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E-grāmata: Dynamic Vulnerability Assessment and Intelligent Control: For Sustainable Power Systems

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Dynamic Vulnerability Assessment and Intelligent Control: For Sustainable Power SystemsPalielināt
  • Formāts: PDF+DRM
  • Sērija : IEEE Press
  • Izdošanas datums: 25-Jan-2018
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  • Valoda: eng
  • ISBN-13: 9781119214977
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Identifying, assessing, and mitigating electric power grid vulnerabilities is a growing focus in short-term operational planning of power systems.

Identifying, assessing, and mitigating electric power grid vulnerabilities is a growing focus in short-term operational planning of power systems. Through illustrated application, this important guide surveys state-of-the-art methodologies for the assessment and enhancement of power system security in short term operational planning and real-time operation. The methodologies employ advanced methods from probabilistic theory, data mining, artificial intelligence, and optimization, to provide knowledge-based support for monitoring, control (preventive and corrective), and decision making tasks.

Key features:

  • Introduces behavioural recognition in wide-area monitoring and security constrained optimal power flow for intelligent control and protection and optimal grid management.
  • Provides in-depth understanding of risk-based reliability and security assessment, dynamic vulnerability assessment methods, supported by the underpinning mathematics.
  • Develops expertise in mitigation techniques using intelligent protection and control, controlled islanding, model predictive control, multi-agent and distributed control systems
  • Illustrates implementation in smart grid and self-healing applications with examples and real-world experience from the WAMPAC (Wide Area Monitoring Protection and Control) scheme.
  • Supplementary material, including Matlab codes, available through the companion website: www.wiley.com/go/rueda_torres/dynamic

Dynamic Vulnerability Assessment and Intelligent Control for Power Systems is a valuable reference for postgraduate students and researchers in power system stability as well as practicing engineers working in power system dynamics, control, and network operation and planning.

List of Contributors xv
Foreword xix
Preface xxi
1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment 1(20)
Jaime C. Cepeda
Jose Luis Rueda-Torres
1.1 Introduction
1(1)
1.2 Power System Vulnerability
2(3)
1.2.1 Vulnerability Assessment
2(2)
1.2.2 Timescale of Power System Actions and Operations
4(1)
1.3 Power System Vulnerability Symptoms
5(3)
1.3.1 Rotor Angle Stability
6(1)
1.3.1.1 Transient Stability
6(1)
1.3.1.2 Oscillatory Stability
6(1)
1.3.2 Short-Term Voltage Stability
7(1)
1.3.3 Short-Term Frequency Stability
7(1)
1.3.4 Post-Contingency Overloads
7(1)
1.4 Synchronized Phasor Measurement Technology
8(5)
1.4.1 Phasor Representation of Sinusoids
8(1)
1.4.2 Synchronized Phasors
9(1)
1.4.3 Phasor Measurement Units (PMUs)
9(1)
1.4.4 Discrete Fourier Transform and Phasor Calculation
10(1)
1.4.5 Wide Area Monitoring Systems
10(2)
1.4.6 WAMPAC Communication Time Delay
12(1)
1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment
13(3)
1.6 Concluding Remarks
16(1)
References
17(4)
2 Steady-state Security 21(20)
Evelyn Heylen
Steven De Boeck
Marten Ovaere
Hakan Ergun
Dirk Van Hertem
2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control
22(9)
2.1.1 Reliability Assessment
23(1)
2.1.2 Reliability Control
24(10)
2.1.2.1 Credible and Non-Credible Contingencies
25(1)
2.1.2.2 Operating State of the Power System
25(3)
2.1.2.3 System State Space Representation
28(3)
2.2 Reliability Under Various Timeframes
31(2)
2.3 Reliability Criteria
33(1)
2.4 Reliability and Its Cost as a Function of Uncertainty
34(3)
2.4.1 Reliability Costs
34(1)
2.4.2 Interruption Costs
35(1)
2.4.3 Minimizing the Sum of Reliability and Interruption Costs
36(1)
2.5 Conclusion
37(1)
References
38(3)
3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems 41(22)
Bart W. Tuinema
Nikoleta Kandalepa
Jose Luis Rueda-Torres
3.1 Introduction
41(1)
3.2 Time Horizons in the Planning and Operation of Power Systems
42(3)
3.2.1 Time Horizons
42(1)
3.2.2 Overlapping and Interaction
42(1)
3.2.3 Remedial Actions
42(3)
3.3 Reliability Indicators
45(4)
3.3.1 Security-of-Supply Related Indicators
45(2)
3.3.2 Additional Indicators
47(2)
3.4 Reliability Analysis
49(4)
3.4.1 Input Information
49(1)
3.4.2 Pre-calculations
50(1)
3.4.3 Reliability Analysis
50(3)
3.4.4 Output: Reliability Indicators
53(1)
3.5 Application Example: EHV Underground Cables
53(5)
3.5.1 Input Parameters
54(2)
3.5.2 Results of Analysis
56(2)
3.6 Conclusions
58(2)
References
60(3)
4 An Enhanced WAMS-based Power System Oscillation Analysis Approach 63(32)
Qing Liu
Hassan Bevrani
Yasunori Mitani
4.1 Introduction
63(2)
4.2 HHT Method
65(6)
4.2.1 EMD
65(1)
4.2.2 Hilbert Transform
65(1)
4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum
66(1)
4.2.4 HHT Issues
67(4)
4.2.4.1 The Boundary End Effect
69(1)
4.2.4.2 Mode Mixing and Pseudo-IMF Component
70(1)
4.2.4.3 Parameter Identification
71(1)
4.3 The Enhanced HHT Method
71(10)
4.3.1 Data Pre-treatment Processing
71(4)
4.3.1.1 DC Removal Processing
72(1)
4.3.1.2 Digital Band-Pass Filter Algorithm
72(3)
4.3.2 Inhibiting the Boundary End Effect
75(5)
4.3.2.1 The Boundary End Effect Caused by the EMD Algorithm
75(1)
4.3.2.2 Inhibiting the Boundary End Effects Caused by the EMD
76(1)
4.3.2.3 The Boundary End Effect Caused by the Hilbert Transform
76(3)
4.3.2.4 Inhibiting the Boundary End Effect Caused by the HT
79(1)
4.3.3 Parameter Identification
80(1)
4.4 Enhanced HHT Method Evaluation
81(7)
4.4.1 Case I
81(3)
4.4.2 Case II
84(1)
4.4.3 Case III
85(3)
4.5 Application to Real Wide Area Measurements
88(4)
Summary
92(1)
References
93(2)
5 Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction 95(24)
Jaime C. Cepeda
Jose Luis Rueda-Torres
Delia G. Colome
Istvan Erlich
5.1 Introduction
95(1)
5.2 Post-contingency Dynamic Vulnerability Regions
96(1)
5.3 Recognition of Post-contingency DVRs
97(12)
5.3.1 N-1 Contingency Monte Carlo Simulation
98(2)
5.3.2 Post-contingency Pattern Recognition Method
100(3)
5.3.3 Definition of Data-Time Windows
103(1)
5.3.4 Identification of Post-contingency DVRs-Case Study
104(5)
5.4 Real-Time Vulnerability Status Prediction
109(6)
5.4.1 Support Vector Classifier (SVC) Training
112(1)
5.4.2 SVC Real-Time Implementation
113(2)
5.5 Concluding Remarks
115(1)
References
115(4)
6 Performance Indicator-Based Real-Time Vulnerability Assessment 119(30)
Jaime C Cepeda
Jose Luis Rueda-Torres
Delia G. Colome
lstvan Erlich
6.1 Introduction
119(1)
6.2 Overview of the Proposed Vulnerability Assessment Methodology
120(2)
6.3 Real-Time Area Coherency Identification
122(3)
6.3.1 Associated PMU Coherent Areas
122(3)
6.4 TVFS Vulnerability Performance Indicators
125(12)
6.4.1 Transient Stability Index (TSI)
125(3)
6.4.2 Voltage Deviation Index (VDI)
128(3)
6.4.3 Frequency Deviation Index (FDI)
131(1)
6.4.4 Assessment of TVFS Security Level for the Illustrative Examples
131(2)
6.4.5 Complete TVFS Real-Time Vulnerability Assessment
133(4)
6.5 Slower Phenomena Vulnerability Performance Indicators
137(8)
6.5.1 Oscillatory Index (OSI)
137(4)
6.5.2 Overload Index (OVI)
141(4)
6.6 Concluding Remarks
145(1)
References
145(4)
7 Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems 149(28)
Florin Capitanescu
7.1
Chapter Overview
149(1)
7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF)
150(8)
7.2.1 Introduction
150(1)
7.2.2 Abstract Mathematical Formulation of the OPF Problem
150(1)
7.2.3 OPF Solution via Interior-Point Method
151(3)
7.2.3.1 Obtaining the Optimality Conditions In IPM
151(1)
7.2.3.2 The Basic Primal Dual Algorithm
152(2)
7.2.4 Illustrative Example
154(4)
7.2.4.1 Description of the Test System
154(1)
7.2.4.2 Detailed Formulation of the OPF Problem
155(1)
7.2.4.3 Analysis of Various Operating Modes
156(1)
7.2.4.4 Iterative OPF Methodology
157(1)
7.3 Risk-Based OPF
158(4)
7.3.1 Motivation and Principle
158(1)
7.3.2 Risk-Based OPF Problem Formulation
159(1)
7.3.3 Illustrative Example
160(2)
7.3.3.1 Detailed Formulation of the RB-OPF Problem
160(1)
7.3.3.2 Numerical Results
161(1)
7.4 OPF Under Uncertainty
162(7)
7.4.1 Motivation and Potential Approaches
162(1)
7.4.2 Robust Optimization Framework
162(1)
7.4.3 Methodology for Solving the R-OPF Problem
163(1)
7.4.4 Illustrative Example
164(5)
7.4.4.1 Detailed Formulation of the Worst Uncertainty Pattern Computation With Respect to a Contingency
164(2)
7.4.4.2 Detailed Formulation of the OPF to Check Feasibility in the Presence of Corrective Actions
166(1)
7.4.4.3 Detailed Formulation of the R-OPF Relaxation
166(2)
7.4.4.4 Numerical Results
168(1)
7.5 Advanced Issues and Outlook
169(4)
7.5.1 Conventional OPF
169(3)
7.5.1.1 Overall OPF Solution Methodology
169(2)
7.5.1.2 Core Optimizers: Classical Methods Versus Convex Relaxations
171(1)
7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid
172(1)
References
173(4)
8 Modeling Preventive and Corrective Actions Using Linear Formulation 177(16)
Tom Van Acker
Dirk Van Hertem
8.1 Introduction
177(1)
8.2 Security Constrained OPF
178(1)
8.3 Available Control Actions in AC Power Systems
178(2)
8.3.1 Generator Redispatch
179(1)
8.3.2 Load Shedding and Demand Side Management
179(1)
8.3.3 Phase Shifting Transformer
179(1)
8.3.4 Switching Actions
180(1)
8.3.5 Reactive Power Management
180(1)
8.3.6 Special Protection Schemes
180(1)
8.4 Linear Implementation of Control Actions in a SCOPF Environment
180(5)
8.4.1 Generator Redispatch
181(1)
8.4.2 Load Shedding and Demand Side Management
182(1)
8.4.3 Phase Shifting Transformer
183(1)
8.4.4 Switching
184(1)
8.5 Case Study of Preventive and Corrective Actions
185(6)
8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1)
186(1)
8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2)
187(3)
8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3)
190(1)
8.6 Conclusions
191(1)
References
191(2)
9 Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems 193(24)
Da Wang
9.1 Introduction
193(1)
9.2 MPC Basic Theory & Damping Controller Models
194(4)
9.2.1 What is MPC?
194(2)
9.2.2 Damping Controller Models
196(2)
9.3 MPC for Damping Oscillations
198(6)
9.3.1 Outline of Idea
198(1)
9.3.2 Mathematical Formulation
199(1)
9.3.3 Proposed Control Schemes
200(4)
9.3.3.1 Centralized MPC
200(1)
9.3.3.2 Decentralized MPC
200(2)
9.3.3.3 Hierarchical MPC
202(2)
9.4 Test System & Simulation Setting
204(1)
9.5 Performance Analysis of MPC Schemes
204(9)
9.5.1 Centralized MPC
204(5)
9.5.1.1 Basic Results in Ideal Conditions
204(2)
9.5.1.2 Results Considering State Estimation Errors
206(2)
9.5.1.3 Consideration of Control Delays
208(1)
9.5.2 Distributed MPC
209(1)
9.5.3 Hierarchical MPC
209(4)
9.6 Conclusions and Discussions
213(1)
References
214(3)
10 Voltage Stability Enhancement by Computational Intelligence Methods 217(16)
Worawat Nakawiro
10.1 Introduction
217(1)
10.2 Theoretical Background
218(5)
10.2.1 Voltage Stability Assessment
218(1)
10.2.2 Sensitivity Analysis
219(1)
10.2.3 Optimal Power Flow
220(1)
10.2.4 Artificial Neural Network
220(1)
10.2.5 Ant Colony Optimisation
221(2)
10.3 Test Power System
223(1)
10.4 Example 1: Preventive Measure
224(2)
10.4.1 Problem Statement
224(1)
10.4.2 Simulation Results
225(1)
10.5 Example 2: Corrective Measure
226(3)
10.5.1 Problem Statement
226(1)
10.5.2 Simulation Results
227(2)
10.6 Conclusions
229(1)
References
230(3)
11 Knowledge-Based Primary and Optimization-Based Secondary Control of Multi-terminal HVDC Grids 233(18)
Adedotun J. Agbemuko
Mario Ndreko
Marjan Popov
Jose Luis Rueda-Torres
Mart A.M.M. van der Meijden
11.1 Introduction
234(1)
11.2 Conventional Control Schemes in HV-MTDC Grids
234(2)
11.3 Principles of Fuzzy-Based Control
236(1)
11.4 Implementation of the Knowledge-Based Power-Voltage Droop Control Strategy
236(6)
11.4.1 Control Scheme for Primary and Secondary Power-Voltage Control
237(1)
11.4.2 Input/Output Variables
238(3)
11.4.2.1 Membership Functions and Linguistic Terms
239(2)
11.4.3 Knowledge Base and Inference Engine
241(1)
11.4.4 Defuzzification and Output
241(1)
11.5 Optimization-Based Secondary Control Strategy
242(3)
11.5.1 Fitness Function
242(2)
11.5.2 Constraints
244(1)
11.6 Simulation Results
245(2)
11.6.1 Set Point Change
245(1)
11.6.2 Constantly Changing Reference Set Points
246(1)
11.6.3 Sudden Disconnection of Wind Farm for Undefined Period
246(1)
11.6.4 Permanent Outage of VSC 3
247(1)
11.7 Conclusion
247(1)
References
248(3)
12 Model Based Voltage/Reactive Control in Sustainable Distribution Systems 251(18)
Hoan Van Pham
Sultan Nasiruddin Ahmed
12.1 Introduction
251(1)
12.2 Background Theory
252(6)
12.2.1 Voltage Control
252(1)
12.2.2 Model Predictive Control
253(2)
12.2.3 Model Analysis
255(2)
12.2.3.1 Definition of Sensitivity
255(1)
12.2.3.2 Computation of Sensitivity
255(2)
12.2.4 Implementation
257(1)
12.3 MPC Based Voltage/Reactive Controller-an Example
258(4)
12.3.1 Control Scheme
258(1)
12.3.2 Overall Objective Function of the MPC Based Controller
259(2)
12.3.3 Implementation of the MPC Based Controller
261(1)
12.4 Test Results
262(4)
12.4.1 Test System and Measurement Deployment
262(1)
12.4.2 Parameter Setup and Algorithm Selection for the Controller
263(1)
12.4.3 Results and Discussion
263(7)
12.4.3.1 Loss Minimization Performance of the Controller
263(1)
12.4.3.2 Voltage Correction Performance of the Controller
264(2)
12.5 Conclusions
266(1)
References
267(2)
13 Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems 269(14)
Hoan Van Pham
Sultan Nasiruddin Ahmed
13.1 Introduction
269(1)
13.2 System Model and Problem Formulation
270(5)
13.2.1 Power System Model
270(1)
13.2.2 Optimal Reactive Control Problem Formulation
271(1)
13.2.3 Multi-Agent Sensitivity Model
272(3)
13.2.3.1 Calculation of the First Layer
273(1)
13.2.3.2 Calculation of the Second Layer
273(2)
13.3 Multi-Agent Based Approach
275(2)
13.3.1 Augmented Lagrange Formulation
275(1)
13.3.2 Implementation Algorithm
275(2)
13.4 Case Studies and Simulation Results
277(3)
13.4.1 Case Studies
277(1)
13.4.2 Simulation Results
277(11)
13.4.2.1 Performance Comparison Between Multi-Agent Based and Single-Agent Based System
278(1)
13.4.2.2 Impacts of General Parameters on the Proposed Control Scheme's Performance
279(1)
13.4.2.3 Impacts of Multi-Agent Parameters on the Proposed Control Scheme's Performance
279(1)
13.5 Conclusions
280(1)
References
281(2)
14 Operation of Distribution Systems Within Secure Limits Using Real-Time Model Predictive Control 283(28)
Hamid Soleimani Bidgoli
Gustavo Valverde
Petros Aristidou
Mevludin Glavic
Thierry Van Cutsem
14.1 Introduction
283(2)
14.2 Basic MPC Principles
285(1)
14.3 Control Problem Formulation
285(3)
14.4 Voltage Correction With Minimum Control Effort
288(3)
14.4.1 Inclusion of LTC Actions as Known Disturbances
289(1)
14.4.2 Problem Formulation
290(1)
14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References
291(5)
14.5.1 Mode 1
292(1)
14.5.2 Mode 2
292(2)
14.5.3 Mode 3
294(1)
14.5.4 Problem Formulation
295(1)
14.6 Test System
296(2)
14.7 Simulation Results: Voltage Correction with Minimal Control Effort
298(4)
14.7.1 Scenario A
299(1)
14.7.2 Scenario B
300(2)
14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference
302(4)
14.8.1 Scenario C: Mode 1
302(2)
14.8.2 Scenario D: Modes 1 and 2 Combined
304(1)
14.8.3 Scenario E: Modes 1 and 3 Combined
305(1)
14.9 Conclusion
306(2)
References
308(3)
15 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks 311(26)
Gustavo Valverde
Petros Aristidou
Thierry Van Cutsem
15.1 Introduction
311(2)
15.2 Long-Term Voltage Stability
313(3)
15.2.1 Countermeasures
314(2)
15.3 Impact of Volt-VAR Control on Long-Term Voltage Stability
316(3)
15.3.1 Countermeasures
318(1)
15.4 Test System Description
319(4)
15.4.1 Test System
319(2)
15.4.2 VVC Algorithm
321(1)
15.4.3 Emergency Detection
322(1)
15.5 Case Studies and Simulation Results
323(11)
15.5.1 Results in Stable Scenarios
323(3)
15.5.1.1 Case Al
323(1)
15.5.1.2 Case A2
324(2)
15.5.2 Results in Unstable Scenarios
326(2)
15.5.2.1 Case B1
326(1)
15.5.2.2 Case B2
326(2)
15.5.3 Results with Emergency Support From Distribution
328(9)
15.5.3.1 Case Cl
328(1)
15.5.3.2 Case C2
329(4)
15.5.3.3 Case C3
333(1)
15.6 Conclusion
334(1)
References
334(3)
16 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints 337(24)
Nelson Granda
Delia G. Colome
16.1 Introduction
337(3)
16.1.1 Stage One: Vulnerability Assessment
337(1)
16.1.2 Stage Two: Islanding Process
338(2)
16.2 Network Splitting Mechanism
340(4)
16.2.1 Graph Modeling, Update, and Reduction
341(1)
16.2.2 Graph Partitioning Procedure
342(1)
16.2.3 Load Shedding/Generation Tripping Schemes
343(1)
16.2.4 Tie-Lines Determination
344(1)
16.3 Power Imbalance Constraint Limits
344(4)
16.3.1 Reduced Frequency Response Model
345(2)
16.3.2 Power Imbalance Constraint Limits Determination
347(1)
16.4 Overload Assessment and Control
348(1)
16.5 Test Results
349(7)
16.5.1 Power System Collapse
349(2)
16.5.2 Application of Proposed Methodology
351(3)
16.5.3 Performance of Proposed ACIS
354(2)
16.6 Conclusions and Recommendations
356(1)
References
357(4)
17 High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions 361(28)
Rommel P. Aguilar
Fabian E. Perez-Yauli
17.1 Introduction
361(2)
17.2 Empirical Orthogonal Functions
363(2)
17.2.1 Formulation
363(2)
17.3 Applications of EOFs for Transmission Line Protection
365(4)
17.3.1 Fault Direction
366(1)
17.3.2 Fault Classification
367(2)
17.3.2.1 Required EOF
368(1)
17.3.2.2 Fault Type Surfaces
368(1)
17.3.2.3 Defining the Fault Type
368(1)
17.3.3 Fault Location
369(1)
17.4 Study Case
369(14)
17.4.1 Transmission Line Model and Simulation
369(1)
17.4.2 The Power System and Transmission Line
370(1)
17.4.3 Training Data
370(1)
17.4.4 Training Data Matrix
370(3)
17.4.4.1 Data Window
372(1)
17.4.4.2 Sampling Frequency
372(1)
17.4.5 Signal Conditioning
373(1)
17.4.5.1 Superimposed Component
373(1)
17.4.5.2 Centering the Variables
373(1)
17.4.5.3 Scaling
373(1)
17.4.6 Energy Patterns
373(3)
17.4.7 EOF Analysis
376(3)
17.4.7.1 Computing the EOFs
376(2)
17.4.7.2 Fault Patterns Using EOF
378(1)
17.4.8 Evaluation of the Protection Scheme
379(1)
17.4.8.1 Fault Direction
379(1)
17.4.9 Fault Classification
380(2)
17.4.9.1 Classification
381(1)
17.4.10 Fault Location
382(1)
17.5 Conclusions
383(1)
Appendix 17.A
384(1)
Study Cases: WECC 9-bus, ATPDraw Models and Parameters
384(2)
References
386(3)
18 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System 389(24)
Pablo X. Verdugo
Jaime C. Cepeda
Aharon B. De La Torre
Diego E. Echeverria
18.1 Introduction
389(1)
18.2 PMU Location in the Ecuadorian SNI
390(1)
18.3 Steady-State Angle Stability
391(4)
18.4 Steady-State Voltage Stability
395(3)
18.5 Oscillatory Stability
398(9)
18.5.1 Power System Stabilizer Tuning
402(5)
18.6 Ecuadorian Special Protection Scheme (SPS)
407(3)
18.6.1 SPS Operation Analysis
409(1)
18.7 Concluding Remarks
410(1)
References
410(3)
Index 413
Edited by

José Luis Rueda-Torres received the Electrical Engineer Diploma from Escuela Politécnica Nacional, Quito, Ecuador, cum laude honors, in August 2004. In November 2009, he received a Ph.D. in electrical engineering from the National University of San Juan, obtaining the highest mark 'Sobresaliente' (Outstanding). He is currently working as an Assistant Professor for Intelligent Electrical Power Grids at the Department of Electrical Sustainable Energy, Technical University Delft, Netherlands. He is vice-chair of the Working Group on Modern Heuristic Optimization (WGMHO) under the IEEE PES Power System Analysis, Computing, and Economics Committee. Dr. Rueda-Torres is a member of CIGRE and a senior member of the IEEE. His current research interests include power system planning, power system stability and control, and probabilistic and artificial intelligence methods.



Francisco Gonzįlez-Longatt received an Electrical Engineering degree from Instituto Universitario Politécnico de la Fuerza Armada Nacional (1994), Master of Business Administration from Universidad Bicentenaria de Aragua (1999), a Ph.D. in Electrical Power Engineering from the Universidad Central de Venezuela (2008), and a Postgraduate Certificate in Higher Education Professional Practice from Coventry University (2013). He is a Lecturer in Electrical Power Systems in the School of Electronic, Electrical and Systems Engineering at Loughborough University, UK, and the Vice-President of the Venezuelan Wind Energy Association. Dr Gonzįlez-Longatt is a member of CIGRE and a senior member of the IEEE. His current research interests include innovative (operation/control) schemes to optimize the performance of future energy systems.
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