Nonlinear Model Predictive Control of Photovoltaic-Battery System for Short-Term Power Dispatch

被引：0

作者：

Li, Yang ^{[1
]}

Vilathgamuwa, D. Mahinda ^{[1
]}

Choi, San Shing ^{[1
]}

Farrell, Troy W. ^{[2
]}

Ngoc Tham Tran ^{[1
]}

Teague, Joseph ^{[2
]}

机构：

[1] Queensland Univ Technol, Sch Elect Engn & Comp Sci, Brisbane, Qld, Australia

[2] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia

来源：

IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2018年

基金：

澳大利亚研究理事会;

关键词：

Battery energy storage system; lithium-ion battery; battery degradation; PV; model predictive control; dynamic programming;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper focuses on developing power flow control strategy for electricity end users installed with photovoltaic-battery systems. The control objective is to reduce the overall cost to the end users while meeting the users' load demands. By taking into consideration the cost associated with the degradation of the battery, a nonlinear model predictive control technique is used to determine the short-term power exchange with the external grid system. The formulated nonlinear optimization problem is solved using dynamic programming technique, and an algorithm is developed to reduce the computational load. Numerical examples show the efficacy of the proposed method.

引用

页码：1884 / 1889

页数：6

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