Optimization Methods Applied to Power Systems: Volume 1
| 1. Verfasser: |
Montoya, Francisco G.
, [VerfasserIn]
Baños Navarro, Raúl , [VerfasserIn] |
|---|---|
| Umfang/Format: |
1 online resource (382 pages). |
| Schlagworte: | |
| Online Zugang: |
DOAB: download the publication DOAB: description of the publication |
| LEADER | 07620namaa2202329ui 4500 | ||
|---|---|---|---|
| 001 | 003027794 | ||
| 005 | 20221228154200.0 | ||
| 003 | DE-2553 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20210211s2019 xx |||||o ||| 0|eng d | ||
| 020 | |a books978-3-03921-131-9 | ||
| 020 | |a 9783039211319 | ||
| 020 | |a 9783039211302 | ||
| 040 | |a oapen |c oapen |b eng |d DE-2553 |e rda | ||
| 024 | 7 | |a 10.3390/books978-3-03921-131-9 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 100 | 1 | |a Montoya, Francisco G. |e author | |
| 264 | |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2019. | ||
| 700 | 1 | |a Baños Navarro, Raúl |e author | |
| 245 | 1 | 0 | |a Optimization Methods Applied to Power Systems: Volume 1 |
| 300 | |a 1 online resource (382 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a n/a | ||
| 653 | |a Stackelberg game | ||
| 653 | |a MILP | ||
| 653 | |a optimal congestion threshold | ||
| 653 | |a magnetic field mitigation | ||
| 653 | |a simulation | ||
| 653 | |a multi-objective particle swarm optimization | ||
| 653 | |a virtual power plant | ||
| 653 | |a internal defect | ||
| 653 | |a day-ahead load forecasting | ||
| 653 | |a neural network | ||
| 653 | |a modular predictor | ||
| 653 | |a multi-objective particle swarm optimization algorithm | ||
| 653 | |a stochastic optimization | ||
| 653 | |a dragonfly algorithm | ||
| 653 | |a unit commitment | ||
| 653 | |a metaheuristic | ||
| 653 | |a multi-population method (MP) | ||
| 653 | |a optimization | ||
| 653 | |a tabu search | ||
| 653 | |a considerable decomposition | ||
| 653 | |a loss minimization | ||
| 653 | |a active distribution system | ||
| 653 | |a islanded microgrid | ||
| 653 | |a dynamic solving framework | ||
| 653 | |a feature selection | ||
| 653 | |a electric energy costs | ||
| 653 | |a power factor compensation | ||
| 653 | |a dependability | ||
| 653 | |a interactive load | ||
| 653 | |a overhead | ||
| 653 | |a energy internet | ||
| 653 | |a evolutionary computation | ||
| 653 | |a wind power | ||
| 653 | |a developed grew wolf optimizer | ||
| 653 | |a underground | ||
| 653 | |a ETAP | ||
| 653 | |a fuzzy algorithm | ||
| 653 | |a electric vehicles | ||
| 653 | |a Schwarz's equation | ||
| 653 | |a evolutionary algorithms | ||
| 653 | |a electric power contracts | ||
| 653 | |a congestion management | ||
| 653 | |a optimizing-scenarios method | ||
| 653 | |a building energy management system | ||
| 653 | |a particle encoding method | ||
| 653 | |a ringdown detection | ||
| 653 | |a HOMER software | ||
| 653 | |a DC optimal power flow | ||
| 653 | |a prosumer | ||
| 653 | |a constrained parameter estimation | ||
| 653 | |a distributed generations (DGs) | ||
| 653 | |a strong track filter (STF) | ||
| 653 | |a transient stability | ||
| 653 | |a calibration | ||
| 653 | |a cost minimization | ||
| 653 | |a radiance | ||
| 653 | |a decentralized and collaborative optimization | ||
| 653 | |a hybrid renewable energy system | ||
| 653 | |a renewable energy sources | ||
| 653 | |a rural electrification | ||
| 653 | |a distribution network reconfiguration | ||
| 653 | |a interval variables | ||
| 653 | |a optimization methods | ||
| 653 | |a particle swarm optimization | ||
| 653 | |a hierarchical scheduling | ||
| 653 | |a micro grid | ||
| 653 | |a AC/DC hybrid active distribution | ||
| 653 | |a consensus | ||
| 653 | |a artificial bee colony | ||
| 653 | |a CCHP system | ||
| 653 | |a data center | ||
| 653 | |a support vector machine | ||
| 653 | |a affinity propagation clustering | ||
| 653 | |a extended Kalman filter | ||
| 653 | |a affine arithmetic | ||
| 653 | |a linear discriminant analysis (LDA) | ||
| 653 | |a current margins | ||
| 653 | |a heterogeneous networks | ||
| 653 | |a Cameroon | ||
| 653 | |a hybrid method | ||
| 653 | |a distributed heat-electricity energy management | ||
| 653 | |a discrete wind driven optimization | ||
| 653 | |a fitness function | ||
| 653 | |a cross-entropy | ||
| 653 | |a GenOpt | ||
| 653 | |a wind energy | ||
| 653 | |a demand uncertainty | ||
| 653 | |a UC | ||
| 653 | |a off-design performance | ||
| 653 | |a genetic algorithm | ||
| 653 | |a energy storage | ||
| 653 | |a the biomimetic membrane computing | ||
| 653 | |a power system optimization | ||
| 653 | |a electric vehicle | ||
| 653 | |a power architectures | ||
| 653 | |a economic load dispatch problem (ELD) | ||
| 653 | |a runner-root algorithm (RRA) | ||
| 653 | |a Cable joint | ||
| 653 | |a battery energy storage system | ||
| 653 | |a load curtailment | ||
| 653 | |a integration assessment | ||
| 653 | |a power system unit commitment | ||
| 653 | |a artificial lighting | ||
| 653 | |a power flow | ||
| 653 | |a hybrid membrane computing | ||
| 653 | |a two-point estimation method | ||
| 653 | |a low-voltage networks | ||
| 653 | |a demand bidding | ||
| 653 | |a non-sinusoidal circuits | ||
| 653 | |a energy flow model | ||
| 653 | |a power transfer distribution factors | ||
| 653 | |a sustainability | ||
| 653 | |a HVAC system | ||
| 653 | |a voltage deviation | ||
| 653 | |a street light points | ||
| 653 | |a radial basis function | ||
| 653 | |a energy storage system | ||
| 653 | |a charging/discharging | ||
| 653 | |a power systems | ||
| 653 | |a intelligent scatter search | ||
| 653 | |a MV/LV substation | ||
| 653 | |a optimal power flow | ||
| 653 | |a stochastic state estimation | ||
| 653 | |a eight searching sub-regions | ||
| 653 | |a chaos optimization algorithm (COA) | ||
| 653 | |a mutual information theory | ||
| 653 | |a inter-turn shorted-circuit fault (ISCF) | ||
| 653 | |a C&I particle swarm optimization | ||
| 653 | |a multiobjective optimization | ||
| 653 | |a passive shielding | ||
| 653 | |a sub-Saharan Africa | ||
| 653 | |a micro-phasor measurement unit | ||
| 653 | |a geometric algebra | ||
| 653 | |a bio-inspired algorithms | ||
| 653 | |a adaptive consensus algorithm | ||
| 653 | |a energy management | ||
| 653 | |a PCS efficiency | ||
| 653 | |a multi-stakeholders | ||
| 653 | |a generalized generation distribution factors | ||
| 653 | |a the genetic algorithm based P system | ||
| 653 | |a JAYA algorithm | ||
| 653 | |a thermal probability density | ||
| 653 | |a power optimization | ||
| 653 | |a pumped-hydro energy storage | ||
| 653 | |a smart grid | ||
| 653 | |a two-stage feature selection | ||
| 653 | |a piecewise linear techniques | ||
| 653 | |a photovoltaic | ||
| 653 | |a SOCP relaxations | ||
| 653 | |a switched reluctance machine (SRM) | ||
| 653 | |a optimal reactive power dispatch | ||
| 653 | |a optimal operation | ||
| 653 | |a controllable response | ||
| 653 | |a off-grid | ||
| 653 | |a active shielding | ||
| 653 | |a transformer-fault diagnosis | ||
| 653 | |a IEEE Std. 80-2000 | ||
| 653 | |a principal component analysis | ||
| 653 | |a demand response | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1449 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/55330 |7 0 |z DOAB: description of the publication |
| 590 | |a Online publication | ||
| 590 | |a ebookoa1222 | ||
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| 942 | |2 z |c EB | ||
| 999 | |c 3027794 |d 1431549 | ||
| 952 | |0 0 |1 0 |2 z |4 0 |6 ONLINE |7 1 |9 973433 |R 2022-12-28 14:42:00 |a DAIG |b DAIG |l 0 |o Online |r 2022-12-28 |y EB | ||
