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arXiv 1802.08047eess.SYcs.SY
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当可再生能源与建筑热质量相遇:一种实时负荷管理方案

When Renewable Energy Meets Building Thermal Mass: A Real-time Load Management Scheme

Yan Shen, Zhonghao Sun, Qinglong Wang

AI总结:

本文提出在噪声干扰条件下,通过用户满意度与电力消费平衡模型优化智能建筑微电网的功率管理,设计了基于Bregemen投影的镜像下降算法并验证其收敛性与有效性。

摘要:

本文考虑在噪声干扰条件下,智能建筑微电网的最优功率管理作为随机优化问题。我们首先提出用户满意度与电力消费平衡(USECB)利润模型作为最优功率管理的目标函数。随后,我们将噪声干扰条件下的问题归类为具有凸约束的期望最大化类随机优化问题。为此,我们设计了一种基于Bregemen投影的镜像下降算法作为对随机优化问题的近似解。本文还提供了算法的收敛性及上界证明。最后,我们在模拟中进行了广泛类型实验以测试模型的合理性及算法的有效性。

英文摘要:

We consider the optimal power management in renewable driven smart building MicroGrid under noise corrupted conditions as a stochastic optimization problem. We first propose our user satisfaction and electricity consumption balanced (USECB) profit model as the objective for optimal power management. We then cast the problem in noise corrupted conditions into the class of expectation maximizing in stochastic optimization problem with convex constraints. For this task, we design a Bregemen projection based mirror decent algorithm as an approximation solution to our stochastic optimization problem. Convergence and upper-bound of our algorithm with proof are also provided in our paper. We then conduct a broad type of experiment in our simulation to test the justification of our model as well as the effectiveness of our algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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