原文信息
Customized decentralized autonomous organization based optimal energy management for smart buildings
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261924016064
Highlights
(1) 首次提出了一种基于分布式自治组织的定制化智能建筑能量管理框架。
(2) 充分考虑了主体的参与意愿，设计了两阶段偏好满足机制。
(3) 提出了一种能抵抗建筑间潜在通信故障的分布式优化算法。
Abstract
In recent years, the unprecedented rising urbanization level and proliferation of distributed energy sources (DES) are motivating the transition of smart building from consumer to prosumer. In the context of mitigating upstream emission via reducing reliance on utility grids, the key challenge becomes realizing autonomous and economically optimal energy management within smart buildings. This paper innovatively proposes a Decentralized Autonomous Organization (DAO)-based energy management framework for smart buildings, where the mechanism of day-ahead scheduling DAO considering peer-to-peer (P2P) energy sharing is specifically investigated. Besides, a sequential preference satisfaction mechanism is designed to customize participants’ special demands, thereby achieving effective energy matching. Considering potential communication failures between buildings in practical operations, this study develops a communication failure robust distributed algorithm to minimize operational costs and safeguard building’s privacy. Extensive numerical studies for highly DES penetrated smart buildings successfully offers an operation cost reduction of 33.7% when considering preference satisfaction and P2P energy sharing in the day-ahead scheduling problem. Simulation results also demonstrates that the algorithm in this work excels in resisting potential communication failure during operation.
Keywords
Smart Building
Peer-to-peer Energy Sharing
Preference Satisfaction
Communication Robust
Day-ahead Scheduling
Decentralized Autonomous Organization
Graphics
Fig. 1. DAO-based Energy Management Framework for Smart Buildings.
Fig. 2. IEEE-15 Nodes Distribution Network.
Fig. 3. Optimal Scheduling Results Before Energy Sharing.
Fig. 4. Optimal Scheduling Results After Energy Sharing.
Fig. 5. Utility Trading Results Comparison.
Fig. 6. Preference-driven Energy Sharing Strategies.
Fig. 7. Energy Sharing Strategies Without Preference.
作者简介
团队介绍：
本研究由香港理工大学、香港理工大学深圳研究院、香港理工大学智慧能源研究院的研究人员共同完成。
通讯作者简介：
许昭，博士，香港理工大学教授，博士生导师，担任香港理工大学电力系统和智能微电网实验室主任、香港理工大学-中国南方电网联合研究院副院长、IEEE PES Joint Chapter主席。主要研究方向为电力系统运行与控制、概率预测、电力市场以及人工智能。
第一作者简介：
丁奕博，香港理工大学博士研究生。主要研究方向为电力系统优化、能源交易、低碳转型等。
关于Applied Energy
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