کاربردهای تصمیم‌گیری در سیستم‌های قدرت مدرن

__Decision Making Applications in Modern Power Systems__ presents an enhanced decision-making framework for power systems. Designed as an introduction to enhanced electricity system analysis using decision-making tools, it provides an overview of the different elements, levels and actors involved within an integrated framework for decision-making in the power sector. In addition, it presents a state-of-play on current energy systems, strategies, alternatives, viewpoints and priorities in support of decision-making in the electric power sector, including discussions of energy storage and smart grids. As a practical training guide on theoretical developments and the application of advanced methods for practical electrical energy engineering problems, this reference is ideal for use in establishing medium-term and long-term strategic plans for the electric power and energy sectors. Cover Decision Making Applications in Modern Power Systems Copyright Contents List of contributors 1 Multicriteria decision-making methodologies and their applications in sustainable energy system/microgrids 1.1 Introduction 1.1.1 A general perspective 1.2 Multicriteria decision-making in energy planning 1.2.1 Weighted sum method 1.2.2 Weighted product method 1.2.3 Analytic hierarchy process 1.2.4 Technique for order preference by similarity to ideal solutions 1.2.5 Elimination and choice translating reality 1.2.5.1 Elimination and choice translating reality I 1.2.5.2 Elimination and choice translating reality II 1.2.5.3 Elimination and choice translating reality III 1.2.5.4 Elimination and choice translating reality IV 1.2.6 Preference ranking organization method for enrichment evaluation 1.3 Fuzzy logic in multicriteria decision-making 1.3.1 Fuzzy–analytical hierarchical process 1.3.2 Fuzzy technique for order preference by similarity to ideal solutions 1.4 Conclusion References 2 Uncertainty management in decision-making in power system operation 2.1 Introduction 2.2 Uncertainty management in power system: a review 2.2.1 Probabilistic method 2.2.2 Information gap decision theory 2.2.3 Robust optimization 2.3 Problem formulation 2.3.1 Constraints 2.3.1.1 Power balance constraint 2.3.1.2 Inequality constraints 2.3.1.3 Energy storage constraints 2.3.1.4 Minimum up/down time constraint 2.3.1.5 Ramp up/down constraint 2.3.2 Uncertainty modeling 2.3.2.1 Scenario generation 2.3.2.1.1 WT power output 2.3.2.1.2 PV power output 2.3.2.1.3 Demand variation modeling 2.3.2.2 Scenario reduction 2.4 Case study 2.4.1 Simulation and results 2.5 Conclusion Acknowledgments References 3 Uncertainty analysis and risk assessment for effective decision-making using wide-area synchrophasor measurement system Abbreviations 3.1 Introduction 3.1.1 Phasor measurement unit 3.1.2 Synchrophasor communication system 3.1.3 Phasor data concentrator 3.2 Risk assessment and uncertainty analysis of wide area synchrophasor measurement system 3.2.1 Basics of estimating the probability of failure 3.2.2 Monte Carlo simulation models for phasor measurement unit and their communication networks 3.2.3 Risk assessment of a sample wide area synchrophasor measurement system 3.3 Optimal placement of phasor measurement unit for power system observability 3.4 Simulation results 3.5 Conclusion References 4 Power quality issues of smart microgrids: applied techniques and decision making analysis 4.1 Introduction 4.1.1 Power quality definition and standards 4.2 Smart microgrids 4.2.1 Challenges in smart grid power quality 4.2.1.1 Power electronic devices 4.2.1.2 Plug-in hybrid electrical vehicles integration 4.2.1.3 Renewable energy sources integration 4.2.2 New tools of smart grids 4.2.2.1 Advanced metering infrastructure 4.2.2.2 Modern monitoring devices 4.2.2.3 Information and communication technology 4.2.2.4 Smart appliances 4.2.2.5 Storage devices 4.2.2.6 Computational intelligence 4.2.2.7 Advanced control methods 4.2.2.8 Active demand-side management and demand response 4.2.2.9 Multiagent technology 4.2.2.10 Internet of things 4.3 Power quality improvement devices 4.3.1 First generation of power quality improvement devices 4.3.2 Second generation of power quality improvement devices 4.3.3 Transition condition, a bridge between conventional and smart electrical systems 4.3.4 Third generation of power quality improvement devices 4.3.4.1 Smart impedance 4.3.4.2 Electrical spring 4.3.4.3 Multifunctional distributed generations 4.3.4.4 Applied control methods to multifunctional distributed generations to enhance power quality 4.3.4.4.1 The Proportional+Resonant control method Current-controlled method Voltage-controlled method Hybrid control method 4.3.4.4.2 Model-based predictive control (MPC) 4.3.4.4.3 Multiobjective model-based predictive control 4.4 Conclusion References 5 Modeling and simulation of active electrical distribution systems using the OpenDSS 5.1 Introduction 5.2 Active electrical distribution systems 5.2.1 Impact of high penetration of distributed generation on power distribution systems 5.2.1.1 Voltage issues 5.2.1.2 The influence of protection 5.2.1.3 Issues on the electric performance metrics (power quality) 5.2.1.4 Operation of the power grid 5.2.1.5 Socioeconomic impact problems 5.2.2 Smart functions on power inverters 5.2.3 Final considerations 5.3 Modeling and simulation using OpenDSS 5.3.1 The OpenDSS 5.3.2 Power flow in OpenDSS: the current injection method 5.3.3 The photovoltaic system model 5.3.4 The OpenDSS storage 5.3.5 The load model 5.4 Application in case studies 5.4.1 Case 1: Voltage control in distribution systems with high penetration of photovoltaics through smart functions 5.4.1.1 Simulation with and without distributed generation photovoltaic insertion 5.4.1.2 Simulation of smart controls 5.4.2 Case 2: Harmonic studies in OpenDSS considering renewable distributed generation and aggregate linear load models 5.4.3 Harmonic studies 5.4.3.1 Model sensitivity 5.4.3.2 Load composition 5.5 Result analysis 5.5.1 Case 1: Volt/Var and Volt/Watt controls 5.5.2 Case 2: Harmonics 5.6 Conclusion Acknowledgment References 6 Adaptive estimation and tracking of power quality disturbances with classification for smart grid applications 6.1 Introduction 6.2 Methodologies for efficient estimation of power quality disturbances by using adaptive filters 6.2.1 Signal model for power quality disturbances and harmonics estimation 6.2.1.1 Signal model for power quality disturbances estimation 6.2.1.2 Signal model for harmonic estimation 6.2.2 Adaptive filtering algorithms for power quality estimation 6.2.2.1 Least mean square algorithm 6.2.2.2 Recursive least square algorithm 6.2.2.3 Kalman filtering algorithm 6.2.3 Sparse model–based adaptive filters 6.2.4 FPGA implementation of adaptive filters used in power quality estimation 6.2.5 Simulation results and discussion 6.3 Methodologies for feature extraction and classification of power quality disturbances 6.3.1 Empirical mode decomposition 6.3.2 Hilbert transform 6.3.3 Artificial neural network 6.3.4 Probabilistic neural network classifier 6.3.5 Support vector machine 6.3.6 Power quality event classification 6.3.7 Results and discussion 6.3.7.1 Classification of power quality events by using ANN and PNN 6.3.7.2 Classification of power quality events using support vector machine 6.3.8 Conclusion Appendix Parameters of ANN Parameters of probabilistic neural network Parameters of particle swarm optimization References 7 Role of microphasor measurement unit for decision making based on enhanced situational awareness of a modern distribution... 7.1 Introduction 7.2 Need of microphasor measurement unit in modern distribution system 7.3 Synchrophasor technology 7.4 Principal components of a basic microphasor measurement unit 7.5 Decision application of microphasor measurement unit in modern distribution system 7.6 Open microphasor measurement unit data for research study 7.7 Conclusion References 8 Effects of electrical infrastructures in grid with high penetration of renewable sources Nomenclature 8.1 Introduction 8.2 Coordinated operation of local generation and flexible resources 8.3 Flexible resources applied to distribution network assistance 8.4 Islanded microgrids operation 8.4.1 Primary control 8.4.2 Secondary control 8.5 Smart coordinated methodology 8.6 Results 8.7 Conclusion Acknowledgments References 9 Distributed generation in deregulated energy markets and probabilistic hosting capacity decision-making challenges 9.1 Introduction 9.2 Decision-making techniques and its applications in hosting capacity studies 9.3 Hosting capacity assessment under uncertainty of renewable energy resources 9.4 Overview of related applications 9.5 Problem formulation 9.5.1 Objective function 9.5.2 Constraints 9.5.3 Load model 9.5.4 Distributed generation unit models 9.5.5 Deterministic hosting capacity approach 9.5.6 Probabilistic hosting capacity approach 9.6 Case study 9.6.1 Deterministic hosting capacity results 9.6.2 Probabilistic hosting capacity results 9.7 Conclusion References Further reading 10 Particle swarm optimization applied to reactive power dispatch considering renewable generation 10.1 Introduction 10.2 Voltage collapse indexes 10.2.1 Tangent vector 10.2.2 PV curve 10.2.3 QV curve 10.3 Active power losses 10.4 Identification of candidate buses for renewable generation allocation 10.4.1 RES allocation by voltage stability criteria 10.4.2 RES allocation by loss sensitivity criteria 10.5 Identification of generators for reactive power dispatch using particle swarm optimization 10.6 Particle swarm optimization for reactive power dispatch 10.7 Methodology for particle swarm optimization application to reactive power dispatch considering tangent-vector-based ge... 10.8 Results and analysis 10.9 Conclusion Acknowledgments References 11 Decision-making-based optimal generation-side secondary-reserve scheduling and optimal LFC in deregulated interconnected... Nomenclatures and abbreviations 11.1 Introduction 11.2 Power system operation and decision-making 11.2.1 Real-time operation 11.2.2 Decision-making-based planning and economic operation 11.2.3 Operation and planning problems to be addressed 11.3 Decision-making application to reserve scheduling 11.3.1 Problem setup and reserve representation 11.4 Probabilistic security-constrained reserve scheduling 11.4.1 Deterministic constraints 11.4.2 Probabilistic constraints 11.5 Decision-making-based optimal automatic generation control in deregulated environment 11.5.1 An overview of the fractional calculus 11.5.2 Load-frequency control and automatic generation control based on fractional calculus 11.5.2.1 Load-frequency control under the deregulation environment 11.5.2.2 Design of load-frequency controller based on the fractional calculus 11.5.3 Optimal tuning of the controller parameter 11.5.3.1 The proposed objective function 11.5.3.2 Imperialist competitive algorithm–based fractional-order proportional–integral–derivative controller’s optimization 11.6 Case study 11.6.1 The studied deregulated power system 11.6.2 Simulation results and discussions 11.7 Conclusion References 12 Heuristic methods for the evaluation of environmental impacts in the power plants 12.1 Introduction 12.2 Materials and methods 12.2.1 Heuristic optimization techniques 12.2.2 Genetic algorithms 12.2.3 Nondominated sorting genetic algorithm II 12.2.3.1 Selection process, crossover, and mutation 12.2.3.2 Stacking operator 12.2.3.3 Selection by tournament second stacking operator 12.2.3.4 Determination of final set descending 12.2.3.5 Pseudocode for the nondominated sorting genetic algorithm II 12.2.4 The emission ratio as a parameter to assess the environmental contamination 12.2.5 Emission index of gas engines 12.2.6 Index engine emissions of heavy fuel oil 12.2.7 Contamination caused by plant 12.2.8 Specific emission index 12.2.9 Permissible values of emission Index 12.2.10 Obtaining primary data 12.2.11 Price of carbon emissions 12.3 A mathematical model for the optimization of EED considering the emission index 12.3.1 Mathematical model for environmental economic dispatch 12.3.1.1 Minimizing costs 12.3.1.2 Minimizing the environmental impact 12.3.1.3 Load dispatch restrictions considering emissions 12.3.1.4 Objective functions 12.3.2 Order environmental economic load: case studies 12.3.2.1 Problem formulation 12.3.3 Analysis and discussion of results 12.4 Conclusions References 13 Maintenance management with application of computational intelligence generating a decision support system for the load ... 13.1 Introduction 13.2 Maintenance systems and their application in thermoelectric plants 13.3 Fragments used for implantation end methodology TPM program 13.4 Predictive maintenance using computational (fuzzy logic) decision support tool in preload dispatch 13.5 Fuzzy simulation 13.6 Case study (fuzzy logic with predictive maintenance) 13.6.1 Results achieved Acknowledgment References 14 Integration of fixed-speed wind energy conversion systems into unbalanced and harmonic distorted power grids 14.1 Introduction 14.2 Problem statement and description 14.2.1 Modeling of the fixed-speed wind energy conversion systems 14.2.2 Determination of the permissible penetration level 14.2.3 Modeling of the Steinmetz compensator 14.2.4 Modeling of the single-tuned harmonic filter 14.3 Problem formulation and solution algorithm 14.3.1 Objective function 14.3.2 Nonequality constraints 14.3.3 Particle swarm optimization algorithm 14.4 Simulation results and discussion 14.4.1 Performance evaluation of the proposed compensator 14.4.2 Sensitivity analysis of the proposed optimal compensator design under variation of utility and load-side conditions 14.5 Conclusion References 15 Impact of demand-side management system in autonomous DC microgrid 15.1 Introduction 15.2 Analysis of AC microgrid and DC microgrid 15.2.1 Converter stages 15.2.2 Energy demand 15.2.3 Estimation of photovoltaic and battery size 15.3 State of charge of battery bank 15.4 Autonomous DC microgrid 15.4.1 Conceptual diagram of DC microgrid 15.4.2 Hardware setup of DC microgrid 15.4.3 Control and monitoring unit of DC microgrid 15.5 Demand-side management algorithm 15.6 Results and discussions 15.6.1 Performance results of demand-side management scheme with sufficient photovoltaic power 15.6.2 Performance results of demand-side management scheme with insufficient photovoltaic power 15.7 Conclusion References Further reading 16 Multistage and decentralized operations of electric vehicles within the California demand response markets 16.1 Introduction 16.2 System overview 16.2.1 Smart electric vehicle charging control system 16.2.2 Communication information exchange 16.3 Deterministic problem formulation 16.3.1 Tariff and demand response markets 16.3.2 Aggregation of electric vehicles 16.3.3 Time-of-use tariff structure 16.3.4 Integration with peak-day pricing plan 16.3.5 Integration with ancillary service market 16.3.6 Integration with PDR market 16.4 Cost-saving performance in different markets 16.4.1 Ancillary service market participation 16.4.2 PDR market participation 16.4.3 Demand-based bid program participation 16.4.4 Peak-day pricing participation 16.4.5 Impact of the flexibility and market participation threshold 16.4.5.1 Impact of baseline calculation 16.5 Distributed optimization with asynchronous ADMM and V2G capabilities 16.6 Conclusion References Further reading 17 Pattern-recognition methods for decision-making in protection of transmission lines 17.1 Introduction 17.2 Pattern recognition 17.2.1 Feature extraction 17.2.2 Feature selection 17.2.3 Decision-making 17.2.3.1 Classification 17.2.3.2 Prediction 17.3 Pattern recognition application on protection of transmission line 17.3.1 Fault detection, classification, and location 17.3.1.1 Fault detection, classification, and location in single-circuit transmission line 17.3.1.2 Fault detection, classification, and location in double-circuit transmission lines 17.3.2 High-impedance fault detection 17.3.3 Power swing detection 17.3.4 Symmetrical fault detection during power swing 17.4 Decision-making based on smart relays 17.4.1 Structure of smart relays 17.4.2 Advantages 17.4.3 Disadvantages 17.5 Conclusion References 18 A reliable decision-making algorithm for fault during power swing in 400kV double-circuit transmission line: a case stud... 18.1 Introduction 18.2 Protection challenges in case of a fault during power swing 18.3 Modeling and simulation of Chhattisgarh state power transmission network 18.4 Proposed wavelet packet energy and bagged decision tree decision-making algorithm 18.4.1 Wavelet packet energy 18.4.2 Bagged decision tree 18.4.3 Wavelet packet energy and bagged decision tree–based decision-making algorithm 18.5 Simulation results and discussions 18.5.1 Detection of system condition (no-fault, fault, and power swing) 18.5.2 Discrimination of power swing condition (stable and unstable) 18.5.3 Detection of faults during power swing condition 18.5.4 Performance evaluation of proposed wavelet packet energy and bagged decision tree-4 module for classification of faults 18.5.4.1 Performance in case of varying fault parameters 18.5.4.2 Performance during variation in power flow angle 18.5.4.3 Performance during variation in source impedance ratio 18.5.4.4 Performance during variation in sampling frequency 18.5.4.5 Performance during current transformer–saturation and capacitor coupling voltage transformer transient 18.5.4.6 Performance in case of a fault in circuit-II 18.5.4.7 Real-time validation using real-time digital simulation 18.6 Overall performance assessment of proposed decision-making scheme 18.7 Comparative assessment 18.8 Conclusion Acknowledgments References 19 Modeling and processing of smart grids big data: study case of a university research building 19.1 Introduction 19.2 Big data 19.3 Big data requirements 19.3.1 Big data analytics for smart grid 19.3.2 Big data analytics—challenges and trends 19.3.3 Security (cyber and physical) 19.4 Big data social impact 19.5 Laboratory building data and analysis 19.5.1 Phase current 19.5.2 Power factor 19.5.3 Frequency 19.5.4 Apparent power 19.5.5 Active power 19.5.6 Reactive power 19.5.7 Energy production 19.5.8 Voltage phase 19.6 Conclusion References Index Back Cover Decision Making Applications in Modern Power Systems presents an enhanced decision-making framework for power systems. Designed as an introduction to enhanced electricity system analysis using decision-making tools, it provides an overview of the different elements, levels and actors involved within an integrated framework for decision-making in the power sector. In addition, it presents a state-of-play on current energy systems, strategies, alternatives, viewpoints and priorities in support of decision-making in the electric power sector, including discussions of energy storage and smart grids. As a practical training guide on theoretical developments and the application of advanced methods for practical electrical energy engineering problems, this reference is ideal for use in establishing medium-term and long-term strategic plans for the electric power and energy sectors. Provides panoramic coverage of state-of-the-art energy systems, strategies and priorities in support of electrical power decision-making Introduces innovative research outcomes, programs, algorithms and approaches to address challenges in understanding, creating and managing complex techno-socio-economic engineering systems Includes practical training on theoretical developments and the application of advanced methods for realistic electrical energy engineering problems Decision Making Applications in Modern Power Systems presents an enhanced decision-making framework for power systems. Designed as an introduction to enhanced electricity system analysis using decision-making tools, it provides an overview of the different elements, levels and actors involved within an integrated framework for decision-making in the power sector. In addition, it presents a state-of-play on current energy systems, strategies, alternatives, viewpoints and priorities in support of decision-making in the electric power sector, including discussions of energy storage and smart grids. As a practical training guide on theoretical developments and the application of advanced methods for practical electrical energy engineering problems, this reference is ideal for use in establishing medium-term and long-term strategic plans for the electric power and energy sectors. -- Provided by publisher