A Novel Fitted Rolling Horizon Control Approach for Real-Time Policy Making in Microgrid

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.