Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling

Energies

Mohamad Alzayed¹  Shichao Liu¹  Hicham Chaoui¹  Qiangqiang Cheng²  Yiqi Yan²  Chunsheng Yang³ 

[1] Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada;The Key Laboratory of Nondestructive Testing, Nanchang Hangkong University, Nanchang 330063, China;The National Research Council, Ottawa, ON K1L 5M4, Canada;

关键词: electricity load prediction;    day-ahead scheduling;    particle filter;    microgrid energy management;   

DOI  :  10.3390/en13246489

来源: DOAJ

【 摘 要 】

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.

【 授权许可】

Unknown