Multi-Stage Stochastic Programming for Power System Planning Considering Nonanticipative Constraints

To address theuncertainty of injected power in power system planning, a novel multi-stage stochastic programming model is set up in this paper. Firstly, a scenario tree is constructed with respect to the uncertainty of injected power and then a multi-stages stochasticprogramming model is formulated, where the current investment decision does not depend on any future information, called "nonanticipative constraints". In numerical analysis, a 6-bus system is studied to compare with the traditional stochastic programming model and verifies the effectiveness of the proposed model. Results show that the proposed model can significantly reduce the total cost, including investment cost and operation cost. Meanwhile, the decision is more flexible than that obtained by thetraditional method. It can be adjusted according to thefuture uncertainty information. In particular, with the increase of the investment cost, the proposed method can save more total cost.