Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications

A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). Existing offline energy management approaches for day-ahead scheduling of BES suffer 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 these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming 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 intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods.