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ISAP 2013 : 17th International Conference on Intelligent Systems Application to Power Systems


When Jul 1, 2013 - Jul 4, 2013
Where Tokyo. Japan
Submission Deadline Feb 20, 2013
Notification Due Mar 10, 2013
Final Version Due Apr 20, 2013

Call For Papers

The Conference welcomes papers on Intelligent Systems Application to Electric Power and Energy Systems (Generation, Transmission, Distribution, Markets, Operations, and Planning) with preference to :

Modern Grid Operation and Control
• Smart grids
• Automated metering applications
• Demand-side participation and response
• Isolated and weakly interconnected power systems
• Microgrids and small island systems
• Self-healing grids and active distribution automation
• Wide area system monitoring and control
• Fault detection and diagnostics
• Monitoring and state analysis of power system components
• Protection

Electric Power and Energy System Analysis, Uncertainty and Risk Assessment
• Power system security, reliability and adequacy
• Power system economics and pricing
• State and load estimation
• Load, price and renewable generation output forecasting
• Grid integration of renewables including solar and wind
• Optimal charging of EV and energy storage systems
• Complex interactive cyber-physical systems
• Decision-making in electricity markets, energy derivatives, load shedding, etc.
• Probabilistic optimization under uncertain environment
• Asset management and real options

Intelligent System Techniques including but not limited to:
• Intelligent estimation and classification techniques including neural nets, fuzzy systems, data mining, decision trees, kernel machines, etc.
• Knowledge based systems including rule-base systems expert systems, model-based reasoning
• Advanced optimization techniques including GA, TS, PSO, ACO, MVO (Mean-Variance Optimization), immune systems, and bacterial foraging
• Multi-agent systems, evolutionary intelligent agents, adaptive distributed computing and control
• Hybrid intelligent systems
• Computational neuroscience
• Game theory
• Molecular and quantum computing
• Bayesian models and networks
• Ensemble learning models
• Distributed/parallel algorithms

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