Abstract This paper deals with structured multi-agent optimization problems that involve coupled local and global decision variables. We propose an iterative distributed algorithm that explicitly accounts for this structure, and requires the agents to communicate only their tentative solutions for the global variables throughout iterations. Our approach extends to structured multi-agent optimization a proximal-based distributed methodology that has recently appeared in the literature. Privacy of local information is preserved and communication effort is reduced with respect to alternative distributed solutions where local and global optimization variables are grouped together and treated as a single decision vector. Multi-agent optimization problems with the considered structural properties appear in various contexts. In this paper, we apply our approach to energy management in a district where multiple buildings can communicate over a possibly time-varying network and aim at optimizing the use of shared and local resources. We illustrate the efficacy of the resulting distributed energy management algorithm by means of a detailed simulation study on a cooling problem.

中文翻译：

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结构化程序的分布式优化及其在建筑小区能源管理中的应用

摘要 本文处理涉及耦合局部和全局决策变量的结构化多智能体优化问题。我们提出了一种迭代分布式算法，该算法明确说明了这种结构，并要求代理在整个迭代过程中仅就全局变量的临时解决方案进行交流。我们的方法扩展到结构化多代理优化，这是一种最近出现在文献中的基于近端的分布式方法。相对于其中局部和全局优化变量被组合在一起并被视为单个决策向量的替代分布式解决方案，本地信息的隐私得到保护并减少了通信工作。具有所考虑结构特性的多智能体优化问题出现在各种环境中。在本文中，我们将我们的方法应用于多个建筑物可以通过可能随时间变化的网络进行通信的区域的能源管理，旨在优化共享和本地资源的使用。我们通过对冷却问题的详细模拟研究来说明所得分布式能源管理算法的功效。