2019 IEEE Industry Applications Society Annual Meeting | 2019
Real-time Stochastic Optimization of Energy Storage Management using Rolling Horizon Forecasts for Residential PV Applications
Abstract
In this paper, an energy management method for a residential PV-storage hybrid system composed of a solar photovoltaic (PV) generation and a battery energy storage (BES) is formulated as an offline optimization model concurrent with a real-time rule-based controller. Existing offline energy management approaches for day ahead scheduling of BES generally suffers from energy loss in real-time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome such limitations, we propose an integrated framework where optimization is performed in receding horizon utilizing the forecasted load and solar generation profiles from long short term memory (LSTM) in rolling horizon to reduce daily electricity purchase costs. The optimization model is formulated as a multi-stage stochastic program where we use the stochastic dual dynamic programming (SDDP) algorithm in the receding horizon to update the optimal set-point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update instants, we propose a rule-based controller beneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a realtime controller-hardware-in-the-Ioop (CHIL) test platform in an Opal-RT simulator. The proposed real-time method reduces the net electricity purchase cost relative to other existing energy management methods.