Distribution network expansion planning under uncertainty: a hedging algorithm in an evolutionary approach

The paper addresses the problem of distribution network expansion planning under uncertainty. The deterministic problem is itself a large-scale, complex, difficult-to-define one; the combinatorial nature of the decision making can be efficiently handled by a specific evolutionary approach. The authors develop, in the evolutionary optimization context, a hedging algorithm to deal with scenario representation of uncertainty; decision making results in a convergence process for the first-stage naturally conflicting decisions. The process robustness is discussed and illustration is provided for a comprehensive example.