Optimization Methods Applied to Power Systems: Volume 2
This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programmin...
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| LEADER | 07823naaaa2202173uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_55331 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03921-157-9 | ||
| 020 | |a 9783039211562 | ||
| 020 | |a 9783039211579 | ||
| 024 | 7 | |a 10.3390/books978-3-03921-157-9 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Montoya, Francisco G. |4 auth | |
| 700 | 1 | |a Baños Navarro, Raúl |4 auth | |
| 245 | 1 | 0 | |a Optimization Methods Applied to Power Systems: Volume 2 |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (306 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc. | ||
| 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/1450 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/55331 |7 0 |z DOAB: description of the publication |