让我们让PED发挥作用——当前的知识如何为未来的正能量区做出贡献

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Jelena Nikolic, Jakob Zinck Thellufsen, Peter Sorknæs, Poul Thøis Madsen, Lasse Schytt Nørgaard
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引用次数: 0

摘要

正能量区(PEDs)的概念,即产生剩余能源的城市单元,已被认为是能源变化的可能促成因素。在文献中,ped主要以三种方式定义:虚拟、动态和自治,每种方式都为能源生产提供不同的系统边界。本文考察了这些定义,同时对能源部门(工业、交通、建筑)进行了不同的纳入,使ped评估成为量化地区规模和部门覆盖范围影响的工具。研究中提出的一般方法已应用于丹麦奥尔堡东区,以证明其实际效用。结果表明,系统复杂性显著影响PED可行性,受天气和土地可利用性等当地条件的影响。PED定义的选择决定了哪些能源部门可以可行地包括在内。在这种情况下,当考虑工业和运输等能源密集型部门时,最可行的PED是通过虚拟方法实现的。与严格在系统边界内产生能量的PED模型相比,虚拟PED模型的年成本比动态PED模型低6%。此外,即使PED仅包括家庭,虚拟模型中PED内产生但未利用的能源量比自主模型低77%,比动态模型低20%。最后,该研究强调了根据当地情况调整PED战略并将其整合到更广泛的城市能源网络中的重要性。这确保了地区间的电力交换,支持了国家的脱碳目标,促进了社会包容和气候中和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Let’s make PED work - How current knowledge can contribute to future positive energy districts
The concept of Positive Energy Districts (PEDs) i.e. urban units that produce surplus energy, has been recognized as a possible enabler of energy change. In literature, PEDs are defined in three main ways: virtual, dynamic, and autonomous, each offering different system boundaries for energy production. This paper examines these definitions while varying the inclusion of energy sectors (industry, transportation, buildings), enabling an assessment of PEDs as a tool to quantify the impact of district size and sectoral coverage. The general methodology presented in the study has been applied to district of Aalborg East in Denmark, to demonstrate its practical utility. Results indicate that system complexity significantly affects PED feasibility, influenced by local conditions such as weather and land availability. The choice of PED definition determines which energy sectors can be feasibly included. In this case, when energy-intensive sectors like industry and transportation are considered, the most feasible PED is achieved through the virtual approach. Compared to PEDs in which energy is strictly produced within the system boundaries, the annual costs of the PED virtual are 6 % lower than those of the PED dynamic model. Furthermore, even when the PED includes only households, the amount of energy produced but not utilized within the PED in the virtual model is 77 % lower compared to the autonomous model, and 20 % lower compared to the dynamic model.
Finally, the study highlights the importance of tailoring PED strategies to local contexts and integrating them into broader urban energy networks. This ensures electricity exchange between districts, supports national decarbonization goals, and promotes social inclusion and climate neutrality.
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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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