A scenario-based robust investment planning model for multi-type distributed generation under uncertainties

Abstract

This paper presented a scenario-based robust distributed generation investment planning (DGIP) model, which considered the uncertainties of wind turbine (WT) generation, photovoltaic (PV) generation and load demand. The robust economic model aims to maximize the net present value (NPV) from the distribution network operator's (DNO's) perspective. The uncertainties are described by an uncertainty matrix based on a heuristic moment matching (HMM) method that captures the stochastic features, i.e. expectation, standard deviation, skewness and kurtosis. The notable feature of the HMM method is that it diminishes the computational burden considerably by representing the uncertainties through a reduced number of representative scenarios. The uncertainty matrix is integrated with deterministic power flow equations to formulate a cost-benefit analysis based robust DGIP model with the objective of maximizing the DNO's net present value. The effectiveness of the proposed DGIP model is firstly verified in a 53-bus distribution test feeder, and then its scalability is further validated in a 138- bus distribution network. The numerical results confirm that the proposed DGIP solution is more robust for all representative network scenarios against the deterministic solution.