考虑多个聚合器的合作能源社区稳健的日前调度

IF 10.5 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Marcos Tostado-Véliz , Juan S. Giraldo , Daniel Icaza Álvarez , Carlos Cruz , Francisco Jurado
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引用次数: 0

摘要

未来的城市必须在减少能源消耗和电力部门去碳化方面发挥重要作用,从而从被动结构向更高效的智能城市发展。能源社区可以促进这一转变。这一新兴模式包括将一系列配备现场可再生能源发电机和储能资产的住宅设施(即专业消费者)集体化,最终实现资源共享,以追求集体福利。本文的重点是合作社区,在这些社区中,消费者共享资源,而不追求自私的金钱回报。能源社区的能源管理和调度尽管具有明显的优势,但由于高度的不确定性(特别是由于间歇性可再生能源发电和随机需求)以及对消费者隐私的担忧,对传统工具来说是一个挑战。本文就是要解决这些问题。具体而言,本文提出了一种基于多个聚合器的新型管理结构。这种模式既能保护用户的保密特征,又能让他们充分挖掘其资产的潜力。为了有效管理不确定性条件下的各种可用资产,本文开发了一个自适应稳健日前调度模型,该模型是一个可求解、可移植的混合整数线性规划框架,便于在现实世界中实施。新建议采用不确定性集合的多面体表示法来处理不确定的发电量和需求量。为验证所开发的模型,我们进行了一项案例研究,结果表明该模型大有可为。此外,还获得并分析了不同的结果。最后,值得注意的是稳健性水平如何影响集体账单,在假设规避风险的条件下,集体账单会增加 75%。此外,还指出了储能资产在悲观条件下的作用,指出这些资产而不是可再生能源发电机决定着社区的调度计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators
Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
期刊介绍: Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;
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