Integrating Small Scale Green Energy into Smart Grids: Prediction for Peak Load Reduction

The emerging Smart Grid technologies allow for the integration of clean energy from small-scale energy generators (SEGs). In this paper, we investigate a model by which an electric grid operator (EGOs) schedules the integration of clean energy from residential homes acting as SEGs. These SEGs are equipped with rooftop photovoltaic (PV) and a bank of utility grade battery systems. The challenge facing the electric grid operator (EGO) is that home-based battery systems require several hours of sunlight to charge from rooftop PV panels, and an average 90 minutes to be discharged to 30% original capacity. The EGO must be able to schedule the discharging cycle so that it coincides with the time of the highest peak load during the day for efficient cost reduction. In this paper, we propose a model that allows the EGO to predict the highest peak of energy consumption on the distribution feed where the SEGs are connected. For the realization of the system, we have acquired a dataset which includes time series of energy consumption data for approximately 1500 houses including 3 SEGs. We performed our prediction using the multivariate autoregressive integrated moving average (MARIMA) method and achieved 92.64% accuracy. The real-life implementation of the system and the prediction model are described in this paper.