LEADER 03310nam  22003493a 450 
001 9910765840503321
005 20250203235427.0
010   $a9783038977193
010   $a3038977195
024 8 $a10.3390/books978-3-03897-719-3
035   $a(CKB)5400000000000126
035   $a(ScCtBLL)d256f0ac-e495-43d0-9484-217417a1c765
035   $a(OCoLC)1105777016
035   $a(EXLCZ)995400000000000126
100   $a20250203i20192019  uu 
101 0 $aeng
135   $auru||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aDistributed Energy Resources Management$fPedro Faria
210  1$aBasel, Switzerland :$cMDPI,$d2019.
215   $a1 online resource (1 p.)
330   $aAt present, the impact of distributed energy resources in the operation of power and energy systems is unquestionable at the distribution level, but also at the whole power system management level. Increased flexibility is required to accommodate intermittent distributed generation and electric vehicle charging. Demand response has already been proven to have a great potential to contribute to an increased system efficiency while bringing additional benefits, especially to the consumers. Distributed storage is also promising, e.g., when jointly used with the currently increasing use of photovoltaic panels. This book addresses the management of distributed energy resources. The focus includes methods and techniques to achieve an optimized operation, to aggregate the resources, namely, by virtual power players, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as a main drive for their efficient use.
610 00$aac/dc hybrid microgrid; adaptive droop control; autonomous operation; distributed generation; energy management system; aggregator; optimal bidding; electricity markets; probabilistic programming; microgrid; uncertainty; hierarchical game; non-cooperative game (NCG); energy trading; pricing strategy; demand response; distributed generation; microgrid; real-time simulation; consensus algorithm; diffusion strategy; distributed system; energy management system; microgrid operation; optimal operation; microgrids; renewable energy; storage; scheduling; co-generation; decision-making under uncertainty; domestic energy management system; energy flexibility; interval optimization; stochastic programming; Unit Commitment (UC); Demand Response (DR); Demand Response Unit Commitment (DRUC); Cat Swarm Optimization (CSO); average consensus algorithm (ACA); black start; local controller; microgrid (MG); multi-agent system (MAS); power system restoration (PSR); demand-side energy management; multiplier method; Powell direction acceleration method; advance and retreat method; thermal comfort; transmission line; fault localization; time series; ARIMA; discrete wavelet transformer; demand response; virtual power plant; energy flexibility potential; aggregators; business model; building energy flexibility; aggregator; clustering; demand response; distributed generation; n/a
700   $aFaria$b Pedro$01312838
801  0$bScCtBLL
801  1$bScCtBLL
906   $aBOOK
912   $a9910765840503321
996   $aDistributed Energy Resources Management$93031013
997   $aUNINA