Probabilistic transmission expansion planning considering distributed generation and demand response programs

Transmission expansion planning (TEP) is generally determined by peak demands. To improve the efficiency and sustainability of energy systems, attention has been paid to demand response programs (DRPs) and distributed generation (DG). DRPs and DG will also have significant impacts on the controllability and economics of power systems, from short-term scheduling to long-term planning. In this study, a non-linear economic design for responsive loads is introduced, based on the price flexibility of demand and the customers' benefit function. Moreover, a probabilistic multi-objective TEP model which considers DRPs is also proposed. A probabilistic analysis method, the so-called Monte-Carlo simulation method, is implemented to handle the uncertainty of the loads, DRPs and DG in the TEP problems. Due to the problems' non-convex formulations, a non-dominated sorting differential evolution program is used to solve the TEP problems. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The planning methodology is then demonstrated on an IEEE 118-bus system in order to show the feasibility of the proposed algorithm.