Power Network Expansion Planning Integrated with Wind Farms Based on Discrete Probabilistic Load Flow

The discrete probabilistic formulation is utilized to describe the uncertain load flow resulted from uncertain nature of wind power generation and load. Monte Carlo simulation technique is adopted to simulate probabilistic distribution character of the integrated wind power, and normal distribution is utilized to describe the uncertainty of load. The uncertain wind power and load are converted into discrete representations with certain precision, and the probabilistic distributions of line flow are subsequently calculated based on the linear direct current power flow model. The minimized objective function of the planning model is the investment costs for the network expansion, and the chance constrained methodology is utilized to cope with the constraints on line load in the scenarios of normal and N-1 conditions. The basic Genetic Algorithm is employed to resolve the planning model. Finally, the simulation results on the modified Garver six-bus test system demonstrates the feasibility of the proposed methodology.