Distributed economic dispatch via a predictive scheme: Heterogeneous delays and privacy preservation
Distributed economic dispatch via a predictive scheme: Heterogeneous delays and privacy preservation
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DOI：
10.1016/j.automatica.2020.109356
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Fei Chen;Xiaozheng Chen;Linying Xiang;W. Ren
中科院分区：
文献类型：
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作者：
Fei Chen;Xiaozheng Chen;Linying Xiang;W. Ren
本文研究了智能电网分布式经济调度问题，其中二次发电费用在一个由等式约束和盒子约束共同确定的可行集上最小。我们的主要目标是寻求一种分布式设计，能够在处理异类时延的同时保护代理的隐私-这是一个对网络物理系统逐渐重要的基本先决条件。为此，我们为每个代理设计了一个状态预测器来补偿不同时延的影响，允许代理在两个连续的更新时间之间预测丢失的状态。基于预报器，我们提出了一种分布式梯度下降算法来局部更新生成器的输出，保证了以渐近方式获得最优解。其中，我们将隐私保护方案融入到所提出的算法中，以保护代理的隐私，并精细地刻画了其收敛、差分隐私性质以及准确性。
This paper studies distributed economic dispatch problems for smart grids, in which a quadratic generation cost is to be minimized over a feasible set that is determined jointly by an equality constraint and a box constraint. Our primary objective is to seek a distributed design that can handle heterogeneous time-delays, while preserving agents’ privacy—a fundamental prerequisite that has become gradually important for cyber–physical systems. For this purpose, we design a state predictor for each agent to compensate for the effect of heterogeneous time-delays, which allows the agents to predict the missing states between two consecutive update times. Based upon the predictor, we present a distributed gradient-descent algorithm to locally update the outputs of the generators, which guarantees that the optimal solution is attained in an asymptotic manner. Among other things, we incorporate a privacy preservation scheme to the proposed algorithm in order to preserve agents’ privacy and delicately characterize its convergence, differential privacy properties, as well as accuracy.