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Energy efficiency of modern power and energy systems /

Energy Efficiency and Management of Power and Energy Systems introduces students and researchers to a broad range of power system management challenges, technologies, and solutions.This book begins with an analysis of system technology's current state, the most pressing problems, and the backgr...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Abdel Aleem, Shady H. E.
Format: eBook
Language:English
Published: Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA : Elsevier, [2024]
Subjects:
Electric power systems.
Energy consumption.
Réseaux électriques (Énergie)
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Energy Efficiency of Modern Power and Energy Systems
  • Copyright Page
  • Contents
  • List of contributors
  • Preface
  • 1 Introductory chapter: effects of power quality problems on energy efficiency of power systems
  • 1.1 Introduction
  • 1.2 Harmonic distortion problem
  • 1.2.1 Harmonic distortion definition
  • 1.2.2 Harmonic distortion indices and standards
  • 1.3 Imbalance problem
  • 1.3.1 Imbalance definition
  • 1.3.2 Indices and standards
  • 1.4 Harmonic distortion's effects on losses
  • 1.4.1 Cable losses
  • 1.4.2 Power transformer losses
  • 1.4.2.1 Losses under high-order harmonics
  • 1.4.2.2 Losses under DC or subharmonic components
  • 1.4.3 Other equipment's losses
  • 1.5 Imbalance's effects on losses
  • 1.5.1 Line losses
  • 1.5.2 Induction motor losses
  • 1.5.3 Other equipment's losses
  • 1.6 Conclusions
  • References
  • 2 Energy transition in Ecuador, a proposal to improve the growth of renewable energy and storage systems in a developing co...
  • 2.1 Introduction
  • 2.2 Current energy status in Ecuador and Latin America
  • 2.2.1 Situation of renewable energies in South America
  • 2.2.2 Situation of renewable energies in Ecuador
  • 2.3 Availability of renewable resources in Ecuador
  • 2.4 Simulation in EnergyPlan with real database
  • 2.4.1 Long-term demand forecast
  • 2.4.2 EnergyPlan model
  • 2.4.3 Proposed scenarios
  • 2.4.3.1 Business as usual scenario (S0)
  • 2.4.3.2 Alternative scenario (S1)
  • 2.5 Analysis of results
  • 2.5.1 Critical excess electricity production
  • 2.5.2 Environmental results
  • 2.5.3 Economic results
  • 2.5.4 Imported electricity
  • 2.5.5 Sensitivity analysis based on technical and economic indices
  • 2.6 Conclusion
  • Acknowledgments
  • References
  • 3 Network reconfiguration to allocate open points in distribution networks using soft computing
  • Nomenclature
  • 3.1 Introduction.
  • 3.1.1 Power losses and optimal switch configuration
  • 3.1.2 Literature review
  • 3.2 Distribution radiality
  • 3.3 Problem formulation: objective and constraints
  • 3.3.1 Objectives
  • 3.3.2 Constraints
  • 3.3.2.1 Power constraints
  • 3.3.2.2 Bus voltage limits
  • 3.3.2.3 Feeder's current transfer capability
  • 3.4 Solution algorithm of the studied problem: equilibrium optimizer
  • 3.4.1 Fundamentals of equilibrium optimizer algorithm
  • 3.4.2 Implementation of equilibrium optimizer algorithm for discrete binary solution space: binary equilibrium optimizer
  • 3.5 Simulations, results, and discussions
  • 3.5.1 Descriptions of test cases
  • 3.5.2 Obtained results for the 33-node test case
  • 3.5.3 Obtained results for the 118-node test case
  • 3.6 Conclusion
  • References
  • 4 Harmonics suppression in polluted renewable isolated/grid-connected microgrids
  • 4.1 Introduction
  • 4.1.1 Harmonic filters types
  • 4.1.2 Literature review
  • 4.2 Detailed modeling of renewable sources and nonlinear loads
  • 4.2.1 The mathematical model of wind turbine
  • 4.2.2 The mathematical analysis of an induction generator
  • 4.2.3 The mathematical model of the PV
  • 4.2.4 The mathematical model of the connected loads
  • 4.3 Harmonics measures and standards
  • 4.3.1 Harmonic factor
  • 4.3.2 Total harmonic distortion
  • 4.3.3 Total demand distortion
  • 4.4 Harmonics mitigation methods
  • 4.5 Problem formulation
  • 4.6 Simulation results and discussions
  • 4.6.1 Without filters test case
  • 4.6.2 Scenario no. 1-utility-connected mode
  • 4.6.3 Scenario no. 2-various loads with utility connected mode
  • 4.6.4 Scenario no. -torque ripples with grid connected mode
  • 4.6.5 Scenario no. 4-isolation mode of hybrid microgrid
  • 4.6.6 Scenario no. 5-simulations with real field measurements
  • 4.7 Conclusions
  • Appendix A Data of the network under study.
  • Utility and synchronous generator
  • PV and wind system parameters
  • Transformer data
  • Line and feeder data
  • Load data
  • References
  • 5 Modelling, analysis, and improvement of energy consumption in data centres via demand side management
  • 5.1 Introduction
  • 5.2 Modeling of energy requirements for data centers
  • 5.2.1 Efficiency measures for data centers
  • 5.2.2 Power consumption modeling of data centers' subcomponents
  • 5.3 Data centers as a significant player in demand side management
  • 5.3.1 The existing approaches for participation of data centers in demand side management
  • 5.3.2 Effects of data centers' participation in demand side management on the load curve
  • 5.3.3 Financial gains from data centers' participation in demand side management
  • 5.3.4 Environmental impacts of data centers' participation in demand side management
  • 5.4 Energy management and optimization models in data centers
  • 5.5 Developing energy management system for data centers: a case study
  • 5.5.1 Forecasting module
  • 5.5.1.1 Nonlinear regression method
  • 5.5.1.2 Determination of predictors used in the forecasting model
  • 5.5.1.3 Determination of predictors for the estimation of IT power consumption
  • 5.5.1.4 Determination of predictors for the estimation of cooling units power consumption
  • 5.5.1.5 Determination of predictors for the estimation of data center total power consumption
  • 5.5.1.6 Estimation of data center power demand
  • 5.5.2 Optimization module
  • 5.5.2.1 Energy cost calculation for data center using single-rate electricity tariff
  • 5.5.2.1.1 Mathematical model
  • 5.5.2.1.2 Analysis results
  • 5.5.2.2 Energy cost calculation for data center using time-of-use electricity tariff
  • 5.5.2.2.1 Mathematical model
  • 5.5.2.2.2 Analysis results.
  • 5.5.2.3 Energy cost calculations for DC by adopting ToU electricity tariff, IT load shifting method and utilizing UPS, and ...
  • 5.5.2.3.1 Mathematical model
  • 5.5.2.3.2 Constraints for IT load shifting and power balance
  • 5.5.2.3.3 Constraints for uninterruptible power supply
  • 5.5.2.3.4 Constraints for generator
  • 5.5.2.3.5 Analysis results
  • 5.5.2.4 Discussion
  • 5.6 Conclusion
  • References
  • 6 Recent maximum power point tracking methods for wind energy conversion system
  • 6.1 Introduction
  • 6.2 Wind energy conversion system modeling
  • 6.2.1 Wind turbine modeling
  • 6.2.2 Steady-state model of doubly fed induction generator
  • 6.3 Maximum power point tracking methods for wind energy conversion system
  • 6.3.1 Indirect power controller maximum power point tracking methods
  • 6.3.1.1 Power signal feedback maximum power point tracking method
  • 6.3.1.2 Tip speed ratio maximum power point tracking method
  • 6.3.1.3 Optimal torque maximum power point tracking method
  • 6.3.2 Direct power controller based maximum power point tracking methods
  • 6.3.2.1 Perturb and observe maximum power point tracking method
  • 6.3.2.2 Incremental conductance maximum power point tracking method
  • 6.3.2.3 Optimal relation based maximum power point tracking method
  • 6.3.3 Hybrid maximum power point tracking methods
  • 6.3.4 Smart maximum power point tracking methods
  • 6.3.4.1 Fuzzy logic controller based maximum power point tracking method
  • 6.3.4.2 Neural network based maximum power point tracking method
  • 6.3.4.3 Adaptive based maximum power point tracking method
  • 6.3.4.4 Multivariable perturb and observe maximum power point tracking method
  • 6.4 Comparative analysis and discussion
  • 6.5 Optimization algorithms for maximum power point tracking methods.
  • 6.6 Case study: optimal maximum power point tracking of wind turbine doubly fed induction generator using optimization algo...
  • 6.6.1 Scenario no. 1 (maximum power point tracking with unity power factor)
  • 6.6.2 Scenario no. 2 (maximum power point tracking with minimum losses)
  • 6.6.3 Comparison between two scenarios
  • 6.7 Conclusions
  • References
  • 7 Energy saving of isolated microgrids comprising proton exchange membrane fuel cells stacks feeding variable loads based o...
  • 7.1 Introduction
  • 7.2 Steady-state modeling of proton exchange membrane fuel cells' stack
  • 7.2.1 Mathematical representation
  • 7.2.2 Model's parameters definition and objective function
  • 7.2.3 Meta-heuristic optimizers for proton exchange membrane fuel cell's steady-state model parameters identification
  • 7.3 Efficiency improvement of proton exchange membrane fuel cells stacks feeding variable load
  • 7.3.1 System modeling and construction
  • 7.3.1.1 A dynamic model of proton exchange membrane fuel cells stack
  • 7.3.1.2 DC converter
  • 7.3.2 Problem formulation
  • 7.3.3 Tuna-swarm optimizer
  • 7.3.4 Simulation outcomes and discussions
  • 7.3.4.1 Various tuna-swarm optimizer-based optimization phases
  • 7.3.4.1.1 Phase (1): optimizing five parameters
  • 7.3.4.1.2 Phase (2): optimizing two parameters
  • 7.3.4.1.3 Statistical indices
  • 7.3.4.2 Artificial neuro-fuzzy inference system-based methodology
  • 7.4 Conclusions and future sights
  • References
  • 8 Design of energy-efficient power transformers by considering nonlinear loads
  • 8.1 Introduction
  • 8.2 Energy efficiency on power transformers
  • 8.3 Finite element analysis method for transformers
  • 8.4 Transformer losses
  • 8.4.1 No-load losses
  • 8.4.2 Winding eddy current losses
  • 8.4.2.1 Evaluation of the eddy effect on a sample winding structure
  • 8.4.3 Structural part losses.