DocumentCode
2414804
Title
Planning large-scale distribution networks for robust expansion under deregulation
Author
Carvalho, P.M.S. ; Ferreira, L.A.F.M.
Author_Institution
Energy Section, Inst. Superior Tecnico, Lisbon, Portugal
Volume
3
fYear
2000
fDate
2000
Firstpage
1305
Abstract
Deregulation is changing the distribution planning practice. The independent generation access to the distribution network introduces a new important source of uncertainty into planning. The paper addresses the problem of planning large-scale distribution networks for operation under generation uncertainty. The planning problem is formulated as a stochastic decision problem. For radial distribution networks, the power flow is monotonic with the generation profile. This monotonicity property is applied to convert the stochastic problem formulation into a two-scenario under uncertainty formulation. The two-scenario problem is then proposed to be solved throughout an evolutionary hedging process. A new evolutionary algorithm is presented. The algorithm is designed to solve multiple-scenario problems by providing the optimal set of investments together with first-order information on each of the decisions´ robustness. An application example is presented to illustrate the algorithm. Robustness is discussed in the context of the distribution deregulation
Keywords
electricity supply industry; evolutionary computation; load flow; power distribution planning; stochastic processes; deregulation; evolutionary algorithm; evolutionary hedging process; generation profile; generation uncertainty; large-scale distribution networks planning; multiple-scenario problems; optimal investments set; power flow; radial distribution networks; robust expansion; stochastic decision problem; two-scenario problem; Costs; Decision making; Evolutionary computation; Investments; Large-scale systems; Mathematical programming; Power generation; Robustness; Stochastic processes; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Engineering Society Summer Meeting, 2000. IEEE
Conference_Location
Seattle, WA
Print_ISBN
0-7803-6420-1
Type
conf
DOI
10.1109/PESS.2000.868711
Filename
868711
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