Work

A model predictive approach for community battery energy storage system optimization

Year: 2014

Type: conference-paper

Abstract: This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load. © 2014 IEEE.

Source: IEEE Power Engineering Society General Meeting

DOI: 10.1109/pesgm.2014.6938788

Authors: H. Pezeshki, P. Wolfs, G. Ledwich

Institutions: Queensland University of Technology, Central Queensland University

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

Funders: Australian Government, Queensland Government