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210512 ||| eng |
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|a 9783039211579
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|a 9783039211562
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|a books978-3-03921-157-9
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| 100 |
1 |
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|a Montoya, Francisco G.
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| 245 |
0 |
0 |
|a Optimization Methods Applied to Power Systems: Volume 2
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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| 300 |
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|a 1 electronic resource (306 p.)
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| 653 |
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|a mutual information theory
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| 653 |
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|a off-grid
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| 653 |
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|a renewable energy sources
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| 653 |
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|a overhead
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| 653 |
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|a genetic algorithm
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| 653 |
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|a adaptive consensus algorithm
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| 653 |
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|a distribution network reconfiguration
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| 653 |
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|a evolutionary computation
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| 653 |
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|a fitness function
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| 653 |
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|a two-point estimation method
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| 653 |
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|a decentralized and collaborative optimization
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| 653 |
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|a feature selection
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|a constrained parameter estimation
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| 653 |
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|a micro grid
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|a internal defect
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|a consensus
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| 653 |
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|a active shielding
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| 653 |
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|a transient stability
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| 653 |
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|a stochastic state estimation
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| 653 |
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|a HOMER software
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| 653 |
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|a switched reluctance machine (SRM)
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| 653 |
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|a interactive load
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| 653 |
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|a power flow
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| 653 |
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|a congestion management
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| 653 |
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|a low-voltage networks
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| 653 |
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|a thermal probability density
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| 653 |
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|a electric vehicles
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| 653 |
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|a electric vehicle
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| 653 |
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|a optimal power flow
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| 653 |
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|a dragonfly algorithm
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| 653 |
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|a Cable joint
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| 653 |
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|a micro-phasor measurement unit
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| 653 |
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|a demand uncertainty
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| 653 |
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|a MV/LV substation
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| 653 |
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|a intelligent scatter search
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| 653 |
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|a load curtailment
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| 653 |
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|a photovoltaic
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| 653 |
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|a discrete wind driven optimization
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| 653 |
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|a energy management
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| 653 |
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|a wind energy
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| 653 |
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|a support vector machine
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| 653 |
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|a hybrid renewable energy system
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| 653 |
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|a hierarchical scheduling
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| 653 |
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|a optimization methods
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| 653 |
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|a wind power
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| 653 |
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|a prosumer
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| 653 |
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|a cost minimization
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| 653 |
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|a cross-entropy
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| 653 |
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|a GenOpt
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| 653 |
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|a SOCP relaxations
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| 653 |
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|a tabu search
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| 653 |
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|a ETAP
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| 653 |
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|a radiance
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| 653 |
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|a sub-Saharan Africa
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| 653 |
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|a underground
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| 653 |
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|a data center
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| 653 |
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|a day-ahead load forecasting
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| 653 |
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|a power systems
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| 653 |
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|a two-stage feature selection
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| 653 |
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|a passive shielding
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| 653 |
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|a multi-stakeholders
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| 653 |
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|a artificial bee colony
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| 653 |
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|a optimal reactive power dispatch
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| 653 |
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|a runner-root algorithm (RRA)
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| 653 |
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|a HVAC system
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a interval variables
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| 653 |
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|a building energy management system
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| 653 |
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|a generalized generation distribution factors
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| 653 |
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|a non-sinusoidal circuits
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| 653 |
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|a dependability
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| 653 |
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|a artificial lighting
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| 653 |
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|a optimal congestion threshold
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| 653 |
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|a energy storage
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| 653 |
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|a IEEE Std. 80-2000
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| 653 |
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|a bio-inspired algorithms
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| 653 |
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|a n/a
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| 653 |
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|a Cameroon
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| 653 |
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|a power optimization
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| 653 |
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|a calibration
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| 653 |
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|a multi-objective particle swarm optimization algorithm
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| 653 |
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|a current margins
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| 653 |
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|a multiobjective optimization
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| 653 |
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|a hybrid membrane computing
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| 653 |
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|a rural electrification
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| 653 |
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|a energy internet
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| 653 |
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|a affine arithmetic
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| 653 |
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|a radial basis function
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a hybrid method
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| 653 |
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|a multi-objective particle swarm optimization
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| 653 |
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|a ringdown detection
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| 653 |
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|a strong track filter (STF)
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| 653 |
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|a active distribution system
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| 653 |
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|a Stackelberg game
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| 653 |
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|a simulation
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| 653 |
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|a inter-turn shorted-circuit fault (ISCF)
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| 653 |
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|a CCHP system
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| 653 |
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|a dynamic solving framework
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| 653 |
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|a eight searching sub-regions
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| 653 |
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|a virtual power plant
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| 653 |
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|a the genetic algorithm based P system
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| 653 |
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|a fuzzy algorithm
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| 653 |
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|a power architectures
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| 653 |
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|a electric energy costs
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| 653 |
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|a voltage deviation
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| 653 |
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|a particle encoding method
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| 653 |
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|a principal component analysis
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| 653 |
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|a unit commitment
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| 653 |
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|a power factor compensation
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| 653 |
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|a chaos optimization algorithm (COA)
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| 653 |
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|a integration assessment
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| 653 |
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|a linear discriminant analysis (LDA)
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| 653 |
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|a evolutionary algorithms
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| 653 |
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|a PCS efficiency
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| 653 |
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|a energy storage system
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| 653 |
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|a electric power contracts
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| 653 |
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|a optimizing-scenarios method
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| 653 |
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|a piecewise linear techniques
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| 653 |
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|a controllable response
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| 653 |
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|a UC
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| 653 |
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|a MILP
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| 653 |
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|a demand response
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| 653 |
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|a distributed heat-electricity energy management
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| 653 |
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|a charging/discharging
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| 653 |
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|a developed grew wolf optimizer
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| 653 |
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|a geometric algebra
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| 653 |
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|a smart grid
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| 653 |
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|a transformer-fault diagnosis
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| 653 |
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|a extended Kalman filter
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| 653 |
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|a islanded microgrid
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| 653 |
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|a heterogeneous networks
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| 653 |
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|a off-design performance
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| 653 |
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|a energy flow model
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| 653 |
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|a optimal operation
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| 653 |
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|a power transfer distribution factors
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| 653 |
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|a stochastic optimization
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| 653 |
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|a considerable decomposition
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| 653 |
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|a pumped-hydro energy storage
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| 653 |
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|a magnetic field mitigation
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| 653 |
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|a battery energy storage system
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| 653 |
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|a power system unit commitment
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| 653 |
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|a distributed generations (DGs)
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| 653 |
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|a Schwarz's equation
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| 653 |
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|a AC/DC hybrid active distribution
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| 653 |
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|a sustainability
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| 653 |
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|a modular predictor
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| 653 |
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|a demand bidding
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| 653 |
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|a street light points
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| 653 |
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|a multi-population method (MP)
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| 653 |
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|a the biomimetic membrane computing
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| 653 |
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|a optimization
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| 653 |
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|a loss minimization
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| 653 |
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|a neural network
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| 653 |
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|a C&I particle swarm optimization
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| 653 |
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|a affinity propagation clustering
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| 653 |
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|a metaheuristic
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| 653 |
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|a power system optimization
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| 653 |
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|a DC optimal power flow
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| 653 |
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|a economic load dispatch problem (ELD)
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| 653 |
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|a JAYA algorithm
|
| 700 |
1 |
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|a Baños Navarro, Raúl
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-03921-157-9
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/1450
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/55331
|z DOAB: description of the publication
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|a 330
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|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.
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