• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

赋能配电系统运营商:分布式能源资源预测技术综述

Empowering distribution system operators: A review of distributed energy resource forecasting techniques.

作者信息

Fose Nande, Singh Arvind R, Krishnamurthy Senthil, Ratshitanga Mukovhe, Moodley Prathaban

机构信息

Department of Electrical , Electronics and Computer Engineering, Cape Peninsula University of Technology, Bellville Campus, South Africa.

South African National Energy Development Institute (SANEDI), South Africa.

出版信息

Heliyon. 2024 Jul 22;10(15):e34800. doi: 10.1016/j.heliyon.2024.e34800. eCollection 2024 Aug 15.

DOI:10.1016/j.heliyon.2024.e34800
PMID:39157304
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11327517/
Abstract

Effective management of Distributed Energy Resources (DERs) and optimization of grid operations are crucial responsibilities of Distribution System Operators (DSOs). Hence, this comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. These goals underscore the significance of clean and accessible energy, advancements in infrastructure, sustainable urban development, climate change mitigation, and collaborative partnerships. The review's core focuses on the DER forecasting techniques employed by DSOs. It explores various aspects, including data collection methods, load forecasting models, DER generation forecasting, aggregation and integration techniques, and the role of advanced technologies like machine learning and artificial intelligence. The review highlights the critical role of accurate DER forecasts in optimizing grid operations, managing energy flows, and facilitating the integration of renewable energy sources. Furthermore, the review examines the implications of DER forecasting for DSOs in achieving the SDGs. It discusses how DER forecasting facilitates the transition to affordable and clean energy, enhances industry innovation and infrastructure, builds sustainable cities and communities, drives climate action efforts, and fosters stakeholder partnerships. However, the review also identifies challenges and limitations in DER forecasting, including data availability, forecasting accuracy, uncertainty management, and regulatory barriers. It emphasizes further research and development in improved forecasting algorithms, advanced data analytics, and enhanced communication and coordination mechanisms. Finally, this comprehensive critical review highlights the importance of DER forecasting for DSOs in achieving the SDGs. Accurate forecasting can promote sustainable and clean energy practices, drive innovation, build resilient communities, mitigate climate change, and foster collaborative partnerships. The review emphasizes the necessity of advancing DER forecasting techniques and addressing associated challenges to fully realize the potential of DERs in contributing to a sustainable and inclusive future. This comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. As Distributed Energy Resources (DERs) play an increasingly significant role in the transition to sustainable energy systems, accurate forecasting techniques are essential for optimizing grid operations and facilitating the integration of renewable energy sources. By effectively managing DERs, Distribution System Operators (DSOs) contribute to the advancement of several SDGs, including affordable and clean energy (SDG 7), sustainable infrastructure (SDG 9), climate action (SDG 13), and partnerships for the goals (SDG 17). This review explores the intersection of DER forecasting with the SDGs, highlighting how forecasting initiatives can support national and global efforts toward sustainable development by providing insights into energy demand, grid stability, and renewable energy integration. The goals and targets are derived from an analysis of current trends and the identification of potential development scenarios by 2030. Both optimistic and pessimistic projections are utilized for communicating with the general public and national governments concerning DSM network planning. Utilizing data from various nations enables the identification of effective strategies and the prediction of similar trends in other areas. Simultaneously, the magnitude of activities related to Sustainable Development Goals (SDGs) enables the improvement and efficient organization of data gathering on a global basis. This, in turn, provides a foundation for future forecasting endeavours.

摘要

有效管理分布式能源资源(DERs)以及优化电网运行是配电系统运营商(DSOs)的关键职责。因此,本次全面的批判性综述旨在分析DSOs的DER预测实践现状及其对实现可持续发展目标(SDGs)的影响。这些目标强调了清洁且可获取的能源、基础设施的进步、可持续城市发展、缓解气候变化以及合作伙伴关系的重要性。该综述的核心聚焦于DSOs所采用的DER预测技术。它探讨了各个方面,包括数据收集方法、负荷预测模型、DER发电预测、聚合与整合技术,以及机器学习和人工智能等先进技术的作用。该综述强调了准确的DER预测在优化电网运行、管理能量流以及促进可再生能源整合方面的关键作用。此外,该综述考察了DER预测对DSOs实现可持续发展目标的影响。它讨论了DER预测如何促进向可负担且清洁能源的转型、增强行业创新与基础设施、建设可持续城市和社区、推动气候行动努力以及促进利益相关者伙伴关系。然而,该综述也指出了DER预测中的挑战和局限,包括数据可用性、预测准确性、不确定性管理以及监管障碍。它强调在改进预测算法、先进数据分析以及增强通信与协调机制方面需要进一步开展研究与开发。最后,本次全面的批判性综述强调了DER预测对DSOs实现可持续发展目标的重要性。准确的预测能够促进可持续和清洁能源实践、推动创新、建设有韧性的社区、缓解气候变化以及促进合作伙伴关系。该综述强调了推进DER预测技术并应对相关挑战的必要性,以充分发挥DERs在为可持续和包容性未来做出贡献方面的潜力。本次全面的批判性综述旨在分析DSOs的DER预测实践现状及其对实现可持续发展目标的影响。随着分布式能源资源(DERs)在向可持续能源系统转型中发挥越来越重要的作用,准确的预测技术对于优化电网运行和促进可再生能源整合至关重要。通过有效管理DERs,配电系统运营商(DSOs)为推进多个可持续发展目标做出贡献,包括可负担且清洁能源(可持续发展目标7)、可持续基础设施(可持续发展目标9)、气候行动(可持续发展目标13)以及为实现这些目标的伙伴关系(可持续发展目标17)。本综述探讨了DER预测与可持续发展目标的交叉点,强调预测举措如何通过提供有关能源需求、电网稳定性和可再生能源整合的见解来支持国家和全球的可持续发展努力。这些目标和指标源自对当前趋势的分析以及对2030年潜在发展情景的识别。乐观和悲观的预测均用于与公众和各国政府就需求侧管理(DSM)网络规划进行沟通。利用来自不同国家的数据能够确定有效策略并预测其他地区的类似趋势。同时,与可持续发展目标(SDGs)相关活动的规模能够在全球范围内改进并高效组织数据收集。这反过来又为未来的预测工作奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/aced21a96622/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/a59e76c7a8fb/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/56355a418fed/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/a47f0f8d9914/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/102b68602042/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/8ed0e479e76b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/757f0d98ea11/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/5f6f719df997/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/57ba13b3316f/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/68453d5ea873/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/8075841b0e86/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/ba212f987b8c/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/3bad7c8199dc/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/aced21a96622/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/a59e76c7a8fb/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/56355a418fed/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/a47f0f8d9914/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/102b68602042/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/8ed0e479e76b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/757f0d98ea11/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/5f6f719df997/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/57ba13b3316f/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/68453d5ea873/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/8075841b0e86/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/ba212f987b8c/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/3bad7c8199dc/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ce/11327517/aced21a96622/gr13.jpg

相似文献

1
Empowering distribution system operators: A review of distributed energy resource forecasting techniques.赋能配电系统运营商:分布式能源资源预测技术综述
Heliyon. 2024 Jul 22;10(15):e34800. doi: 10.1016/j.heliyon.2024.e34800. eCollection 2024 Aug 15.
2
Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.基于机器学习的含多个分布式能源的并网微电网能量管理与功率预测
Sci Rep. 2024 Aug 19;14(1):19207. doi: 10.1038/s41598-024-70336-3.
3
Urban health: an example of a "health in all policies" approach in the context of SDGs implementation.城市健康:在实现可持续发展目标背景下“所有政策促进健康”方法的一个范例。
Global Health. 2019 Dec 18;15(1):87. doi: 10.1186/s12992-019-0529-z.
4
The 2023 Latin America report of the Countdown on health and climate change: the imperative for health-centred climate-resilient development.《2023年健康与气候变化倒计时拉丁美洲报告:以健康为中心的气候适应型发展的必要性》
Lancet Reg Health Am. 2024 Apr 23;33:100746. doi: 10.1016/j.lana.2024.100746. eCollection 2024 May.
5
Assessing the contribution of semiconductors to the sustainable development goals (SDGs) from 2017 to 2022.评估2017年至2022年半导体对可持续发展目标(SDGs)的贡献。
Heliyon. 2023 Oct 30;9(11):e21306. doi: 10.1016/j.heliyon.2023.e21306. eCollection 2023 Nov.
6
Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.衡量 1990 年至 2017 年期间的进展情况,并预测 195 个国家和地区在 2030 年实现与健康相关的可持续发展目标的情况:基于 2017 年全球疾病负担研究的系统分析。
Lancet. 2018 Nov 10;392(10159):2091-2138. doi: 10.1016/S0140-6736(18)32281-5. Epub 2018 Nov 8.
7
Advancing Solar Energy for Primary Healthcare in Developing Nations: Addressing Current Challenges and Enabling Progress Through UNICEF and Collaborative Partnerships.为发展中国家的初级医疗保健推进太阳能:应对当前挑战并通过联合国儿童基金会及合作伙伴关系推动进展。
Cureus. 2024 Jan 3;16(1):e51571. doi: 10.7759/cureus.51571. eCollection 2024 Jan.
8
Role of microalgae in achieving sustainable development goals and circular economy.微藻在实现可持续发展目标和循环经济中的作用。
Sci Total Environ. 2023 Jan 1;854:158689. doi: 10.1016/j.scitotenv.2022.158689. Epub 2022 Sep 13.
9
Maximise your impact: Sustainable Development Goals-Focussed content in communication intervention and teaching.最大化影响力:可持续发展目标导向的沟通干预和教学内容。
Int J Speech Lang Pathol. 2023 Feb;25(1):188-192. doi: 10.1080/17549507.2022.2153165. Epub 2022 Dec 28.
10
Unraveling interactions and priorities under sustainable development goals in less-developed mountainous areas: case study on the National Innovation Demonstration Zone for the 2030 Agenda for Sustainable Development, China.解析欠发达山区可持续发展目标下的相互作用与优先事项:以中国2030年可持续发展议程国家创新示范区为例
Environ Sci Pollut Res Int. 2024 Jan;31(4):5254-5274. doi: 10.1007/s11356-023-31478-5. Epub 2023 Dec 19.

本文引用的文献

1
Metal accumulation in riverine macroinvertebrates from a platinum mining region.河流大型无脊椎动物中的金属积累来自铂矿区。
Sci Total Environ. 2020 Feb 10;703:134738. doi: 10.1016/j.scitotenv.2019.134738. Epub 2019 Oct 31.
2
Another look at the relationship between energy consumption, carbon dioxide emissions, and economic growth in South Africa.再看南非的能源消耗、二氧化碳排放与经济增长之间的关系。
Sci Total Environ. 2019 Mar 10;655:759-765. doi: 10.1016/j.scitotenv.2018.11.271. Epub 2018 Nov 19.
3
A multivariate heuristic model for fuzzy time-series forecasting.
一种用于模糊时间序列预测的多变量启发式模型。
IEEE Trans Syst Man Cybern B Cybern. 2007 Aug;37(4):836-46. doi: 10.1109/tsmcb.2006.890303.