	4	0	_3EBSCOhost _uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1727772
938			_aKnowledge Unlatched _bKNOW _nf45f7394-0d37-412c-86d2-f98573a60f48
938			_aEBL - Ebook Library _bEBLB _nEBL5313486
938			_aYBP Library Services _bYANK _n15212074
938			_aEBSCOhost _bEBSC _n1727772
994			_a92 _bN$T
999			_c45481 _d45481