CN112465271B - 一种面向储能平抑风电波动场景的储能电池选型方法 - Google Patents
一种面向储能平抑风电波动场景的储能电池选型方法 Download PDFInfo
- Publication number
- CN112465271B CN112465271B CN202011490337.7A CN202011490337A CN112465271B CN 112465271 B CN112465271 B CN 112465271B CN 202011490337 A CN202011490337 A CN 202011490337A CN 112465271 B CN112465271 B CN 112465271B
- Authority
- CN
- China
- Prior art keywords
- battery
- energy storage
- neuron
- type
- input
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 115
- 230000000087 stabilizing effect Effects 0.000 title claims abstract description 31
- 238000010187 selection method Methods 0.000 title claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 41
- 239000013598 vector Substances 0.000 claims description 73
- 239000011159 matrix material Substances 0.000 claims description 37
- 210000004205 output neuron Anatomy 0.000 claims description 31
- 210000002569 neuron Anatomy 0.000 claims description 24
- 210000002364 input neuron Anatomy 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 20
- 230000004927 fusion Effects 0.000 claims description 16
- 238000005070 sampling Methods 0.000 claims description 12
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 4
- 229910052744 lithium Inorganic materials 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 claims description 2
- 230000007423 decrease Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 claims description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 claims description 2
- 229910052720 vanadium Inorganic materials 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000013507 mapping Methods 0.000 abstract description 4
- 238000010276 construction Methods 0.000 abstract description 3
- 238000010248 power generation Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/061—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using biological neurons, e.g. biological neurons connected to an integrated circuit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Human Resources & Organizations (AREA)
- Computing Systems (AREA)
- Strategic Management (AREA)
- Software Systems (AREA)
- Neurology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Biology (AREA)
- Marketing (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Secondary Cells (AREA)
Abstract
Description
n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
Claims (1)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011490337.7A CN112465271B (zh) | 2020-12-16 | 2020-12-16 | 一种面向储能平抑风电波动场景的储能电池选型方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011490337.7A CN112465271B (zh) | 2020-12-16 | 2020-12-16 | 一种面向储能平抑风电波动场景的储能电池选型方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112465271A CN112465271A (zh) | 2021-03-09 |
CN112465271B true CN112465271B (zh) | 2023-05-23 |
Family
ID=74803005
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011490337.7A Active CN112465271B (zh) | 2020-12-16 | 2020-12-16 | 一种面向储能平抑风电波动场景的储能电池选型方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112465271B (zh) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113219355B (zh) * | 2021-03-29 | 2022-04-08 | 安徽江淮汽车集团股份有限公司 | 电池选型方法、装置、设备及存储介质 |
CN117933764B (zh) * | 2024-03-22 | 2024-08-20 | 江西师范大学 | 基于可持续发展指数的区域生态安全格局构建方法及系统 |
CN118381466B (zh) * | 2024-06-21 | 2024-09-17 | 江苏协航能源科技有限公司 | 基于大数据的光伏设备运行监测方法 |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104166790A (zh) * | 2014-07-24 | 2014-11-26 | 广东电网公司电力科学研究院 | 基于逼近理想解理论的锂离子动力电池性能评价方法 |
CN104899459A (zh) * | 2015-06-16 | 2015-09-09 | 北京亿利智慧能源科技有限公司 | 基于层次分析法的电池性能评价方法 |
CN105129109A (zh) * | 2015-09-30 | 2015-12-09 | 北京航空航天大学 | 一种基于多重分形理论和自组织映射网络的飞机副翼作动器系统健康评估方法 |
CN105654175A (zh) * | 2015-12-24 | 2016-06-08 | 北方民族大学 | 一种面向轴承制造企业的零件供应商多目标优选方法 |
CN107437135A (zh) * | 2016-05-26 | 2017-12-05 | 中国电力科学研究院 | 一种新型储能选型方法 |
CN109800950A (zh) * | 2018-12-17 | 2019-05-24 | 国家电网有限公司 | 基于层次分析法的梯次利用电池储能系统性能评估方法 |
CN110929791A (zh) * | 2019-11-27 | 2020-03-27 | 北京交通大学 | 梯次利用电池的应用场景选择方法 |
CN111487532A (zh) * | 2020-04-09 | 2020-08-04 | 北方工业大学 | 一种基于层次分析法和熵值法的退役电池筛选方法及系统 |
-
2020
- 2020-12-16 CN CN202011490337.7A patent/CN112465271B/zh active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104166790A (zh) * | 2014-07-24 | 2014-11-26 | 广东电网公司电力科学研究院 | 基于逼近理想解理论的锂离子动力电池性能评价方法 |
CN104899459A (zh) * | 2015-06-16 | 2015-09-09 | 北京亿利智慧能源科技有限公司 | 基于层次分析法的电池性能评价方法 |
CN105129109A (zh) * | 2015-09-30 | 2015-12-09 | 北京航空航天大学 | 一种基于多重分形理论和自组织映射网络的飞机副翼作动器系统健康评估方法 |
CN105654175A (zh) * | 2015-12-24 | 2016-06-08 | 北方民族大学 | 一种面向轴承制造企业的零件供应商多目标优选方法 |
CN107437135A (zh) * | 2016-05-26 | 2017-12-05 | 中国电力科学研究院 | 一种新型储能选型方法 |
CN109800950A (zh) * | 2018-12-17 | 2019-05-24 | 国家电网有限公司 | 基于层次分析法的梯次利用电池储能系统性能评估方法 |
CN110929791A (zh) * | 2019-11-27 | 2020-03-27 | 北京交通大学 | 梯次利用电池的应用场景选择方法 |
CN111487532A (zh) * | 2020-04-09 | 2020-08-04 | 北方工业大学 | 一种基于层次分析法和熵值法的退役电池筛选方法及系统 |
Non-Patent Citations (4)
Title |
---|
Selection of storage energy technologies in a power quality scenario - the AHP and the fuzzy logic;Alexandre Barin等;2009 35th Annual Conference of IEEE Industrial Electronics;第3615-3620页 * |
不同场景下基于AHP-TOPSIS退役电池梯次利用综合评价;吴威;唐雨晨;叶荣;林章岁;江岳文;温步瀛;;电网与清洁能源(第04期);第115-122页 * |
基于区间层次分析法的电化学储能选型方案;李建林;马会萌;田春光;惠东;;高电压技术(第09期);第2707-2714页 * |
基于电压曲线的退役电池模组分选方法;王帅;尹忠东;郑重;王银顺;邹涵宇;严玉廷;;中国电机工程学报(第08期);第2691-2705页 * |
Also Published As
Publication number | Publication date |
---|---|
CN112465271A (zh) | 2021-03-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112465271B (zh) | 一种面向储能平抑风电波动场景的储能电池选型方法 | |
CN106532778B (zh) | 一种计算分布式光伏并网最大准入容量的方法 | |
CN108205717A (zh) | 一种光伏发电功率多时间尺度预测方法 | |
CN111008728A (zh) | 一种用于分布式光伏发电系统短期出力的预测方法 | |
CN105207253B (zh) | 考虑风电及频率不确定性的agc随机动态优化调度方法 | |
CN104732300B (zh) | 一种基于模糊分区理论的神经网络风功率短期预测方法 | |
CN109599872B (zh) | 基于堆栈降噪自动编码器的电力系统概率潮流计算方法 | |
CN108960491A (zh) | 基于rbf神经网络的光伏发电量预测方法 | |
CN110009141B (zh) | 基于sdae特征提取和svm分类模型的爬坡事件预测方法及系统 | |
CN110110434B (zh) | 一种概率潮流深度神经网络计算的初始化方法 | |
CN110212551B (zh) | 基于卷积神经网络的微网无功自动控制方法 | |
CN106777521B (zh) | 基于双链量子遗传算法的发电机组涉网参数优化方法 | |
CN111105005B (zh) | 一种风电功率预测方法 | |
CN118134290B (zh) | 一种基于改进anfis的光伏阵列运行状态评估及故障诊断方法 | |
CN108694475B (zh) | 基于混合模型的短时间尺度光伏电池发电量预测方法 | |
CN107330248A (zh) | 一种基于改进型神经网络的短期风功率预测方法 | |
CN112418504B (zh) | 一种基于混合变量选择优化深度信念网络风速预测方法 | |
CN114094599A (zh) | 一种多站融合参与调峰调频调压潜力评估方法及装置 | |
CN117875752A (zh) | 基于自组织映射决策树的电力系统灵活运行域评估方法 | |
CN117117827A (zh) | 一种基于卷积神经网络的新型配电网状态估计方法 | |
CN115864491A (zh) | 一种处于拓扑不明状态台区的电压灵敏度拟合方法 | |
CN114781244A (zh) | 一种风电场内分群与参数优化方法 | |
CN112531725B (zh) | 一种对静止无功发生器的参数进行识别的方法及系统 | |
Cho et al. | A variable step size incremental conductance MPPT of a photovoltaic system using DC-DC converter with direct control scheme | |
CN104850914B (zh) | 一种基于特征建模的新能源发电量预测方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
TA01 | Transfer of patent application right |
Effective date of registration: 20220506 Address after: 100144 Beijing City, Shijingshan District Jin Yuan Zhuang Road No. 5 Applicant after: NORTH CHINA University OF TECHNOLOGY Applicant after: Beijing Lianzhi Huineng Technology Co.,Ltd. Applicant after: Xinyuan Zhichu energy development (Beijing) Co.,Ltd. Address before: 100144 Beijing City, Shijingshan District Jin Yuan Zhuang Road No. 5 Applicant before: NORTH CHINA University OF TECHNOLOGY Applicant before: Beijing Lianzhi Huineng Technology Co.,Ltd. |
|
TA01 | Transfer of patent application right | ||
GR01 | Patent grant | ||
GR01 | Patent grant |