Differential Power Processing Based Control Framework for Multiple Battery Energy Storage Systems in DC Microgrids

储能 电池(电) 计算机科学 蓄电池储能 差速器(机械装置) 电气工程 功率(物理) 功率控制 汽车工程 工程类 控制工程 量子力学 物理 航空航天工程
作者
Jialei Su,Kang Li
出处
期刊:IEEE Transactions on Sustainable Energy [Institute of Electrical and Electronics Engineers]
卷期号:15 (4): 2417-2427 被引量:1
标识
DOI:10.1109/tste.2024.3421358
摘要

Multiple battery energy storage systems (BESSs) have been widely used in the DC microgrids to balance generation and demand. To achieve this, the BESS converters need to deliver the full required input/output power imposed on BESSs under the conventional BESS-DC bus configuration, which often demands high power ratings for the converters, hence leads to high installation cost as well as high power losses. To reduce the power ratings for BESS converters while delivering the same power from BESSs, this paper proposes a new differential power processing (DPP) based control framework where the DPP techniques and BESSs are firstly combined without losing the following control objectives, namely, the accurate current-sharing and state of charge (SoC) balance of BESSs as well as DC bus voltage regulation. This is achieved first by introducing inverted bidirectional buck converters to function as a front-end converter and DPP converters. Then, a virtual state variable combining BESS output current and its SoC is proposed, based on which a consensus control strategy is proposed. The stability of the proposed DPP-based control framework is also analyzed. Finally, the real-time hardware-in-loop (HIL) tests confirm the effectiveness of the proposed control framework, showing that the proposed DPP-based control framework reduces the power ratings of the converters to less than 20 $\%$ of BESS converters used in conventional BESS-DC bus configuration even in the worst operating scenario, while delivering the same required power from BESSs, paving a way for an innovative BESS DC microgrid design with much down-sized converters for BESSs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
wmy0607发布了新的文献求助10
1秒前
yuanziqiao完成签到,获得积分10
1秒前
科研通AI2S应助忧郁的吐司采纳,获得10
2秒前
2秒前
3秒前
FashionBoy应助科研小贩采纳,获得10
3秒前
4秒前
yuanziqiao发布了新的文献求助10
4秒前
Lucas应助胡志宇采纳,获得30
5秒前
灰灰发布了新的文献求助10
5秒前
6秒前
6秒前
Orange应助猛龙FC20采纳,获得10
6秒前
7秒前
嘿嘿发布了新的文献求助10
7秒前
小通通完成签到 ,获得积分10
7秒前
9秒前
sanqi完成签到,获得积分10
10秒前
10秒前
10秒前
10秒前
糯米多多发布了新的文献求助10
10秒前
kingwill应助早点睡觉丶采纳,获得20
11秒前
于小淘发布了新的文献求助10
11秒前
11秒前
Hey发布了新的文献求助10
12秒前
Louisa完成签到,获得积分10
12秒前
mr_wang发布了新的文献求助10
12秒前
12秒前
超帅的语雪完成签到,获得积分10
13秒前
13秒前
个性友蕊完成签到,获得积分10
14秒前
沉默小虾米完成签到,获得积分10
14秒前
mmingyu发布了新的文献求助10
14秒前
宠溺Ovo发布了新的文献求助10
14秒前
番茄鱼完成签到 ,获得积分10
15秒前
傲娇时光完成签到,获得积分10
15秒前
小二郎应助灰灰采纳,获得10
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Propeller Design 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6015644
求助须知:如何正确求助?哪些是违规求助? 7594624
关于积分的说明 16149567
捐赠科研通 5163536
什么是DOI,文献DOI怎么找? 2764394
邀请新用户注册赠送积分活动 1745072
关于科研通互助平台的介绍 1634798