Expansion planning of electric power distribution systems with microgeneration and EV charging stations

Abstract

Abstract This chapter presents a scenario-based stochastic model for the multistage joint distribution system expansion planning (DSEP) with distributed (micro-) generation and electric vehicle charging stations (EVCSs), taking into account the associated uncertainties. The uncertainty modeling methods found in literature can be classified as stochastic optimization, robust optimization, Monte Carlo simulation, Latin hypercube sampling technique, Taguchi s orthogonal array testing, and probability statistical methods. Different from these methods, a scenario matrix, based on the heuristic moment matching method, is utilized to characterize the stochastic features and correlation among historical wind and photovoltaic generation and conventional loads and EV demands. The historical EV charging demand data are projected using the Markovian analysis of EV driving patterns and charging demand. The scenario matrix is then incorporated into the formulation of the expansion planning framework that aims at minimizing the investment and operational costs. The planning solution outlines the optimal construction/reinforcement of substations, EVCSs, and feeders, along with the placement of wind and photovoltaic generators and capacitor banks over the three-stage planning horizon. The effectiveness of the scenario-based model is assessed through case studies in the 18-bus and IEEE 123-bus distribution systems. Test results obtained in the 18-bus distribution system demonstrate the effectiveness of scenario-based DSEP in terms of minimizing the total investment and operational costs. Moreover the comparative analysis of scenario-based DSEP against the deterministic and robust approaches confirms its superiority in dealing with the uncertainties. Lastly the scalability of scenario-based DSEP is confirmed using the IEEE 123-bus distribution system.