论文标题
部分可观测时空混沌系统的无模型预测
Optimal Regulation of Prosumers and Consumers in Smart Energy Communities
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
In smart energy communities, households of a particular geographical location make a cooperative group to achieve the community's social welfare. Prosumers are the users that both consume and produce energy. In this paper, we develop stochastic and distributed algorithms to regulate the number of consumers and the number of prosumers with heterogeneous energy sources in the smart energy community. In the community, each prosumer has one of the heterogeneous energy sources such as solar photovoltaic panels or wind turbines installed in their household. The prosumers and consumers decide in a probabilistic way when to be active. They keep their information private and do not need to share it with other prosumers or consumers in the community. Moreover, we consider a central server that keeps track of the total number of active prosumers and consumers and sends feedback signals in the community at each time step; the prosumers and consumers use these signals to calculate their probabilistic intent. We present experimental results to check the efficacy of the algorithms. We observe that the average number of times prosumers and consumers are active reaches the optimal value over time, and the community asymptotically achieves the social optimum value.
