Renewable Energy Maximum Penetration Rate of Island Network Considering Spatial-temporal Correlativity

Abstract

Based on modified Markov Chain Monte Carlo (MCMC), this paper presents a sampling method of renewable energy (RE) station considering spatial-temporal correlativity, and a probabilistic method to determine RE maximum penetration rate in an island network. The method contains two steps, first, establishing time series production simulation model of island network whose optimized objective is maximum renewable energy accommodation, and simulate RE penetration rate under the constraints of reserves inadequacy. Second, simulating distribution of power flow and voltage after long term RE integration by probabilistic power flow, and analyzing RE penetration rate under constraints of static stability. Combining results of production simulation and probabilistic power flow, this paper gives suggested value of RE penetration rate of island network. At last, an example verifies the effectiveness of presented algorithm and model.