Energy management systems (EMS) are viable techniques to reduce the customers billing cost as well as enhancing the grid reliability and efficiency. Uncertainty and information privacy of users are serious concerns in designing EMS of a residential nanogrid with renewable energy resources. This paper addresses a decentralized energy management algorithm for day-ahead scheduling of residential nanogrids using the concept of Mean Field (MF). The aim of each user is to find its optimal demand strategy by minimizing an objective function consisting of energy consumption cost, battery degradation cost, and cost of user discomfort while meeting a suite of constraints. The strategy of a user affects the objective functions of other users through the electricity price; such interaction among the users is hence modeled as a game problem. Each user, without any information exchange with other users, sends its electricity demand to the utility company. The utility company broadcasts the aggregated demand as a common information of the grid (MF term) to all users. The robustness of the algorithm in presence of uncertainties in decision making of the clients and non-residential load prediction error is analytically verified. In particular, it is shown that the algorithm converges to $\epsilon$ -Nash equilibrium point of the game and $\epsilon$ uniformly converges to its minimum value as the population size of the users goes to infinity. Simulation results show the advantages of the proposed method.

中文翻译：

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通过分散平均场控制实现住宅纳米电网的强大能量管理

能源管理系统（EMS）是可行的技术，可以降低客户的账单成本并提高电网的可靠性和效率。在设计具有可再生能源的住宅纳米电网的EMS时，用户的不确定性和信息隐私是严重的问题。本文利用均值域（MF）的概念，提出了一种用于住宅纳米电网日间提前调度的分散式能量管理算法。每个用户的目的是通过在满足一系列约束的同时最小化包括能耗成本，电池降级成本和用户不适成本的目标函数，找到最佳的需求策略。用户的策略通过电价影响其他用户的目标功能；用户之间的这种交互因此被建模为游戏问题。每个用户，在不与其他用户进行任何信息交换的情况下，将其用电需求发送给公用事业公司。公用事业公司将汇总的需求作为网格的公共信息（MF术语）广播给所有用户。通过分析验证了该算法在客户决策中存在不确定性和非住宅负荷预测误差的鲁棒性。特别地，表明算法收敛到$ \ epsilon $ -游戏的纳什均衡点和 $ \ epsilon $随着用户的人口规模达到无穷大，统一收敛到其最小值。仿真结果表明了该方法的优越性。