Shenzhen Shangfang Clean Energy Co.,Ltd 的作品 Residential Pv 储能系统

设计师 Shenzhen Shangfang Clean Energy Co.,Ltd 的作品 Residential Pv 储能系统荣获了极具声望的 2024 A' Design Award 银奖，竞赛类别为能源产品、项目与设备设计。

银能源· 2024

Residential Pv 储能系统

设计师： Shenzhen Shangfang Clean Energy Co.,Ltd

客户: Shenzhen Shangfang Clean Energy Co., Ltd.

能源类别A' Design Award 银奖获奖者

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关于 Residential Pv 储能系统

Residential PV 是一款面向家庭的光伏储能系统。该系统可储存太阳能，供家庭日常使用，并可满足离网状态下的用电需求。用户可通过移动应用程序在家中或远程监测系统运行状况、查看能耗图表并设置运行模式。系统与逆变器之间采用无线数据传输，简化了安装流程并降低了线缆成本。氮化镓半导体的应用使最高转换效率达到98.4%。电池模块可堆叠设计，最多支持八组并联运行。防护涂层具备防锈、耐盐雾与防潮性能。

荣誉认可

关于A' Design Award 银奖

Residential Pv 储能系统是 A' Design Award 银奖得主，这是授予顶级、华美而令人印象深刻的设计的极为崇高的认可。银奖得主是极其卓越、极具创造力的作品，展现出其设计师杰出的专业技能、才华与敏锐。预计Residential Pv 储能系统将成为一件出色的典范之作，兼具非凡的专业性与深刻的创新性。这件赢得 A' Design Award 银奖的设计是一项备受瞩目、令人钦佩的发明，造福并拓展艺术、科学、设计和技术的边界。Residential Pv 储能系统是一件令人羡慕的创作，它唤起渴望，带来积极的情感，并激发惊叹与赞叹。

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媒体资料包

由设计师提供的致谢、团队成员及技术详情。

独特属性: The residential PV energy storage system is predicated upon intelligence, efficiency, and expandability. It facilitates energy management for users via mobile apps, both at home and remotely. The wireless data transmission between the system and the inverter reduces installation difficulties and costs. The stable connection and ability to stack battery modules allow up to 8 groups to run simultaneously, making it an ideal solution for off grid scenarios requiring high power and large capacity.
尺寸与包装: 2000*1000*400mm
标签: Residential PV energy storage system, Intelligent management, wireless connection function, efficient photovoltaic conversion, multiscene expansion
生产技术: The Gallium Nitride semiconductor material, known for its higher thermal and electrical conductivity, which greatly reduces internal losses during the power conversion process, allows the product to operate at higher frequencies, and achieves the maximum conversion efficiency of 98.4 per cent.
Meanwhile, the protective coating of the battery module safeguards the battery from rust, salt and moisture, reducing the risk of failure due to environmental factors and enhancing outdoor usage lifespan.
设计挑战: The challenge is to design an intelligent energy storage system solution that caters to the needs of users while keeping in mind the scalability of the scene. In light of this, the design team has introduced an intelligent management system that streamlines the control process through mobile apps and AI technology. The energy storage system and inverter seamlessly transmit data wirelessly, making it easy to access the IoT in the home at a later stage. Additionally, users can conveniently move the modular battery components outdoors, thereby enabling flexible deployment.
研究: The progression of renewable energy technology and the increasing social concern for the environment have resulted in the development of residential PV energy storage systems. However, traditional residential systems lack intelligent management, scene expansion, and photovoltaic conversion efficiency. Consequently, the system introduces intelligent monitoring systems and AI, which suit various conditions and achieve a higher conversion rate with modular design, fulfilling the needs of the user.
灵感: The energy storage system has been designed with a comprehensive understanding of user friendliness, with the support app to facilitate energy management. The wireless data transmission simplifies installation and reduces the cable cost. The battery modules are stackable and expandable, making it a versatile solution for various scenarios. The logo integrated into the battery combines aesthetics and functionality to enable users to visualize the operating status and make timely adjustments.
项目周期: The project started research and development in Beijing in June 2023, and the product was finalised in September, and the project mass production will be put on line according to the market situation.
操作流程: Users can easily view relevant information and configure operating modes through smart interconnection with the support mobile app. The app performs real time monitoring and self checks, identifying and fixing potential problems and presenting comprehensive energy data charts. The cloud native architecture with machine learning capabilities allows the system to tailor energy plans based on actual conditions, leading to cost savings and sustainable development.
团队成员: Junqing Fan
Xi Gui
Xin Jin
Xinjian Chen
Facai Zhou
Yixuan Li