Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

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

Sustainable Cities and Society Pub Date : 2025-02-01 DOI:10.1016/j.scs.2024.106077

Safoura Faghri , Hamed Tahami , Reza Amini , Haniyeh Katiraee , Amir Saman Godazi Langeroudi , Mahyar Alinejad , Mobin Ghasempour Nejati

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引用次数: 0

Abstract

Nowadays, the presence of electric vehicles (EVs) in power distribution networks (DNs) is increasing significantly, where these facilities' smart charging and discharging are mandatory. In response to this challenge, strategic control of EV charging/discharging power can improve power system flexibility and reduce the underlying operation costs. Real-time charging and discharging of EVs is a time-consuming and complicated problem that might suffer from uncertainties in the behavior of EV owners. Respecting the potential to provide fast responses in complex environments, state-of-the-art deep reinforcement learning (DRL) methods can be an appropriate solution for EVs' real-time charging and discharging. This paper studies the application of DRL to real-time energy scheduling of autonomous smart building communities (SBCs) integrated with EV parking lots (EVPLs). A model-free DRL approach based on a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to reach a near-optimal solution for autonomous SBCs’ real-time energy scheduling problem. In addition, a convex mathematical optimal power flow (OPF) is developed to guarantee DN's reliable operation. The findings reflect that real-time strategic charging and discharging of EVs can enhance the flexibility of DN in order to provide energy flexibility in the real-time electricity market.

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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;