Risk-Averse Joint Capacity Evaluation of PV Generation and Electric Vehicle Charging Stations in Distribution Networks

TL;DR

This paper introduces a risk-averse, distributionally robust method for jointly evaluating the capacity of PV generation and EV charging stations in distribution networks, accounting for uncertainties.

Contribution

It develops a novel joint chance constrained programming model with WC-CVaR approximation and an SDP-based iterative algorithm for capacity evaluation under uncertainty.

Findings

Effective capacity estimation considering uncertainties

Successful numerical testing on IEEE 33-bus system

Provides a risk threshold setting function for capacity assessment

Abstract

Increasing penetration of distribution generation (DG) and electric vehicles (EVs) calls for an effective way to estimate the achievable capacity connected to the distribution systems, but the exogenous uncertainties of DG outputs and EV charging loads make it challengeable. This study provides a joint capacity evaluation method with a risk threshold setting function for photovoltaic (PV) generation and EV charging stations (EVCSs). The method is mathematically formulated as a distributionally robust joint chance constrained programming model. And the worst-case conditional value at risk (WC-CVaR) approximation and an iterative algorithm based on semidefinite program (SDP) are used to solve the model. Finally, the method test is carried out numerically on IEEE 33-bus radial distribution system.