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CoDIT-UMTFPS 2017 : Special Session: Uncertainty modelling techniques in future power systems | |||||||||||||||
Link: http://codit2017.com/index.php/special-sessions | |||||||||||||||
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Call For Papers | |||||||||||||||
Papers submission deadline has been extended to January 04, 2017 2017 01 04
This special session deals with the problem of decision making under uncertainty techniques in operation and planning of future power systems. The methods and tools used in decision-making play a vital role in electric power system management. The objective and information available to each participant determines the best techniques that they should utilise when making decisions in investment, operation and planning. Furthermore, the interactions of each of these entities highly influence the overall outcome. Uncertainty is inherent in almost every aspect of power systems from technical parameters (demand fluctuation, renewable power generation, and generation/transmission/distribution outages), economic parameters (market price, interest rates, and economic growth rate), regulatory frameworks and technology development (smart grid, demand response, flexibility services). The sources of uncertainty are set to rise in future power systems with increased penetration of renewables, changes to market structures, disruptive technologies and increased demand-side participation. Future power systems will be equipped with even more ICT with an abundance of data sources to manage these changes. This data and the uncertainty of each source will require advances in methods able to efficiently deal with uncertainty. In order to have a secure, reliable, resilient and economically efficient power system we need new methods to deal with this uncertainty. This special issue intends to address the emerging concepts, methodologies and applications of decision making tools under uncertainty in future power systems. The topics of interest include, but are not limited to: Information gap decision theory Fuzzy arithmetic Stochastic optimization Monte Carlo Simulation Point Estimate technique Robust optimization Z-numbers Risk modeling in presence of large scale renewable energy resources in transmission and distribution networks Uncertainty modeling in resiliency assessment/enhancement of power systems Uncertainty modeling in power system security evaluation and optimization Uncertainty modeling techniques in energy system integration Uncertainty modeling in power system security evaluation and optimization |
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