Dynamic Demand and Mean-Field Games
Dynamic Demand and Mean-Field Games
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动态需求和平均场博弈
DOI：
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发表时间：
2017
影响因子：
6.8
通讯作者：
D. Bauso
中科院分区：
文献类型：
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作者：
D. Bauso
在智能建筑和智能城市领域，动态响应管理发挥着越来越大的作用，吸引了不同学科科学家的关注。动态需求响应管理涉及一系列旨在分散大型复杂电力网络中负载控制的操作。每个设备都完全响应并根据整体网络负载重新调整其能源需求。一个主要问题与供需不平衡导致的电源频率振荡有关。简而言之，本文通过为每个消费者提供战略洞察力来为该主题做出贡献。我们特别强调三个主要贡献和其他一些次要贡献。首先，我们为一组恒温控制负载设计了平均场博弈，研究了确定性平均场博弈的平均场平衡，并研究了微观动力学的渐近稳定性。其次，我们将分析和设计扩展到不确定模型，其中涉及随机或确定性扰动。这导致稳健的平均场平衡策略分别保证随机和最坏情况的稳定性。次要贡献涉及使用随机控制策略而不是确定性控制策略，以及一些说明所提出策略的有效性的数值研究。
Within the realm of smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies.