A Novel Fitted Rolling Horizon Control Approach for Real-Time Policy Making in Microgrid
A Novel Fitted Rolling Horizon Control Approach for Real-Time Policy Making in Microgrid
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DOI：
10.1109/tsg.2020.2966931
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发表时间：
2020-07-01
影响因子：
9.6
通讯作者：
Ni, Zhen
中科院分区：
文献类型：
--
作者：
Das, Avijit;Ni, Zhen
近年来，滚动水平控制（RHC）方法因其减少实时/在线操作和优化的预测误差的特点而受到越来越多的关注。然而，如果由于互联网或云服务提供商中断、软件/硬件故障和许多其他因素导致日内预测数据不可用或丢失，现有 RHC 方法的性能就会下降。在本文中，我们提出了一种新的拟合 RHC 方法来克服这一挑战。所提出的拟合 RHC 框架采用回归算法设计，当日内预测数据不可用时，该算法利用经验知识做出实时决策。回归算法利用统计相对概率方法来计算每个决策向量的相对概率，并输出适当的优化策略。此外，我们采用了外源信息转换函数的修改版本，更适合在实时环境中进行模拟。微电网中的仿真结果表明，所提出的拟合 RHC 方法即使在数据缺失的情况下也可以实现确定性情况下的最优策略，并且在随机案例研究中的不确定环境中有效执行。相比之下，所提出的拟合 RHC 方法优于其他几种优化技术。
In recent years, rolling horizon control (RHC) approaches have attracted growing attention due to its feature of reducing forecast errors for real-time/online operation and optimization. However, the performance of the existing RHC approach degrades if the intra-day forecast data is unavailable or missing due to Internet or cloud service provider outages, software/hardware failures, and many other factors. In this paper, we propose a new fitted-RHC approach to overcome this challenge. The proposed fitted-RHC framework is designed with a regression algorithm which utilizes the empirical knowledge to make the real-time decisions whenever the intra-day forecast data is unavailable. The regression algorithm utilizes a statistical relative probability method to calculate the relative probability for each decision vector, and output the proper optimization policy. In addition, we adopt a modified version of exogenous information transition function that is more suitable to conduct the simulations in a real-time environment. Simulation results in microgird show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case even with the missing data, and perform efficiently with the uncertain environment in stochastic case study. In comparison, the proposed fitted-RHC approach outperforms several other optimization techniques.