Optimisation-based Switch Allocation To Improve Energy Losses And Service Restoration In Radial Electrical Distribution Systems

dc.creatorLopez
dc.creatorJuan Camilo; Franco
dc.creatorJohn Fredy; Rider
dc.creatorMarcos J.
dc.date2016
dc.dateagos
dc.date2017-11-13T13:24:30Z
dc.date2017-11-13T13:24:30Z
dc.date.accessioned2018-03-29T05:57:01Z
dc.date.available2018-03-29T05:57:01Z
dc.identifierIet Generation Transmission & Distribution. Inst Engineering Technology-iet, v. 10, p. 2792 - 2801, 2016.
dc.identifier1751-8687
dc.identifier1751-8695
dc.identifierWOS:000382791400026
dc.identifier10.1049/iet-gtd.2015.1509
dc.identifierhttp://ieeexplore.ieee.org/document/7542774/
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328313
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365338
dc.descriptionThis study presents a new methodology for the optimal allocation of switching devices in radial electrical distribution systems (EDSs). A specialised greedy randomised adaptive search procedure (GRASP) algorithm defines the location of a number of switching devices in order to simultaneously improve the following optimisation subproblems related to the use of the allocated switches: (i) the optimal reconfiguration of EDS and (ii) the optimal service restoration of EDS. Eventually, the objective function of the proposed switch allocation algorithm minimises the cost of the total expected energy not supplied, computed after deploying the service restoration, plus the cost of the total annual energy loss computed for every load level in a year, plus the investment costs associated with the number of installed switches. Both optimisation subproblems, i.e. the reconfiguration and the restoration of EDS, are represented by mixed-integer non-linear programming (MINLP) models and transformed into mixed-integer linear programming (MILP) models, using linearisation strategies. MILP models guarantee convergence to optimality by using convex optimisation techniques. Finally, all tests were carried out using a real 136-node distribution system, considering dispatchable and non-dispatchable distributed generation resources.
dc.description10
dc.description11
dc.description2792
dc.description2801
dc.languageEnglish
dc.publisherInst Engineering Technology-IET
dc.publisherHertford
dc.relationIET Generation Transmission & Distribution
dc.rightsfechado
dc.sourceWOS
dc.subjectPower System Restoration
dc.subjectPower Distribution Reliability
dc.subjectSearch Problems
dc.subjectRandomised Algorithms
dc.subjectGreedy Algorithms
dc.subjectPower Distribution Economics
dc.subjectCost Reduction
dc.subjectInteger Programming
dc.subjectLinear Programming
dc.subjectLinearisation Techniques
dc.subjectConvex Programming
dc.subjectPower Generation Dispatch
dc.subjectDistributed Power Generation
dc.subjectPower Generation Economics
dc.subjectOptimisation-based Switch Allocation
dc.subjectEnergy Loss Improvement
dc.subjectRadial Electrical Distribution System
dc.subjectGreedy Randomised Adaptive Search Procedure Algorithm
dc.subjectOptimisation Subproblem
dc.subjectEds Optimal Reconfiguration
dc.subjectEds Optimal Service Restoration
dc.subjectCost Minimisation
dc.subjectSwitch Allocation Algorithm Objective Function
dc.subjectInvestment Cost
dc.subjectMixed Integer Nonlinear Programming Model
dc.subjectMixed Integer Linear Programming Model
dc.subjectMilp Model
dc.subjectLinearisation Strategy
dc.subjectConvex Optimisation Tools
dc.subject136-node Distribution System
dc.subjectDispatchable Dg Resource
dc.subjectNondispatchable Dg Resource
dc.titleOptimisation-based Switch Allocation To Improve Energy Losses And Service Restoration In Radial Electrical Distribution Systems
dc.typeArtículos de revistas


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