基于人工智能的住宅需求侧管理

Ajith Vijayan, Venugopalan Kurupath, J. Das
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引用次数: 5

摘要

在过去十年中,由于全面发展,特别是在工业部门,全球电力需求呈指数级增长。需求侧管理(DSM)是电网的一项关键功能,它鼓励用户对他们的能源使用做出决策,使能源供应商能够最大限度地减少高峰需求,并重塑负荷的轮廓。使用像DSM这样的电网控制算法,可以在特定的时间间隔内将能源需求最小化。它正在规划、实施和监测电力公用事业的活动,鼓励消费者改变用电水平和模式,确保电网的稳定,并平衡全年的电力需求。本文提出了一种负荷转移的需求侧管理方法,将低优先级的用户负荷从高峰时段转移到非高峰时段,从而降低高峰需求,从而降低成本。对住宅基础设施进行了模拟。结果表明,所提出的优化策略可实现显著的成本节约。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Residential Demand Side Management Using Artificial Intelligence
There is an exponential increase for the global electricity demand during the last decade owing to overall development, especially in the industrial sector. Demand side management (DSM) is a critical function of a grid that encourages users to make decisions about their energy usage and enables energy suppliers minimize peak demand and reshape the profile of load. Energy demand could be minimized at specific time intervals using grid control algorithms like DSM. It is planning, implementing, and monitoring activities of electrical utilities which encourage consumers to modify their level and pattern of electricity usage, ensuring stability on the electricity grid and balance the electrical demand throughout the year. This paper presents a load shifting demand side management which transfers low priority consumer loads from peak to off peak periods, which can reduce peak demand and thereby cost. Simulations are carried out for a residential infrastructure. The results show that significant cost savings are achievable with the proposed optimization strategy.
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