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dc.contributor.supervisorThulasiram, Ruppa (Computer Science) Thulasiraman, Parimala (Computer Science)en_US
dc.contributor.authorSingh, Sameer
dc.date.accessioned2015-09-17T12:33:17Z
dc.date.available2015-09-17T12:33:17Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/1993/30818
dc.description.abstractFinancial risk management is of high priority for participants in wholesale deregulated electricity markets due to substantial price and volume risks. Due to high complexity of a wholesale electricity market, prices can exhibit high volatility at times of peak demand and supply shortages. Electricity markets are partly dependent on characteristics such as generation, demand, weather patterns etc. Transmission Rights (TRs) are designed to provide a financial hedge for markets participants in a deregulated electricity market. TRs are financial instruments that entitle the holder to a stream of revenues based on the hourly congestion price difference across a transmission path. The dynamic change in electricity prices poses challenges to price and compute payouts from TRs. TR pricing pose challenges because traditional methods to value financial derivatives do not directly apply to price TRs. To the best of my knowledge, there has not been any attempt on pricing TRs (without obligation) in research community and there are no current standards for pricing them by industry. This lack of pricing technique to price TRs is a prime motivation for proposed work. In this work, I propose a technique to price TRs which can be used in real world. I have observed various similarities between the TRs and financial options in financial market and few differences as well. As a next step, I model TR as a financial option pricing problem. Then, I use a nature inspired meta-heuristic algorithm, Ant Colony Optimization (ACO) to compute option prices and determine future Transmission Rights payouts. Goals of this research are to see suitability of ACO to price TRs and to compute TR payouts. Both these goals are achieved in feasibility study. Further, ACO algorithm is parallellized on a shared memory system. After confirmation of suitability, two variants of ACO are proposed and implemented to price TR. This work suggests that ACO searches solution space focusing on areas of interest. Computational time using ACO is compared to two exhaustive searches and Monte-Carlo technique. ACO without evaporation is the algorithm of choice for computation time and payouts from a TR. This work opens up research opportunities to apply other established optimization techniques to price TRs.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAnt colony optimizationen_US
dc.titleAnt colony optimization to price transmission rights in electricity marketsen_US
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.typedoctoral thesisen_US
dc.degree.disciplineComputer Scienceen_US
dc.contributor.examiningcommitteeAbraham, Ajith (Director, Machine Intelligence Research Labs) Annakkage, Udaya (Electrical and Computer Engineering) Li, Ben (Computer Science)en_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.noteOctober 2015en_US


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