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This paper presents a rolling horizon optimal energy management mechanism using real-time grid monitoring data. The proposed mechanism includes novel approaches in terms of real-time data processing, net-load forecasting, and optimal scheduling of battery energy storage systems. The proposed real-time data processing and net-load forecasting techniques use fast training and computationally efficient methods based on grid monitoring data. The data processing and parameter forecasting methods are based on auto-regressive with exogenous variables (ARX). Two sets of features including similar values in previous hours, days, and weeks as well as calendar effects are used for training the forecast model. Furthermore, the impact of additional non-synchronized weather features adopted from meteorology databases on the forecasts&#x2019; accuracy is discussed. Finally, a real-time optimal scheduling is proposed to optimize battery energy storage systems (BESS), maximizing the self-consumption at grid and community levels. The application of real-time grid measurement in the proposed algorithm allows handling the impacts of loads and generators behind the meter without having their detailed information. The developed method is being effectively used in a real low voltage distribution grid in Switzerland.

ieee open access journal of power and energy

Application of Real-Time Distribution Grid Monitoring for Grid Forecasting and Control Considering Incomplete Information of Resources Behind-the-Meter