dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:26:03Z
dc.date.accessioned2022-10-05T18:29:21Z
dc.date.available2014-05-27T11:26:03Z
dc.date.available2022-10-05T18:29:21Z
dc.date.created2014-05-27T11:26:03Z
dc.date.issued2011-10-05
dc.identifier2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.
dc.identifierhttp://hdl.handle.net/11449/72740
dc.identifier10.1109/PTC.2011.6019300
dc.identifier2-s2.0-80053350010
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3921779
dc.description.abstractDistributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.
dc.languageeng
dc.relation2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectdistributed generation
dc.subjectmulti-objective optimization
dc.subjectReactive power support
dc.subjecttransmission systems
dc.subjectActive power
dc.subjectActive power generation
dc.subjectAncillary service
dc.subjectDemand response programs
dc.subjectDistributed generators
dc.subjectMicro grid
dc.subjectMonte Carlo Simulation
dc.subjectMulti objective
dc.subjectMulti-objective optimal power flow
dc.subjectOpportunity costs
dc.subjectOptimization methodology
dc.subjectOptimization process
dc.subjectReactive power capacity
dc.subjectSales opportunities
dc.subjectSmart grid
dc.subjectTest systems
dc.subjectTwo way communications
dc.subjectVoltage stability margins
dc.subjectCommunication systems
dc.subjectComputer simulation
dc.subjectCosts
dc.subjectDistributed power generation
dc.subjectFuzzy logic
dc.subjectMarkov processes
dc.subjectMonte Carlo methods
dc.subjectMultiobjective optimization
dc.subjectReactive power
dc.subjectSmart power grids
dc.subjectSustainable development
dc.subjectTime series
dc.subjectTransmissions
dc.subjectTurbines
dc.subjectVoltage stabilizing circuits
dc.subjectElectric power transmission
dc.titlePricing of reactive power support provided by distributed generators in transmission systems
dc.typeTrabalho apresentado em evento


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