A new methodology for reducing carbon emissions using multi-renewable energy systems and artificial intelligence

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-08-03 DOI:10.1016/j.scs.2024.105721

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引用次数: 0

Abstract

Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396 % per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437 % per day. From the obtained results the proposed algorithm gives superior performance.

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利用多再生能源系统和人工智能减少碳排放的新方法

微电网成本管理是一个重大难题，因为微电网产生的能源通常来自各种可再生和不可再生资源。此外，为了满足自由能源市场的要求和确保负荷需求，微电网与国家电网之间的连接总是首选。鉴于上述原因，为了最大限度地降低运营成本，必须设计一种智能能源管理装置来调节微电网内的各种能源资源。本研究提出了一种用于多源微电网运行和成本管理的智能单元理念。所提出的装置利用改进的人工兔子优化算法（IAROA），根据当前的负载需求、能源价格和发电能力来优化运行成本。此外，还使用蜜獾算法（HBA）和鲸鱼优化算法（WOA）对优化结果进行了比较。结果证明了所提方法在 SMG 需求管理系统中的适用性和可行性。应用 HBA 算法后的价格为 6244.5783（ID）。但应用鲸鱼优化算法后，成本为 4283.9755（ID），应用人工兔子优化算法后，成本为 1227.4482（ID）。通过将所提出的方法与传统方法进行比较，鲸鱼优化算法每天节省了 31.396 %，而所提出的人工兔优化算法每天节省了 80.3437 %。从获得的结果来看，提议的算法性能优越。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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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