Coordinated generation and transmission expansion planning approach based on chance-constraints using genetic algorithm

Abstract

The grid planning is a crucial and basic work in the power system, but there are various uncertain factors in the power system, which cause dramatic influences to the size of the grid power investment, as well as the power system security and stability for next few years. Traditional planning methods are gradually hard to adapt the grid planning under situations especially with large amounts of uncertainties. To reduce the impact of uncertain factors of the power system, it is necessary to consider increasingly significant uncertainties in system planning. However, if all extreme scenarios all taken into account, generally, the optimization results will be really conservative. Therefore, to prevent a relatively conservative optimization scheme, this paper proposes a grid-based expansion planning model based on chance constraints which take fully considerations of system uncertain factors using genetic algorithm. Compared with other planning schemes, this method comprehensively considers the investment cost and the level of load loss. Finally, a test system with six nodes is used to verify the effectiveness of the proposed method. The economics of the optimization scheme using the proposed approaches are observed using the test system.