Adaptive stochastic optimization techniques with applications

Content: Part 1: Classical Optimization TechniquesStatic Optimization OverviewDefinitionApplications of Static OptimizationConstraints and Limitation of Static Optimization TechniquesTools/Solution TechniquesDynamic Optimization Techniques and Optimal ControlDefinitionStrengths and Limitations of Dynamic Optimization TechniquesFunctional Optimization or Dynamic Programming (DP)Optimal ControlPontryagin Minimum PrincipleDecision Analysis ToolsConcepts and Definitions for Decision AnalysisDecision Analysis (DA)Analytical Hierarchical Programming (ARP)Analytical Network Process (ANP)Cost/Benefit Analysis (CBA)Risk AssessmentGame TheoryIntelligent SystemExpert SystemsFuzzy Logic SystemsArtificial Neural NetworksGenetic AlgorithmEvolutionary Programming/Heuristic OptimizationParticle Swann OptimizationAnt Colony OptimizationTabu SearchAnnealing MethodPareto Multiples OptimizationAdaptive Dynamic Programming (ADP)OverviewStrengths and Limitations of ADPVariants of ADPImplementation ApproachADP Formulation Part 2: Applications to Power SystemsIntroduction to Power System ApplicationsOverview of Power System ApplicationsAnalysis of Possible Optimization TechniquesOPFFormulationVariantsChallengesSolution TechniquesDesignVulnerabilityStabilityReal Time AssessmentLimitationsFramework for DesignSchedulingFormulationAlgorithm for Multiple ObjectivesTools / Proposed ApproachesPricingFormulationStatic vs. Dynamic ApplicationsTools / Proposed ApproachesUnit CommitmentFormulationVariants: Static vs. Dynamic ApplicationsAlgorithm and Computational StrategyControl & Voltage/ VAR regulationFormulationVariantsLimitationsAlgorithms and Computational StrategySmart Grid and Adaptive Dynamic Stochastic Optimization ApplicationEvaluation of stochastic optimization for smart grid designImplementation system