Techno-Economic Analysis of Candidate Oxide Materials for Thermochemical Storage in Concentrating Solar Power Systems

按来源划分的电力成本 热能储存 工艺工程 集中太阳能 储能 太阳能 材料科学 发电 环境科学 废物管理 工程类 功率(物理) 生态学 量子力学 生物 物理
作者
Reiner Buck,Christos Agrafiotis,Stefania Tescari,Nicole Neumann,Martin Schmücker
出处
期刊:Frontiers in Energy Research [Frontiers Media]
卷期号:9 被引量:14
标识
DOI:10.3389/fenrg.2021.694248
摘要

The thermal storage capability is an important asset of state-of-the-art concentrating solar power plants. The use of thermochemical materials, such as redox oxides, for hybrid sensible/thermochemical storage in solar power plants offers the potential for higher specific volume and mass storage capacity and as a consequence reduced levelized cost of electricity making such plants more competitive. For the techno-economic system analysis, three candidate redox materials were analyzed for their cost reduction potential: cobalt-based, manganese–iron–based, and perovskite-based oxide materials. As a reference process the use of inert commercial bauxite particles (sensible-only storage) was considered. A solar thermal power plant with a nominal power of 125 MW e and a storage capacity of 12 h was assumed for the analysis. For each storage material a plant layout was made, taking the specific thermophysical properties of the material into account. Based on this layout a particle break-even cost for the specific material was determined, at which levelized cost of electricity parity is achieved with the reference system. Cost factors mainly influenced by the material selection are storage cost and steam generator cost. The particle transport system cost has only a minor impact. The results show differences in the characteristics of the materials, for example, regarding the impact on storage size and cost and the steam generator cost. Regarding the economic potential of the candidate redox materials, the perovskite-based particles promise to have advantages, as they might be produced from inexpensive raw materials.
最长约 10秒,即可获得该文献文件

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
淡定访枫发布了新的文献求助10
刚刚
1秒前
1秒前
夏至发布了新的文献求助10
1秒前
2秒前
3秒前
3秒前
烟嗓小宋完成签到,获得积分10
3秒前
perrier发布了新的文献求助150
4秒前
无花果发布了新的文献求助10
4秒前
酷波er应助科研通管家采纳,获得10
4秒前
Akim应助科研通管家采纳,获得10
4秒前
youxianlang发布了新的文献求助10
5秒前
今后应助科研通管家采纳,获得10
5秒前
八九发布了新的文献求助10
5秒前
5秒前
汉堡包应助科研通管家采纳,获得10
5秒前
乐乐应助科研通管家采纳,获得10
5秒前
5秒前
ding应助科研通管家采纳,获得10
6秒前
橙汁寒关注了科研通微信公众号
6秒前
6秒前
大模型应助科研通管家采纳,获得10
6秒前
归零者应助科研通管家采纳,获得10
6秒前
huiwanfeifei发布了新的文献求助10
6秒前
春天的熊完成签到,获得积分10
7秒前
大胆的衬衫完成签到 ,获得积分10
7秒前
CHEN发布了新的文献求助10
7秒前
yangliu发布了新的文献求助10
7秒前
一一发布了新的文献求助10
8秒前
方青松应助淡定访枫采纳,获得10
8秒前
大模型应助淡定访枫采纳,获得10
8秒前
Jasper应助果冻采纳,获得10
9秒前
yanseyibian发布了新的文献求助10
9秒前
11秒前
12秒前
华仔应助huiwanfeifei采纳,获得10
13秒前
NaCl发布了新的文献求助10
13秒前
13秒前
13秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583507
求助须知:如何正确求助?哪些是违规求助? 9162219
关于积分的说明 19606485
捐赠科研通 7165541
什么是DOI,文献DOI怎么找? 3266283
关于科研通互助平台的介绍 2431182
邀请新用户注册赠送积分活动 2257764