使用多目标算法的发电和建筑电力管理、sDA和DGP的自适应BIPV遮阳优化

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy Pub Date : 2025-10-11 DOI:10.1016/j.apenergy.2025.126860

Sepideh Miraba , Ali Salehi , Mohammad Rostamzadeh-Renani , Reza Ehteshami , Armin Shahbazi , Reza Rostamzadeh-Renani , Seyed Amir Hossein Hashemi Dehkordi , Mohammadreza Baghoolizadeh

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引用次数: 0

摘要

自适应建筑集成光伏遮阳系统（BIPVS）提供了一个有前途的解决方案，同时解决可持续能源生产，日光优化和办公大楼的视觉舒适性。然而，目前的研究大多集中在静态或半静态遮阳解决方案上，对实时太阳条件的适应性有限。本研究提出了一种全自适应BIPVS遮阳配置，并通过综合多目标优化评估其性能，以最大发电量（EP）、最小耗电量（EC）、提高空间日光自主性（sDA）和降低日光眩光概率（DGP）为目标，在伊朗德黑兰的一座办公大楼中进行了研究。MethodologyEnergyPlus、Radiance和Python脚本被集成到一个新颖的仿真优化框架中。采用多目标灰狼优化算法（MOGWO）、多目标鲸鱼优化算法（MOWOA）、多目标蚁群优化算法（MOACO）、多目标蛾焰优化算法（MOMFO）四种先进的多目标算法对倾角、方位角、遮光窗距、窗尺寸、窗墙比等设计变量进行优化。采用8个指标(代际距离（GD）、倒代际距离（IGD）、间隔（SP）、最大传播（MS）、时间(T)、质量（Q)、平均理想距离（MID）和帕累托前沿点（NPS））进行评价，采用Shannon熵排序，并采用TOPSIS方法确定最佳解决方案。结果自适应遮阳可使净发电量提高200%以上，达到6661.11 kWh /年，同时降低建筑能耗22.29%，提高日光自主性80%。此外，眩光概率显著降低，提高了居住者的视觉舒适度。这项研究强调了自适应bipv系统的潜力，结合先进的优化方法，显著提高建筑能源效率和室内环境质量，特别是在半干旱气候下。

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Adaptive BIPV shading optimization for electricity generation and building electricity management, sDA, and DGP using multi-objective algorithms

Background

Adaptive Building-Integrated Photovoltaic Shading Systems (BIPVS) offer a promising solution to simultaneously address sustainable energy generation, daylight optimization, and visual comfort in office buildings. However, current studies mostly focus on static or semi-static shading solutions with limited adaptability to real-time solar conditions.

Objective

This study proposes a fully adaptive BIPVS shading configuration and evaluates its performance through comprehensive multi-objective optimization, targeting maximum electricity production (EP), minimum electricity consumption (EC), improved spatial daylight autonomy (sDA), and reduced daylight glare probability (DGP) in an office building located in Tehran, Iran.

Methodology

EnergyPlus, Radiance, and Python scripting were integrated into a novel simulation-optimization framework. Four advanced multi-objective algorithms (Multi-Objective Grey Wolf Optimizer Algorithm (MOGWO), Multi-Objective Whale Optimization Algorithm (MOWOA), Multi-Objective Ant Colony Optimization Algorithm (MOACO), Multi-Objective Moth Flame Optimization Algorithm (MOMFO)) were applied to optimize design variables, including tilt angle, azimuth angle, shading-to-window distance, window dimensions, and window-to-wall ratio. Performance was evaluated using eight criteria (Generational distance (GD), Inverted generational distance (IGD), Spacing (SP), Maximum Spread (MS), Time (T), Quality (Q), Mean ideal distance (MID), and the number of Pareto front points (NPS)), ranked by Shannon entropy, and the best solution identified by the TOPSIS method.

Results

The findings show that adaptive solar shading can boost net electricity generation by over 200 %, achieving up to 6661.11 kWh annually, while reducing building energy consumption by 22.29 % and improving daylight autonomy by 80 %. Additionally, glare probability was significantly reduced, enhancing occupant visual comfort. This research highlights the potential of adaptive BIPVS systems, combined with advanced optimization methods, to significantly improve building energy efficiency and indoor environmental quality, particularly in semi-arid climates.

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来源期刊

Applied Energy 工程技术-工程：化工

CiteScore

21.20

自引率

10.70%

发文量

1830

审稿时长

41 days

期刊介绍： Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.