Expansion Planning of Distribution Networks Considering Uncertainties

An approach is proposed to solve the power system network expansion planning problems considering future uncertainties, guiding the planner from generation of expansion plans, evaluation of the plans under various future uncertain scenarios, to the selection of the best strategy. The balanced genetic algorithm (BGA) is invented for this purpose. It is not only able to search for the optimal solution, but has the capability of efficiently producing a variety of sub-optimal solutions for the planner to take into consideration. Traditional data envelopment analysis (DEA) is modified and improved to assess the overall performance of each plan under different uncertain scenarios and thus assist the planner in deciding the best solution to adopt. The approach is applied to a green-field distribution network expansion problem considering scenarios for the location of future loads. The results obtained by the BGA are compared with a conventional GA, clearly showing the advantages of BGA. The modified DEA allows more realistic evaluation of each planning strategy than the conventional DEA, assisting the planner in taking the right decisions.