Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

Author:

Ghafoor Aras1,Aldahmashi Jamal23,Apsley Judith1ORCID,Djurović Siniša1ORCID,Ma Xiandong2ORCID,Benbouzid Mohamed4ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK

2. School of Engineering, Lancaster University, Lancaster LA1 4YW, UK

3. Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73213, Saudi Arabia

4. Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France

Abstract

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Funder

UKRI/EPSRC

Deanship of Scientific Research at the Northern Border University, Arar, KSA

Publisher

MDPI AG

Reference121 articles.

1. RUSI (2024, January 31). How Will Growth in Renewables Change the UK’s Approach to Energy Security?. Available online: https://rusi.org/explore-our-research/publications/commentary/how-will-growth-renewables-change-uks-approach-energy-security.

2. NEMA (2023, June 12). NEMA MG-1 Motor-Generator Standard. Available online: https://law.resource.org/pub/us/cfr/ibr/005/nema.mg-1.2009.pdf.

3. Improved reliability of power modules: A review of online junction temperature measurement methods;Baker;IEEE Ind. Electron. Mag.,2014

4. Siemens Gamesa (2023, April 08). Asset Optimization Services. Available online: https://www.siemensgamesa.com/global/en/home/products-and-services/service-wind/asset-optimization.html.

5. Vestas (2023, April 05). Fleet Optimisation. Available online: https://www.vestas.com/en/services/fleet-optimisation#accordion-d626793722-item-03a4e4001e.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3