Uncertainties in Modern Power Systems 0128204915, 9780128204917

Table of contents :
Uncertainties in Modern Power Systems
Copyright
Contributors
Preface
1. Overview of uncertainties in modern power systems: uncertainty models and methods
1. Introduction
2. Uncertainty models of parameters in power systems
2.1 Load demand uncertainty model
2.2 Wind energy uncertainty model
2.3 PV energy uncertainty model
2.4 PEVs uncertainty model
2.5 Electricity price uncertainty model
2.6 Load growth uncertainty model
3. Uncertainty modeling methods
3.1 Probabilistic methods
3.1.1 Numerical methods
3.1.1.1 Monte Carlo simulation methods
3.1.1.2 Sequential MCS method
3.1.1.3 Pseudosequential MCS
3.1.1.4 Markov chain MCS
3.1.1.5 Nonsequential MCS
3.1.2 Analytical methods
3.1.2.1 Scenario-based method
3.1.2.2 PDF approximation methods
3.1.2.2.1 Unscented transformation method
3.1.2.2.2 Point estimation method
3.1.2.3 Linearization methods
3.1.2.3.1 Convolution method
3.1.2.3.2 Cumulants method
3.1.2.3.3 Taylor series expansion
3.1.2.3.4 First-order second-moment method
3.2 Possibilistic methods
3.2.1 α-Cut method
3.2.2 Defuzzification
3.3 Hybrid methods
3.3.1 Fuzzy scenario
3.3.2 Fuzzy MCS
3.4 Information gap decision theory
3.5 Robust optimization
3.6 Interval analysis
4. Future trends
References
2. Expansion planning of transmission networks
1. Introduction
2. Definition of uncertainties in the structure of modern power systems
3. Deterministic programing–based expansion planning of transmission networks
4. Stochastic programing–based expansion planning of transmission networks
4.1 Point estimation method–based transmission expansion planning formulation
4.2 Chance constraint programming–based transmission expansion planning formulation
4.3 Scenario-based transmission expansion planning formulation
4.3.1 Robustness criterion approach
4.3.2 Flexibility criterion approach
5. Robust programming–based expansion planning of transmission networks
5.1 Robust optimization–based transmission expansion planning formulation
5.2 Information gap decision theory–based transmission expansion planning formulation
6. Hybrid robust/stochastic programming–based expansion planning of transmission networks
6.1 Data-driven robust optimization–based transmission expansion planning formulation
6.2 Short-term and long-term uncertainty modeling-based transmission expansion planning formulation
7. Conclusion
References
3. A stochastic robust approach to deal with the generation and transmission expansion planning problem embedding renewable so ...
1. Introduction
1.1 Problem statement
1.2 A brief review of previous work
1.3 Chapter objectives
1.4 Contributions
2. Mathematical uncertainty
2.1 Stochastic optimization
2.2 Robust optimization
2.3 Current challenges
2.4 Operational flexibility
2.5 Uncertainties treatment
2.6 Integration of renewable energies into power systems
3. Problem formulation
3.1 Notation
3.2 Hybrid robust-stochastic approach
3.3 Uncertainty
3.4 Three-level problem description
3.4.1 Upper-level problem
3.4.2 Middle-level problems
3.4.3 Lower-level problems
4. Solution approach
4.1 Master problem
4.2 Subproblem
4.3 Algorithm
5. Study case
5.1 Modeling the uncertainty for the study cases
6. Conclusions
References
4. Data-driven robust stochastic optimization for power systems operations
1. Uncertain optimization in the operation of modern power systems
1.1 Model of uncertain optimization
1.2 Robust optimization
1.2.1 Box uncertainty set
1.2.2 Ellipsoidal uncertainty set
1.2.3 Polyhedral uncertainty set
1.2.4 Budget uncertainty set
1.3 Stochastic optimization
1.4 Challenges of using RO in the operation scheduling problem of power system
2. RO-based distribution network reconfiguration
2.1 Model of network reconfiguration using hard and soft strategies
2.1.1 Branch state constraints
2.1.2 System operation constraints
2.1.3 Power flow equation constraints
2.1.4 The compressed-air-assisted wind turbine system
2.2 Robust optimization method for ONR considering the uncertainty of wind power
2.2.1 Uncertainty set
2.2.2 RO model extended based on ONR
2.2.3 Computable conversion of the robust optimization model
2.3 Case study
2.3.1 IEEE-16 system in stage 1
2.3.2 Comparison and analysis in stage 2
3. Data-driven ACOPF with wind power uncertainty
3.1 Robust optimal power flow problem with wind uncertainty
3.1.1 Power flow constraints
3.1.2 Affinely adjustable policy
3.1.3 Operation constraints
3.1.4 Objective functions
3.2 The straightforward data-driven framework
3.3 Case study
4. Wasserstein metric-based DRO for unit commitment problem
4.1 Wasserstein metric-based ambiguity set
4.2 Wasserstein metric-based uncertainty set
4.3 DRO model of unit commitment problems
4.3.1 Constraints
4.3.2 Power balance constraints
4.3.3 Spinning reserve constraints
4.3.4 Thermal unit constraints
4.3.5 Objective function
4.4 Model reformulation
4.4.1 Wasserstein metric-based distributionally robust counterpart
4.4.2 Wasserstein metric-based distributionally robust approximation
4.5 Case study
5. RSO model for dynamic economic dispatch problem
5.1 Uncertain optimization model
5.1.1 The DRO model under generalized moment information ambiguity set
5.1.2 The DRO model under Wasserstein ambiguity set
5.1.3 RSO model
5.1.4 A simple example
5.2 Dynamic economic dispatch model considering wind power
5.2.1 Objective function
5.2.2 constraints
5.3 The RSO model and its computational reformulation
5.4 Case study
5.4.1 The relation between RSO and RO/SO
5.4.2 The comparison of the RSO and DRO model under different ambiguity set
6. Conclusion
References
5. Optimal power flow for distribution systems with uncertainty
1. Introduction
2. Distributed generator's benefits and uncertainty
2.1 Distributed generator's benefits
2.1.1 Economical benefits
2.1.2 Technical benefits
2.1.3 Environmental benefits
2.2 Uncertainty with distributed generators
2.2.1 Economic parameters
2.2.2 Technical parameters
2.3 Uncertainty modeling methods
2.3.1 Interval analysis
2.3.2 Robust optimization
2.3.3 Information gap decision theory
2.3.4 Possibilistic approach
2.3.5 Probabilistic approaches
2.3.5.1 Analytical methods
2.3.5.1.1 Based on linearization
2.3.5.1.2 Based on probability distribution function approximation
2.3.5.2 Numerical approaches
2.3.5.2.1 Sequential Monte Carlo simulation
2.3.5.2.2 Pseudosequential Monte Carlo simulation
2.3.5.2.3 Nonsequential Monte Carlo simulation
3. Problem formulation
3.1 Objective function
3.2 System constraints
3.2.1 Equality constraints
3.2.2 Inequality constraints
4. The uncertainties of load demand, solar, and wind power resources
4.1 Load uncertainty modeling
4.2 Wind energy subsystem modeling
4.3 Photovoltaic energy subsystem modeling
5. Equilibrium optimizer
5.1 Inspiration
6. Simulation results
7. Conclusion
References
6. Distributed demand-side management for microgrids in modern power system
1. Introduction
2. Distributed literature review
3. Distributed demand response in campus microgrids
3.1 System model
3.2 Distributed alternating direction method of multipliers–based demand response strategy
4. Distributed demand response for microgrid-aided restoration
4.1 System model and problem formulation
4.2 Game theoretic solution
5. Case study
5.1 UConn campus demand response for peak load shedding
5.2 57-Bus test system
6. Summary
A. Section in appendix
A.1 Economic incentive rewards
A.2 Stability incentive rewards
References
7. Evaluation of DG impacts on distribution networks
1. Introduction
2. Network losses
3. Voltage profile of the system
4. Reliability
References
8. A comprehensive review of islanding detection methods
1. Introduction
2. International standards
3. Criteria to evaluate islanding detection methods
3.1 Nondetection zone
3.2 Quality factor and parallel RLC load
3.3 Nuisance tripping
4. Islanding detection methods
4.1 Remote islanding detection methods
4.1.1 Power line carrier communication
4.1.2 Supervisory control and data acquisition
4.1.3 Transfer trip method
4.2 Local islanding detection methods
4.2.1 Passive methods
4.2.1.1 Voltage unbalance method
4.2.1.2 Total harmonic distortion method
4.2.1.3 Over/undervoltage and over/underfrequency method
4.2.1.4 Rate of change of power method
4.2.1.5 Rate of change of frequency method
4.2.1.6 Rate of change of frequency over power method
4.2.1.7 Phase jump detection method
4.2.2 Active methods
4.2.2.1 Impedance measurement method
4.2.2.2 Active frequency drift method
4.2.2.3 Frequency jump method
4.2.2.4 Active frequency drift with positive feedback method
4.2.2.5 Sandia frequency shift method
4.2.2.6 Sandia voltage shift method
4.2.2.7 Slip mode frequency shift method
4.2.2.8 Variation of active and reactive power method
4.2.2.9 Negative-sequence current injection method
4.2.2.10 High-frequency signal injection method
4.2.2.11 Virtual capacitor/inductor method
4.2.2.12 Phase-locked loop perturbation method
4.2.2.13 Positive feedback-based method
4.2.3 Hybrid methods
4.2.3.1 Voltage unbalance and frequency setpoint method
4.2.3.2 Voltage and real power shift method
4.2.3.3 Voltage fluctuation injection method
4.2.3.4 Hybrid Sandia frequency shift and Q–f method
4.2.4 Signal processing–based methods
4.2.4.1 Fourier transform–based methods
4.2.4.2 Wavelet transform–based methods
4.2.4.3 S-transform-based methods
4.2.4.4 Time–time transform-based methods
4.2.4.5 Hilbert–Huang transform-based methods
4.2.4.6 Variational mode decomposition–based methods
4.2.4.7 Mathematical morphology-based methods
4.2.4.8 Transient monitoring function–based methods
4.2.4.9 Miscellaneous signal processing–based methods
4.2.5 Computational intelligence–based methods
4.2.5.1 Neural network–based methods
4.2.5.2 Fuzzy logic–based methods
4.2.5.3 Neuro-fuzzy-based methods
4.2.5.4 Probabilistic neural network–based methods
4.2.5.5 Decision tree–based methods
4.2.5.6 Naive Bayesian classifier-based methods
4.2.5.7 Support vector machine classifier–based methods
4.2.5.8 Extreme learning machine–based methods
4.2.5.9 Ensemble classifier–based methods
4.2.5.10 Deep learning–based methods
4.2.5.11 Miscellaneous computational intelligence–based methods
5. Conclusions and future scope
References
9. Protection system failure and power system blackout
1. Introduction
1.1 General introduction
1.2 Zone-3 maloperation in distance protection
1.2.1 Philosophy of distance protection
1.2.2 Zone-3 maloperation
1.3 Causes of zone-3 maloperation
1.3.1 System stressed events
1.3.1.1 Voltage instability
1.3.1.2 Power swing
1.3.1.3 Load encroachment
1.4 Power system blackout
1.4.1 Uncertainties in the power system
1.4.2 Power system security model
1.4.3 Potential causes and impacts of blackout
1.4.4 Lessons learned from the blackouts
2. Performance evaluation of distance relay under power system disturbances
2.1 Performance of the distance relay during system stressed events
2.1.1 Voltage instability
2.1.2 Stable power swing
2.1.3 Unstable power swing
2.1.4 Load encroachment
2.2 Performance of the distance relay under fault during system stressed events
2.2.1 Fault during voltage instability
2.2.2 Fault during stable power swing
2.2.3 Fault during unstable power swing
2.2.4 Fault during load encroachment
3. Performance of conventional techniques during system stressed conditions
3.1 Voltage instability
3.1.1 Rate of change of voltage
3.2 Power swing
3.2.1 Concentric circle method
3.2.2 Blinder-based method
3.2.2.1 Single blinder method
3.2.2.2 Double blinder method
3.3 Load encroachment
3.3.1 Antiencroachment zone design
4. Available solutions
5. Conclusion
References
10. Testing of influence of SVC and energy storage device’s location on power system using GAMS
1. GAMS
1.1 GAMS structure
1.2 General rules in GAMS
1.3 Models and solvers in GAMS
1.4 Example
2. SVC devices
2.1 SVC device structure
2.2 Thyristor-controlled reactor
2.3 Thyristor switched capacitor
2.4 Current situation regarding SVCs in the world
3. Energy storage systems
3.1 Reversible hydropower plants
3.2 Compressed air energy storage
3.3 Battery energy storage systems
3.4 Flow battery
3.5 Hydrogen-based energy storage system
3.6 Flywheel energy storage system
3.7 Superconducting magnetic energy storage
3.8 Supercapacitors
3.9 Power conversion systems
3.10 The current situation regarding SVCs in the world
4. Optimal location of SVC and ESS devices in a power network
4.1 IEEE 9-test bus system
4.2 Problem formulation
4.3 Case 1—optimal SVC location for constant load
4.4 Case 2—optimal SVC location for variable load
4.5 Case 3—optimal SVC location for variable load and with wind power
4.6 Case 4—optimal ESS location for variable load
4.7 Case 5—optimal ESS location for variable load and with wind power
References
11. DG investment and allocation in active distribution networks
1. Introduction
2. Allocation of DGs in distribution networks
3. Modeling and simulation
4. Modifications in Newton–Raphson load flow
4.1 Modifications with PQVδ−Zero bus pair
4.2 Elements of the Jacobian matrix (JE)
5. Average load demand and renewable generation pattern with uncertainty modeling
5.1 Average load demand pattern
5.2 Average solar generation pattern
5.3 Average wind generation pattern
6. Objective functions with technical constraints
6.1 Objective Function-1
6.2 Objective Function-2
6.3 Objective Function-3
6.4 Equality and inequality constraints
7. Mixed discrete particle swarm optimization
8. Uncertainty analysis with renewable generation and load
8.1 Hong's point estimation method
8.2 Gram-Charlier expansion
9. Annual cost–benefit analysis for DG placement
10. The methodology of DGs placement using the proposed method
11. Essential assumptions and parameters
12. Case study (33 bus RDNR)
12.1 Objective Function-1
12.2 Objective Function-2
12.3 Objective Function-3
13. Conclusions
Appendix
Modified Jacobian matrices for five bus sample network
Gram-Charlier expansion method
Panel generating factor determination
Data required for renewable and loads uncertainty analysis
Line and load data for radial distribution network
References
12. Technical assessment of the impacts of distributed energy resources on distribution feeders
1. Introduction
1.1 Distribution network design and planning objectives
1.2 The evolution of residential loads and distributed generation
1.3 Operation configurations of new technologies
1.3.1 Ordinary distributed generation (power export only)
1.3.2 Loads with variable profiles (power import only)
1.3.3 Dynamic storage systems (export and import: bidirectional operation)
1.3.4 Implications of new technologies on planning
1.4 The need for new planning tools
1.4.1 Regulatory frameworks for uptake
1.4.1.1 Definition of penetration limits
1.4.1.2 Autonomous or central control of inverters
1.4.2 Formulation of new network planning principles
2. Framework for impact assessment studies
2.1 Stochasticity and variability of system loads
2.2 Uncertainty associated with system structure and operation
2.3 Scope of technical variables included
2.4 Purpose of assessment
3. A stochastic–probabilistic approach
3.1 Overview of the stochastic–analytic probabilistic approach
3.2 Probabilistic load modeling
3.2.1 Selection of statistical model
3.2.2 Customer loads
3.2.3 Distributed energy resources
3.3 Stochastic simulation of distributed energy resource placement
3.3.1 Random simulation method
3.3.2 Determining the appropriate number of Monte Carlo simulation trials
3.4 Probabilistic load flow computation
3.4.1 Applicability of the Herman-Beta Extended transform
3.4.2 Formulation of the Herman-Beta Extended algorithm
3.4.2.1 Voltage drop calculation
3.4.2.1.1 System of equations
3.4.2.1.2 Calculation of statistical moments
3.4.2.1.3 Extension to multiple nodes
3.4.2.2 Calculation of branch currents
3.4.2.3 Transformer loading and unbalance
3.4.2.4 Extension to voltage-dependent loads
3.4.2.5 Representation of outputs using statistical distributions
3.5 Analyzing the range of outputs
4. Case studies
4.1 Simulation methodology
4.2 Simulation specifications
4.2.1 Test network
4.2.2 Input models
4.3 Case 1: impact of unbalanced single-phase photovoltaic systems
4.4 Case 2: impact of balanced three-phase distributed energy resources
4.5 Case 3: constraints to uptake
5. Conclusion
References
Chapter 13 - Large-scale integration of distributed generation in reconfigured distribution networks considering load uncer ...
1. Introduction
2. Problem statement
2.1 Power flow equations
2.2 Distributed generation modeling
2.3 Fuzzy c-means clustering
3. Problem formulation
3.1 Deterministic HC
3.1.1 Objective function
3.1.2 Constraints
3.2 Probabilistic HC
3.2.1 Objective function
3.3 Harris hawks optimization
4. Simulation results and discussions
4.1 Deterministic HC
4.2 Probabilistic HC
5. Conclusions
References
Appendix
14. Steady-state and time-varying harmonics in distribution system
1. Introduction
2. Harmonic data acquisition systems
3. Steady-state harmonic analysis
3.1 Sinusoidal voltages and currents
3.2 Nonsinusoidal voltages and currents
3.3 Measurements under nonsinusoidal conditions
3.4 Harmonic analysis algorithm
3.5 Digital signal processing
4. Design of harmonic analyzer
5. Time-varying harmonics analysis
5.1 Choice of sensors
5.2 Signal conditioning
5.3 Analog-to-digital conversion
6. Results and discussions
6.1 Fan harmonics
6.2 Computer harmonics
6.3 DC power supply harmonics
6.4 Oscilloscope harmonics
7. Estimation of time constant, time varying, and interharmonics
8. Mitigation of harmonics
8.1 Design of notch filter
8.2 Evaluation of harmonic filters
8.3 Design of C-type filter
9. Conclusions
References
15. A case study with power quality analysis on building integrated PV (BIPV) system
1. Introduction to power quality issues
2. Solar photovoltaic as a renewable source of energy
3. Power quality standards
4. Voltage disturbances on electrical equipment
5. Guilty party for the power quality issues
6. Solar photovoltaic energy system incorporation
7. A case study on power quality analysis
8. Discussion of building integrated photovoltaic system issues of chosen building—Chennai automobile showroom
9. System and component description
10. Connected load for automobile showroom
11. Battery design consideration and sizing method
12. Procedure to estimate energy delivered by a photovoltaic panel
13. Example case
13.1 Case study of automobile show room with solar energy system integration
14. Conclusion
References
16. Reliability evaluation of Li-ion batteries for electric vehicles applications from the thermal perspectives
1. Introduction
2. Electric vehicles trend
3. Effects of the temperature and control strategies on the reliability of the Li-ion batteries
4. Passive and active battery thermal management systems and their limitations
4.1 Passive cooling systems for battery thermal management system
4.1.1 Heat pipe
4.1.1.1 Literature review on heat pipe-based battery thermal management system
4.1.2 Phase change material
4.1.2.1 Literature review on phase change material–based battery thermal management system
4.2 Active cooling systems for battery thermal management system
4.2.1 Liquid cooling system
4.2.1.1 Literature review on liquid-based battery thermal management system
4.2.2 Air cooling system
4.2.2.1 Literature review on air-based battery thermal management system
4.3 Challenges for active/passive battery thermal management system from reliability points of view
5. Case study: thermal management system and reliability assessment in Li-ion batteries
5.1 Test setup requirement
5.1.1 General requirements
5.1.2 Cell datasheet
5.1.3 Simulation results
5.1.3.1 Passive cooling systems
5.1.3.1.1 Natural convection cooling system
5.1.3.1.2 Phase change material cooling system
5.1.3.2 Active cooling systems
5.1.3.2.1 Liquid cooling system
5.1.3.2.2 Air cooling system
6. Conclusion
References
17. Role of compensators' hybrid participation for isolated wind-driven electrical system in presence of input and load uncerta ...
1. Introduction
2. Operational issues for wind-driven renewable energy system
3. Policy framework for wind-driven renewable energy–based system
4. Introduction to wind-diesel based isolated hybrid electrical system
5. Reactive power compensation issues in isolated hybrid electrical system
6. Reactive power compensation as techno-economic issues
7. Mathematical study of isolated hybrid electrical system
7.1 Diesel power plant
7.2 Wind power plant
7.3 Reactive power compensator
7.3.1 Static reactive power compensator
7.3.2 Dynamic reactive power compensator
7.4 Isolated hybrid electrical system's overall reactive power balance equation
8. Challenges in load modeling
9. Modeling for composite loads
9.1 Static load model
9.2 Dynamic load model
9.3 Composite load model
10. Effect of load and input uncertainties on system dynamics
11. Load and input uncertainties’ related findings for system
11.1 Deterministic load pattern or possibilistic uncertainties
11.2 Probabilistic load pattern or probabilistic uncertainties
12. Modeling of load and input uncertainties for system
13. Role of compensators to mitigate the load and input disturbances
14. Importance of tuning methods for probabilistic load model
15. Investigations of hybrid compensation for economic benefits
16. Conclusion
References
Appendices
18. Bidding strategies of a power producer in power market: measurement indices and evaluation
1. Introduction
2. Problem formulation
3. Sinusoidal function–enabled chaotic grasshopper optimization algorithm
3.1 Brief overview of grasshopper optimization algorithm
4. Sinusoidal function–enabled chaotic grasshopper optimization algorithm
4.1 Mathematical representation
4.2 Sine chaotic map–enabled crossover scheme
5. Results analysis
5.1 Architectures and their details
5.2 Error indices and evaluation
5.3 Prediction of market-clearing price, profitable bids, and profit
6. Conclusion
References
Author Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
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U
V
W
X
Y
Z
Subject Index
A
B
C
D
E
F
G
H
I
J
L
M
N
O
P
Q
R
S
T
U
V
W
Z