OPTIMIZATION ADVANCESIN ELECTRIC POWER SYSTEMS -- CONTENTS -- PREFACE -- ABOUT THE EDITOR -- A MATHEMATICAL PROGRAMMING APPROACHTO STATE ESTIMATION -- Abstract -- 1. Introduction -- 2. Formulation -- 3. Observability -- 4. Classical Solution -- 5. Mathematical Programming Solution -- 6. Bad Measurement Detection -- 7. Identification of Erroneous Measurements -- 8. Sensitivity -- 9. Decomposition -- 10. Conclusions -- A. Vectors and Matrices for Sensitivity Analysis -- References -- TRUST REGION OPTIMIZATION METHODSVIA GIVENS ROTATIONS APPLIED TO POWERSYSTEM STATE ESTIMATION -- 1. Introduction -- 2. State Estimation Background -- 2.1. Measurement Model andWeighted Least Squares Estimator -- 2.2. Solution through Gauss-Newton Method -- 2.3. A Priori State Information in Least-Squares Problems -- 2.4. Solution through Givens Rotations -- 2.4.1. A Macro View of PSSE via Givens Rotations -- 2.4.2. The Elementary G3M Givens Rotations - Interpretation of Scaling Factors -- 3. Trust Region Theoretical Basis -- 3.1. Rationale of Trust Region Approach -- 3.2. Basic Trust Region Algorithm -- 3.3. Ensuring Step Feasibility via > 0 -- 4. Trust Region Methods through Givens Rotations -- 4.1. The -−Refactorization Method -- 4.2. The APSI Method -- 4.3. Qualitative Appraisal -- 5. Simulation Results -- 5.1. 6-bus System -- 5.2. 30-bus System -- 5.3. 118-bus and 300-bus Systems -- 5.4. Comparison of Numerical Performance -- 6. Conclusions -- Appendix -- A. Definition of the Trust Region -- Computation of ′2 -- Computation of -- B. Data for the 6-Bus Test System -- References -- THE IMPACT OF DEREGULATION ONMATHEMATICAL MODELS USING OPTIMIZATIONTECHNIQUES TO AID SYSTEM PLANNINGAND OPERATIONS -- Abstract -- The Issue of Reactive Power -- Transmission Rights -- Generation and Transmission additions.

Relation between Mathematical Models and Markets, andOperating Philosophy -- Greenhouse Gases -- Unit Commitment -- Epilogue -- References -- METAHEURISTIC-BASED OPTIMIZATION METHODSFOR TRANSMISSION EXPANSION PLANNINGCONSIDERING UNRELIABILITY COSTS -- Abstract -- 1. Introduction -- 2. Heuristic-Based Methodologies -- 2.1. Evolution Strategies -- 2.1.1. Basic Concepts -- 2.1.2. Application to TEP Problems -- 2.2. Tabu Search -- 2.2.1. Basic Concepts -- 2.2.2. Application to TEP Problems -- 2.3. Ant Colony Optimization -- 2.3.1. Basics Concepts -- 2.3.2. Application to TEP Problems -- 3. Proposed Methodologies -- 3.1. Initialization Process -- 3.2. Transmission Loss Costs -- 3.3. Chronological Aspects -- 3.4. Unreliability Costs -- 3.5. Proposed Algorithms -- 3.5.1. ES and TS Algorithms -- 3.5.2. ACO Algorithm -- 4. Results -- 4.1. Small Test System -- 4.2. Brazilian Sub-transmission Network -- 4.2.1. ES Results -- 4.2.2. TS Results -- 4.2.3. ACO Results -- 4.2.4. Performance Comparison of ES, TS and ACO Algorithms -- 4.1. Small Test System -- 4.1.1. ES Results -- 4.1.2. TS Results -- 4.1.3. ACO Results -- 5. Conclusion -- 6. Appendix: Simple Test System Data -- References -- A VOLTAGE CONTROL OPTIMIZATION FORDISTRIBUTION NETWORKS WITH DG ANDMICROGRIDS -- Abstract -- Acronyms and Abbreviations -- 1. Introduction -- 2. Hierarchical Voltage Control -- 3. Characterization of the Voltage Control Optimization Problem -- 3.1. Mathematical Formulation -- 3.1.1. Defining an Objective Function -- 3.1.2. Defining the Control Variables -- 3.1.3. Optimization Tool -- 3.2. Algorithm -- 4. Test Networks -- 5. Main Results -- 5.1. Objective Function 1 -- 5.2. Objective Function 2 -- 6. Conclusion -- Annex I. Electrical Distance Calculation -- References -- TOOLS FOR THE EFFECTIVE INTEGRATIONOF LARGE AMOUNTS OF WIND ENERGYIN THE SYSTEM -- Abstract -- 1. Introduction.

2. Wind Generation Bids in Pool-Based Electricity Markets -- 2.1. Nomenclature -- 2.2. Justification -- 2.3. Mathematical Model for the Individual Wind Power Bid -- 2.4. Mathematical Model for the Combined Bid -- 2.5. Results for Individual Wind Power Bid -- 2.6. Results for Combined Hydro-wind Power Bid -- 2.6.1. Hydro Generation Data -- 2.6.2. Wind Generation Data -- 2.6.3. Results -- 3. Coordination among Wind Farms and Water Pump Stations -- 4. Coordination among Different Wind Farms, DelegatedDispatches -- 5. Voltage Stability in Power Networks with Large Amount ofWind Energy -- 5.1. Two bus system with a Wind Farm -- 5.1.1. Increasing the Demand -- 5.1.2. Loss of Transmission and Generation Equipments -- 5.2. Voltage Stability Enhancement in Network with Wind Farms -- 5.3. Optimal Power Flow (OPF) Formulation -- 5.4. Modified IEEE 14-bus System -- 6. Conclusion -- References -- APPLICATION OF COST FUNCTIONS FOR LARGESCALE INTEGRATION OF WIND POWER USINGA MULTI-SCHEME ENSEMBLE PREDICTIONTECHNIQUE -- Abstract -- 1. Introduction -- 2. The Optimisation Problem -- 2.1. Energy Prices and Market Structures -- 3. Optimisation Objectives -- 3.1. Market Considerations -- 3.2. Transition from Fixed Prices to the Liberalised Market -- 3.3. The Skew Competition in the Trading of Wind -- 3.4. Uncertainty Considerations -- 4. Optimisation Schemes -- 4.1. Pooling of Energy -- 4.2. The "Price Maker" Optimisation Problem -- 4.3. The "Price Taker" Optimisation Problem -- 4.4. The Combi-Pool Optimisation Scheme -- 5. Wind Power Forecasting Methods -- 5.1. Different Approaches to Forecast Power Output -- 5.2. Ensemble Prediction Systems -- 5.3. The MSEPS Forecasting System -- 6. Aspects of the Forecasting Error -- 6.1. Wind Power Error Decomposition -- 7. Reserve Prediction and Optimisation -- 7.1. Optimisation of Reserve Predictions: Example Denmark.

7.2. Optimisation of the Reserve Prediction: Example Canada -- 7.2.1. Optimisation Scenarios -- 8. Summary and Discussion -- 9. Conclusion -- References -- SECURITY OPTIMIZATION OF BULK POWERSYSTEMS IN THE MARKET ENVIRONMENT -- 1. Abstract -- 2. Introduction -- 3. Optimization of the Voltage Profile in the Electricity MarketEnvironment -- 3.1. Objective Functions for ORPF Problems -- 3.1.1. Minimization of Real Losses -- 3.1.2. Modified Minimum Losses -- 3.1.3. Minimum Reactive Power Produced -- 3.1.4. Proximity to the Voltage Collapse and Voltage Control -- 3.1.5. Discussion on the Use of σmax -- 3.2. Integration of the ORPF into the Electricity Market Operation -- 4. Modern Approaches for Solving ORPF Problems -- 4.1. The Interior Point Method -- 4.2. An Artificial Intelligence Approach: Genetic Algorithms -- 4.2.1. An ORPF Based on GA: Implementation -- 4.2.1.1. Coding -- 4.2.1.2. Initial Population -- 4.2.1.3. Fitness Function and Ranking -- 4.2.1.4. Selection -- 4.2.1.5. Crossover -- 4.2.1.6. Mutation and Diversity -- 4.2.1.7. Penalty Coefficient -- 4.2.2. Tests of the GA ORPF on the Italian Power System -- 4.2.2.1. Real Losses -- 4.2.2.2. Modified Real Losses -- 4.2.2.3. Minimum Reactive Power Produced -- 4.2.2.4. Objective Function: σmax (Model 4) -- 5. Multiobjective Optimization -- 5.1. Multiobjective Methodologies -- 5.1.1. The Weight Method -- 5.1.2. The ε-Constraint Method -- 5.1.3. The Validation of the Pareto Set -- 5.1.4. The Choice of the Best Solution Using the Surrogate Worth Trade off Analysis -- 5.2. The Multiobjective Approach for the ORPF Problem -- 5.2.1. Minimization of Real Losses and Minimization of the Reactive PowerProduced -- 5.2.2. Security and Cost -- 6. Conclusions -- References -- OPTIMAL PLACEMENT IN POWER SYSTEM -- Abstract -- Optimal Monitoring Program for Voltage Sag Characterization ofPower Systems.

Introduction -- Assessing Voltage Sags Performance -- Optimization Problem -- Application -- System Indices -- Redundancy -- Voltage Sag Estimation -- Conclusion -- Economic Evaluation of FACTS for Congestion Management inPool Markets -- Introduction -- Congestion Management Models -- Market Settlement of the Pool Model -- Economic Evaluation of FACTS -- Simulation Study -- Market Settlement -- Congestion Management Solutions -- Minimization of the Congestion Cost -- Optimal Placement of FACTS Devices -- Conclusions -- Appendix -- References -- NON-LINEAR MATHEMATICAL PROGRAMMINGAPPLIED TO ELECTRIC POWER SYSTEMS STABILITY -- Abstract -- Introduction -- Definitions: -- Voltage Stability - Long Term Stability -- Rotor Angle Stability -- Algorithms for Rotor Angle Stability -- Incorporating Transient Stability Constraints -- Generalized Reduced Gradient method -- Successive Linear Programming Method -- Primal-Dual Interior Point Method -- Solution in the Euclidean Space -- Quasi-Newton Method -- Primal-Dual Interior Points Method -- Conclusion -- References -- INDEX.